title: Mapping sentence form onto meaning: The syntax–semantic interface

abstract: Abstract The understanding of sentences involves not only the retrieval of the meaning of single words, but the identification of the relation between a verb and its arguments. The way the brain manages to process word meaning and syntactic relations during language comprehension on-line still is a matter of debate. Here we review the different views discussed in the literature and report data from crucial experiments investigating the temporal and neurotopological parameters of different information types encoded in verbs, i.e. word category information, the verb’s argument structure information, the verb’s selectional restriction and the morphosyntactic information encoded in the verb’s inflection. The neurophysiological indices of the processes dealing with these different information types suggest an initial independence of the processing of word category information from other information types as the basis of local phrase structure building, and a later processing stage during which different information types interact. The relative ordering of the subprocesses appears to be universal, whereas the absolute timing of when during later phrases interaction takes places varies as a function of when the relevant information becomes available. Moreover, the neurophysiological indices for non-local dependency relations vary as a function of the morphological richness of the language.


title: Explaining 'Explaining Away'

abstract: 'Explaining away' is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also an occur, where the confirmation of one cause increases belief in another. A general qualitative probabilistic analysis of intercausal reasoning is provided and the property of the interaction among the causes (product synergy) that determines which form of reasoning is appropriate is identified. Product synergy extends the qualitative probabilistic network (QPN) formalism to support qualitative intercausal inference about the directions of change in probabilistic belief. The intercausal relation also justifies Occam's razor, facilitating pruning in the search for likely diagnoses. >


title: Performance measures of game controllers in a three-dimensional environment

abstract: Little work exists on the testing and evaluation of computer-game related input devices. This paper presents five new performance metrics and utilizes two tasks from the literature to quantify differences between input devices in constrained three-dimensional environments, similar to "first-person"-genre games. The metrics are Mean Speed Variance, Mean Acceleration Variance, Percent View Moving, Target Leading Analysis, and Mean Time-to-Reacquire. All measures are continuous, as they evaluate movement during a trial. The tasks involved tracking a moving target for several seconds, with and without target acceleration. An evaluation between an X-Box gamepad and a standard PC mouse demonstrated the ability of the metrics to help reveal and explain performance differences between the devices.


title: Supporting Children to Participate Successfully in Everyday Life by Using Sensory Processing Knowledge

abstract: There is an accumulating literature describing sensory processing in young children and suggesting the importance of this knowledge for understanding the characteristics of vulnerable children. Professionals and families need a working knowledge about sensory processing because it enables them to un


title: RGB-D Object Recognition and Grasp Detection Using Hierarchical Cascaded Forests

abstract: This paper presents an efficient framework to perform recognition and grasp detection of objects from RGB-D images of real scenes. The framework uses a novel architecture of hierarchical cascaded forests, in which object-class and grasp-pose probabilities are computed at different levels of an image hierarchy (e.g., patch and object levels) and fused to infer the class and the grasp of unseen objects. We introduce a novel training objective function that minimizes the uncertainties of the class labels and the grasp ground truths at the leaves of the forests, thereby enabling the framework to perform the recognition and grasp detection of objects. Our objective function is learned from features that are extracted from RGB-D point clouds of the objects. For that, we propose a novel method to encode an RGB-D point cloud into a representation that facilitates the use of large convolution neural networks to extract discriminative features from RGB-D images. We evaluate our framework on challenging object datasets, where we demonstrate that our framework outperforms the state-of-the-art methods in terms of object-recognition and grasp-detection accuracies. We also show experiments by using live video streams from a Kinect mounted on our in-house robotic platform.


title: Software testing research and software engineering education

abstract: Software testing research has not kept up with modern software system designs and applications, and software engineering education falls short of providing students with the type of knowledge and training that other engineering specialties require. Testing researchers should pay more attention to areas that are currently relevant for practicing software developers, such as embedded systems, mobile devices, safety-critical systems and other modern paradigms, in order to provide usable results and techniques for practitioners. We identify a number of skills that every software engineering student and faculty should have learned, and also propose that education for future software engineers should include significant exposure to real systems, preferably through hands-on training via internships at software-producing firms.


title: Talking microbes: When gut bacteria interact with diet and host organs

abstract: Obesity and diabetes have reached epidemic proportions. Evidence suggests that besides dietary habits and physical activity, other environmental factors, such as gut microbes, are recognized as additional partners implicated in the control of energy homeostasis. Studies on the human gut microbiota have shown that the general population can be stratified on the sole basis of three dominant bacteria (i.e., the concept of enterotypes), while some others have suggested categorizing the population according to their microbiome gene richness. Both aspects have been strengthened by recent studies investigating the impact of nutrients (e.g., dietary fibers, fat feeding) and dietary habits (i.e., vegans versus omnivores) of different populations. Using preclinical models, quite a few novel mechanisms have been proposed in these gut microbiota-host interactions, including the role of novel bacteria, the regulation of antimicrobial peptide production, the maintenance of the gut barrier function and intestinal innate immunity. In this review, we discuss several of the aforementioned aspects. Nonetheless, determining the overall mechanisms by which microbes dialogue with host cells will require further investigations before anticipating the development of next-generation nutritional interventions using prebiotics, probiotics, synbiotics, or even specific nutrients for promoting health benefits.


title: Classification by Pairwise Coupling

abstract: We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the Bradley-Terry method for paired comparisons. We study the nature of the class probability estimates that arise, and examine the performance of the procedure in simulated datasets. The classifiers used include linear discriminants and nearest neighbors: application to support vector machines is also briefly described.


title: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform

abstract: The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. The detection of epileptiform discharges in the electroencephalogram (EEG) is an important part in the diagnosis of epilepsy. All data set were obtained from EEG signals of healthy subjects and subjects suffering from epilepsy diseases. For healthy subjects is background EEG (scalp) with open eyes and for epileptic patients correspond to a seizure recorded in hippocampus (epileptic focus) with depth electrodes. The evolution of proposed system was conducted using k-fold cross-validation, classification accuracy, and sensitivity and specificity values. We have obtained 98.68% and 98.72% classification accuracies using 5- and 10-fold cross-validation. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.


title: A Trusted Virtual Machine in an Untrusted Management Environment

abstract: Virtualization is a rapidly evolving technology that can be used to provide a range of benefits to computing systems, including improved resource utilization, software portability, and reliability. Virtualization also has the potential to enhance security by providing isolated execution environments for different applications that require different levels of security. For security-critical applications, it is highly desirable to have a small trusted computing base (TCB), since it minimizes the surface of attacks that could jeopardize the security of the entire system. In traditional virtualization architectures, the TCB for an application includes not only the hardware and the virtual machine monitor (VMM), but also the whole management operating system (OS) that contains the device drivers and virtual machine (VM) management functionality. For many applications, it is not acceptable to trust this management OS, due to its large code base and abundance of vulnerabilities. For example, consider the "computing-as-a-service” scenario where remote users execute a guest OS and applications inside a VM on a remote computing platform. It would be preferable for many users to utilize such a computing service without being forced to trust the management OS on the remote platform. In this paper, we address the problem of providing a secure execution environment on a virtualized computing platform under the assumption of an untrusted management OS. We propose a secure virtualization architecture that provides a secure runtime environment, network interface, and secondary storage for a guest VM. The proposed architecture significantly reduces the TCB of security-critical guest VMs, leading to improved security in an untrusted management environment. We have implemented a prototype of the proposed approach using the Xen virtualization system, and demonstrated how it can be used to facilitate secure remote computing services. We evaluate the performance penalties incurred by the proposed architecture, and demonstrate that the penalties are minimal.


title: Optimization by direct search: New perspectives on some classical and modern methods

abstract: Direct search methods are best known as unconstrained optimization techniques that do not explicitly use derivatives. Direct search methods were formally proposed and widely applied in the 1960s but fell out of favor with the mathematical optimization community by the early 1970s because they lacked coherent mathematical analysis. Nonetheless, users remained loyal to these methods, most of which were easy to program, some of which were reliable. In the past fifteen years, these methods have seen a revival due, in part, to the appearance of mathematical analysis, as well as to interest in parallel and distributed com- puting. This review begins by briefly summarizing the history of direct search methods and considering the special properties of problems for which they are well suited. Our focus then turns to a broad class of methods for which we provide a unifying framework that lends itself to a variety of convergence results. The underlying principles allow general- ization to handle bound constraints and linear constraints. We also discuss extensions to problems with nonlinear constraints.


title: Mode-detection via median-shift

abstract: Median-shift is a mode seeking algorithm that relies on computing the median of local neighborhoods, instead of the mean. We further combine median-shift with Locality Sensitive Hashing (LSH) and show that the combined algorithm is suitable for clustering large scale, high dimensional data sets. In particular, we propose a new mode detection step that greatly accelerates performance. In the past, LSH was used in conjunction with mean shift only to accelerate nearest neighbor queries. Here we show that we can analyze the density of the LSH bins to quickly detect potential mode candidates and use only them to initialize the median-shift procedure. We use the median, instead of the mean (or its discrete counterpart - the medoid) because the median is more robust and because the median of a set is a point in the set. A median is well defined for scalars but there is no single agreed upon extension of the median to high dimensional data. We adopt a particular extension, known as the Tukey median, and show that it can be computed efficiently using random projections of the high dimensional data onto 1D lines, just like LSH, leading to a tightly integrated and efficient algorithm.


