title: A framework for intelligence analysis using spatio-temporal storytelling

abstract: Social media have ushered in alternative modalities to propagate news and developments rapidly. Just as traditional IR matured to modeling storylines from search results, we are now at a point to study how stories organize and evolve in additional mediums such as Twitter, a new frontier for intelligence analysis. This study takes as input news articles as well as social media feeds and extracts and connects entities into interesting storylines not explicitly stated in the underlying data. First, it proposes a novel method of spatio-temporal analysis on induced concept graphs that models storylines propagating through spatial regions in a time sequence. Second, it describes a method to control search space complexity by providing regions of exploration. And third, it describes ConceptRank as a ranking strategy that differentiates strongly-typed connections from weakly-bound ones. Extensive experiments on the Boston Marathon Bombings of April 15, 2013 as well as socio-political and medical events in Latin America, the Middle East, and the United States demonstrate storytelling's high application potential, showcasing its use in event summarization and association analysis that identifies events before they hit the newswire.


Comparing Automatic and Human Evaluation of Local Explanations for Text Classification


title: Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization

abstract: We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.


title: Prefrontal cortex as a meta-reinforcement learning system

abstract: Over the past 20 years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine 'stamps in' associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. We now draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.


title: Elastic Queue: A Universal SSD Lifetime Extension Plug-in for Cache Replacement Algorithms

abstract: Flash-based solid-state drives (SSDs) are getting popular to be deployed as the second-level cache in storage systems because of the noticeable performance acceleration and transparency for the original software. However, the frequent data updates of existing cache replacement algorithms (e.g. LRU, LIRS, and LARC) causes too many writes on SSDs, leading to short lifetime and high costs of devices. SSD-oriented cache schemes with less SSD writes have fixed strategies of selecting cache blocks, so we cannot freely choose a suitable cache algorithm to adapt to application features for higher performance. Therefore, a universal SSD lifetime extension plug-in called Elastic Queue (EQ), which can cooperate with any cache algorithm to extend the lifetime of SSDs, is proposed in this paper. EQ reduces the data updating frequency by extending the eviction border of cache blocks elastically, making SSD devices serve much longer. The experimental results based on some real-world traces indicate that for the original LRU, LIRS, and LARC schemes, adding the EQ plug-in reduces their SSD write amounts by 39.03 times, and improves the cache hit rates by 17.30% on average at the same time.


title: Neural networks for signal classification

abstract: In this paper the instructions for using neural networks as classifier signals will use the lab-on-chip without using a PC, making them more mobile. Conducted the analysis structure of subsystem and neural network structure.


title: IMI -- A Multilingual Semantic Annotation Environment

abstract: Semantic annotated parallel corpora, though rare, play an increasingly important role in natural language processing. These corpora provide valuable data for computational tasks like sense-based machine translation and word sense disambiguation, but also to contrastive linguistics and translation studies. In this paper we present the ongoing development of a web-based corpus semantic annotation environment that uses the Open Multilingual Wordnet (Bond and Foster, 2013) as a sense inventory. The system includes interfaces to help coordinating the annotation project and a corpus browsing interface designed specifically to meet the needs of a semantically annotated corpus. The tool was designed to build the NTU-Multilingual Corpus (Tan and Bond, 2012). For the past six years, our tools have been tested and developed in parallel with the semantic annotation of a portion of this corpus in Chinese, English, Japanese and Indonesian. The annotation system is released under an open source license (MIT).


title: Traffic Accidents Classification and Injury Severity Prediction

abstract: Traffic accidents constitutes the first cause of death and injury in many developed countries. However, traffic accidents information and data provided by public organisms can be exploited to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. This article presents a case study of traffic accidents classification and severity prediction in Spain. Raw data are from Spanish traffic agency covering a period of six years ranging from 2011 to 2015. To this end, are compared three different machine learning classification techniques, such as Gradient Boosting Trees, Deep Learning and Naive Bayes.


title: Forecast-Assisted NFV Service Chain Deployment Based on Affiliation-Aware vNF Placement

abstract: This paper studies the problem of service chain (SC) deployment. Specifically, we try to place virtual network functions (vNFs) on network nodes and connect the vNFs in sequence through link mapping. We start with the offline problem. An integer linear programming (ILP) model is formulated to minimize the total SC deployment cost. With the ILP, we prove that the offline problem is NP-hard and propose a time-efficient heuristic based on affiliation-aware vNF placement. Then, we move to the online problem, and design a forecast-assisted online SC deployment algorithm that includes the prediction of future vNF requirements. Simulation results show that the online algorithms can reduce the blocking probability of SC requests and increase the service provider's profit from SC deployment effectively.


title: Security Threats and Countermeasures for Intra-vehicle Networks

abstract: Controller Area Network (CAN) is the leading serial bus system for embedded control. More than two billion CAN nodes have been sold since the protocol's development in the early 1980s. CAN is a mainstream network and was internationally standardized (ISO 11898–1) in 1993. This paper describes an approach to implementing security services on top of a higher level Controller Area Network (CAN) protocol, in particular, CANopen. Since the CAN network is an open, unsecured network, every node has access to all data on the bus. A system which produces and consumes sensitive data is not well suited for this environment. Therefore, a general-purpose security solution is needed which will allow secure nodes access to the basic security services such as authentication, integrity, and confidentiality.


title: Exploring Sequence Alignment Algorithms on FPGA-based Heterogeneous Architectures

abstract: With the rapid development of DNA sequencer, the rate of data generation is rapidly outpacing the rate at which it can be computationally processed. Traditional sequence alignment based on PC cannot fulfill the increasing demand. Accelerating the algorithm using FPGA provides the better performance compared to the other platforms. This paper will explain and classify the current sequence alignment algorithms. In addition, we analyze the different types of sequence alignment algorithms and present the taxonomy of FPGA-based sequence alignment implementations. This work will conclude the current solutions and provide a reference to further accelerating sequence alignment on a FPGA-based heterogeneous architecture.


Metaphor, Inference and Domain Independent Mappings


title: Prototype rotary percussive drill for the Mars Sample Return mission

abstract: Since 1990s Honeybee Robotics has been developing and testing surface coring drills for future planetary missions. Recently, we focused on developing a rotary-percussive core drill for the 2018 Mars Sample Return mission and in particular for the Mars Astrobiology Explorer-Cacher, MAX-C mission. The goal of the 2018 MAX-C mission is to acquire approximately 20 cores from various rocks and outcrops on the surface of Mars. The acquired cores, 1 cm diameter and 5 cm long, would be cached for return back to Earth either in 2022 or 2024, depending which of the MSR architectures is selected. We built a testbed coring drill that was used to acquire drilling data, such as power, rate of penetration, and Weight on Bit, in various rock formations. Based on these drilling data we designed a prototype Mars Sample Return coring drill. The proposed MSR drill is an arm-mounted, standalone device, requiring no additional arm actuation once positioned and preloaded. A low mass, compact transmission internal to the housing provides all of the actuation of the tool mechanisms. The drill uses a rotary-percussive drilling approach and can acquire a 1 cm diameter and 5 cm long core in Saddleback basalt in less than 30 minutes with only ∼20 N Weight on Bit and less than 100 Watt of power. The prototype MSR drill weighs approximately 5 kg1,2.


title: Development of slider-crank based pole climbing robot

abstract: In this paper, a slider-crank based pole climbing robot will be discussed. The robot is designed to climb a pole of perimeter 35cm upward and downward, and its weight is limited to 3kg. It utilizes the combination of slider-crank mechanism for its climbing module and modular mechanism for its gripping module. In this work, the overall climbing motion can be completed in 6 steps. The benefits of this robot are low cost, simple mechanism, easy to control and fabricate. The robot has been built and successfully tested on a PVC tube of diameter 35cm. The average upward and downward climbing speed of the developed robot is 0.375cm/s and 0377cm/s respectively.


title: BA-Net: Dense Bundle Adjustment Networks

abstract: This paper introduces a neural network to solve the structure-from-motion (SfM) problem via feature bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature reprojection error. The whole pipeline is differentiable, so that the network can learn suitable feature representations that make the BA problem more trackable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates some bases depth maps according to the input image, and optimizes the final depth as a linear combination of these bases via feature BA. The bases depth map generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and machine learning (i.e. feature learning and basis depth map generator learning) to address the challenging SfM problem. Experiments on large scale real data prove the success of the proposed method.


