title: NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model

abstract: Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.


A COMPLIANT QUADRUPEDAL ROBOT FOR FAST , EFFICIENT , AND VERSATILE


title: Mining the Sentiment Expectation of Nouns Using Bootstrapping Method

abstract: We propose an unsupervised bootstrapping method to generate a new type of affect knowledge base: the sentiment expectation of nouns (e.g., “high salary” is desirable while “high price” is usually undesirable, because people have opposite sentiment expectation towards “salary” and “price”). A bootstrapping framework is designed to retrieve patterns that might be used to express complaints from the Web. The sentiment expectation of a noun could be automatically predicted with the output patterns. We evaluate the retrieved patterns and show that our method yields good results. Also, they are applied to improve both sentence and document level sentiment analysis results.


title: Learning Compact Face Representation: Packing a Face into an int32

abstract: This paper addresses the problem of producing very compact representation of a face image for large-scale face search and analysis tasks. In tradition, the compactness of face representation is achieved by a dimension reduction step after representation extraction. However, the dimension reduction usually degrades the discriminative ability of the original representation drastically. In this paper, we present a deep learning framework which optimizes the compactness and discriminative ability jointly. The learnt representation can be as compact as 32 bit (same as the int32) and still produce highly discriminative performance (91.4% on LFW benchmark). Based on the extreme compactness, we show that traditional face analysis tasks (e.g. gender analysis) can be effectively solved by a Look-Up-Table approach given a large-scale face data set.


title: Review of Wheeled Mobile Robots’ Navigation Problems and Application Prospects in Agriculture

abstract: Robot navigation in the environment with obstacles is still a challenging problem. In this paper, the navigation problems with wheeled mobile robots (WMRs) are reviewed, the navigation mechanism of WMRs is analyzed in detail, the methods of solving the sub problems such as mapping, localization and path planning which all both related to robot navigation are summarized and the advantages and disadvantages of the existing methods are expounded. Especially in the agricultural field, the precise navigation of robots in the complex agricultural environment is the prerequisite for the completion of various tasks. This paper is aimed at the special complexity of the agricultural environment, prospected the application of the solution to the navigation problem of WMRs in agricultural engineering, put forward the research direction to solve the problems of precise navigation in agricultural environments.


title: Vehicle Tracking and Monitoring System to Enhance the Safety and Security Driving Using IoT

abstract: In this work, an advanced vehicle monitoring and tracking system is proposed and designed for the purpose of monitoring the vehicles which are moving from one place to the other in order to provide safety and security. The proposed method puts together superior exercise of contemporary technology by means of Embedded C programming language and the unit developed via LPC2148 and its sophisticated features of storing database. The work includes Global Positioning System (GPS) and Global System for Mobile Communication (GSM) for vehicle tracking and monitoring purpose using SIM800 module. The GPS provides present site of the vehicle; GPRS sends the tracking information to the server and thus an alert message generated is transmitted to the owner of the vehicle. This scheme is deployed in the interior of the vehicle whose location is to be determined on the web page and supervised at real time. Hence, if the driver drives the vehicle on the wrong path then the alert message will be sent from the proposed system to the vehicle’s owner mobile and if the vehicle’s driver feels drowsy or drunk then also the warning sound is produced by Buzzer. The proposed system take care of the traveler’s safety by using Alcohol sensor to find the status of the driver and Temperature sensor to monitor vehicle Engine Temperature to avoid sparking of the vehicle their by preventing from the disaster.


title: Parental stress, family quality of life, and family-teacher partnerships: Families of children with autism spectrum disorder.

abstract: Abstract Background Reducing parental stress and improving family quality of Life (FQOL) are continuing concerns for families of children with autism spectrum disorder (ASD). Family-teacher partnerships have been identified as a positive factor to help parents reduce their stress and improve their FQOL. However, the interrelations among parental stress, FQOL, and family-teacher partnerships need to be further examined so as to identify the possible paths to help parents reduce their stress and improve their FQOL. The purpose of this study was to examine the interrelations among these three variables. Method A total of 236 parents of school children with ASD completed questionnaires, which included three measures: (a) the Beach Center Family Quality of Life Scale, (b) the Parental Stress Scale, and (c) the Beach Center Family-Professional Partnerships Scale. The structural equation modeling was used to analyze the interrelations among these three variables. Results Perceived parental stress had a direct effect on parental satisfaction concerning FQOL and vice versa. Perceived family-teacher partnerships had a direct effect on FQOL, but did not have a direct effect on parental stress. However, family-teacher partnerships had an indirect effect on parental stress through FQOL. Conclusions and implications Reducing parental stress could improve FQOL for families of children with ASD and vice versa. Strong family-teacher partnerships could help parents of children with ASD improve their FQOL and indirectly reduce their stress.


title: Two Techniques for Assessing Virtual Agent Personality

abstract: Personality can be assessed with standardized inventory questions with scaled responses such as “How extraverted is this character?” or with open-ended questions assessing first impressions, such as “What personality does this character convey?” Little is known about how the two methods compare to each other, and even less is known about their use in the personality assessment of virtual agents. We tested what personality virtual agents conveyed through gesture alone when the agents were programmed to display introversion versus extraversion (Experiment 1) and high versus low emotional stability (Experiment 2). In Experiment 1, both measures indicated participants perceived the extraverted agent as extraverted, but the open-question technique highlighted the perception of both agents as highly agreeable whereas the inventory indicated that the extraverted agents were also perceived as more open to new experiences. In Experiment 2, participants perceived agents expressing high versus low emotional stability differently depending on assessment style. With inventory questions, the agents differed on both emotional stability and agreeableness. With the open-ended question, participants perceived the high stability agent as extraverted and the low stability agent as disagreeable. Inventory and open-ended questions provide different information about what personality virtual agents convey and both may be useful in agent development.


title: Hierarchical place trees: a portable abstraction for task parallelism and data movement

abstract: Modern computer systems feature multiple homogeneous or heterogeneous computing units with deep memory hierarchies, and expect a high degree of thread-level parallelism from the software. Exploitation of data locality is critical to achieving scalable parallelism, but adds a significant dimension of complexity to performance optimization of parallel programs. This is especially true for programming models where locality is implicit and opaque to programmers. In this paper, we introduce the hierarchical place tree (HPT) model as a portable abstraction for task parallelism and data movement. The HPT model supports co-allocation of data and computation at multiple levels of a memory hierarchy. It can be viewed as a generalization of concepts from the Sequoia and X10 programming models, resulting in capabilities that are not supported by either. Compared to Sequoia, HPT supports three kinds of data movement in a memory hierarchy rather than just explicit data transfer between adjacent levels, as well as dynamic task scheduling rather than static task assignment. Compared to X10, HPT provides a hierarchical notion of places for both computation and data mapping. We describe our work-in-progress on implementing the HPT model in the Habanero-Java (HJ) compiler and runtime system. Preliminary results on general-purpose multicore processors and GPU accelerators indicate that the HPT model can be a promising portable abstraction for future multicore processors.


title: tiqr: a novel take on two-factor authentication

abstract: Authentication is of paramount importance for all modern networked applications. The username/password paradigm is ubiquitous. This paradigm suffices for many applications that require a relatively low level of assurance about the identity of the end user, but it quickly breaks down when a stronger assertion of the user's identity is required. Traditionally, this is where two-or multi-factor authentication comes in, providing a higher level of assurance. There is a multitude of two-factor authentication solutions available, but we feel that many solutions do not meet the needs of our community. They are invariably expensive, difficult to roll out in heterogeneous user groups (like student populations), often closed source and closed technology and have usability problems that make them hard to use. In this paper we will give an overview of the two-factor authentication landscape and address the issues of closed versus open solutions. We will introduce a novel open standards-based authentication technology that we have developed and released in open source. We will then provide a classification of two-factor authentication technologies, and we will finish with an overview of future work.


title: An alternative to push, press, and tap-tap-tap: gesturing on an isometric joystick for mobile phone text entry

abstract: A gestural text entry method for mobile is presented. Unlike most mobile phone text entry methods, which rely on repeatedly pressing buttons, our gestural method uses an isometric joystick and the EdgeWrite alphabet to allow users to write by making letter-like "pressure strokes." In a 15-session study comparing character-level EdgeWrite to Multitap, subjects' speeds were statistically indistinguishable, reaching about 10 WPM. In a second 15-session study comparing word-level EdgeWrite to T9, the same subjects were again statistically indistinguishable, reaching about 16 WPM. Uncorrected errors were low, around 1% or less for each method. In addition, subjective results favored EdgeWrite. Overall, results indicate that our isometric joystick-based method is highly competitive with two commercial keypad-based methods, opening the way for keypad-less designs and text entry on tiny devices. Additional results showed that a joystick on the back could be used at about 70% of the speed of the front, and the front joystick could be used eyes-free at about 80% of the speed of normal use.


