title: Redesign the Memory Allocator for Non-Volatile Main Memory

abstract: The non-volatile memory (NVM) has the merits of byte-addressability, fast speed, persistency and low power consumption, which make it attractive to be used as main memory. Commonly, user process dynamically acquires memory through memory allocators. However, traditional memory allocators designed with in-place data writes are not appropriate for the non-volatile main memory (NVRAM) due to the limited endurance. In this article, first, we quantitatively analyze the wear-oblivious of DRAM-oriented designed allocator—glibc malloc and the inefficiency of wear-conscious allocator NVMalloc. Then, we propose WAlloc, an efficient wear-aware manual memory allocator designed for NVRAM: (1) decouples metadata and data management; (2) distinguishes metadata with volatility; (3) redirects the data writes around to achieve wear-leveling; (4) redesigns an efficient and effective NVM copy mechanism, bypassing the CPU cache partially and prefetching data explicitly. Finally, experimental results show that the wear-leveling of WAlloc outperforms that of NVMalloc about 30% and 60% under random workloads and well-distributed workloads, respectively. Besides, WAlloc reduces the average data memory writes in 64 bytes block by 1.5 times comparing with glibc malloc. With the fulfillment of data persistency, cache bypassing NVM copy is better than cache line flushing NVM copy with performance improvement circa 14%.


title: The English Lexicon Project.

abstract: The English Lexicon Project is a multiuniversity effort to provide a standardized behavioral and descriptive data set for 40,481 words and 40,481 nonwords. It is available via the Internet at elexicon.wustl.edu. Data from 816 participants across six universities were collected in a lexical decision task (approximately 3400 responses per participant), and data from 444 participants were collected in a speeded naming task (approximately 2500 responses per participant). The present paper describes the motivation for this project, the methods used to collect the data, and the search engine that affords access to the behavioral measures and descriptive lexical statistics for these stimuli.


title: Faster R-CNN Features for Instance Search

abstract: Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN. We take advantage of the object proposals learned by a Region Proposal Network (RPN) and their associated CNN features to build an instance search pipeline composed of a first filtering stage followed by a spatial reranking. We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve. We assess the performance of our proposed system with the Oxford Buildings 5k, Paris Buildings 6k and a subset of TRECVid Instance Search 2013, achieving competitive results.


title: Breast-feeding as a Protection against Gastroenteritis and Other Infections

abstract: This editorial reviews current knowledge of the mechanisms by which breastfeeding protects infants against gastroenteritis and other infections. Breast milk averts the disadvantage of contamination with micro-organisms in the handling process common to indigenous foods used in the weaning period and commercial milk products. The infections caused by contaminated food and water compound the malnutrition already caused by inadequate weaning food. Human milk is thought to contain several nonspecific and specific factors that provide protection against infections such as gastroenteristis and upper respiratory tract infections. Analogues to epithelial cell receptors may prevent the mucosal attachment of potential pathogens. Protection of the breast-fed infant against cholera has been shown to be related to the level of the IgA antibodies in the mothers milk against the cholera lipoplysaccharide and enterotoxin.


title: The Unreasonable Effectiveness of Address Clustering

abstract: Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe the primary reasons behind this effectiveness: address reuse, avoidable merging, super-clusters with high centrality,, the incremental growth of address clusters. We quantify their impact during Bitcoin's first seven years of existence.


title: Towards an optimized model of incident ticket correlation

abstract: In recent years, IT Service Management (ITSM) has become one of the most researched areas of IT. Incident and Problem Management are two of the Service Operation processes in the IT Infrastructure Library (ITIL). These two processes aim to recognize, log, isolate and correct errors which occur in the environment and disrupt the delivery of services. Incident Management and Problem Management form the basis of the tooling provided by an Incident Ticket Systems (ITS).


title: Corporate responsibility and financial performance: the role of intangible resources

abstract: This paper examines the effects of a firm's intangible resources in mediating the relationship between corporate responsibility and financial performance. We hypothesize that previous empirical findings of a positive relationship between social and financial performance may be spurious because the researchers failed to account for the mediating effects of intangible resources. Our results indicate that there is no direct relationship between corporate responsibility and financial performance—merely an indirect relationship that relies on the mediating effect of a firm's intangible resources. We demonstrate our theoretical contention with the use of a database comprising 599 companies from 28 countries. Copyright © 2009 John Wiley & Sons, Ltd.


title: CAD/CAM monolithic restorations and full-mouth adhesive rehabilitation to restore a patient with a past history of bulimia: the modified three-step technique.

abstract: Abstract Due to an increasing awareness about dental erosion, many clinicians would like to propose treatments even at the initial stages of the disease. However, when the loss of tooth structure is visible only to the professional eye, and it has not affected the esthetics of the smile, affected patients do not usually accept a full-mouth rehabilitation. Reducing the cost of the therapy, simplifying the clinical steps, and proposing noninvasive adhesive techniques may promote patient acceptance. In this article, the treatment of an ex-bulimic patient is illustrated. A modified approach of the three-step technique was followed. The patient completed the therapy in five short visits, including the initial one. No tooth preparation was required, no anesthesia was delivered, and the overall (clinical and laboratory) costs were kept low. At the end of the treatment, the patient was very satisfied from a biologic and functional point of view.


title: A new compact wide band 8-way SIW power divider at X-band

abstract: Ultra wideband components have been developed using SIW technology. The various components including a GCPW transition with less than 0.4dB insertion loss are developed. In addition to, T and Y-junctions are optimized with relatively wide bandwidth of greater than 63% and 40% respectively that have less than 0.6 dB insertion loss. The developed transition was utilized to design an X-band 8 way power divider that demonstrated excellent performance over a 5 GHz bandwidth with less than ±4o and ±0.9 dB phase and amplitude imbalance, respectively. The developed SIW power divider has a low profile and is particularly suitable for circuits' integration.


title: Manipulation of online reviews: An analysis of ratings, readability, and sentiments

abstract: As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of products to increase their sales. Despite the suspicion on the existence of such manipulation, the amount of such manipulation is unknown, and deciding which reviews to believe in is largely based on the reader's discretion and intuition. Therefore, the success of the manipulation of reviews by firms in generating sales of products is unknown. In this paper, we propose a simple statistical method to detect online reviews manipulation, and assess how consumers respond to products with manipulated reviews. In particular, the writing style of reviewers is examined, and the effectiveness of manipulation through ratings, sentiments, and readability is investigated. Our analysis examines textual information available in online reviews by combining sentiment mining techniques with readability assessments. We discover that around 10.3% of the products are subject to online reviews manipulation. In spite of the deliberate use of sentiments and ratings in manipulated products, consumers are only able to detect manipulation taking place through ratings, but not through sentiments. The findings from this research ensue a note of caution for all consumers that rely on online reviews of books for making purchases, and encourage them to delve deep into the book reviews without being deceived by fraudulent manipulation.


title: Analysis and design of phase shift full bridge converter with series-connected two transformers

abstract: A new phase shift full bridge (PSFB) converter with series-connected two transformers is proposed. The proposed converter shows wide zero voltage switching (ZVS) ranges and no output inductor is needed since each transformer individually acts as an inductor or a transformer during different times of the switching cycle. The operational principle, large signal modeling, and design equations are presented. Experimental results demonstrate that the proposed converter can achieve a significant improvement in the efficiency for a 100W (5 V, 20 A) telecommunication on-board power supply.


title: Low kickback noise techniques for CMOS latched comparators

abstract: The latched comparator is utilized in virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. Such high voltage variations in the regeneration nodes are coupled to the input voltage - kickback noise. This paper reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations verify the effectiveness of our techniques.


title: Oligonucleotide structure affects behavior of oligoplexes

abstract: The objective of this work is to investigate the effect of oligonucleotide structure on the formation and biological properties of oligonucleotide/poly-L-lysine (ODN/PLL) complexes, or "oligoplexes". Two structurally different ODN sequences RBG (rabbit /spl beta/ globin) 33 and RBG147 were used in the study. The difference in ODN binding and hence complex formation was analyzed by a gel retardation assay. In addition, complex formation in buffer versus in serum-free medium was studied. The oligoplexes also displayed different cellular uptake efficiencies. Differences in the biological activity were attributed to the varying characteristics of the oligoplexes due to ODN structural difference.


title: An efficient compression scheme for 4-D medical images using hierarchical vector quantization and motion compensation

abstract: This paper proposes an efficient compression scheme for compressing time-varying medical volumetric data. The scheme uses 3-D motion estimation to create a homogenous preprocessed data to be compressed by a 3-D image compression algorithm using hierarchical vector quantization. A new block distortion measure, called variance of residual (VOR), and three 3-D fast block matching algorithms are used to improve the motion estimation process in term of speed and data fidelity. The 3-D image compression process involves the application of two different encoding techniques based on the homogeneity of input data. Our method can achieve a higher fidelity and faster decompression time compared to other lossy compression methods producing similar compression ratios. The combination of 3-D motion estimation using VOR and hierarchical vector quantization contributes to the good performance.


