title: JPEG Quantization Step Estimation and Its Applications to Digital Image Forensics

abstract: The goal of this paper is to propose an accurate method for estimating quantization steps from an image that has been previously JPEG-compressed and stored in lossless format. The method is based on the combination of the quantization effect and the statistics of discrete cosine transform (DCT) coefficient characterized by the statistical model that has been proposed in our previous works. The analysis of quantization effect is performed within a mathematical framework, which justifies the relation of local maxima of the number of integer quantized forward coefficients with the true quantization step. From the candidate set of the true quantization step given by the previous analysis, the statistical model of DCT coefficients is used to provide the optimal quantization step candidate. The proposed method can also be exploited to estimate the secondary quantization table in a double-JPEG compressed image stored in lossless format and detect the presence of JPEG compression. Numerical experiments on large image databases with different image sizes and quality factors highlight the high accuracy of the proposed method.


title: TACO: Learning Task Decomposition via Temporal Alignment for Control

abstract: Many advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks. By reusing the corresponding sub-policies within and between tasks, they provide training data for each policy from different high-level tasks and compose them to perform novel ones. Existing approaches to modular LfD focus either on learning a single high-level task or depend on domain knowledge and temporal segmentation. In contrast, we propose a weakly supervised, domain-agnostic approach based on task sketches, which include only the sequence of sub-tasks performed in each demonstration. Our approach simultaneously aligns the sketches with the observed demonstrations and learns the required sub-policies. This improves generalisation in comparison to separate optimisation procedures. We evaluate the approach on multiple domains, including a simulated 3D robot arm control task using purely image-based observations. The results show that our approach performs commensurately with fully supervised approaches, while requiring significantly less annotation effort.


title: Intrinsic Versus Extrinsic Goal Contents in Self-Determination Theory: Another Look at the Quality of Academic Motivation

abstract: Examination of motivational dynamics in academic contexts within self-determination theory has centered primarily around both the motives (initially intrinsic vs. extrinsic, later autonomous vs. controlled) that regulate learners' study behavior and the contexts that promote or hinder these regulations. Less attention has been paid to the goal contents (intrinsic vs. extrinsic) that learners hold and to the different goal contents that are communicated in schools to increase the perceived relevance of the learning. Recent field experiments are reviewed showing that intrinsic goal framing (relative to extrinsic goal framing and no-goal framing) produces deeper engagement in learning activities, better conceptual learning, and higher persistence at learning activities. These effects occur for both intrinsically and extrinsically oriented individuals. Results are discussed in terms of self-determination theory's concept of basic psychological needs for autonomy, competence, and relatedness.


title: Vulnerable Narcissism Is (Mostly) a Disorder of Neuroticism

abstract: Objective ::: Increasing attention has been paid to the distinction between the dimensions of narcissistic grandiosity and vulnerability. We examine the degree to which basic traits underlie vulnerable narcissism, with a particular emphasis on the importance of Neuroticism and Agreeableness. ::: ::: Method ::: Across four samples (undergraduate, online community, clinical-community), we conduct dominance analyses to partition the variance predicted in vulnerable narcissism by the Five-Factor Model personality domains, as well as compare the empirical profiles generated by vulnerable narcissism and Neuroticism. ::: ::: Results ::: These analyses demonstrate that the lion's share of variance is explained by Neuroticism (65%) and Agreeableness (19%). Similarity analyses were also conducted in which the extent to which vulnerable narcissism and Neuroticism share similar empirical networks was tested using an array of criteria, including self-, informant, and thin slice ratings of personality; interview-based ratings of personality disorder and pathological traits; and self-ratings of adverse events and functional outcomes. The empirical correlates of vulnerable narcissism and Neuroticism were nearly identical (MrICC = .94). Partial analyses demonstrated that the variance in vulnerable narcissism not shared with Neuroticism is largely specific to disagreeableness-related traits such as distrustfulness and grandiosity. ::: ::: Conclusions ::: These findings demonstrate the parsimony of using basic personality to study personality pathology and have implications for how vulnerable narcissism might be approached clinically.


Syddansk Universitet Acromelic frontonasal dysostosis and ZSWIM 6 mutation


Systematic Review on the Suspicious Profiles Detection on Online Social Media Data


title: Revisiting the problem of a crack impinging on an interface: a modeling framework for the interaction between the phase field approach for brittle fracture and the interface cohesive zone model

abstract: Abstract The problem of a crack impinging on an interface has been thoroughly investigated in the last three decades due to its important role in the mechanics and physics of solids. In the current investigation, this problem is revisited in view of the recent progresses on the phase field approach of brittle fracture. In this concern, a novel formulation combining the phase field approach for modeling brittle fracture in the bulk and a cohesive zone model for pre-existing adhesive interfaces is herein proposed to investigate the competition between crack penetration and deflection at an interface. The model, implemented within the finite element method framework using a monolithic fully implicit solution strategy, is applied to provide a further insight into the understanding of the role of model parameters on the above competition. In particular, in this study, the role of the fracture toughness ratio between the interface and the adjoining bulks and of the characteristic fracture-length scales of the dissipative models is analyzed. In the case of a brittle interface, the asymptotic predictions based on linear elastic fracture mechanics criteria for crack penetration, single deflection or double deflection are fully captured by the present method. Moreover, by increasing the size of the process zone along the interface, or by varying the internal length scale of the phase field model, new complex phenomena are emerging, such as simultaneous crack penetration and deflection and the transition from single crack penetration to deflection and penetration with subsequent branching into the bulk. The obtained computational trends are in very good agreement with previous experimental observations and the theoretical considerations on the competition and interplay between both fracture mechanics models open new research perspectives for the simulation and understanding of complex fracture patterns.