title: GlassPass: Tapping Gestures to Unlock Smart Glasses

abstract: Wearable technologies such as smart-glasses can sense, store and display sensitive personal contents. In order to protect this data, users need to securely authenticate to their devices. However, current authentication techniques, such as passwords or PINs, are a poor fit for the limited input and output spaces available on wearables. This paper focuses on eyewear and addresses this problem with a novel authentication system that uses an alphabet of simple tapping patterns optimized for rapid and accurate input on the temples (or arms) of glasses. Furthermore, it explores how an eyewear display can support password memorization by privately presenting a visualization of entered symbols. A pair of empirical studies confirm that performance during input of both individual password symbols and full passwords is rapid and accurate. A follow-up session one week after the main study suggests using a private display to show entered password symbols effectively supports memorization.


title: A Proactive Fault Tolerance Approach to High Performance Computing (HPC) in the Cloud

abstract: Cloud computing offers new computing paradigms, capacity, and flexibility to high performance computing (HPC) applications with provisioning of a large number of Virtual Machines (VMs) for computation-intensive applications using the Hardware as a Service (HaaS) model. Due, however, to the large number of VMs and electronic components in HPC systems in the cloud, any fault during the execution would result in re-running the application, which will cost time, money and energy. In this paper we present a proactive Fault Tolerance (FT) approach to HPC systems in the cloud to reduce the wall clock execution time in the presence of faults. We develop a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We analyze the dollar cost of provisioning spare nodes to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall clock execution time of the computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of check pointing of computation-intensive applications can be reduced to 50% with our fault tolerance approach for HPC in the cloud, compared to current FT approaches.


title: A Double-Stage Kalman Filter for Orientation Tracking With an Integrated Processor in 9-D IMU

abstract: This paper presents an application-specific integrated processor for an angular estimation system that works with 9-D inertial measurement units. The application-specific instruction-set processor (ASIP) was implemented on field-programmable gate array and interfaced with a gyro-plus-accelerometer 6-D sensor and with a magnetic compass. Output data were recorded on a personal computer and also used to perform a live demo. During system modeling and design, it was chosen to represent angular position data with a quaternion and to use an extended Kalman filter as sensor fusion algorithm. For this purpose, a novel two-stage filter was designed: The first stage uses accelerometer data, and the second one uses magnetic compass data for angular position correction. This allows flexibility, less computational requirements, and robustness to magnetic field anomalies. The final goal of this work is to realize an upgraded application-specified integrated circuit that controls the microelectromechanical systems (MEMS) sensor and integrates the ASIP. This will allow the MEMS sensor gyro plus accelerometer and the angular estimation system to be contained in a single package; this system might optionally work with an external magnetic compass.


title: PSYCHOLOGICAL SCIENCE Research Article The Effect of Construal Level on Subjective Probability Estimates

abstract: In a series of studies, we examined novel predictions drawn from a conceptualization of probability as psychological distance. Manipulating construal level in a number of different ways and examining a variety of probability judgments, we found that participants led to adopt a high-level-construal mind-set made lower probability assessments than did those led to adopt a low-level-construal mind-set. Moreover, this occurred even when construal level was manipulated in a context separate from the judgment task and the manipulation was unrelated in content to the events being judged. These findings suggest that broad processing variables can exert a widespread influence on probability judgment.


title: Strategy-as-practice: A review and future directions for the field

abstract: This review maps and critically evaluates the rapidly growing body of research in the strategy-as-practice field. Following an introduction on the emergence and foundations of strategy-as-practice, the review is structured in three main parts, based on the terminology, issues and research agendas outlined in the field. First, the paper examines the concepts of practitioners and praxis. A typology of nine possible domains for strategy-as-practice research is developed, based on the way that different studies conceptualize the strategy practitioner and the level of strategy praxis that they aim to explain. Second, the paper reviews the concept of practices, which has been adopted widely but inconsistently within the strategy-as-practice literature. While there is no dominant view on practices, the review maps the various concepts of practices that inform the strategy-as-practice field and outlines avenues for future research. The final section attends to the call for strategy-as-practice research to develop and substantiate outcomes that may better explain or inform strategy praxis. Five categories of outcomes are found within existing empirical studies, and an agenda for building upon this evidence is advanced. The paper concludes with a summation of the current state of the field and some recommendations on how to take strategy-as-practice research forward.


title: Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification

abstract: Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We empirically show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better performance on many time series datasets without further changes to the model. Our empirical study showed that the proposed GRU-FCN model also outperforms the state-of-the-art classification performance in many univariate time series datasets without additional supporting algorithms requirement. Furthermore, since the GRU uses simpler architecture than the LSTM, it has fewer training parameters, less training time, smaller memory storage requirements, and simpler hardware implementation, compared to the LSTM-based models.


title: Human Evaluation of Grammatical Error Correction Systems

abstract: The paper presents the results of the first large-scale human evaluation of automatic grammatical error correction (GEC) systems. Twelve participating systems and the unchanged input of the CoNLL-2014 shared task have been reassessed in a WMT-inspired human evaluation procedure. Methods introduced for the Workshop of Machine Translation evaluation campaigns have been adapted to GEC and extended where necessary. The produced rankings are used to evaluate standard metrics for grammatical error correction in terms of correlation with human judgment.


title: Extreme Learning Machines [Trends & Controversies]

abstract: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data," Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for learning feature representations using singular values. In "A Secure and Practical Mechanism for Outsourcing ELMs in Cloud Computing," Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, and Victor C.M. Leung propose a method for handling large data applications by outsourcing to the cloud that would dramatically reduce ELM training time. In "ELM-Guided Memetic Computation for Vehicle Routing," Liang Feng, Yew-Soon Ong, and Meng-Hiot Lim consider the ELM as an engine for automating the encapsulation of knowledge memes from past problem-solving experiences. In "ELMVIS: A Nonlinear Visualization Technique Using Random Permutations and ELMs," Anton Akusok, Amaury Lendasse, Rui Nian, and Yoan Miche propose an ELM method for data visualization based on random permutations to map original data and their corresponding visualization points. In "Combining ELMs with Random Projections," Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, and Sergio Decherchi analyze the relationships between ELM feature-mapping schemas and the paradigm of random projections. In "Reduced ELMs for Causal Relation Extraction from Unstructured Text," Xuefeng Yang and Kezhi Mao propose combining ELMs with neuron selection to optimize the neural network architecture and improve the ELM ensemble's computational efficiency. In "A System for Signature Verification Based on Horizontal and Vertical Components in Hand Gestures," Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, and Jaihie Kim propose a novel paradigm for hand signature biometry for touchless applications without the need for handheld devices. Finally, in "An Adaptive and Iterative Online Sequential ELM-Based Multi-Degree-of-Freedom Gesture Recognition System," Hanchao Yu, Yiqiang Chen, Junfa Liu, and Guang-Bin Huang propose an online sequential ELM-based efficient gesture recognition algorithm for touchless human-machine interaction.


The application of dual coding theory in multi-representational virtual mathematics environments


title: On the Use of Fuzzy Modeling in Virtualized Data Center Management

abstract: One of the most important goals of data-center management is to reduce cost through efficient use of resources. Virtualization techniques provide the opportunity of carving individual physical servers into multiple virtual containers that can be run and managed separately. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared resources to virtual containers and the management of their capacities to meet service quality targets at the least cost. This paper proposes a two-level resource management system with local controllers at the virtual-container level and a global controller at the resource-pool level. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the controller designs is their use of fuzzy logic to efficiently and robustly deal with the complexity of the virtualized data center and the uncertainties of the dynamically changing workloads. Experimental results obtained through a prototype implementation demonstrate that, for the scenarios under consideration, the proposed resource management system can significantly reduce resource consumption while still achieving application performance targets.


title: Psychotherapy provision, socioeconomic deprivation, and the inverse care law

abstract: Abstract In this study of routine service data and census data, the authors explore the influence of socioeconomic status on patients entering an integrated psychotherapy service. Between January 1, 2000, and September 30, 725 patients were assessed and had their Townsend Index of Material Deprivation (TIMD) scores derived from postcodes. Results showed no correlation between TIMD score and those attending their assessment and no significant difference in TIMD score between those allocated to psychoanalytic psychotherapy (PAP) or cognitive–behavioral psychotherapy (CBP) and between those accepted and not accepted for each treatment. No correlation was found between Townsend score and numbers presenting to the service, suggesting that the inverse care law applies. Deprivation is not a predictor of patient allocation to or acceptance for PAP or CBP.