title: Dhwani: secure peer-to-peer acoustic NFC

abstract: Near Field Communication (NFC) enables physically proximate devices to communicate over very short ranges in a peer-to-peer manner without incurring complex network configuration overheads. However, adoption of NFC-enabled applications has been stymied by the low levels of penetration of NFC hardware. In this paper, we address the challenge of enabling NFC-like capability on the existing base of mobile phones. To this end, we develop Dhwani, a novel, acoustics-based NFC system that uses the microphone and speakers on mobile phones, thus eliminating the need for any specialized NFC hardware. A key feature of Dhwani is the JamSecure technique, which uses self-jamming coupled with self-interference cancellation at the receiver, to provide an information-theoretically secure communication channel between the devices. Our current implementation of Dhwani achieves data rates of up to 2.4 Kbps, which is sufficient for most existing NFC applications.


title: Swarms, Phase Transitions, and Collective Intelligence

abstract: A spacially extended model of the collective behavior of a large number of locally acting organisms is proposed in which organisms move probabilistically between local cells in space, but with weights dependent on local morphogenetic substances, or morphogens. The morphogens are in turn are effected by the passage of an organism. The evolution of the morphogens, and the corresponding flow of the organisms constitutes the collective behavior of the group. Such models have various types of phase transitions and self-organizing properties controlled both by the level of the noise, and other parameters. ::: The model is then applied to the specific case of ants moving on a lattice. The local behavior of the ants is inspired by the actual behavior observed in the laboratory, and analytic results for the collective behavior are compared to the corresponding laboratory results. ::: It is hoped that the present model might serve as a paradigmatic example of a complex cooperative system in nature. In particular swarm models can be used to explore the relation of nonequilibrium phase transitions to at least three important issues encountered in artificial life. Firstly, that of emergence as complex adaptive behavior. Secondly, as an exploration of continuous phase transitions in biological systems. Lastly, to derive behavioral criteria for the evolution of collective behavior in social organisms.


title: The twin crises: the causes of banking and balance-ofpayments problems

abstract: In the wake of the Mexican and Asian currency turmoil, the subject of financial crises has come to the forefront of academic and policy discussions. This paper analyzes the links between banking and currency crises. We find that: problems in the banking sector typically precede a currency crisis--the currency crisis deepens the banking crisis, activating a vicious spiral; financial liberalization often precedes banking crises. The anatomy of these episodes suggests that crises occur as the economy enters a recession, following a prolonged boom in economic activity that was fueled by credit, capital inflows and accompanied by an overvalued currency.


title: NeuroRex: A clinical neural interface roadmap for EEG-based brain machine interfaces to a lower body robotic exoskeleton

abstract: In this communication, a translational clinical brain-machine interface (BMI) roadmap for an EEG-based BMI to a robotic exoskeleton (NeuroRex) is presented. This multi-faceted project addresses important engineering and clinical challenges: It addresses the validation of an intelligent, self-balancing, robotic lower-body and trunk exoskeleton (Rex) augmented with EEG-based BMI capabilities to interpret user intent to assist a mobility-impaired person to walk independently. The goal is to improve the quality of life and health status of wheelchair-bounded persons by enabling standing and sitting, walking and backing, turning, ascending and descending stairs/curbs, and navigating sloping surfaces in a variety of conditions without the need for additional support or crutches.


title: Autonomous vehicle social behavior for highway entrance ramp management

abstract: “Socially cooperative driving” is an integral part of our everyday driving, hence requiring special attention to imbue the autonomous driving with a more natural driving behavior. In this paper, an intention-integrated Prediction- and Cost function-Based algorithm (iPCB) framework is proposed to enable an autonomous vehicle to perform cooperative social behavior. An intention estimator is developed to extract the probability of surrounding agents' intentions in real time. Then for each candidate strategy, a prediction engine considering the interaction between host and surrounding agents is used to predict future scenarios. A cost function-based evaluation is applied to compute the cost for each scenario and select the decision corresponding to the lowest cost. The algorithm was tested in simulation on an autonomous vehicle cooperating with vehicles merging from freeway entrance ramps with 10,000 randomly generated scenarios. Compared with approaches that do not take social behavior into account, the iPCB algorithm shows a 41.7% performance improvement based on the chosen cost functions.


title: Genetic modification of lignin biosynthesis for improved biofuel production

abstract: The energy in cellulosic biomass largely resides in plant cell walls. Cellulosic biomass is more difficult than starch to break down into sugars because of the presence of lignin and the complex structure of cell walls. Transgenic down-regulation of major lignin genes led to reduced lignin content, increased dry matter degradability, and improved accessibility of cellulases for cellulose degradation. This review provides background information on lignin biosynthesis and focuses on genetic manipulation of lignin genes in important monocot species as well as the dicot potential biofuel crop alfalfa. Reduction of lignin in biofuel crops by genetic engineering is likely one of the most effective ways of reducing costs associated with pretreatment and hydrolysis of cellulosic feedstocks, although some potential fitness issues should also be addressed.


A study of interactive 3D point location in a computer simulated virtual environment


title: Online Gradient Boosting

abstract: We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online learning algorithm with smooth convex loss functions that competes with a larger class of regression functions. Our main result is an online gradient boosting algorithm that converts a weak online learning algorithm into a strong one where the larger class of functions is the linear span of the base class. We also give a simpler boosting algorithm that converts a weak online learning algorithm into a strong one where the larger class of functions is the convex hull of the base class, and prove its optimality.


The welchADF Package for Robust Hypothesis Testing in Unbalanced Multivariate Mixed Models with Heteroscedastic and Non-normal Data


title: To Lock, Swap, or Elide: On the Interplay of Hardware Transactional Memory and Lock-Free Indexing

abstract: The release of hardware transactional memory (HTM) in commodity CPUs has major implications on the design and implementation of main-memory databases, especially on the architecture of high-performance lock-free indexing methods at the core of several of these systems. This paper studies the interplay of HTM and lock-free indexing methods. First, we evaluate whether HTM will obviate the need for crafty lock-free index designs by integrating it in a traditional B-tree architecture. HTM performs well for simple data sets with small fixed-length keys and payloads, but its benefits disappear for more complex scenarios (e.g., larger variable-length keys and payloads), making it unattractive as a general solution for achieving high performance. Second, we explore fundamental differences between HTM-based and lock-free B-tree designs. While lock-freedom entails design complexity and extra mechanism, it has performance advantages in several scenarios, especially high-contention cases where readers proceed uncontested (whereas HTM aborts readers). Finally, we explore the use of HTM as a method to simplify lock-free design. We find that using HTM to implement a multi-word compare-and-swap greatly reduces lock-free programming complexity at the cost of only a 10-15% performance degradation. Our study uses two state-of-the-art index implementations: a memory-optimized B-tree extended with HTM to provide multi-threaded concurrency and the Bw-tree lock-free B-tree used in several Microsoft production environments.


title: Towards IP over LPWANs technologies: LoRaWAN, DASH7, NB-IoT

abstract: In this paper, we discuss a set of solutions that are proposed in order to run IPv6 over IoT and we investigate its applicability over the three Low Power Wide Area Networks (LPWANs) technologies: LoRaWAN, DASH7, NB-IoT. LPWANs are wireless technologies that are used to connect things to the Internet. These technologies are characterized by their large coverage area, long battery operation, low bandwidth, small frame payload size, and the use of asymmetric and non-synchronized communication. Based on this investigation, we highlight the schemes that can be adopted for IP-based LPWANs technologies.


title: Dynamic behavior evaluation for malware detection

abstract: The number of malicious applications, their diversity and complexity is continuously growing. To provide the best protection against these advanced threats, there is a need to develop proactive detection solutions, that are able to detect malware based on their behavior. One of the essential concerns when developing such solutions is identifying specific actions based on which malicious applications will be detected. Almost all actions commonly performed by malware can also be encountered in the behavior of clean applications, making it necessary to develop a scoring engine able to evaluate the potential malicious behavior of a process, based on its entire set of actions. We propose a set of dynamic behavior evaluation concepts that should be considered by any proactive security researcher. Using these concepts we developed a scoring engine for a behavior-based solution that has a high detection rate, a small number of false positives, and is highly adaptable and extensible.


