title: Gene-environment interplay and psychopathology: multiple varieties but real effects

abstract: Gene–environment interplay is a general term that covers several divergent concepts with different meanings and different implications. In this review, we evaluate research evidence on four varieties of gene–environment interplay. First, we consider epigenetic mechanisms by which environmental influences alter the effects of genes. Second, we focus on variations in heritability according to environmental circumstances. Third, we discuss what is known about gene–environment correlations. Finally, we assess concepts and findings on the interaction between specific identified genes and specific measured environmental risks. In order to provide an understanding of what may be involved in gene–environment interplay, we begin our presentation with a brief historical review of prevailing views about the role of genetic and environmental factors in the causation of mental disorders, and we provide a simplified account of some of the key features of how genes ‘work’. Over the past half-century there has been a series of changes in the generally prevailing views about the role of genetic and environmental factors in the causation of mental disorders. These changes were preceded in the first half of the 20th century (Cameron, 1956; Cairns, 1983; Kanner, 1959) by two background features. First, there was the introduction of unethical and abhorrent eugenic interventions on the misguided view of deterministic genetic effects (Devlin, Fienberg, Resnick, & Roeder, 1997). This background created a distrust of genetics in many behavioural scientists that has not entirely disappeared today despite genetic thinking and practice having changed completely (see Rutter, in press a). Second, there were numerous research reports of substantial associations between various environmental risk factors and the development of mental disorder. The Mental Hygiene movement placed great emphasis on the role of adverse experiences, both within and outside the family, in the predisposition to some mental disorders. This period also constituted the heyday of the influence of psychoanalysis on concepts and thinking in most of psychiatry and psychology. Behaviourism, led by the theorising of Pavlov and of Skinner, as well as by the advocacy of Watson, placed predominate emphasis on the power of learning with respect to all forms of behaviour. Within the field of child mental health, Bowlby’s (1951) review for the WHO of ‘maternal deprivation’, followed by Ainsworth’s (1962) reassessment a decade or so later, put forward a powerful case for the overwhelming influence of children’s upbringing in the early years. Reviews such as those by Pringle (1974) did much to popularise these views and foster


title: FPGA-based MIMO testbed for LTE applications

abstract: This paper addresses the designing a pragmatic appliance of multi-antenna, 2.437GHz ISM band multiple-input multiple-output (MIMO) testbed systems developed for evaluating wireless MIMO algorithms. Since using Sundance demo package for the development of a flexible field-programmable gate array (FPGA) processing system. The objective of this contribution is to provide an understanding of basic MIMO-LTE techniques and the practical concept, the setup, and the handling of our MIMO testbed based on SMT hardware and 3L diamond software platform in facts. The testbed manipulated as well as a MATLAB interface allowing real channel measurements become used in evolution and algorithm development. We have proposed a 2×2 antenna MIMO transceiver testbed system with Alamouti STBC and 64-QAM research model which efficiently implemented the MIMO algorithms.


title: A switched reluctance machine-based starter/alternator for more electric cars

abstract: This paper addresses various aspects of the switched reluctance (SR) machine based starter/alternator (S/A) system for a more electric car. Inherent simplicity of the machine geometry and control, offering high efficiency and a very long constant power speed ratio along with a rugged structure and safe operation are among the most salient attributes of an SRM drive. In addition, due to the absence of any magnetic source on the rotor, a robust performance in the presence of high temperatures is expected. The authors also introduce a novel method in active damping of the engine torque pulsation by controlling the braking torque of the SRG. This will reduce the engine shaft pulsation thereby enhancing the durability of the system while presenting a quiet engine/generator combination. Furthermore, to enhance the compactness of the system components a novel method for control of the phase current is presented.


title: Access to pictorial material: A review of current research and future prospects

abstract: Rapid expansion in the digitization of image and image collections has vastly increased the numbers of images available to scholars and researchers through electronic means. This research review will familiarize the reader with current research applicable to the development of image retrieval systems and provides additional material for exploring the topic further, both in print and online. The discussion will cover several broad areas, among them classification and indexing systems used for describing image collections and research initiatives into image access focusing on image attributes, users, queries, tasks, and cognitive aspects of searching. Prospects for the future of image access, including an outline of future research initiatives, are discussed. Further research in each of these areas will provide basic data which will inform and enrich image access system design and will hopefully provide a richer, more flexible, and satisfactory environment for searching for and discovering images. Harnessing the true power of the digital image environment will only be possible when image retrieval systems are coherently designed from principles derived from the fullest range of applicable disciplines, rather than from isolated or fragmented perspectives.


Exploring Individual Differences in Online Addictions: the Role of Identity and Attachment


title: A switched-coupling-capacitor equalizer for series-connected battery strings

abstract: An automatic switched-coupling-capacitor equalizer (SCCE) is proposed, which can realize the any-cells-to-any-cells equalization for a battery string. Only two switches and one capacitor are needed for each battery cell. All MOSFETs are controlled by a pair of complementary PWM signals, and energy can be automatically and directly delivered from any higher voltage cells to any lower voltage ones without the need of monitoring circuits. The inherent advantages of the proposed system are the simple control, high efficiency, and easy modularization. A prototype for four lithium-ion battery cells is implemented, and a comparison between the proposed circuit and the conventional one is presented. Experimental results show the proposed circuit exhibits a substantially improved balancing performance, and the measured peak efficiency is about 92.7%.


title: A Compressive-sensing based Testing Vehicle for 3D TSV Pre-bond and Post-bond Testing Data

abstract: Online testing vehicle is required for 3D TSV pre-bond and post-bond testing due to high probability of TSV failures. It has become a challenge to deal with large sets of generated testing data with limited probing when transmitting the data out. In this paper, a lossless compressive-sensing based testing vehicle is developed for online testing of TSVs. By exploring sparsity of the testing data under constraint of failure bound of TSV, sparse-representation based encoding can be deployed by XOR and AND network on chip to deal with large volume of testing data. Experimental results (with benchmarks) have shown that 89.70% pre-bond data compression rate can be achieved under 0.5% probability of failures; and 88.18% post-bond data compression rate can be achieved with 5% probability of failures.


title: Perfectionism, Fear of Failure, and Affective Responses to Success and Failure: The Central Role of Fear of Experiencing Shame and Embarrassment

abstract: This study investigated how aspects of perfectionism in athletes (N = 388) related to the fears of failure proposed by Conroy et al. (2002), and how perfectionism and fears of failure predicted positive and negative affect after imagined success and failure in sports competitions. Results showed that perfectionistic personal standards showed a negative relationship with fear of experiencing shame and embarrassment and a positive relationship with positive affect after success, whereas perfectionistic concern over mistakes and perceived parental pressure showed a positive relationship with fear of experiencing shame and embarrassment and with negative affect after failure. Moreover, fear of experiencing shame and embarrassment fully mediated the relationship between perfectionistic concern and negative affect and between coach pressure and negative affect. The findings demonstrate that fear of experiencing shame and embarrassment is central in the relationship between perfectionism and fear of failure, and that perfectionistic concern about mistakes and perceived coach pressure are aspects of perfectionism that predict fear of experiencing shame and embarrassment and negative affect after failure.


title: A Programming Language With a POMDP Inside

abstract: We present POAPS, a novel planning system for defining Partially Observable Markov Decision Processes (POMDPs) that abstracts away from POMDP details for the benefit of non-expert practitioners. POAPS includes an expressive adaptive programming language based on Lisp that has constructs for choice points that can be dynamically optimized. Non-experts can use our language to write adaptive programs that have partially observable components without needing to specify belief/hidden states or reason about probabilities. POAPS is also a compiler that defines and performs the transformation of any program written in our language into a POMDP with control knowledge. We demonstrate the generality and power of POAPS in the rapidly growing domain of human computation by describing its expressiveness and simplicity by writing several POAPS programs for common crowdsourcing tasks.


title: Privacy-preserving photo sharing based on a public key infrastructure

abstract: A significant number of pictures are posted to social media sites or exchanged through instant messaging and cloud-based sharing services. Most social media services offer a range of access control mechanisms to protect users privacy. As it is not in the best interest of many such services if their users restrict access to their shared pictures, most services keep users' photos unprotected which makes them available to all insiders. This paper presents an architecture for a privacy-preserving photo sharing based on an image scrambling scheme and a public key infrastructure. A secure JPEG scrambling is applied to protect regional visual information in photos. Protected images are still compatible with JPEG coding and therefore can be viewed by any one on any device. However, only those who are granted secret keys will be able to descramble the photos and view their original versions. The proposed architecture applies an attribute-based encryption along with conventional public key cryptography, to achieve secure transmission of secret keys and a fine-grained control over who may view shared photos. In addition, we demonstrate the practical feasibility of the proposed photo sharing architecture with a prototype mobile application, ProShare, which is built based on iOS platform.