title: Abstract Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

abstract: Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, we compare its performance with those of multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.


title: Asynchronous accelerated stochastic gradient descent

abstract: Stochastic gradient descent (SGD) is a widely used optimization algorithm in machine learning. In order to accelerate the convergence of SGD, a few advanced techniques have been developed in recent years, including variance reduction, stochastic coordinate sampling, and Nesterov's acceleration method. Furthermore, in order to improve the training speed and/or leverage larger-scale training data, asynchronous parallelization of SGD has also been studied. Then, a natural question is whether these techniques can be seamlessly integrated with each other, and whether the integration has desirable theoretical guarantee on its convergence. In this paper, we provide our formal answer to this question. In particular, we consider the asynchronous parallelization of SGD, accelerated by leveraging variance reduction, coordinate sampling, and Nesterov's method. We call the new algorithm asynchronous accelerated SGD (AASGD). Theoretically, we proved a convergence rate of AASGD, which indicates that (i) the three acceleration methods are complementary to each other and can make their own contributions to the improvement of convergence rate; (ii) asynchronous parallelization does not hurt the convergence rate, and can achieve considerable speedup under appropriate parameter setting. Empirically, we tested AASGD on a few benchmark datasets. The experimental results verified our theoretical findings and indicated that AASGD could be a highly effective and efficient algorithm for practical use.


title: Face Search at Scale: 80 Million Gallery

abstract: Due to the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to process and search for persons of interest among the billions of shared photos on these websites. Facebook revealed in a 2013 white paper that its users have uploaded more than 250 billion photos, and are uploading 350 million new photos each day. Due to this humongous amount of data, large-scale face search for mining web images is both important and challenging. Despite significant progress in face recognition, searching a large collection of unconstrained face images has not been adequately addressed. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top-k most similar faces using deep features generated from a convolutional neural network. The k candidates are re-ranked by combining similarities from deep features and the COTS matcher. We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images. Experimental results demonstrate that the deep features are competitive with state-of-the-art methods on unconstrained face recognition benchmarks (LFW and IJB-A). Further, the proposed face search system offers an excellent trade-off between accuracy and scalability on datasets consisting of millions of images. Additionally, in an experiment involving searching for face images of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed face search system could find the younger brother's (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a 5M gallery and at rank 8 in 7 seconds on an 80M gallery.


title: Box in the Box: Joint 3D Layout and Object Reasoning from Single Images

abstract: In this paper we propose an approach to jointly infer the room layout as well as the objects present in the scene. Towards this goal, we propose a branch and bound algorithm which is guaranteed to retrieve the global optimum of the joint problem. The main difficulty resides in taking into account occlusion in order to not over-count the evidence. We introduce a new decomposition method, which generalizes integral geometry to triangular shapes, and allows us to bound the different terms in constant time. We exploit both geometric cues and object detectors as image features and show large improvements in 2D and 3D object detection over state-of-the-art deformable part-based models.


title: Reinforcement Learning with Soft State Aggregation

abstract: It is widely accepted that the use of more compact representations than lookup tables is crucial to scaling reinforcement learning (RL) algorithms to real-world problems. Unfortunately almost all of the theory of reinforcement learning assumes lookup table representations. In this paper we address the pressing issue of combining function approximation and RL, and present 1) a function approximator based on a simple extension to state aggregation (a commonly used form of compact representation), namely soft state aggregation, 2) a theory of convergence for RL with arbitrary, but fixed, soft state aggregation, 3) a novel intuitive understanding of the effect of state aggregation on online RL, and 4) a new heuristic adaptive state aggregation algorithm that finds improved compact representations by exploiting the non-discrete nature of soft state aggregation. Preliminary empirical results are also presented.


title: Incorporating Discrete Translation Lexicons into Neural Machine Translation

abstract: Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-frequency words. We describe a method to calculate the lexicon probability of the next word in the translation candidate by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. We test two methods to combine this probability with the standard NMT probability: (1) using it as a bias, and (2) linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3 BLEU and 0.13-0.44 NIST score, and faster convergence time.


title: AutoRVO: Local Navigation with Dynamic Constraints in Dense Heterogeneous Traffic

abstract: We present a novel algorithm for computing collision-free navigation for heterogeneous road-agents such as cars, tricycles, bicycles, and pedestrians in dense traffic. Our approach currently assumes the positions, shapes, and velocities of all vehicles and pedestrians are known and computes smooth trajectories for each agent by taking into account the dynamic constraints. We describe an efficient optimization-based algorithm for each road-agent based on reciprocal velocity obstacles that takes into account kinematic and dynamic constraints. Our algorithm uses tight fitting shape representations based on medial axis to compute collision-free trajectories in dense traffic situations. We evaluate the performance of our algorithm in real-world dense traffic scenarios and highlight the benefits over prior reciprocal collision avoidance schemes.


title: Occluded Person Re-identification

abstract: Person re-identification (re-id) suffers from a serious occlusion problem when applied to crowded public places. In this paper, we propose to retrieve a full-body person image by using a person image with occlusions. This differs significantly from the conventional person re-id problem where it is assumed that person images are detected without any occlusion. We thus call this new problem the occluded person re-identitification. To address this new problem, we propose a novel Attention Framework of Person Body (AFPB) based on deep learning, consisting of 1) an Occlusion Simulator (OS) which automatically generates artificial occlusions for full-body person images, and 2) multi-task losses that force the neural network not only to discriminate a person's identity but also to determine whether a sample is from the occluded data distribution or the full-body data distribution. Experiments on a new occluded person re-id dataset and three existing benchmarks modified to include full-body person images and occluded person images show the superiority of the proposed method.


title: Remote control laboratory via Internet using Matlab and Simulink

abstract: This article describes the general architecture and application of a remote laboratory for teaching control theory based in Matlab/Simulink. The proposed system allows solving the time and spatial limitations of laboratories that rely on real physical systems used in control courses. In this way, control lab assignments with various physical processes present in the remote laboratories can be performed. Also, some examples that show the validity and applicability of the presented architecture are introduced. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 694–702, 2010; View this article online at wileyonlinelibrary.com; DOI 10.1002/cae.20274


title: A NOVEL PRINCIPLE OF NON-UNIT PROTECTION BASED ON TRANSIENT COMPONENTS FOR EHV TRANSMISSION LINES

abstract: This paper proposes a new principle of Transient based Protection for Extra High Voltage (EHV) transmission lines based on large amount of studying singularity degree of transient current signal at sudden change point utilizing wavelet multi scale edge detection method. During the fault period, there exist differences in singularity degree of transient current wave forms between internal and external fault as the result of the influence of bus bar capacitance, line traps and coupling capacitance. The difference is extracted and amplified by wavelet to construct a novel non\|unit ultra high speed protection principle.The Electromagnetic Transient Program (EMTP) simulation shows that the theory and methods proposed in the paper is correct.


title: Optical wireless power transmission using VCSELs

abstract: The power transmission technology to the equipment is remained behind in contrast to the progress of wireless communication. The existence of wiring and its connections greatly restrict the location of use, installation, maintenance, and configuration of the equipment. Solutions of wireless power transmission may lead to major changes in society, such as new services and new industries as well as convenience. Wireless power transmission such as electromagnetic induction is beginning to be put into practical use in smartphone, etc., however there are issues in extremely short transmission distance, large size, heavy, and so on. Optical wireless power transmission will be an important technology for expanding the field of wirelessly usable equipment due to features such as small size, short, mid and long distance transmission. On the other hand, there are some problems to be solved such as efficiency and safety. In this presentation, I would like to discuss the possibility and challenges of optical wireless power transmission using VCSEL as a high output and high efficiency light source.


title: Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism

abstract: As the size of Deep Neural Networks (DNNs) continues to grow to increase accuracy and solve more complex problems, their energy footprint also scales. Weight pruning reduces DNN model size and the computation by removing redundant weights. However, we implemented weight pruning for several popular networks on a variety of hardware platforms and observed surprising results. For many networks, the network sparsity caused by weight pruning will actually hurt the overall performance despite large reductions in the model size and required multiply-accumulate operations. Also, encoding the sparse format of pruned networks incurs additional storage space overhead. To overcome these challenges, we propose Scalpel that customizes DNN pruning to the underlying hardware by matching the pruned network structure to the data-parallel hardware organization. Scalpel consists of two techniques: SIMD-aware weight pruning and node pruning. For low-parallelism hardware (e.g., microcontroller), SIMD-aware weight pruning maintains weights in aligned fixed-size groups to fully utilize the SIMD units. For high-parallelism hardware (e.g., GPU), node pruning removes redundant nodes, not redundant weights, thereby reducing computation without sacrificing the dense matrix format. For hardware with moderate parallelism (e.g., desktop CPU), SIMD-aware weight pruning and node pruning are synergistically applied together. Across the microcontroller, CPU and GPU, Scalpel achieves mean speedups of 3.54x, 2.61x, and 1.25x while reducing the model sizes by 88%, 82%, and 53%. In comparison, traditional weight pruning achieves mean speedups of 1.90x, 1.06x, 0.41x across the three platforms.


