Accurate Scale Estimation for Robust Visual Tracking

Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation. Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8% in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-theart trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6% in median distance precision, while operating at real-time.


ClaimVerif: A Real-time Claim Verification System Using the Web and Fact Databases

Our society is increasingly digitalized. Every day, a tremendous amount of information is being created, shared, and digested through all kinds of cyber channels. Although people can easily acquire information from various sources (social media, news articles, etc.), the truthfulness of most received information remains unverified. In many real-life scenarios, false information has become the de facto cause that leads to detrimental decision makings, and techniques that can automatically filter false information are highly demanded. However, verifying whether a piece of information is trustworthy is difficult because: (1) selecting candidate snippets for fact checking is nontrivial; and (2) detecting supporting evidences, i.e. stances, suffers from the difficulty of measuring the similarity between claims and related evidences. We build ClaimVerif, a claim verification system that not only provides credibility assessment for any user-given query claim, but also rationales the assessment results with supporting evidences. ClaimVerif can automatically select the stances from millions of documents and employs two-step training to justify the opinions of the stances. Furthermore, combined with the credibility of stances sources, ClaimVerif degrades the score of stances from untrustworthy sources and alleviates the negative effects from rumor spreaders. Our empirical evaluations show that ClaimVerif achieves both high accuracy and efficiency in different claim verification tasks. It can be highly useful in practical applications by providing multi-dimension analysis for the suspicious statements, including the stances, opinions, source credibility and estimated judgements.


Software metrics : usability and evaluation of software quality

It is difficult to understand, let alone improve, the quality of software without the knowledge of its software development process and software products. There must be some measurement process to predict the software development, and to evaluate the software products. This thesis provides a brief view on Software Quality, Software Metrics, and Software Metrics methods that will predict and measure the specified quality factors of software. It further discusses about the Quality as given by the standards such as ISO, principal elements required for the Software Quality and Software Metrics as the measurement technique to predict the Software Quality. This thesis was performed by evaluating a source code developed in Java, using Software Metrics, such as Size Metrics, Complexity Metrics, and Defect Metrics. Results show that, the quality of software can be analyzed, studied and improved by the usage of software metrics.


Learning Embedding Adaptation for Few-Shot Learning

Learning with limited data is a key challenge for visual recognition. Few-shot learning methods address this challenge by learning an instance embedding function from seen classes, and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them is the target task. In this paper, we propose a novel approach to adapt the embedding model to the target classification task, yielding embeddings that are task-specific and are discriminative. To this end, we employ a type of self-attention mechanism called Transformer to transform the embeddings from task-agnostic to task-specific by focusing on relating instances from the test instances to the training instances in both seen and unseen classes. Our approach also extends to both transductive and generalized few-shot classification, two important settings that have essential use cases. We verify the effectiveness of our model on two standard benchmark few-shot classification datasets — MiniImageNet and CUB, where our approach demonstrates state-of-the-art empirical performance.


Fabrication of zero-speed sensor using weakly coupled NiFe/CoFe multilayer films

A magnetic sensor consisting of NiFe/Al/sub 2/O/sub 3//CoFe thin film was fabricated. Magnetization reversal of the NiFe soft layer was asymmetric in switching to parallel and antiparallel alignments to magnetization of CoFe because of magnetostatic coupling between the NiFe and CoFe layers. When the magnetization of the NiFe was paralleled to that of CoFe, a steep switching accompanied a large Barkhausen jump-induced pulse voltage in a pickup coil as sensor output. Amplitude of the pulse was not dependent on frequency of an external magnetic field. The pulse voltage output was obtained from the sensor element consisting of the optimized film structure excited by the magnetic field at almost zero frequency. These features are also achieved by twisted FeCoV and other wire materials. However, an integrated on-chip sensor with electronic circuits can be fabricated with film-based materials. The film-based sensor elements fabricated in this study can be utilized for speed sensor, rotation sensor, flow meter, and other applications.


Metamaterial-Based Low-Profile Broadband Mushroom Antenna

A metamaterial-based broadband low-profile mushroom antenna is presented. The proposed antenna is formed using an array of mushroom cells and a ground plane, and fed by a microstrip line through a slot cut onto the ground plane. With the feeding slot right underneath the center gap between the mushroom cells, the dual resonance modes are excited simultaneously for the radiation at boresight. A transmission-line model integrated with the dispersion relation of a composite right/left-handed mushroom structure is applied to analyze the modes. The proposed dielectric-filled (εr=3.38) mushroom antenna with a low profile of 0.06λ0 ( λ0 is the operating wavelength in free space) and a ground plane of 1.10λ0×1.10λ0 attains 25% measured bandwidth with(|S11| <; - 10dB) 9.9-dBi average gain at 5-GHz band. Across the bandwidth, the antenna efficiency is greater than 76%, and cross-polarization levels are less than -20 dB.


Integrating shape grammars into a generative system for Zhuang ethnic embroidery design exploration

The integration of shape grammars within a generative system is thought to have good potential to support design applications in the art and cultural domains. Based on a shape decomposing method, two-level generative shape grammars are developed within the application of Zhuang ethnic embroidery design. In our approach, free B-spline curves are used to derive initial design concepts together with a new interpolation algorithm. In order to cope with the potentially huge solution space, a novel aesthetic evaluation model and a design exploration algorithm are developed for designers to find their favorite design solutions based on their subjective assessments. A system is introduced to demonstrate this research with an example of Zhuang ethnic embroidery design. © 2012 Elsevier Ltd. All rights reserved.


Foot Design for a Large Walking Delta Robot

A large Delta robot has been modified to pick up and place a triangular shaped foot. The robot pushes the foot down to lift the body and the three associated fixed support legs. It then moves the foot sideways so that the body moves in the opposite direction to a new position where the fixed legs are lowered to the ground and the foot lifted ready for a new placement. In this way the robot is able to move in any direction with a tripod gait. The use of a combined foot and gripper for moving heavy objects is investigated. In the implementation reported, the gearboxes used are found to limit the operation and spring assistance is investigated as a trade off with payload. The small prototype, stands as tall as a person, weighs a third of a ton, and has a payload capability of near 100%. It is envisaged as a prototype for a much larger walking robot capable of general-purpose heavy lifting.


From a Set of Shapes to Object Discovery

This paper presents an approach to object discovery in a give n unlabeled image set, based on mining repetitive spatial config urations of image contours. Contours that similarly deform from one image to a n ther are viewed as collaborating, or, otherwise, conflicting. This is captu red by a graph over all pairs of matching contours, whose maximum a posteriori mult icoloring assignment is taken to represent the shapes of discovered objects. Multicoloring is conducted by our new Coordinate Ascent Swendsen-Wang cut (CASW ). CASW uses the Metropolis-Hastings (MH) reversible jumps to probabil istically sample graph edges, and color nodes. CASW extends SW cut by introducing a r e ularization in the posterior of multicoloring assignments that prevent s the MH jumps to arrive at trivial solutions. Also, CASW seeks to learn paramet ers of the posterior via maximizing a lower bound of the MH acceptance rate. This s peeds up multicoloring iterations, and facilitates MH jumps from local mi nima. On benchmark datasets, we outperform all existing approaches to unsuper vised object discovery.