title: Significance and popularity in music production

abstract: Creative industries constantly strive for fame and popularity. Though highly desirable, popularity is not the only achievement artistic creations might ever acquire. Leaving a longstanding mark in the global production and influencing future works is an even more important achievement, usually acknowledged by experts and scholars. ‘Significant’ or ‘influential’ works are not always well known to the public or have sometimes been long forgotten by the vast majority. In this paper, we focus on the duality between what is successful and what is significant in the musical context. To this end, we consider a user-generated set of tags collected through an online music platform, whose evolving co-occurrence network mirrors the growing conceptual space underlying music production. We define a set of general metrics aiming at characterizing music albums throughout history, and their relationships with the overall musical production. We show how these metrics allow to classify albums according to their current popularity or their belonging to expert-made lists of important albums. In this way, we provide the scientific community and the public at large with quantitative tools to tell apart popular albums from culturally or aesthetically relevant artworks. The generality of the methodology presented here lends itself to be used in all those fields where innovation and creativity are in play.


title: Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing

abstract: We introduce Segment-Phrase Table (SPT), a large collection of bijective associations between textual phrases and their corresponding segmentations. Leveraging recent progress in object recognition and natural language semantics, we show how we can successfully build a high-quality segment-phrase table using minimal human supervision. More importantly, we demonstrate the unique value unleashed by this rich bimodal resource, for both vision as well as natural language understanding. First, we show that fine-grained textual labels facilitate contextual reasoning that helps in satisfying semantic constraints across image segments. This feature enables us to achieve state-of-the-art segmentation results on benchmark datasets. Next, we show that the association of high-quality segmentations to textual phrases aids in richer semantic understanding and reasoning of these textual phrases. Leveraging this feature, we motivate the problem of visual entailment and visual paraphrasing, and demonstrate its utility on a large dataset.


title: Improved Text Extraction from PDF Documents for Large-Scale Natural Language Processing

abstract: The inability of reliable text extraction from arbitrary documents is often an obstacle for large scale NLP based on resources crawled from the Web. One of the largest problems in the conversion of PDF documents is the detection of the boundaries of common textual units such as paragraphs, sentences and words. PDF is a file format optimized for printing and encapsulates a complete description of the layout of a document including text, fonts, graphics and so on. This paper describes a tool for extracting texts from arbitrary PDF files for the support of large-scale data-driven natural language processing. Our approach combines the benefits of several existing solutions for the conversion of PDF documents to plain text and adds a language-independent post-processing procedure that cleans the output for further linguistic processing. In particular, we use the PDF-rendering libraries pdfXtk, Apache Tika and Poppler in various configurations. From the output of these tools we recover proper boundaries using on-the-fly language models and language-independent extraction heuristics. In our research, we looked especially at publications from the European Union, which constitute a valuable multilingual resource, for example, for training statistical machine translation models. We use our tool for the conversion of a large multilingual database crawled from the EU bookshop with the aim of building parallel corpora. Our experiments show that our conversion software is capable of fixing various common issues leading to cleaner data sets in the end.


title: Failure mode and effects analysis by data envelopment analysis

abstract: Failure mode and effects analysis (FMEA) is a method that examines potential failures in products or processes and has been used in many quality management systems. One important issue of FMEA is the determination of the risk priorities of failure modes. In this paper we propose an FMEA which uses data envelopment analysis (DEA), a well-known performance measurement tool, to determine the risk priorities of failure modes. The proposed FMEA measures the maximum and minimum risks of each failure mode. The two risks are then geometrically averaged to measure the overall risks of failure modes. The risk priorities are determined in terms of overall risks rather than maximum or minimum risks only. Two numerical examples are provided and examined using the proposed FMEA to show its potential applications and benefits.


title: The Effect of an Augmented Reality Enhanced Mathematics Lesson on Student Achievement and Motivation.

abstract: The purpose of the study was to assess student achievement and motivation during a high school augmented reality mathematics activity focused on dimensional analysis. Included in this article is a review of the literature on the use of augmented reality in mathematics and the combination of print with augmented reality, also known as interactive print. The design and development process of the interactive print document is described in detail. Participants in the quasi-experimental study included 61 students and measures included the IMMS Survey as well as pre, post, and delay post achievement tests. Findings support claims that technology use within a mathematics lesson increases student achievement and augmented reality enhances student motivation to learn mathematics. However, analyzing technologies impact on student conceptual or technical use of mathematics shows variance of degree emphasizing the need for continued exploration to determine technologies impact not only on overall mathematical achievement but also on the specific type of mathematical activity, technical or conceptual.


title: NUMA-aware graph-structured analytics

abstract: Graph-structured analytics has been widely adopted in a number of big data applications such as social computation, web-search and recommendation systems. Though much prior research focuses on scaling graph-analytics on distributed environments, the strong desire on performance per core, dollar and joule has generated considerable interests of processing large-scale graphs on a single server-class machine, which may have several terabytes of RAM and 80 or more cores. However, prior graph-analytics systems are largely neutral to NUMA characteristics and thus have suboptimal performance. This paper presents a detailed study of NUMA characteristics and their impact on the efficiency of graph-analytics. Our study uncovers two insights: 1) either random or interleaved allocation of graph data will significantly hamper data locality and parallelism; 2) sequential inter-node (i.e., remote) memory accesses have much higher bandwidth than both intra- and inter-node random ones. Based on them, this paper describes Polymer, a NUMA-aware graph-analytics system on multicore with two key design decisions. First, Polymer differentially allocates and places topology data, application-defined data and mutable runtime states of a graph system according to their access patterns to minimize remote accesses. Second, for some remaining random accesses, Polymer carefully converts random remote accesses into sequential remote accesses, by using lightweight replication of vertices across NUMA nodes. To improve load balance and vertex convergence, Polymer is further built with a hierarchical barrier to boost parallelism and locality, an edge-oriented balanced partitioning for skewed graphs, and adaptive data structures according to the proportion of active vertices. A detailed evaluation on an 80-core machine shows that Polymer often outperforms the state-of-the-art single-machine graph-analytics systems, including Ligra, X-Stream and Galois, for a set of popular real-world and synthetic graphs.


title: DP-MCDBSCAN: Differential Privacy Preserving Multi-Core DBSCAN Clustering for Network User Data

abstract: The proliferation of ubiquitous Internet and mobile devices has brought about the exponential growth of individual data in big data era. The network user data has been confronted with serious privacy concerns for extracting valuable information during the process of data mining. Differential privacy preservation is a new paradigm independent of the adversaries’ prior knowledge, which protects sensitive data while maintaining certain statistical properties by adding random noise. In this paper, we put forward a differential privacy preservation multiple cores DBSCAN clustering schema based on the powerful differential privacy and DBSCAN algorithm for network user data to effectively leverage the privacy leakage issue in the process of data mining, enhancing data clustering efficaciously by adding Laplace noise. We perform extensive theoretical analysis and simulations to evaluate our schema and the results show better efficiency, accuracy, and privacy preservation effect than previous schemas.


title: Aluminium at terapascal pressures

abstract: What happens to a crystal placed under a huge pressure? In the case of aluminium, it is now shown that the standard, low-pressure close-packed structure transforms into an open one, with incommensurate host–guest arrangement. The findings could have important implications for a wider range of elements.


title: Two simple full-text indexes based on the suffix array

abstract: We propose two suffix array inspired full-text indexes. One, called SA-hash, augments the suffix array with a hash table to speed up pattern searches due to significantly narrowed search interval before the binary search phase. The other, called FBCSA, is a compact data structure, similar to M{\"a}kinen's compact suffix array, but working on fixed sized blocks. Experiments on the Pizza~\&~Chili 200\,MB datasets show that SA-hash is about 2--3 times faster in pattern searches (counts) than the standard suffix array, for the price of requiring $0.2n-1.1n$ bytes of extra space, where $n$ is the text length, and setting a minimum pattern length. FBCSA is relatively fast in single cell accesses (a few times faster than related indexes at about the same or better compression), but not competitive if many consecutive cells are to be extracted. Still, for the task of extracting, e.g., 10 successive cells its time-space relation remains attractive.