title: Quaternion-based parallel feature extraction: Extending the horizon of quantitative analysis using TLC-SERS sensing

abstract: Abstract Quantitative analysis using thin-layer chromatography coupled in tandem with surface-enhanced Raman scattering (TLC-SERS) still remains a grand challenge due to many uncontrollable variations during the TLC developing process and the random nature of the SERS substrates. Traditional chemometric methods solve this problem by sampling multiple SERS spectra in the sensing spot and then conducting statistical analysis of the SERS signals to mitigate the variation of quantitative analysis, while still ignoring the spatial distribution of the target species and the correlation among the multiple sampling points. In this paper, we proposed for the first time a parallel feature extraction and fusion method based on quaternion signal processing techniques, which can enable quantitative analysis using recently established TLC-SERS techniques. By marking three deterministic sampling points, we recorded spatially correlated SERS spectra to constitute an integral representation model of triple-spectra by a pure quaternion matrix. Quaternion principal component analysis (QPCA) was utilized for features extraction and followed by feature crossing among the quaternion principal components to obtain final fusion spectral feature vectors. Support vector regression (SVR) was then used to establish the quantitative model of melamine-contaminated milk samples with seven concentrations (1 ppm–250 ppm). Compared with traditional TLC-SERS analysis methods, QPCA method significantly improved the accuracy of quantification by reaching only 7% and 2% quantization errors at 20 and 105 ppm concentration. Validation testing based on reasonable amount of statistic measurement results showed consistently smaller measurement errors and variance, which proved the effectiveness of QPCA method for TLC-SERS based quantitative sensing applications.


title: Genetic algorithm-based clustering technique

abstract: Abstract A genetic algorithm-based clustering technique, called GA-clustering, is proposed in this article. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centres of a fixed number of clusters. The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.


title: Humanitarian logistics network design under mixed uncertainty

abstract: In this paper, we address a two-echelon humanitarian logistics network design problem involving multiple central warehouses (CWs) and local distribution centers (LDCs) and develop a novel two-stage scenario-based possibilistic-stochastic programming (SBPSP) approach. The research is motivated by the urgent need for designing a relief network in Tehran in preparation for potential earthquakes to cope with the main logistical problems in pre- and post-disaster phases. During the first stage, the locations for CWs and LDCs are determined along with the prepositioned inventory levels for the relief supplies. In this stage, inherent uncertainties in both supply and demand data as well as the availability level of the transportation network's routes after an earthquake are taken into account. In the second stage, a relief distribution plan is developed based on various disaster scenarios aiming to minimize: total distribution time, the maximum weighted distribution time for the critical items, total cost of unused inventories and weighted shortage cost of unmet demands. A tailored differential evolution (DE) algorithm is developed to find good enough feasible solutions within a reasonable CPU time. Computational results using real data reveal promising performance of the proposed SBPSP model in comparison with the existing relief network in Tehran. The paper contributes to the literature on optimization based design of relief networks under mixed possibilistic-stochastic uncertainty and supports informed decision making by local authorities in increasing resilience of urban areas to natural disasters.


title: Image segmentation by nested cuts

abstract: We present a new image segmentation algorithm based on graph cuts. Our main tool is separation of each pixel p from a special point outside the image by a cut of a minimum cost. Such a cut creates a group of pixels C/sub p/ around each pixel. We show that these groups C/sub p/ are either disjoint or nested in each other and so they give a natural segmentation of the image. In addition this property allows an efficient implementation of the algorithms because for most pixels p the computation of C/sub p/ is not performed on the whole graph. We inspect all C/sub p/ and discard those which are not interesting, for example if they are too small. This procedure automatically groups small components together or merges them into nearby large clusters. Effectively, our segmentation is performed by extracting significant non-intersecting closed contours. We present interesting segmentation results on real and artificial images.


title: IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics

abstract: Over the past few years, the semantics community has developed several ontologies to describe concepts and relationships for internet of things (IoT) applications. A key problem is that most of the IoT-related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time, and therefore, they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis and assess IoT-Lite against another solution in terms of round trip time performance for query-response times. We have linked IoT-Lite with stream annotation ontology, to allow queries over stream data annotations, and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.


abstract: For any professional designer or developer working in the world of web technologies or multimedia, Flash is the must-have application, and knowing how to use it effectively is vital. Add in ActionScript 3.0, and Flash becomes an even more powerful tool for the development of high-performance web applications. The Essential Guide to Flash CS4 with ActionScript introduces and explores the possibilities available to you through using Flash CS4. You will learn the aspects basics of Flash's working environment and, using a step-by-step approach, work through the features of Flash and ActionScript needed to quickly achieve results. From navigating the interface to using graphic tools to write scripts and troubleshoot, you'll master all of the of deploying highly interactive applications with rich audio, video, and animation. Written for the beginning-to-intermediate Flash user, this book uses complete examples that highlight best practices that you can apply to your daily work as a developer or designer in today's rich media world. Learn to create and manage assets in Flash using the graphics tools and the library. Acquire a comprehensive understanding of animation using the new Motion Editor and inverse kinematics tools. Discover the dynamic world of ActionScript development including classes, events, and XML. Learn to use and customize Flash's UI components. Become familiar with audio and video assets including preparation and integration. Debug, publish, and deploy Flash applications for the Web with Flash Player and the desktop using the Adobe Integrated Runtime (AIR). Summary of Contents Part One: Basics Chapter 1: How It All Began Chapter 2: The New Interface of Flash CS4 Chapter 3: Drawing with Flash's Vector Tools Chapter 4: External Assets and Symbol Management Chapter 5: Working with Blends, Filters, and 3D Transformations Part Two: Animation Chapter 6: Basic Animation in Flash Chapter 7: Playing with Dolls: Introducing Flash IK Part Three: ActionScript Chapter 8: The Programming Primer: A Flash Designer's Intro to ActionScript 3.0 Chapter 9: The Building Blocks of Interactivity Chapter 10: Learning the Display Model and Bringing It All Together Chapter 11: Managing External Assets and Communication Chapter 12: XML: The Best Way In and Out of Flash Part Four: Additional User Interfaces Chapter 13: Working with Text and Strings Chapter 14: Using Components Part Five: Enhanced Media Development Chapter 15: Using the Adobe Media EncoderA Crash Course Chapter 16: Working with Audio in Flash Chapter 17: Working with Video in Flash Part Six: Preparing Your Project for Deployment Chapter 18: Publishing, Exporting, and Debugging Your Flash Project Chapter 19: Building AIR Applications with Flash CS4


title: Growth Factors and Feeder Cells Promote Differentiation of Human Embryonic Stem Cells into Dopaminergic Neurons: A Novel Role for Fibroblast Growth Factor-20

abstract: Human embryonic stem cells (hESCs) are a potential source of dopaminergic neurons for treatment of Parkinson’s disease (PD). Dopaminergic neurons can be derived from hESCs and display a characteristic midbrain phenotype. Once transplanted, they can induce partial behavioral recovery in animal models of PD. The potential research field faces several challenges that need to be overcome before clinical application of hESCs in a transplantation therapy in PD can be considered. These include low survival of the hESC-derived, grafted dopaminergic neurons after transplantation; unclear functional integration of the grafted neurons in the host brain; and, the risk of teratoma/tumor formation from the transplanted cells. This review is focused on our recent efforts to improve the survival of hESC-dervied dopaminergic neurons. We have examined the effect of fibroblast growth factor (FGF)-20 in the differentiation of hESCs into dopaminergic neurons. We supplemented cultures of hESCs with FGF-20 during differentiation on PA6 mouse stromal cells for three weeks. When we added FGF-20 the yield of neurons expressing tyrosine hydroxylase increased. We demonstrated that at least part of the effect is contributed by enhanced cell differentiation towards the dopaminergic phenotype as well as reduced cell death. We compare our results with those obtained in other published protocols using different sets of growth factors. Our data indicate that FGF-20 has potent effects to generate large number of dopaminergic neurons derived from hESCs, which may be useful for cell therapy in PD.