Person Re-identification by Discriminatively Selecting Parts and Features

This paper presents a novel appearance-based method for person re-identification. The core idea is to rank and select different body parts on the basis of the discriminating power of their characteristic features. In our approach, we first segment the pedestrian images into meaningful parts, then we extract features from such parts as well as from the whole body and finally, we perform a salience analysis based on regression coefficients. Given a set of individuals, our method is able to estimate the different importance (or salience) of each body part automatically. To prove the effectiveness of our approach, we considered two standard datasets and we demonstrated through an exhaustive experimental section how our method improves significantly upon existing approaches, especially in multiple-shot scenarios.


A Pipelined CRC Calculation Using Lookup Tables

We present a fast cyclic redundancy check (CRC) algorithm that performs CRC computation for any length of message in parallel. Traditional CRC implementations have feedbacks, which make pipelining problematic. In the proposed approach, we eliminate feedbacks and implement a pipelined calculation of 32-bit CRC in the SMIC 0.13 μm CMOS technology. For a given message, the algorithm first chunks the message into blocks, each of which has a fixed size equal to the degree of the generator polynomial. Then it computes CRC for the chunked blocks in parallel using lookup tables, and the results are combined together by performing XOR operations. The simulation results show that our proposed pipelined CRC is more efficient than existing CRC implementations.


Periodic Leaky-Wave Antenna Array With Horizontally Polarized Omnidirectional Pattern

A novel periodic leaky-wave antenna for broadside radiation with horizontally polarized omnidirectional pattern is proposed, designed and demonstrated experimentally. The objective is to achieve a horizontally polarized omnidirectional antenna array with highly directive beam. The proposed structure is based on combining the rotating electric field method and a leaky-wave antenna design. By properly designing a unit cell with a pair of crossed dipoles spaced at a distance approximately equal to λg/4 at the center frequency, not only omnidirectional radiation pattern with horizontal polarization is obtained by crossed dipoles fed in phase quadrature, but also the open-stopband effect exhibited at broadside scan is significantly reduced. The analysis and design of this structure are performed on the basis of a simple equivalent network model. A finite structure formed by 16-unit cell (32-element) for broadside beam is developed at the 2.4 GHz band, which offers a horizontally polarized omnidirectional radiation pattern with enhanced gain of 9.9-10.6 dBi and measured radiation efficiency of 90% for the covering bands.


A comprehensive study of visual event computing

This paper contains a survey on aspects of visual event computing. We start by presenting events and their classifications, and continue with discussing the problem of capturing events in terms of photographs, videos, etc, as well as the methodologies for event storing and retrieving. Later, we review an extensive set of papers taken from well-known conferences and journals in multiple disciplines. We analyze events, and summarize the procedure of visual event actions. We introduce each component of a visual event computing system, and its computational aspects, we discuss the progress of each component and review its overall status. Finally, we suggest future research trends in event computing and hope to introduce a comprehensive profile of visual event computing to readers.


Estimation of safe sensor measurements of autonomous system under attack

The introduction of automation in cyber-physical systems (CPS) has raised major safety and security concerns. One attack vector is the sensing unit whose measurements can be manipulated by an adversary through attacks such as denial of service and delay injection. To secure an autonomous CPS from such attacks, we use a challenge response authentication (CRA) technique for detection of attack in active sensors data and estimate safe measurements using the recursive least square algorithm. For demonstrating effectiveness of our proposed approach, a car-follower model is considered where the follower vehicle's radar sensor measurements are manipulated in an attempt to cause a collision.


How robust are prediction effects in language comprehension? Failure to replicate article-elicited N400 effects

Current psycholinguistic theory proffers prediction as a central, explanatory mechanism in language processing. However, widely-replicated prediction effects may not mean that prediction is necessary in language processing. As a case in point, C. D. Martin et al. [2013. Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language, 69(4), 574–588. doi:10.1016/j.jml.2013.08.001] reported ERP evidence for prediction in nativebut not in non-native speakers. Articles mismatching an expected noun elicited larger negativity in the N400 time window compared to articles matching the expected noun in native speakers only. We attempted to replicate these findings, but found no evidence for prediction irrespective of language nativeness. We argue that pre-activation of phonological form of upcoming nouns, as evidenced in article-elicited effects, may not be a robust phenomenon. A view of prediction as a necessary computation in language comprehension must be re-evaluated. ARTICLE HISTORY Received 1 June 2016 Accepted 22 September 2016


Filterbank-based fingerprint matching

With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.


On-line fingerprint verification

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy. Index Terms —Biometrics, fingerprints, matching, verification, minutia, orientation field, ridge extraction. —————————— ✦ ——————————


FVC2002: Second Fingerprint Verification Competition

Two years after the first edition, a new Fingerprint Verification Competition (FVC2002) was organized by the authors, with the aim of determining the state-of-theart in this challenging pattern recognition application. The experience and the feedback received from FVC2000 allowed the authors to improve the organization of FVC2002 and to capture the attention of a significantly higher number of academic and commercial organizations (33 algorithms were submitted). This paper discusses the FVC2002 database, the test protocol and the main differences between FVC2000 and FVC2002. The algorithm performance evaluation will be presented at the 16 ICPR.


Cover your ACKs: pitfalls of covert channel censorship circumvention

In response to increasingly sophisticated methods of blocking access to censorship circumvention schemes such as Tor, recently proposed systems such as Skypemorph, FreeWave, and CensorSpoofer have used voice and video conferencing protocols as "cover channels" to hide proxy connections. We demonstrate that even with perfect emulation of the cover channel, these systems can be vulnerable to attacks that detect or disrupt the covert communications while having no effect on legitimate cover traffic. Our attacks stem from differences in the channel requirements for the cover protocols, which are peer-to-peer and loss tolerant, and the covert traffic, which is client-proxy and loss intolerant. These differences represent significant limitations and suggest that such protocols are a poor choice of cover channel for general censorship circumvention schemes.


Smart Agents in Industrial Cyber–Physical Systems

Future industrial systems can be realized using the cyber-physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system's functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.


Katyusha: the first direct acceleration of stochastic gradient methods

Nesterovâs momentum trick is famously known for accelerating gradient descent, and has been proven useful in building fast iterative algorithms. However, in the stochastic setting, counterexamples exist and prevent Nesterovâs momentum from providing similar acceleration, even if the underlying problem is convex.
We introduce Katyusha, a direct, primal-only stochastic gradient method to fix this issue. It has a provably accelerated convergence rate in convex (off-line) stochastic optimization. The main ingredient is Katyusha momentum, a novel ânegative momentumâ on top of Nesterovâs momentum that can be incorporated into a variance-reduction based algorithm and speed it up. Since variance reduction has been successfully applied to a growing list of practical problems, our paper suggests that in each of such cases, one could potentially give Katyusha a hug.


A maximum entropy approach to adaptive statistical language modelling

An adaptive statistical language model is described, which successfully integrates long distance linguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document’s history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. The ME framework is extremely general: any phenomenon that can be described in terms of statistics of the text can be readily incorporated. An adaptive language model based on the ME approach was trained on the Wall Street Journal corpus, and showed a 32–39% perplexity reduction over the baseline. When interfaced to SPHINX-II, Carnegie Mellon’s speech recognizer, it reduced its error rate by 10–14%. This thus illustrates the feasibility of incorporating many diverse knowledge sources in a single, unified statistical framework.  1996 Academic Press Limited


Expressing Interactivity with States and Constraints

......................................................................................................................... iii Acknowledgements ......................................................................................................... iv Table of


Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching

Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.