title: Stirring up experience through movement in game play: effects on engagement and social behaviour

abstract: The recent development of controllers designed around natural body movements has altered the nature of gaming and contributed towards it being marketed as a more social activity. The study reported here compares the use of Donkey Konga bongos with a standard controller to examine how affording motion through an input device affects social interaction. Levels of engagement with the game were also measured to explore whether increases in social behaviour in the 'real world' would result in reduced involvement with the 'game world'. Social interaction was significantly higher when the bongos were used, but this did not detract from engagement. Instead, engagement was also found to increase when body movement was afforded.


title: Predicting the popularity of micro-reviews

abstract: We tackle the problem of predicting the future popularity level of micro-reviews, focusing on Foursquare tips, whose high degree of informality and briefness offer extra difficulties to the design of effective popularity prediction methods. Such predictions can greatly benefit the future design of content filtering and recommendation methods. Towards our goal, we first propose a rich set of features related to the user who posted the tip, the venue where it was posted, and the tip's content to capture factors that may impact popularity of a tip. We evaluate different regression and classification based models using this rich set of proposed features as predictors in various scenarios. As fas as we know, this is the first work to investigate the predictability of micro-review popularity (or helpfulness) exploiting spatial, temporal, topical and, social aspects that are rarely exploited conjointly in this domain.


title: Constructing general dynamic group key distribution schemes with decentralized user join

abstract: In a dynamic group key distribution scheme, members of a group themselves generate private common keys with the help of a group controller in an initialization phase. The system must enable the revocation and the addition of members to the group in the successive periods of time. If the addition of new members can also be performed by the existing members themselves, then the scheme is said to have decentralized user join. ::: ::: In this work we construct a general family of dynamic group key distribution schemes with decentralized user join by using linear secret sharing schemes as a tool. This allows to obtain new schemes with more flexible characteristics than the previous threshold-based constructions.


title: A Compact Polyimide-Based UWB Antenna for Flexible Electronics

abstract: In this letter, we present a compact ultrawideband (UWB) antenna printed on a 50.8-μm Kapton polyimide substrate. The antenna is fed by a linearly tapered coplanar waveguide (CPW) that provides smooth transitional impedance for improved matching. The proposed design is tuned to cover the 2.2-14.3-GHz frequency range that encompasses both the 2.45-GHz Industrial, Scientific, Medical (ISM) band and the standard 3.1-10.6-GHz UWB band. Furthermore, the antenna is compared to a conventional CPW-fed antenna to demonstrate the significance of the proposed design. A parametric study is first performed on the feed of the proposed design to achieve the desired impedance matching. Next, a prototype is fabricated; measurement results show good agreement with the simulated model. Moreover, the antenna demonstrates a very low susceptibility to performance degradation due to bending effects in terms of impedance matching and far-field radiation patterns, which makes it suitable for integration within modern flexible electronic devices.


title: The relationship between Precision-Recall and ROC curves

abstract: Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.


title: Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks

abstract: In this paper, we propose an efficient algorithm for estimating the natural policy gradient using parameter-based exploration; this algorithm samples directly in the parameter space. Unlike previous methods based on natural gradients, our algorithm calculates the natural policy gradient using the inverse of the exact Fisher information matrix. The computational cost of this algorithm is equal to that of conventional policy gradients whereas previous natural policy gradient methods have a prohibitive computational cost. Experimental results show that the proposed method outperforms several policy gradient methods.


title: Incremental hierarchical clustering of text documents

abstract: Incremental hierarchical text document clustering algorithms are important in organizing documents generated from streaming on-line sources, such as, Newswire and Blogs. However, this is a relatively unexplored area in the text document clustering literature. Popular incremental hierarchical clustering algorithms, namely Cobweb and Classit , have not been widely used with text document data. We discuss why, in the current form, these algorithms are not suitable for text clustering and propose an alternative formulation that includes changes to the underlying distributional assumption of the algorithm in order to conform with the data. Both the original Classit algorithm and our proposed algorithm are evaluated using Reuters newswire articles and Ohsumed dataset.


title: Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D

abstract: The $k$-Means clustering problem on $n$ points is NP-Hard for any dimension $d\ge 2$, however, for the 1D case there exists exact polynomial time algorithms. Previous literature reported an $O(kn^2)$ time dynamic programming algorithm that uses $O(kn)$ space. It turns out that the problem has been considered under a different name more than twenty years ago. We present all the existing work that had been overlooked and compare the various solutions theoretically. Moreover, we show how to reduce the space usage for some of them, as well as generalize them to data structures that can quickly report an optimal $k$-Means clustering for any $k$. Finally we also generalize all the algorithms to work for the absolute distance and to work for any Bregman Divergence. We complement our theoretical contributions by experiments that compare the practical performance of the various algorithms.


title: Teaching a Machine to Read Maps with Deep Reinforcement Learning

abstract: The ability to use a 2D map to navigate a complex 3D environment is quite remarkable, and even difficult for many humans. Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community. In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before. Our system combines several state-of-the-art methods such as A3C and incorporates novel elements such as a recurrent localization cell. Our agent learns to localize itself based on 3D first person images and an approximate orientation angle. The agent generalizes well to bigger mazes, showing that it learned useful localization and navigation capabilities.


title: Understanding data survivability in archival storage systems

abstract: Preserving data for a long period of time in the face of faults, large and small, is crucial for designing reliable archival storage systems. However, the survivability of data is different from the reliability of storage because typically, data are stored in more than one storage at a given moment. Previous studies of reliability ignore the former. We present a framework for relating data survivability and storage reliability, and use the framework to gauge the impact of rare but large-scale events on data survivability. We also present a method to track all copies of data and the condition of all the online and offline media, devices and systems on which they are stored uninterruptedly over the whole lifetime of the data. With this method, the survivability of the data can be closely monitored, and potential dangers can be handled in a timely manner. A better understanding of data survivability can be used in reducing unnecessary data replicas, thus reducing the cost.


title: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

abstract: This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.


title: Weak Target Tracking Based on Improved Particle Filter Algorithm

abstract: The traditional particle filter detection algorithm cannot detect weak targets efficiently in the face of the current complex environment. To solve that problem, an improved particle filtering algorithm is proposed in this paper. Firstly, the basic theory of particle filter is introduced, the defects of particle filter method are pointed out. Utilizing auxiliary particle filter and resampling in filtering algorithm is proposed in this paper. So that particle degeneration can be solved which is the significant problem in particle filtering algorithm. A simulation experiment has been conducted to verify the effectiveness of the algorithm.


title: Random Forests

abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.


title: Hash-based enhancement of location privacy for radio-frequency identification devices using varying identifiers

abstract: Radio-frequency identification devices (RFID) may emerge as one of the most pervasive computing technologies in history. On the one hand, with tags affixed to consumer items as well as letters, packets or vehicles costs in the supply chain can be greatly reduced and new applications introduced. On the other hand, unique means of identification in each tag like serial numbers enable effortless traceability of persons and goods. But data protection and privacy are worthwhile civil liberties. We introduce a simple scheme relying on one way hash-functions that greatly enhances location privacy by changing traceable identifiers on every read getting by with only a single, unreliable message exchange. Thereby the scheme is safe from many threats like eavesdropping, message interception, spoofing, and replay attacks.


Research Design: Qualitative, Quantitative, and Mixed Methods Approaches


title: SCIENTIFIC PRINCIPLES OF GROUP TECHNOLOGY

abstract: Novel hydroxyhalogenated elastomers are produced by first partially epoxidizing a high molecular diene elastomer in a solvent medium and then reacting the partially epoxidized product with a hydrohalogen acid in a solvent medium. Diene elastomers such as polymer and copolymers of butadiene, isoprene, dimethyl-2,3-butadiene, chloroprene, cyanobutadienes and piperylene are first epoxidized and then reacted with hydrohalogenated acids such as hydrochloric, hydrobromic, hydriodic and mixtures thereof. The hydroxyhalogenated elastomers thus produced can be vulcanized by an extremely broad range of vulcanization agents and covulcanized with many other elastomers. The vulcanizates of these elastomers are characterized by such properties as very good resistance to aromatic and aliphatic solvents and oils, excellent stability at high or low temperatures and low moisture absorption.


title: A Critical Review on Outlier Detection Techniques

abstract: Outlier Detection is a Data Mining Application. Outlier contains noisy data which is researched in various domains. The various techniques are already being researched that is more generic. We surveyed on various techniques and applications of outlier detection that provides a novel approach that is more useful for the beginners. The proposed approach helps to clean data at university level in less time with great accuracy. This survey includes the existing outlier techniques and applications where the noisy data exists. Our paper defines critical review on various techniques used in different applications of outlier detection that are to be researched further and they gives a particular type of knowledge based data i.e. more useful in research activities. So where the Anomalies is present it will be detected through outlier detection techniques and monitored accordingly especially in educational Data Mining.


title: Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

abstract: Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit novel features from the collective dynamics of the system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach.


title: A Survey on Hybrid Circuit-Breaker Topologies

abstract: The applications of power semiconductors in circuit-breaker (CB) technology can help achieve considerable improvement in its performance and possible new capabilities. In this paper, new trends in power electronics for the applications in CBs are presented. It also summarizes and reviews the appropriate hybrid mechanical-static CB topologies. Various conventional and derived topologies for ac as well as dc applications are described. Discussions about common and future trends in this technology development are presented. This study will provide a useful framework and point of reference for the future development of hybrid CBs for various different applications.