title: Design of a piezoelectric inchworm actuator and compliant end effector for minimally invasive surgery

abstract: Recent advances in robotics, tele-robotics, smart material actuators, and mechatronics raise new possibilities for innovative developments in millimeter-scale robotics capable of manipulating objects only fractions of a millimeter in size. These advances can have a wide range of applications in the biomedical community. A potential application of this technology is in minimally invasive surgery (MIS). The focus of this paper is the development of a single degree of freedom prototype to demonstrate the viability of smart materials, force feedback and compliant mechanisms for minimally invasive surgery. The prototype is a compliant gripper that is 7-mm by 17-mm, made from a single piece of titanium that is designed to function as a needle driver for small scale suturing. A custom designed piezoelectric "inchworm" actuator drives the gripper. The integrated system is computer controlled providing a user interface device capable of force feedback. The design methodology described draws from recent advances in three emerging fields in engineering: design of innovative tools for MIS, design of compliant mechanisms, and design of smart materials and actuators. The focus of this paper is on the design of a millimeter-scale inchworm actuator for use with a compliant end effector in MIS.


title: Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data

abstract: This paper demonstrates the potential for synthesis of medical images in one modality (e.g. MR) from images in another (e.g. CT) using a CycleGAN [24] architecture. The synthesis can be learned from unpaired images, and applied directly to expand the quantity of available training data for a given task. We demonstrate the application of this approach in synthesising cardiac MR images from CT images, using a dataset of MR and CT images coming from different patients. Since there can be no direct evaluation of the synthetic images, as no ground truth images exist, we demonstrate their utility by leveraging our synthetic data to achieve improved results in segmentation. Specifically, we show that training on both real and synthetic data increases accuracy by 15% compared to real data. Additionally, our synthetic data is of sufficient quality to be used alone to train a segmentation neural network, that achieves 95% of the accuracy of the same model trained on real data.


title: Learning scene gist with convolutional neural networks to improve object recognition

abstract: Advancements in convolutional neural networks (CNNs) have made significant strides toward achieving high performance levels on multiple object recognition tasks. While some approaches utilize information from the entire scene to propose regions of interest, the task of interpreting a particular region or object is still performed independently of other objects and features in the image. Here we demonstrate that a scene's 'gist' can significantly contribute to how well humans can recognize objects. These findings are consistent with the notion that humans foveate on an object and incorporate information from the periphery to aid in recognition. We use a biologically inspired two-part convolutional neural network ('GistNet') that models the fovea and periphery to provide a proof-of-principle demonstration that computational object recognition can significantly benefit from the gist of the scene as contextual information. Our model yields accuracy improvements of up to 50% in certain object categories when incorporating contextual gist, while only increasing the original model size by 5%. This proposed model mirrors our intuition about how the human visual system recognizes objects, suggesting specific biologically plausible constraints to improve machine vision and building initial steps towards the challenge of scene understanding.


title: Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method

abstract: High blood pressure (BP) is a major cardiovascular risk factor that is treatable, yet hypertension awareness and control rates are low. Ubiquitous BP monitoring technology could improve hypertension management, but existing devices require an inflatable cuff and are not compatible with such anytime, anywhere measurement of BP. We extended the oscillometric principle, which is used by most automatic cuff devices, to develop a cuff-less BP monitoring device using a smartphone. As the user presses her/his finger against the smartphone, the external pressure of the underlying artery is steadily increased while the phone measures the applied pressure and resulting variable-amplitude blood volume oscillations. A smartphone application provides visual feedback to guide the amount of pressure applied over time via the finger pressing and computes systolic and diastolic BP from the measurements. We prospectively tested the smartphone-based device for real-time BP monitoring in human subjects to evaluate usability ( n = 30) and accuracy against a standard automatic cuff-based device ( n = 32). We likewise tested a finger cuff device, which uses the volume-clamp method of BP detection. About 90% of the users learned the finger actuation required by the smartphone-based device after one or two practice trials. The device yielded bias and precision errors of 3.3 and 8.8 mmHg for systolic BP and −5.6 and 7.7 mmHg for diastolic BP over a 40 to 50 mmHg range of BP. These errors were comparable to the finger cuff device. Cuff-less and calibration-free monitoring of systolic and diastolic BP may be feasible via a smartphone.


title: The Effects of White Noise on Agitated Behaviors, Mental Status, and Activities of Daily Living in Older Adults With Dementia

abstract: BACKGROUND ::: The aging of society is a global trend, and care of older adults with dementia is an urgent challenge. As dementia progresses, patients exhibit negative emotions, memory disorders, sleep disorders, and agitated behavior. Agitated behavior is one of the most difficult problems for family caregivers and healthcare providers to handle when caring for older adults with dementia. ::: ::: ::: PURPOSE ::: The aim of this study was to investigate the effectiveness of white noise in improving agitated behavior, mental status, and activities of daily living in older adults with dementia. ::: ::: ::: METHODS ::: An experimental research design was used to study elderly participants two times (pretest and posttest). Six dementia care centers in central and southern Taiwan were targeted to recruit participants. There were 63 participants: 28 were in the experimental group, and 35 were in the comparison group. Experimental group participants received 20 minutes of white noise consisting of ocean, rain, wind, and running water sounds between 4 and 5 P.M. daily over a period of 4 weeks. The comparison group received routine care. Questionnaires were completed, and observations of agitated behaviors were collected before and after the intervention. ::: ::: ::: RESULTS ::: Agitated behavior in the experimental group improved significantly between pretest and posttest. Furthermore, posttest scores on the Mini-Mental Status Examination and Barthel Index were slightly better for this group than at pretest. However, the experimental group registered no significant difference in mental status or activities of daily living at posttest. For the comparison group, agitated behavior was unchanged between pretest and posttest. ::: ::: ::: CONCLUSIONS ::: The results of this study support white noise as a simple, convenient, and noninvasive intervention that improves agitated behavior in older adults with dementia. These results may provide a reference for related healthcare providers, educators, and administrators who care for older adults with dementia.


title: Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

abstract: This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model


title: Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks

abstract: Correlation networks were used to detect characteristics which, although fixed over time, have an important influence on the evolution of prices over time. Potentially important features were identified using the websites and whitepapers of cryptocurrencies with the largest userbases. These were assessed using two datasets to enhance robustness: one with fourteen cryptocurrencies beginning from 9 November 2017, and a subset with nine cryptocurrencies starting 9 September 2016, both ending 6 March 2018. Separately analysing the subset of cryptocurrencies raised the number of data points from 115 to 537, and improved robustness to changes in relationships over time. Excluding USD Tether, the results showed a positive association between different cryptocurrencies that was statistically significant. Robust, strong positive associations were observed for six cryptocurrencies where one was a fork of the other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the existence of a group of cryptocurrencies particularly associated with Cardano, and a separate group correlated with Ethereum. The data was not consistent with a token's functionality or creation mechanism being the dominant determinants of the evolution of prices over time but did suggest that factors other than speculation contributed to the price.


title: UAV aerial imaging applications for post-disaster assessment, environmental management and infrastructure development

abstract: This paper discusses the use of a low-cost unmanned aerial vehicle (UAV)-based remote sensing system for different applications, namely post-disaster assessment, environmental management and monitoring of infrastructure development. A collaborative research consortium was established to promote the acquisition, post processing, analysis and sharing of UAV-based aerial imagery. A streamlined workflow - flight planning and data acquisition, post-processing, data delivery and collaborative sharing - was created in order to deliver acquired images and orthorectified maps to various stakeholders within this consortium. Various use case examples of UAV aerial imagery work are still in ongoing development. Initial experience shows that the combination of aerial surveys, ground observations and collaborative sharing with domain experts results in richer information content and a more effective decision support system.


title: Logo localization and recognition in natural images using homographic class graphs

abstract: We propose a method for localization and classification of brand logos in natural images. The system has to overcome multiple challenges such as perspective deformations, warping, variations of the shape and colors, occlusions, background variations. To deal with perspective variation, we rely on homography matching between the SIFT keypoints of logo instances of the same class. To address the changes in color, we construct a weighted graph of logo interconnections that is further analyzed to extract potentially multiple instances of the class. The main instance is built by grouping the keypoints of the graph connected logos onto the central image. The secondary instance is needed for color inverted logos and is obtained by inverting the orientation of the main instance. The constructed logo recognition system is tested on two databases (FlickrLogos-32 and BelgaLogos), outperforming state of the art with more than 10 % accuracy.


title: A 90 nm CMOS Low-Power 60 GHz Transceiver With Integrated Baseband Circuitry

abstract: This paper presents a low power 60 GHz transceiver that includes RF, LO, PLL and BB signal paths integrated into a single chip. The transceiver has been fabricated in a standard 90 nm CMOS process and includes specially designed ESD protection on all mm-wave pads. With a 1.2 V supply the chip consumes 170 mW while transmitting 10 dBm and 138 mW while receiving. Data transmission up to 5 Gb/s on each of I and Q channels has been measured, as has data reception over a 1 m wireless link at 4 Gb/s QPSK with less than 10-11 BER.