title: PID Controller Tuning in Reverse Osmosis System based on Particle Swarm Optimization

abstract: This paper describes PID Controller tuning based on Particle Swarm Optimization in the Reverse Osmosis application. Particle Swarm Optimization is powerful method for tuning PID controller comparison to other conventional optimization techniques. It has some advance features such as easy implementation, stable characteristics; self-tuning, flexible design In this paper a model has presented that focus on the PH and Conductivity control in the Reverse Osmosis (RO). PSO algorithm is presented to tune the parameters of PID controller used in the RO Model. Computer Simulation in terms of a virtual process is designed to check the algorithm and compare the results with other optimization techniques.


title: Designing the digital workplace of the future – what scholars recommend to practitioners

abstract: The digital workplace is widely acknowledged as an important organizational asset for optimizing knowledge worker productivity. While there is no particular research stream on the digital workplace, scholars have conducted intensive research on related topics. This study aims to summarize the practical implications of the current academic body of knowledge on the digital workplace. For this purpose, a screening of academic-practitioner literature was conducted, followed by a systematic review of academic top journal literature. The screening revealed four main research topics on the digital workplace that are present in academic-practitioner literature: 1) Collaboration, 2) Compliance, 3) Mobility, and 4) Stress and overload. Based on the four topics, this study categorizes practical implications on the digital workplace into 15 concepts. Thereby, it provides two main contributions. First, the study delivers condensed information for practitioners about digital workplace design. Second, the results shed light on the relevance of IS research.


title: Dopamine, learning and motivation

abstract: The hypothesis that dopamine is important for reward has been proposed in a number of forms, each of which has been challenged. Normally, rewarding stimuli such as food, water, lateral hypothalamic brain stimulation and several drugs of abuse become ineffective as rewards in animals given performance-sparing doses of dopamine antagonists. Dopamine release in the nucleus accumbens has been linked to the efficacy of these unconditioned rewards, but dopamine release in a broader range of structures is implicated in the 'stamping-in' of memory that attaches motivational importance to otherwise neutral environmental stimuli.


title: Synthesis and antimicrobial activity of copper nanoparticle loaded regenerated bacterial cellulose membranes

abstract: A series of copper nanoparticle (CuNP) loaded regenerated bacterial cellulose (RC) membranes were fabricated. The prepared membranes were characterized in terms of their morphology and chemical composition. The results of SEM, EDS and elemental mapping indicated that CuNPs were successfully loaded into the RC matrix and RC–Cu membranes exhibited a homogeneous structure. XPS and XRD results showed copper is mainly present in the form of nanoparticles in the RC membranes. Antibacterial activity was evaluated against S. aureus ATCC 6538, B. subtilis ATCC 9372, C. albicans CMCC(F) 98001, E. coli ATCC 25922 and P. aeruginosa ATCC 27853 in vitro and RC–Cu membranes showed a significant antibacterial activity influenced by the CuNP content. Hence, RC–Cu membranes might have a great potential for use in the wound dressings and other biomedical areas.


title: Bacteria Biotope Detection, Ontology-based Normalization, and Relation Extraction using Syntactic Rules

abstract: The absence of a comprehensive database of locations where bacteria live is an im- portant obstacle for biologists to under- stand and study the interactions between bacteria and their habitats. This paper re- ports the results to a challenge, set forth by the Bacteria Biotopes Task of the BioNLP Shared Task 2013. Two systems are ex- plained: Sub-task 1 system for identifying habitat mentions in unstructured biomedi- cal text and normalizing them through the OntoBiotope ontology and Sub-task 2 sys- tem for extracting localization and part- of relations between bacteria and habitats. Both approaches rely on syntactic rules designed by considering the shallow lin- guistic analysis of the text. Sub-task 2 system also makes use of discourse-based rules. The two systems achieve promising results on the shared task test data set.


Current status and impact of early mortality syndrome (EMS)/acute hepatopancreatic necrosis disease (AHPND) and hepatopancreatic microsporidiosis (HPM) outbreaks on Thailand s shrimp farming


title: Website Quality And Consumer Online Purchase Intention Of Air Ticket

abstract: Internet application has been used by many firms especially the low-cost airline service providers in supporting their marketing activities. Thus, it is crucial to know whether firms’ website is giving an added advantage that could trigger online purchase intention. In this study, we examine six website quality factors: usability, website design, information quality, trust, perceived risk and empathy as antecedents of online purchase intention of air ticket. A self-administered questionnaire was used and 208 usable responses were collected in Klang Valley, Malaysia areas. Correlations and regressions were used to analyze the data. The findings showed that empathy and trust are the most direct influential factors in predicting online purchase intention. To improve consumers onlinepurchase intention, service providers should provide service with empathy and enhance customers’ trust. The results of study provide a valuable insight on the direct impact of website quality factors towards online purchase intention of air tickets.


title: Centripetal Democratic Governance: A Theory and Global Inquiry

abstract: Why are some democracies better governed than others? Why are many plagued by corruption and ineptitude, whereas others manage to implement policies effectively and efficiently? Why are some borne down by inefficient markets and lowstandardsofliving,whereasothersenjoylowtransaction costs, high capital investment, and strong economic performance? Why are rates of morbidity, mortality,illiteracy,andotheraspectsofhumandeprivation so depressingly high in some democracies, and so impressively low in others? What can account, in short, for the immense variation we observe in the quality of governance across democratic polities in the world today? In this paper, we focus on the role of democratic political institutions in the achievement of good governance. The survival of democracy is understood as


title: Spector: An OpenCL FPGA benchmark suite

abstract: High-level synthesis tools allow programmers to use OpenCL to create FPGA designs. Unfortunately, these tools have a complex compilation process that can take several hours to synthesize a single design. This creates a significant barrier for design optimization since even experts typically need to test many designs due to the non-obvious interactions between the different optimizations. Thus, understanding the design space, and guiding the optimization process is a crucial requirement for enabling the widespread adoption of these high-level synthesis tools. However this requires a significant amount of design space data that is currently unavailable or difficult to generate. To solve this problem, we present an OpenCL FPGA benchmark suite. We outfitted each benchmark with a range of optimization parameters (or knobs), compiled over 8300 unique designs using the Altera OpenCL SDK, executed them on a Terasic DE5 board, and recorded their corresponding performance and utilization characteristics. We describe the resulting design spaces, and perform a statistical analysis of the optimization configurations which provides valuable architecture insights to FPGA developers. We make the benchmarks and results completely open-source to give opportunities for the community to perform additional analyses and provide a repository of well-documented designs for follow-on research.


title: Analysis of vulnerabilities in Internet firewalls

abstract: Firewalls protect a trusted network from an untrusted network by filtering traffic according to a specified security policy. A diverse set of firewalls is being used today. As it is infeasible to examine and test each firewall for all possible potential problems, a taxonomy is needed to understand firewall vulnerabilities in the context of firewall operations. This paper describes a novel methodology for analyzing vulnerabilities in Internet firewalls. A firewall vulnerability is defined as an error made during firewall design, implementation, or configuration, that can be exploited to attack the trusted network that the firewall is supposed to protect. We examine firewall internals, and cross-reference each firewall operation with causes and effects of weaknesses in that operation, analyzing twenty reported problems with available firewalls. The result of our analysis is a set of matrices that illustrate the distribution of firewall vulnerability causes and effects over firewall operations. These matrices are useful in avoiding and detecting unforeseen problems during both firewall implementation and firewall testing. Two case studies of Firewall-1 and Raptor illustrate our methodology.


title: Ten-Year Results for Brånemark Implants Immediately Loaded With Fixed Prostheses at Implant Placement

abstract: This investigation was initiated to develop a method to provide patients with a fixed provisional prosthesis placed at the time of implant placement. Sixty-three standard 3.75-mm Nobel Biocare implants of varying lengths were placed into mandibular sites in 10 patients and followed for up to 10 years. Twenty-eight implants were immediately loaded at implant placement, providing support for fixed provisional prostheses, while 35 adjacent implants were allowed to heal submerged and stress-free. Following a 3-month healing period, the submerged implants were exposed and definitive reconstruction was accomplished. All 10 prostheses supported by 28 implants placed into immediate function at the time of implant placement were successful during the 3-month healing period. Of these 28 implants placed into immediate function, 4 ultimately failed. Of the 35 submerged implants, all are osseointegrated and in function to date. Life-table analysis demonstrates an overall 10-year survival rate of 93.4% for all implants. The 10-year life-table analysis of survival is 84.7% for immediately loaded implants and 100% for submerged implants. Statistical analysis of the submerged versus immediately loaded implants demonstrates failure rates for immediately loaded implants to be significantly higher (P = .022 by the log rank test). These data demonstrate that although mandibular implants can be successfully placed into immediate function in the short term to support fixed provisional prostheses, long-term prognosis is guarded for those implants placed into immediate function distal to the incisor region.