Algorithms for Unevenly Spaced Time Series : Moving Averages and Other Rolling Operators


title: Regularization and Optimization strategies in Deep Convolutional Neural Network

abstract: Convolution Neural Networks, known as ConvNets exceptionally perform well in many complex machine learning tasks. The architecture of ConvNets demands the huge and rich amount of data and involves with a vast number of parameters that leads the learning takes to be computationally expensive, slow convergence towards the global minima, trap in local minima with poor predictions. In some cases, architecture overfits the data and make the architecture difficult to generalise for new samples that were not in the training set samples. To address these limitations, many regularization and optimization strategies are developed for the past few years. Also, studies suggested that these techniques significantly increase the performance of the networks as well as reducing the computational cost. In implementing these techniques, one must thoroughly understand the theoretical concept of how this technique works in increasing the expressive power of the networks. This article is intended to provide the theoretical concepts and mathematical formulation of the most commonly used strategies in developing a ConvNet architecture.


title: Privacy Regulation and Online Advertising

abstract: Advertisers use online customer data to target their marketing appeals. This has heightened consumers' privacy concerns, leading governments to pass laws designed to protect consumer privacy by restricting the use of data and by restricting online tracking techniques used by websites. We use the responses of 3.3 million survey takers who had been randomly exposed to 9,596 online display (banner) advertising campaigns to explore how privacy regulation in the European Union (EU) has influenced advertising effectiveness. This privacy regulation restricted advertisers' ability to collect data on Web users in order to target ad campaigns. We find that, on average, display advertising became far less effective at changing stated purchase intent after the EU laws were enacted, relative to display advertising in other countries. The loss in effectiveness was more pronounced for websites that had general content (such as news sites), where non-data-driven targeting is particularly hard to do. The loss of effectiveness was also more pronounced for ads with a smaller presence on the webpage and for ads that did not have additional interactive, video, or audio features. ::: ::: This paper was accepted by Pradeep Chintagunta, marketing.


OSINT-based Data-driven Cybersecurity Discovery


title: Hymenal Characteristics in Girls with and without a History of Sexual Abuse

abstract: This article reviews the recent literature on physical findings related to the hymen in pubertal and prepubertal girls with and without a history of sexual abuse. Characteristics of normal hymenal anatomy, acute traumatic findings, and characteristics of healed trauma are discussed, particularly with regard to changes in the interpretation of these findings that have occurred over time.


title: Criminal Liability of Autonomous Agents: From the Unthinkable to the Plausible

abstract: The evolution of information technologies have brought us to a point where we are confronted with the existence of agents - computational entities - which are able to act autonomously with little or no human intervention. And their behavior can damage individual or collective interests that are protected by criminal law. Based on the analysis of different models of criminal responsibility of legal persons - which constituted an interesting advance in the criminal law in relation to what was hitherto traditionally accepted -, we will appraise whether the necessary legal elements to have direct criminal liability of artificial entities are present.


Religion as Resinstance in Jamaican Peasant Life: The Baptist Church, Revival Worldview and Rastafari Movement


title: The TangibleK Robotics Program: Applied Computational Thinking for Young Children.

abstract: This article describes the TangibleK robotics program for young children. Based on over a decade of research, this program is grounded on the belief that teaching children about the human-made world, the realm of technology and engineering, is as important as teaching them about the natural world, numbers, and letters. The TangibleK program uses robotics as a tool to engage children in developing computational thinking and learning about the engineering design process. It includes a research-based, developmentally appropriate robotics kit that children can use to make robots and program their behaviors. The curriculum has been piloted in kindergarten classrooms and in summer camps and lab settings. The author presents the theoretical framework that informs TangibleK and the “powerful ideas” from computer science and robotics on which the curriculum is based, linking them to other disciplinary areas and developmental characteristics of early childhood. The article concludes with a description of classroom pedagogy and activities, along with assessment tools used to evaluate learning outcomes.


title: A learning mechanism for adaptive fitness function in auto 3D graphics layout using genetic algorithm

abstract: In computer-aided drafting and designing area, interactive graphics are used for designing components, systems, layouts, and structures. There are currently several approaches using for automated graphical layout tools in designing the graphic. Our research aims to reduce design-working time, to learn user preferences, and to generate 3D graphical layout design automatically. An attempt to enable computers to generate 3D graphical layout design and presentation automatically by using the genetic algorithm (GA) has been discussed for a decade. An effective use of GA in automated graphical layout design relies on how closely it is possible to define a fitness function that can reflect the user preferences. In this paper, we introduce methods to define chromosome structures and fitness functions of the selected objects. A learning mechanism is employed to adjust the fitness values of the objects in the selected layout chosen by the users. By this approach, the fitness functions can be changed adaptively reflecting the selection and user preferences.


Notes on Vertex Classification Problem in Hypergraph Setting


Mathematical Modeling and Analysis of an Electro-Hydrostatic Servo-Actuator with Brushless d . c . Motor


title: Multiuser detection algorithm for PDMA uplink system based on SIC and MMSE

abstract: As the promising novel multiple access scheme for the 5th generation (5G) wireless communication system, pattern division multiple access (PDMA) technology can improve the spectral efficiency and support the massive connection based on the reasonable unequal diversity between users. Compared with traditional multiple access technologies, PDMA brings more serious multiple address interference to hinder the multiuser detection. The universal detection algorithm with low complexity and high performance is one of the main challenges of PDMA technology. In this paper, we proposed a new iterative detection and decoding algorithm, which is a successive interference cancellation (SIC) iterative processing based on the minimum mean square error (MMSE) detection and channel decoding algorithm. It is essentially a SIC iterative processing based on some users' information has been correctly decoded by the MMSE detection and the channel decoder. The proposed multiuser detection algorithm can prevent the error propagation by the cyclic redundancy check (CRC). The simulation results show that the proposed algorithm can get significant performance gains over traditional multiuser detection algorithms.


Scene Recognition Using Mid-level features from CNN


title: Breathing at a rate of 5.5 breaths per minute with equal inhalation-to-exhalation ratio increases heart rate variability

abstract: Abstract Objectives Prior studies have found that a breathing pattern of 6 or 5.5 breaths per minute (bpm) was associated with greater heart rate variability (HRV) than that of spontaneous breathing rate. However, the effects of combining the breathing rate with the inhalation-to-exhalation ratio (I:E ratio) on HRV indices are inconsistent. This study aimed to examine the differences in HRV indices and subjective feelings of anxiety and relaxation among four different breathing patterns. Methods Forty-seven healthy college students were recruited for the study, and a Latin square experimental design with a counterbalance in random sequences was applied. Participants were instructed to breathe at two different breathing rates (6 and 5.5 breaths) and two different I:E ratios (5:5 and 4:6). The HRV indices as well as anxiety and relaxation levels were measured at baseline (spontaneous breathing) and for the four different breathing patterns. Results The results revealed that a pattern of 5.5 bpm with an I:E ratio of 5:5 produced a higher NN interval standard deviation and higher low frequency power than the other breathing patterns. Moreover, the four different breathing patterns were associated with significantly increased feeling of relaxation compared with baseline. Conclusion The study confirmed that a breathing pattern of 5.5 bpm with an I:E ratio of 5:5 achieved greater HRV than the other breathing patterns. This finding can be applied to HRV biofeedback or breathing training in the future.