title: A multi‐scale convolutional neural network for phenotyping high‐content cellular images

abstract: Identifying phenotypes based on high-content cellular images is challenging. Conventional ::: image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters. Here we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images’ pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared to state-of-the-art results, including those of other deep CNN architectures. In addition to using the network to simply obtain a yes-or-no prediction for a given phenotype, we use the probability outputs calculated by the network to quantitatively describe the phenotypes. Our study shows that these probability values correlate with chemical treatment concentrations. This finding validates further our approach and enables chemical treatment potency estimation via convolutional neural networks.


title: An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images

abstract: Automatic extraction of features from MRI brain scans and diagnosis of Alzheimer's Disease (AD) remain a challenging task. In this paper, we propose an efficient and simple three-dimensional convolutional network (3D ConvNet) architecture that is able to achieve high performance for detection of AD on a relatively large dataset. The proposed 3D ConvNet consists of five convolutional layers for feature extraction, followed by three fully-connected layers for AD/NC classification. The main contributions of the paper include: (a) propose a novel and effective 3D ConvNet architecture; (b) study the impact of hyper-parameter selection on the performance of AD classification; (c) study the impact of pre-processing; (d) study the impact of data partitioning; (e) study the impact of dataset size. Experiments conducted on an ADNI dataset containing 340 subjects and 1198 MRI brain scans have resulted good performance (with the test accuracy of 98.74%, 100% AD detection rate and 2,4% false alarm). Comparisons with 7 existing state-of-the-art methods have provided strong support to the robustness of the proposed method.


title: On the difficulty of benchmarking inductive program synthesis methods

abstract: A variety of inductive program synthesis (IPS) techniques have recently been developed, emerging from different areas of computer science. However, these techniques have not been adequately compared on general program synthesis problems. In this paper we compare several methods on problems requiring solution programs to handle various data types, control structures, and numbers of outputs. The problem set also spans levels of abstraction; some would ordinarily be approached using machine code or assembly language, while others would ordinarily be approached using high-level languages. The presented comparisons are focused on the possibility of success; that is, on whether the system can produce a program that passes all tests, for all training and unseen testing inputs. The compared systems are Flash Fill, MagicHaskeller, TerpreT, and two forms of genetic programming. The two genetic programming methods chosen were PushGP and Grammar Guided Genetic Programming. The results suggest that PushGP and, to an extent, TerpreT and Grammar Guided Genetic Programming are more capable of finding solutions than the others, albeit at a higher computational cost. A more salient observation is the difficulty of comparing these methods due to drastically different intended applications, despite the common goal of program synthesis.


title: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

abstract: Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.


title: Rethinking schizophrenia

abstract: How will we view schizophrenia in 2030? Schizophrenia today is a chronic, frequently disabling mental disorder that affects about one per cent of the world's population. After a century of studying schizophrenia, the cause of the disorder remains unknown. Treatments, especially pharmacological treatments, have been in wide use for nearly half a century, yet there is little evidence that these treatments have substantially improved outcomes for most people with schizophrenia. These current unsatisfactory outcomes may change as we approach schizophrenia as a neurodevelopmental disorder with psychosis as a late, potentially preventable stage of the illness. This 'rethinking' of schizophrenia as a neurodevelopmental disorder, which is profoundly different from the way we have seen this illness for the past century, yields new hope for prevention and cure over the next two decades.


title: A network of physiological neurons with differentiated excitatory and inhibitory units possessing pattern recognition capacity

abstract: The incorporation of properties derived from the function of physiological neurons to artificial networks is actively sought in order to enrich the range of dynamic behavior of the networks. In the present work a novel neural network is proposed with different excitatory and inhibitory neural populations. This property conforms with the so-called Dale's hypothesis that applies to neurons of the mammalian brain. The network improves the quality of the associative memory abilities which are shown to exist in an earlier model, where the synaptic activity derived from a neuron could be of both types. >


title: Cascaded PID temperature controller for FOPDT model of shell-and-tube heat exchanger based on Matlab/Simulink

abstract: Cascade control is one of the most successful methods for improving single loop control performance. This control can stabilize the system, minimalize both of oscillation and integral error of interference in manipulation variables. Cascade control structure consists of two controllers where loop feedback in the loop feedback. This research takes a case on original shell-and-tube heat exchanger and simulated using Matlab/Simulink. Cascade control is used to stabilize outlet temperature even there is interference. Shell-and-tube heat exchanger is modeled using first order plus delay time (FOPDT). The type of controller is used PID standard with Ziegler-Nichols tuning method. Performance of cascade control is tested by providing inlet fluid flow disturbance. The experiment got the best performance on PI control (secondary controller) with parameter of Kp and Ti worth 1.375 and 2. PI control produces smallest IAE amount 34.57. Test control of cascade (primary controller) obtained a result that control of PID produces the best response. PID control with parameter values Kp, Ti, and Td of 2.85, 53.06 and 13.265 gave the smallest IAE of 601.54 and the fastest rise time of 121.5 seconds. From this research, it is concluded that the shell-and-tube heat exchanger system can use cascade control to regulate the temperature stably even with an unstable fluid flow.


Health checks for cardiometabolic diseases in primary care: One size does not fit all


title: Feature Selection Library (MATLAB Toolbox)

abstract: Feature Selection Library (FSLib) is a widely applicable MATLAB library for Feature Selection (FS). FS is an essential component of machine learning and data mining which has been studied for many years under many different conditions and in diverse scenarios. These algorithms aim at ranking and selecting a subset of relevant features according to their degrees of relevance, preference, or importance as defined in a specific application. Because feature selection can reduce the amount of features used for training classification models, it alleviates the effect of the curse of dimensionality, speeds up the learning process, improves model's performance, and enhances data understanding. This short report provides an overview of the feature selection algorithms included in the FSLib MATLAB toolbox among filter, embedded, and wrappers methods.


title: Continuous Distributed Representations of Words as Input of LSTM Network Language Model

abstract: The continuous skip-gram model is an efficient algorithm for learning quality distributed vector representations that are able to capture a large number of syntactic and semantic word relationships. Artificial neural networks have become the state-of-the-art in the task of language modelling whereas Long-Short Term Memory (LSTM) networks seem to be efficient training algorithm.


title: Phineas gauged: decision-making and the human prefrontal cortex

abstract: Poor social judgment and decision-making abilities have often been attributed to people who have suffered injury to the ventromedial prefrontal cortex (VMPFC). However, few laboratory tests of decision-making have been conducted on these patients. The exception to this is the Iowa Gambling Task which has often, but not always, demonstrated differential performance between patients and controls. Results from patients with prefrontal cortex lesions on a novel test of decision-making are presented. Participants explored and chose from pairs of gambles that differed in their underlying distributions, primarily in the variance of their respective outcomes. In accordance with many findings from the behavioral decision-making literature, both young normal participants and older patient controls demonstrated a marked avoidance of risk and selected largely from secure, low variance gambles. In contrast, patients with ventromedial lesions were divided into two clear sub-groups. One group behaved similarly to normals, showing a risk-averse strategy. The other group displayed a distinctive risk-seeking behavior pattern, choosing predominantly from the high-variance, high-risk decks. This research demonstrates some of the advantages of using methods and theories from traditional decision-making research to study the behavior of patients, as well as the benefits of examining individual participants, and provides new insights into the nature of the decision-making deficit in patients with ventromedial prefrontal cortex lesions.


title: High- ${Q}$ Inductors Utilizing Thick Metals and Dense-Tapered Spirals

abstract: Ultra-thick metals are known to be counter productive for quality factor ( ${Q}$ ) improvement at high frequencies in multiturn inductors. From the experimental results, assisted by electromagnetic simulations, it is first shown that ${Q}$ improvement with ultra-thick metals can be pushed to higher frequencies using variable width and spaced (tapered) spirals. Furthermore, it is also shown that the extent of tapering is proportional to the metal thickness for the highest performance. Second, dense-tapered layouts are shown to be good candidates for higher ${Q}$ rather than hollow ones for a given inductance density.


title: Mission-focused cyber situational understanding via graph analytics

abstract: This paper describes CyGraph, a prototype tool for improving network security posture, maintaining situational understanding in the face of cyberattacks, and focusing on protection of mission-critical assets. CyGraph captures complex relationships among entities in the cyber security domain, along with how mission elements depend on cyberspace assets. Pattern-matching queries traverse the graph of interrelations according to user-specified constraints, yielding focused clusters of high-risk activity from the swarm of complex interrelationships. Analytic queries are expressed in CyGraph Query Language (CyQL), a domain-specific language for expressing graph patterns of interest, which CyGraph translates to the backend native query language. CyGraph automatically infers the structure of its underlying graph model through analysis of the ingested data, which it presents to the user for generating queries in an intuitive way. CyGraph has been experimentally validated in both enterprise and tactical military environments.