title: ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features

abstract: The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits. However, each ssEM plane contains densely packed structures that vary from one section to the next, which makes matching features across images a challenge. Advances in deep learning has resulted in unprecedented performance in similar computer vision problems, but to our knowledge, they have not been successfully applied to ssEM image co-registration. In this paper, we introduce a novel deep network model that combines a spatial transformer for image deformation and a convolutional autoencoder for unsupervised feature learning for robust ssEM image alignment. This results in improved accuracy and robustness while requiring substantially less user intervention than conventional methods. We evaluate our method by comparing registration quality across several datasets.


title: Modeling Multimodal Expression of User’s Affective Subjective Experience

abstract: With the growing importance of information technology in our everyday life, new types of applications are appearing that require the understanding of information in a broad sense. Information that includes affective and subjective content plays a major role not only in an individual’s cognitive processes but also in an individual’s interaction with others. We identify three key points to be considered when developing systems that capture affective information: embodiment (experiencing physical reality), dynamics (mapping experience and emotional state with its label) and adaptive interaction (conveying emotive response, responding to a recognized emotional state). We present two computational systems that implement those principles: MOUE (Model Of User Emotions) is an emotion recognition system that recognizes the user’s emotion from his/her facial expressions, and from it, adaptively builds semantic definitions of emotion concepts using the user’s feedback; MIKE (Multimedia Interactive Environment for Kansei communication) is an interactive adaptive system that, along with the user, co-evolves a language for communicating over subjective impressions.


title: Kinect depth sensor for computer vision applications in autonomous vehicles

abstract: Unmanned vehicles (Smart cars, drones, robots) need to understand and respond to the surrounding environment in order to perform their tasks. Therefore, they must be equipped with vision capabilities (vision sensors) to let them to detect the presence of objects in their paths or measure how far away objects are from the sensor depending of the type of application. Several technologies have been used to deal with this computer vision application such stereo vision, LIDAR and RADAR but each of them still has advantages and disadvantages in term of limitations and price. In this paper, we developed one of the functions of unmanned vehicles to detect the presence of objects and measure how far they are using Kinect depth sensor, a low-cost range sensor along with its higher depth fidelity and attractive alternative in computer vision. The results showed that Kinect sensor can detect along with segmentation techniques the presence of objects in its field of view and measure how far they are from the sensor. Based on results, Kinect can be mounted on unmanned vehicles like a vision sensor for obstacle avoidance application or other applications which require a vision sensor to measure how far detected objects are from it as well for also manned vehicles to alert drivers but mostly computer vision has a crucial role in vehicles without human intervention.


title: Validating the Quality of Crowdsourced Psychometric Personality Test Items.

abstract: Personality questionnaire items are normally written by experts in personality theory or psychometrics. This makes personality test development expensive, especially since as many as 40% of the items are discarded after quantitative analysis. Could the crowd create personality items that are as good as the ones developed by professionals? We analyze the psychometric quality of 164 Big Five personality items generated by the crowd (n=82) and show that non-expert item writers are capable of designing high quality personality items. The analysis revealed that 59% of the crowdsourced items had adequate psychometric properties, and thus could be used as an alternative instrument to measure Big Five personality traits. We conclude that crowdsourcing personality items is a possible strategy to generate new items rapidly. We also discuss some of the challenges of crowdsourcing general psychological items.


title: Replay Attacks on Zero Round-Trip Time: The Case of the TLS 1.3 Handshake Candidates

abstract: We investigate security of key exchange protocols supporting so-called zero round-trip time (0-RTT), enabling a client to establish a fresh provisional key without interaction, based only on cryptographic material obtained in previous connections. This key can then be already used to protect early application data, transmitted to the server before both parties interact further to switch to fully secure keys. Two recent prominent examples supporting such 0-RTT modes are Google's QUIC protocol and the latest drafts for the upcoming TLS version 1.3. We are especially interested in the question how replay attacks, enabled through the lack of contribution from the server, affect security in the 0-RTT case. Whereas the first proposal of QUIC uses state on the server side to thwart such attacks, the latest version of QUIC and TLS 1.3 rather accept them as inevitable. We analyze what this means for the key secrecy of both the preshared-key-based 0-RTT handshake in draft-14 of TLS 1.3 as well as the Diffie-Hellman-based 0-RTT handshake in TLS 1.3 draft-12. As part of this we extend previous security models to capture such cases, also shedding light on the limitations and options for 0-RTT security under replay attacks.


title: ExpNet: Landmark-Free, Deep, 3D Facial Expressions

abstract: We describe a deep learning based method for estimating 3D facial expression coefficients. Unlike previous work, our process does not relay on facial landmark detection methods as a proxy step. Recent methods have shown that a CNN can be trained to regress accurate and discriminative 3D morphable model (3DMM) representations, directly from image intensities. By foregoing facial landmark detection, these methods were able to estimate shapes for occluded faces appearing in unprecedented in-the-wild viewing conditions. We build on those methods by showing that facial expressions can also be estimated by a robust, deep, landmark-free approach. Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients. We propose a unique method for collecting data to train this network, leveraging on the robustness of deep networks to training label noise. We further offer a novel means of evaluating the accuracy of estimated expression coefficients: by measuring how well they capture facial emotions on the CK+ and EmotiW-17 emotion recognition benchmarks. We show that our ExpNet produces expression coefficients which better discriminate between facial emotions than those obtained using state of the art, facial landmark detection techniques. Moreover, this advantage grows as image scales drop, demonstrating that our ExpNet is more robust to scale changes than landmark detection methods. Finally, at the same level of accuracy, our ExpNet is orders of magnitude faster than its alternatives.


title: Barista - a Graphical Tool for Designing and Training Deep Neural Networks

abstract: In recent years, the importance of deep learning has significantly increased in pattern recognition, computer vision, and artificial intelligence research, as well as in industry. However, despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs). In this paper, we introduce Barista, an open-source graphical high-level interface for the Caffe deep learning framework. While Caffe is one of the most popular frameworks for training DNNs, editing prototext files in order to specify the net architecture and hyper parameters can become a cumbersome and error-prone task. Instead, Barista offers a fully graphical user interface with a graph-based net topology editor and provides an end-to-end training facility for DNNs, which allows researchers to focus on solving their problems without having to write code, edit text files, or manually parse logged data.


title: Increase Physical Fitness and Create Health Awareness through Exergames and Gamification - The Role of Individual Factors, Motivation and Acceptance.

abstract: Demographic change and the aging population push health and welfare system to its limits. Increased physical fitness and increased awareness for health issues will help elderly to live independently for longer and will thereby reduce the costs in the health care system. Exergames seem to be a promising solution for promoting physical fitness. Still, there is little evidence under what conditions Exergames will be accepted and used by elderly. To investigate promoting and hindering factors we conducted a user study with a prototype of an Exergame. We contrasted young vs. elderly players and investigated the role of gamer types, personality factors and technical expertise on the performance within the game and changes in the attitude towards individual health after the game. Surprisingly, performance within the game is not affected by performance motivation but by gamer type. More importantly, a universal positive effect on perceived pain is detected after the Exergame intervention.


Determinants of Repurchase Intention in Online Shopping : a Turkish Consumer ’ s Perspective


title: Negative Cognitive Styles and Stress-Reactive Rumination Interact to Predict Depression: A Prospective Study

abstract: Research on cognitive theories of depression has identified negative cognitive styles and rumination in response to depressed mood as risk factors for depressive episodes. In addition, a general self-focusing style has been suggested to increase vulnerability to depression. The present study used a behavioral high-risk paradigm to test whether the interaction of negative cognitive styles and rumination predicted the prospective onset, number, and duration of depressive episodes in a sample of 148 initially nondepressed undergraduates over a 2.5-year follow-up. In addition, rumination was assessed specifically as the tendency to focus on maladaptive self-referential thoughts following stressful events (stress-reactive rumination; SRR). The principal hypotheses tested were (1) the interaction of negative cognitive styles and SRR increases risk for developing depressive episodes as well as longer duration depressive episodes; and (2) this interaction would not be obtained when a trait measure of general self-focus or a measure of rumination in response to depressed mood is used instead of the measure of SRR. After controlling for subsyndromal depressive symptoms and the main effects of negative cognitive styles and SRR, the interaction of negative cognitive styles and SRR was found to predict the prospective onset, number, and duration of major depressive and hopelessness depressive episodes. These interactions were not obtained when other measures of trait self-focus and depressive rumination were used instead of SRR.


title: Domain adaptation for object recognition: An unsupervised approach

abstract: Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.


title: PupilNet: Convolutional Neural Networks for Robust Pupil Detection

abstract: Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking. However, automated pupil detection in real-world scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non centered and off-axis eye recording, and physiological eye characteristics. In this paper, we propose and evaluate a method based on a novel dual convolutional neural network pipeline. In its first stage the pipeline performs coarse pupil position identification using a convolutional neural network and subregions from a downscaled input image to decrease computational costs. Using subregions derived from a small window around the initial pupil position estimate, the second pipeline stage employs another convolutional neural network to refine this position, resulting in an increased pupil detection rate up to 25% in comparison with the best performing state-of-the-art algorithm. Annotated data sets can be made available upon request.