title: DOI: 10.1093/bioinformatics/btg224 PISCES: a protein sequence culling server

abstract: Summary: PISCES is a public server for culling sets of protein sequences from the Protein Data Bank (PDB) by sequence identity and structural quality criteria. PISCES can provide lists culled from the entire PDB or from lists of PDB entries or chains provided by the user. The sequence identities are obtained from PSI-BLAST alignments with position-specific substitution matrices derived from the non-redundant protein sequence database. PISCES therefore provides better lists than servers that use BLAST, which is unable to identify many relationships below 40% sequence identity and often overestimates sequence identity by aligning only well-conserved fragments. PDB sequences are updated weekly. PISCES can also cull non-PDB sequences provided by the user as a list of GenBank identifiers, a FASTA format file, or BLAST/PSI-BLAST output. Availability: The server is located at http://www.fccc.edu/


title: A new correlated double sampling (CDS) technique for low voltage design environment in advanced CMOS technology

abstract: In this paper, a new Correlated Double Sampling (CDS) Technique based on Fixed Voltage Difference (FVD) is introduced. Compared with the traditional CDS technique with voltage sampling for A/D conversion, this method has the advantage of low voltage capability, which relieves the high resolution requirement of the subsequent A/D converter as a result of the limited voltage swing in advanced deep-submicron CMOS technologies. The new technique also allows the use of reference voltages to control the dynamic range of the circuit. The FVD CDS technique has been applied to the readout circuit of a low voltage CMOS Active Pixel Sensor (APS) circuits with an array size of 128×128 fabricated by a 0.25µm CMOS process from TSMC. The circuit is proven to be functional at extremely low V DD with added dynamic range.


title: Women In Prison: A Review of the Current Female Prison System: Future Directions and Alternatives

abstract: A review of relevant literature has indicated that women are sentenced inconsistently by the criminal justice system showing differences between harsh, equal or lenient treatment. This dissertation highlights that sending women to prison, who tend not commit serious crime and drawing a prison population who have the characteristics of economic and social deprivation, is completely unnecessary. It also highlights the penal institutions to which women are sentenced, are failing to provide for the specific needs of women or to equip them with ways to improve their future prospects. Whilst it is recognised that the government is aware of such problems, through the publication of the Corston Report (2007) and their subsequent response, radical shifts in sentencing policy and practices to restrict the numbers of women sent to prison are desperately needed. Considering the secondary research findings, a number of recommendations for future policy and practice have been made in four main areas; increasing the use of non-custodial penalties, a substantial reduction in custodial remand, reform of custody and increased diversion from prosecution.


title: Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

abstract: Natural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on non-textual data or from meaning representations. This is a significant area which empowers human-computer interaction. It has also given rise to a variety of theoretical as well as empirical approaches. This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. The paper explores NLG architectures and tasks classed under document planning, micro-planning and surface realization modules. Additionally, this paper also identifies the gaps existing in the NLG research which require further work in order to make NLG a widely usable technology.


title: Aladdin: Automating Release of Android Deep Links to In-App Content

abstract: Unlike the Web where each web page has a global URL to reach, a specific "content page" inside a mobile app cannot be opened unless the user explores the app with several operations from the landing page. Recently, deep links have been advocated by major companies to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier (URI). In this paper, we present an empirical study of deep links over 20,000 Android apps, and find that deep links do not get wide adoption among current Android apps, and non-trivial manual efforts are required for app developers to support deep links. To address such an issue, we propose the Aladdin approach and supporting tool to release deep links to access arbitrary locations of existing apps. We evaluate Aladdin with popular apps and demonstrate its effectiveness and performance.


title: An efficiency enhancement technique for CMOS rectifiers with low start-up voltage for UHF RFID tags

abstract: A high-efficiency CMOS rectifier with low start-up voltage for ultra-high-frequency (UHF) radio-frequency identification (RFID) applications is presented. To enhance the power conversion efficiency (PCE) of the conventional rectifier when the input voltage (power) is low, appropriate gate-drive voltages for each stage of the rectifier are generated using a chain of auxiliary floating rectifier cells. Floating rectifier cells are optimized to generate shifted versions of the intermediate voltage of each stage to boost the drive voltage of NMOS and PMOS switching transistors and accordingly improve the PCE. The proposed rectifier architecture is designed in a standard 0.13μm CMOS technology. For a 950 MHz RF input and 50 kΩ output load, simulation results show that the rectifier achieves a PCE of 54% for a small input signal with an amplitude of 200 mV (-19 dBm) which is well below the nominal standard threshold voltage of MOS transistors in the technology used.


title: SELF-EFFICACY, STRESS, AND ACADEMIC SUCCESS IN COLLEGE

abstract: This paper investigates the joint effects of academic self-efficacy and stress on the academic performance of 107 nontraditional, largely immigrant and minority, college freshmen at a large urban commuter institution. We developed a survey instrument to measure the level of academic self-efficacy and perceived stress associated with 27 college-related tasks. Both scales have high reliability, and they are moderately negatively correlated. We estimated structural equation models to assess the relative importance of stress and self-efficacy in predicting three academic performance outcomes: first-year college GPA, the number of accumulated credits, and college retention after the first year. The results suggest that academic self-efficacy is a more robust and consistent predictor than stress of academic success.


title: Regulation of astrocyte glutamate transporter-1 (GLT1) and aquaporin-4 (AQP4) expression in a model of epilepsy

abstract: Astrocytes regulate extracellular glutamate and water homeostasis through the astrocyte-specific membrane proteins glutamate transporter-1 (GLT1) and aquaporin-4 (AQP4), respectively. The role of astrocytes and the regulation of GLT1 and AQP4 in epilepsy are not fully understood. In this study, we investigated the expression of GLT1 and AQP4 in the intrahippocampal kainic acid (IHKA) model of temporal lobe epilepsy (TLE). We used real-time polymerase chain reaction (RT-PCR), Western blot, and immunohistochemical analysis at 1, 4, 7, and 30days after kainic acid-induced status epilepticus (SE) to determine hippocampal glial fibrillary acidic protein (GFAP, a marker for reactive astrocytes), GLT1, and AQP4 expression changes during the development of epilepsy (epileptogenesis). Following IHKA, all mice had SE and progressive increases in GFAP immunoreactivity and GFAP protein expression out to 30days post-SE. A significant initial increase in dorsal hippocampal GLT1 immunoreactivity and protein levels were observed 1day post SE and followed by a marked downregulation at 4 and 7days post SE with a return to near control levels by 30days post SE. AQP4 dorsal hippocampal protein expression was significantly downregulated at 1day post SE and was followed by a gradual return to baseline levels with a significant increase in ipsilateral protein levels by 30days post SE. Transient increases in GFAP and AQP4 mRNA were also observed. Our findings suggest that specific molecular changes in astrocyte glutamate transporters and water channels occur during epileptogenesis in this model, and suggest the novel therapeutic strategy of restoring glutamate and water homeostasis.