title: An embedded key management system for PUF-based security enclosures

abstract: Hardware Security Modules (HSMs) are embedded systems which provide a physically secured environment for data storage and handling. The device is protected by an enclosure against adversaries. A supervisor circuit monitors the enclosure's integrity and deletes all Critical Security Parameters (CSPs), such as keys, upon a tamper event. While current solutions store CSPs in battery-backed memory, our novel batteryless solution exploits the Physical Unclonable Function (PUF) of the enclosure to derive a key encryption key (KEK). However, such a PUF-based solution requires a more complex Embedded Key Management System (EKMS) for integrity verification, PUF usage, and key management. In this paper, we address this issue by discussing an adversary model, deriving design requirements, and presenting a hardened firmware architecture for PUF-based security enclosures. We present the complementing security extensions for FreeRTOS that enhance the operating system's security. To verify the concept's feasibility, we implement the proposed system and evaluate its performance. Our results show that this security architecture for an EKMS can serve as a firmware basis for novel PUF-based HSMs.


title: Recurrent Neural Networks for Enhancement of Signature-based Network Intrusion Detection Systems

abstract: Security of information passing through the Internet is threatened by today's most advanced malware ranging from orchestrated botnets to much simpler polymorphic worms. These threads, as examples of zero-day attacks, are able to change their behavior several times at the early phases of their existence to bypass the network intrusion detection systems (NIDS). It is known that even well- designed, and frequently-updated signature-based NIDS cannot detect the zero-day treats due to the lack of an adequate signature database, adaptive to intelligent attacks on the Internet. On the other hand, applying traditional machine learning methods could not narrow this gap. More importantly, having an NIDS, it should be tested on malicious traffic dataset that not only represents known attacks, but also can to some extent reflect the characteristics of unknown, zero-day attacks. Generating such traffic is identified in the literature as one of the main obstacles for evaluating the effectiveness of NIDS. To address these issues, we apply Recurrent Neural Networks (RNNs) known as powerful tools in finding complex patterns and generating similar ones. In this regard, we first examine whether it is possible to generate new, unseen mutants of a polymorphic worm. Our results demonstrate that our synthetic mutants exhibit the same characteristics as the original mutants, i.e., known mutants fed into the RNN. Besides, we assess the ability of RNNs to generate synthetic signatures from the most advanced malware. We claim that by adding the RNN-generated, synthetic signatures to the set of the signatures of a signature-based NIDS it is possible to improve the malware detection rate of that. To support this and evaluate the feasibility of our approach, we conduct extensive experiments and provide exhaustive discussion on our experimental results.


Finding Undervalued Stocks with Machine Learning


title: A survey on object detection and tracking algorithms

abstract: Object detection and tracking are important and challenging task in many computer vision applications such as surveillance, vehicle navigation and autonomous robot navigation. Video surveillance in dynamic environment, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. The work involves designing of efficient video surveillance system in complex environments. In video surveillance, detection of moving objects from a video is important for object detection, target tracking, and behaviour understanding. Detection of moving objects in video streams is the first relevant step of information and background subtraction is a very popular approach for foreground segmentation. In this thesis, we have simulated different background subtraction methods to overcome the problem of illumination variation, background clutter and shadows. Detecting and tracking of human body parts is important in understanding human activities. Intelligent and automated security surveillance systems have become an active research area in recent time due to an increasing demand for such systems in public areas such as airports, underground stations and mass events. In this context, tracking of stationary foreground regions is one of the most critical requirements for surveillance systems based on the tracking of abandoned or stolen objects or parked vehicles.


title: Medical student mentorship in radiation oncology at a single academic institution: A 10-year analysis.

abstract: PURPOSE ::: Mentorship has been identified by medical students, residents, and faculty as an important component of specialty selection and research productivity in radiation oncology. This study quantitatively analyzes the impact of a mentorship program in radiation oncology targeted to medical students at our institution. ::: ::: ::: METHODS AND MATERIALS ::: We performed a retrospective review of 76 current or former medical students who were mentored by faculty radiation oncologists at our institution between 2004 and 2013. Data were collected from the medical school's Office of Student Affairs and from internal departmental records. Mentees were organized by mentorship tracks, which included a clinical track and a research track. For each track, data were compiled and analyzed for student specialty selection, and Fisher exact tests were used to determine the relative significance of exposure to clinical, research, or both tracks on student likelihood of pursuing residency in radiation oncology relative to other specialties. We further tracked the research productivity of mentees in the program, as determined by the number publications that were coauthored by mentees and mentors each year. ::: ::: ::: RESULTS ::: The absolute number of mentees has grown each year, with a total of 76 mentees, including 58 alumni, at the end of 2013. Mentees in the program have produced a total of 53 manuscripts, given 75 presentations at national conferences, and received numerous national and internal medical school research awards. Of the 58 alumni, 17 (29.3%) applied to and matched into radiation oncology residencies. Alumni of both the research and the clinical track were 5.76 (P < .01) times more likely to enter a radiation oncology residency program than the average single-track alumnus. ::: ::: ::: CONCLUSIONS ::: Mentorship in medical school is an important factor in the development of future radiation oncologists. These results demonstrate the positive impact mentorship has on specialty selection and research productivity.


title: Coordinated Use of Structure-Integrated Bistable Actuation Modules for Agile Locomotion

abstract: It is difficult to design agile soft-bodied robots owing to their inherent softness. To overcome this problem, we propose a structure-integrated bistable module that uses snap-through buckling for agile motions. First, we confirmed that a 0.05-m-long module was able to jump to 0.13 m high. Through investigation, we found a range of command parameters within which the module jumps consistently. Moreover, we showed that jumping performance had strong relation to the bending amplitude. Next, we induced a robot with two serially connected modules to roll forward and jump over an obstacle. In rolling, the robot became round for quick locomotion. In jumping, we found that buckling in one module induced buckling in the other module. The difference in buckling time between the two modules was shortened from the order of 0.1 s to the order of 0.01 s. This might be effective for error correction or useful for coordinated motions. These results show the effectiveness of the proposed structure-integrated bistable modules for making agile soft-bodied robots, and suggest ways of exploiting them.


title: A combined rotated general regression neural network method for river flow forecasting

abstract: ABSTRACT This study focused on the performance of the rotated general regression neural network (RGRNN), as an enhancement of the general regression neural network (GRNN), in monthly-mean river flow forecasting. The study of forecasting of monthly mean river flows in Heihe River, China, was divided into two steps: first, the performance of the RGRNN model was compared with the GRNN model, the feed-forward error back-propagation (FFBP) model and the soil moisture accounting and routing (SMAR) model in their initial model forms; then, by incorporating the corresponding outputs of the SMAR model as an extra input, the combined RGRNN model was compared with the combined FFBP and combined GRNN models. In terms of model efficiency index, R2, and normalized root mean squared error, NRMSE, the performances of all three combined models were generally better than those of the four initial models, and the RGRNN model performed better than the GRNN model in both steps, while the FFBP and the SMAR were consistently the worst two models. The results indicate that the combined RGRNN model could be a useful river flow forecasting tool for the chosen arid and semi-arid region in China. Editor D. Koutsoyiannis; Associate editor not assigned


Making Travel Smarter: Extracting Travel Information From E-Mail Itineraries Using Named Entity Recognition


title: Generalized Label Propagation Methods for Semi-Supervised Learning.

abstract: The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance. The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions. In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier. By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning. GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications. Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms. Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.


title: MgNet: A Unified Framework of Multigrid and Convolutional Neural Network

abstract: We develop a unified model, known as MgNet, that simultaneously recovers some convolutional neural networks (CNN) for image classification and multigrid (MG) methods for solving discretized partial differential equations (PDEs). This model is based on close connections that we have observed and uncovered between the CNN and MG methodologies. For example, pooling operation and feature extraction in CNN correspond directly to restriction operation and iterative smoothers in MG, respectively. As the solution space is often the dual of the data space in PDEs, the analogous concept of feature space and data space (which are dual to each other) is introduced in CNN. With such connections and new concept in the unified model, the function of various convolution operations and pooling used in CNN can be better understood. As a result, modified CNN models (with fewer weights and hyperparameters) are developed that exhibit competitive and sometimes better performance in comparison with existing CNN models when applied to both CIFAR-10 and CIFAR-100 data sets.


title: Being good at doing good? Quality and accountability of humanitarian NGOs.

abstract: Quality enhancement of humanitarian assistance is far from a technical task. It is interwoven with debates on politics of principles and people are intensely committed to the various outcomes these debates might have. It is a field of strongly competing truths, each with their own rationale and appeal. The last few years have seen a rapid increase in discussions, policy paper and organisational initiatives regarding the quality of humanitarian assistance. This paper takes stock of the present initiatives and of the questions raised with regard to the quality of humanitarian assistance.