title: Connected arrays of dipoles for broad band, wide angle scanning, dual polarized applications: A novel solution to the common mode problem

abstract: Wideband connected arrays of dipoles are a very promising solution for broadband, wide-angle scanning phased arrays. Unlike tapered slot antennas, connected arrays can operate over wide bandwidth maintaining polarization purity. However, an open issue for the practical implementation of these arrays is the propagation of common-mode currents along the differential feeding lines. A solution for this problem is here proposed and is constituted by a loop-shaped transformer that acts as common-mode rejection circuit. Simulated results are presented for connected arrays of dipoles for both linear and dual polarization, with 30% relative bandwidth (from 10.7 to 14.5 GHz) and scanning capability up to 45° for any azimuth.


title: Latent Fingerprint Matching: A Survey

abstract: Latent fingerprint has been used as evidence in the court of law for over 100 years. However, even today, a completely automated latent fingerprint system has not been achieved. Researchers have identified several important challenges in latent fingerprint recognition: 1) low information content; 2) presence of background noise and nonlinear ridge distortion; 3) need for an established scientific procedure for matching latent fingerprints; and 4) lack of publicly available latent fingerprint databases. The process of automatic latent fingerprint matching is divided into five definite stages, and this paper discusses the existing algorithms, limitations, and future research directions in each of the stages.


title: Copy-move image forgery detection based on SIFT descriptors and SVD-matching

abstract: In recent years, more data are created in digital form allowing easy control over storage and manipulation due to the technology progress. Unfortunately, this progress may have dragged along a lot of risks, especially the ones related to the security of digital files. In particular, digital forgery becomes a worry for many organizations because it has become easier to create fake images without leaving any obvious perceptual traces of tampering. A specific form of image forgery operation called “copy-move” is considered one of the most difficult problems in the case of forgery detection. For this case, a part of the image is copied and pasted on another location of the same image to conceal undesirable objects in the scene. In this paper, we propose a method that automatically detects duplicated regions in the same image. Duplicated detection is performed by identifying the local characteristics of the images (points of interest) using the Scale Invariant Feature Transform (SIFT) method and by matching between identical features using the Singular Value Decomposition (SVD) method. Obtained results show that our proposed hybrid method is robust to geometrical transformations and is able to detect with high performance duplicated regions.


title: Animating articulated characters using wiggly splines

abstract: We propose a new framework for spacetime optimization that can generate artistic motion with a long planning horizon for complex virtual characters. The scheme can be used for generating general types of motion and neither requires motion capture data nor an initial motion that satisfies the constraints. Our modeling of the spacetime optimization combines linearized dynamics and a novel warping scheme for articulated characters. We show that the optimal motions can be described using a combination of vibration modes, wiggly splines, and our warping scheme. This enables us to restrict the optimization to low-dimensional spaces of explicitly parametrized motions. Thereby the computation of an optimal motion is reduced to a low-dimensional non-linear least squares problem, which can be solved with standard solvers. We show examples of motions created by specifying only a few constraints for positions and velocities.


title: Quantifying Wikipedia Usage Patterns Before Stock Market Moves

abstract: Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making.


title: Bandwidth improvement of band-notched printed dipole antenna for DTV signal reception

abstract: This paper presents the design and experiment of wideband using band-notched printed dipole antenna operates covering frequency band for digital television (DTV) signal reception in the 470-862 MHz. Besides this proposed antenna is required to be compact, light-weight, and easy to fabricate. The antenna is of rectangular shape of width (W) 35 mm and length (L) 223 mm, and comprises two asymmetric radiating portions of left-arm and right-arm, which are separated by step-shaped feed gap with its one open end at the center of antenna's one long side edge and the other open end at about L/4 away from the center of the opposite long side edge. The results of the return loss (S11), gain and radiation patterns of the proposed antenna have been simulated by using antenna analysis software CST Microwave Studio program. It is found that the proposed antenna is accessible to bandwidth about 62.77% at frequency range 470-900 MHz. Measured maximum gains were approximately 2.43 dBi, 2.53 dBi and 3.69 dBi for 514 MHz, 650 MHz and 786 MHz band, respectively. Finally, simulation and measurement results for the design example are presented and a conclusion follows.


title: Coloring-based coalescing for graph coloring register allocation

abstract: Graph coloring register allocation tries to minimize the total cost of spilled live ranges of variables. Live-range splitting and coalescing are often performed before the coloring to further reduce the total cost. Coalescing of split live ranges, called sub-ranges, can decrease the total cost by lowering the interference degrees of their common interference neighbors. However, it can also increase the total cost because the coalesced sub-ranges can become uncolorable. In this paper, we propose coloring-based coalescing, which first performs trial coloring and next coalesces all copyrelated sub-ranges that were assigned the same color. The coalesced graph is then colored again with the graph coloring register allocation. The rationale is that coalescing of differently colored sub-ranges could result in spilling because there are some interference neighbors that prevent them from being assigned the same color. Experiments on Java programs show that the combination of live-range splitting and coloring-based coalescing reduces the static spill cost by more than 6% on average, comparing to the baseline coloring without splitting. In contrast, well-known iterated and optimistic coalescing algorithms, when combined with splitting, increase the cost by more than 20%. Coloring-based coalescing improves the execution time by up to 15% and 3% on average, while the existing algorithms improve by up to 12% and 1% on average.


title: Modeling Relational Information in Question-Answer Pairs with Convolutional Neural Networks

abstract: In this paper, we propose convolutional neural networks for learning an optimal representation of question and answer sentences. Their main aspect is the use of relational information given by the matches between words from the two members of the pair. The matches are encoded as embeddings with additional parameters (dimensions), which are tuned by the network. These allows for better capturing interactions between questions and answers, resulting in a significant boost in accuracy. We test our models on two widely used answer sentence selection benchmarks. The results clearly show the effectiveness of our relational information, which allows our relatively simple network to approach the state of the art.


title: Single-Phase Modified Quasi-Z-Source Cascaded Hybrid Five-Level Inverter

abstract: This paper proposes the combination of a novel modified quasi-Z-source (MqZS) inverter with a single-phase symmetrical hybrid three-level inverter in order to boost the inverter three-level output voltage. The proposed single-phase MqZS hybrid three-level inverter provides a higher boost ability and reduces the number of inductors in the source impedance, compared with both the single-phase three-level neural-point clamped quasi-Z-source inverter and the single-phase quasi-Z-source cascaded multilevel inverter. Additionally, it can be extended to obtain the nine-level output voltage by cascading two three-level pulse width modulation switching cells with a separate MqZS and a dc source, which herein is called a single-phase MqZS cascaded hybrid five-level inverter (MqZS-CHI). A modified modulation technique based on an alternative phase opposition disposition scheme is suggested to effectively control the shoot-through state for boosting the dc-link voltage and balancing the two series capacitor voltages of the MqZS. The performances of both the proposed MqZS-CHI and the modulation techniques are verified through simulation and experimental results.


title: Use of Triple-Convergence Polypropylene Thread™ for the Aesthetic Correction of Partial Facial Paralysis

abstract: Rehabilitation and reanimation of the paralyzed face remains a challenge. A variety of autografts and allografts have been used for static facial suspension. We report two cases of long-standing partial facial paralysis treated with 3C triple-convergence polypropylene thread™. A 39-year-old woman with right-sided partial facial paralysis underwent an endoscopy-assisted facial suspension using the 3C threads and a 60-year-old woman with right-sided partial facial paralysis underwent a round face-lifting combined with endoscopic brow lift and placement of 3C triple-convergence polypropylene threads. Its use for partial facial paralysis has not been previously described. The 1-year follow-up shows effective preservation of the surgical result and patient satisfaction.


title: Recent developments and highlights in biomarkers in allergic diseases and asthma

abstract: The potential of precision medicine in allergy and asthma has only started to be explored. A significant clarification in the pathophysiology of rhinitis, chronic rhinosinusitis, asthma, food allergy and drug hypersensitivity was made in the last decade. This improved understanding led to a better classification of the distinct phenotypes and to the discovery of new drugs such as biologicals, targeting phenotype-specific mechanisms. Nevertheless, many conditions remain poorly understood such as non-eosinophilic airway diseases or non-IgE-mediated food allergy. Moreover, there is a need to predict the response to specific therapies and the outcome of drug and food provocations. The identification of patients at risk of progression towards severity is also an unmet need in order to establish adequate preventive or therapeutic measures. The implementation of precision medicine in the clinical practice requires the identification of phenotype-specific markers measurable in biological matrices. To become useful, these biomarkers need to be quantifiable by reliable systems, and in samples obtained in an easy, rapid and cost-efficient way. In the last years, significant research resources have been put in the identification of valid biomarkers for asthma and allergic diseases. This review summarizes these recent advances with focus on the biomarkers with higher clinical applicability.