An anatomically-constrained local deformation model for monocular face capture

We present a new anatomically-constrained local face model and fitting approach for tracking 3D faces from 2D motion data in very high quality. In contrast to traditional global face models, often built from a large set of blendshapes, we propose a local deformation model composed of many small subspaces spatially distributed over the face. Our local model offers far more flexibility and expressiveness than global blendshape models, even with a much smaller model size. This flexibility would typically come at the cost of reduced robustness, in particular during the under-constrained task of monocular reconstruction. However, a key contribution of this work is that we consider the face anatomy and introduce subspace skin thickness constraints into our model, which constrain the face to only valid expressions and helps counteract depth ambiguities in monocular tracking. Given our new model, we present a novel fitting optimization that allows 3D facial performance reconstruction from a single view at extremely high quality, far beyond previous fitting approaches. Our model is flexible, and can be applied also when only sparse motion data is available, for example with marker-based motion capture or even face posing from artistic sketches. Furthermore, by incorporating anatomical constraints we can automatically estimate the rigid motion of the skull, obtaining a rigid stabilization of the performance for free. We demonstrate our model and single-view fitting method on a number of examples, including, for the first time, extreme local skin deformation caused by external forces such as wind, captured from a single high-speed camera.


ESD reliability improvement of the 0.25-µm 60-V power nLDMOS with discrete embedded SCRs separated by STI structures

In this paper, the electrostatic-discharge (ESD) robustness improvement by modulating the drain-side embedded SCR of an HV nLDMOS device is investigated via a TSMC 0.25 μm 60 V process. After a systematic layout design and data analysis, it can be found that the holding voltage (Vh) of an nLDMOS with a parasitic SCR “npn”-arranged type & thin oxide (OD) discrete (i.e. separated by the shallow-trench isolation (STI) structure) distribution in the drain-side have greatly increased with the parasitic SCR OD decreasing. Therefore, a high Vh value in the OD discrete parameter 2 (DIS-2) can be obtained about 13.3 V. On the other hand, the trigger voltage (Vt1) values and the holding voltage (Vh) values of the nLDMOS DUTs with a parasitic SCR “pnp”-arranged type & OD discrete distribution in the drain-side are slowly increased with the parasitic-SCR OD decreasing. The best Vh value in the “pnp”-arranged type & OD discrete parameter 2 (DIS-2) is about 14.4 V. Meanwhile, the secondary breakdown current (It2) values of this type are greater than 7 A except for the OD discrete parameter 2 and 3. Therefore, an appropriate layout of nLDMOS embedded with a drain-side “pnp” arranged type & OD discrete distribution that can yield high ESD and latch-up (LU) robustness levels.


The huge Package for High-dimensional Undirected Graph Estimation in R

We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data generation and graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected; (5) the package allows the user to apply both lossless and lossy screening rules to scale up large-scale problems, making a tradeoff between computational and statistical efficiency.


Semantic Attachments for Domain-Independent Planning Systems

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attach-


Visualising convolutional neural network decisions in automated sleep scoring

Current sleep medicine relies on the supervised analysis of polysomnographic recordings, which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework for automated sleep classification, however, the lack of interpretability of its results has hampered CNN’s further use in medicine. In this study, we train a CNN using as input Continuous Wavelet transformed EEG, EOG and EMG recordings from a publicly available dataset. The network achieved a 10-fold cross-validation Cohen’s Kappa score of κ = 0.71±0.01. Further, we provide insights on how this network classifies individual epochs of sleep using Guided Gradient-weighted Class Activation Maps (Guided Grad-CAM). The proposed approach is able to produce fine-grained activation maps on time-frequency domain for each signal providing a useful tool for identifying relevant features in CNNs.


Long-Term Treatment with High-Dose Thiamine in Parkinson Disease: An Open-Label Pilot Study.

OBJECTIVES
To investigate the potential clinical, restorative, and neuroprotective effects of long-term treatment with thiamine in Parkinson disease (PD).

DESIGN
Observational open-label pilot study.

SETTING
Outpatient neurologic rehabilitation clinic.

PATIENTS AND METHODS
Starting in June 2012, we have recruited 50 patients with PD (33 men and 17 women; mean age, 70.4 ± 12.9 years; mean disease duration, 7.3 ± 6.7 years). All the patients were assessed at baseline with the Unified Parkinson's Disease Rating Scale (UPDRS) and the Fatigue Severity Scale (FSS) and began treatment with 100 mg of thiamine administered intramuscularly twice a week, without any change to personal therapy. All the patients were re-evaluated after 1 month and then every 3 months during treatment.

RESULTS
Thiamine treatment led to significant improvement of motor and nonmotor symptoms: mean UPDRS scores (parts I-IV) improved from 38.55 ± 15.24 to 18.16 ± 15.08 (p = 2.4 × 10(-14), t test for paired data) within 3 months and remained stable over time; motor UPDRS part III score improved from 22.01 ± 8.57 to 9.92 ± 8.66 (p = 3.1 × 10(-22)). Some patients with a milder phenotype had complete clinical recovery. FSS scores, in six patients who had fatigue, improved from 53.00 ± 8.17 to 23.60 ± 7.77 (p < 0.0001, t test for paired data). Follow-up duration ranged from 95 to 831 days (mean, 291.6 ± 207.2 days).

CONCLUSIONS
Administration of parenteral high-dose thiamine was effective in reversing PD motor and nonmotor symptoms. The clinical improvement was stable over time in all the patients. From our clinical evidence, we hypothesize that a dysfunction of thiamine-dependent metabolic processes could cause selective neural damage in the centers typically affected by this disease and might be a fundamental molecular event provoking neurodegeneration. Thiamine could have both restorative and neuroprotective action in PD.


Information quality in information fusion

Designing fusion systems for decision support in complex dynamic situations calls for an integrated human-machine information environment, in which some processes are best executed automatically while for others the judgment and guidance of human experts and end-users are critical. Thus decision making in such environment requires constant information exchange between human and automated agents that utilize operational data, data obtained from sensors, intelligence reports, and open source information. The quality of decision making strongly depends on the success of being aware of, and compensating for, insufficient information quality at each step of information exchange. Designing the methods of representing and incorporating information quality into this environment is a relatively new and a rather difficult problem. The paper discusses major challenges and suggests some approaches to address this problem.


An Intelligent Tutoring System for Entity Relationship Modelling

The paper presents KERMIT, a Knowledge-based Entity Relationship Modelling Intelligent Tutor. KERMIT is a problem-solving environment for the university-level students, in which they can practise conceptual database design using the Entity-Relationship data model. KERMIT uses Constraint-Based Modelling (CBM) to model the domain knowledge and generate student models. We have used CBM previous in tutors that teach SQL and English punctuation rules. The research presented in this paper is significant because we show that CBM can be used to support students learning design tasks, which are very different from domains we dealt with in earlier tutors. The paper describes the system’s architecture and functionality. The system observes students’ actions and adapts to their knowledge and learning abilities. KERMIT has been evaluated in the context of genuine teaching activities. We present the results of two evaluation studies with students taking database courses, which show that KERMIT is an effective system. The students have enjoyed the system’s adaptability and found it a valuable asset to their learning.


A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. A general formulation for editing graphs is used to derive a graph edit distance that is proven to be a metric, provided the cost function for individual edit operations is a metric. Then, a binary linear program is developed for computing this graph edit distance, and polynomial time methods for determining upper and lower bounds on the solution of the binary program are derived by applying solution methods for standard linear programming and the assignment problem. A recognition problem of comparing a sample input graph to a database of known prototype graphs in the context of a chemical information system is presented as an application of the new method. The costs associated with various edit operations are chosen by using a minimum normalized variance criterion applied to pairwise distances between nearest neighbors in the database of prototypes. The new metric is shown to perform quite well in comparison to existing metrics when applied to a database of chemical graphs


Blogging as social activity, or, would you let 900 million people read your diary?