Pollen-mediated gene flow and differential male reproductive success in a tropical pioneer tree, Cecropia obtusifolia Bertol. (Moraceae): a paternity analysis


title: Multi-exposure imaging on mobile devices

abstract: Many natural scenes have a dynamic range that is larger than the dynamic range of a camera's image sensor. A popular approach to producing an image without under- and over-exposed areas is to capture several input images with varying exposure settings, and later merge them into a single high-quality result using offline image processing software. In this paper, we describe a system for creating images of high-dynamic-range (HDR) scenes that operates entirely on a mobile camera. Our system consists of an automatic HDR metering algorithm that determines which exposures to capture, a video-rate viewfinder preview algorithm that allows the user to verify the dynamic range that will be recorded, and a light-weight image merging algorithm that computes a high-quality result directly on the camera. By using our system, a photographer can capture, view, and share images of HDR scenes directly on the camera, without using offline image processing software.


title: Emotion Recognition based on Phoneme Classes

abstract: Recognizing human emotions/attitudes from speech cues has gained increased attention recently. Most previous work has focused primarily on suprasegmental prosodic features calculated at the utterance level for this purpose. Notably, not much attention is paid to details at the segmental phoneme level in the modeling. Based on the hypothesis that different emotions have varying effects on the properties of the different speech sounds, this paper investigates the usefulness of phoneme-level modeling for the classification of emotional states from speech. Hidden Markov models (HMM) based on short-term spectral features are used for this purpose using data obtained from a recording of an actress’ expressing 4 different emotional states anger, happiness, neutral, and sadness. We designed and compared two sets of HMM classifiers: a generic set of ”emotional speech” HMMs (one for each emotion) a set of broad phoneticclass based HMMs for each emotion type considered. Five broad phonetic classes were used to explore the effect of emotional coloring on different phoneme classes, and it was found that (spectral properties of) vowel sounds were the best indicator to emotions in terms of the classification performance. The experiments also showed that the best performance can be obtained by using phoneme-class classifiers over generic “emotional” HMM classifier and classifiers based on global prosodic features. To see complementary effect of the prosodic and spectral features, two classifiers were combined at the decision level. The improvement was 0.55% in absolute compared with the result from phoneme-class based HMM classifier.


title: Predicting online e-marketplace sales performances: A big data approach

abstract: Confirming the predictive power of product review volume and rating on sales.Examining product type, answers, discount and information usefulness as moderators.Using big data architecture to collect data for model testing. To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework.


title: LTP and LTD An Embarrassment of Riches

abstract: LTP and LTD, the long-term potentiation and depression of excitatory synaptic transmission, are widespread phenomena expressed at possibly every excitatory synapse in the mammalian brain. It is now clear that "LTP" and "LTD" are not unitary phenomena. Their mechanisms vary depending on the synapses and circuits in which they operate. Here we review those forms of LTP and LTD for which mechanisms have been most firmly established. Examples are provided that show how these mechanisms can contribute to experience-dependent modifications of brain function.


title: CSRR Loaded Microstrip Array Antenna With Low Sidelobe Level

abstract: A novel technique for obtaining low sidelobes pattern in a planar microstrip array antenna is proposed. This technique involves the addition of two external complementary split ring resonators (CSRRs) at both the ends of each row of the antenna in the ground plane. These two CSRRs together produce an interferometer pattern for the reduction of sidelobes. An $8\times4$ planar array at 9.9 GHz is designed and fabricated to demonstrate the concept and a sidelobe reduction of 4.3 dB achieved.


title: DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks

abstract: Deep Q-Network (DQN), as one type of deep reinforcement learning model, targets to train an intelligent agent that acquires optimal actions while interacting with an environment. The model is well known for its ability to surpass professional human players across many Atari 2600 games. Despite the superhuman performance, in-depth understanding of the model and interpreting the sophisticated behaviors of the DQN agent remain to be challenging tasks, due to the long-time model training process and the large number of experiences dynamically generated by the agent. In this work, we propose DQNViz , a visual analytics system to expose details of the blind training process in four levels, and enable users to dive into the large experience space of the agent for comprehensive analysis. As an initial attempt in visualizing DQN models, our work focuses more on Atari games with a simple action space, most notably the Breakout game. From our visual analytics of the agent's experiences, we extract useful action/reward patterns that help to interpret the model and control the training. Through multiple case studies conducted together with deep learning experts, we demonstrate that DQNViz can effectively help domain experts to understand, diagnose, and potentially improve DQN models.


title: ARCHER: Aggressive Rewards to Counter bias in Hindsight Experience Replay

abstract: Experience replay is an important technique for addressing sample-inefficiency in deep reinforcement learning (RL), but faces difficulty in learning from binary and sparse rewards due to disproportionately few successful experiences in the replay buffer. Hindsight experience replay (HER) was recently proposed to tackle this difficulty by manipulating unsuccessful transitions, but in doing so, HER introduces a significant bias in the replay buffer experiences and therefore achieves a suboptimal improvement in sample-efficiency. In this paper, we present an analysis on the source of bias in HER, and propose a simple and effective method to counter the bias, to most effectively harness the sample-efficiency provided by HER. Our method, motivated by counter-factual reasoning and called ARCHER, extends HER with a trade-off to make rewards calculated for hindsight experiences numerically greater than real rewards. We validate our algorithm on two continuous control environments from DeepMind Control Suite - Reacher and Finger, which simulate manipulation tasks with a robotic arm - in combination with various reward functions, task complexities and goal sampling strategies. Our experiments consistently demonstrate that countering bias using more aggressive hindsight rewards increases sample efficiency, thus establishing the greater benefit of ARCHER in RL applications with limited computing budget.


Automated Negotiation: Prospects, Methods and Challenges


title: Feature discovery by competitive learning

abstract: This paper reports the results of our studies with an unsupervised learning paradigm which we have called “Competitive Learning.” We have examined competitive learning using both computer simulation and formal analysis and have found that when it is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. We were attracted to competitive learning because it seems to provide a way to discover the salient, general features which can be used to classify a set of patterns. We show how a very simply competitive mechanism can discover a set of feature detectors which capture important aspects of the set of stimulus input patterns. We also show how these feature detectors can form the basis of a multilayer system that can serve to learn categorizations of stimulus sets which are not linearly separable. We show how the use of correlated stimuli con serve as a kind of “teaching” input to the system to allow the development of feature detectors which would not develop otherwise. Although we find the competitive learning mechanism a very interesting and powerful learning principle, we do not, of course, imagine thot it is the only learning principle. Competitive learning is an essentially nonassociative statistical learning scheme. We certainly imagine that other kinds of learning mechanisms will be involved in the building of associations among patterns of activation in a more complete neural network. We offer this analysis of these competitive learning mechanisms to further our understanding of how simple adaptive networks can discover features important in the description of the stimulus environment in which the system finds itself.


title: Printed wearable temperature sensor for health monitoring

abstract: We developed a printed high sensitive temperature sensor by synthesizing poly (3, 4-ethlenedioxythiophene) poly (styrenesulfonate) (PEDOT:PSS) and carbon nanotubes (CNTs) composition ink on a flexible substrate. There are two advantages of this flexible temperature sensor, which are (1) high sensitivity (~0.6%/°C) compared to other reported flexible temperature sensors, (2) fully-printable low-cost process on macroscale flexible substrates due to solution-based process. The key point to achieve the high sensitivity was a mixture of PEDOT:PSS and CNTs for the temperature sensor material. Based on the analyses, the mechanism of this high sensitivity is most likely due to electron hopping at the interface of PEDOT:PSS and CNTs. In addition to the sensor analyses, real-time human skin temperature was monitored by attaching on a human skin like a bandage as a proof-of-concept, and it was successfully conducted to observe small skin temperature change during activities.