title: A recursive crawler algorithm to detect crash in Android application

abstract: Android mobile testing is challenging as it involves several applications to be tested. Automated testing involves lot of script writing or instrumentation of source code. In this paper, designing a crawler algorithm without any source code instrumentation (Block box approach) is discussed. In this approach, UI Automator is the tool used to learn User Interface (UI) objects of each screen recursively. The properties of these objects are stored in a database for navigating through menu tree to detect application crash. The effectiveness of this method is studied and compared with other methods.


title: Characterization of the granular packing and percolation threshold of reactive powder concrete

abstract: Reactive powder concrete (RPC) is a new cement-based material developed through microstructural engineering. RPC is composed of very fine powders: sand, crushed quartz and silica fume, all with particle sizes comprised between 300 and 0.02 μm, and a low water content, W/C < 0.20. A very dense matrix is achieved by optimizing the granular packing of these powders. This compactness confers to RPC ultrahigh strength and durability. The hydration kinetic of the cementitious matrix using electrical conductivity and isothermal calorimetry is presented. A linear relationship between the logarithm of conductivity In(σ) and the degree of hydration o, has been found: In(σ) - A v - B v × 0 where A depends on the nature and the dosage of the different mineral and organic components, and B v is an invariant related to the granular packing. From 19 different RPC compositions, an average value of 12.1 with a standard deviation of 1.7 were found for B v . The capillary porosity percolation threshold was also deduced from this relationship. It has been found that when the degree of hydration is equal to 26%, the capillary pore space is discontinuous. This last result is in agreement with the prediction of the NIST microstructural model by Bentz and Garboczi [D., Bentz, E. Garboczi, Percolation of phases in a three-dimensional cement paste microstructural model, Cem Coner Res 21 (2) (1991) 325-344]. A continuous determination of the degree of hydration based on electrical conductivity is proposed.


SmartLTM: Smart Larger-Than-Memory Storage for Hybrid Database Systems


title: Correcting Errors Using the Framework of Argumentation: Towards Generating Argumentative Correction Propositions from Error Annotation Schemas

abstract: b IRIT, Abstract. This paper presents a first step towards the automatic generation of argumentative responses to accompany the corrections proposed by a correction and writing-aid system. This system focuses on pairs of languages (e.g. French speakers writing in English), and incorporates a strong didactic orientation. We show how, in case several corrections are available, error annotations can be used to design argumentations weighing the pros and cons of each correction. Argumentation is paired with decision theory in order to help the user pick out the most appropriate correction. Argumentative responses produced manually are used to create the generation schemas required to implement the automatic generation of such texts in the future.


title: Effects of Phlomis umbrosa Root on Longitudinal Bone Growth Rate in Adolescent Female Rats

abstract: This study aimed to investigate the effects of Phlomis umbrosa root on bone growth and growth mediators in rats. Female adolescent rats were administered P. umbrosa extract, recombinant human growth hormone or vehicle for 10 days. Tetracycline was injected intraperitoneally to produce a glowing fluorescence band on the newly formed bone on day 8, and 5-bromo-2′-deoxyuridine was injected to label proliferating chondrocytes on days 8–10. To assess possible endocrine or autocrine/paracrine mechanisms, we evaluated insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein-3 (IGFBP-3) or bone morphogenetic protein-2 (BMP-2) in response to P. umbrosa administration in either growth plate or serum. Oral administration of P. umbrosa significantly increased longitudinal bone growth rate, height of hypertrophic zone and chondrocyte proliferation of the proximal tibial growth plate. P. umbrosa also increased serum IGFBP-3 levels and upregulated the expressions of IGF-1 and BMP-2 in growth plate. In conclusion, P. umbrosa increases longitudinal bone growth rate by stimulating proliferation and hypertrophy of chondrocyte with the increment of circulating IGFBP-3. Regarding the immunohistochemical study, the effect of P. umbrosa may also be attributable to upregulation of local IGF-1 and BMP-2 expressions in the growth plate, which can be considered as a GH dependent autocrine/paracrine pathway.


title: Unsupervised Degradation Learning for Single Image Super-Resolution

abstract: Deep Convolution Neural Networks (CNN) have achieved significant performance on single image super-resolution (SR) recently. However, existing CNN-based methods use artificially synthetic low-resolution (LR) and high-resolution (HR) image pairs to train networks, which cannot handle real-world cases since the degradation from HR to LR is much more complex than manually designed. To solve this problem, we propose a real-world LR images guided bi-cycle network for single image super-resolution, in which the bidirectional structural consistency is exploited to train both the degradation and SR reconstruction networks in an unsupervised way. Specifically, we propose a degradation network to model the real-world degradation process from HR to LR via generative adversarial networks, and these generated realistic LR images paired with real-world HR images are exploited for training the SR reconstruction network, forming the first cycle. Then in the second reverse cycle, consistency of real-world LR images are exploited to further stabilize the training of SR reconstruction and degradation networks. Extensive experiments on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against state-of-the-art single image SR methods.


title: In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

abstract: Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attention from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate content delivery and improve the quality of mobile services. In order to bring more intelligence to edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with mobile edge systems, for optimizing mobile edge computing, caching and communication. And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. "In-Edge AI" is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to mobile communication systems. Finally, we discuss several related challenges and opportunities for unveili


title: Design and development of non invasive glucose measurement system

abstract: One of the biggest health challenges of 21st century is diabetics due to its exponential increase in the diabetics patients in the age group of 20–79 years. To prevent the complication due to diabetics it is essential to monitor the blood glucose level continuously. Most of the regular glucose measurement systems are invasive in nature. Invasive methods cause pain, time consumption, high cost and potential risk of spreading infection diseases. Therefore there is a great demand to have reliable cost effective and comfortable non invasive system for the detection of blood glucose level continuously. The proposed method is based on the direct effect of glucose on the scattering properties of the organ. Glucose decreases the mismatch in refractive index between scatterers and their surrounding media, leading to a smaller scattering coefficient and, consequently, a shorter optical path. The reduction in scattering is due to an increase in glucose concentration. As a result, with the growing concentration of glucose, fewer photons are absorbed and the light intensity increases. In the present work, we have used PPG technique. An algorithm was developed from the PPG data for monitoring blood glucose. The result obtained from this technique was compared with ARKRAY, Glucocard tm01-mini and found good agreement.


AIAA 94-3054 Modeling Pyrotechnic Shock in a NASA Standard Initiator Driven Pin Puller


title: New Algorithms for SIMD Alignment ⋆

abstract: Optimizing programs for modern multiprocessor or vector platforms is a major important challenge for compilers today. In this work, we focus on one challenging aspect: the SIMD ALIGNMENT problem. Previously, only heuristics were used to solve this problem, without guarantees on the number of shifts in the obtained solution.We study two interesting and realistic special cases of the SIMD ALIGNMENT problem and present two novel and efficient algorithms that provide optimal solutions for these two cases. The new algorithms employ dynamic programming and a MIN-CUT/MAX-FLOWalgorithm as subroutines.We also discuss the relation between the SIMD ALIGNMENT problem and the MULTIWAY CUT and NODE MULTIWAY CUT problems; and we show how to derive an approximated solution to the SIMD ALIGNMENT problem based on approximation algorithms to these two known problems.


title: Real-time 3D Reconstruction on Construction Site using Visual SLAM and UAV

abstract: 3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the image-based 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in Visual Simultaneous Localization and Mapping (SLAM) make it possible to reconstruct a 3D map of construction site in real-time. Integrated with Unmanned Aerial Vehicle (UAV), the obstacles areas that are inaccessible for the ground equipment can also be sensed. Despite these advantages of visual SLAM and UAV, until now, such technique has not been fully investigated on construction site. Therefore, the objective of this research is to present a pilot study of using visual SLAM and UAV for real-time construction site reconstruction. The system architecture and the experimental setup are introduced, and the preliminary results and the potential applications using Visual SLAM and UAV on construction site are discussed.


title: Feature Mining for Image Classification

abstract: The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).


title: Decision making among alternative routes for UAVs in dynamic environments

abstract: This paper presents an approach to trajectory generation for unmanned aerial vehicles (UAV) by using mixed integer linear programming (MILP) and a modification of the A* algorithm to optimize paths in dynamic environments, particularly having pop-ups with a known future probability of appearance. Each pop-up leads to one or several possible evasion maneuvers, characterized with a set of values used as decision making parameters in an integer linear programming (ILP) model that optimizes the final route by choosing the most suitable alternative trajectories, according to the imposed constrains such as maximum fuel consumption and spent time. The model of the system in MILP and A* algorithms is presented, as well as the ILP formulation for decision making. Results and discussions are given to promote future real time implementations.


