Estimating 3D Hand Pose from a Cluttered Image

A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclidean space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this cluttertolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.


Thermal degradation of DRAM retention time: Characterization and improving techniques

Variation of DRAM retention time and reliability problem induced by thermal stress was investigated. Most of the DRAM cells revealed 2-state retention time with thermal stress. The effects of hydrogen annealing condition and fluorine implantation on the variation of retention time and reliability are discussed.


Data Science , Predictive Analytics , and Big Data : A Revolution That Will Transform Supply Chain Design and Management

W e illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.


An integrated charger using segmented windings of interior permanent magnet motor based on 3 phase with 9 windings

Connecting the plug of electric vehicles (EVs) into the grid source directly means to charge the battery. The electric power generated from the grid goes through the electric motor, crosses the bidirectional inverter used as three-boost rectifier and is received by the load (battery). An innovative fast-integrated charger based on segmented windings of permanent magnet synchronous motor (PMSM) is introduced for plug-in electric vehicles (PEV). The three-phase with nine windings of PMSM is used during the traction and charging modes. Three (or single) phases of grid source are directly connected to the neutral points of segmented PMSM windings to provide high power density to the battery. The system configuration and operation of charging mode are explained in detail for proposed integrated on-board charger. The simulation models are designed by using Ansoft Maxwell, Ansys Maxwell circuit editor, and Matlab/Simulink softwares, and simulation results are presented to verify the performance of the proposed system. In addition, the experimental setup of the integrated system is under progress.


From the Internet of Computers to the Internet of Things

This paper1 discusses the vision, the challenges, possible usage scenarios and technological building blocks of the “Internet of Things”. In particular, we consider RFID and other important technological developments such as IP stacks and web servers for smart everyday objects. The paper concludes with a discussion of social and governance issues that are likely to arise as the vision of the Internet of Things becomes a reality.


of the Thesis Classification of Imbalanced Data Using Synthetic OverSampling Techniques by

of the Thesis Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques


Query Optimization Techniques In Graph Databases

Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for storing and managing data with graph-like structure. Today, they represent a requirementfor many applications that manage graph-like data,like social networks.Most of the techniques, applied to optimize queries in graph databases, have been used in traditional databases, distribution systems,... or they are inspired from graph theory. However, their reuse in graph databases should take care of the main characteristics of graph databases, such as dynamic structure, highly interconnected data, and ability to efficiently access data relationships. In this paper, we survey the query optimization techniques in graph databases. In particular,we focus on the features they have introduced to improve querying graph-like data.


Wait-Free Synchronization

A wait-free implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a wait-free implementation of one data object from another lies at the heart of much recent work in concurrent algorithms, concurrent data structures, and multiprocessor architectures. First, we introduce a simple and general technique, based on reduction to a concensus protocol, for proving statements of the form, “there is no wait-free implementation of X by Y.” We derive a hierarchy of objects such that no object at one level has a wait-free implementation in terms of objects at lower levels. In particular, we show that atomic read/write registers, which have been the focus of much recent attention, are at the bottom of the hierarchy: thay cannot be used to construct wait-free implementations of many simple and familiar data types. Moreover, classical synchronization primitives such astest&set and fetch&add, while more powerful than read and write, are also computationally weak, as are the standard message-passing primitives. Second, nevertheless, we show that there do exist simple universal objects from which one can construct a wait-free implementation of any sequential object.


MapReduce: Simplified Data Processing on Large Clusters

MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.


Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.


Adaptive median filters: new algorithms and results

Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L(p) filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters.


AP3: cooperative, decentralized anonymous communication

This paper describes a cooperative overlay network that provides anonymous communication services for participating users. The Anonymizing Peer-to-Peer Proxy (AP3) system provides clients with three primitives: (i) anonymous message delivery, (ii) anonymous channels, and (iii) secure pseudonyms. AP3 is designed to be lightweight, low-cost and provides "probable innocence" anonymity to participating users, even under a large-scale coordinated attack by a limited fraction of malicious overlay nodes. Additionally, we use AP3's primitives to build novel anonymous group communication facilities (multicast and anycast), which shield the identity of both publishers and subscribers.