title: The Development of control system via Brain Computer Interface (BCI) - Functional Electrical Stimulation (FES) for paraplegic subject

abstract: Brain is known to be one of the powerful systems in human body because of its ability to give command and communicate throughout the body. The spinal cord is the pathway for impulses from the brain to the body as well as from the body to the brain. However, the bounty of this pathway could be lost due to spinal cord injury (SCI) and that results in a loss of function especially mobility. A combination of Brain Computer Interface (BCI) and Functional Electrical Stimulation (FES) is among one of the technique to regain the mobility function of human body which will be the focused area of this research. In this study, Electroencephalography (EEG) system will be used to capture the brain signal which will then drive the FES. A paraplegic subject will be involved in this study. The subject will be required to move the knee joint with involvement few muscle contraction. Overall, in this paper the combination of BCI-FES methods for development of rehabilitation system will be proposed. From this preliminary study, it can be summarized that the combination between BCI and FES potentially would provide a better rehabilitation system for SCI patient in comparison to the conventional FES system.


title: A simple multi-armed bandit algorithm with optimal variation-bounded regret

abstract: We pose the question of whether it is possible to design a simple, linear-time algorithm for the basic multi-armed bandit problem in the adversarial setting which has a regret bound of O( p Q logT ), where Q is the total quadratic variation of all the arms.


Diagnosis Code Prediction from Electronic Health Records as Multilabel Text Classification : A Survey


title: Wideband Substrate-Integrated Waveguide-Fed Endfire Metasurface Antenna Array

abstract: A vertically polarized substrate-integrated waveguide (SIW)-fed endfire metasurface antenna array is proposed for wideband operation. Each of the metasurfaces consists of $3\times 3$ rectangular patches and is printed on the two surfaces of a single-layered substrate with a thickness of $0.16\lambda _{0}$ (where $\lambda _{0}$ is the wavelength at 32.65 GHz in free space). The antenna is fed at one side of metasurface by an open-end SIW for wideband endfire radiation. The proximity-coupled interdigital strips are introduced between the SIW and metasurface for fault-tolerant coupling. The wideband operating mechanism is revealed by studying the propagation mode and multiple resonant modes of metasurface. To verify the proposed antenna, a $1\times 4$ array is presented with uniform excitation including two SIW Y junctions and an SIW T junction. Moreover, the connected SIW Y junction improved the impedance matching at the low frequencies based on the cavity mode analysis. The proposed design shows that the measured impedance bandwidth (10 dB return loss) is 26.6–38.7 GHz (37%) with the achieved gain of 9.1–13.8 dBi.


title: Impact of interventions on malaria in internally displaced persons along the China–Myanmar border: 2011–2014

abstract: BackgroundInternally displaced persons (IDP) represent vulnerable populations whose public health conditions merit special attention. In the China–Myanmar border area, human movement and resettlements of IDP can influence malaria transmission. Comparison of disease incidence and vector densities between IDP camps and surrounding local villages allows for better understanding of current epidemiology and to evaluate the effectiveness of interventions in the region.MethodsMalaria and vector surveillance was conducted in three IDP camps and three local villages neighbouring the camps along the China–Myanmar border in Myanmar. Clinical malaria cases were collected from seven hospitals/clinics from April 2011 to December 2014. Malaria vector population dynamics were monitored using CDC light traps. The use of malaria preventive measures and information on aid agencies and their activities was obtained through questionnaire surveys.ResultsMalaria was confirmed in 1832 patients. Of these cases, 85.4 % were Plasmodium vivax and 11.4 % were Plasmodium falciparum malaria. Annual malaria incidence rates were 38.8 and 127.0 cases/1000 person year in IDP camps and local villages, respectively. Older children of 5–14 years had the highest incidence rate in the camps regardless of gender, while male adults had significantly higher incidence rates than females in local villages and females child-bearing age had significantly lower risk to malaria in IDP camps compare to local villages. Seasonal malaria outbreaks were observed both in the IDP camps and in the local villages from May to August 2013. The proportion of P. vivax remained unchanged in local villages but increased by approximately tenfold in IDP camps from 2011 to 2014. Anopheles vector density was tenfold higher in local villages compared to IDP camps (2.0:0.2 females/trap/night). Over 99 % of households in both communities owned bed nets. While long-lasting insecticidal nets accounted for 61 % of nets used in IDPs, nearly all residents of local villages owned regular nets without insecticide-impregnation. There were more active aid agencies in the camps than in local villages.ConclusionMalaria in IDP camps was significantly lower than the surrounding villages through effective control management. The observation of P. vivax outbreaks in the study area highlights the need for increased control efforts. Expansion of malaria intervention strategies in IDP camps to local surrounding villages is critical to malaria control in the border area.


title: Early enteral nutrition in mechanically ventilated patients in the prone position

abstract: ObjectiveTo assess the tolerance of early enteral nutrition in critically ill patients receiving invasive mechanical ventilation in the prone position.DesignProspective, comparative study.SettingGeneral intensive care unit in a university-affiliated hospital.PatientsA total of 71 consecutive patient


title: The reliability, minimal detectable change and concurrent validity of a gravity-based bubble inclinometer and iphone application for measuring standing lumbar lordosis.

abstract: PURPOSE ::: To investigate the reliability, minimal detectable change (MDC90) and concurrent validity of a gravity-based bubble inclinometer (inclinometer) and iPhone® application for measuring standing lumbar lordosis. ::: ::: ::: METHODS ::: Two investigators used both an inclinometer and an iPhone® with an inclinometer application to measure lumbar lordosis of 30 asymptomatic participants. ::: ::: ::: RESULTS ::: ICC models 3,k and 2,k were used for the intrarater and interrater analysis, respectively. Good interrater and intrarater reliability was present for the inclinometer with Intraclass Correlation Coefficients (ICC) of 0.90 and 0.85, respectively and the iPhone® application with ICC values of 0.96 and 0.81. The minimal detectable change (MDC90) indicates that a change greater than or equal to 7° and 6° is needed to exceed the threshold of error using the iPhone® and inclinometer, respectively. The concurrent validity between the two instruments was good with a Pearson product-moment coefficient of correlation (r) of 0.86 for both raters. Ninety-five percent limits of agreement identified differences ranging from 9° greater in regards to the iPhone® to 8° less regarding the inclinometer. ::: ::: ::: CONCLUSION ::: Both the inclinometer and iPhone® application possess good interrater reliability, intrarater reliability and concurrent validity for measuring standing lumbar lordosis. This investigation provides preliminary evidence to suggest that smart phone applications may offer clinical utility comparable to inclinometry for quantifying standing lumbar lordosis. Clinicians should recognize potential individual differences when using these devices interchangeably.