Algorithms for Trip-Vehicle Assignment in Ride-Sharing


title: Layout-dependent aging mitigation for critical path timing

abstract: Layout-dependent effects (LDEs) are becoming increasingly important as technology node continues to shrink into the regime of FinFET transistors. Prior LDE studies mainly focus on accurate transistor modeling and fast circuit performance evaluations at the early lifetime of a design. Few studies have been performed on the layout dependency of the circuit aging towards the end of life (EOL). This study demonstrates that, due to transistor-level layout-dependent aging (LDA) behaviors, circuit-level timing degradations are greatly impacted by layout configurations, including length of diffusion and oxide spacing. In this paper, we propose the first circuit-level aging mitigation framework to improve the critical-path timing towards the EOL. Our framework features comprehensive LDA evaluations for standard cell timing, which shows that multiple-row height cells lead to worse EOL timing than single-row height cells due to length-of-diffusion effects. We further propose a min-cost-flow-based placement approach to concurrently allocate the oxide spacing among neighboring standard cells, which generates much better EOL timing than a conventional greedy approach. Experimental results demonstrate that under the concurrent approach in the proposed aging mitigation framework, the total and worst negative slacks for EOL timing are on average reduced by 42% and 25%, respectively.


title: Machine learning-based network modeling: An artificial neural network model vs a theoretical inspired model

abstract: Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. The application of ML to networking brings several use-cases as well as challenges. In this paper we focus on a rather fundamental problem in networking: estimating the delays of a network. In particular, we aim to assess the performance of models inspired by the existing knowledge of network modeling in comparison to generic adaptive machine learning models. To do so, we present a M/M/1-inspired ML regressor and compare its performance to a neural network.


title: A Nonparametric Delayed Feedback Model for Conversion Rate Prediction

abstract: Predicting conversion rates (CVRs) in display advertising (e.g., predicting the proportion of users who purchase an item (i.e., a conversion) after its corresponding ad is clicked) is important when measuring the effects of ads shown to users and to understanding the interests of the users. There is generally a time delay (i.e., so-called {\it delayed feedback}) between the ad click and conversion. Owing to the delayed feedback, samples that are converted after an observation period may be treated as negative. To overcome this drawback, CVR prediction assuming that the time delay follows an exponential distribution has been proposed. In practice, however, there is no guarantee that the delay is generated from the exponential distribution, and the best distribution with which to represent the delay depends on the data. In this paper, we propose a nonparametric delayed feedback model for CVR prediction that represents the distribution of the time delay without assuming a parametric distribution, such as an exponential or Weibull distribution. Because the distribution of the time delay is modeled depending on the content of an ad and the features of a user, various shapes of the distribution can be represented potentially. In experiments, we show that the proposed model can capture the distribution for the time delay on a synthetic dataset, even when the distribution is complicated. Moreover, on a real dataset, we show that the proposed model outperforms the existing method that assumes an exponential distribution for the time delay in terms of conversion rate prediction.


title: No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles Using Cameras and LiDARs

abstract: Online multi-object tracking (MOT) is extremely important for high-level spatial reasoning and path planning for autonomous and highly-automated vehicles. In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks. This work is a generalization of the MDP framework for MOT proposed by Xiang et al. , with some key extensions - First, we track objects across multiple cameras and across different sensor modalities. This is done by fusing object proposals across sensors accurately and efficiently. Second, the objects of interest (targets) are tracked directly in the real world . This is a departure from traditional techniques where objects are simply tracked in the image plane. Doing so allows the tracks to be readily used by an autonomous agent for navigation and related tasks. To verify the effectiveness of our approach, we test it on real world highway data collected from a heavily sensorized testbed capable of capturing full-surround information. We demonstrate that our framework is well-suited to track objects through entire maneuvers around the ego-vehicle, some of which take more than a few minutes to complete. We also leverage the modularity of our approach by comparing the effects of including/excluding different sensors, changing the total number of sensors, and the quality of object proposals on the final tracking result.


SMOKING AND CANCER


title: Influence of pulse shaping on bit error rate performance and out of band radiation of Generalized Frequency Division Multiplexing

abstract: Generalized Frequency Division Multiplexing (GFDM) is a multicarrier transmission scheme that offers flexible pulse shaping of individual subcarriers. The application of pulse shaping per subcarrier can control the out of band (OOB) radiation and create non-orthogonal waveforms. In this paper, the influence of the pulse shaping to the overall system performance, namely bit error rate (BER) over AWGN channels and OOB radiation, is investigated. Closed from expressions for the BER and power spectral density (PSD) of GFDM are derived. Simulation results show that GFDM reduces the OOB radiation by 46dB compared to OFDM, while at the same time, the OFDM BER can be achieved when using the Dirichlet pulse filter. In case some self-interference is allowed, the OOB radiation can be reduced even more, which is a key aspect for cognitive radio (CR) applications.


title: Machine learning methods for fully automatic recognition of facial expressions and facial actions

abstract: We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the facial action coding system (FACS), as well as recognition of fall facial expressions. Each video-frame is first scanned in real-time to detect approximately upright frontal faces. The faces found are scaled into image patches of equal size, convolved with a bank of Gabor energy filters, and then passed to a recognition engine that codes facial expressions into 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques. Best results were obtained by selecting a subset of Gabor filters using AdaBoost and then training support vector machines on the outputs of the filters selected by AdaBoost. The generalization performance to new subjects for recognition of full facial expressions in a 7-way forced choice was 93% correct, the best performance reported so far on the Cohn-Kanade FACS-coded expression dataset. We also applied the system to fully automated facial action coding. The present system classifies 18 action units, whether they occur singly or in combination with other actions, with a mean agreement rate of 94.5% with human FACS codes in the Cohn-Kanade dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics.


title: Do security toolbars actually prevent phishing attacks?

abstract: Security toolbars in a web browser show security-related information about a website to help users detect phishing attacks. Because the toolbars are designed for humans to use, they should be evaluated for usability -- that is, whether these toolbars really prevent users from being tricked into providing personal information. We conducted two user studies of three security toolbars and other browser security indicators and found them all ineffective at preventing phishing attacks. Even though subjects were asked to pay attention to the toolbar, many failed to look at it; others disregarded or explained away the toolbars' warnings if the content of web pages looked legitimate. We found that many subjects do not understand phishing attacks or realize how sophisticated such attacks can be.


title: MINIATURIZATION OF ANTENNA FOR WIRELESS APPLICATION WITH DIFFERENCE METAMATERIAL STRUCTURES

abstract: In this paper, periodic structures are investigated in antenna design for wireless applications. These antennas were compared with CRLH miniaturization method. Three difierent models of patch antenna with coaxial feed on EBG ground, metamaterial substrate or EBG/AMC structure have been presented here. Also two compact dual-band antennas have been designed and fabricated based on CRLH techniques for wireless and GSM applications. The flrst antenna has directional pattern and operates at 1760, 2550 and 3850MHz (three-band antenna) with gain 2.1, i3:9 and 2.5dBi, and it is dual polarized. The size of prototype patch antenna is 20 £ 20mm 2 which is reduced about 47% in comparison to conventional patch antenna at 2.5GHz. The second antenna is designed by the use of interdigital capacitor and spiral inductor. Dimensions of antenna are 15:5£12mm 2 , so the size is reduced about 69% in comparison to conventional microstrip patch antennas at 1.8GHz. The second tri-band antenna operates at 1060MHz, 1800MHz and 2500MHz in which two frequencies (1.8 and 2.5GHz) are suitable for GMS and WLAN applications. Both structures have been designed and fabricated on FR4 low cost substrate with "r = 4:4 and thickness of 1.6mm. All simulations are done with CST and HFSS. Equivalent circuit and experimental results are also presented and compared.


title: Wave steered FSMs

abstract: In this paper we address the problem of designing very high throughput finite state machines (FSMs). The presence of loops in sequential circuits prevents a straightforward and generalized application of pipelining techniques, which work so well for combinational circuits, to increase FSM performance. We observe that appropriate extensions of the "wave steering" technique are possible to partially overcome the problem. Additionally we use FSM decomposition theory to decouple state variable dependencies. Application of these two techniques to MCNC benchmarks resulted in a factor of 3 average throughput increase as compared to a standard cell implementation, at the expense of factor 3.7 area and less than factor 2 latency penalties.


title: SPORC: Group Collaboration using Untrusted Cloud Resources

abstract: Cloud-based services are an attractive deployment model for user-facing applications like word processing and calendaring. Unlike desktop applications, cloud services allow multiple users to edit shared state concurrently and in real-time, while being scalable, highly available, and globally accessible. Unfortunately, these benefits come at the cost of fully trusting cloud providers with potentially sensitive and important data. ::: ::: To overcome this strict tradeoff, we present SPORC, a generic framework for building a wide variety of collaborative applications with untrusted servers. In SPORC, a server observes only encrypted data and cannot deviate from correct execution without being detected. SPORC allows concurrent, low-latency editing of shared state, permits disconnected operation, and supports dynamic access control even in the presence of concurrency. We demonstrate SPORC's flexibility through two prototype applications: a causally-consistent key-value store and a browser-based collaborative text editor. ::: ::: Conceptually, SPORC illustrates the complementary benefits of operational transformation (OT) and fork* consistency. The former allows SPORC clients to execute concurrent operations without locking and to resolve any resulting conflicts automatically. The latter prevents a misbehaving server from equivocating about the order of operations unless it is willing to fork clients into disjoint sets. Notably, unlike previous systems, SPORC can automatically recover from such malicious forks by leveraging OT's conflict resolution mechanism.


title: Comparative Exudate Classification using Support Vector Machines and Neural Networks

abstract: After segmenting candidate exudates regions in colour retinal images we present and compare two methods for their classification. The Neural Network based approach performs marginally better than the Support Vector Machine based approach, but we show that the latter are more flexible given criteria such as control of sensitivity and specificity rates. We present classification results for different learning algorithms for the Neural Net and use both hard and soft margins for the Support Vector Machines. We also present ROC curves to examine the trade-off between the sensitivity and specificity of the classifiers.