Probabilistic Encryption


THE ROLE OF MUSIC COMMUNICATION IN CINEMA

[Authors’ note: This paper is an abbreviated version of a chapter included in a forthcoming book entitled Music Communication (D. Miell, R. MacDonald, & D. Hargreaves, Eds.), to be published by Oxford University Press.] Past research leaves no doubt about the efficacy of music as a means of communication. In the following pages, after presenting a general model of music communication, the authors will introduce models – both empirical and theoretical – of film music perception and the role of music in film, referencing some of the most significant research investigating the relationship between sound and image in the cinematic context. We shall then enumerate the many ways in which the motion picture soundtrack can supplement, enhance, and expand upon the meaning of a film’s narrative. The relationship between the auditory and visual components in cinema is both active and dynamic, affording a multiplicity of possible relations than can evolve – sometimes dramatically – as the narrative unfolds. This paper will take a cognitive approach to the study of musical communication in cinema. As a result, much credence will be given to the results of empirical research investigating human cognitive processing in response to the motion picture experience. In conclusion, the present authors will argue for a more inclusive definition of the term “film music” than that utilized or implied in previous publications. In our view, film music is one component of a sonic fabric that includes the musical score, ambient sound, dialogue, sound effects, and silence. The functions of these constituent elements often overlap or interact with one another, creating a harmonious counterpoint to the visual image. 1. A MODEL OF MUSIC COMMUNICATION Many studies have investigated various aspects of musical communication as a form of expression (Bengtsson & Gabrielsson, 1983; Clarke, 1988; Clynes, 1983, Gabrielsson, 1988, Seashore, 1967/1938; Senju & Ohgushi, 1987; Sundberg, 1988; Sundberg et al., 1983). A tripartite communication model was proposed by Campbell and Heller (1980), consisting simply of a composer, performer, and listener. Using this previous model as a foundation, Kendall and Carterette (1990) elegantly expanded upon this model of music communication, detailing a process involving multiple states of coding, decoding, and recoding. Kendall and Carterette suggest that this process involves the “grouping and parsing of elementary thought units” (p. 132), these “thought units” (metasymbols) are mental representations involved in the process of creating, performing, and listening to musical sound. 2. MODELS OF FILM MUSIC COMMUNICATION 2.1 Empirical Evidence Several researchers have proposed models specific to the perception and cognition of music within the cinematic context. Initiating this systematic effort, Marshall and Cohen’s (1988) bipartite “congruence-associationist” model suggests that the meaning of a film is altered by the music as the result of two complex cognitive processes. Based upon subject responses, the researchers determined that musical sound directly effects subject ratings on the Potency (strong-weak) and Activity (active-passive) dimensions, while the Evaluative dimension (good-bad) relies on the degree of congruence between the audio and visual components on all three dimensions, as determined by a “comparator” component. The second part of the model describes how musical meaning is ascribed to the film. Marshall and Cohen claim that attention is directed to the overlapping congruent meaning of the music and the film. Referential meanings associated with the music are ascribed to the overlapped (congruent) audio-visual components upon which attention is focused. As a result, “the music alters meaning of a particular aspect of the film” (1988, p. 109). Marshall and Cohen also acknowledge the important role played by temporal characteristics of the sound and image, stating that “the assignment of accent to events will affect retention, processing, and interpretation” (1988, p. 108). Incorporation of this important component of the developing model was provided by Lipscomb and Kendall’s (1994) Film Music Paradigm, in which two implicit processes are considered as the basis for whether attentional focus is shifted to the musical component or whether it is likely to remain at the subconscious – cognitively “inaudible” – level. The authors suggested that these two implicit processes include an association judgment (similar to Marshall and Cohen’s assessment of “congruence”) and an evaluation of the accent structure relationship between the auditory and visual components. Based on the results of a series of three experiments utilizing stimuli ranging from extremely simple, single-object animations to actual movie excerpts, Lipscomb (1995) determined that the role of the two implicit judgments appears to be dynamic such that, with simple stimuli (such as that used in Lipscomb, 1995, Experiment 1 and Marshall & Cohen, 1988), accent structure alignment plays a dominant role. As the stimuli become more complex (e.g., multi-object animations and actual movie excerpts) the primary determinant of meaning in the auditory domain appears to shift to the associational judgment, with the accent structure alignment aspect receding to a supporting role, i.e., focusing audience attention on certain aspects of the visual image (Boltz, 2001). The most complex and fully developed model of film music perception proposed to date is Cohen’s (2001) “congruence-associationist framework for understanding film-music communication” (p. 259; see Figure 1). This multi-stage model attempts to account for meaning derived from the spoken narrative, visual images, and musical sound. Level A represents bottom-up processing based on physical features derived from input to each perceptual modality. Level B represents the determination of cross-modal congruence, based on both semantic (associational) and syntactic (temporal) grouping features. Level D represents top-down processing, determined by an individual’s past experience and the retention of that experience in long term memory. According to this model, the input from levels B (bottom-up) and D (top-down) meet in the observer’s conscious mind (level C), where information is prepared for transfer to short term memory. In its details, clearly documented in Cohen (2001), this model is based on an assumption of visual primacy, citing several studies that have suggested a subservient role for the auditory component (Bolivar et al., 1994; Driver, 1997; Thompson et al., 1994). Though a common assumption throughout the literature, the present authors would like to express reservation about this assumption and suggest that additional research is required before such a claim can be supported definitively. Figure 1. Cohen’s “congruence-associationist framework. 2.2 Theoretical Evidence Richard Wagner, creator of the idealized Gesamtkunstwerk in the form of the 19 century music drama, claimed that “as pure organ of the feeling, [music] speaks out the very thing which word speech in itself can not speak out ... that which, looked at from the standpoint of our human intellect, is the unspeakable” (Wagner 1849/1964, p. 217). According to Suzanne K. Langer, “music has all the earmarks of a true symbolism, except one: the existence of an assigned connotation” and, though music is clearly a symbolic form, it remains an “unconsummated symbol” (1942, p. 240). In order for a film to make the greatest possible impact, there must be an interaction between the verbal dialogue (consummated symbol), the cinematic images (also, typically, a consummated symbol), and the musical score (unconsummated symbol). To answer the question “How does music in film narration create a point of experience for the spectator?,” Gorbman (1987) suggests three methods by which music can “signify” in the context of a narrative film. Purely musical signification results from the highly coded syntactical relationships inherent in the association of one musical tone with another. Patterns of tension and release provide a sense of organization and meaning to the musical sound, apart from any extramusical association that might exist. Cultural musical codes are exemplified by music that has come to be associated with a certain mood or state of mind. These associations have been further canonized by the Hollywood film industry into certain conventional expectations – implicitly anticipated by enculturated audience members – determined by the narrative content of a given scene. Finally, cinematic codes influence musical meaning merely due to the placement of musical sound within the filmic context. Opening credit and end title music illustrate this type of signification, as well as recurring musical themes that come to represent characters or situations within the film. There is a commonly held belief that film music is not to be heard (Burt, 1994; Gorbman, 1987). Instead, it is believed to fulfill its role in communicating the underlying psychological drama of the narrative at a subconscious level (Lipscomb, 1989). There is, however, certain music that is intended to be heard by the audience as part of the cinematic diegesis, i.e., “the narratively implied spatiotemporal world of the actions and characters” (Gorbman 1987, p. 21). This “world” includes, naturally, a sonic component. Therefore, all sounds that are understood to be heard by characters in the narrative – including music – are referred to as diegetic, while those that are not part of the diegesis (e.g., the orchestral score) are referred to as nondiegetic. This would suggest that diegetic music is more likely to be processed at the conscious level while nondiegetic music might remain at the subconscious level, though research is needed to determine whether this is true, in fact. It is worth noting also, that the source of diegetic sound can be either seen or unseen. Michel Chion (199


Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey

Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.


A Successive Approximation Recursive Digital Low-Dropout Voltage Regulator With PD Compensation and Sub-LSB Duty Control

This paper presents a recursive digital low-dropout (RLDO) regulator that improves response time, quiescent power, and load regulation dynamic range over prior digital LDO designs by 1–2 orders of magnitude. The proposed RLDO enables a practical digital replacement to analog LDOs by using an SAR-like binary search algorithm in a coarse loop and a sub-LSB pulse width modulation duty control scheme in a fine loop. A proportional-derivative compensation scheme is employed to ensure stable operation independent of load current, the size of the output decoupling capacitor, and clock frequency. Implemented in 0.0023 mm2 in 65 nm CMOS, the 7-bit RLDO achieves, at a 0.5-V input, a response time of 15.1 ns with a figure of merit of 199.4 ps, along with stable operation across a 20 000 $\times $  dynamic load range.


Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/ motion), language (written/spoken word recognition, spoken/ no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial-temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial-temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.


Exploring Synergies between Machine Learning and Knowledge Representation to Capture Scientific Knowledge

In this paper we explore synergies between the machine learning and knowledge representation fields by considering how scientific knowledge is represented in these areas. We illustrate some of the knowledge obtained through machine learning methods, providing two contrasting examples of such models: probabilistic graphical models (aka Bayesian networks) and artificial neural networks (including deep learning networks). From knowledge representation, we give an overview of ontological representations, qualitative reasoning, and planning. Then we discuss potential synergies that would benefit both areas.


Coupling Matrix Synthesis for a New Class of Microwave Filter Configuration

In this paper a new approach to the synthesis of coupling matrices for microwave filters is presente d. The new approach represents an advance on existing direct a nd optimization methods for coupling matrix synthesis in that it will exhaustively discover all possible coupling matrix solutions for a network if more than one exists. This enables a se lection to be made of the set of coupling values, resonator frequ ency offsets, parasitic coupling tolerance etc that will be best suited to the technology it is intended to realize the microwave filter with. To demonstrate the use of the method, the case of the r cently – introduced ‘extended box’ (EB) coupling matrix configuration is taken. The EB represents a new class of filter con figuration featuring a number of important advantages, one of which is the existence of multiple coupling matrix solutions for each prototype filtering function, eg 16 for 8 degree cases. This case is taken as an example to demonstrate the use of the synthesis method – yielding one solution suitable for dual-mode realiz ation and one where some couplings are small enough to neglect. Index Terms — Coupling matrix, filter synthesis, Groebner basis, inverted characteristic, multiple solutions.


An Artificial Bee Colony-Based COPE Framework for Wireless Sensor Network

In wireless communication, network coding is one of the intelligent approaches to process the packets before transmitting for efficient information exchange. The goal of this work is to enhance throughput by using the intelligent technique, which may give comparatively better optimization. This paper introduces a biologically-inspired coding approach called Artificial Bee Colony Network Coding (ABC-NC), a modification in the COPE framework. The existing COPE and its variant are probabilistic approaches, which may not give good results in all of the real-time scenarios. Therefore, it needs some intelligent technique to find better packet combinations at intermediate nodes before forwarding to optimize the energy and maximize the throughput in wireless networks. This paper proposes ABC-NC over the existing COPE framework for the wireless environment.


Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition

Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost of the optimization algorithms required to compute the sparse representation. In this work we propose a simple and efficient algorithm to learn basis functions. After training, this model also provides a fast and smooth approximator to the optimal representation, achieving even better accuracy than exact sparse coding algorithms on visual object recognition tasks.