title: Bayesian inference with Stan: A tutorial on adding custom distributions

abstract: When evaluating cognitive models based on fits to observed data (or, really, any model that has free parameters), parameter estimation is critically important. Traditional techniques like hill climbing by minimizing or maximizing a fit statistic often result in point estimates. Bayesian approaches instead estimate parameters as posterior probability distributions, and thus naturally account for the uncertainty associated with parameter estimation; Bayesian approaches also offer powerful and principled methods for model comparison. Although software applications such as WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, Statistics and Computing, 10, 325–337, 2000) and JAGS (Plummer, 2003) provide “turnkey”-style packages for Bayesian inference, they can be inefficient when dealing with models whose parameters are correlated, which is often the case for cognitive models, and they can impose significant technical barriers to adding custom distributions, which is often necessary when implementing cognitive models within a Bayesian framework. A recently developed software package called Stan (Stan Development Team, 2015) can solve both problems, as well as provide a turnkey solution to Bayesian inference. We present a tutorial on how to use Stan and how to add custom distributions to it, with an example using the linear ballistic accumulator model (Brown & Heathcote, Cognitive Psychology, 57, 153–178. doi: 10.1016/j.cogpsych.2007.12.002, 2008).


title: Cooperative lateral control for automatic valet parking

abstract: This paper presents the cooperative lateral control algorithm for automatic valet parking (AVP). While a position measurement system such as DGPS is in general used for autonomous driving on highway, the position information is provided for AVP via vehicle to infrastructure (V2I) communication. Both noise and time delay for position measurement via V2I is considered. Therefore, it is necessary for the lateral controller to be robust enough to compensate for the noise and time delay. With the assumptions of low speed and no sideslip angle, a nonlinear kinematic model is used for lateral control. Based on the nonlinear model, a nonlinear control technique called dynamic surface control (DSC) is applied to design the lateral controller. Furthermore, the preview and nonlinear damping ideas are incorporated with DSC to redesign the lateral controller. Finally the proposed lateral control algorithm is validated through simulations.


title: & Research Article A Contextual Theory of Learning and the Learning Organization

abstract: Learning and accumulation of new knowledge in an organization always require two transformation processes: one transformation process from data to information and another from information to (new) knowledge. This is so because only information, and not knowledge, can be shared and spread among the members of the organization. This article describes these transformations processes as social processes that take place in a concrete context. The processes lead from Data→Information→Knowledge→Action→Learning→New Knowledge. But not all these processes have the same progression or produce the same kind of results. One can differentiate between single-loop, double-loop and triple-loop learning. ::: ::: These findings are analysed for the consequences they provide for the learning individual and the learning organization. The qualitative difference between the learning organization and other organizations is shown to be the coordination and cooperation that the individuals perform in a close working relationship. Against this background achievements and shortcomings of attempts to become a learning organization are summarized. Copyright © 2005 John Wiley & Sons, Ltd.


title: RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning

abstract: In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.


title: Virtuously Safe Reinforcement Learning

abstract: We show that when a third party, the adversary, steps into the two-party setting (agent and operator) of safely interruptible reinforcement learning, a trade-off has to be made between the probability of following the optimal policy in the limit, and the probability of escaping a dangerous situation created by the adversary. So far, the work on safely interruptible agents has assumed a perfect perception of the agent about its environment (no adversary), and therefore implicitly set the second probability to zero, by explicitly seeking a value of one for the first probability. We show that (1) agents can be made both interruptible and adversary-resilient, and (2) the interruptibility can be made safe in the sense that the agent itself will not seek to avoid it. We also solve the problem that arises when the agent does not go completely greedy, i.e. issues with safe exploration in the limit. Resilience to perturbed perception, safe exploration in the limit, and safe interruptibility are the three pillars of what we call \emph{virtuously safe reinforcement learning}.


title: Differential regional gray matter volumes in patients with on-line game addiction and professional gamers

abstract: Patients with on-line game addiction (POGA) and professional video game players play video games for extended periods of time, but experience very different consequences for their on-line game play. Brain regions consisting of anterior cingulate, thalamus and occpito-temporal areas may increase the likelihood of becoming a pro-gamer or POGA. Twenty POGA, seventeen pro-gamers, and eighteen healthy comparison subjects (HC) were recruited. All magnetic resonance imaging (MRI) was performed on a 1.5 Tesla Espree MRI scanner (SIEMENS, Erlangen, Germany). Voxel-wise comparisons of gray matter volume were performed between the groups using the two-sample t-test with statistical parametric mapping (SPM5). Compared to HC, the POGA group showed increased impulsiveness and perseverative errors, and volume in left thalamus gray matter, but decreased gray matter volume in both inferior temporal gyri, right middle occipital gyrus, and left inferior occipital gyrus, compared with HC. Pro-gamers showed increased gray matter volume in left cingulate gyrus, but decreased gray matter volume in left middle occipital gyrus and right inferior temporal gyrus compared with HC. Additionally, the pro-gamer group showed increased gray matter volume in left cingulate gyrus and decreased left thalamus gray matter volume compared with the POGA group. The current study suggests that increased gray matter volumes of the left cingulate gyrus in pro-gamers and of the left thalamus in POGA may contribute to the different clinical characteristics of pro-gamers and POGA.


title: A Novel Primary-Side Controlled Universal-Input AC–DC LED Driver Based on a Source-Driving Control Scheme

abstract: A novel primary-side controlled universal-input ac–dc LED driver based on the source-driving control scheme is proposed in this paper, which employs low-voltage control mosfet $M_{2}$ to drive high-voltage power mosfet $M_{1}$ without an auxiliary winding commonly used in the conventional primary-side controlled scheme. The proposed control IC adopts minimum voltage detection circuit to monitor the zero crossing information of secondary winding current. The demagnetization time signal is generated by demagnetization time detection circuit. In addition, the ratio between the secondary winding demagnetization time $T_{{\rm Demag}}$ and switching period $T_{S}$ is maintained constant by adopting the intelligent charging and discharging circuit, finally achieving high-precision constant output current. A control IC for the proposed LED driver has been fabricated in TSMC 0.35 μm 5 V/600 V CMOS/LDMOS process. Experimental results of a 3-W circuit prototype show that the constant current precision is within ±1% in a wide range of universal-input ac voltage from 85 to 264 V, and that above 80% efficiency is obtained when driving three 1-W LEDs. The start-up time is only 46 ms under 90 Vac and 60 Hz-input, and the standby power is tested to be lower than 142 mW under 220 Vac and 50 Hz-input.


title: Pharmacogenomics variation in drug metabolizing enzymes and transporters in relation to docetaxel toxicity in Lebanese breast cancer patients: paving the way for OMICs in low and middle income countries.

abstract: We investigated the association of genetic polymorphisms in drug metabolizing enzymes (DMEs) and transporters in patients with docetaxel-induced febrile neutropenia, by a new high-throughput DMEs and transporters (DMETPlus) microarray platform, characterizing 1936 single nucleotide polymorphisms (SNPs) in 225 genes. We recruited 100 Lebanese breast cancer patients from a consecutive cohort of 277 patients who received docetaxel either alone, or in combination with trastuzumab. Out of 100 patients, 18 had developed febrile neutropenia (cases). They were age- and treatment- matched with 18 patients who did not develop febrile neutropenia on docetaxel (controls). We found that 12 SNPs in seven genes (ABCC6, ABCG1, ABCG2, CYP1A2, CYP2D6, FMO2, and FMO3) were significantly associated with febrile neutropenia after docetaxel treatment. Many of these SNPs have not been previously reported to be associated with toxicity due to docetaxel treatment. Interestingly, one SNP in the FMO3 gene (rs909530) was significantly associated with three clinical endpoints: febrile neutropenia, reduced absolute neutrophil count, and hemoglobin reduction. To the best of our knowledge, this is the first study that evaluated the effect of a large array of nearly 2000 polymorphisms in DMEs and transporters on docetaxel toxicity in breast cancer patients, and in a previously understudied population. Additionally, it attests to the feasibility of genomics research in low- and middle-income countries (LMICs). In light of the current global epidemic of noncommunicable diseases (NCDs) such as breast cancer impacting LMICs, we suggest pharmacogenomics is considered as an integral part of the global health research agenda for NCDs and personalized therapeutics.


title: Strategies for Vietnamese keyword search

abstract: We propose strategies for a state-of-the-art Vietnamese keyword search (KWS) system developed at the Institute for Infocomm Research (I 2 R). The KWS system exploits acoustic features characterizing creaky voice quality peculiar to lexical tones in Vietnamese, a minimal-resource transliteration framework to alleviate out-ofvocabulary issues from foreign loan words, and a proposed system combination scheme FusionX. We show that the proposed creaky voice quality features complement pitch-related features, reaching fusion gains of 17.7% relative (6.9% absolute). To the best of our knowledge, the proposed transliteration framework is the first reported rule-based system for Vietnamese; it outperforms statisticalapproach baselines up to 14.93 - 36.73% relative on foreign loan word search tasks. Using FusionX to combine 3 sub-systems, the actual term-weighted value (ATWV) reaches 0.4742, exceeding the ATWV=0.3 benchmark for IARPA Babel participants in the NIST OpenKWS13 Evaluation.


title: Antimycin A-induced apoptosis of HL-60 cells.