title: Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques

abstract: Producing chili is a daunting task as the plant is exposed to the attacks from various micro-organisms and bacterial diseases and pests. The symptoms of the attacks are usually distinguished through the leaves, stems or fruit inspection. This paper discusses the effective way used in performing early detection of chili disease through leaf features inspection. Leaf image is captured and processed to determine the health status of each plant. Currently the chemicals are applied to the plants periodically without considering the requirement of each plant. This technique will ensure that the chemicals only applied when the plants are detected to be effected with the diseases. The image processing techniques are used to perform hundreds of chili disease images. The plant chili disease detection through leaf image and data processing techniques is very useful and inexpensive system especially for assisting farmers in monitoring the big plantation area.


title: Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function

abstract: A wide range of evidence points toward the existence of a common algorithm underlying the processing of information throughout the cerebral cortex. Several hypothesized features of this cortical algorithm are reviewed, including sparse distributed representation, Bayesian inference, hierarchical organization composed of alternating template matching and pooling layers, temporal slowness and predictive coding. Hierarchical Temporal Memory (HTM) is a family of learning algorithms and corresponding theories of cortical function that embodies these principles. HTM has previously been applied mainly to perceptual tasks typical of posterior cortex. In order to evaluate HTM as a candidate model of cortical function, it is necessary also to investigate its compatibility with the requirements of frontal cortical function. To this end, a variety of models of frontal cortical function are reviewed and integrated, to arrive at the hypothesis that frontal functions including attention, working memory and action selection depend largely upon the same basic algorithms as do posterior functions, with the notable additions of a mechanism for the active maintenance of representations and of multiple cortico-striato-thalamo-cortical loops that allow communication between regions of frontal cortex to be gated in an adaptive manner. Computational models of this system are reviewed. Finally, there is a discussion of how HTM can contribute to the understanding of frontal cortical function, and of what the requirements of frontal cortical function mean for the future development of HTM.


title: Understanding Big Data Analytics Workloads on Modern Processors

abstract: Big data analytics workloads are very significant ones in modern data centers, and it is more and more important to characterize their representative workloads and understand their behaviors so as to improve the performance of data center computer systems. In this paper, we embark on a comprehensive study to understand the impacts and performance implications of the big data analytics workloads on the systems equipped with modern superscalar out-of-order processors. After investigating three most important application domains in Internet services in terms of page views and daily visitors, we choose 11 representative data analytics workloads and characterize their micro-architectural behaviors by using hardware performance counters. Our study reveals that the big data analytics workloads share many inherent characteristics, which place them in a different class from the traditional workloads and the scale-out services. To further understand the characteristics of big data analytics workloads, we perform correlation analysis to identify the most key factors that affect cycles per instruction (CPI). Also, we reveal that the increasing complexity of the big data software stacks will put higher pressures on the modern processor pipelines.


title: Problematic involvement in online games: A cluster analytic approach

abstract: ADV=advancement (MPOGQ); SOC=socializing (MPOGQ); RP=role-play (MPOGQ); ESC=escapism (MPOGQ); URG=urgency (UPPS-P); LPR=lack of premeditation (UPPS-P); LPE=lack of perseverance (UPPS-P); SS=sensation seeking (UPPS-P); SE=self-esteem (SISE). Dashed lines refer to mean Z-scores of the entire sample. Bold lines refer to mean Z-scores for each specific cluster.Display Omitted The current study identified distinct subtypes of online gamers.The study combines self-report measures and ecological data (in-game behaviors).Cluster comparison revealed that problematic online gaming is a heterogeneous disorder.Problematic online gamer subtypes exist and individualized treatment is favored. Playing online games can become problematic and engender adverse consequences. Several psychological factors have been shown to influence the development and the maintenance of this problematic behavior, including impulsivity traits, motives to play (immersion, achievement, social affiliation), and self-esteem. The aim of the current study is to determine whether reliable subtypes of problematic online gamers can be identified. A sample of 1057 online gamers was recruited. Validated questionnaires were used to measure established psychological risk factors (impulsivity, motives to play, self-esteem) and potential consequences of playing (addiction symptoms, positive and negative affect). Actual in-game behaviors were also monitored. Five reliable clusters of gamers were identified (three problematic and two nonproblematic clusters). Cluster comparison revealed that the psychological factors considered are differentially involved in problematic online gaming. At the theoretical level, the results emphasized that problem online gaming depends on a wide range of psychological factors. At the clinical level, the diversity of psychological profiles shown supports the development of personalized (custom-made) interventions targeting specific psychological mechanisms. Overall, our findings suggest that conceptualizing the problematic use of massively multiplayer online role-playing games as "behavioral addiction" is too restrictive and might result in the simplification of heterogeneous and multi-determined problematic behaviors.


The CAPM: Theoretical Validity, Empirical Intractability and Practical Applications


title: Predictable Low-Latency Event Detection With Parallel Complex Event Processing

abstract: The tremendous number of sensors and smart objects being deployed in the Internet of Things (IoT) pose the potential for IT systems to detect and react to live-situations. For using this hidden potential, complex event processing (CEP) systems offer means to efficiently detect event patterns (complex events) in the sensor streams and therefore, help in realizing a “distributed intelligence” in the IoT. With the increasing number of data sources and the increasing volume at which data is produced, parallelization of event detection is crucial to limit the time events need to be buffered before they actually can be processed. In this paper, we propose a pattern-sensitive partitioning model for data streams that is capable of achieving a high degree of parallelism in detecting event patterns, which formerly could only consistently be detected in a sequential manner or at a low parallelization degree. Moreover, we propose methods to dynamically adapt the parallelization degree to limit the buffering imposed on event detection in the presence of dynamic changes to the workload. Extensive evaluations of the system behavior show that the proposed partitioning model allows for a high degree of parallelism and that the proposed adaptation methods are able to meet a buffering limit for event detection under high and dynamic workloads.