What are mobile developers asking about? A large scale study using stack overflow

The popularity of mobile devices has been steadily growing in recent years. These devices heavily depend on software from the underlying operating systems to the applications they run. Prior research showed that mobile software is different than traditional, large software systems. However, to date most of our research has been conducted on traditional software systems. Very little work has focused on the issues that mobile developers face. Therefore, in this paper, we use data from the popular online Q&A site, Stack Overflow, and analyze 13,232,821 posts to examine what mobile developers ask about. We employ Latent Dirichlet allocation-based topic models to help us summarize the mobile-related questions. Our findings show that developers are asking about app distribution, mobile APIs, data management, sensors and context, mobile tools, and user interface development. We also determine what popular mobile-related issues are the most difficult, explore platform specific issues, and investigate the types (e.g., what, how, or why) of questions mobile developers ask. Our findings help highlight the challenges facing mobile developers that require more attention from the software engineering research and development communities in the future and establish a novel approach for analyzing questions asked on Q&A forums.


Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to further enhance the retrieval accuracy. In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion. The primary contribution of this work is that two adversarial networks are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations. Such information guides the feature learning process and preserves the modality relationships in both the common semantic space and the Hamming space. Extensive experiments carried out on three benchmark datasets validate that the proposed SSAH surpasses the state-of-the-art methods.


Arthrodesis with Intramedular Fixation in Posttraumatic Arthrosis of the Midfoot: A Case Report

We present two middle-aged men with posttraumatic arthrosis of the midfoot. Both patients suffered from severe pain and their foot was unable to bear weight. Both were operated using new fusion bolt 6.5 mm and additional screws. In their case, arthrodesis was mandatory and effective intervention. After surgical treatment both patients were pain free and able to walk without crutches and return to daily work.


Wavelet analysis of EEG for seizure detection: Coherence and phasesynchrony estimation.

This paper deals with the wavelet analysis method f or seizure detection in EEG time series and coherence estimation. The main part of the paper pr esents the basic principles of signal decomposition in connection with the EEG frequency ba nds. Wavelet analysis method has been used for detection of seizure onset. The wavelet fi ltered signal is used for the computation of spectral power ratio. The results show that our met hod can identify pre seizure, seizure, post seizure and non seizure phases. When dealing with s eizure detection and prediction problems it is important to identify seizure precursor dynamics and necessary to identify information about onset and spread of seizures. Therefore in th e second part the coherence and phase synchrony during pre seizure, seizure, post seizure an d non seizure are computed. We expect this method to provide more insight into dynamic aspects of the seizure generating process.


Evaluating Digital Forensic Options for the Apple iPad

The iPod Touch, iPhone and iPad from Apple are among the most popular mobile computing platforms in use today. These devices are of forensic interest because of their high adoption rate and potential for containing digital evidence. The uniformity in their design and underlying operating system (iOS) also allows forensic tools and methods to be shared across product types. This paper analyzes the tools and methods available for conducting forensic examinations of the Apple iPad. These include commercial software products, updated methodologies based on existing jailbreaking processes and the analysis of the device backup contents provided by iTunes. While many of the available commercial tools offer promise, the results of our analysis indicate that most comprehensive examination of the iPad requires jailbreaking to perform forensic duplication and manual analysis of its media content.


Enhancing scholarly use of digital libraries: A comparative survey and review of bibliographic metadata ontologies

The HathiTrust Research Center (HTRC) is engaged in the development of tools that will give scholars the ability to analyze the HathiTrust digital library's 14 million volume corpus. A cornerstone of the HTRC's digital infrastructure is the workset -- a kind of scholar-built research collection intended for use with the HTRC's analytics platform. Because more than 66% of the digital corpus is subject to copyright restrictions, scholarly users remain dependent upon the descriptive accounts provided by traditional metadata records in order to identify and gather together bibliographic resources for analysis. This paper compares the MADSRDF/MODSRDF, Bibframe, schema.org, BIBO, and FaBiO ontologies by assessing their suitability for employment by the HTRC to meet scholars' needs. These include distinguishing among multiple versions of the same work; representing the complex historical and physical relationships among those versions; and identifying and providing access to finer grained bibliographic entities, e.g., poems, chapters, sections, and even smaller segments of content.


Decreased gut microbiota diversity, delayed Bacteroidetes colonisation and reduced Th1 responses in infants delivered by caesarean section.

OBJECTIVE
The early intestinal microbiota exerts important stimuli for immune development, and a reduced microbial exposure as well as caesarean section (CS) has been associated with the development of allergic disease. Here we address how microbiota development in infants is affected by mode of delivery, and relate differences in colonisation patterns to the maturation of a balanced Th1/Th2 immune response.

DESIGN
The postnatal intestinal colonisation pattern was investigated in 24 infants, born vaginally (15) or by CS (nine). The intestinal microbiota were characterised using pyrosequencing of 16S rRNA genes at 1 week and 1, 3, 6, 12 and 24 months after birth. Venous blood levels of Th1- and Th2-associated chemokines were measured at 6, 12 and 24 months.

RESULTS
Infants born through CS had lower total microbiota diversity during the first 2 years of life. CS delivered infants also had a lower abundance and diversity of the Bacteroidetes phylum and were less often colonised with the Bacteroidetes phylum. Infants born through CS had significantly lower levels of the Th1-associated chemokines CXCL10 and CXCL11 in blood.

CONCLUSIONS
CS was associated with a lower total microbial diversity, delayed colonisation of the Bacteroidetes phylum and reduced Th1 responses during the first 2 years of life.


Whole-function vectorization

Data-parallel programming languages are an important component in today's parallel computing landscape. Among those are domain-specific languages like shading languages in graphics (HLSL, GLSL, RenderMan, etc.) and "general-purpose" languages like CUDA or OpenCL. Current implementations of those languages on CPUs solely rely on multi-threading to implement parallelism and ignore the additional intra-core parallelism provided by the SIMD instruction set of those processors (like Intel's SSE and the upcoming AVX or Larrabee instruction sets). In this paper, we discuss several aspects of implementing dataparallel languages on machines with SIMD instruction sets. Our main contribution is a language- and platform-independent code transformation that performs whole-function vectorization on low-level intermediate code given by a control flow graph in SSA form. We evaluate our technique in two scenarios: First, incorporated in a compiler for a domain-specific language used in realtime ray tracing. Second, in a stand-alone OpenCL driver. We observe average speedup factors of 3.9 for the ray tracer and factors between 0.6 and 5.2 for different OpenCL kernels.


Argumentation Theory: A Very Short Introduction

Since the time of the ancient Greek philosophers and rhetoricians, argumentation theorists have searched for the requirements that make an argument correct, by some appropriate standard of proof, by examining the errors of reasoning we make when we try to use arguments. These errors have long been called fallacies, and the logic textbooks have for over 2000 years tried to help students to identify these fallacies, and to deal with them when they are encountered. The problem was that deductive logic did not seem to be much use for this purpose, and there seemed to be no other obvious formal structure that could usefully be applied to them. The radical approach taken by Hamblin (1970) was to refashion the concept of an argument to think of it not just as an arbitrarily designated set of propositions, but as a move one party makes in a dialog to offer premises that may be acceptable to another party who doubts the conclusion of the argument. Just after Hamblin's time a school of thought called informal logic grew up that wanted to take a new practical approach to teaching students skills of critical thinking by going beyond deductive logic to seek other methods for analyzing and evaluating arguments. Around the same time, an interdisciplinary group of scholars associated with the term 'argumentation', coming from fields like speech communication, joined with the informal logic group to help build up such practical methods and apply them to real examples of argumentation (Johnson and Blair, 1987). The methods that have been developed so far are still in a process of rapid evolution. More recently, improvements in them have been due to some computer scientists joining the group, and to collaborative research efforts between argumentation theorists and computer scientists. Another recent development has been the adaption of argumentation models and techniques to fields in artificial intelligence, like multi-agent systems and artificial intelligence for legal reasoning. In a short paper, it is not possible to survey all these developments. The best that can be done is to offer an introduction to some of the basic concepts and methods of argumentation theory as they have evolved to the present point, and to briefly indicate some problems and limitations in them. 1. Arguments and Argumentation There are four tasks undertaken by argumentation, or informal logic, as it is also often called: identification, analysis, evaluation and invention. The task of identification …


Super Normal Vector for Activity Recognition Using Depth Sequences

This paper presents a new framework for human activity recognition from video sequences captured by a depth camera. We cluster hypersurface normals in a depth sequence to form the polynormal which is used to jointly characterize the local motion and shape information. In order to globally capture the spatial and temporal orders, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time grids. We then propose a novel scheme of aggregating the low-level polynormals into the super normal vector (SNV) which can be seen as a simplified version of the Fisher kernel representation. In the extensive experiments, we achieve classification results superior to all previous published results on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.