Overview of the Algorithms for Solving the Multidimensional Knapsack Problems


title: Approach and Avoidance Motivation

abstract: In this article, we introduce this special issue by establishing a conceptual foundation for the distinction between approach and avoidance motivation. We do so primarily by explicating several reasons why the approach–avoidance distinction should be viewed as fundamental and basic to the study of human behavior. In addition, we compare and contrast the “approach–avoidance” designation with other designations that have been used in the motivational literature to cover the same or similar conceptual ground. Finally, we conclude by briefly overviewing the other contributions to this special issue, specifically highlighting how they make use of the approach–avoidance distinction.


title: Automatic Detection of Psychological Distress Indicators and Severity Assessment from Online Forum Posts

abstract: Psychological disorders are frequently under-diagnosed and consequently have an irreversible impact on individuals and society. The stigma associated with such disorders makes face-to-face discussions with family members and clinicians difficult for many individuals. In contrast, people openly relate experiences on Internet forums. This paper describes a novel system that analyses forum posts to: (1) detect distress indicators that directly map to the Diagnostic and Statistical Manual of Mental Disorders (DSM) IV constructs, and (2) assess the severity of distress for prioritizing individuals who should seek clinical help (i.e. triage). For distress indicator detection, we use support vector machines (SVMs) trained on a suite of innovative intraand inter-message features. We show significant improvements in multi-label classification accuracy using humangenerated rationales in support of annotated distress labels. For triage assessment, we demonstrate the effectiveness of Markov Logic Networks (MLNs) in dealing with noisy distress label detections and encoding expert rules.


title: Quantum Teleportation Between Distant Matter Qubits

abstract: Quantum teleportation is the faithful transfer of quantum states between systems, relying on the prior establishment of entanglement and using only classical communication during the transmission. We report teleportation of quantum information between atomic quantum memories separated by about 1 meter. A quantum bit stored in a single trapped ytterbium ion (Yb+) is teleported to a second Yb+ atom with an average fidelity of 90% over a replete set of states. The teleportation protocol is based on the heralded entanglement of the atoms through interference and detection of photons emitted from each atom and guided through optical fibers. This scheme may be used for scalable quantum computation and quantum communication.


title: The evolution of a generalized neural learning rule

abstract: Evolution is extremely creative. The mere availability of a mechanism for synaptic change seems to be enough for evolution to derive a learning rule. Many simulations of evolution have evolved learning in a highly guided manner. Either by constraining the update function to a Hebbian form, or by supplying an error/teaching signal. In this paper, we aim to evolve a more general learning rule. And since neural networks are so versatile, we construct the learning function itself out of a neural network. Our evolved networks excel at the foraging task they evolved in. Amazingly, they even function robustly when tested outside of their historical niche. The same cannot be said for the Hebbian learning networks we compare to.


title: The estimation of the gradient of a density function, with applications in pattern recognition

abstract: Nonparametric density gradient estimation using a generalized kernel approach is investigated. Conditions on the kernel functions are derived to guarantee asymptotic unbiasedness, consistency, and uniform consistency of the estimates. The results are generalized to obtain a simple mcan-shift estimate that can be extended in a k -nearest-neighbor approach. Applications of gradient estimation to pattern recognition are presented using clustering and intrinsic dimensionality problems, with the ultimate goal of providing further understanding of these problems in terms of density gradients.


title: Dielectric properties of epoxy nanocomposites

abstract: The dielectric properties of epoxy nanocomposites with insulating nano-fillers, viz., TiO2, ZnO and AI2O3 were investigated at low filler concentrations by weight. Epoxy nanocomposite samples with a good dispersion of nanoparticles in the epoxy matrix were prepared and experiments were performed to measure the dielectric permittivity and tan delta (400 Hz-1 MHz), dc volume resistivity and ac dielectric strength. At very low nanoparticle loadings, results demonstrate some interesting dielectric behaviors for nanocomposites and some of the electrical properties are found to be unique and advantageous for use in several existing and potential electrical systems. The nanocomposite dielectric properties are analyzed in detail with respect to different experimental parameters like frequency (for permittivity/tan delta), filler size, filler concentration and filler permittivity. In addition, epoxy microcomposites for the same systems were synthesized and their dielectric properties were compared to the results already obtained for nanocomposites. The interesting dielectric characteristics for epoxy based nanodielectric systems are attributed to the large volume fraction of interfaces in the bulk of the material and the ensuing interactions between the charged nanoparticle surface and the epoxy chains.


Deep Learning meets Semantic Web: A feasibility study with the Cardiovascular Disease Ontology and PubMed citations


Imagined visual representations as multimodal embeddings


title: Angular-Based Word Meta-Embedding Learning

abstract: Ensembling word embeddings to improve distributed word representations has shown good success for natural language processing tasks in recent years. These approaches either carry out straightforward mathematical operations over a set of vectors or use unsupervised learning to find a lower-dimensional representation. This work compares meta-embeddings trained for different losses, namely loss functions that account for angular distance between the reconstructed embedding and the target and those that account normalized distances based on the vector length. We argue that meta-embeddings are better to treat the ensemble set equally in unsupervised learning as the respective quality of each embedding is unknown for upstream tasks prior to meta-embedding. We show that normalization methods that account for this such as cosine and KL-divergence objectives outperform meta-embedding trained on standard $\ell_1$ and $\ell_2$ loss on \textit{defacto} word similarity and relatedness datasets and find it outperforms existing meta-learning strategies.


title: A Study of Air Pollution Smart Sensors LPWAN via NB-IoT for Thailand Smart Cities 4.0

abstract: The problem of air pollutant has to improve urgently, in particular, approach to smart city in 2024 of Thailand 4.0. This paper presents a development of smart sensors of air pollution to monitor the air quality in smart city. We propose the smart sensors that consist of the particulate matter (PM 10 ) or dust sensor, carbon monoxide (CO), carbon dioxide (CO 2 ), noise level (dB), and ozone (O 3 ) respectively. These sensors are solution of a low power wide area network (LPWAN). In the experiment, the measurement has been investigated in Bangkok metropolitan, and the results show air quality index (AQI) via Norrowband Internet of Things (NB-IoT). The proposed of this paper can help the people know the real-time air quality via IoT as a service.


title: Large-Scale Study of Curiosity-Driven Learning

abstract: Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://pathak22.github.io/large-scale-curiosity/


title: Deep Learning-Based Methodological Approach for Vineyard Early Disease Detection Using Hyperspectral Data

abstract: Machine Learning (ML) progressed significantly in the last decade, evolving the computer-based learning/prediction paradigm to a much more effective class of models known as Deep learning (DL). Since then, hyperspectral data processing relying on DL approaches is getting more popular, competing with the traditional classification techniques. In this paper, a valid ML/DL-based works applied to hyperspectral data processing is reviewed in order to get an insight regarding the approaches available for the effective meaning extraction from this type of data. Next, a general DL-based methodology focusing on hyperspectral data processing to provide farmers and winemakers effective tools for earlier threat detection is proposed.


title: DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization

abstract: We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary. In experiments, our model not only achieves state-of-the-art ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1 F-1 score of 39.41 on CNN/Daily Mail test set with merely 1/100 training set, demonstrating a tremendous data efficiency.


title: Embedding Images and Sentences in a Common Space with a Recurrent Capsule Network

abstract: Associating texts and images is an easy and intuitive task for a human being, but it raises some issues if we want that task to be accomplished by a computer. Among these issues, there is the problem of finding a common representation for images and sentences. Based on recent research about capsule networks, we define a novel model to tackle that issue. This model is trained and compared to other recent models on the Flickr8k database on Image Retrieval and Image Annotation (or Sentence Retrieval) tasks. We propose a new recurrent architecture inspired from capsule networks to replace the traditional LSTM/GRU and show how it leads to improved performances. Moreover, we show that the interest of our model goes beyond its performances and includes its intrinsic characteristics, which can explain why it performs particularly well on the Image Annotation task. In addition, we propose a routing procedure between capsules which is fully learned during the training of our model.


title: Fast Weather Simulation for Inverse Procedural Design of 3D Urban Models

abstract: We present the first realistic, physically based, fully coupled, real-time weather design tool for use in urban procedural modeling. We merge designing of a 3D urban model with a controlled long-lasting spatiotemporal interactive simulation of weather. Starting from the fundamental dynamical equations similar to those used in state-of-the-art weather models, we present a novel simplified urban weather model for interactive graphics. Control of physically based weather phenomena is accomplished via an inverse modeling methodology. In our results, we present several scenarios of forward design, inverse design with high-level and detailed-level weather control and optimization, and comparisons of our method against well-known weather simulation results and systems.