abstract: Background ::: ::: Previous experiments in our laboratory investigating apoptosis induced in HL-60 cells by camptothecin (CAM) have revealed that the sequence and rapidity of the apoptotic phenomena in an individual cell depend on the proliferative state of that cell when it encounters CAM. The role of mitochondria in HL-60 apoptosis was explored using an inhibitor of oxidative phosphorylation, antimycin A (AMA). ::: ::: ::: ::: Methods ::: ::: Changes in cell light scatter, binding of annexin V-fluorescein isothiocyanate (FITC), uptake of propidium iodide (PI), and DNA content after membrane fixation/permeabilization were monitored by flow cytometry. Z-VAD-FMK was used to inhibit caspases. Fluorescence microscopy was used to examine cell morphology. ::: ::: ::: ::: Results ::: ::: Cells in the G1 phase of the cell cycle were the first to exhibit signs of apoptosis in response to 100 μM AMA and some of these cells disintegrated without exposing to phosphatidylserine (PS). Caspase inhibition prevented fragmentation of DNA, the nucleus, and the cell, but only delayed PS exposure and loss of plasma membrane integrity. ::: ::: ::: ::: Conclusions ::: ::: The highly active mitochondria of G1-phase HL-60 cells make them particularly sensitive to AMA. PS exposure and plasma membrane damage are mediated by noncaspase molecules released from mitochondria. We hypothesize that if mitochondria are subjected to a sufficiently severe insult, whether indirectly as a result of extensive CAM-induced DNA damage or directly by the effect of AMA on electron transport, the nature and quantities of the proapoptotic molecules released are such that apoptosis proceeds to the point of cell disintegration before the PS exposure pathway is complete. Cytometry 49:106–112, 2002. © 2002 Wiley-Liss, Inc.


title: Model independent inference of the expansion history and implications for the growth of structure

abstract: Author(s): Joudaki, Shahab; Kaplinghat, Manoj; Keeley, Ryan; Kirkby, David | Abstract: We model the expansion history of the Universe as a Gaussian Process and find constraints on the dark energy density and its low-redshift evolution using distances inferred from the Luminous Red Galaxy (LRG) and Lyman-alpha (Ly$\alpha$) datasets of the Baryon Oscillation Spectroscopic Survey, supernova data from the Joint Light-curve Analysis (JLA) sample, Cosmic Microwave Background (CMB) data from the Planck satellite, and local measurement of the Hubble parameter from the Hubble Space Telescope ($\mathsf H0$). Our analysis shows that the CMB, LRG, Ly$\alpha$, and JLA data are consistent with each other and with a $\Lambda$CDM cosmology, but the ${\mathsf H0}$ data is inconsistent at moderate significance. Including the presence of dark radiation does not alleviate the ${\mathsf H0}$ tension in our analysis. While some of these results have been noted previously, the strength here lies in that we do not assume a particular cosmological model. We calculate the growth of the gravitational potential in General Relativity corresponding to these general expansion histories and show that they are well-approximated by $\Omega_{\rm m}^{0.55}$ given the current precision. We assess the prospects for upcoming surveys to measure deviations from $\Lambda$CDM using this model-independent approach.


title: Adult Learning Theory: Applications to Non-Traditional College Students

abstract: IntroductionAlexis graduated from high school but lacked the financial resources to go to college. Instead, she went to work in a local machine shop working on a punch press machine. During the recent recession, her employer went out of business and she lost her job. While she quickly mastered the repetitive skills involved in managing her press machine, this employment experience did not provide her with the critical thinking skills and particular analytical ability that would be required in the collegiate environment. A few weeks into her first semester, Alexis realized the learning skills that were effective in her former job may not be as effective in the academic environment. College was going to be a challenge and she was not sure even how to start to adapt.While Alexis is not a real student, she is a composite of many of the adult learners that enter today's higher education community. These new adult learners bring learning styles and life experiences that may either be critical foundations for future success or deeply entrenched beliefs that hinder learning in the academic environment. As adult learners enroll in their entry level courses, college instructors will need to realize that these adult learners differ from the traditional college student. Although these differences present challenges for educators, they also provide opportunities for educators to embrace the life experiences and wisdom that these adult learners bring to the collegiate community.While adult learners can be classified in many ways, this piece will focus on how best to understand and teach entry-level adult learners who are between the ages of 25 and 50, have a high school diploma or a GED, are financially independent, and have one semester or less of college-level coursework.Three main groups of students lead the charge in the growing number of adult learners entering college developmental education courses: (a) workers who have lost their jobs because of the recession of 2008 and who require developmental coursework to refresh their entry level collegiate skills, (b) veterans returning from Afghanistan and Iraq who delayed their education to serve in the armed forces (Katapos, 2009), and, (c) adults who have just completed their GED and are moving onto higher education classes ("GED classes," 2009). By understanding what makes adult learners different from traditional students, developmental educators can provide specific tools that help adult learners integrate into the college or university environment and increase their chances for success.Horn's (1996) ranking of students on a scale from minimally nontraditional to highly nontraditional recognizes the challenges facing adult learners, such as Alexis, who move into higher education. Older students (those more than 25 years) generally have at least four non-traditional factors: financial independence, full-time employment, dependents, and part-time enrollment. Therefore, many older students fall into Horn's highly nontraditional category, placing them at significant risk for not completing their degree (Lane, 2004).Andragogy and Adult Learning TheoryMuch adult learning theory comes from the organizational development (OD) field where the focus on learning theory is seen as a way of providing employees with the tools they needed to perform better in the workplace. In the 1950s and 1960s, OD practitioners created new learning models because traditional higher education pedagogical models did not translate well into the workplace training environment. OD practitioners coined the term andragogy to recognize the needs and features of this distinct learning population and to separate adult learning theory from traditional pedagogy (Knowles, 1974). Building upon theories from organizational development, Knowles identified four principles that characterize adult learners:a. They are self directed, take responsibility for their own actions, and resist having information arbitrarily imposed on them. …


title: Characterizing WiFi link performance in open outdoor networks

abstract: We present an experimental performance evaluation study of WiFi links in an open-space outdoor environment. We consider a large scale wireless sensor network scenario of seismic data collection from sensors that are buried in ground and a set of access points (APs) form the hierarchical aggregation layer and the backbone of the network. We conduct two different link characterization studies. First, we evaluate the links between the sensor nodes and a wireless AP using IEEE 802.11a/b/g. We construct the path loss model and investigate the reachability distance of this link for different protocols and different sensor node antenna heights. We then characterize the long distance wireless backhaul links between the APs. We use 802.11n and high gain directional antenna for high throughput and long distance. We evaluate how different PHY and MAC layer enhancements of 802.11n impacts its performance in an open outdoor environment. We observed up to 148 Mb/s throughput at 800 meter line-of-sight links without sophisticated tuning of antenna orientation. We believe our findings can be a benchmark for WiFi based outdoor network deployment, especially for high throughput long distance links.


title: Short communication Monte Carlo approaches for determining power and sample size in low-prevalence applications

abstract: The prevalence of disease in many populations is often low. For example, the prevalence of tuberculosis, brucellosis, and bovine spongiform encephalopathy range from 1 per 100,000 to less than 1 per 1,000,000 in many countries. When an outbreak occurs, epidemiological investigations often require comparing the prevalence in an exposed population with that of an unexposed population. To determine if the level of disease in the two populations is significantly different, the epidemiologist must consider the test to be used, desired power of the test, and determine the appropriate sample size for both the exposed and unexposed populations. Commonly available software packages provide estimates of the required sample sizes for this application. This study shows that these estimated sample sizes can exceed the necessary number of samples by more than 35% when the prevalence is low. We provide a Monte Carlo-based solution and show that in low-prevalence applications this approach can lead to reductions in the total samples size of more than 10,000 samples. Published by Elsevier B.V.


title: The Big-Data-Driven Intelligent Wireless Network: Architecture, Use Cases, Solutions, and Future Trends

abstract: The concept of using big data (BD) for wireless communication network optimization is no longer new. However, previous work has primarily focused on long-term policies in the network, such as network planning and management. Apart from this, the source of the data collected for analysis/model training is mostly limited to the core network (CN). In this article, we introduce a novel data-driven intelligent radio access network (RAN) architecture that is hierarchical and distributed and operates in real time. We also identify the required data and respective workflows that facilitate intelligent network optimizations. It is our strong belief that the wireless BD (WBD) and machine-learning/artificial-intelligence (AI)-based methodology applies to all layers of the communication system. To demonstrate the superior performance gains of our proposed methodology, two use cases are analyzed with system-level simulations; one is the neural-network-aided optimization for Transmission Control Protocol (TCP), and the other is prediction-based proactive mobility management.


title: Application and Simulation of SVPWM in three phase inverter

abstract: Comparing with commonly used sine-wave pulse-width modulation(SPWM) method, Space Vector Pulse Width Modulation (SVPWM) method has higher utilization rate of DC voltage and smaller output waveform distortion. This article introduced SVPWM control technology into three-phase grid-connected inverter, created a main circuit mathematical model of three-phase grid-connected inverter and detailed the implementation of SVPWM modulation method with Matlab software. Through the establishment of a complete closed-loop system to simulate and research, giving the simulation waveforms and output grid-connected current waveform. The simulation results showed the advantages of SVPWM technique are more suitable for high-power applications.