A Seed-Based Method for Generating Chinese Confusion Sets

In natural language, people often misuse a word (called a “confused word”) in place of other words (called “confusing words”). In misspelling corrections, many approaches to finding and correcting misspelling errors are based on a simple notion called a “confusion set.” The confusion set of a confused word consists of confusing words. In this article, we propose a new method of building Chinese character confusion sets.
 Our method is composed of two major phases. In the first phase, we build a list of seed confusion sets for each Chinese character, which is based on measuring similarity in character pinyin or similarity in character shape. In this phase, all confusion sets are constructed manually, and the confusion sets are organized into a graph, called a “seed confusion graph” (SCG), in which vertices denote characters and edges are pairs of characters in the form (confused character, confusing character).
 In the second phase, we extend the SCG by acquiring more pairs of (confused character, confusing character) from a large Chinese corpus. For this, we use several word patterns (or patterns) to generate new confusion pairs and then verify the pairs before adding them into a SCG. Comprehensive experiments show that our method of extending confusion sets is effective. Also, we shall use the confusion sets in Chinese misspelling corrections to show the utility of our method.


Learning Over Long Time Lags

The advantage of recurrent neural networks (RNNs) in learni ng dependencies between time-series data has distinguished RNNs from other deep learning models. Recent ly, many advances are proposed in this emerging field. However, there is a lack of comprehensive review on mem ory models in RNNs in the literature. This paper provides a fundamental review on RNNs and long short te rm memory (LSTM) model. Then, provides a surveys of recent advances in different memory enhancement s and learning techniques for capturing long term dependencies in RNNs.


Ethical Artificial Intelligence

First Edition Please send typo and error reports, and any other comments, to hibbard@wisc.edu.


Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data

Part-of-speech information is a pre-requisite in many NLP algorithms. However, Twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. We present a detailed error analysis of existing taggers, motivating a series of tagger augmentations which are demonstrated to improve performance. We identify and evaluate techniques for improving English part-of-speech tagging performance in this genre. Further, we present a novel approach to system combination for the case where available taggers use different tagsets, based on voteconstrained bootstrapping with unlabeled data. Coupled with assigning prior probabilities to some tokens and handling of unknown words and slang, we reach 88.7% tagging accuracy (90.5% on development data). This is a new high in PTB-compatible tweet part-of-speech tagging, reducing token error by 26.8% and sentence error by 12.2%. The model, training data and tools are made available.


Photobook: Content-based manipulation of image databases

We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We discuss three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with text-based descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data.


Face recognition/detection by probabilistic decision-based neural network

This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.


Eigenfaces for Recognition

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.


The multilayer perceptron as an approximation to a Bayes optimal discriminant function

The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear.


An Efficient Active Learning Framework for New Relation Types

Supervised training of models for semantic relation extraction has yielded good performance, but at substantial cost for the annotation of large training corpora. Active learning strategies can greatly reduce this annotation cost. We present an efficient active learning framework that starts from a better balance between positive and negative samples, and boosts training efficiency by interleaving self-training and co-testing. We also studied the reduction of annotation cost by enforcing argument type constraints. Experiments show a substantial speed-up by comparison to the previous state-of-the-art pure co-testing active learning framework. We obtain reasonable performance with only 150 labels for individual ACE 2004 relation


Complications during root canal irrigation--literature review and case reports.

LITERATURE REVIEW AND CASE REPORTS: The literature concerning the aetiology, symptomatology and therapy of complications during root canal irrigation is reviewed. Three cases of inadvertent injection of sodium hypochlorite and hydrogen peroxide beyond the root apex are presented. Clinical symptoms are discussed, as well as preventive and therapeutic considerations.


Neural Machine Translation Advised by Statistical Machine Translation

Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016; He et al. 2016). This is in contrast to conventional Statistical Machine Translation (SMT), which usually yields adequate but non-fluent translations. It is natural, therefore, to leverage the advantages of both models for better translations, and in this work we propose to incorporate SMT model into NMT framework. More specifically, at each decoding step, SMT offers additional recommendations of generated words based on the decoding information from NMT (e.g., the generated partial translation and attention history). Then we employ an auxiliary classifier to score the SMT recommendations and a gating function to combine the SMT recommendations with NMT generations, both of which are jointly trained within the NMT architecture in an end-to-end manner. Experimental results on Chinese-English translation show that the proposed approach achieves significant and consistent improvements over state-of-the-art NMT and SMT systems on multiple NIST test sets.


Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation

General solutions to augment Knowledge Graphs (KGs) with facts extracted from Web tables aim to associate pairs of columns from the table with a KG relation based on the matches between pairs of entities in the table and facts in the KG. These approaches suffer from intrinsic limitations due to the incompleteness of the KGs. In this paper we investigate an alternative solution, which leverages the patterns that occur on the schemas of a large corpus of Wikipedia tables. Our experimental evaluation, which used DBpedia as reference KG, demonstrates the advantages of our approach over state-of-the-art solutions and reveals that we can extract more than 1.7M of facts with an estimated accuracy of 0.81 even from tables that do not expose any fact on the KG.


Breaking out of the box of recommendations: from items to packages

Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend top-k packages for the user to choose from.
 Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender system, focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of generating the top recommendation (package) is NP-complete, we devise several approximation algorithms for generating top-k packages as recommendations. We analyze their efficiency as well as approximation quality. Finally, using two real and two synthetic data sets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for top-k packages compared to exact algorithms.


Guided filtering for PRNU-based localization of small-size image forgeries

PRNU-based techniques guarantee a good forgery detection performance irrespective of the specific type of forgery. The presence or absence of the camera PRNU pattern is detected by a correlation test. Given the very low power of the PRNU signal, however, the correlation must be averaged over a pretty large window, reducing the algorithm's ability to reveal small forgeries. To improve resolution, we estimate correlation with a spatially adaptive filtering technique, with weights computed over a suitable pilot image. Implementation efficiency is achieved by resorting to the recently proposed guided filters. Experiments prove that the proposed filtering strategy allows for a much better detection performance in the case of small forgeries.


Intel MPX Explained: An Empirical Study of Intel MPX and Software-based Bounds Checking Approaches

Memory-safety violations are a prevalent cause of both reliability and security vulnerabilities in systems software written in unsafe languages like C/C++. Unfortunately, all the existing software-based solutions to this problem exhibit high performance overheads preventing them from wide adoption in production runs. To address this issue, Intel recently released a new ISA extension—Memory Protection Extensions (Intel MPX), a hardware-assisted full-stack solution to protect against memory safety violations. In this work, we perform an exhaustive study of the Intel MPX architecture to understand its advantages and caveats. We base our study along three dimensions: (a) performance overheads, (b) security guarantees, and (c) usability issues. To put our results in perspective, we compare Intel MPX with three prominent software-based approaches: (1) trip-wire—AddressSanitizer, (2) objectbased—SAFECode, and (3) pointer-based—SoftBound. Our main conclusion is that Intel MPX is a promising technique that is not yet practical for widespread adoption. Intel MPX’s performance overheads are still high (~50% on average), and the supporting infrastructure has bugs which may cause compilation or runtime errors. Moreover, we showcase the design limitations of Intel MPX: it cannot detect temporal errors, may have false positives and false negatives in multithreaded code, and its restrictions on memory layout require substantial code changes for some programs. This paper presents only the general discussion and aggregated data; for the complete evaluation, please see the supporting website: https://Intel-MPX.github.io/. Evaluation plots and section headings have hyperlinks to the complete experimental description and results.