title: Mathematical modeling for the ionic inclusion process inside conducting polymer-based thin-films

abstract: Ionic inclusion inside thin conducting polymer (CP) film is a major and common feature for actuator as well as membrane-based drug release. In this study, an electro-active polymeric thin-film system has been conceptualized. PNP-electro-neutrality (Poisson–Nernst– Planck) based modeling framework with customized boundary conditions is used to depict the electrochemical phenomena. In dynamic model, kinetics of probable redox reactions is included along with electromigration and diffusion terms in the overall PNP framework. At steady state, interfacial voltage seems to hold the critically important role, while ionic migration and reaction kinetics play very crucial roles in determining the dynamics of such systems. The validated model predicts that lowering in the standard potential of the polymeric electrode accelerates the process of ionic ingress. Higher ionic flux is obtained using slower voltage scan. Variation of diffusivity shows the large spectrum of relatively unexplored dynamics for such electro-active thin-film-based system. The significance is in designing actuator- or membrane-based controlled molecular release systems. POLYM. ENG. SCI., 48:2229– 2237, 2008. a 2008 Society of Plastics Engineers


title: ESD protection devices placed inside keep-out zone (KOZ) of through Silicon Via (TSV) in 3D stacked integrated circuits

abstract: Through Silicon Via (TSV) has been utilized in vertically stacking IC dice to implement real systemin-chip applications. However, threshold voltage and mobility of MOSFETs can be influenced by induced mechanical strain of the TSV, causing degradation or non-stability of functional circuits. Therefore, a Keep-out Zone (KOZ) is defined, meaning that active devices are forbidden in this area. This paper investigates the impact on ESD protection devices placed inside this KOZ in bulk FinFET process.


title: Secure Firmware Updates over the Air in Intelligent Vehicles

abstract: Modern intelligent vehicles have electronic control units containing firmware that enables various functions in the vehicle. New firmware versions are constantly developed to remove bugs and improve functionality. Automobile manufacturers have traditionally performed firmware updates over cables but in the near future they are aiming at conducting firmware updates over the air, which would allow faster updates and improved safety for the driver. In this paper, we present a protocol for secure firmware updates over the air. The protocol provides data integrity, data authentication, data confidentiality, and freshness. In our protocol, a hash chain is created of the firmware, and the first packet is signed by a trusted source, thus authenticating the whole chain. Moreover, the packets are encrypted using symmetric keys. We discuss the practical considerations that exist for implementing our protocol and show that the protocol is computationally efficient, has low memory overhead, and is suitable for wireless communication. Therefore, it is well suited to the limited hardware resources in the wireless vehicle environment.


title: Mapping between fMRI responses to movies and their natural language annotations

abstract: Abstract Several research groups have shown how to map fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock ( Chen et al., 2017 ), and learn bidirectional mappings between fMRI responses and natural language representations. By leveraging data from multiple subjects watching the same movie, we were able to perform scene classification with 72% accuracy (random guessing would give 4%) and scene ranking with average rank in the top 4% (random guessing would give 50%). The key ingredients underlying this high level of performance are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA ( Chen et al., 2015 , Zhang et al., 2016 ) to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature ( Arora et al., 2017 ) that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data. These optimizations in how we featurize the fMRI data and text annotations provide a substantial improvement in classification performance, relative to standard approaches.


title: Error Detecting and Error Correcting Codes

abstract: The author was led to the study given in this paper from a consideration of large scale computing machines in which a large number of operations must be performed without a single error in the end result. This problem of “doing things right” on a large scale is not essentially new; in a telephone central office, for example, a very large number of operations are performed while the errors leading to wrong numbers are kept well under control, though they have not been completely eliminated. This has been achieved, in part, through the use of self-checking circuits. The occasional failure that escapes routine checking is still detected by the customer and will, if it persists, result in customer complaint, while if it is transient it will produce only occasional wrong numbers. At the same time the rest of the central office functions satisfactorily. In a digital computer, on the other hand, a single failure usually means the complete failure, in the sense that if it is detected no more computing can be done until the failure is located and corrected, while if it escapes detection then it invalidates all subsequent operations of the machine. Put in other words, in a telephone central office there are a number of parallel paths which are more or less independent of each other; in a digital machine there is usually a single long path which passes through the same piece of equipment many, many times before the answer is obtained.


AADE-08-DF-HO-08 Modeling Cement Placement Using a New 3-D Flow Simulator


title: Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity

abstract: Brain---computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are potentially vulnerable to cybercriminality. This opens the prospect of "neurocrime": extending the range of computer-crime to neural devices. This paper explores a type of neurocrime that we call brain-hacking as it aims at the illicit access to and manipulation of neural information and computation. As neural computation underlies cognition, behavior and our self-determination as persons, a careful analysis of the emerging risks of malicious brain-hacking is paramount, and ethical safeguards against these risks should be considered early in design and regulation. This contribution is aimed at raising awareness of the emerging risk of malicious brain-hacking and takes a first step in developing an ethical and legal reflection on those risks.


title: Digital Signal Processing Designing for FPGA Architectures

abstract: This paper presents the discussion on efficiency of differen t implementation methodologies of DSP algorithms targeted for modern FPGA architectures. Modern programmable structures are equipped with specialized DSP embedded blocks that allow implementing digital signal processing algorithms with use of the methodology known from digital signal processors. On the first place, how ever, programmable ar- chitectures give the designer the possibility to increase e fficiency of designed system by exploitation of parallelism of implemented algorithms. Moreover, it is possible to apply special techniques, such as distributed arithmetic ( DA) that will boost the per- formance of designed processing systems. Additionally, application of the functional decomposition based methods, known to be best suited for FPGA structures, allows utilizing possibilities of programmable technology in ver y high degree. The paper presents results of comparison of different design approac hes in this area.


Design of a hand exoskeleton (handexos) for the rehabilitation of the hand


title: Cyber–Physical System Security for the Electric Power Grid

abstract: The development of a trustworthy smart grid requires a deeper understanding of potential impacts resulting from successful cyber attacks. Estimating feasible attack impact requires an evaluation of the grid's dependency on its cyber infrastructure and its ability to tolerate potential failures. A further exploration of the cyber-physical relationships within the smart grid and a specific review of possible attack vectors is necessary to determine the adequacy of cybersecurity efforts. This paper highlights the significance of cyber infrastructure security in conjunction with power application security to prevent, mitigate, and tolerate cyber attacks. A layered approach is introduced to evaluating risk based on the security of both the physical power applications and the supporting cyber infrastructure. A classification is presented to highlight dependencies between the cyber-physical controls required to support the smart grid and the communication and computations that must be protected from cyber attack. The paper then presents current research efforts aimed at enhancing the smart grid's application and infrastructure security. Finally, current challenges are identified to facilitate future research efforts.


title: Explaining Transition Systems through Program Induction

abstract: Explaining and reasoning about processes which underlie observed black-box phenomena enables the discovery of causal mechanisms, derivation of suitable abstract representations and the formulation of more robust predictions. We propose to learn high level functional programs in order to represent abstract models which capture the invariant structure in the observed data. We introduce the $\pi$-machine (program-induction machine) -- an architecture able to induce interpretable LISP-like programs from observed data traces. We propose an optimisation procedure for program learning based on backpropagation, gradient descent and A* search. We apply the proposed method to three problems: system identification of dynamical systems, explaining the behaviour of a DQN agent and learning by demonstration in a human-robot interaction scenario. Our experimental results show that the $\pi$-machine can efficiently induce interpretable programs from individual data traces.


title: Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima

abstract: We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation function, i.e., $f(\mathbf{Z}; \mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^\top\mathbf{Z}_j)$, in which both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$ are parameters to be learned. We prove that with Gaussian input $\mathbf{Z}$, there is a spurious local minimum that is not a global mininum. Surprisingly, in the presence of local minimum, starting from randomly initialized weights, gradient descent with weight normalization can still be proven to recover the true parameters with constant probability (which can be boosted to arbitrarily high accuracy with multiple restarts). We also show that with constant probability, the same procedure could also converge to the spurious local minimum, showing that the local minimum plays a non-trivial role in the dynamics of gradient descent. Furthermore, a quantitative analysis shows that the gradient descent dynamics has two phases: it starts off slow, but converges much faster after several iterations.