title: Design of a bright polarized head-mounted projection display

abstract: In optical see-through head-mounted displays, it has been a common challenge that the displayed image lacks brightness and contrast compared with the direct view of a real-world scene. Consequently, such displays are usually used in dimmed lighting conditions, which limits the feasibility of applying such information displays outdoors or in scenarios where well-lit environments, such as in operation rooms, are required. The lack of image brightness is aggravated in the design of a see-through head-mounted projection display (HMPD). For instance, the overall flux transfer efficiency of existing HMPD designs is less than 10%. ::: The design of a polarized head-mounted projection display (p-HMPD) is presented. The images of a p-HMPD system can potentially be three times brighter than those in existing HMPD designs. It is further demonstrated that the p-HMPD design is able to dramatically improve image brightness, contrast, and color vividness with experimental results. Finally, the design of a compact optical system and helmet prototype is described.


title: Towards a Basic Theory to Model Model Driven Engineering

abstract: What is a model? What is a metamodel? What is a language? What is a transformation? How are these concepts related? It is striking to see that, though MDE is supposed to be about precise modelling, MDE core concepts are usually described in natural language or at best, using sketchy UML diagrams. When precise descriptions are provided, it is only to describe a specific technology. But Model Driven Engineering is about supporting multiple Technological Spaces (TS). The concepts of model, metamodel, and transformation must be understood, not only in the context of the MDA TS, but also in the Grammarware TS, the Documentware TS, the Dataware TS, etc. This paper shows how the set theory and language theory could help in understanding essential MDE concepts. A first version of a very rudimentary theory for reasoning about MDE concepts is provided in the form of a megamodel.


title: Intelligence 32 (2004) 227–243 Why beautiful people are more intelligent

abstract: Empirical studies demonstrate that individuals perceive physically attractive others to be more intelligent than physically unattractive others. While most researchers dismiss this perception as a “bias” or “stereotype,” we contend that individuals have this perception because beautiful people indeed are more intelligent. The conclusion that beautiful people are more intelligent follows from four assumptions. (1) Men who are more intelligent are more likely to attain higher status than men who are less intelligent. (2) Higher-status men are more likely to mate with more beautiful women than lower-status men. (3) Intelligence is heritable. (4) Beauty is heritable. If all four assumptions are empirically true, then the conclusion that beautiful people are more intelligent is logically true, making it a proven theorem. We present empirical evidence for each of the four assumptions. While we concentrate on the relationship between beauty and intelligence in this paper, our evolutionary psychological explanation can account for a correlation between physical attractiveness and any other heritable trait that helps men attain higher status (such as aggression and social skills).


title: Cross polarization ratio analysis of circular polarized patch antenna

abstract: Cross polarization ratio (CPR) analysis can be used to determine the quality of circular polarization of an antenna as compared to the commonly used axial ratio (AR) analysis. The analysis is independent of E-field left and right polarity measurement which requires two circular polarized reference antennas of left and right. Through CPR, the quality of circular polarization of an antenna can be measured from a reference linear polarity antenna. A low complexity 2.4 GHz circular polarized truncated corner patch antenna is designed and fabricated on printed circuit board (PCB) to show the simple process to analyse circular polarization quality of the antenna. Simulation result is compared to the theoretical considerations and measurement of ratio and E-field results.


title: Towards Zero Copy Dataflows using RDMA

abstract: Remote Direct Memory Access (RDMA) offers ultra-low latency and CPU bypass networking to application programmers. Existing applications are often designed around socket based software stack that manages application buffers separately from networking buffers and do memory copies between them when sending/receiving data. With large sized (up to hundreds MB) application buffers, the cost of such copies adds non trivial overhead to the end-to-end communication pipeline. In this work, we made an attempt to design a zero copy transport for distribute dataflow frameworks that unifies application and networking buffer management and completely eliminates unnecessary memory copies. Our prototype on top of TensorFlow shows 2.43x performance improvement over gRPC based transport and 1.21x performance improvement over an alternative RDMA transport with private buffers and memory copies.


title: Position and force control of a robotic finger with twisted strings actuation

abstract: Actuation and control of robotic hands is one of the difficult problems in mechatronics that still needs to be solved. In this paper we present a kinematic analysis of a robotic finger that is actuated by twisted strings mechanism, which we named Twist Drive. The principle of actuation is briefly described in the paper and a structure of the developed robotic finger is presented. Forward kinematics for a finger with nonlinearly coupled joints is given. The obtained Jacobian is used in position control and in force control on the finger's tip. Experimental results are presented. Force control in two directions is successfully demonstrated.


title: Digital Technology Shaping Teaching Practices in Higher Education

abstract: In their quest on being effective, educators have always experimented with the art of teaching. Teaching has evolved over centuries by adopting new approaches, methods, tools, and technologies to reach a wider audience. As technologies advance, educators should carefully use, evaluate, and adopt the changes to utilize the technologies and track of their impacts. This article provides a mini review to briefly describe some of the existing technical achievements that are used in higher education along with their challenges.


title: A Tough Call: Mitigating Advanced Code-Reuse Attacks at the Binary Level

abstract: Current binary-level Control-Flow Integrity (CFI) techniques are weak in determining the set of valid targets for indirect control flow transfers on the forward edge. In particular, the lack of source code forces existing techniques to resort to a conservative address-taken policy that overapproximates this set. In contrast, source-level solutions can accurately infer the targets of indirect calls and thus detect malicious control-flow transfers more precisely. Given that source code is not always available, however, offering similar quality of protection at the binary level is important, but, unquestionably, more challenging than ever: recent work demonstrates powerful attacks such as Counterfeit Object-oriented Programming (COOP), which made the community believe that protecting software against control-flow diversion attacks at the binary level is rather impossible. In this paper, we propose binary-level analysis techniques to significantly reduce the number of possible targets for indirect branches. More specifically, we reconstruct a conservative approximation of target function prototypes by means of use-def analysis at possible callees. We then couple this with liveness analysis at each indirect callsite to derive a many-to-many relationship between callsites and target callees with a much higher precision compared to prior binary-level solutions. Experimental results on popular server programs and on SPEC CPU2006 show that TypeArmor, a prototype implementation of our approach, is efficient - with a runtime overhead of less than 3%. Furthermore, we evaluate to what extent TypeArmor can mitigate COOP and other advanced attacks and show that our approach can significantly reduce the number of targets on the forward edge. Moreover, we show that TypeArmor breaks published COOP exploits, providing concrete evidence that strict binary-level CFI can still mitigate advanced attacks, despite the absence of source information or C++ semantics.


title: Electromagnetic Inspection of Carbon-Fiber- Reinforced Polymer Composites With Coupled Spiral Inductors

abstract: This paper presents a new type of an electromagnetic sensor for nondestructive testing of carbon-fiber-reinforced polymer composites. The sensor utilizes coupled planar spiral inductors operating typically in the range of 10-500 MHz. The method proposed here shows some similarity to the eddy current technique, but as will be shown, the principles of operation are different as the sensitivity to defects is mostly due to the magnetic field components tangential to the surface of a material. It is shown that the method is applicable to 3-D inspection of carbon-fiber-reinforced composites widely employed in the aerospace industry.


title: Object-oriented Neural Programming (OONP) for Document Understanding

abstract: We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.


title: Object Counts! Bringing Explicit Detections Back into Image Captioning

abstract: The use of explicit object detectors as an intermediate ::: step to image captioning – which ::: used to constitute an essential stage in early ::: work – is often bypassed in the currently dominant ::: end-to-end approaches, where the language ::: model is conditioned directly on a midlevel ::: image embedding. We argue that explicit ::: detections provide rich semantic information, ::: and can thus be used as an interpretable representation ::: to better understand why end-to-end ::: image captioning systems work well. We provide ::: an in-depth analysis of end-to-end image ::: captioning by exploring a variety of cues that ::: can be derived from such object detections. ::: Our study reveals that end-to-end image captioning ::: systems rely on matching image representations ::: to generate captions, and that encoding ::: the frequency, size and position of objects ::: are complementary and all play a role in ::: forming a good image representation. It also ::: reveals that different object categories contribute ::: in different ways towards image captioning.


title: Passive ultrasonic irrigation of the root canal: a review of the literature

abstract: Ultrasonic irrigation of the root canal can be performed with or without simultaneous ultrasonic instrumentation. When canal shaping is not undertaken the term passive ultrasonic irrigation (PUI) can be used to describe the technique. In this paper the relevant literature on PUI is reviewed from a MEDLINE database search. ::: ::: Passive ultrasonic irrigation can be performed with a small file or smooth wire (size 10–20) oscillating freely in the root canal to induce powerful acoustic microstreaming. PUI can be an important supplement for cleaning the root canal system and, compared with traditional syringe irrigation, it removes more organic tissue, planktonic bacteria and dentine debris from the root canal. PUI is more efficient in cleaning canals than ultrasonic irrigation with simultaneous ultrasonic instrumentation. PUI can be effective in curved canals and a smooth wire can be as effective as a cutting K-file. The taper and the diameter of the root canal were found to be important parameters in determining the efficacies of dentine debris removal. Irrigation with sodium hypochlorite is more effective than with water and ultrasonic irrigation is more effective than sonic irrigation in the removal of dentine debris from the root canal. The role of cavitation during PUI remains inconclusive. No detailed information is available on the influence of the irrigation time, the volume of the irrigant, the penetration depth of the instrument and the shape and material properties of the instrument. The influence of irrigation frequency and intensity on the streaming pattern as well as the complicated interaction of acoustic streaming with the adherent biofilm needs to be clarified to reveal the underlying physical mechanisms of PUI.