Childhood predictors differentiate life-course persistent and adolescence-limited antisocial pathways among males and females.

This article reports a comparison on childhood risk factors of males and females exhibiting childhood-onset and adolescent-onset antisocial behavior, using data from the Dunedin longitudinal study. Childhood-onset delinquents had childhoods of inadequate parenting, neurocognitive problems, and temperament and behavior problems, whereas adolescent-onset delinquents did not have these pathological backgrounds. Sex comparisons showed a male-to-female ratio of 10:1 for childhood-onset delinquency but a sex ratio of only 1.5:1 for adolescence-onset delinquency. Showing the same pattern as males, childhood-onset females had high-risk backgrounds but adolescent-onset females did not. These findings are consistent with core predictions from the taxonomic theory of life-course persistent and adolescence-limited antisocial behavior.


Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.


CAPTCHA Design: Color, Usability, and Security

Most user interfaces use color, which can greatly enhance their design. Because the use of color is typically a usability issue, it rarely causes security failures. However, using color when designing CAPTCHAs, a standard security technology that many commercial websites apply widely, can have an impact on usability and interesting but critical implications for security. Here, the authors examine some CAPTCHAs to determine whether their use of color negatively affects their usability, security, or both.


Development and validation of measures of social phobia scrutiny fear and social interaction anxiety.

The development and validation of the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) two companion measures for assessing social phobia fears is described. The SPS assesses fear of being scrutinised during routine activities (eating, drinking, writing, etc.), while the SIAS assesses fear of more general social interaction, the scales corresponding to the DSM-III-R descriptions of Social Phobia--Circumscribed and Generalised types, respectively. Both scales were shown to possess high levels of internal consistency and test-retest reliability. They discriminated between social phobia, agoraphobia and simple phobia samples, and between social phobia and normal samples. The scales correlated well with established measures of social anxiety, but were found to have low or non-significant (partial) correlations with established measures of depression, state and trait anxiety, locus of control, and social desirability. The scales were found to change with treatment and to remain stable in the face of no-treatment. It appears that these scales are valid, useful, and easily scored measures for clinical and research applications, and that they represent an improvement over existing measures of social phobia.


Using Machine Learning to Refine Black-Box Test Specifications and Test Suites

In the context of open source development or software evolution, developers often face test suites which have been developed with no apparent rationale and which may need to be augmented or refined to ensure sufficient dependability, or even reduced to meet tight deadlines. We refer to this process as the re-engineering of test suites. It is important to provide both methodological and tool support to help people understand the limitations of test suites and their possible redundancies, so as to be able to refine them in a cost effective manner. To address this problem in the case of black-box testing, we propose a methodology based on machine learning that has shown promising results on a case study.


An architecture for the aggregation and analysis of scholarly usage data

Although recording of usage data is common in scholarly information services, its exploitation for the creation of value-added services remains limited due to concerns regarding, among others, user privacy, data validity, and the lack of accepted standards for the representation, sharing and aggregation of usage data. This paper presents a technical, standards-based architecture for sharing usage information, which we have designed and implemented. In this architecture, OpenURL-compliant linking servers aggregate usage information of a specific user community as it navigates the distributed information environment that it has access to. This usage information is made OAI-PMH harvestable so that usage information exposed by many linking servers can be aggregated to facilitate the creation of value-added services with a reach beyond that of a single community or a single information service. This paper also discusses issues that were encountered when implementing the proposed approach, and it presents preliminary results obtained from analyzing a usage data set containing about 3,500,000 requests aggregated by a federation of linking servers at the California State University system over a 20 month period.


Optimising thermal efficiency of direct contact membrane distillation by brine recycling for small-scale seawater desalination Revised Manuscript Submitted to Desalination

A technique to optimise thermal efficiency using brine recycling during direct contact membrane distillation (DCMD) of seawater was investigated. By returning the hot brine to the feed tank, the system water recovery could be increased and the sensible heat of the hot brine was recovered to improve thermal efficiency. The results show that in the optimal water recovery range of 20 to 60% facilitated by brine recycling, the specific thermal energy consumption of the process could be reduced by more than half. It is also noteworthy that within this optimal water recovery range, the risk of membrane scaling is negligible DCMD of seawater at a constant water recovery of 70% was achieved for over 24. h without any scale formation on the membrane surface. In contrast, severe membrane scaling was observed when water recovery reached 80%. In addition to water recovery, other operating conditions such as feed temperature and water circulation rates could influence the process thermal efficiency. Increasing the feed temperature and reducing the circulation flow rates increased thermal efficiency. Increasing the feed temperature could also mitigate the negative effect of elevated feed concentration on the distillate flux, particularly at a high water recovery. Disciplines Engineering | Science and Technology Studies Publication Details Duong, H. C., Cooper, P., Nelemans, B., Cath, T. Y. & Nghiem, L. D. (2015). Optimising thermal efficiency of direct contact membrane distillation by brine recycling for small-scale seawater desalination. Desalination, 374 1-9. This journal article is available at Research Online: http://ro.uow.edu.au/eispapers/4237 1 Optimising thermal efficiency of direct contact membrane distillation by brine recycling for small-scale seawater desalination Revised Manuscript Submitted to


A self-charging power unit by integration of a textile triboelectric nanogenerator and a flexible lithium-ion battery for wearable electronics.

A novel integrated power unit realizes both energy harvesting and energy storage by a textile triboelectric nanogenerator (TENG)-cloth and a flexible lithium-ion battery (LIB) belt, respectively. The mechanical energy of daily human motion is converted into electricity by the TENG-cloth, sustaining the energy of the LIB belt to power wearable smart electronics.


Learning Plannable Representations with Causal InfoGAN

In recent years, deep generative models have been shown to ‘imagine’ convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans – a plausible sequence of observations that transition a dynamical system from its current configuration to a desired goal state, which can later be used as a reference trajectory for control. We focus on systems with high-dimensional observations, such as images, and propose an approach that naturally combines representation learning and planning. Our framework learns a generative model of sequential observations, where the generative process is induced by a transition in a low-dimensional planning model, and an additional noise. By maximizing the mutual information between the generated observations and the transition in the planning model, we obtain a low-dimensional representation that best explains the causal nature of the data. We structure the planning model to be compatible with efficient planning algorithms, and we propose several such models based on either discrete or continuous states. Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations. We demonstrate our method on imagining plausible visual plans of rope manipulation3.


A gut (microbiome) feeling about the brain.

PURPOSE OF REVIEW
There is an increasing realization that the microorganisms which reside within our gut form part of a complex multidirectional communication network with the brain known as the microbiome-gut-brain axis. In this review, we focus on recent findings which support a role for this axis in modulating neurodevelopment and behavior.

RECENT FINDINGS
A growing body of research is uncovering that under homeostatic conditions and in response to internal and external stressors, the bacterial commensals of our gut can signal to the brain through a variety of mechanisms to influence processes such neurotransmission, neurogenesis, microglia activation, and modulate behavior. Moreover, the mechanisms underlying the ability of stress to modulate the microbiota and also for microbiota to change the set point for stress sensitivity are being unraveled. Dysregulation of the gut microbiota composition has been identified in a number of psychiatric disorders, including depression. This has led to the concept of bacteria that have a beneficial effect upon behavior and mood (psychobiotics) being proposed for potential therapeutic interventions.