title: A Multi-robot Control Policy for Information Gathering in the Presence of Unknown Hazards

abstract: This paper addresses the problem of deploying a network of robots to gather information in an environment, where the environment is hazardous to the robots. This may mean that there are adversarial agents in the environment trying to disable the robots, or that some regions of the environment tend to make the robots fail, for example due to radiation, fire, adverse weather, or caustic chemicals. A probabilistic model of the environment is formulated, under which recursive Bayesian filters are used to estimate the environment events and hazards online. The robots must control their positions both to avoid sensor failures and to provide useful sensor information by following the analytical gradient of mutual information computed using these online estimates. Mutual information is shown to combine the competing incentives of avoiding failure and collecting informative measurements under a common objective. Simulations demonstrate the performance of the algorithm.


title: Memory Forensics of a Java Card Dump

abstract: Nowadays several papers have shown the ability to dump the EEPROM area of several Java Cards leading to the disclosure of already loaded applet and data structure of the card. Such a reverse engineering process is costly and prone to errors. Currently there are no tools available to help the process. We propose here an approach to find in the raw data obtained after a dump, the area containing the code and the data. Then, once the code area has been identified, we propose to rebuilt the original binary Cap file in order to be able to obtain the source code of the applet stored in the card.


title: Accelerating Multiagent Reinforcement Learning by Equilibrium Transfer

abstract: An important approach in multiagent reinforcement learning (MARL) is equilibrium-based MARL, which adopts equilibrium solution concepts in game theory and requires agents to play equilibrium strategies at each state. However, most existing equilibrium-based MARL algorithms cannot scale due to a large number of computationally expensive equilibrium computations (e.g., computing Nash equilibria is PPAD-hard) during learning. For the first time, this paper finds that during the learning process of equilibrium-based MARL, the one-shot games corresponding to each state’s successive visits often have the same or similar equilibria (for some states more than 90% of games corresponding to successive visits have similar equilibria). Inspired by this observation, this paper proposes to use equilibrium transfer to accelerate equilibrium-based MARL. The key idea of equilibrium transfer is to reuse previously computed equilibria when each agent has a small incentive to deviate. By introducing transfer loss and transfer condition, a novel framework called equilibrium transfer-based MARL is proposed. We prove that although equilibrium transfer brings transfer loss, equilibrium-based MARL algorithms can still converge to an equilibrium policy under certain assumptions. Experimental results in widely used benchmarks (e.g., grid world game, soccer game, and wall game) show that the proposed framework: 1) not only significantly accelerates equilibrium-based MARL (up to 96.7% reduction in learning time), but also achieves higher average rewards than algorithms without equilibrium transfer and 2) scales significantly better than algorithms without equilibrium transfer when the state/action space grows and the number of agents increases.


title: Bullet trains: a study of NIC burst behavior at microsecond timescales

abstract: While numerous studies have examined the macro-level behavior of traffic in data center networks---overall flow sizes, destination variability, and TCP burstiness---little information is available on the behavior of data center traffic at packet-level timescales. Whereas one might assume that flows from different applications fairly share available link bandwidth, and that packets within a single flow are uniformly paced, the reality is more complex. To meet increasingly high link rates of 10 Gbps and beyond, batching is typically introduced across the network stack---at the application, middleware, OS, transport, and NIC layers. This batching results in short-term packet bursts, which have implications for the design and performance requirements of packet processing devices along the path, including middleboxes, SDN-enabled switches, and virtual machine hypervisors. In this paper, we study the burst behavior of traffic emanating from a 10-Gbps end host across a variety of data center applications. We find that at 10--100 microsecond timescales, the traffic exhibits large bursts (i.e., 10s of packets in length). We further find that this level of burstiness is largely outside of application control, and independent of the high-level behavior of applications.


title: Automatic Data Augmentation by Learning the Deterministic Policy

abstract: Aiming to produce sufficient and diverse training samples, data augmentation has been demonstrated for its effectiveness in training deep models. Regarding that the criterion of the best augmentation is challenging to define, we in this paper present a novel learning-based augmentation method termed as DeepAugNet, which formulates the final augmented data as a collection of several sequentially augmented subsets. Specifically, the current augmented subset is required to maximize the performance improvement compared with the last augmented subset by learning the deterministic augmentation policy using deep reinforcement learning. By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model. We extensively evaluated our proposed DeepAugNet on various benchmark datasets including Fashion MNIST, CUB, CIFAR-100 and WebCaricature. Compared with the current state-of-the-arts, our method can achieve a significant improvement in small-scale datasets, and a comparable performance in large-scale datasets. Code will be available soon.


title: Trusted Execution Environments: A Look under the Hood

abstract: Smartphones are now handling personal and extremely important data and applications. This increases mobile environment threats and makes smartphones one of attackers' preferred targets. To mitigate threats, many proposals and counter-measures have been proposed. In this paper, we focus on one of them, the Trusted Execution Environment (TEE) which is a new family of architecture having its own hardware and software environment completely isolated from the mobile ones. It intends to offer better security than the one provided by mobile platforms. It proposes for example secure storage to applications running on top of the mobile Operating System. We give a complete overview of standardization activities on TEE. We first detail the software and hardware architecture of the TEE as introduced by GlobalPlatform specifications. Then, we describe the different available TEE solutions. Finally, we compare these solutions according to key performance parameters with a special interest to security aspects.


title: Typed syntactic meta-programming

abstract: We present a novel set of meta-programming primitives for use in a dependently-typed functional language. The types of our meta-programs provide strong and precise guarantees about their termination, correctness and completeness. Our system supports type-safe construction and analysis of terms, types and typing contexts. Unlike alternative approaches, they are written in the same style as normal programs and use the language's standard functional computational model. We formalise the new meta-programming primitives, implement them as an extension of Agda, and provide evidence of usefulness by means of two compelling applications in the fields of datatype-generic programming and proof tactics.


title: Development of liposomes loaded with anti-leishmanial drugs for the treatment of cutaneous leishmaniasis

abstract: AbstractCutaneous leishmaniasis is caused by different species of Leishmania parasites and its available treatments have not yet provided a strong consistent result. The weak response of current chemotherapeutics is due to their deficient effects on stealth parasites inside macrophages, rapid clearance from the site of action and systemic side effects in high doses. Liposomal formulation of anti-leishmanial drugs could overcome these problems. In this study, different liposomal formulations of three famous anti-leishmanial drugs: Glucantime®, miltefosine and paromomycin were prepared by a modified freeze-drying double emulsion method. Liposome size, zeta potential and encapsulation efficiency were evaluated, and their imaging was carried out by means of atomic force microscopy. Three formulations were evaluated in vivo by subcutaneous injection into skin lesions caused by Leishmania major in BALB/c mice. Encapsulation efficiency of prepared liposomes was up to 90%; however, they inherited a bimodal size d...


title: A Physical Education trial improves adolescents' cognitive performance and academic achievement: the EDUFIT study

abstract: To analyze the effects of an intervention focused on increasing the time and intensity of Physical Education (PE), on adolescents' cognitive performance and academic achievement. A 4-month group-randomized controlled trial was conducted in 67 adolescents from South-East Spain, 2007. Three classes were randomly allocated into control group (CG), experimental group 1 (EG1) and experimental group 2 (EG2). CG received usual PE (two sessions/week), EG1 received four PE sessions/week and EG2 received four PE sessions/week of high intensity. Cognitive performance (non-verbal and verbal ability, abstract reasoning, spatial ability, verbal reasoning and numerical ability) was assessed by the Spanish Overall and Factorial Intelligence Test, and academic achievement by school grades. All the cognitive performance variables, except verbal reasoning, increased more in EG2 than in CG (all P < 0.05). Average school grades (e.g., mathematics) increased more in EG2 than in CG. Overall, EG2 improved more than EG1, without differences between EG1 and CG. Increased PE can benefit cognitive performance and academic achievement. This study contributes to the current knowledge by suggesting that the intensity of PE sessions might play a role in the positive effect of physical activity on cognition and academic success. Future studies involving larger sample sizes should confirm or contrast these preliminary findings.


title: Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights

abstract: In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner. Motivated by prolific research on homogeneous networks, especially spectral graph theory, we firstly conduct a systematic empirical study on the spectrum and embedding quality of different meta-graphs on multiple HINs, which leads to an efficient method of meta-graph assessment. It also helps us to gain valuable insight into the higher-order organization of HINs and indicates a practical way of selecting useful embedding dimensions. Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with $\ell_{2,1}$-loss, which finds the most informative meta-graphs and embeddings in an end-to-end unsupervised manner. Finally, empirical analysis suggests a unified workflow to close the gap between our meta-graph assessment and combination methods. To the best of our knowledge, this is the first research effort to provide rich theoretical and empirical analyses on the utility of meta-graphs and their combinations, especially regarding HIN embedding. Extensive experimental comparisons with various state-of-the-art neural network based embedding methods on multiple real-world HINs demonstrate the effectiveness and efficiency of our framework in finding useful meta-graphs and generating high-quality HIN embeddings.