Multi-modal Attention Mechanisms in LSTM and Its Application to Acoustic Scene Classification


title: Semantic Segmentation On Medium-Resolution Satellite Images Using Deep Convolutional Networks With Remote Sensing Derived Indices

abstract: Semantic Segmentation is a fundamental task in computer vision and remote sensing imagery. Many applications, such as urban planning, change detection, and environmental monitoring, require the accurate segmentation; hence, most segmentation tasks are performed by humans. Currently, with the growth of Deep Convolutional Neural Network (DCNN), there are many works aiming to find the best network architecture fitting for this task. However, all of the studies are based on very-high resolution satellite images, and surprisingly; none of them are implemented on medium resolution satellite images. Moreover, no research has applied geoinformatics knowledge. Therefore, we purpose to compare the semantic segmentation models, which are FCN, SegNet, and GSN using medium resolution images from Landsat-8 satellite. In addition, we propose a modified SegNet model that can be used with remote sensing derived indices. The results show that the model that achieves the highest accuracy RGB bands of medium resolution aerial imagery is SegNet. The overall accuracy of the model increases when includes Near Infrared (NIR) and Short-Wave Infrared (SWIR) band. The results showed that our proposed method (our modified SegNet model, named RGB-IR-IDX-MSN method) outperforms all of the baselines in terms of mean F1 scores.


title: Improving Color Modeling for Alpha Matting

abstract: This paper addresses the problem of extracting an alpha matte from a single photograph given a user-defined trimap. A crucial part of this task is the color modeling step where for each pixel the optimal alpha value, together with its confidence, is estimated individually. This forms the data term of the objective function. It comprises of three steps: (i) Collecting a candidate set of potential foreand background colors; (ii) Selecting high confidence samples from the candidate set; (iii) Estimating a sparsity prior to remove blurry artifacts. We introduce novel ideas for each of these steps and show that our approach considerably improves over state-of-the-art techniques by evaluating it on a large database of 54 images with known high-quality ground truth.


title: Mining the modern code review repositories: a dataset of people, process and product

abstract: In this paper, we present a collection of Modern Code Review data for five open source projects. The data showcases mined data from both an integrated peer review system and source code repositories. We present an easy–to–use andricher data structure to retrieve the 1.) People 2.) Process and 3.) Product aspects of the peer review. This paperpresents the extraction methodology, the dataset structure, and a collection of database dumps.


title: Detecting failures in distributed systems with the Falcon spy network

abstract: A common way for a distributed system to tolerate crashes is to explicitly detect them and then recover from them. Interestingly, detection can take much longer than recovery, as a result of many advances in recovery techniques, making failure detection the dominant factor in these systems' unavailability when a crash occurs. This paper presents the design, implementation, and evaluation of Falcon, a failure detector with several features. First, Falcon's common-case detection time is sub-second, which keeps unavailability low. Second, Falcon is reliable: it never reports a process as down when it is actually up. Third, Falcon sometimes kills to achieve reliable detection but aims to kill the smallest needed component. Falcon achieves these features by coordinating a network of spies, each monitoring a layer of the system. Falcon's main cost is a small amount of platform-specific logic. Falcon is thus the first failure detector that is fast, reliable, and viable. As such, it could change the way that a class of distributed systems is built.


title: A trust model for online peer-to-peer lending: a lender's perspective

abstract: Online peer-to-peer (P2P) lending is a new but essential financing method for small and micro enterprises that is conducted on the Internet and excludes the involvement of collateral and financial institutions. To tackle the inherent risk of this new financing method, trust must be cultivated. Based on trust theories, the present study develops an integrated trust model specifically for the online P2P lending context, to better understand the critical factors that drive lenders' trust. The model is empirically tested using surveyed data from 785 online lenders of PPDai, the first and largest online P2P platform in China. The results show that both trust in borrowers and trust in intermediaries are significant factors influencing lenders' lending intention. However, trust in borrowers is more critical, and not only directly nurtures lenders' lending intention more efficiently than trust in intermediaries, but also carries the impact of trust in intermediaries on lenders' lending intention. To develop lenders' trust, borrowers should provide high-quality information for their loan requests and intermediaries should provide high-quality services and sufficient security protection. The findings provide valuable insights for both borrowers and intermediaries.


title: Modeling Control Objectives for Business Process Compliance

abstract: Business process design is primarily driven by process improvementobjectives. However, the role of control objectives stemming from regulationsand standards is becoming increasingly important for businesses in light ofrecent events that led to some of the largest scandals in corporate history. Asorganizations strive to meet compliance agendas, there is an evident need toprovide systematic approaches that assist in the understanding of the interplaybetween (often conflicting) business and control objectives during businessprocess design. In this paper, our objective is twofold. We will firstly present aresearch agenda in the space of business process compliance, identifying majortechnical and organizational challenges. We then tackle a part of the overallproblem space, which deals with the effective modeling of control objectivesand subsequently their propagation onto business process models. Controlobjective modeling is proposed through a specialized modal logic based onnormative systems theory, and the visualization of control objectives onbusiness process models is achieved procedurally. The proposed approach isdemonstrated in the context of a purchase-to-pay scenario.


Migration and measles


title: in press-uncorrected proof Quality Indicators in Laboratory Medicine : from theory to practice Preliminary data from the IFCC Working Group Project ‘ ‘ Laboratory Errors and Patient Safety ’ ’

abstract: BACKGROUND ::: The adoption of Quality Indicators (QIs) has prompted the development of tools to measure and evaluate the quality and effectiveness of laboratory testing, first in the hospital setting and subsequently in ambulatory and other care settings. While Laboratory Medicine has an important role in the delivery of high-quality care, no consensus exists as yet on the use of QIs focussing on all steps of the laboratory total testing process (TTP), and further research in this area is required. ::: ::: ::: METHODS ::: In order to reduce errors in laboratory testing, the IFCC Working Group on "Laboratory Errors and Patient Safety" (WG-LEPS) developed a series of Quality Indicators, specifically designed for clinical laboratories. In the first phase of the project, specific QIs for key processes of the TTP were identified, including all the pre-, intra- and post-analytic steps. The overall aim of the project is to create a common reporting system for clinical laboratories based on standardized data collection, and to define state-of-the-art and Quality Specifications (QSs) for each QI independent of: a) the size of organization and type of activities; b) the complexity of processes undertaken; and c) different degree of knowledge and ability of the staff. The aim of the present paper is to report the results collected from participating laboratories from February 2008 to December 2009 and to identify preliminary QSs. ::: ::: ::: RESULTS AND CONCLUSIONS ::: The results demonstrate that a Model of Quality Indicators managed as an External Quality Assurance Program can serve as a tool to monitor and control the pre-, intra- and post-analytical activities. It might also allow clinical laboratories to identify risks that lead to errors resulting in patient harm: identification and design of practices that eliminate medical errors; the sharing of information and education of clinical and laboratory teams on practices that reduce or prevent errors; the monitoring and evaluation of improvement activities.


title: The genetics of alcohol dependence: Twin and SNP-based heritability, and genome-wide association study based on AUDIT scores.

abstract: Alcohol dependence (AD) is among the most common and costly public health problems contributing to morbidity and mortality throughout the world. In this study, we investigate the genetic basis of AD in a Dutch population using data from the Netherlands Twin Register (NTR) and the Netherlands Study of Depression and Anxiety (NESDA). The presence of AD was ascertained via the Alcohol Use Disorders Identification Test (AUDIT) applying cut-offs with good specificity and sensitivity in identifying those at risk for AD. Twin-based heritability of AD-AUDIT was estimated using structural equation modeling of data in 7,694 MZ and DZ twin pairs. Variance in AD-AUDIT explained by all SNPs was estimated with genome-wide complex trait analysis (GCTA). A genome-wide association study (GWAS) was performed in 7,842 subjects. GWAS SNP effect concordance analysis was performed between our GWAS and a recent AD GWAS using DSM-IV diagnosis. The twin-based heritability of AD-AUDIT was estimated at 60% (55-69%). GCTA showed that common SNPs jointly capture 33% (SE = 0.12, P = 0.002) of this heritability. In the GWAS, the top hits were positioned within four regions (4q31.1, 2p16.1, 6q25.1, 7p14.1) with the strongest association detected for rs55768019 (P = 7.58 × 10(-7) ). This first GWAS of AD using the AUDIT measure found results consistent with previous genetic studies using DSM diagnosis: concordance in heritability estimates and direction of SNPs effect and overlap with top hits from previous GWAS. Thus, the use of appropriate questionnaires may represent cost-effective strategies to phenotype samples in large-scale biobanks or other population-based datasets.