title: Template-Based Conformal Shape-from-Motion from Registered Laparoscopic Images.

abstract: One of the current limits of laparosurgery is the absence of a 3D vision facility for standard laparoscopes. While there has been significant progress made in visual SLAM (Simultaneous Localization And Mapping) with a single camera, most of the current approaches relies on the assumption that the tissues are rigid or undergo a cyclic deformation. However, in laparoscopic surgery none of these assumptions commonly apply, due to unpredictable and non-isometric deformation of the tissues. Our main contribution in this paper is a new sequential 3D reconstruction method well-adapted to the peritoneal environment. We draw on recent computer vision results exploiting a template of the environment. The state-of-the-art methods assume that the environment can be modeled as an isometric developable surface, i.e. which deforms isometrically to a plane. While this assumption holds for paper-like surfaces, it certainly does not fit for peritoneal surfaces. Our new method tackle these limits: it uses a full 3D template and copes with non-isometric 3D deformations, thanks to two phases. First the 3D template is reconstructed using rigid SfM (Shape-from-Motion) while the surgeon is exploring – but not deforming – the peritoneal environment. Second the 3D template is used during surgery to infer a quasi-conformal deformation to the current 3D shape from a single laparoscopic image only. This makes both sequential processing and effective self-recovery from tracking errors possible. The proposed approach has been validated on in-vivo laparoscopic videos of the abdominal wall and a uterus. Experimental results illustrate the ability of our method to deal with extensible deformations of the tissues.


title: Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks

abstract: Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc features. The noise that originates from the feature extraction process can cause poor performance. In this paper, we propose a novel model dubbed the Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address these two problems. To solve the first problem, distant supervised relation extraction is treated as a multi-instance problem in which the uncertainty of instance labels is taken into account. To address the latter problem, we avoid feature engineering and instead adopt convolutional architecture with piecewise max pooling to automatically learn relevant features. Experiments show that our method is effective and outperforms several competitive baseline methods.


title: Independent navigation and functioning of intelligent agents by social interaction

abstract: The focus of the effort underlying this paper is to evolve a co-operation based navigation scheme and a mechanism for ad hoc task division between members of a socially interactive swarm of intelligent robots that may find application in strategic warfare and in landmine detection and neutralization. A novel approach has been adopted for evolving a vision scheme for the intelligent agents that endows each member of the swarm with a considerable level of autonomy thereby allowing for re-configurability of the swarm.


title: DAWT: Densely Annotated Wikipedia Texts across multiple languages

abstract: In this work, we open up the DAWT dataset - Densely Annotated Wikipedia Texts across multiple languages. The annotations include labeled text mentions mapping to entities (represented by their Freebase machine ids) as well as the type of the entity. The data set contains total of 13.6M articles, 5.0B tokens, 13.8M mention entity co-occurrences. DAWT contains 4.8 times more anchor text to entity links than originally present in the Wikipedia markup. Moreover, it spans several languages including English, Spanish, Italian, German, French and Arabic. We also present the methodology used to generate the dataset which enriches Wikipedia markup in order to increase number of links. In addition to the main dataset, we open up several derived datasets including mention entity co-occurrence counts and entity embeddings, as well as mappings between Freebase ids and Wikidata item ids. We also discuss two applications of these datasets and hope that opening them up would prove useful for the Natural Language Processing and Information Retrieval communities, as well as facilitate multi-lingual research.


title: Bank Direct Marketing Analysis of Data Mining Techniques

abstract: bank marketing campaigns are dependent on customers' huge electronic data. The size of these data sources is impossible for a human analyst to come up with interesting information that will help in the decision-making process. Data mining models are completely helping in the performance of these campaigns. This paper introduces analysis and applications of the most important techniques in data mining; multilayer perception neural network (MLPNN), tree augmented Naive Bayes (TAN) known as Bayesian networks, Nominal regression or logistic regression (LR), and Ross Quinlan new decision tree model (C5.0). The objective is to examine the performance of MLPNN, TAN, LR and C5.0 techniques on a real-world data of bank deposit subscription. The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a success (the deposit subscribed by the client) based on MLPNN, TAN, LR and C5.0. The experimental results demonstrate, with higher accuracies, the success of these models in predicting the best campaign contact with the clients for subscribing deposit. The performances are calculated by three statistical measures; classification accuracy, sensitivity, and specificity.


title: c ○ 2000 Kluwer Academic Publishers. Printed in The Netherlands. Design and Control of Autonomous Underwater Robots: A Survey

abstract: During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.


title: Fully Convolutional Networks to Detect Clinical Dermoscopic Features

abstract: The presence of certain clinical dermoscopic features within a skin lesion may indicate melanoma, and automatically detecting these features may lead to more quantitative and reproducible diagnoses. We reformulate the task of classifying clinical dermoscopic features within superpixels as a segmentation problem, and propose a fully convolutional neural network to detect clinical dermoscopic features from dermoscopy skin lesion images. Our neural network architecture uses interpolated feature maps from several intermediate network layers, and addresses imbalanced labels by minimizing a negative multilabel Dice-F $_1$ score, where the score is computed across the minibatch for each label. Our approach ranked first place in the 2017 ISIC-ISBI Part 2: Dermoscopic Feature Classification Task , challenge over both the provided validation and test datasets, achieving a 0.895% area under the receiver operator characteristic curve score. We show how simple baseline models can outrank state-of-the-art approaches when using the official metrics of the challenge, and propose to use a fuzzy Jaccard Index that ignores the empty set (i.e., masks devoid of positive pixels) when ranking models. Our results suggest that the classification of clinical dermoscopic features can be effectively approached as a segmentation problem, and the current metrics used to rank models may not well capture the efficacy of the model. We plan to make our trained model and code publicly available.


title: Techniques for Restoring Worn Anterior Teeth with Direct Composite Resin

abstract: Toothwear is increasing in prevalence. Traditional treatment methods for moderate or advanced toothwear, such as indirect restorations and crown lengthening surgery, are invasive and destructive of remaining tissues. The ‘Dahl technique’ has been used to obtain space for anterior restorations and has been modified such that direct composite restorations are placed at increased occlusal vertical dimension. These restorations have proved durable and aesthetic, protect tooth structure and posterior occlusal contact is predictably re-established. The authors describe and compare two techniques using composite resin to treat localized anterior toothwear in a general practice setting.