title: Towards understanding phishing victims' profile

abstract: Today it is known that the weakest link in the cyber security chain is the computer user. Social engineering attacks are commonly used to deceive computer users to perform actions that could leak private information. Such attacks psychologically manipulate the computer users to reveal his/her confidential information. Therefore, the computer user has been carefully studied by security researchers to understand the relationship between cyber security incidents and the victim background. In this paper, we present a breadth-first survey of recent studies that aim to understand the relationship between victims' backgrounds and phishing attacks. We summarize the characteristics of the phishing victims, following a review of their demographic and personality traits.


title: Purification and Some Properties of GTP Cyclohydrolase I from Spinach Leaves

abstract: GTP cyclohydrolase I (EC 3.5.4.16) has been purified for the first time from a higher plant, spinach leaves. The purified preparation appeared to be homogeneous on polyacrylamide gel electrophoresis. The molecular weight of this enzyme was estimated at 135,000 by gel filtration and the subunit molecular weight was estimated at 35,000 by SDS-PAGE. The latter method also suggested that this enzyme was composed of four identical subunits. The enzyme was stable to heat treatment at 50°C for 10min, and the activity was maintained for at least six months when stored at −30°C. The enzyme had an optimum pH of around 8.0 in Tris buffer. The Hill coefficient of the enzyme was calculated to be 2.2. The pI of the enzyme was measured as 5.1 by chromatofocusing.


title: The Matching Analysis of Structure Construction for Enterprises' Technology Innovation Network

abstract: The technological innovation network is a new organization form of technological innovation activity. As the external innovative enterprises join into network, and the relationships among the inside enterprises increase and reproduce, the structure of the technological innovation network will evolve. On the basis of the one-to-one matching model of Gale and Shapley, this paper discusses the evolution of the structure in the process of the technological innovation network formation, develops the one-to-more network matching model, and gives the corresponding algorithm. Through the example research, discussed the technical characteristics of the innovation network structure, pointed out that technological innovation network matching process will lead to the emergence of the core node, will cause the network structure to carry on the evolution to the centralized direction.


title: Salient region detection for stereoscopic images

abstract: In this paper, we propose an effective saliency model, which combines region-level depth, color and spatial information, to detect salient regions in stereoscopic images. Based on region segmentation results of stereoscopic images, depth contrast, depth weighted color contrast, and spatial compactness of color distribution are measured for each region, and combined to generate the region-level saliency map. Experimental results on a public stereoscopic image dataset with ground truths of salient objects demonstrate that the proposed saliency model outperforms the state-of-the-art saliency models.


Applications of magnetic nanoparticles in biomedicine


title: Minimum-Cost Cloud Storage Service Across Multiple Cloud Providers

abstract: Many Cloud Service Providers (CSPs) provide data storage services with datacenters distributed worldwide. These datacenters provide different Get/Put latencies and unit prices for resource utilization and reservation. Thus, when selecting different CSPs' datacenters, cloud customers of globally distributed applications (e.g., online social networks) face two challenges: i) how to allocate data to worldwide datacenters to satisfy application SLO (service level objective) requirements including both data retrieval latency and availability, and ii) how to allocate data and reserve resources in datacenters belonging to different CSPs to minimize the payment cost. To handle these challenges, we first model the cost minimization problem under SLO constraints using integer programming. Due to its NP-hardness, we then introduce our heuristic solution, including a dominant-cost based data allocation algorithm and an optimal resource reservation algorithm. We finally introduce an infrastructure to enable the conduction of the algorithms. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the effectiveness of our algorithms for SLO guaranteed services and customer cost minimization.


title: Memory self-appraisal and depressive symptoms in people at genetic risk for Alzheimer's disease.

abstract: Objectives ::: ::: A previous study found that subjective memory loss in middle-aged and older persons is associated with the major genetic risk for Alzheimer's disease, the apolipoprotein E-4 (APOE-4) allele. No previous study has focused on subjective memory complaints and depressive symptoms in the same subject population at genetic risk for Alzheimer's disease. ::: ::: ::: ::: Method ::: ::: Sixty-six persons (mean age = 64 years, range = 43 to 82 years) without major depression or dementia but with mild age-related memory complaints were rated for severity of depressive symptoms, using the Hamilton Depression Rating Scale, and assessed for the presence of the APOE-4 allele. Severity of subjective memory loss was assessed using the Memory Functioning Questionnaire, which measures four memory domains: frequency of forgetting, seriousness of forgetting, retrospective functioning, and mnemonics usage. ::: ::: ::: ::: Results ::: ::: Depressive symptoms were significantly associated with subjective memory loss in subjects without the APOE-4 allele, for retrospective functioning (perceived change in memory) and mnemonics usage, but not in APOE-4 carriers. The same significant associations were found when the analysis was limited to the 44 subjects in the mid-age range (55–74 years), wherein APOE-4 confers its greatest effects on risk for Alzheimer's disease. ::: ::: ::: ::: Conclusion ::: ::: These results confirm that mild depressive symptoms are related to subjective memory loss, but for some forms of memory complaint, the relationship holds true only for people without the major known genetic risk for Alzheimer's disease. Copyright © 2001 John Wiley & Sons, Ltd.


title: A novel shape octagonal koch fractal MIMO antenna for WLAN and WiMAX application

abstract: In this paper, a tri band compact octagonal fractal monopole MIMO antenna is presented. The proposed antenna is microstrip line fed and its structure is based on fractal geometry where the resonance frequency of antenna is lowered by applying iteration techniques. The simulated bandwidth of the antenna are 2.3706GHz to 2.45GHz, 3.398GHz to 3.677GHz and 4.9352GHz to 5.8988GHz (S11 < −10 dB), covering the bands of WLAN and WiMAX. The characteristics of small size, nearly omnidirectional radiation pattern and moderate gain make the proposed MIMO antenna entirely applicable to WLAN and WiMAX applications. The proposed antenna has compact size of 50 mm × 50 mm. Details of the proposed antenna design and performance are presented and discussed.


title: Real-Time Painterly Rendering of Terrains

abstract: We present a non-photo realistic, real-time painterly rendering technique for terrains. The painterly appearance and the impression of terrains is created by effectively rendering several brush strokes. The strokes have fixed locations on the surfaces of the terrain during animation to enable frame to frame coherency. The strokes are rendered as alpha blended sprites in two-dimensions and are oriented along the slope of terrain analogous to the way artists paint on canvas. By exploiting the regular nature of terrain data, we create pre-decided rendering depth orders for primitives for any camera orientation. With this, we avoid the necessity of sorting the primitives of sprites required for alpha blending. We use DirectX10/SM4.0 based shaders to render strokes to improve performance. Being distributed on terrain, strokes get cluttered when they are closely located on screen. We follow a level of detail scheme that maintains a uniform stroke density in screen space. Various styles can be achieved with different stroke variations. Phong shading the rendered output in real-time is possible for more varied styles. We achieve painterly rendering in real-time with a combination of object space positioning and image space rendering of strokes. We illustrate our method with images and performance results.


title: IPARK: Location-Aware-Based Intelligent Parking Guidance over Infrastructureless VANETs

abstract: It has been pointed out that about 30% of the traffic congestion is caused by vehicles cruising around their destination and looking for a place to park. Therefore, addressing the problems associated with parking in crowded urban areas is of great significance. One effective solution is providing guidance for the vehicles to be parked according to the occupancy status of each parking lot. However, the existing parking guidance schemes mainly rely on deploying sensors or RSUs in the parking lot, which would incur substantial capital overhead. To reduce the aforementioned cost, we propose IPARK, which taps into the unused resources (e.g., wireless device, rechargeable battery, and storage capability) offered by parked vehicles to perform parking guidance. In IPARK, the cluster formed by parked vehicles generates the parking lot map automatically, monitors the occupancy status of each parking space in real time, and provides assistance for vehicles searching for parking spaces. We propose an efficient architecture for IPARK and investigate the challenging issues in realizing parking guidance over this architecture. Finally, we investigate IPARK through realistic experiments and simulation. The numerical results obtained verify that our scheme achieves effective parking guidance in VANETs.


title: Per panel photovoltaic energy extraction with multilevel output DC-DC switched capacitor converters

abstract: Switched capacitor multilevel output DC-DC converters are evaluated as panel integrated modules in a solar maximum power point tracking system. The recommended system includes a central input current-controlled ripple port inverter. Potential benefits include per panel MPPT without inter-panel communication, electrolytic capacitors or per panel magnetics. Statistical methods are used to predict average tracking and conversion efficiencies. A particular implementation of the switched capacitor module is studied — the Marx converter. Average total efficiencies (tracking × conversion) greater than 93% are predicted for a simulated 510 W, 3 panel, DC-DC system.