"Blogging" is a Web-based form of communication that is rapidly becoming mainstream. In this paper, we report the results of an ethnographic study of blogging, focusing on blogs written by individuals or small groups, with limited audiences. We discuss motivations for blogging, the quality of social interactivity that characterized the blogs we studied, and relationships to the blogger's audience. We consider the way bloggers related to the known audience of their personal social networks as well as the wider "blogosphere" of unknown readers. We then make design recommendations for blogging software based on these findings.


Domain Adaptation for Statistical Machine Translation with Domain Dictionary and Monolingual Corpora

tra Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to perform domain adaptation for statistical machine translation, where in-domain bilingual corpora do not exist. This method first uses out-of-domain corpora to train a baseline system and then uses in-domain translation dictionaries and in-domain monolingual corpora to improve the indomain performance. We propose an algorithm to combine these different resources in a unified framework. Experimental results indicate that our method achieves absolute improvements of 8.16 and 3.36 BLEU scores on Chinese to English translation and English to French translation respectively, as compared with the baselines using only out-ofdomain corpora.


Recommendations for designers and researchers resulting from the world-wide failure exercise

The World-Wide Failure Exercise (WWFE) contained a detailed assessment of 19 theoretical approaches for predicting the deformation and failure response of polymer composite laminates when subjected to complex states of stress. The leading five theories are explored in greater detail to demonstrate their strengths and weaknesses in predicting various types of structural failure. Recommendations are then derived, as to how the theories can be best utilised to provide safe and economic predictions in a wide range of engineering design applications. Further guidance is provided for designers on the level of confidence and bounds of applicability of the current theories. The need for careful interpretation of initial failure predictions is emphasised, as is the need to allow for multiple sources of non-linearity (including progressive damage) where accuracy is sought for certain classes of large deformation and final failure strength predictions. Aspects requiring further experimental and theoretical investigation are identified. Direction is also provided to the research community by highlighting specific, tightly focussed, experimental and theoretical studies that, if carried out in the very near future, would pay great dividends from the designer’s perspective, by increasing their confidence in the theoretical foundations. # 2003 QinetiQ Ltd. Published by Elsevier Ltd. All rights reserved.


A case study of cooperative learning and communication pedagogy : Does working in teams make a difference ?

Cooperative learning has increasingly become a popular form of active pedagogy employed in academic institutions. This case study explores the relationship between cooperative learning and academic performance in higher education, specifically in the field of communication. Findings from a questionnaire administered to undergraduate students in a communication research course indicate that involvement in cooperative learning is a strong predictor of a student’s academic performance. A significant positive relationship was found between the degree to which grades are important to a student and his or her active participation in cooperative learning. Further, the importance of grades and sense of achievement are strong predictors of performance on readiness assessment tests.


Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.


Characterizing debate performance via aggregated twitter sentiment

Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.


Semantic Sentiment Analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.


From bias to opinion: a transfer-learning approach to real-time sentiment analysis

Real-time interaction, which enables live discussions, has become a key feature of most Web applications. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. We identify a task - opinion holder bias prediction - which is strongly related to the sentiment analysis task; however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution.
 Instead of learning textual models to predict content polarity (i.e., the traditional sentiment analysis approach), we first measure the bias of social media users toward a topic, by solving a relational learning task over a network of users connected by endorsements (e.g., retweets in Twitter). We then analyze sentiments by transferring user biases to textual features. This approach works because while new terms may arise and old terms may change their meaning, user bias tends to be more consistent over time as a basic property of human behavior. Thus, we adopted user bias as the basis for building accurate classification models. We applied our model to posts collected from Twitter on two topics: the 2010 Brazilian Presidential Elections and the 2010 season of Brazilian Soccer League. Our results show that knowing the bias of only 10% of users generates an F1 accuracy level ranging from 80% to 90% in predicting user sentiment in tweets.


Hedonic and instrumental motives in anger regulation.

What motivates individuals to regulate their emotions? One answer, which has been highlighted in emotion-regulation research, is that individuals are motivated by short-term hedonic goals (e.g., the motivation to feel pleasure). Another answer, however, is that individuals are motivated by instrumental goals (e.g., the motivation to perform certain behaviors). We suggest that both answers have merit. To demonstrate the role instrumental goals may play in emotion regulation, we pitted short-term hedonic motives and instrumental motives against each other, by testing whether individuals were motivated to experience a potentially useful, albeit unpleasant, emotion. We found that (a) individuals preferred activities that would increase their level of anger (but not their level of excitement) when they were anticipating confrontational, but not nonconfrontational, tasks and that (b) anger improved performance in a confrontational, but not a nonconfrontational, task. These findings support a functional view of emotion regulation, and demonstrate that in certain contexts, individuals may choose to experience emotions that are instrumental, despite short-term hedonic costs.


Building Poker Agent Using Reinforcement Learning with Neural Networks

Poker is a game with incomplete and imperfect information. The ability to estimate opponent and interpret its actions makes a player as a world class player. Finding optimal game strategy is not enough to win poker game. As in real life as in online poker game, the most time of it consists of opponent analysis. This paper illustrates a development of poker agent using reinforcement learning with neural networks. 1 STAGE OF THE RESEARCH Poker is a game with incomplete and imperfect information. The ability to estimate opponent and interpret its actions makes a player as a world class player. Finding optimal game strategy is not enough to win poker game. As in real life as in online poker game, the most time of it consists of opponent analysis. Author is working on development of poker agent that would find optimal game strategy using reinforcement learning (RL) in combination with artificial neural network (ANN) for value function approximation. 2 OUTLINE OF OBJECTIVES This paper illustrates a development of poker agent using reinforcement learning with neural networks. Complete poker agent should have an ability to create optimal game strategy that makes decisions based on information:  Hand strength/potential estimation;  Table card estimation;  Opponent hand strength prediction;  Opponent classification (tight/loose passive/ aggressive);  Current state evaluation using neural network. AI Poker agent should be able to find an optimal strategy by itself (unsupervised learning) using information given above. It also should be able to adapt opponent play style and change its strategy during the game.


MATLAB Based BackPropagation Neural Network for Automatic Speech Recognition

Speech interface to computer is the next big step that the technology needs to take for general users. Automatic speech recognition (ASR) will play an important role in taking technology to the people. There are numerous applications of speech recognition such as direct voice input in aircraft, data entry, speech-to-text processing, voice user interfaces such as voice dialling. ASR system can be divided into two different parts, namely feature extraction and feature recognition. In this paper we present MATLAB based feature recognition using backpropagation neural network for ASR. The objective of this research is to explore how neural networks can be employed to recognize isolated-word speech as an alternative to the traditional methodologies. The general techniques developed here can be further extended to other applications such as sonar target recognition, missile tracking and classification of underwater acoustic signals. Back-propagation neural network algorithm uses input training samples and their respective desired output values to learn to recognize specific patterns, by modifying the activation values of its nodes and weights of the links connecting its nodes. Such a trained network is later used for feature recognition in ASR systems.