A Case Study into the Development Process of Cyber Physical Systems


title: Cryptography based digital image watermarking algorithm to increase security of watermark data

abstract: Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. This technique is better than Digital Signatures and other methods because it does not increase overhead. Digital Watermarking describes methods and technologies that hide information, for example a number or text, in digital media, such as images, video or audio. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. In this paper cryptography based Blind image watermarking technique presented that can embed more number of watermark bits in the gray scale cover image without affecting the imperceptibility and increase the security of watermarks.


title: Morpholexical and Discriminative Language Models for Turkish Automatic Speech Recognition

abstract: This paper introduces two complementary language modeling approaches for morphologically rich languages aiming to alleviate out-of-vocabulary (OOV) word problem and to exploit morphology as a knowledge source. The first model, morpholexical language model, is a generative $n$ -gram model, where modeling units are lexical-grammatical morphemes instead of commonly used words or statistical sub-words. This paper also proposes a novel approach for integrating the morphology into an automatic speech recognition (ASR) system in the finite-state transducer framework as a knowledge source. We accomplish that by building a morpholexical search network obtained by the composition of lexical transducer of a computational lexicon with a morpholexical language model. The second model is a linear reranking model trained discriminatively with a variant of the perceptron algorithm using morpholexical features. This variant of the perceptron algorithm, WER-sensitive perceptron, is shown to perform better for reranking $n$ -best candidates obtained with the generative model. We apply the proposed models in Turkish broadcast news transcription task and give experimental results. The morpholexical model leads to an elegant morphology-integrated search network with unlimited vocabulary. Thus, it is highly effective in alleviating OOV problem and improves the word error rate (WER) over word and statistical sub-word models by 1.8% and 0.4% absolute, respectively. The discriminatively trained morpholexical model further improves the WER of the system by 0.8% absolute.


title: Corpus-based Question Answering for why-Questions

abstract: This paper proposes a corpus-based approach for answering why-questions. Conventional systems use hand-crafted patterns to extract and evaluate answer candidates. However, such hand-crafted patterns are likely to have low coverage of causal expressions, and it is also difficult to assign suitable weights to the patterns by hand. In our approach, causal expressions are automatically collected from corpora tagged with semantic relations. From the collected expressions, features are created to train an answer candidate ranker that maximizes the QA performance with regards to the corpus of why-questions and answers. NAZEQA, a Japanese why-QA system based on our approach, clearly outperforms a baseline that uses hand-crafted patterns with a Mean Reciprocal Rank (top-5) of 0.305, making it presumably the best-performing fully implemented why-QA system.


title: Processing Capacity Defined by Relational Complexity: Implications for Comparative, Developmental, and Cognitive Psychology

abstract: Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A binary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by "segmentation" or "conceptual chunking." In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are "collapsed" to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed.


title: Safe and Efficient Off-Policy Reinforcement Learning

abstract: In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\lambda$), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from any behaviour policy, whatever its degree of "off-policyness"; and (3) it is efficient as it makes the best use of samples collected from near on-policy behaviour policies. We analyze the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. We believe this is the first return-based off-policy control algorithm converging a.s. to $Q^*$ without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q($\lambda$), which was an open problem since 1989. We illustrate the benefits of Retrace($\lambda$) on a standard suite of Atari 2600 games.


title: Beamforming in Noninvasive Brain–Computer Interfaces

abstract: Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.


title: Colwell's Castle Defence: A Custom Game Using Dynamic Difficulty Adjustment to Increase Player Enjoyment

abstract: Dynamic Difficulty Adjustment (DDA) is a mechanism used in video games that automatically tailors the individual gaming experience to match an appropriate difficulty setting. This is generally achieved by removing pre-defined difficulty tiers such as Easy, Medium and Hard; and instead concentrates on balancing the gameplay to match the challenge to the individual's abilities. The work presented in this paper examines the implementation of DDA in a custom survival game developed by the author, namely Colwell's Castle Defence. The premise of this arcade-style game is to defend a castle from hordes of oncoming enemies. The AI system that we developed adjusts the enemy spawn rate based on the current performance of the player. Specifically, we read the Player Health and Gate Health at the end of each level and then assign the player with an appropriate difficulty tier for the proceeding level. We tested the impact of our technique on thirty human players and concluded, based on questionnaire feedback, that enabling the technique led to more enjoyable gameplay.


title: Monthly energy consumption forecast: A deep learning approach

abstract: Every year, energy consumption grows world widely. Therefore, power companies need to investigate models to better forecast and plan the energy use. One approach to address this problem is the estimation of energy consumption in the customer level. Energy consumption forecasting problem is a time series regression task. It consists of predicting the energy consumption for the next month given a finite history of a customer. Machine learning techniques have shown promising results in a variety of problems including time series and regression problems. Part of these promising results are attributed to deep neural networks. Although investigated in other domains, deep architectures have not been used to address the energy consumption prediction problem. In this work, we propose a system to predict monthly energy consumption using deep learning techniques. Three deep learning models were studied: Deep Fully Connected, Convolutional and Long Short-Term Memory Neural Networks. Due to the sensitivity of these models to the input range, normalization techniques were also investigated. The proposed system was validated with real data of almost a million customers (resulting in over 9 million samples). Results showed that our system can predict monthly energy consumption with an absolute error of 31.83 kWh and a relative error of 17.29%.


title: cTPM: a cloud TPM for cross-device trusted applications

abstract: Current Trusted Platform Modules (TPMs) are ill-suited for use in mobile services because they hinder sharing data across multiple devices seamlessly, they lack access to a trusted real-time clock, and their non-volatile storage performs poorly. We present cloud TPM (cTPM), an extension of the TPM’s design, to address these problems. cTPM includes two features: a cloud seed shared between the TPM and the cloud, and remote storage in addition to the on-chip storage. cTPM allows the cloud to create and share TPM-protected keys across multiple devices, to manage a portion of a mobile device’s TPM storage, and to provide each TPM with a trusted real-time clock and with high-performance non-volatile storage.


title: SentiWordNet: A High-Coverage Lexical Resource for Opinion Mining

abstract: Opinion mining (OM) is a recent subdiscipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a document is about, but with the opinions it expresses. OM has a rich set of applications, ranging from tracking users’ opinions about products or about political candidates as expressed in online forums, to customer relationship management. In order to aid the extraction of opinions from text, recent research has tried to automatically determine the “PN-polarity” of subjective terms, i.e. identify whether a term that indicates the presence of an opinion has a positive or a negative connotation. Research on determining the “SO-polarity” of terms, i.e. whether a term indeed indicates the presence of an opinion (a subjective term) or not (an objective, or neutral term) has been instead much scarcer. In this paper we describe SentiWordNet, a lexical resource produced by asking an automated classifier Φ to associate to each synset s of WordNet (version 2.0) a triplet of scores Φ(s, p) (for p ∈ P ={Positive, Negative, Objective}) describing how strongly the terms contained in s enjoy each of the three properties. The method used to develop SentiWordNet is based on the quantitative analysis of the glosses associated to synsets, and on the use of the resulting vectorial term representations for semi-supervised synset classification. The score triplet is derived by combining the results produced by a committee of eight ternary classifiers, all characterized by similar accuracy levels but extremely different classification behaviour. We present the results of evaluating the accuracy of the automatically assigned triplets on a publicly available benchmark. SentiWordNet is freely available for research purposes, and is endowed with a Web-based graphical user interface.