Energy efficient opportunistic edge computing for the Internet of Things


title: Training Deep Nets with Sublinear Memory Cost

abstract: We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O( √ n) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the memory cost to store the intermediate feature maps and gradients during training. Computation graph analysis is used for automatic in-place operation and memory sharing optimizations. We show that it is possible to trade computation for memory giving a more memory efficient training algorithm with a little extra computation cost. In the extreme case, our analysis also shows that the memory consumption can be reduced to O(logn) with as little as O(n logn) extra cost for forward computation. Our experiments show that we can reduce the memory cost of a 1,000-layer deep residual network from 48G to 7G on ImageNet problems. Similarly, significant memory cost reduction is observed in training complex recurrent neural networks on very long sequences.


title: The impact of CSR on casino employees' organizational trust, job satisfaction, and customer orientation: an empirical examination of responsible gambling strategies.

abstract: Abstract Corporate social responsibility (CSR) has emerged as a critical philosophy and a strategy that affects both internal (i.e., employee) and external (i.e., customer) attitudes. The importance of CSR extends to the casino industry, especially when it pertains to compulsory and supplementary responsible gambling (RG) strategies. To this end, this study surveys casino employees in South Korea to examine the impact of CSR on casino employees’ organizational trust, job satisfaction, and customer orientation. Results indicate that legal CSR and supplementary RG have a positive effect on organizational trust, whereas compulsory RG strategies have a negative effect on organizational trust. Also, it appears that organizational trust positively influences job satisfaction, which in turn has a positive effect on customer orientation. In building off of these findings, this study provides practical implications for casino managers when implementing CSR and RG strategies.


title: Characterising Behavior and Emotions on Social Media for Safety: Exploring Online Communication between Police and Citizens

abstract: Increased use of social media by police to connect with citizens has encouraged researchers to study different aspects of information exchange (e.g. type of information, credibility and propagation) during emergency and crisis situation. Research studies lack understanding of human behavior such as engagement, emotions and social interaction between citizen and police department on social media. Several social media studies explore and show technological implications of human behavioral aspects in various contexts such as workplace interaction and depression in young mothers. In this paper, we study online interactions between citizens and Indian police in context of day-to-day policing, including safety concerns, advisories, etc. Indian police departments use Facebook to issue advisories, send alerts and receive citizen complaints and suggestions regarding safety issues and day-to-day policing. We explore how citizens express their emotions and social support on Facebook. Our work discusses technological implications of behavioral aspects on social well being of citizens.


title: The superhighway to nursing science and practice.

abstract: Nursing is evolving within a societal context of shifting paradigms and pervasive technology. Information technology offers untold possibilities to enhance the mechanisms of health care delivery. The capacity for comprehensive access to and integration of information and knowledge suggests a holistic approach that transcends time, settings, and providers. Moreover, the interface between nursing practice and nursing informatics provides an opportunity to create new science and to expand the boundaries of nursing knowledge.


title: C/EBPalpha inactivation in FAK-overexpressed HL-60 cells impairs cell differentiation.

abstract: We previously demonstrated that focal adhesion kinase (FAK)-overexpressed (HL-60/FAK) cells have marked resistance against various apoptotic stimuli such as oxidative stress, ionizing radiation and TNF-receptor-induced ligand (TRAIL) compared with vector-transfected (HL-60/Vect) cells. Here, we show that HL-60/FAK cells are highly resistant to all-trans retinoic acid (ATRA)-induced differentiation, whereas original HL-60 or HL-60/Vect cells are sensitive. Treatment with ATRA at 1 muM for 5 days markedly inhibited the proliferation and increased the expression of differentiation markers (CD38, CD11b) in HL-60/Vect cells, but showed no such effect in HL-60/FAK cells. Electrophoretic mobility shift assay (EMSA) using an oligonucleotide for the c/EBP consensus binding sequence showed that c/EBPalpha was activated in ATRA-treated HL-60/Vect cells but not in HL-60/FAK cells, indicating that c/EBPalpha activation by ATRA was impaired in HL-60/FAK cells. In addition, the association of retinoblastoma protein (pRb) and c/EBPalpha after treatment with ATRA was seen in HL-60/Vect cells but not in HL-60/FAK cells. Further, hyperphosphorylation of pRb was observed in HL-60/FAK cells. Finally, the introduction of FAK siRNA into HL-60/FAK cells resulted in the recovery of sensitivity to ATRA-induced differentiation, confirming that the inhibition of HL-60/FAK differentiation resulted from both the induction of pRb hyperphosphorylation and the inhibition of association of pRb and c/EBPalpha.


title: Character rigging, deformations, and simulations in film and game production

abstract: This course focuses on rigging, deformations, dynamics, and production practices in animation, visual effects and game development. Topics include analysis of performance requirements, motion system set-up, procedural rigging for secondary animation, and efficient extension of techniques over a wide range of primary and secondary characters.


title: Building Machines That Learn and Think Like People

abstract: Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.


title: Non-surgical treatments for hidradenitis suppurativa: A systematic review

abstract: Summary Introduction The management of hidradenitis suppurativa is multidisciplinary, involving general measures, medical treatment and surgery. Non-surgical treatments, often first-line procedures, mainly concern forms of low-to-moderate severity or, conversely, very severe forms in non-operable patients or those refusing surgery. While many treatments have been attempted, few randomized controlled trials have been conducted, so the choice of treatments is most often based on the personal experience of the clinicians. The objective of this systematic review is to propose a synthetic analysis of the currently available non-surgical procedures. Methods This systematic review of the literature was conducted in accordance with the PRISMA criteria. We searched for articles in the Medline ® , PubMed Central , Embase and Cochrane databases published between January 2005 and September 2015. Results Sixty-four articles were included. They generally had a low level of evidence; indeed, the majority of them were retrospective observational studies. They involved biotherapy (44%), dynamic phototherapy (16%), antibiotics (11%), Laser (8%), retinoids (6%) and immunosuppressive therapies, anti-inflammatory drugs, zinc, metformin, gammaglobulins and fumarates. Conclusions None of the non-surgical treatments can treat all stages of the disease and offer long-term remission. Antibiotics and biotherapy seem to have real effectiveness but their effect remains suspensive and the disease is almost certain to reappear once they are stopped. As regards antibiotics, no association has shown their superiority in a study with a high level of evidence. And while some biotherapies seem quite effective, due to their side effects they should be reserved for moderate-to-severe, resistant or inoperable forms of the disease. Randomized controlled studies are needed before valid conclusions can be drawn. In the resistant or disabling forms, it is consequently advisable to orientate to the greatest possible extent towards radical surgery, which is the only treatment allowing hope for cure.


title: Who does what on Facebook? Age, sex, and relationship status as predictors of Facebook use

abstract: Previous studies have focused on why people use Facebook and on the effects of ''Facebooking'' on well being. This study focused more on how people use Facebook. An international sample of 1,026 Facebook users (284 males, 735 females; mean age=30.24) completed an online survey about their Facebook activity. Females, younger people, and those not currently in a committed relationship were the most active Facebook users, and there were many age-, sex-, and relationship-related main effects. Females spent more time on Facebook, had more Facebook friends, and were more likely to use profile pictures for impression management; women and older people engaged in more online family activity. Relationship status had an impact on the Facebook activity of males, but little effect on the activity of females. The results are interpreted within a framework generated by an evolutionary perspective and previous research on the psychology of gossip.


title: A Comparison of Autonomic Decision Making Techniques

abstract: Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This paper proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application, which provides direct performance feedback at runtime. The Application Heartbeats framework is used to provide the sensor data (feedback), and a variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks.