Automated Fingerprint Identification and Imaging Systems

More than a century has passed since Alphonse Bertillon first conceived and then industriously practiced the idea of using body measurements for solving crimes [1]. Just as his idea was gaining popularity, it faded into relative obscurity by a far more significant and pract ic l discovery of the uniqueness of the human fingerprints 1. Soon after this discovery, many major law enforcement departments embraced the idea of first “booking” the fingerprints of criminals, so tha t their records are readily available and later using leftover fingerprint smudges (latent s), the identity of criminals can be determined. These agencies sponsored a rigorous study of fingerprints, developed s cientific 1In 1893, the Home Ministry Office, UK, accepted that no two ind ividuals have the same fingerprints.


Automatic Linguistic Annotation of Large Scale L 2 Databases : The EF-Cambridge Open Language Database ( EFCamDat )

∗Naturalistic learner productions are an important empirical resource for SLA research. Some pioneering works have produced valuable second language (L2) resources supporting SLA research.1 One common limitation of these resources is the absence of individual longitudinal data for numerous speakers with different backgrounds across the proficiency spectrum, which is vital for understanding the nature of individual variation in longitudinal development.2 A second limitation is the relatively restricted amounts of data annotated with linguistic information (e.g., lexical, morphosyntactic, semantic features, etc.) to support investigation of SLA hypotheses and obtain patterns of development for different linguistic phenomena. Where available, annotations tend to be manually obtained, a situation posing immediate limitations to the quantity of data that could be annotated with reasonable human resources and within reasonable time. Natural Language Processing (NLP) tools can provide automatic annotations for parts-of-speech (POS) and syntactic structure and are indeed increasingly applied to learner language in various contexts. Systems in computer-assisted language learning (CALL) have used a parser and other NLP tools to automatically detect learner errors and provide feedback accordingly.3 Some work aimed at adapting annotations provided by parsing tools to accurately describe learner syntax (Dickinson & Lee, 2009) or evaluated parser performance on learner language and the effect of learner errors on the parser. Krivanek and Meurers (2011) compared two parsing methods, one using a hand-crafted lexicon and one trained on a corpus. They found that the former is more successful in recovering the main grammatical dependency relations whereas the latter is more successful in recovering optional, adjunction relations. Ott and Ziai (2010) evaluated the performance of a dependency parser trained on native German (MaltParser; Nivre et al., 2007) on 106 learner answers to a comprehension task in L2 German. Their study indicates that while some errors can be problematic for the parser (e.g., omission of finite verbs) many others (e.g., wrong word order) can be parsed robustly, resulting in overall high performance scores. In this paper we have two goals. First, we introduce a new English L2 database, the EF Cambridge Open Language Database, henceforth EFCAMDAT. EFCAMDAT was developed by the Department of Theoretical and Applied Linguistics at the University of Cambridge in collaboration with EF Education First, an international educational organization. It contains writings submitted to Englishtown, the


A Radius and Ulna TW3 Bone Age Assessment System

An end-to-end system to automate the well-known Tanner-Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to properly semi-automatically segment the contour of the bones. Second, up to 89 features are defined and extracted from bone contours and gray scale information inside the contour, followed by some well- founded feature selection mathematical criteria, based on the ideas of maximizing the classes' separability. Third, bone age is estimated with the help of a generalized softmax perceptron (GSP) neural network (NN) that, after supervised learning and optimal complexity estimation via the application of the recently developed posterior probability model selection (PPMS) algorithm, is able to accurately predict the different development stages in both radius and ulna from which and with the help of the TW3 methodology, we are able to conveniently score and estimate the bone age of a patient in years, in what can be understood as a multiple- class (multiple stages) pattern recognition approach with posterior probability estimation. Finally, numerical results are presented to evaluate the system performance in predicting the bone stages and the final patient bone age over a private hand image database, with the help of the pediatricians and the radiologists expert diagnoses.


An Approach to Data Privacy in Smart Home using Blockchain Technology

Nowadays, numerous applications of smart home systems provide recommendations for users, including reducing their energy consumption, warnings of defective devices, selecting reliable devices and software, diagnoses, etc [1]. The internet connected, dynamic and heterogeneous nature of the smart home environment creates new security, authentication, and privacy challenges [2]. To solve those challenges, an approach to data privacy in smart home using blockchain technology, which is called smart home based the IoT-Blockchain (SHIB), is proposed in this paper. In order to demonstrate the proposed architecture, an experimental scenario using Ganache, Remix, and web3. js is built among the user, service provider, and smart home to evaluate the performance of the smart contract in the SHIB. Based on the experiment results, the SHIB architecture brings the advantages like data privacy, trust access control, and high extension ability. In addition, the comparison between the proposed architecture and existing models in different parameters such as smart contract, the privacy of data, usage of tokens, updating the policies, and misbehavior judging are performed.


Semi-supervised Density-Based Clustering

Most of the effort in the semi-supervised clustering literature was devoted to variations of the K-means algorithm. In this paper we show how background knowledge can be used to bias a partitional density-based clustering algorithm. Our work describes how labeled objects can be used to help the algorithm detecting suitable density parameters for the algorithm to extract density-based clusters in specific parts of the feature space. Considering the set of constraints estabilished by the labeled dataset we show that our algorithm, called SSDBSCAN, automatically finds density parameters for each natural cluster in a dataset. Four of the most interesting characteristics of SSDBSCAN are that (1) it only requires a single, robust input parameter, (2) it does not need any user intervention, (3) it automaticaly finds the noise objects according to the density of the natural clusters and (4) it is able to find the natural cluster structure even when the density among clusters vary widely. The algorithm presented in this paper is evaluated with artificial and real-world datasets, demonstrating better results when compared to other unsupervised and semi-supervised density-based approaches.


Shake-your-head: revisiting walking-in-place for desktop virtual reality

The Walking-In-Place interaction technique was introduced to navigate infinitely in 3D virtual worlds by walking in place in the real world. The technique has been initially developed for users standing in immersive setups and was built upon sophisticated visual displays and tracking equipments.
 In this paper, we propose to revisit the whole pipeline of the Walking-In-Place technique to match a larger set of configurations and apply it notably to the context of desktop Virtual Reality. With our novel "Shake-Your-Head" technique, the user is left with the possibility to sit down, and to use small screens and standard input devices such as a basic webcam for tracking. The locomotion simulation can compute various motions such as turning, jumping and crawling, using as sole input the head movements of the user. We also introduce the use of additional visual feedback based on camera motions to enhance the walking sensations.
 An experiment was conducted to compare our technique with classical input devices used for navigating in desktop VR. Interestingly, the results showed that our technique could even allow faster navigations when sitting, after a short learning. Our technique was also perceived as more fun and increasing presence, and was generally more appreciated for VR navigation.


Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view

We investigate projection methods, for evaluating a linear approximation of the value function of a policy in a Markov Decision Process context. We consider two popular approaches, the one-step Temporal Difference fix-point computation (TD(0)) and the Bellman Residual (BR) minimization. We describe examples, where each method outperforms the other. We highlight a simple relation between the objective function they minimize, and show that while BR enjoys a performance guarantee, TD(0) does not in general. We then propose a unified view in terms of oblique projections of the Bellman equation, which substantially simplifies and extends the characterization of Schoknecht (2002) and the recent analysis of Yu & Bertsekas (2008). Eventually, we describe some simulations that suggest that if the TD(0) solution is usually slightly better than the BR solution, its inherent numerical instability makes it very bad in some cases, and thus worse on average.