title: Internet of things: from internet scale sensing to smart services

abstract: The internet of things (IoT) is the latest web evolution that incorporates billions of devices (such as cameras, sensors, RFIDs, smart phones, and wearables), that are owned by different organizations and people who are deploying and using them for their own purposes. Federations of such IoT devices (we refer to as IoT things) can deliver the information needed to solve internet-scale problems that have been too difficult to obtain and harness before. To realize this unprecedented IoT potential, we need to develop IoT solutions for discovering the IoT devices each application needs, collecting and integrating their data, and distilling the high value information each application needs. We also need to provide solutions that permit doing these tasks in real-time, on the move, in the cloud, and securely. In this paper we present an overview of a collection of IoT solutions (which we have developed in partnerships with other prominent IoT innovators and refer to them collectively as IoT platform) for addressing these technical challenges and help springboard IoT to its potential. We also describe a variety of IoT applications that have utilized the proposed IoT platform to provide smart IoT services in the areas of smart farming, smart grids, and smart manufacturing. Finally, we discuss future research and a vision of the next generation IoT infrastructure.


title: Transformational Leadership and Job Behaviors: The Mediating Role of Core Job Characteristics

abstract: Although the effects of transformational leadership on task performance and organizational citizenship behavior (OCB) are well-documented, the mechanisms that explain those effects remain unclear. We propose that transformational leadership is associated with the way followers view their jobs, in terms of Hackman and Oldham’s (1976) core job characteristics. Results of our study support a structural model whereby indirect effects supplement the direct effects of transformational leadership on task performance and OCB through the mechanisms of job characteristics, intrinsic motivation, and goal commitment. Additional analyses revealed that transformational leadership relationships were significantly stronger for followers who perceived highquality leader-member exchange.


title: Institutions Do Not Rule: Reassessing the Driving Forces of Economic Development

abstract: The pursuit to uncover the driving forces behind cross-country income gaps has divided economists into two major camps: One emphasizes institutions, while the other stresses non-institutional forces such as geography. Each school of thought has its own theoretical foundation and empirical support, but they share an implicit hypothesis—the forces driving economic development remain the same regardless of a country’s stage of development. Such hypothesis implies a theory that the process of development in human history is a continuous improvement in income levels, driven by the same forces, and that structural changes do not dictate the influences of geography and institutions on national income. This paper tests this theory and found it not supported by the data. Specifically, non-institutional factors predominantly explain the cross-country income variations among agrarian countries, while institutional factors largely account for the income differences across industrialized economies. In addition, we find evidence of developmental trap in which noninstitutional forces explain a country’s lack of industrialization, while institutions do not. The finding that institutions cannot account for the absence/presence of industrialization lends support to views held by many prominent historians who have cast serious doubts on the notion that institutional changes caused the British Industrial Revolution.


title: Sentence Simplification as Tree Transduction

abstract: In this paper, we introduce a syntax-based sentence simplifier that models simplification using a probabilistic synchronous tree substitution grammar (STSG). To improve the STSG model specificity we utilize a multi-level backoff model with additional syntactic annotations that allow for better discrimination over previous STSG formulations. We compare our approach to T3 (Cohn and Lapata, 2009), a recent STSG implementation, as well as two state-of-the-art phrase-based sentence simplifiers on a corpus of aligned sentences from English and Simple English Wikipedia. Our new approach performs significantly better than T3, similarly to human simplifications for both simplicity and fluency, and better than the phrasebased simplifiers for most of the evaluation metrics.


Artificial Intelligence, Machine Learning, and Music Understanding


title: Bureaucratic protocols for secure two-party sorting, selection, and permuting

abstract: In this paper, we introduce a framework for secure two-party (S2P) computations, which we call bureaucratic computing, and we demonstrate its efficiency by designing practical S2P computations for sorting, selection, and random permutation. In a nutshell, the main idea behind bureaucratic computing is to design data-oblivious algorithms that push all knowledge and influence of input values down to small black-box circuits, which are simulated using Yao's garbled paradigm. The practical benefit of this approach is that it maintains the zero-knowledge features of secure two-party computations while avoiding the significant computational overheads that come from trying to apply Yao's garbled paradigm to anything other than simple two-input functions.


title: Generating Stimuli for Neuroscience Using PsychoPy

abstract: PsychoPy is a software library written in Python, using OpenGL to generate very precise visual stimuli on standard personal computers. It is designed to allow the construction of as wide a variety of neuroscience experiments as possible, with the least effort. By writing scripts in standard Python syntax users can generate an enormous variety of visual and auditory stimuli and can interact with a wide range of external hardware (enabling its use in fMRI, EEG, MEG etc.). The structure of scripts is simple and intuitive. As a result, new experiments can be written very quickly, and trying to understand a previously written script is easy, even with minimal code comments. PsychoPy can also generate movies and image sequences to be used in demos or simulated neuroscience experiments. This paper describes the range of tools and stimuli that it provides and the environment in which experiments are conducted.


title: Forecasting Exchange Rates Using Feedforward And Recurrent Neural Networks

abstract: In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample prediction error relative to the random walk model. Copyright 1995 by John Wiley & Sons, Ltd.


title: A multimedia gesture dataset for human robot communication: Acquisition, tools and recognition results

abstract: Motivated by the recent advances in human-robot interaction we present a new dataset, a suite of tools to handle it and state-of-the-art work on visual gestures and audio commands recognition. The dataset has been collected with an integrated annotation and acquisition web-interface that facilitates on-the-way temporal ground-truths for fast acquisition. The dataset includes gesture instances in which the subjects are not in strict setup positions, and contains multiple scenarios, not restricted to a single static configuration. We accompany it by a valuable suite of tools as the practical interface to acquire audio-visual data in the robotic operating system, a state-of-the-art learning pipeline to train visual gesture and audio command models, and an online gesture recognition system. Finally, we include a rich evaluation of the dataset providing rich and insightfull experimental recognition results.


title: Stereo SLAM Using Two Estimators

abstract: This paper proposes a new algorithm for Simultaneous Localization and Mapping (SLAM) with omnidirectional stereo vision. In our approach, stereo matching is solved efficiently by using the estimated spatial information of the environment and robot motion. The use of two EKF (Extended Kalman Filter) estimators brings about a more reliable robot trajectory and a better map of the environment with more landmarks. The first estimator (a binocular estimator) mostly focuses on the robot trajectory; the second estimator (a monocular estimator) is devoted to the map building. Reliably matched landmarks are entered into the first estimator to establish the reliable robot position and some of the landmark positions. Other landmarks, which have more uncertainty of stereo combination or are observed by only one camera, are passed to the second estimator to establish their positions. This structure results in more precise estimation of robot trajectory and a map of the environment with more landmarks.


Comparative studies on 3 G , 4 G and 5 G wireless technology


title: Financial Liberalization and Democracy: The Role of Reform Reversals

abstract: The relationship between economic and political liberalization has received a great deal of attention lately, yet the possibility of a non-linear relationship and the role of reversals remain largely neglected. Focusing on democratization and financial reform, this paper offers evidence for a U-shaped relationship across countries, over time as well as in a panel setting using a wide range of estimators for various reform measures. We link this non-linear relationship to the notion of partial or captured democracy. We provide as well econometric support showing that even when de facto is modelled as a function of de jure financial liberalization, this non-linearity obtains.


title: Polynomial method for Procedural Terrain Generation

abstract: A new method is presented, allowing for the generation of 3D terrain and texture from coherent noise. The method is significantly faster than prevailing fractal brownian motion approaches, while producing results of equivalent quality. The algorithm is derived through a systematic approach that generalizes to an arbitrary number of spatial dimensions and gradient smoothness. The results are compared, in terms of performance and quality, to fundamental and efficient gradient noise methods widely used in the domain of fast terrain generation: Perlin noise and OpenSimplex noise. Finally, to objectively quantify the degree of realism of the results, a fractal analysis of the generated landscapes is performed and compared to real terrain data.


title: Real-Time Printed Document Authentication Using Watermarked QR Code

abstract: Printed documents are vulnerable to forgery through the latest technology development and it becomes extremely important. Most of the forgeries can be resulting loss of personal identity or ownership of a certain valuable object. This paper proposes novel authentication technique and schema for printed document authentication using watermarked QR (Quick Response) code. The technique is based Watermarked QR code generated with embedding logo belongs to the owner of the document which contain validation link, and the schema is checking the validation link of the printed document which linked to the web server and database server through internet connection by scanning it over camera phone and QR code reader, the result from this technique and schema is the validation can be done in real-time using smart phone such as smart phone based Android, Black Berry, and iOS. To get a good performance in extracting and validating printed document, it can be done by preparing in advance the validation link via internet connection to get the authentication of information hidden. Finally, this paper provide experimental results to demonstrate the authenticated of printed documents using watermarked QR code.


title: Grand Theft Auto is a “Sandbox” Game, but There are Weapons, Criminals, and Prostitutes in the Sandbox: Response to Ferguson and Donnellan (2017)

abstract: In this issue, (Ferguson, C. J., & Donnellan, B. D., Journal of Youth and and Adolescence, published online 21 June 2017) criticize one of our studies (Gabbiadini, A., Riva, P., Andrighetto, L., Volpato, C., & Bushman, B. J., PLoS ONE, 11: 1–14, 2016) that found violent sexist video games can reduce empathy for female violence victims in male players who identify with violent male game characters, and do so by increasing masculine beliefs. Their main criticism is a “straw person” argument built on a claim that we never made (i.e., a direct effect of sexist-violent video games on empathy). They also made several other criticisms of our article. We appreciate the opportunity to respond to their criticisms in this article. We also point out some flaws in their reanalysis. Despite their criticisms, the core contributions of our original article remain intact.