title: DEFO-NET: Learning Body Deformation Using Generative Adversarial Networks

abstract: Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (Defo-Net), able to predict body deformations under external forces from a single RGB-D image. The network is based on an invertible conditional Generative Adversarial Network (IcGAN) and is trained on a collection of different objects of interest generated by a physical finite element model simulator. Defo-Net inherits the generalisation properties of GANs. This means that the network is able to reconstruct the whole 3-D appearance of the object given a single depth view of the object and to generalise to unseen object configurations. Contrary to traditional finite element methods, our approach is fast enough to be used in real-time applications. We apply the network to the problem of safe and fast navigation of mobile robots carrying payloads over different obstacles and floor materials. Experimental results in real scenarios show how a robot equipped with an RGB-D camera can use the network to predict terrain deformations under different payload configurations and use this to avoid unsafe areas.


title: Telephone Intervention to Improve Diabetes Control: A Randomized Trial in the New York City A1c Registry

abstract: Introduction Scalable self-management interventions are necessary to address suboptimal diabetes control, especially among minority populations. The study tested the effectiveness of a telephone behavioral intervention in improving glycemic control among adults with diabetes in the New York City A1c Registry. Design RCT comparing a telephone intervention to print-only intervention in the context of the A1c Registry program. Setting/participants Nine hundred forty-one adults with diabetes and hemoglobin A1c (A1c) >7% from a low-income, predominantly Latino population in the South Bronx were recruited from the A1c Registry. Intervention All study participants were mailed print diabetes self-management materials at baseline and modest lifestyle incentives quarterly. Only the telephone participants received four calls from health educators evenly spaced over 1 year if baseline A1c was >7%–9%, or eight calls if baseline A1c was >9%. Medication adherence was the main behavioral focus and, secondarily, nutrition and exercise. Main outcome measures Primary outcome was difference between two study arms in change in A1c from baseline to 1 year. Secondary outcomes included diabetes self-care activities, including self-reported medication adherence. Data were collected in 2008–2012 and analyzed in 2012–2014. Results Participants were predominantly Latino (67.7%) or non-Latino black (28%), with 69.7% foreign-born and 55.1% Spanish-speaking. Among 694 (74%) participants with follow-up A1c, mean A1c decreased by 0.9 (SD=0.1) among the telephone group compared with 0.5 (SD=0.1) among the print-only group, a difference of 0.4 (95% CI=0.09, 0.74, p =0.01). The intervention had significant effect when baseline A1c was >9%. Both groups experienced similar improvements in self-care activities, medication adherence, and intensification. Conclusions A telephone intervention delivered by health educators can be a clinically effective tool to improve diabetes control in diverse populations, specifically for those with worse metabolic control identified using a registry. This public health approach could be adopted by health systems supported by electronic record capabilities. ClinicalTrials.gov Registration NCT00797888.


title: Implementing a surgical checklist: more than checking a box.

abstract: BACKGROUND ::: Perioperative checklists are mandated by many hospitals as determined by the reduction in morbidity and mortality seen with the use of the World Health Organization's Surgical Safety Checklist. An adapted perioperative checklist was implemented within our hospital system, and compliance with the checklist was reported to be 100%. We hypothesized that compliance does not measure the fidelity of implementation. ::: ::: ::: METHODS ::: During a 7-week period, a prospective study was performed to evaluate the completion of all preincision components of the surgical checklist. Pediatric surgical operations were selected for direct observation. In addition, a poststudy survey was used to assess perception and understanding of the checklist process. ::: ::: ::: RESULTS ::: A total of 142 pediatric surgical cases were observed. Hospital reported data demonstrated 100% compliance with the preincision phase of the checklist for these cases. None of the cases completely executed all items on the checklist, and the average number of checklist items performed in the observed cases was 4 of 13. The most commonly performed checkpoint were the confirmation of patient name and procedure (99%) and the "timeout" at the start of the checklist (97%). The rest of the checkpoints were performed in less than 60% of cases. Adherence did not increase during the observation period. ::: ::: ::: CONCLUSION ::: These data show that despite the 100% documented completion of the preincision phase of the checklist; most of the individual checkpoints are either not executed as designed or not executed at all. These findings demonstrate lack of checklist implementation fidelity, which may be a reflection a poor implementation and dissemination strategy.


Concerning AOP and Inheritance


title: BlueCache: A Scalable Distributed Flash-based Key-value Store

abstract: A key-value store (KVS), such as memcached and Redis, is widely used as a caching layer to augment the slower persistent backend storage in data centers. DRAM-based KVS provides fast key-value access, but its scalability is limited by the cost, power and space needed by the machine cluster to support a large amount of DRAM. This paper offers a 10X to 100X cheaper solution based on flash storage and hardware accelerators. In BlueCache key-value pairs are stored in flash storage and all KVS operations, including the flash controller are directly implemented in hardware. Furthermore, BlueCache includes a fast interconnect between flash controllers to provide a scalable solution. We show that BlueCache has 4.18X higher throughput and consumes 25X less power than a flash-backed KVS software implementation on x86 servers. We further show that BlueCache can outperform DRAM-based KVS when the latter has more than 7.4% misses for a read-intensive aplication. BlueCache is an attractive solution for both rack-level appliances and data-center-scale key-value cache.


title: Self-Transfer Learning for Weakly Supervised Lesion Localization

abstract: Recent advances of deep learning have achieved remarkable performances in various computer vision tasks including weakly supervised object localization. Weakly supervised object localization is practically useful since it does not require fine-grained annotations. Current approaches overcome the difficulties of weak supervision via transfer learning from pre-trained models on large-scale general images such as ImageNet. However, they cannot be utilized for medical image domain in which do not exist such priors. In this work, we present a novel weakly supervised learning framework for lesion localization named as self-transfer learning (STL). STL jointly optimizes both classification and localization networks to help the localization network focus on correct lesions without any types of priors. We evaluate STL framework over chest X-rays and mammograms, and achieve significantly better localization performance compared to previous weakly supervised localization approaches.


title: Stimulation by polyols of the two ryanodine receptor isoforms of frog skeletal muscle

abstract: While the stimulating effect of concentrated salts on ryanodine receptor (RyR) is widely accepted in Ca2+-induced Ca2+ release (CICR) and [3H]ryanodine binding, the effect of non-ionic solutes on RyR is controversial. We investigated the effects of polyols on [3H]ryanodine binding to α- and β-RyR purified from bullfrog skeletal muscle, and on CICR from sarcoplasmic reticulum (SR) in a skinned frog skeletal muscle fibre. Addition of polyols (glucose, sucrose, sorbitol, glycerol and ethylene glycol) in submolar to molar concentrations to an isotonic salt medium increased dose-dependently Ca2+-activated [3H]ryanodine binding to α- and β-RyR of a similar magnitude. The increase is due to the rise in both apparent affinity (1/KD) and maximal numbers of binding sites (Bmax) for ryanodine. In addition to this stimulating effect, glucose sensitized both isoforms to Ca2+ in the Ca2+-activated reaction, which is distinct in mechanism(s) from caffeine. These stimulating effects of polyols were not observed unless some NaCl was present, which might explain the discrepancy among reported results. Consistent with these findings, polyols reversibly enhanced the rate of CICR from SR in skinned fibres with an increase in the Ca2+ sensitivity. The enhanced CICR was still sensitive to well-known modulators for CICR (Ca2+, Mg2+, adenine nucleotides and procaine), as with [3H]ryanodine binding. The results of this study reveal that polyols stimulate α- and β-RyR in frog skeletal muscle, bringing about increased CICR activity. The finding that the specific activity of polyols in stimulation of [3H]ryanodine binding was approximately proportional to their molecular weights leads us to discuss the possible modification of protein surface--water molecule interaction as an underlying mechanism


title: A location-based personal task reminder for mobile users

abstract: Personal task reminders have been indispensable for modern people, in order to remind them of their tasks at specific circumstances. Traditional paper-based reminders are still useful, but they cannot be organized efficiently. Electronic reminders based on the calendar in cell phones are more efficient and gaining popularity, but such reminders are mostly triggered by time. In many situations, tasks are only meaningful to be performed at a specific location, so it would be useful if reminders for those tasks can be triggered only when the person to be reminded is physically near or located at that location. Therefore, in this research, we develop a location-based personal task reminder for Android-based smartphones and tablets. To distinguish our work from existing ones that rely solely on the GPS technology, we take advantage of the ubiquity of IEEE 802.11 WLAN infrastructure to compliment the "blind spots" of GPS location sensing. Combining the two technologies makes it possible for the personal task reminder to be effective in both indoor and outdoor environments. We also propose two operating models for the personal task reminder to boost the usability of the application. Furthermore, as long as the WLAN infrastructure is available, our work as a foundation of location-based services can easily be extended to be used in many other scenarios, such as guiding in public transportation systems or tourist attractions, location-based learning, and even caring of the Dementia residents.