Analysis Enhanced Particle-based Flow Visualization

Particle-based fluid simulation (PFS), such as Smoothed Particle Hydrodynamics (SPH) and Position-based Fluid (PBF), is a mesh-free method that has been widely used in various fields, including astrophysics, mechanical engineering, and biomedical engineering for the study of liquid behaviors under different circumstances. Due to its meshless nature, most analysis techniques that are developed for mesh-based data need to be adapted for the analysis of PFS data. In this work, we study a number of flow analysis techniques and their extension for PFS data analysis, including the FTLE approach, Jacobian analysis, and an attribute accumlation framework. In particular, we apply these analysis techniques to free surface fluids. We demonstrate that these analyses can reveal some interesting underlying flow patterns that would be hard to see otherwise via a number of PFS simulated flows with different parameters and boundary settings. In addition, we point out that an in-situ analysis framework that performs these analyses can potentially be used to guide the adaptive PFS to allocate the computation and storage power to the regions of interest during the simulation.


Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark

Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named “Look into Person (LIP)” that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The datasets, code and models are available at http://www.sysu-hcp.net/lip/.


OctoPocus: a dynamic guide for learning gesture-based command sets

We describe OctoPocus, an example of a dynamic guide that combines on-screen feedforward and feedback to help users learn, execute and remember gesture sets. OctoPocus can be applied to a wide range of single-stroke gestures and recognition algorithms and helps users progress smoothly from novice to expert performance. We provide an analysis of the design space and describe the results of two experi-ments that show that OctoPocus is significantly faster and improves learning of arbitrary gestures, compared to con-ventional Help menus. It can also be adapted to a mark-based gesture set, significantly improving input time compared to a two-level, four-item Hierarchical Marking menu.


Controversies surrounding nonspeech oral motor exercises for childhood speech disorders.

The use of nonspeech oral motor exercises (NSOMEs) for influencing children’s speech sound productions is a common therapeutic practice used by speech-language pathologists (SLPs) in the United States, Canada, and the United Kingdom. Reports from these countries have documented that between 71.5% and 85% of practicing clinicians use some type of NSOMEs in therapy to change children’s speech productions. NSOMEs can be defined as any therapy technique that does not require the child to produce a speech sound but is used to influence the development of speaking abilities. The National Center for Evidence-Based Practice in Communication Disorders (NCEP) of the American Speech-Language-Hearing Association (ASHA) developed this definition: ‘‘Oral-motor exercises are activities that involve sensory stimulation to or actions of the lips, jaw, tongue, soft palate, larynx, and respiratory muscles which are intended to influence the physiologic underpinnings of the oropharyngeal mechanism and thus improve its functions; oral-motor exercises may include active muscle exercise, muscle stretching, passive exercise, and sensory stimulation.’’ The term ‘‘oral motor,’’ which relates to movements and placements of the oral musculature, is established in the field of SLP. Although the existence and importance of the oral-motor aspects of speech production is well understood, the use and effectiveness of nonspeech oral-motor activities is disputed because of the lack of theoretical and empirical support. To understand more about the use of NSOMEs for changing disordered productions of speech, a colleague and I conducted a nationwide survey of 537 practicing clinicians from 48 states. We found that SLPs frequently used the exercises of blowing, tongue wagging and pushups, cheek puffing, the alternating movement of pucker-smile, ‘‘big smile,’’ and tongue-to-nose-to-chin. The clinicians believed these NSOMEs would help their clients obtain tongue elevation, protrusion, and lateralization; increase their tongue and lip strength; become more aware of their articulators; stabilize the jaw; control drooling; and increase velopharyngeal and sucking abilities. The respondents to the survey reported using these techniques for children with a wide variety of different speech disorders stemming from a wide variety of etiologies: dysarthria, childhood apraxia of speech (CAS), Down syndrome, late talkers, phonological impairment, functional misarticulations, and hearing impairment. It makes one curious why clinicians would select these nonspeech therapeutic techniques because they lack adequate theoretical underpinnings for their use. Additionally, the research studies that have evaluated treatment efficacy using the NSOME techniques, although admittedly scant and not at the highest levels of scientific rigor, do not show therapeutic effectiveness. Not only are these nonspeech tasks lacking in theoretical and data support for their use, their application to improve speech intelligibility also often defies logical reasoning. So why do clinicians use these techniques? As I have previously pointed out, SLPs have several possible reasons for using NSOMEs. Some of these reasons may be that


Spatial as Deep: Spatial CNN for Traffic Scene Understanding

Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road surface as shown in Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-byslice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer. Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a newly released very challenging traffic lane detection dataset and Cityscapse dataset. The results show that SCNN could learn the spatial relationship for structure output and significantly improves the performance. We show that SCNN outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet) in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an accuracy of 96.53%.


Multi-modality web video categorization

This paper reports a first comprehensive study and large-scale test on web video (so-called user generated video or micro video) categorization. Observing that web videos are characterized by a much higher diversity of quality, subject, style, and genres compared with traditional video programs, we focus on studying the effectiveness of different modalities in dealing with such high variation. Specifically, we propose two novel modalities including a semantic modality and a surrounding text modality, as effective complements to most commonly used low-level features. The semantic modality includes three feature representations, i.e., concept histogram, visual word vector model and visual word Latent Semantic Analysis (LSA), while text modality includes the titles, descriptions and tags of web videos. We conduct a set of comprehensive experiments for evaluating the effectiveness of the proposed feature representations over different classifiers such as Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and Manifold Ranking (MR). Our experiments on a large-scale dataset with 11k web videos (nearly 450 hours in total) demonstrate that (1) the proposed multimodal feature representation is effective for web video categorization, and (2) SVM outperforms GMM and MR on nearly all the modalities.


A Fast-Response Pseudo-PWM Buck Converter With PLL-Based Hysteresis Control

Hysteresis voltage-mode control is a simple and fast control scheme for switched-mode power converters. However, it is well-known that the switching frequency of a switched-mode power converter with hysteresis control depends on many factors such as loading current and delay of the controller which vary from time to time. It results in a wide noise spectrum and leads to difficulty in shielding electro-magnetic interference. In this work, a phase-lock loop (PLL) is utilized to control the hysteresis level of the comparator used in the controller, while not interfering with the intrinsic behavior of the hysteresis controller. Some design techniques are used to solve the integration problem and to improve the settling speed of the PLL. Moreover, different control modes are implemented. A buck converter with proposed control scheme is fabricated using a commercial 0.35-μ m CMOS technology. The chip area is 1900 μm × 2200 μm. The switching frequency is locked to 1 MHz, and the measured frequency deviation is within ±1%. The measured load transient response between 160 and 360 mA is 5 μ s only.


Memristor-The Missing Circuit Element LEON 0

A new two-terminal circuit element-called the memrirtorcharacterized by a relationship between the charge q(t) s St% i(7J d7 and the flux-linkage (p(t) = J-‘-m vfrj d T is introduced os the fourth boric circuit element. An electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell’s equations is presented. Many circuit~theoretic properties of memdstorr are derived. It is shown that this element exhibiis some peculiar behavior different from that exhibited by resistors, inductors, or capacitors. These properties lead to a number of unique applications which cannot be realized with RLC networks alone. I + ” -3 nl Although a physical memristor device without internal power supply has not yet been discovered, operational laboratory models have been built with the help of active circuits. Experimental results ore presented to demonstrate the properties and potential applications of memristors. (a) I. 1NTR00~cnoN I + Y -3 T HIS PAPER presents the logical and scientific basis for the existence of a new two-terminal circuit element called the memristor (a contraction for memory (b) resistor) which has every right to be as basic as the three classical circuit elements already in existence, namely, the resistor, inductor, and capacitor. Although the existence of a memristor in the form of a physical device without internal power supply has not yet been discovered, its laboratory realization in the form of active circuits will be presented in Section II.’ Many interesting circuit-theoretic properties possessed by the memristor, the most important of which is perhaps the passivity property which provides the circuit-theoretic basis for its physical realizability, will be derived in Section III. An electromagnetic field interpretation of the memristor characterization will be presented in Section IV with the help of a quasi-static expansion of Maxwell’s equations. Finally, some novel applications of memristors will be presented in Section V. 1 + ” -3


Millimeter-wave antennas for radio access and backhaul in 5G heterogeneous mobile networks

Millimeter-wave communications are expected to play a key role in future 5G mobile networks to overcome the dramatic traffic growth expected over the next decade. Such systems will severely challenge antenna technologies used at mobile terminal, access point or backhaul/fronthaul levels. This paper provides an overview of the authors' recent achievements in the design of integrated antennas, antenna arrays and high-directivity quasi-optical antennas for high data-rate 60-GHz communications.