SUMMARY
Understanding the mechanisms by which the bacterial commensals of our gut are involved in brain function may lead to the development of novel microbiome-based therapies for these mood and behavioral disorders.


A Dual-Polarized Pattern Reconfigurable Yagi Patch Antenna for Microbase Stations

A two-port pattern reconfigurable three-layered Yagi-Uda patch antenna with ±45° dual-polarization characteristic is presented. A driven patch (DP) and two large parasitic patches (LPPs) are printed on the top side of the middle layer, and the middle and bottom layers share a common metal ground. Two microstrip feedlines printed orthogonally on the bottom side of the bottom layer are used to feed the DP through two H-shaped slots etched in the ground. The LPPs are connected to/disconnected from the ground controlled by switches. By adjusting the connection states of the LPPs, one wide-beam mode and three narrow-beam modes can be obtained in both polarizations. A small parasitic patch is printed on the top side of the top layer to improve the pattern coherence of the two polarizations. This antenna is studied by both simulation and measurement. The measured common bandwidth of the four modes in both polarizations is 3.32–3.51 GHz, and the isolations between two polarizations in all the modes are higher than 20 dB. The low-profile antenna is very suitable for microbase-station applications.


Dynamic Malware Detection Using API Similarity

Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.


Interactive Block Games for Assessing Children's Cognitive Skills: Design and Preliminary Evaluation

Background: This paper presents design and results from preliminary evaluation of Tangible Geometric Games (TAG-Games) for cognitive assessment in young children. The TAG-Games technology employs a set of sensor-integrated cube blocks, called SIG-Blocks, and graphical user interfaces for test administration and real-time performance monitoring. TAG-Games were administered to children from 4 to 8 years of age for evaluating preliminary efficacy of this new technology-based approach. Methods: Five different sets of SIG-Blocks comprised of geometric shapes, segmented human faces, segmented animal faces, emoticons, and colors, were used for three types of TAG-Games, including Assembly, Shape Matching, and Sequence Memory. Computational task difficulty measures were defined for each game and used to generate items with varying difficulty. For preliminary evaluation, TAG-Games were tested on 40 children. To explore the clinical utility of the information assessed by TAG-Games, three subtests of the age-appropriate Wechsler tests (i.e., Block Design, Matrix Reasoning, and Picture Concept) were also administered. Results: Internal consistency of TAG-Games was evaluated by the split-half reliability test. Weak to moderate correlations between Assembly and Block Design, Shape Matching and Matrix Reasoning, and Sequence Memory and Picture Concept were found. The computational measure of task complexity for each TAG-Game showed a significant correlation with participants' performance. In addition, age-correlations on TAG-Game scores were found, implying its potential use for assessing children's cognitive skills autonomously.


Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach

Observed in many real applications, a top-k query often consists of two components to reflect a user's preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may want to have a thorough study of the data by initiating a multi-dimensional analysis of the top-k query results. Previous work on top-k query processing mainly focuses on optimizing data access according to the ranking function only. The problem of efficient answering top-k queries with multi-dimensional selections has not been well addressed yet.This paper proposes a new computational model, called ranking cube, for efficient answering top-k queries with multi-dimensional selections. We define a rank-aware measure for the cube, capturing our goal of responding to multi-dimensional ranking analysis. Based on the ranking cube, an efficient query algorithm is developed which progressively retrieves data blocks until the top-k results are found. The curse of dimensionality is a well-known challenge for the data cube and we cope with this difficulty by introducing a new technique of ranking fragments. Our experiments on Microsoft's SQL Server 2005 show that our proposed approaches have significant improvement over the previous methods.


IC-CRIME : A Collaborative , Web-Based , 3 D System for the Investigation , Analysis , and Annotation of Crime Scenes

Modern day crime scene investigation methods are continually being enhanced by the application of new technologies to improve the analysis and presentation of crime scene information, helping to solve and prosecute crimes. This paper describes a new system called IC-CRIME that integrates several new technologies to meet both of these needs. IC-CRIME employs laser scanning to produce realistic 3D models of a crime scene which it then incorporates into a 3D virtual environment. ICCRIME creates an integrated platform that encourages investigators and forensic experts to view, explore, and annotate these models at any time from any web browser. A key goal of the system is to expand opportunities for collaboration between professionals whose geographic locations and time commitments would otherwise limit their ability to contribute to the analysis of the actual crime scene.


Investment and Financing Constraints : Evidence from the Funding of Corporate Pension Plans

I exploit sharply nonlinear funding rules for defined benefit pension plans in order to identify the dependence of corporate investment on internal financial resources in a large sample. Capital expenditures decline with mandatory contributions to defined benefit pension plans, even when controlling for correlations between the pension funding status itself and the firm’s unobserved investment opportunities. The effect is particularly evident among firms that face financing constraints based on observable variables such as credit ratings. Investment also displays strong negative correlations with the part of mandatory contributions resulting solely from unexpected asset market movements. * Graduate School of Business, University of Chicago. I thank James Poterba, Stew Myers, Jonathan Gruber, Dirk Jenter, Heitor Almeida, Daniel Bergstresser, Mihir Desai, Michael Greenstone, Robin Greenwood, David Scharfstein, Antoinette Schoar, Jeremy Stein, and Amir Sufi for helpful comments and discussions. I would also like to thank Rob Stambaugh (the editor) and the referees. This work benefited greatly from the thoughts of economics seminar participants at MIT, Harvard, Princeton, and the Kennedy School of Government, and of finance seminar participants at the University of Chicago, the University of Pennsylvania (Wharton), Harvard, Stanford, Columbia, NYU (Stern), Dartmouth (Tuck), Michigan (Ross), Boston College (Carroll), Northwestern (Kellogg), Duke (Fuqua), and the 2004 Western Finance Association meetings in Vancouver. I am grateful to the Center for Retirement Research at Boston College and the National Bureau of Economic Research for financial support.


Abstractive Multi-Document Summarization : An Overview

In recent times, the necessity of generating single document summary has gained popularity among the researchers due to its extensive applicability. The text summarization can be categorized with different approaches like: extractive and abstractive from single document or multi document, goal of text summarization (intent, focus and coverage), characteristic of text summarization (frequency-based, knowledge-based and discoursebased), level of processing (surface level, entities level and discourse level) and kind of information (lexicon, structure information and deep understanding). Recently, the efforts in research are transferred from single document summarization to multi document summarization Multi-document summarization considerably differs from single in issues related to compression, speed, redundancy and passage selection are critical in the formation of useful summaries. In this paper, we review the techniques that have done for multi document summarization. Next, we describe evaluation method In conclusion; we propose our future work for multi document


Point & Teleport Locomotion Technique for Virtual Reality

With the increasing popularity of virtual reality (VR) and new devices getting available with relatively lower costs, more and more video games have been developed recently. Most of these games use first person interaction techniques since it is more natural for Head Mounted Displays (HMDs). One of the most widely used interaction technique in VR video games is locomotion that is used to move user's viewpoint in virtual environments. Locomotion is an important component of video games since it can have a strong influence on user experience. In this study, a new locomotion technique we called "Point & Teleport" is described and compared with two commonly used VR locomotion techniques of walk-in-place and joystick. In this technique, users simply point where they want to be in virtual world and they are teleported to that position. As a major advantage, it is not expected to introduce motion sickness since it does not involve any visible translational motion. In this study, two VR experiments were designed and performed to analyze the Point & Teleport technique. In the first experiment, Point & Teleport was compared with walk-in-place and joystick locomotion techniques. In the second experiment, a direction component was added to the Point & Teleport technique so that the users could specify their desired orientation as well. 16 users took part in both experiments. Results indicated that Point & Teleport is a fun and user friendly locomotion method whereas the additional direction component degraded the user experience.