title: The Influence of Body Movement on Subjective Presence in Virtual Environments

abstract: We describe an experiment to assess the influence of body movements on presence in a virtual environment. In the experiment 20 participants were to walk through a virtual field of trees and count the trees with diseased leaves. A 2 × 2 between-subjects design was used to assess the influence of two factors on presence: tree height variation and task complexity. The field with greater variation in tree height required participants to bend down and look up more than in the lower variation tree height field. In the higher complexity task participants were told to remember the distribution of diseased trees in the field as well as to count them. The results showed a significant positive association between reported presence and the amount of body movement - in particular, head yaw - and the extent to which participants bent down and stood up. There was also a strong interaction effect between task complexity and gender: Women in the morecomplex task reported a much lower sense of presence than in the simpler ...


title: A framework for adaptive training and games in virtual reality rehabilitation environments

abstract: The training of severely disabled individuals on the use of electric power wheelchairs creates many challenges, particularly in the case of children. The adjustment of equipment and training on a per-patient basis in an environment with limited specialists and resources often leads to a reduced amount of training time per patient. Virtual reality rehabilitation has recently been proven an effective way to supplement patient rehabilitation, although some important challenges remain including high setup/equipment costs and time-consuming continual adjustments to the simulation as patients improve. We propose a design for a flexible, low-cost rehabilitation system that uses virtual reality training and games to engage patients in effective instruction on the use of powered wheelchairs. We also propose a novel framework based on Bayesian networks for self-adjusting adaptive training in virtual rehabilitation environments. Preliminary results from a user evaluation and feedback from our rehabilitation specialist collaborators support the effectiveness of our approach.


title: Can Music Preference Indicate Mental Health Status in Young People?

abstract: Objective: In the aftermath of the double suicide of two teenage girls in 2007, the media linked the themes of ‘emo’ music and the girls’ mental state. But it is not just emo music that has been the subject of scrutiny by the media. Rap music, country, and heavy metal have also been blamed for antisocial behaviours including violence, theft, promiscuity and drug use. It remains an important research and clinical question as to whether music contributes to the acting out of behaviours described in the music lyrics or whether the preferred music represents the already existing behavioural tendencies in the subject. This paper surveys and discusses the relevant literature on music preference and adolescent music listening behaviours, and their links with adolescent mental health.Conclusion: Studies have found a relationship between various genres of music and antisocial behaviours, vulnerability to suicide, and drug use. However, studies reject that music is a causal factor and suggest that music preference ...


title: Synesthesia: A new approach to understanding the development of perception. Developmental Psychology, 45, 175–189. doi:10.1037/a0014171

abstract: In this article, the authors introduce a new theoretical framework for understanding intersensory development. Their approach is based upon insights gained from adults who experience synesthesia, in whom sensory stimuli induce extra cross-modal or intramodal percepts. Synesthesia appears to represent one way that typical developmental mechanisms can play out by magnifying connections present in early life that are pruned and/or inhibited during development but persist in muted form in all adults. As such, the study of synesthesia provides valuable insights into the nature of intersensory development. The authors review evidence on the perceptual reality and neural basis of synesthesia, then summarize developmental models and evidence that its underlying mechanisms are universal in adults. They illustrate how evidence for consistent sensory associations in adults leads to predictions about toddlers’ perception and present 3 bodies of work that have confirmed those hypotheses. They end by describing novel hypotheses about intersensory development that arise from this framework. Such intersensory associations appear to reflect intrinsic sensory cortical organization that influences the development of perception and of language and that may constrain the learning of environmentally based associations.


title: On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks

abstract: Deep learning-based methods achieved impressive results for the segmentation of medical images. With the development of 3D fully convolutional networks (FCNs), it has become feasible to produce improved results for multi-organ segmentation of 3D computed tomography (CT) images. The results of multi-organ segmentation using deep learning-based methods not only depend on the choice of networks architecture, but also strongly rely on the choice of loss function. In this paper, we present a discussion on the influence of Dice-based loss functions for multi-class organ segmentation using a dataset of abdominal CT volumes. We investigated three different types of weighting the Dice loss functions based on class label frequencies (uniform, simple and square) and evaluate their influence on segmentation accuracies. Furthermore, we compared the influence of different initial learning rates. We achieved average Dice scores of 81.3%, 59.5% and 31.7% for uniform, simple and square types of weighting when the learning rate is 0.001, and 78.2%, 81.0% and 58.5% for each weighting when the learning rate is 0.01. Our experiments indicated a strong relationship between class balancing weights and initial learning rate in training.


title: The Effects of Perceived Corporate Social Responsibility on Employee Attitudes

abstract: We explore the impact on employee attitudes of their perceptions of how others outside the organization are treated (i.e., corporate social responsibility) above and beyond the impact of how employees are directly treated by the organization. Results of a study of 827 employees in eighteen organizations show that employee perceptions of corporate social responsibility (CSR) are positively related to (a) organizational commitment with the relationship being partially mediated by work meaningfulness and perceived organizational support (POS) and (b) job satisfaction with work meaningfulness partially mediating the relationship but not POS. Moreover, in order to address limited micro-level research in CSR, we develop a measure of employee perceptions of CSR through four pilot studies. Employing a bifactor model, we find that social responsibility has an additional effect on employee attitudes beyond environmental responsibility, which we posit is due to the relational component of social responsibility (e.g., relationships with community).


title: Facial point detection using convolutional neural network transferred from a heterogeneous task

abstract: We present a novel training approach that uses convolutional neural network for facial part detection. In the proposed training procedure, we use the parameters of a network obtained for a heterogeneous task as the initial parameters of the network for a target task. We employ a convolutional neural network for facial part labeling in the heterogeneous task, and then transfer the trained network so as to provide initial parameters of the network for facial point detection. This transfer of network is advantageous in the training for a target task in that 1) the network obtains representation kernels for extraction of facial part regions and 2) the network reduces detection errors at distant positions. The performance of the proposed method applied to BioID and Labeled Face Parts in the Wild datasets is comparable to that of state-of-the art methods. In addition, since our network structure is simple, processing takes approximately 3ms for one face on a standard CPU.


title: Fuzzy Model on Human Emotions Recognition

abstract: This paper discusses a fuzzy model for multi-level human emotions recognition by computer systems through keyboard keystrokes, mouse and touchscreen interactions. This model can also be used to detect the other possible emotions at the time of recognition. Accuracy measurements of human emotions by the fuzzy model are discussed through two methods; the first is accuracy analysis and the second is false positive rate analysis. This fuzzy model detects more emotions, but on the other hand, for some of emotions, a lower accuracy was obtained with the comparison with the non-fuzzy human emotions detection methods. This system was trained and tested by Support Vector Machine (SVM) to recognize the users' emotions. Overall, this model represents a closer similarity between human brain detection of emotions and computer systems.


Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data


title: Application of ordinal logistic regression analysis in determining risk factors of child malnutrition in Bangladesh

abstract: BackgroundThe study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004.MethodsBased on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also been adopted for checking the proportional odds assumption.ResultsAll the models determine that age of child, birth interval, mothers' education, maternal nutrition, household wealth status, child feeding index, and incidence of fever, ARI & diarrhoea were the significant predictors of child malnutrition; however, results of PPOM were more precise than those of other models.ConclusionThese findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models.


title: A survey on unsupervised outlier detection in high-dimensional numerical data

abstract: High-dimensional data in Euclidean space pose special challenges to data mining algorithms. These challenges are often indiscriminately subsumed under the term ‘curse of dimensionality’, more concrete aspects being the so-called ‘distance concentration effect’, the presence of irrelevant attributes concealing relevant information, or simply efficiency issues. In about just the last few years, the task of unsupervised outlier detection has found new specialized solutions for tackling high-dimensional data in Euclidean space. These approaches fall under mainly two categories, namely considering or not considering subspaces (subsets of attributes) for the definition of outliers. The former are specifically addressing the presence of irrelevant attributes, the latter do consider the presence of irrelevant attributes implicitly at best but are more concerned with general issues of efficiency and effectiveness. Nevertheless, both types of specialized outlier detection algorithms tackle challenges specific to high-dimensional data. In this survey article, we discuss some important aspects of the ‘curse of dimensionality’ in detail and survey specialized algorithms for outlier detection from both categories. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc.


title: Sturge-Weber syndrome - A case report.

abstract: Sturge-Weber syndrome (SWS), also called as encephalotrigeminal angiomatosis is an uncommon congenital neurological disorder & frequent among the neurocutaneous syndromes specifically with vascular predominance. This disorder is characterized by facial capillary malformation & other neurological condition. The oral manifestations are gingival hemangiomatosis restricting to either side in upper and lower jaw, sometimes bilateral. We report a case of SWS with oral, ocular and neurological features.


title: Image Segmentation and Classification of MRI Brain Tumor Based on Cellular Automata and Neural Networks

abstract: This paper proposes segmentation of MRI brain tumor using cellular automata and classification of tumors using Gray level Co-occurrence matrix features and artificial neural network. In this technique, cellular automata (CA) based seeded tumor segmentation method on magnetic resonance (MR) images, which uses volume of interest (VOI) and seed selection. Seed based segmentation is performed in the image for detecting the tumor region and then highlighting the region with help of level set method. The brain images are classified into three stages that are normal, benign and malignant. For this non knowledge based automatic image classification, image texture features and Artificial Neural Network are employed. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Decision making was performed in two stages: feature extraction using Gray level Co-occurrence matrix and the classification using Radial basis function which is the type of ANN. The performance of the ANN classifier was evaluated in terms of training performance and classification accuracies. Artificial Neural Network gives fast and accurate classification than other neural networks and it is a promising tool for classification of the tumors.


title: Searching for Mindfulness in the Brain: A Process-Oriented Approach to Examining the Neural Correlates of Mindfulness

abstract: There has been great interest of late in trying to capture the benefits of meditation by scanning meditators’ brains. In this paper, we argue that a successful neuroscience of mindfulness needs to be based on an adequate psychological analysis. We present a definition of mindfulness based on four psychological processes that are relatively well understood, and we show how this model may help organize neuroimaging research and create a bridge to clinical applications. This framework provides an approach to neuroscience research grounded in psychological principles and theory. We propose that this is critical for advancing scientific endeavors such that the knowledge gained helps improve the human condition.


title: Triage template pipelines in digital forensic investigations

abstract: This paper addresses the increasing resources overload being experienced by law enforcement digital forensics units with the proposal to introduce triage template pipelines into the investigative process, enabling devices and the data they contain to be examined according to a number of prioritised criteria.


title: 26-42 GHz SOI CMOS low noise amplifier

abstract: A complementary metal-oxide semiconductor (CMOS) single-stage cascode low-noise amplifier (LNA) is presented in this paper. The microwave monolithic integrated circuit (MMIC) is fabricated using digital 90-nm silicon-on-insulator (SOI) technology. All impedance matching and bias elements are implemented on the compact chip, which has a size of 0.6 mm /spl times/ 0.3 mm. The supply voltage and supply current are 2.4 V and 17 mA, respectively. At 35 GHz and 50 /spl Omega/ source/load impedances, a gain of 11.9 dB, a noise figure of 3.6 dB, an output compression point of 4 dBm, an input return loss of 6 dB, and an output return loss of 18 dB are measured. The -3-dB frequency bandwidth ranges from 26 to 42 GHz. All results include the pad parasitics. To the knowledge of the author, the results are by far the best for a silicon-based millimeter-wave LNA reported to date. The LNA is well suited for systems operating in accordance to the local multipoint distribution service (LMDS) standards at 28 and 38 GHz and the multipoint video distribution system (MVDS) standard at 42 GHz.


title: Multi-objective performance optimization of a probabilistic similarity/dissimilarity-based broadcasting scheme for mobile ad hoc networks in disaster response scenarios

abstract: Communications among crewmembers in rescue teams and among victims are crucial to relieve the consequences and damages of a disaster situation. A common communication system for establishing real time communications between the elements (victims, crewmembers, people living in the vicinity of the disaster scenario, among others) involved in a disaster scenario is required. Ad hoc networks have been envisioned for years as a possible solution. They allow users to establish decentralized communications quickly and using common devices like mobile phones. Broadcasting is the main mechanism used to disseminate information in all-to-all fashion in ad hoc networks. The objective of this paper is to optimize a broadcasting scheme based on similarity/dissimilarity coefficient designed for disaster response scenarios through a multi-objective optimization problem in which several performance metrics such as reachability, number of retransmissions and delay are optimized simultaneously.