title: Efficient Processing of Skyline Queries Using MapReduce

abstract: The skyline operator has attracted considerable attention recently due to its broad applications. However, computing a skyline is challenging today since we have to deal with big data. For data-intensive applications, the MapReduce framework has been widely used recently. In this paper, we propose the efficient parallel algorithm SKY-MR $^+$ for processing skyline queries using MapReduce. We first build a quadtree-based histogram for space partitioning by deciding whether to split each leaf node judiciously based on the benefit of splitting in terms of the estimated execution time. In addition, we apply the dominance power filtering method to effectively prune non-skyline points in advance. We next partition data based on the regions divided by the quadtree and compute candidate skyline points for each partition using MapReduce. Finally, we check whether each skyline candidate point is actually a skyline point in every partition using MapReduce. We also develop the workload balancing methods to make the estimated execution times of all available machines to be similar. We did experiments to compare SKY-MR $^+$ with the state-of-the-art algorithms using MapReduce and confirmed the effectiveness as well as the scalability of SKY-MR $^+$ .


title: Drug–target interaction prediction by random walk on the heterogeneous network

abstract: Predicting potential drug–target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug–target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug–target associations. It integrates three different networks (protein–protein similarity network, drug–drug similarity network, and known drug–target interaction networks) into a heterogeneous network by known drug–target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug–target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug–target interaction prediction. Excellent performance enables us to suggest a number of new potential drug–target interactions for drug development.


Video Objects Description in Hindi Text Language


title: Gland segmentation in colon histology images: The glas challenge contest

abstract: Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.


title: The immunopathology of dengue and Zika virus infections

abstract: A large proportion of the world's population live in areas with dengue virus (DENV) transmission resulting in tens of millions of symptomatic dengue cases each year. Serious complications following DENV infection occur more frequently in those suffering from a second or subsequent infection implicating virus-specific immunity as having a role in pathogenesis. In recent years outbreaks of the related Zika virus (ZIKV) have been associated with birth defects and neurological complications. As DENV and ZIKV share a viral vector sequential infections can occur. Given the sequence homology between the two viruses, the generation of cross-reactive immune responses is highly likely. This review examines the role immunopathogenesis plays during DENV infection as well as highlighting recent studies that demonstrate DENV immunity may have an effect on the outcome of ZIKV infection.


title: A Power Line Communication Network Infrastructure for The Smart Home

abstract: Low voltage electrical wiring has largely been dismissed as too noisy and unpredictable to support high-speed communication signals. Advances in communication and modulation methodologies as well as in adaptive digital signal processing and error detection and correction have spawned novel protocols capable of supporting power line communication networks at speeds comparable to wired LANs. We motivate the use of power line LANs as a basic infrastructure for building integrated smart homes, wherein information appliances ranging from simple control or monitoring devices to multimedia entertainment systems are seamlessly interconnected by the very wires that provide them electricity. By simulation and actual measurements using "reference design" prototype commercial powerline products, we show that the HomePlug MAC and PHY layers can guarantee QoS for real-time communications, supporting delay-sensitive data streams for smart home applications.


title: Image Segmentation and Analysis via Multiscale Gradient Watershed Hierarchies

abstract: Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scale-space behavior of intensity extrema, edges, intensity ridges, and grey-level blobs. We investigate the multiscale behavior of gradient watershed regions. These regions are defined in terms of the gradient properties of the gradient magnitude of the original image. Boundaries of gradient watershed regions correspond to the edges of objects in an image. Multiscale analysis of intensity minima in the gradient magnitude image provides a mechanism for imposing a scale-based hierarchy on the watersheds associated with these minima. This hierarchy can be used to label watershed boundaries according to their scale. This provides valuable insight into the multiscale properties of edges in an image without following these curves through scale-space. In addition, the gradient watershed region hierarchy can be used for automatic or interactive image segmentation. By selecting subtrees of the region hierarchy, visually sensible objects in an image can be easily constructed.


title: Design Optimization of Transformerless Grid-Connected PV Inverters Including Reliability

abstract: This paper presents a new methodology for optimal design of transformerless photovoltaic (PV) inverters targeting a cost-effective deployment of grid-connected PV systems. The optimal switching frequency as well as the optimal values and types of the PV inverter components is calculated such that the PV inverter LCOE generated during the PV system lifetime period is minimized. The LCOE is also calculated considering the failure rates of the components, which affect the reliability performance and lifetime maintenance cost of the PV inverter. A design example is presented, demonstrating that compared to the nonoptimized PV inverter structures, the PV inverters designed using the proposed optimization methodology exhibit lower total manufacturing and lifetime maintenance cost and inject more energy into the electric-grid and by that minimizing LCOE.


title: Explaining the Poor Performance of Consumption-based Asset Pricing Models

abstract: The poor performance of consumption-based asset pricing models relative to traditional portfolio-based asset pricing models is one of the great disappointments of the empirical asset pricing literature. We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain this puzzle. Though artificial data from that economy conform to a consumption-based model by construction, the CAPM and its extensions are much better approximate models than is the standard power utility specification of the consumption-based model. Conditioning information is the central reason for this result. The model economy has one shock, so when returns are measured at sufficiently high frequency the consumption-based model and the CAPM are equivalent and perfect conditional asset pricing models. However, the model economy also produces time-varying expected returns, tracked by the dividend-price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture, and so portfolio-based models are better approximate unconditional asset pricing models.


title: Lane detection using B-snake

abstract: We propose a B-snake based lane detection algorithm. Compared with other lane models, the B-snake based lane model is able to describe a wider range of lane structures, since B-spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been formulated here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. A robust algorithm called CHEVP is presented for providing a good initial position for the B-snake. Furthermore, a minimum energy method by MMSE (minimum mean square energy) is suggested to determine the control points of the B-snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images, and also applicable to both the marked and the unmarked roads, and the dash and the solid paint line roads.


title: Aggressive buffer pool warm-up after restart in SQL Server

abstract: In many settings, a database server has to be restarted either in response to a failure event, or in response to an operational decision such as moving a database service from one machine to another. However, such restarts pose a potential performance problem as the new database server starts off with a cold buffer pool. As a result, the database application experiences a dramatic reduction in performance right after the restart, since just before the restart the database buffer pool was filled with hot pages and after the restart the database buffer pool is empty. To address these issues, traditional database systems use mechanisms such as SQL Server's aggressive page expansion and MySQL's buffer pool preloading. However, these approaches have key limitations including long warm-up times, possible early hot page eviction, user query performance saturation, and failure restart. In this paper, we present a new framework for SQL Server that allows continual capturing of the state of the buffer pool, and restoring the server state quickly with a snapshot of the buffer pool at restart. Our empirical evaluation demonstrates that our method reduces the time to regain peak performance by a factor of 2X or more over the previous approaches.


title: Malaysia traffic sign recognition with convolutional neural network

abstract: Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various kind of scenarios, even those which were not used during training. Therefore, a deep architecture neural network is implemented to perform traffic sign classification in order to improve the traffic sign recognition rate. A comparative study for a deep and shallow architecture neural network is presented in this paper. Deep and shallow architecture neural network refer to convolutional neural network (CNN) and radial basis function neural network (RBFNN) respectively. In the simulation result, two types of training modes had been compared i.e. incremental training and batch training. Experimental results show that incremental training mode trains faster than batch training mode. The performance of the convolutional neural network is evaluated with the Malaysian traffic sign database and achieves 99% of the recognition rate.


title: Smart Cities Concept and Challenges: Bases for the Assessment of Smart City Projects

abstract: ASCIMER (Assessing Smart Cities in the Mediterranean Region) is a project developed by the Universidad Politecnica of Madrid (UPM) for the EIBURS call on “Smart City Development: Applying European and International Experience to the Mediterranean Region”. Nowadays, many initiatives aimed at analysing the conception process, deployment methods or outcomes of the — referred as — Smart City projects are being developed in multiple fields. Since its conception, the Smart City notion has evolved from the execution of specific projects to the implementation of global strategies to tackle wider city challenges. ASCIMER's project takes as a departure point that any kind of Smart City assessment should give response to the real challenges that cities of the 21st century are facing. It provides a comprehensive overview of the available possibilities and relates them to the specific city challenges. A selection of Smart City initiatives will be presented in order to establish relations between the identified city challenges and real Smart Projects designed to solve them. As a result of the project, a Projects Guide has been developed as a tool for the implementation of Smart City projects that efficiently respond to complex and diverse urban challenges without compromising their sustainable development and while improving the quality of life of their citizens.