CityGML – OGC Standard for Photogrammetry ?


title: PRECAST CONCRETE WALL PANELS WITH GLASS CONCRETE

abstract: A hard surface liquid detergent with desirable cleansing properties comprises an anionic surfactant, a nonionic surfactant, a solubilizing agent, a solubilizer and water.


title: Reconfigurable wideband circularly polarized stacked square patch antenna for cognitive radios

abstract: An almost square patch, a square patch and a stacked square patch with corner truncation for circular polarization (CP) are researched and developed at X-band for cognitive radios. Experimental results indicate, first, that the impedance bandwidth of a CP almost square patch fed from the edge by a 50 ohm line is 1.70% and second, that of a CP square patch fed from the ground plane side by a surface launch connector is 1.87%. Third, the impedance bandwidth of a CP stacked square patch fed by a surface launch connector is 2.22%. The measured center frequency for the CP square patch fed by a surface launch connector without and with an identical stacked patch is 8.45 and 8.1017 GHz, respectively. By stacking a patch, separated by a fixed air gap of 0.254 mm, the center frequency is observed to shift by as much as 348.3 MHz. The shift in center frequency, brought about by the reconfiguring of the physical layer antenna, can be exploited in a cognitive system since it expands the usable frequency spectrum for software reconfiguration in the presence of interference. In addition, varying the fixed air gap in the stacked antenna geometry by increments of 0.254 mm further expands the usable frequency spectrum.


title: Low-resolution Convolutional Neural Networks for video face recognition

abstract: Security and safety applications such as surveillance or forensics demand face recognition in low-resolution video data. We propose a face recognition method based on a Convolutional Neural Network (CNN) with a manifold-based track comparison strategy for low-resolution video face recognition. The low-resolution domain is addressed by adjusting the network architecture to prevent bottlenecks or significant upscaling of face images. The CNN is trained with a combination of a large-scale self-collected video face dataset and large-scale public image face datasets resulting in about 1.4M training images. To handle large amounts of video data and for effective comparison, the CNN face descriptors are compared efficiently on track level by local patch means. Our setup achieves 80.3 percent accuracy on a 32×32 pixels low-resolution version of the YouTube Faces Database and outperforms local image descriptors as well as the state-of-the-art VGG-Face network [20] in this domain. The superior performance of the proposed method is confirmed on a self-collected in-the-wild surveillance dataset.


title: Overall job satisfaction: How good are single-item measures?

abstract: A meta-analysis of single-item measures of overall job satisfaction (28 correlations from 17 studies with 7,682 people) found an average uncorrected correlation of .63 (SD = .09) with scale measures of overall job satisfaction. The overall mean correlation (corrected only for reliability) is .67 (SD = .08), and it is moderated by the type of measurement scale used. The mean corrected correlation for the best group of scale measures (8 correlations, 1,735 people) is .72 (SD = .05). The correction for attenuation formula was used to estimate the minimum level of reliability for a single-item measure. These estimates range from .45 to .69, depending on the assumptions made.


title: Inter-Cloud Architectures and Application Brokering: Taxonomy and Survey

abstract: Although Cloud computing itself has many open problems, researchers in the field have already made the leap to envision Inter-Cloud computing. Their goal is to achieve better overall Quality of Service QoS, reliability and cost efficiency by utilizing multiple clouds. Inter-Cloud research is still in its infancy, and the body of knowledge in the area has not been well defined yet. In this work, we propose and motivate taxonomies for Inter-Cloud architectures and application brokering mechanisms. We present a detailed survey of the state of the art in terms of both academic and industry developments 20 projects, and we fit each project onto the discussed taxonomies. We discuss how the current Inter-Cloud environments facilitate brokering of distributed applications across clouds considering their nonfunctional requirements. Finally, we analyse the existing works and identify open challenges and trends in the area of Inter-Cloud application brokering. Copyright © 2012 John Wiley & Sons, Ltd.


title: GaussStones: shielded magnetic tangibles for multi-token interactions on portable displays

abstract: This work presents GaussStones, a system of shielded magnetic tangibles design for supporting multi-token interactions on portable displays. Unlike prior works in sensing magnetic tangibles on portable displays, the proposed tangible design applies magnetic shielding by using an inexpensive galvanized steel case, which eliminates interference between magnetic tangibles. An analog Hall-sensor grid can recognize the identity of each shielded magnetic unit since each unit generates a magnetic field with a specific intensity distribution and/or polarization. Combining multiple units as a knob further allows for resolving additional identities and their orientations. Enabling these features improves support for applications involving multiple tokens. Thus, using prevalent portable displays provides generic platforms for tangible interaction design.


title: Excessive Time on Social Networking Sites and Disordered Eating Behaviors Among Undergraduate Students: Appearance and Weight Esteem as Mediating Pathways

abstract: Social networking sites (SNS) are a popular form of communication among undergraduate students. Body image concerns and disordered eating behaviors are also quite prevalent among this population. Maladaptive use of SNS has been associated with disordered eating behaviors; however, the mechanisms remain unclear. The present study examined if body image concerns (e.g., appearance and weight esteem) mediate the relationship between excessive time spent on SNS and disordered eating behaviors (restrained and emotional eating). The sample included 383 (70.2 percent female) undergraduate students (mean age = 23.08 years, standard deviation = 3.09) who completed self-report questionnaires related to SNS engagement, body image, disordered eating behaviors, and demographics. Parallel multiple mediation and moderated mediation analyses revealed that lower weight and appearance esteem mediated the relationship between excessive time on SNS and restrained eating for males and females, whereas appearance esteem mediated the relationship between excessive time on SNS and emotional eating for females only. The study adds to the literature by highlighting mediational pathways and gender differences. Intervention research is needed to determine if teaching undergraduate students more adaptive ways of using SNS or reducing exposure to SNS reduces body dissatisfaction and disordered eating in this high-risk population.


title: Effects of Mindfulness on Meta-Awareness and Specificity of Describing Prodromal Symptoms in Suicidal Depression

abstract: The authors examined the effects of mindfulness training on 2 aspects of mode of processing in depressed participants: degree of meta-awareness and specificity of memory. Each of these has been suggested as a maladaptive aspect of a mode of processing linked to persistence and recurrence of symptoms. Twenty-seven depressed participants, all of whom had experienced suicidal crises, described warning signs for their last crisis. These descriptions were blind-rated independently for meta-awareness and specificity. Participants were then randomly allocated to receive mindfulness-based cognitive therapy (MBCT) plus treatment as usual (TAU) or TAU alone, and retested after 3 months. Results showed that, although comparable at baseline, patients randomized to MBCT displayed significant posttreatment differences in meta-awareness and specificity compared with TAU patients. These results suggest that mindfulness training may enable patients to reflect on memories of previous crises in a detailed and decentered way, allowing them to relate to such experiences in a way that is likely to be helpful in preventing future relapses.


title: Smart home automation system using Wi-Fi low power devices

abstract: Home automation is gaining popularity nowadays. A smart home automation system is based on ensuring security and making user life easier. It contains a large number of sensors which can control or monitor objects distributed in three-dimensional space. The sensors can be specialized in measuring temperature, humidity, pressure, light, noise, dust air, and so on. In this paper, a solution to transform a normal house in a smart house while reducing the energy consumption is proposed. This can be realized with the help of wireless sensor networks and of the LabVIEW™ graphical programming environment, which use the NI LabVIEW™ Statechart Module for collecting data from sensors.