The "visual cliff".

This simple apparatus is used to investigate depth perception in different animals. All species thus far tested seem able to perceive and avoid a sharp drop as soon as they can move about. Human infants at the creeping and toddling stage are notoriously prone to falls from more or less high places. They must be kept from going over the brink by side panels on their cribs, gates on stairways and the vigilance of adults. As their muscular coordination matures they begin to avoid such accidents on their own. Common sense might suggest that the child learns to recognize falling-off places by experience–that is, by falling and hurting himself. But is experience really the teacher? Or is the ability to perceive and avoid a brink part of the child's original endowment? Answers to these questions will throw light on the genesis of space perception in general. Height perception is a special case of distance perception: information in the light reaching the eye provides stimuli that can be utilized for the discrimination both of depth and of receding distance on the level. At what stage of development can an animal respond effectively to these stimuli? Does the onset of such response vary with animals of different species and habitats? At Cornell University we have been investigating these problems by means of a simple experimental setup that we call a visual cliff. The cliff is a simulated one and hence makes it possible not only to control the optical and other stimuli (auditory and tactual, for instance) but also to protect the experimental subjects. It consists of a board laid across a large sheet of heavy glass which is supported a foot or more above the floor. On one side of the board a sheet of patterned material is placed flush against the undersurface of the glass, giving the glass the appearance as well as the substance of solidity. On the other side a sheet of the same material is laid upon the floor; this side of the board thus becomes the visual cliff (Fig. 1). The Classic Visual Cliff Experiment This young explorer has the good sense not to crawl out onto an apparently unsupported surface, even when Mother beckons from the other side. Rats, pups, kittens, and chicks also will not try to walk across to the other side. (So don't bother asking why the chicken crossed the visual cliff.) …


Improved Redirection with Distractors: A large-scale-real-walking locomotion interface and its effect on navigation in virtual environments

Users in virtual environments often find navigation more difficult than in the real world. Our new locomotion interface, Improved Redirection with Distractors (IRD), enables users to walk in larger-than-tracked space VEs without predefined waypoints. We compared IRD to the current best interface, really walking, by conducting a user study measuring navigational ability. Our results show that IRD users can really walk through VEs that are larger than the tracked space and can point to targets and complete maps of VEs no worse than when really walking.


Quantifying immersion in virtual reality

Virtual Reality (VR) has generated much excitement but little formal proof that it is useful. Because VR interfaces are difficult and expensive to build, the computer graphics community needs to be able to predict which applications will benefit from VR. In this paper, we show that users with a VR interface complete a search task faster than users with a stationary monitor and a hand-based input device. We placed users in the center of the virtual room shown in Figure 1 and told them to look for camouflaged targets. VR users did not do significantly better than desktop users. However, when asked to search the room and conclude if a target existed, VR users were substantially better at determining when they had searched the entire room. Desktop users took 41% more time, re-examining areas they had already searched. We also found a positive transfer of training from VR to stationary displays and a negative transfer of training from stationary displays to VR.


When is it biased?: assessing the representativeness of twitter's streaming API

Twitter shares a free 1% sample of its tweets through the "Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.


Information security culture: A management perspective

Information technology has become an integral part of modern life. Today, the use of information permeates every aspect of both business and private lives. Most organizations need information systems to survive and prosper and thus need to be serious about protecting their information assets. Many of the processes needed to protect these information assets are, to a large extent, dependent on human cooperated behavior. Employees, whether intentionally or through negligence, often due to a lack of knowledge, are the greatest threat to information security. It has become widely accepted that the establishment of an organizational sub-culture of information security is key to managing the human factors involved in information security. This paper briefly examines the generic concept of corporate culture and then borrows from the management and economical sciences to present a conceptual model of information security culture. The presented model incorporates the concept of elasticity from the economical sciences in order to show how various variables in an information security culture influence each other. The purpose of the presented model is to facilitate conceptual thinking and argumentation about information security culture. a 2009 Elsevier Ltd. All rights reserved.


Designing Anthropomorphic Robot Hand With Active Dual-Mode Twisted String Actuation Mechanism and Tiny Tension Sensors

In this letter, using the active dual-mode twisted string actuation (TSA) mechanism and tiny tension sensors on the tendon strings, an anthropomorphic robot hand is newly designed in a compact manner. Thanks to the active dual-mode TSA mechanism, which is a miniaturized transmission, the proposed robot hand dose has a wide range of operation in terms of the grasping force and speed. It experimentally produces maximally the fingertip force 31.3 N and minimally the closing time of 0.5 s in average. Also, tiny tension sensor with the dimension of 4.7 (width) × 4.0 (height) × 10.75 (length) mm is newly presented and embedded at the fingertips in order to measure the tension on the tendon strings, which would allow the grasping force control. The kinetic and kinematic analyses are performed and the performance is verified by experiments.


Control of a Quadrotor Using a Smart Self-Tuning Fuzzy PID Controller

This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra’s algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.


A Flooding Warning System based on RFID Tag Array for Energy Facility

Passive radio-frequency identification (RFID) tags are widely used due to its economic cost and satisfactory performance. So far, passive RFID tags are mostly applied to identify certain objects, such as underground pipe identification under buried conditions. However, there is lack of study on the application of buried tags for further application. In this paper, the performances of buried RFID tags are studied to develop a flooding warning system based on RFID tag array for energy facility such as power stations. In this study, the corresponding signal strength received by the RFID reader is evaluated when the RFID tags are buried by seven materials respectively. The results show that flood warning detector can be constructed using passive RFID tag array and reader.


Cognitive Control Deficits in Schizophrenia: Mechanisms and Meaning

Although schizophrenia is an illness that has been historically characterized by the presence of positive symptomatology, decades of research highlight the importance of cognitive deficits in this disorder. This review proposes that the theoretical model of cognitive control, which is based on contemporary cognitive neuroscience, provides a unifying theory for the cognitive and neural abnormalities underlying higher cognitive dysfunction in schizophrenia. To support this model, we outline converging evidence from multiple modalities (eg, structural and functional neuroimaging, pharmacological data, and animal models) and samples (eg, clinical high risk, genetic high risk, first episode, and chronic subjects) to emphasize how dysfunction in cognitive control mechanisms supported by the prefrontal cortex contribute to the pathophysiology of higher cognitive deficits in schizophrenia. Our model provides a theoretical link between cellular abnormalities (eg, reductions in dentritic spines, interneuronal dysfunction), functional disturbances in local circuit function (eg, gamma abnormalities), altered inter-regional cortical connectivity, a range of higher cognitive deficits, and symptom presentation (eg, disorganization) in the disorder. Finally, we discuss recent advances in the neuropharmacology of cognition and how they can inform a targeted approach to the development of effective therapies for this disabling aspect of schizophrenia.


Tampering Detection in Low-Power Smart Cameras

A desirable feature in smart cameras is the ability to autonomously detect any tampering event/attack that would prevent a clear view over the monitored scene. No matter whether tampering is due to atmospheric phenomena (e.g., few rain drops over the camera lens) or to malicious attacks (e.g., occlusions or device displacements), these have to be promptly detected to possibly activate countermeasures. Tampering detection is particularly challenging in battery-powered cameras, where it is not possible to acquire images at full-speed frame-rates, nor use sophisticated image-analysis algorithms. We here introduce a tampering-detection algorithm specifically designed for low-power smart cameras. The algorithm leverages very simple indicators that are then monitored by an outlier-detection scheme: any frame yielding an outlier is detected as tampered. Core of the algorithm is the partitioning of the scene into adaptively defined regions, that are preliminarily defined by segmenting the image during the algorithmconfiguration phase, and which shows to improve the detection of camera displacements. Experiments show that the proposed algorithm can successfully operate on sequences acquired at very low-frame rate, such as one frame every minute, with a very small computational complexity.