title: Trust in Small Military Teams

abstract: Despite relevance for military operations, little work has examined trust in military research and theory. This work first reviews the literature on the development of interpersonal trust, where two distinct forms of trust are considered. Person-based trust develops over time, and is based on direct experience with another person [ Rempel, Holmes, and Zanna, 1985]. Category-based trust emerges in the absence of direct experience and contact with others, and is based on assumptions about the category to which others belong [Kramer, 1999]. Secondly, the relevance of both forms of trust to the development of trust in small military teams is explored. The effects of trust on team processes and performance are also considered. Existing research and theory suggests that trust affects communication processes, the monitoring of other team members, and also impacts on the ability of teams to work cooperatively. With increasing technological demands in the military, trust in automation and relevant research and theory are also considered. The paper concludes with a discussion of an ongoing research program that is examining trust among members of small military teams.


title: Geometric Mechanics of Curved Crease Origami

abstract: Folding a sheet of paper along a curve can lead to structures seen in decorative art and utilitarian packing boxes. Here we present a theory for the simplest such structure: an annular circular strip that is folded along a central circular curve to form a three-dimensional buckled structure driven by geometrical frustration. We quantify this shape in terms of the radius of the circle, the dihedral angle of the fold, and the mechanical properties of the sheet of paper and the fold itself. When the sheet is isometrically deformed everywhere except along the fold itself, stiff folds result in creases with constant curvature and oscillatory torsion. However, relatively softer folds inherit the broken symmetry of the buckled shape with oscillatory curvature and torsion. Our asymptotic analysis of the isometrically deformed state is corroborated by numerical simulations that allow us to generalize our analysis to study structures with multiple curved creases.


title: Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method

abstract: We develop and analyze a procedure for gradient-based optimization that we refer to as stochastically controlled stochastic gradient (SCSG). As a member of the SVRG family of algorithms, SCSG makes use of gradient estimates at two scales, with the number of updates at the faster scale being governed by a geometric random variable. Unlike most existing algorithms in this family, both the computation cost and the communication cost of SCSG do not necessarily scale linearly with the sample size $n$; indeed, these costs are independent of $n$ when the target accuracy is low. An experimental evaluation on real datasets confirms the effectiveness of SCSG.


title: A geometry-based soft shadow volume algorithm using graphics hardware

abstract: Most previous soft shadow algorithms have either suffered from aliasing, been too slow, or could only use a limited set of shadow casters and/or receivers. Therefore, we present a strengthened soft shadow volume algorithm that deals with these problems. Our critical improvements include robust penumbra wedge construction, geometry-based visibility computation, and also simplified computation through a four-dimensional texture lookup. This enables us to implement the algorithm using programmable graphics hardware, and it results in images that most often are indistinguishable from images created as the average of 1024 hard shadow images. Furthermore, our algorithm can use both arbitrary shadow casters and receivers. Also, one version of our algorithm completely avoids sampling artifacts which is rare for soft shadow algorithms. As a bonus, the four-dimensional texture lookup allows for small textured light sources, and, even video textures can be used as light sources. Our algorithm has been implemented in pure software, and also using the GeForce FX emulator with pixel shaders. Our software implementation renders soft shadows at 0.5--5 frames per second for the images in this paper. With actual hardware, we expect that our algorithm will render soft shadows in real time. An important performance measure is bandwidth usage. For the same image quality, an algorithm using the accumulated hard shadow images uses almost two orders of magnitude more bandwidth than our algorithm.


title: CO-RELATION BETWEEN LACTATE DEHYDROGENASE AND CREATINE KINASE-MB IN ACUTE MYOCARDIAL INFARCTION

abstract: The aim of our study was to know the most potent cardiac marker from SGOT, LDH and CK-MB and to evaluate the sensitivity and specificity of that marker in diagnosis of myocardial infarction. For this purpose we were selected 30 patients of acute myocardial infarction from different hospitals of solapur city. Blood samples from 30 healthy volunteers were collected and referred as controls. In acute myocardial infarction (AMI) patients the activity of SGOT and LDH were found significantly higher (p < 0.01) as compared to controls. The values were 71.82 ± 12.26 IU/L (control = 35.48 ± 4.43 IU/L) and 239.79 ± 27.57 IU/L (control = 170.39 ± 16.82 IU/L) respectively for SGOT and LDH. But, as both of above markers are also found to be increased in many other clinical conditions, they are non-specific for diagnosis of acute myocardial infarction. Thus, are useful only along with CK-MB to know severity of myocardial infarction . On other hand, CK-MB also found to be elevated at significant level (p < 0.01). The values were 22.88 ± 3.39 U/L (control = 11.04 ± 2.03 U/L). CK-MB is present in large proportion in myocardium and also it is only specific for myocardial tissue. Thus, it is not found to be elevated in any other clinical conditions. Similarly, CK-MB gets elevated in serum more early as compared to other markers in myocardial infarction hence, it has been considered as the 'Gold Standard' for confirmation of acute myocardial infarction.


title: Orbital Angular Momentum (OAM) Generation by Cylinder Dielectric Resonator Antenna for Future Wireless Communications

abstract: In this paper, we demonstrate that the solid cylinder dielectric resonator antenna (CDRA) is a high radiation efficient device to generate orbital angular momentum (OAM) waves with different OAM modes at different frequencies. Theoretical formulations of its radiation fields are deduced in the cylindrical coordinate system, which shows that all components of the electric and magnetic fields have the azimuth dependence of e ::: jmφ ::: . A full-wave simulation on a practical size CDRA is then implemented to validate the theoretical derivations. The simulation results show that different separated OAM modes can be obtained at different frequencies. Specifically, the higher order OAM modes are generated at higher electron magnetic modes, which makes CDRA a promising method to overcome the narrow band and multiband issues existing in the design of current OAM devices. Furthermore, a maximum radiation efficiency of 97.1% at 13.3 GHz, and a minimum radiation efficiency of 72.4% at 19.45 GHz are obtained in our simulation, which is a high radiation efficiency feature at the OAM resonant frequencies.


title: MULTI-WEAR: A multi-wearable platform for enhancing mobile experiences

abstract: The uptake of wearable technology suggests that the time is ripe to explore new opportunities for improving mobile experiences. Apps, however, are not keeping up with the pace of technological advancement because wearables are treated as standalone devices, although their individual capabilities better classify them as peripherals with complementary roles. We foresee that the next generation of apps will orchestrate multiple wearable devices to enhance mobile user experiences. However, currently there is limited support for combining heterogeneous devices. This paper introduces Multi-Wear, a platform to scaffold the development of apps that span multiple wearables. It demonstrates experimentally how MULTI-WEAR can help bring changes to mobile apps that go beyond conventional practices.


Design of Experiments: Statistical Principles of Research Design and Analysis


title: Error Bounds for Approximate Policy Iteration

abstract: In Dynamic Programming, convergence of algorithms such as Value Iteration or Policy Iteration results -in discounted problems- from a contraction property of the back-up operator, guaranteeing convergence to its fixed-point. When approximation is considered, known results in Approximate Policy Iteration provide bounds on the closeness to optimality of the approximate value function obtained by successive policy improvement steps as a function of the maximum norm of value determination errors during policy evaluation steps. Unfortunately, such results have limited practical range since most function approximators (such as linear regression) select the best fit in a given class of parameterized functions by minimizing some (weighted) quadratic norm. ::: ::: In this paper, we provide error bounds for Approximate Policy Iteration using quadratic norms, and illustrate those results in the case of feature-based linear function approximation.


title: Acceleration and balance in trotting dogs

abstract: During quadrupedal trotting, diagonal pairs of limbs are set down in unison and exert forces on the ground simultaneously. Ground-reaction forces on individual limbs of trotting dogs were measured separately using a series of four force platforms. Vertical and fore-aft impulses were determined for each limb from the force/time recordings. When mean fore-aft acceleration of the body was zero in a given trotting step (steady state), the fraction of vertical impulse on the forelimb was equal to the fraction of body weight supported by the forelimbs during standing (approximately 60 %). When dogs accelerated or decelerated during a trotting step, the vertical impulse was redistributed to the hindlimb or forelimb, respectively. This redistribution of the vertical impulse is due to a moment exerted about the pitch axis of the body by fore-aft accelerating and decelerating forces. Vertical forces exerted by the forelimb and hindlimb resist this pitching moment, providing stability during fore-aft acceleration and deceleration.