Testing an integrative theoretical model of knowledge-sharing behavior in the context of Wikipedia


title: Measures of Presortedness and Optimal Sorting Algorithms

abstract: The concept of presortedness and its use in sorting are studied. Natural ways to measure presortedness are given and some general properties necessary for a measure are proposed. A concept of a sorting algorithm optimal with respect to a measure of presortedness is defined, and examples of such algorithms are given. A new insertion sort algorithm is shown to be optimal with respect to three natural measures. The problem of finding an optimal algorithm for an arbitrary measure is studied, and partial results are proven.


title: Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games

abstract: Monte Carlo Tree Search (MCTS) methods have proven powerful in planning for sequential decision-making problems such as Go and video games, but their performance can be poor when the planning depth and sampling trajectories are limited or when the rewards are sparse. We present an adaptation of PGRD (policy-gradient for reward-design) for learning a reward-bonus function to improve UCT (a MCTS algorithm). Unlike previous applications of PGRD in which the space of reward-bonus functions was limited to linear functions of hand-coded state-action-features, we use PGRD with a multi-layer convolutional neural network to automatically learn features from raw perception as well as to adapt the non-linear reward-bonus function parameters. We also adopt a variance-reducing gradient method to improve PGRD's performance. The new method improves UCT's performance on multiple ATARI games compared to UCT without the reward bonus. Combining PGRD and Deep Learning in this way should make adapting rewards for MCTS algorithms far more widely and practically applicable than before.


title: A Comparative Study of Data Mining Algorithms for Image Classification

abstract: Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It’s also includes image preprocessing, feature extraction, object detection, object classification, object segmentation, object classification and many more techniques. Image classification to produce the accurate prediction results in their target class for each case in the data. It is a very predominant and challenging task in various application domains, including video surveillance, biometry, biomedical imaging, industrial visual inspection, vehicle navigation, remote sensing and robot navigation. The aim of this study compares the some predominant data mining algorithms in image classification. For this review SVM, AdaBoost, CART, KNN, Artificial Neural Network, K-Means, Chaos Genetic Algorithm, EM Algorithm, C4.5 algorithms are taken.


title: Self-localization application for iPhone using only ambient sound signals

abstract: We present a smartphone application to localize a group of networking devices in a mobile environment without the need of any further infrastructure. Ambient sound signals are the only information source. Time marks are assigned to the recorded audio stream for distinctive audio events, out of which we evaluate the time differences of arrival (TDOA) between the devices. In contrast to common multilateration approaches we do not need any positional anchor points - neither any predefined smartphone positions nor the positions of the environmental sounds. As an application scenario we can localize arbitrary devices using only the random environmental noise peaks, e.g. in crowded areas like market places or concerts with the usual soundscape, or for thunderstorm tracking. Especially, our solution becomes useful when established positioning systems (e.g. GPS) are too imprecise or fail, as during indoor self-localization. We use a Wi-Fi connection to synchronize the clocks of the devices and to exchange time marks. In our experiments we evaluated the audio information and synchronized the devices up to an order of 0.1 ms. This results in a positioning precision in the order of 10 cm.


title: Dune: safe user-level access to privileged CPU features

abstract: Dune is a system that provides applications with direct but safe access to hardware features such as ring protection, page tables, and tagged TLBs, while preserving the existing OS interfaces for processes. Dune uses the virtualization hardware in modern processors to provide a process, rather than a machine abstraction. It consists of a small kernel module that initializes virtualization hardware and mediates interactions with the kernel, and a user-level library that helps applications manage privileged hardware features. We present the implementation of Dune for 64- bit x86 Linux. We use Dune to implement three user-level applications that can benefit from access to privileged hardware: a sandbox for untrusted code, a privilege separation facility, and a garbage collector. The use of Dune greatly simplifies the implementation of these applications and provides significant performance advantages.


title: The Student's Guide to Social Neuroscience

abstract: 1. Introduction to Social Neuroscience 2. The Methods of Social Neuroscience 3. Evolutionary Origins of Social Intelligence and Culture 4. Emotion and Motivation 5. Reading Faces and Bodies 6. Understanding Others 7. Interacting with Others 8. Relationships 9. Groups and Identity 10. Morality and Anti-Social Behaviour 11. Developmental Social Neuroscience


title: A Progressive Search Paradigm for the Internet of Things

abstract: As the Internet of Things (IoT) expands, IoT search has tremendous potential applications but also presents many challenges. To solve these challenges, we propose a progressive search paradigm to reduce the search space that has three main strategies: coarse-to-fine search in the feature space, near-to-distant search in the spatial-temporal space, and low-to-high permission search in the security space. Two case studies from a multimedia-based urban sensing network demonstrate that our approach can significantly improve IoT search speed and accuracy.


title: A Residual Encoder-Decoder Network for Semantic Segmentation in Autonomous Driving Scenarios

abstract: In this paper, we propose an encoder-decoder based deep convolutional network for semantic segmentation in autonomous driving scenarios. The architecture of the proposed model is based on VGG16 [1]. Residual learning is introduced to preserve the context while decreasing the size of feature maps between the stacks of convolutional layers. Also, the resolution is preserved through shortcuts from the encoder stage to the decoder stage. Experiments are conducted on popular benchmark datasets CamVid and CityScapes to demonstrate the efficacy of the proposed model. The experiments are corroborated with comparative analysis with popular encoder-decoder networks such as SegNet and Enet architectures demonstrating that the proposed approach outperforms existing methods despite having fewer trainable parameters.


title: The Membrane-bound Cytochromes of an Aerobically Grown, Extremely Thermophilic Bacterium, PS3: Characterization by Spectral Deconvolution Coupled with Potentiometric Analysis

abstract: SUMMARY: Cytochromes of the a, b and c types in membranes from the thermophilic bacterium PS3 have been characterized with respect to their spectral, potentiometric and ligand-binding properties. The integrated approach used (a) conventional potentiometric analysis with non-linear least-square analysis, (b) incorporation of the potentiometric procedures into a spectral decomposition protocol at 298 and 77 K, (c) fourth-order finite difference and fourth derivative analysis, and (d) photodissociation studies of the CO-liganded components. Either one c-type cytochrome with a split α-band (547, 552 nm at 77 K) or two cytochromes c with similar redox potentials lapprox. +230 mV) were found, together with at least three b-type cytochromes (554, 557 and 561 nm at 77 K). On the basis of its high redox potential (+104mV), cytochrome b ::: 551 was tentatively equated with the o-type oxidase of this organism. A c-type cytochrome that formed a photodissociable complex with CO was also detected. The α-band of the previously purified cytochrome c oxidase was resolved into two components, having spectral and potentiometric properties remarkably similar to those of the analogous mitochondrial oxidase.


title: Patch-based image vectorization with automatic curvilinear feature alignment

abstract: Raster image vectorization is increasingly important since vector-based graphical contents have been adopted in personal computers and on the Internet. In this paper, we introduce an effective vector-based representation and its associated vectorization algorithm for full-color raster images. There are two important characteristics of our representation. First, the image plane is decomposed into nonoverlapping parametric triangular patches with curved boundaries. Such a simplicial layout supports a flexible topology and facilitates adaptive patch distribution. Second, a subset of the curved patch boundaries are dedicated to faithfully representing curvilinear features. They are automatically aligned with the features. Because of this, patches are expected to have moderate internal variations that can be well approximated using smooth functions. We have developed effective techniques for patch boundary optimization and patch color fitting to accurately and compactly approximate raster images with both smooth variations and curvilinear features. A real-time GPU-accelerated parallel algorithm based on recursive patch subdivision has also been developed for rasterizing a vectorized image. Experiments and comparisons indicate our image vectorization algorithm achieves a more accurate and compact vector-based representation than existing ones do.


title: Delta-Sigma Modulators for Power-Efficient A/D Conversion in High-Speed Wireless Communications

abstract: This paper describes how the signal path within a DeltaSigma modulator can be designed, independent of its noise-shaping characteristics, in order to significantly reduce the harmonic distortion due to opamp nonidealities and to lower the power dissipation. This paper then presents architectural approaches for designing high-resolution DeltaSigma modulators at low oversampling ratios (OSRs) and low supply voltages. Thus, analog-to-digital converters (ADCs) can be designed in low-voltage nano-scale digital CMOS technologies to achieve high-speed and high-resolution A/D conversion, with high power efficiency (energy dissipation less than lpj per conversion step). Such DeltaSigma ADCs are attractive for emerging broadband communication applications.


title: Forest Density Estimation

abstract: We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estimation, we do not assume the true distribution corresponds to a forest; rather, we form kernel density estimates of the bivariate and univariate marginals, and apply Kruskal's algorithm to estimate the optimal forest on held out data. We prove an oracle inequality on the excess risk of the resulting estimator relative to the risk of the best forest. For graph estimation, we consider the problem of estimating forests with restricted tree sizes. We prove that finding a maximum weight spanning forest with restricted tree size is NP-hard, and develop an approximation algorithm for this problem. Viewing the tree size as a complexity parameter, we then select a forest using data splitting, and prove bounds on excess risk and structure selection consistency of the procedure. Experiments with simulated data and microarray data indicate that the methods are a practical alternative to Gaussian graphical models.