title: The Use of Acceptance and Commitment Therapy to Prevent the Rehospitalization of Psychotic Patients: A Randomized Controlled Trial

abstract: The present study examined the impact of a brief version of an acceptance-based treatment (acceptance and commitment therapy; ACT) that teaches patients to accept unavoidable private events; to identify and focus on actions directed toward valued goals; and to defuse from odd cognition, just noticing thoughts rather than treating them as either true or false. Eighty inpatient participants with positive psychotic symptoms were randomly assigned to treatment as usual (TAU) or to 4 sessions of ACT plus TAU. ACT participants showed significantly higher symptom reporting and lower symptom believability and a rate of rehospitalization half that of TAU participants over a 4-month follow-up period. The same basic pattern of results was seen with all participant subgroups except delusional participants who denied symptoms. Nearly 4% of all schizophrenics who initially are medication responsive and continue to be medication compliant are rehospitalized each month, at a cost to society approaching one billion dollars a year (Weiden & Olfson, 1995). There are many reasons for rehospitalization (Doering et al., 1998), but part of this problem may be traced to the persistence of auditory hallucinations and delusions in the seriously mentally ill (SMI). Even with medication, these positive symptoms persist at least at low levels for many SMI patients (Breier, Schreiber, Dyer, & Pickar, 1991), and such symptoms are among the predictors of rehospitalization in this population (e.g., Sota, 2000). Thus, the SMI patients may require psychosocial interventions to help them cope with those symptoms that medication does not eliminate.


title: DrAcc: a DRAM based Accelerator for Accurate CNN Inference

abstract: Modern Convolutional Neural Networks (CNNs) are computation and memory intensive. Thus it is crucial to develop hardware accelerators to achieve high performance as well as power/energy-efficiency on resource limited embedded systems. DRAM-based CNN accelerators exhibit great potentials but face inference accuracy and area overhead challenges. In this paper, we propose DrAcc, a novel DRAM-based processing-in-memory CNN accelerator. DrAcc achieves high inference accuracy by implementing a ternary weight network using in-DRAM bit operation with simple enhancements. The data partition and mapping strategies can be flexibly configured for the best trade-off among performance, power and energy consumption, and DRAM data reuse factors. Our experimental results show that DrAcc achieves 84.8 FPS (frame per second) at 2W and 2.9× power efficiency improvement over the process-near-memory design.


title: The Impact of Peer Review on Creative Self-efficacy and Learning Performance in Web 2.0 Learning Activities

abstract: Introduction Creativity is considered as one of the core competences in contemporary education (U.S. Department of Education, 2010). Learning to be creative is thus infused into formal educational contexts to foster the creativity of students to address this requirement. It is considered important to develop effective practices and pedagogies for encouraging and enhancing students' creativity in schools (Lassig, 2009). Recently, more and more Web 2.0 technologies, referred to as Internet-based applications, have been developed to empower users to interact and collaborate with each other as creators of user-generated content in an online community (O'Reilly, 2005). Web 2.0 technologies, such as blogs, wikis and social networking platforms, have been increasingly applied to promote open and creative learning experiences in various educational settings. For instance, the studies by Aragon, Poon, Monroy-Hernandez and Aragon (2009) and Liu, Liu, Chen, Lin and Chen (2011b) have demonstrated the effects of such Web 2.0 platforms on augmenting creative activities, as they can afford a platform for students to create and share their creative works. Therefore, it is believed that the Web 2.0 technologies can enhance learner participation and creativity in educational settings (Greenhow, Robelia. & Hughes, 2009; McLoughlin & Lee, 2010; Ravenscroft, 2009). However, many studies have pointed out the significant contrast between the creative nature of Web 2.0 learning activities and the structured learning that takes place in schools (Bennett, Bishop, Dalgarno, Waycott, & Kennedy, 2012; Mao, 2014). Formal education is restricted by the pre-defined curriculum in which students have to attain certain knowledge (Jimoyiannis, Tsiotakis, Roussinos, & Siorenta, 2013). However, the acquisition of the knowledge and assessment required in formal education may restrict the creative process. On the one hand, although the open and creative features of Web 2.0 learning activities may support active learning, previous studies have found that students' work in such activities may be perfunctory and may lack critical construction of knowledge (Tess, 2013). On the other hand, the assessment of students' performance, which is often conducted within a certain framework, may also be inconsistent with and interfere with the open and creative features of Web 2.0 learning activities (Hemmi, Bayne, & Land, 2009). Such assessment may also impact significantly students' motivation to participate in Web 2.0 learning activities. It is widely believed that a critical pathway for developing students' creativity is to model creative practices for students (Sternberg & Williams, 1996). This study thus attempted to propose an approach to leveraging Web 2.0 learning activities and classroom teaching to help students develop both specific knowledge and creativity. This approach is mainly based on Csikzentmihalyi's system model of creativity (1999) which asserts that "creativity is a process that can be observed only at the intersection where individuals, domains, and field interact" (Csikzentmihalyi, 1999, p. 314). From Csikzentmihalyi's perspective, individuals create new elements while knowing and operating old elements in a domain. However, not all elements are accepted as new elements; rather, new elements are "sanctioned by some group entitled to make decisions as to what should or should not be included in the domain" (Csikzentmihalyi, 1999, p. 315). On the one hand, these groups, i.e. the field, determine the value and originality of the new elements. On the other hand, individuals receive critical feedback to improve the elements they have created. From the perspective of Csikzentmihalyi's system model, the review process is a central component of the creative practice that students need to experience in order to understand the creative process. This study thus proposes integrating peer review activities with Web 2. …


Practical Search Techniques in Path Planning for Autonomous Driving


title: Objective assessment of depressive symptoms with machine learning and wearable sensors data

abstract: Depression is the major cause of years lived in disability world-wide; however, its diagnosis and tracking methods still rely mainly on assessing self-reported depressive symptoms, methods that originated more than fifty years ago. These methods, which usually involve filling out surveys or engaging in face-to-face interviews, provide limited accuracy and reliability and are costly to track and scale. In this paper, we develop and test the efficacy of machine learning techniques applied to objective data captured passively and continuously from E4 wearable wristbands and from sensors in an Android phone for predicting the Hamilton Depression Rating Scale (HDRS). Input data include electrodermal activity (EDA), sleep behavior, motion, phone-based communication, location changes, and phone usage patterns. We introduce our feature generation and transformation process, imputing missing clinical scores from self-reported measures, and predicting depression severity from continuous sensor measurements. While HDRS ranges between 0 and 52, we were able to impute it with 2.8 RMSE and predict it with 4.5 RMSE which are low relative errors. Analyzing the features and their relation to depressive symptoms, we found that poor mental health was accompanied by more irregular sleep, less motion, fewer incoming messages, less variability in location patterns, and higher asymmetry of EDA between the right and the left wrists.


title: Statistical Post-editing and Quality Estimation for Machine Translation Systems

abstract: Statistical post-editing (SPE) has been successfully applied to RBMT systems and, to a less successful extent, to some SMT systems. This thesis investigates the impact of SPE on SMT systems. We apply SPE to an SMT system using a new context-modelling approach to preserve some aspects of source information in the second stage translation. This technique yields mixed results, but fails to consistently improve the output over the baseline. Furthermore, we compared the results to those of an RBMT+SPE system and a pure SMT system, using both automatic and human evaluation methods. Results show that while automatic evaluation metrics favour a pure SMT system, manual evaluators prefer the output provided by the combined RBMT+SPE system. We investigate the use machine learning methods to predict which sentences would benefit from post-editing, however, as the oracle score for both SMT and SMT+SPE was not much higher than the two systems alone, we decided to compare two systems that had a higher upper bound. Combining our analysis with machine learning techniques for quality estimation, we are able to improve the overall output by automatically selecting the best sentences from each of the SMT and RBMT+SPE systems.


Hybrid organosiloxane material/metal oxide composite as efficient anticorrosive coatings for mild steel in a saline medium


title: Mini-Batch Primal and Dual Methods for SVMs

abstract: We address the issue of using mini-batches in stochastic optimization of SVMs. We show that the same quantity, the spectral norm of the data, controls the parallelization speedup obtained for both primal stochastic subgradient descent (SGD) and stochastic dual coordinate ascent (SCDA) methods and use it to derive novel variants of mini-batched SDCA. Our guarantees for both methods are expressed in terms of the original nonsmooth primal problem based on the hinge-loss.


title: A novel ternary half adder and multiplier based on carbon nanotube field effect transistors

abstract: A lot of research has been done on multiple-valued logic (MVL) such as ternary logic in these years. MVL reduces the number of necessary operations and also decreases the chip area that would be used. Carbon nanotube field effect transistors (CNTFETs) are considered a viable alternative for silicon transistors (MOSFETs). Combining carbon nanotube transistors and MVL can produce a unique design that is faster and more flexible. In this paper, we design a new half adder and a new multiplier by nanotechnology using a ternary logic, which decreases the power consumption and chip surface and raises the speed. The presented design is simulated using CNTFET of Stanford University and HSPICE software, and the results are compared with those of other studies.


title: 3D Shape Induction from 2D Views of Multiple Objects

abstract: In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints. Our approach called"projective generative adversarial networks"(PrGANs) trains a deep generative model of 3D shapes whose projections match the distributions of the input 2D views. The addition of a projection module allows us to infer the underlying 3D shape distribution without using any 3D, viewpoint information, or annotation during the learning phase. We show that our approach produces 3D shapes of comparable quality to GANs trained on 3D data for a number of shape categories including chairs, airplanes, and cars. Experiments also show that the disentangled representation of 2D shapes into geometry and viewpoint leads to a good generative model of 2D shapes. The key advantage is that our model allows us to predict 3D, viewpoint, and generate novel views from an input image in a completely unsupervised manner.