Vitamin D blood levels, unnecessary prescriptions, and retest in naïve patients


title: Providing ultra-short latency to user-centric 5G applications at the mobile network edge

abstract: Mobile Edge Computing (MEC) will play a key role in next-generation mobile networks to extend the range of supported delay-sensitive applications. Furthermore, an increasing attention is paid to provide user-centric services, to better address the strict requirements of novel immersive applications. In this scenario, MEC solutions need to efficiently cope with user mobility, which requires fast relocation of service instances to guarantee the desired Quality of Experience. However, service migration is still an open issue, especially for resource-constrained edge nodes interconnected by high-latency and low-bandwidth links. In this paper, by leveraging the potential of lightweight container-based virtualization techniques, we investigate a novel approach to support service provisioning in dynamic MEC environments. In particular, we present a framework where proactive service replication for stateless applications is exploited to drastically reduce the time of service migration between different cloudlets and to meet the latency requirements. The performance evaluation shows promising results of our approach with respect to classic reactive service migration.


title: Reduction of contrast medium volume in abdominal aorta CTA: multiphasic injection technique versus a test bolus volume.

abstract: OBJECTIVE ::: The purpose of this study is to reduce the administered contrast medium volume in abdominal CTA by using a test bolus injection, with the preservation of adequate quantitative and qualitative vessel enhancement. ::: ::: ::: STUDY DESIGN ::: For this technical efficacy study 30 patients, who were referred for a CTA examination of the abdominal aorta, were included. Randomly 15 patients were assigned to undergo a multiphasic injection protocol and received 89 mL of contrast medium (Optiray 350) (protocol I). Fifteen patients were assigned to the test bolus injection protocol (protocol II), which implies injection of a 10 mL test bolus of Optiray 350 prior to performing CTA with a 40 mL of contrast medium. Quantitative assessment of vascular enhancement was performed by measuring the amount of Hounsfield Units in the aorta at 30 positions from the celiac trunk to the iliac arteries in both groups. Qualitative assessment was performed by three radiologists who scored the images at a 5-point scale. ::: ::: ::: RESULTS ::: Quantitative assessment showed that there was no significant difference in vascular enhancement for patients between the two protocols, with mean attenuation values of 280.9 ± 50.84 HU and 258.60 ± 39.28 HU, respectively. The image quality of protocol I was rated 4.31 (range: 3.67/5.00) and of protocol II 4.11 (range: 2.67/5.00). These differences were not statistically significant. ::: ::: ::: CONCLUSION ::: This study showed that by using a test bolus injection and the administration of 50 mL of contrast medium overall, CTA of the abdominal aorta can reliably be performed, with regard to quantitative and qualitative adequate vessel enhancement.


title: The use of gaze to control drones

abstract: This paper presents an experimental investigation of gaze-based control modes for unmanned aerial vehicles (UAVs or "drones"). Ten participants performed a simple flying task. We gathered empirical measures, including task completion time, and examined the user experience for difficulty, reliability, and fun. Four control modes were tested, with each mode applying a combination of x-y gaze movement and manual (keyboard) input to control speed (pitch), altitude, rotation (yaw), and drafting (roll). Participants had similar task completion times for all four control modes, but one combination was considered significantly more reliable than the others. We discuss design and performance issues for the gaze-plus-manual split of controls when drones are operated using gaze in conjunction with tablets, near-eye displays (glasses), or monitors.


title: A Practical and Configurable Lip Sync Method for Games

abstract: We demonstrate a lip animation (lip sync) algorithm for real-time applications that can be used to generate synchronized facial movements with audio generated from natural speech or a text-to-speech engine. Our method requires an animator to construct animations using a canonical set of visemes for all pairwise combinations of a reduced phoneme set (phone bigrams). These animations are then stitched together to construct the final animation, adding velocity and lip-pose constraints. This method can be applied to any character that uses the same, small set of visemes. Our method can operate efficiently in multiple languages by reusing phone bigram animations that are shared among languages, and specific word sounds can be identified and changed on a per-character basis. Our method uses no machine learning, which offers two advantages over techniques that do: 1) data can be generated for non-human characters whose faces can not be easily retargeted from a human speaker's face, and 2) the specific facial poses or shapes used for animation can be specified during the setup and rigging stage, and before the lip animation stage, thus making it suitable for game pipelines or circumstances where the speech targets poses are predetermined, such as after acquisition from an online 3D marketplace.


title: Histogram of distances for local surface description

abstract: 3D object recognition is proven superior compared to its 2D counterpart with numerous implementations, making it a current research topic. Local based proposals specifically, although being quite accurate, they limit their performance on the stability of their local reference frame or axis (LRF/A) on which the descriptors are defined. Additionally, extra processing time is demanded to estimate the LRF for each local patch. We propose a 3D descriptor which overrides the necessity of a LRF/A reducing dramatically processing time needed. In addition robustness to high levels of noise and non-uniform subsampling is achieved. Our approach, namely Histogram of Distances is based on multiple L2-norm metrics of local patches providing a simple and fast to compute descriptor suitable for time-critical applications. Evaluation on both high and low quality popular point clouds showed its promising performance.


title: Three-dimensional visualization of skin lymphatic drainage patterns of the head and neck.

abstract: BACKGROUND ::: Lymphatic drainage from skin on the head and neck is complex. We sought to provide improved visualization and analysis of the patterns of head and neck skin lymphatic drainage using aggregated lymphoscintigraphy data. ::: ::: ::: METHODS ::: Lymphoscintigraphy data from 929 patients with cutaneous melanoma on the head and neck collected at the Sydney Melanoma Unit have been mapped onto a 3-dimensional computer model of the skin and lymph nodes. ::: ::: ::: RESULTS ::: Novel heat maps and interactive software have been created, which show subtle differences in lymphatic drainage patterns when compared with those that are previously reported. Posterior head and neck node fields largely drained posterior regions of the head and neck, whereas anterolateral skin regions were generally drained by the other head and neck node fields. ::: ::: ::: CONCLUSIONS ::: The heat maps and interactive software tool provide novel visualization of head and neck lymphatic drainage patterns, which has both educational and clinical utility.


title: Incorporating Stochastic Lead Times Into the Guaranteed Service Model of Safety Stock Optimization

abstract: Effective end-to-end supply chain management and network inventory optimization must account for service levels, demand volatility, lead times, and lead-time variability. Most inventory models incorporate demand variability, but far fewer rigorously account for lead-time variability, particularly in multiechelon supply chain networks. Our research extends the guaranteed service model of safety stock placement to allow random lead times. The main methodological contribution is the creation of closed-form equations for the expected safety stock in the system; this includes a derivation for the early-arrival stock in the system. The main applied contributions are the demonstration of real stochastic lead times in practice and a discussion of how our approach outperforms more traditional heuristics that either ignore lead-time variability or consider the maximum lead time at each stage.


Consideration for Oxygen Adsorption Species on SnO2 Semiconductor Gas Sensors


title: Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs

abstract: Recursive neural networks (RNN) and their recently proposed extension recursive long short term memory networks (RLSTM) are models that compute representations for sentences, by recursively combining word embeddings according to an externally provided parse tree. Both models thus, unlike recurrent networks, explicitly make use of the hierarchical structure of a sentence. In this paper, we demonstrate that RNNs nevertheless suffer from the vanishing gradient and long distance dependency problem, and that RLSTMs greatly improve over RNN's on these problems. We present an artificial learning task that allows us to quantify the severity of these problems for both models. We further show that a ratio of gradients (at the root node and a focal leaf node) is highly indicative of the success of backpropagation at optimizing the relevant weights low in the tree. This paper thus provides an explanation for existing, superior results of RLSTMs on tasks such as sentiment analysis, and suggests that the benefits of including hierarchical structure and of including LSTM-style gating are complementary.


title: Depth from focus with your mobile phone

abstract: While prior depth from focus and defocus techniques operated on laboratory scenes, we introduce the first depth from focus (DfF) method capable of handling images from mobile phones and other hand-held cameras. Achieving this goal requires solving a novel uncalibrated DfF problem and aligning the frames to account for scene parallax. Our approach is demonstrated on a range of challenging cases and produces high quality results.


Use of Information Technology in Mandatory Settings: A Proposal for an Objective View of Appropriation