title: TouchLogger: Inferring keystrokes on touch screen from smartphone motion

abstract: Attacks that use side channels, such as sound and electromagnetic emanation, to infer keystrokes on physical keyboards are ineffective on smartphones without physical keyboards. We describe a new side channel, motion, on touch screen smartphones with only soft keyboards. Since typing on different locations on the screen causes different vibrations, motion data can be used to infer the keys being typed. To demonstrate this attack, we developed TouchLogger, an Android application that extracts features from device orientation data to infer keystrokes. TouchLogger correctly inferred more than 70% of the keys typed on a number-only soft keyboard on a smartphone. We hope to raise the awareness of motion as a significant side channel that may leak confidential data.


title: Autonomous disengagement classification and repair in multiparty child-robot interaction

abstract: As research on robotic tutors increases, it becomes more relevant to understand whether and how robots will be able to keep students engaged over time. In this paper, we propose an algorithm to monitor engagement in small groups of children and trigger disengagement repair interventions when necessary. We implemented this algorithm in a scenario where two robot actors play out interactive narratives around emotional words and conducted a field study where 72 children interacted with the robots three times in one of the following conditions: control (no disengagement repair), targeted (interventions addressing the child with the highest disengagement level) and general (interventions addressing the whole group). Surprisingly, children in the control condition had higher narrative recall than in the two experimental conditions, but no significant differences were found in the emotional interpretation of the narratives. When comparing the two different types of disengagement repair strategies, participants who received targeted interventions had higher story recall and emotional understanding, and their valence after disengagement repair interventions increased over time.


title: Electroencephalogram signatures of loss and recovery of consciousness from propofol

abstract: Unconsciousness is a fundamental component of general anesthesia (GA), but anesthesiologists have no reliable ways to be certain that a patient is unconscious. To develop EEG signatures that track loss and recovery of consciousness under GA, we recorded high-density EEGs in humans during gradual induction of and emergence from unconsciousness with propofol. The subjects executed an auditory task at 4-s intervals consisting of interleaved verbal and click stimuli to identify loss and recovery of consciousness. During induction, subjects lost responsiveness to the less salient clicks before losing responsiveness to the more salient verbal stimuli; during emergence they recovered responsiveness to the verbal stimuli before recovering responsiveness to the clicks. The median frequency and bandwidth of the frontal EEG power tracked the probability of response to the verbal stimuli during the transitions in consciousness. Loss of consciousness was marked simultaneously by an increase in low-frequency EEG power (<1 Hz), the loss of spatially coherent occipital alpha oscillations (8–12 Hz), and the appearance of spatially coherent frontal alpha oscillations. These dynamics reversed with recovery of consciousness. The low-frequency phase modulated alpha amplitude in two distinct patterns. During profound unconsciousness, alpha amplitudes were maximal at low-frequency peaks, whereas during the transition into and out of unconsciousness, alpha amplitudes were maximal at low-frequency nadirs. This latter phase–amplitude relationship predicted recovery of consciousness. Our results provide insights into the mechanisms of propofol-induced unconsciousness, establish EEG signatures of this brain state that track transitions in consciousness precisely, and suggest strategies for monitoring the brain activity of patients receiving GA.


title: Image Description using Visual Dependency Representations

abstract: Describing the main event of an image involves identifying the objects depicted and predicting the relationships between them. Previous approaches have represented images as unstructured bags of regions, which makes it difficult to accurately predict meaningful relationships between regions. In this paper, we introduce visual dependency representations to capture the relationships between the objects in an image, and hypothesize that this representation can improve image description. We test this hypothesis using a new data set of region-annotated images, associated with visual dependency representations and gold-standard descriptions. We describe two template-based description generation models that operate over visual dependency representations. In an image description task, we find that these models outperform approaches that rely on object proximity or corpus information to generate descriptions on both automatic measures and on human judgements.


title: Interactive Procedural Modelling of Coherent Waterfall Scenes

abstract: Combining procedural generation and user control is a fundamental challenge for the interactive design of natural scenery. This is particularly true for modelling complex waterfall scenes where, in addition to taking charge of geometric details, an ideal tool should also provide a user with the freedom to shape the running streams and falls, while automatically maintaining physical plausibility in terms of flow network, embedding into the terrain, and visual aspects of the waterfalls. We present the first solution for the interactive procedural design of coherent waterfall scenes. Our system combines vectorial editing, where the user assembles elements to create a waterfall network over an existing terrain, with a procedural model that parametrizes these elements from hydraulic exchanges; enforces consistency between the terrain and the flow; and generates detailed geometry, animated textures and shaders for the waterfalls and their surroundings. The tool is interactive, yielding visual feedback after each edit.


title: ENGINEERING FLOWS IN SMALL DEVICES : Microfluidics Toward a Lab-ona-Chip

abstract: Microfluidic devices for manipulating fluids are widespread and finding uses in many scientific and industrial contexts. Their design often requires unusual geometries and the interplay of multiple physical effects such as pressure gradients, electrokinetics, and capillarity. These circumstances lead to interesting variants of well-studied fluid dynamical problems and some new fluid responses. We provide an overview of flows in microdevices with focus on electrokinetics, mixing and dispersion, and multiphase flows. We highlight topics important for the description of the fluid dynamics: driving forces, geometry, and the chemical characteristics of surfaces.


title: The many faces of data-centric workflow optimization: a survey

abstract: Workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on data-centric workflows (or workflows for data analytics or data flows), where a key aspect is data passing through and getting manipulated by a sequence of steps. The large volume and variety of data, the complexity of operations performed, and the long time such workflows take to compute give rise to the need for optimization. In general, data-centric workflow optimization is a technology in evolution. This survey focuses on techniques applicable to workflows comprising arbitrary types of data manipulation steps and semantic inter-dependencies between such steps. Further, it serves a twofold purpose: firstly, to present the main dimensions of the relevant optimization problems and the types of optimizations that occur before flow execution and secondly, to provide a concise overview of the existing approaches with a view to highlighting key observations and areas deserving more attention from the community.


title: Linear Large-Swing Push–Pull SiGe BiCMOS Drivers for Silicon Photonics Modulators

abstract: The analysis, design, and experimental characterization of a novel class of large-swing low output-impedance push-pull SiGe BiCMOS drivers are discussed. The circuits, which employ single- and series-stacked emitter follower (EF) and MOS-HBT cascode topologies, are intended to drive optical modulators with high-order m-pulse-amplitude modulated (PAM) signals, while maximizing energy/bit efficiency. The series-stacked EF version, implemented in a 55-nm SiGe BiCMOS technology with 370 GHz ${f_{\mathrm {MAX}}}$ , achieved the highest linearity with 4.8-Vpp differential output swing, 57.5 GHz bandwidth, and an output compression point of 12 dBm per side. Four-PAM and eight-PAM eye diagrams were measured at 56 GBd for a record data rate of 168 Gb/s. Four-PAM 64-GBd eye diagrams were also demonstrated. The circuit consumes 820/600 mW with/without the predriver and has an energy efficiency of 4.88/3.57 pJ/b.


title: Deep neural networks and mixed integer linear optimization

abstract: Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made up of layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero. In this (and other similar cases like max pooling, where the max operation involves more than one input value), for fixed parameters one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where the continuous variables correspond to the output values of each unit, and a binary variable is associated with each ReLU to model its yes/no nature. In this paper we discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution. We also present possible applications of the 0-1 MILP model arising in feature visualization and in the construction of adversarial examples. Computational results are reported, aimed at investigating (on small DNNs) the computational performance of a state-of-the-art MILP solver when applied to a known test case, namely, hand-written digit recognition.


title: The factor structure of the Hopkins Symptoms Checklist (HSCL-25) in a student population: A cautionary tale

abstract: Aims: The Hopkins Symptoms Checklist (HSCL-25) is a widely used self-report measurement for mental health problems, but its factor structure is still uncertain, with divergent results in different social or cultural settings. We aimed to investigate the previously suggested factor structures of the HSCL-25, as well as a model including an explicit somatic factor among students in Norway. Methods: The study population is based on data from the Norwegian study of students’ health and wellbeing, SHoT (‘Studentenes Helse- og Trivselsundersokelse’), and the present study comprises N = 13,525 participants. Using confirmatory factor analyses we investigated previously suggested factor structures, as well as a 3-factor structure, with specific subscales for anxiety, depression and somatic symptoms, suggested by the authors. After identification of the best-fitting model(s), measurement invariance across sexes, as well as associations with self-reported socioeconomic and social factors, use of medication and help-...