Frequency compensation of high-speed, low-voltage CMOS multistage amplifiers

This paper presents the frequency compensation of high-speed, low-voltage multistage amplifiers. Two frequency compensation techniques, the Nested Miller Compensation with Nulling Resistors (NMCNR) and Reversed Nested Indirect Compensation (RNIC), are discussed and employed on two multistage amplifier architectures. A four-stage pseudo-differential amplifier with CMFF and CMFB is designed in a 1.2 V, 65-nm CMOS process. With NMCNR, it achieves a phase margin (PM) of 59° with a DC gain of 75 dB and unity-gain frequency (fug) of 712 MHz. With RNIC, the same four-stage amplifier achieves a phase margin of 84°, DC gain of 76 dB and fug of 2 GHz. Further, a three-stage single-ended amplifier is designed in a 1.1-V, 40-nm CMOS process. The three-stage OTA with RNIC achieves PM of 81°, DC gain of 80 dB and fug of 770 MHz. The same OTA achieves PM of 59° with NMCNR, while maintaining a DC gain of 75 dB and fug of 262 MHz. Pole-splitting, to achieve increased stability, is illustrated for both compensation schemes. Simulations illustrate that the RNIC scheme achieves much higher PM and fug for lower values of compensation capacitance compared to NMCNR, despite the growing number of low voltage amplifier stages.


Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising

In real-world situations, speech is masked by both background noise and reverberation, which negatively affect perceptual quality and intelligibility. In this paper, we address monaural speech separation in reverberant and noisy environments. We perform dereverberation and denoising using supervised learning with a deep neural network. Specifically, we enhance the magnitude and phase by performing separation with an estimate of the complex ideal ratio mask. We define the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech. Our approach is evaluated using simulated and real room impulse responses, and with background noises. The proposed approach improves objective speech quality and intelligibility significantly. Evaluations and comparisons show that it outperforms related methods in many reverberant and noisy environments.


Depth Images-based Human Detection , Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.


Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization

The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. This paper proposes a cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm. The main contribution of our work is to balance the entire system load while trying to minimizing the make span of a given tasks set. The new scheduling strategy was simulated using the CloudSim toolkit package. Experiments results showed the proposed LBACO algorithm outperformed FCFS (First Come First Serve) and the basic ACO (Ant Colony Optimization).


A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that PSO can achieve: a) as much as 3 times cost savings as compared to BRS, and b) good distribution of workload onto resources.


QRSF: QoS-aware resource scheduling framework in cloud computing

Cloud computing harmonizes and delivers the ability of resource sharing over different geographical sites. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair. The efficient management of dynamic nature of resource can be done with the help of cloud workloads. Till cloud workload is deliberated as a central capability, the resources cannot be utilized in an effective way. In literature, very few efficient resource scheduling policies for energy, cost and time constraint cloud workloads are reported. This paper presents an efficient cloud workload management framework in which cloud workloads have been identified, analyzed and clustered through K-means on the basis of weights assigned and their QoS requirements. Further scheduling has been done based on different scheduling policies and their corresponding algorithms. The performance of the proposed algorithms has been evaluated with existing scheduling policies through CloudSim toolkit. The experimental results show that the proposed framework gives better results in terms of energy consumption, execution cost and time of different cloud workloads as compared to existing algorithms.


Systematic literature reviews in software engineering - A systematic literature review

Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners. ! 2008 Elsevier B.V. All rights reserved.


Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

BACKGROUND
The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification.

METHODS
We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors.

RESULTS
The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003-2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist.

CONCLUSION
We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors.


On the Resolution Limits of Superimposed Projection

Multi-projector super-resolution is the dual of multi-camera super-resolution. The goal of projector super-resolution is to produce a high resolution frame via superimposition of multiple low resolution subframes. Prior work claims that it is impossible to improve resolution via superimposed projection except in specialized circumstances. Rigorous analysis has been previously restricted to the special case of uniform display sampling, which reduces the problem to a simple shift-invariant deblurring. To understand the true behavior of superimposed projection as an inverse of classical camera super-resolution, one must consider the effects of non-uniform displacements between component subframes. In this paper, we resolve two fundamental theoretical questions concerning resolution enhancement via superimposed projection. First, we show that it is possible to reproduce frequencies that are well beyond the Nyquist limit of any of the component subframes. Second, we show that nonuniform sampling and pixel reconstruction functions impose fundamental limits on achievable resolution.


Droop control in LV-grids

Remote electrification with island supply systems, the increasing acceptance of the microgrids concept and the penetration of the interconnected grid with DER and RES require the application of inverters and the development of new control algorithms. One promising approach is the implementation of conventional f/U-droops into the respective inverters, thus down scaling the conventional grid control concept to the low voltage grid. Despite contradicting line parameters, the applicability of this proceeding is outlined and the boundary conditions are derived


Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization

We give novel algorithms for stochastic strongly-convex optimization in the gradient oracle model which return a O( 1 T )-approximate solution after T iterations. The first algorithm is deterministic, and achieves this rate via gradient updates and historical averaging. The second algorithm is randomized, and is based on pure gradient steps with a random step size. This rate of convergence is optimal in the gradient oracle model. This improves upon the previously known best rate of O( log(T ) T ), which was obtained by applying an online stronglyconvex optimization algorithm with regret O(log(T )) to the batch setting. We complement this result by proving that any algorithm has expected regret of Ω(log(T )) in the online stochastic strongly-convex optimization setting. This shows that any online-to-batch conversion is inherently suboptimal for stochastic strongly-convex optimization. This is the first formal evidence that online convex optimization is strictly more difficult than batch stochastic convex optimization.


Oxytocin Attenuates Amygdala Reactivity to Fear in Generalized Social Anxiety Disorder

Patients with generalized social anxiety disorder (GSAD) exhibit heightened activation of the amygdala in response to social cues conveying threat (eg, fearful/angry faces). The neuropeptide oxytocin (OXT) decreases anxiety and stress, facilitates social encounters, and attenuates amygdala reactivity to threatening faces in healthy subjects. The goal of this study was to examine the effects of OXT on fear-related amygdala reactivity in GSAD and matched healthy control (CON) subjects. In a functional magnetic resonance imaging study utilizing a double-blind placebo-controlled within-subjects design, we measured amygdala activation to an emotional face matching task of fearful, angry, and happy faces following acute intranasal administration of OXT (24 IU or 40.32 μg) and placebo in 18 GSAD and 18 CON subjects. Both the CON and GSAD groups activated bilateral amygdala to all emotional faces during placebo, with the GSAD group exhibiting hyperactivity specifically to fearful faces in bilateral amygdala compared with the CON group. OXT had no effect on amygdala activity to emotional faces in the CON group, but attenuated the heightened amygdala reactivity to fearful faces in the GSAD group, such that the hyperactivity observed during the placebo session was no longer evident following OXT (ie, normalization). These findings suggest that OXT has a specific effect on fear-related amygdala activity, particularly when the amygdala is hyperactive, such as in GSAD, thereby providing a brain-based mechanism of the impact of OXT in modulating the exaggerated processing of social signals of threat in patients with pathological anxiety.