W-BAND MICROSTRIP-TO-WAVEGUIDE TRANSITION USING VIA FENCES

Abstract—The paper presents integrated probe for direct coupling to the WR-10 waveguide with the use of metal filled vias on both sides of the microstrip line. Design and optimization of this novel microstripto-waveguide transition has been performed using 3-D finite element method based software HFSS (High Frequency Structure Simulator). A back-to-back transition has been fabricated and measured between 75–110GHz. The measured return loss is higher than 10 dB and the insertion loss for a single microstrip-to-waveguide transition is about 1.15 dB.


Hierarchical Discrete Distribution Decomposition for Match Density Estimation

Existing deep learning methods for pixel correspondence output a point estimate of the motion field, but do not represent the full match distribution. Explicit representation of a match distribution is desirable for many applications as it allows direct representation of the correspondence probability. The main difficulty of estimating a full probability distribution with a deep network is the high computational cost of inferring the entire distribution. In this paper, we propose Hierarchical Discrete Distribution Decomposition, dubbed HD, to learn probabilistic point and region matching. Not only can it model match uncertainty, but also region propagation. To achieve this, we estimate the hierarchical distribution of pixel correspondences at different image scales without multi-hypotheses ensembling. Despite its simplicity, our method can achieve competitive results for both optical flow and stereo matching on established benchmarks, while the estimated uncertainty is a good indicator of errors. Furthermore, the point match distribution within a region can be grouped together to propagate the whole region even if the area changes across images.


A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder.

OBJECTIVE
To determine the empirical evidence for deficits in working memory (WM) processes in children and adolescents with attention-deficit/hyperactivity disorder (ADHD).

METHOD
Exploratory meta-analytic procedures were used to investigate whether children with ADHD exhibit WM impairments. Twenty-six empirical research studies published from 1997 to December, 2003 (subsequent to a previous review) met our inclusion criteria. WM measures were categorized according to both modality (verbal, spatial) and type of processing required (storage versus storage/manipulation).

RESULTS
Children with ADHD exhibited deficits in multiple components of WM that were independent of comorbidity with language learning disorders and weaknesses in general intellectual ability. Overall effect sizes for spatial storage (effect size = 0.85, CI = 0.62 - 1.08) and spatial central executive WM (effect size = 1.06, confidence interval = 0.72-1.39) were greater than those obtained for verbal storage (effect size = 0.47, confidence interval = 0.36-0.59) and verbal central executive WM (effect size = 0.43, confidence interval = 0.24-0.62).

CONCLUSION
Evidence of WM impairments in children with ADHD supports recent theoretical models implicating WM processes in ADHD. Future research is needed to more clearly delineate the nature, severity, and specificity of the impairments to ADHD.


Terpene synthases of oregano (Origanum vulgare L.) and their roles in the pathway and regulation of terpene biosynthesis

The aroma, flavor and pharmaceutical value of cultivated oregano (Origanum vulgare L.) is a consequence of its essential oil which consists mostly of monoterpenes and sesquiterpenes. To investigate the biosynthetic pathway to oregano terpenes and its regulation, we identified and characterized seven terpene synthases, key enzymes of terpene biosynthesis, from two cultivars of O. vulgare. Heterologous expression of these enzymes showed that each forms multiple mono- or sesquiterpene products and together they are responsible for the direct production of almost all terpenes found in O. vulgare essential oil. The correlation of essential oil composition with relative and absolute terpene synthase transcript concentrations in different lines of O. vulgare demonstrated that monoterpene synthase activity is predominantly regulated on the level of transcription and that the phenolic monoterpene alcohol thymol is derived from γ-terpinene, a product of a single monoterpene synthase. The combination of heterologously-expressed terpene synthases for in vitro assays resulted in blends of mono- and sesquiterpene products that strongly resemble those found in vivo, indicating that terpene synthase expression levels directly control the composition of the essential oil. These results will facilitate metabolic engineering and directed breeding of O. vulgare cultivars with higher quantity of essential oil and improved oil composition.


A wireless slanted optrode array with integrated micro leds for optogenetics

This paper presents a wireless-enabled, flexible optrode array with multichannel micro light-emitting diodes (μ-LEDs) for bi-directional wireless neural interface. The array integrates wirelessly addressable μ-LED chips with a slanted polymer optrode array for precise light delivery and neural recording at multiple cortical layers simultaneously. A droplet backside exposure (DBE) method was developed to monolithically fabricate varying-length optrodes on a single polymer platform. In vivo tests in rat brains demonstrated that the μ-LEDs were inductively powered and controlled using a wireless switched-capacitor stimulator (SCS), and light-induced neural activity was recorded with the optrode array concurrently.


Efficient projections onto the l1-ball for learning in high dimensions

We describe efficient algorithms for projecting a vector onto the l1-ball. We present two methods for projection. The first performs exact projection in O(n) expected time, where n is the dimension of the space. The second works on vectors k of whose elements are perturbed outside the l1-ball, projecting in O(k log(n)) time. This setting is especially useful for online learning in sparse feature spaces such as text categorization applications. We demonstrate the merits and effectiveness of our algorithms in numerous batch and online learning tasks. We show that variants of stochastic gradient projection methods augmented with our efficient projection procedures outperform interior point methods, which are considered state-of-the-art optimization techniques. We also show that in online settings gradient updates with l1 projections outperform the exponentiated gradient algorithm while obtaining models with high degrees of sparsity.


Integrated on-chip inductors with electroplated magnetic yokes ( invited )

J. Appl. Phys. 111, 07E328 (2012) A single-solenoid pulsed-magnet system for single-crystal scattering studies Rev. Sci. Instrum. 83, 035101 (2012) Solution to the problem of E-cored coil above a layered half-space using the method of truncated region eigenfunction expansion J. Appl. Phys. 111, 07E717 (2012) Array of 12 coils to measure the position, alignment, and sensitivity of magnetic sensors over temperature J. Appl. Phys. 111, 07E501 (2012) Skin effect suppression for Cu/CoZrNb multilayered inductor J. Appl. Phys. 111, 07A501 (2012)


Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.


Dermoscopy of cutaneous leishmaniasis.

BACKGROUND
Dermoscopy has been proposed as a diagnostic tool in the case of skin infections and parasitosis but no specific dermoscopic criteria have been described for cutaneous leishmaniasis (CL).

OBJECTIVES
To describe the dermoscopic features of CL.

METHODS
Dermoscopic examination (using the DermLite Foto; 3Gen, LLC, Dana Point, CA, U.S.A.) of 26 CL lesions was performed to evaluate specific dermoscopic criteria.

RESULTS
We observed the following dermoscopic features: generalized erythema (100%), 'yellow tears' (53%), hyperkeratosis (50%), central erosion/ulceration (46%), erosion/ulceration associated with hyperkeratosis (38%) and 'white starburst-like pattern' (38%). Interestingly, at least one vascular structure described in skin neoplasms was observed in all cases: comma-shaped vessels (73%), linear irregular vessels (57%), dotted vessels (53%), polymorphous/atypical vessels (26%), hairpin vessels (19%), arborizing telangiectasia (11%), corkscrew vessels (7%) and glomerular-like vessels (7%). Combination of two or more different types of vascular structures was present in 23 of 26 CL lesions (88%), with a combination of two vascular structures in 13 cases (50%) and three or more in 10 cases (38%).

CONCLUSIONS
Characteristic dermoscopic structures have been identified in CL. Important vascular patterns seen in melanocytic and nonmelanocytic tumours are frequently observed in this infection.