Generalized State Coherence Transform for multidimensional localization of multiple sources

In our recent work an effective method for multiple source localization has been proposed under the name of cumulative State Coherence Transform (cSCT). Exploiting the physical meaning of the frequency-domain blind source separation and the sparse time-frequency dominance of the acoustic sources, multiple reliable TDOAs can be estimated with only two microphones, regardless of the permutation problem and of the microphone spacing. In this paper we present a multidimensional generalization of the cSCT which allows one to localize several sources in the P-dimensional space. An important approximation is made in order to perform a disjoint TDOA estimation over each dimension which reduces the localization problem to linear complexity. Furthermore the approach is invariant to the array geometry and to the assumed acoustic propagation model. Experimental results on simulated data show a precise 2-D localization of 7 sources by only using an array of three elements.


An Overview of BioCreative II.5

We present the results of the BioCreative II.5 evaluation in association with the FEBS Letters experiment, where authors created Structured Digital Abstracts to capture information about protein-protein interactions. The BioCreative II.5 challenge evaluated automatic annotations from 15 text mining teams based on a gold standard created by reconciling annotations from curators, authors, and automated systems. The tasks were to rank articles for curation based on curatable protein-protein interactions; to identify the interacting proteins (using UniProt identifiers) in the positive articles (61); and to identify interacting protein pairs. There were 595 full-text articles in the evaluation test set, including those both with and without curatable protein interactions. The principal evaluation metrics were the interpolated area under the precision/recall curve (AUC iP/R), and (balanced) F-measure. For article classification, the best AUC iP/R was 0.70; for interacting proteins, the best system achieved good macroaveraged recall (0.73) and interpolated area under the precision/recall curve (0.58), after filtering incorrect species and mapping homonymous orthologs; for interacting protein pairs, the top (filtered, mapped) recall was 0.42 and AUC iP/R was 0.29. Ensemble systems improved performance for the interacting protein task.


Requirements, Traceability and DSLs in Eclipse with the Requirements Interchange Format (ReqIF)

Requirements engineering (RE) is a crucial aspect in systems development and is the area of ongoing research and process improvement. However, unlike in modelling, there has been no established standard that activities could converge on. In recent years, the emerging Requirements Interchange Format (RIF/ReqIF) gained more and more visibility in industry, and research projects start to investigate these standards. To avoid redundant efforts in implementing the standard, the VERDE and Deploy projects cooperate to provide a stable common basis for RIF/ReqIF that could be leveraged by other research projects too. In this paper, we present an Eclipse-based extensible implementation of a RIF/ReqIF-based requirements editing platform. In addition, we are concerned with two related aspects of RE that take advantage of the common platform. First, how can the quality of requirements be improved by replacing or complementing natural language requirements with formal approaches such as domain specific languages or models. Second, how can we establish robust traceability that links requirements and model constructs and other artefacts of the development process. We present two approaches to traceability and two approaches to modelling. We believe that our research represents a significant contribution to the existing tooling landscape, as it is the first clean-room implementation of the RIF/ReqIF standard. We believe that it will help reduce gaps in often heterogeneous tool chains and inspire new conceptual work and new tools.


FRPAs and High-Gain Directional Antennas

Many types of GNSS antennas have been developed in recent years to make them suitable for different applications. Since their designs and performance requirements vary depending on their application, they have been grouped into six different categories in this book: 1. The design and performance of each type of antenna will be described here and throughout the rest of the book. This chapter discusses the design of the first two varieties: FRPAs and high-gain directional antennas. Figure 2.1 shows a representative sample of these antennas. FRPAs are the most popular and widely used of all GNSS antennas. There are many different types of these antennas; hence they will need two chapters—this chapter and Chapter 3—to fully cover all the important types. This chapter will discuss the following FRPA designs: microstrip patch antennas, which are the most ubiquitous of all GNSS antennas, and quadrifilar helix antennas (QHAs), which are also popular for handheld receivers and crossed " drooping " bow-type dipoles, which provide relatively wider bandwidths and good circular polarization. Spiral antennas such as the Archimedean spiral antenna will be discussed in Chapter 3 under multiband antennas since they are ultrawideband and can cover the entire GNSS band from 1.1 to 1.6 GHz. The conical spiral antenna is also a high-gain di


$V_{T}$ Compensation Circuit for AM OLED Displays Composed of Two TFTs and One Capacitor

In this paper, we propose the threshold-voltage compensation pixel circuit that is composed of two thin-film transistors (TFTs) and one capacitor (2T1C). It not only compensates the deviation of the threshold voltage of the driver TFT but also actualizes the large aperture ratio for organic light-emitting diode (OLED) devices as well as the traditional 2T1C circuit. We show the result of SPICE simulation for the pixel circuit; it indicates that the circuit can allocate the relatively large aperture ratio for OLED devices


AGDISTIS - Graph-Based Disambiguation of Named Entities Using Linked Data

Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text. We evaluate our approach on eight different datasets against state-of-theart named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.


Web Spam Taxonomy

Web spamming refers to actions intended to mislead search engines into ranking some pages higher than they deserve. Recently, the amount of web spam has increased dramatically, leading to a degradation of search results. This paper presents a comprehensive taxonomy of current spamming techniques, which we believe can help in developing appropriate countermeasures.


Eye-tracking of men's preferences for waist-to-hip ratio and breast size of women.

Studies of human physical traits and mate preferences often use questionnaires asking participants to rate the attractiveness of images. Female waist-to-hip ratio (WHR), breast size, and facial appearance have all been implicated in assessments by men of female attractiveness. However, very little is known about how men make fine-grained visual assessments of such images. We used eye-tracking techniques to measure the numbers of visual fixations, dwell times, and initial fixations made by men who viewed front-posed photographs of the same woman, computer-morphed so as to differ in her WHR (0.7 or 0.9) and breast size (small, medium, or large). Men also rated these images for attractiveness. Results showed that the initial visual fixation (occurring within 200 ms from the start of each 5 s test) involved either the breasts or the waist. Both these body areas received more first fixations than the face or the lower body (pubic area and legs). Men looked more often and for longer at the breasts, irrespective of the WHR of the images. However, men rated images with an hourglass shape and a slim waist (0.7 WHR) as most attractive, irrespective of breast size. These results provide quantitative data on eye movements that occur during male judgments of the attractiveness of female images, and indicate that assessments of the female hourglass figure probably occur very rapidly.


Static analysis of cable-driven manipulators with non-negligible cable mass

This paper addresses the static analysis of cable-driven robotic manipulators with non-negligible cable mass. An approach to computing the static displacement of a homogeneous elastic cable is presented. The resulting cable-displacement expression is used to solve the inverse kinematics of general cable-driven robotic manipulators. In addition, the sag-induced stiffness of the cables is derived. Finally, two sample robotic manipulators with dimensions and system parameters similar to a large scale cable-driven manipulator currently under development are analyzed. The results show that cable sag can have a significant effect on both the inverse kinematics and stiffness of such manipulators.