title: Spatially embedded co-offence prediction using supervised learning

abstract: Crime reduction and prevention strategies are essential to increase public safety and reduce the crime costs to society. Law enforcement agencies have long realized the importance of analyzing co-offending networks---networks of offenders who have committed crimes together---for this purpose. Although network structure can contribute significantly to co-offence prediction, research in this area is very limited. Here we address this important problem by proposing a framework for co-offence prediction using supervised learning. Considering the available information about offenders, we introduce social, geographic, geo-social and similarity feature sets which are used for classifying potential negative and positive pairs of offenders. Similar to other social networks, co-offending networks also suffer from a highly skewed distribution of positive and negative pairs. To address the class imbalance problem, we identify three types of criminal cooperation opportunities which help to reduce the class imbalance ratio significantly, while keeping half of the co-offences. The proposed framework is evaluated on a large crime dataset for the Province of British Columbia, Canada. Our experimental evaluation of four different feature sets show that the novel geo-social features are the best predictors. Overall, we experimentally show the high effectiveness of the proposed co-offence prediction framework. We believe that our framework will not only allow law enforcement agencies to improve their crime reduction and prevention strategies, but also offers new criminological insights into criminal link formation between offenders.


title: Semantic Text Summarization of Long Videos

abstract: Long videos captured by consumers are typically tied to some of the most important moments of their lives, yet ironically are often the least frequently watched. The time required to initially retrieve and watch sections can be daunting. In this work we propose novel techniques for summarizing and annotating long videos. Existing video summarization techniques focus exclusively on identifying keyframes and subshots, however evaluating these summarized videos is a challenging task. Our work proposes methods to generate visual summaries of long videos, and in addition proposes techniques to annotate and generate textual summaries of the videos using recurrent networks. Interesting segments of long video are extracted based on image quality as well as cinematographic and consumer preference. Key frames from the most impactful segments are converted to textual annotations using sequential encoding and decoding deep learning models. Our summarization technique is benchmarked on the VideoSet dataset, and evaluated by humans for informative and linguistic content. We believe this to be the first fully automatic method capable of simultaneous visual and textual summarization of long consumer videos.


title: A Scor Based Analysis Of Simulation In Supply Chain Management.

abstract: One of the main goals of simulation in supply chain management is to evaluate the performance and how it can be increased by effectively managing a complex supply chain. Simulation supports managers in decision making at strategic, tactical and operational levels through visualizing, understanding and analyzing the dynamics of the supply chain (SC). This paper provides a detailed analysis of the practice of simulation for different supply chain management (SCM) processes. The supply chain operation reference (SCOR) model is used for the classification of SCM processes. It reports the results of a review and analysis of simulation applications based on literature published within peerreviewed journals until 2013 in order to provide an upto-date picture of the role of simulation techniques in SCM. A structured methodology is followed to narrow down the publications (n=569). This research paper mentions different types of simulation in the context to SCM, describes their main characteristics as well as the implementation at different SCM process levels. This leads us to interesting trends and patterns on how different simulation types are applied to different SCM processes in order to answer different managerial questions.


title: A Comparative Study of Classification Techniques for Intrusion Detection

abstract: Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing network traffic data to detect security violations. Mining approach can play very important role in developing an intrusion detection system. The network traffic can be classified into normal and anomalous in order to detect intrusions. In our paper, top-ten classification algorithms namely J48, BayesNet, Logistic, SGD, IBK, JRip, PART, Random Forest, Random Tree and REPTree were selected after experimenting with more than twenty most widely used classification algorithms. The comparison of these top-ten classification algorithms is presented in this paper based upon their performance metrics to find out the best suitable algorithm available. Performance of the classification models is measured using 10-fold cross validation. Experiments and assessments of these methods are performed in WEKA environment using NSL-KDD dataset.


title: Analyzing the Structure and Evolution of Massive Telecom Graphs

abstract: With the ever-growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Existing approaches for telecom business intelligence have almost solely focused on the individual behavior of customers. In this paper, we use the call detail records of a mobile operator to construct call graphs, that is, graphs induced by people calling each other. We determine the structural properties of these graphs and also introduce the Treasure-Hunt model to describe the shape of mobile call graphs. Moreover, we determine how the structure of these call graphs evolve over time. Finally, since short messaging service (SMS) is becoming a preferred mode of communication among many sections of the society, we study the properties of the SMS graph. Our analysis indicates several interesting similarities and differences between the SMS graph and the corresponding call graph. We believe that our analysis techniques can allow telecom operators to better understand the social behavior of their customers and potentially provide major insights for designing effective incentives.


title: Analog Imagery in Mental Model Reasoning : Depictive Models

abstract: Abstract We investigated whether people can use analog imagery to model the behavior of a simple mechanical interaction. Subjects saw a static computer display of two touching gears that had different diameters. Their task was to determine whether marks on each gear would meet if the gears rotated inward. This task added a problem of coordination to the typical analog rotation task in that the gears had a physical interdependency; the angular velocity of one gear depended on the angular velocity of the other gear. In the first experiment, we found the linear relationship between response time and angular disparity that indicates analog imagery. In the second experiment, we found that people can also solve the problem through a non-analog, visual comparison. We also found that people of varying spatial ability could switch between analog and non-analog solutions if instructed to do so. In the third experiment, we examined whether the elicitation of physical knowledge would influence solution strategies. To do so, we manipulated the visual realism of the gear display. Subjects who saw the most realistic gears coordinated their transformations by using the surfaces of the gears, as though they were relying on the friction connecting the surfaces. Subjects who saw more schematic displays relied on analytic strategies, such as comparing the ratios made by the angles and/or diameters of the two gears. To explain the relationship between spatial and physical knowledge found in the experiments, we constructed a computer simulation of what we call depictive modeling. In a depictive model, general spatial knowledge and context-sensitive physical knowledge have the same ontology. This is different from prior simulations in which a non-analog representation would be needed to coordinate the analog behaviors of physical objects. In our simulation, the inference that coordinates the gear motions emerges from the analog rotations themselves. We suggest that mental depictions create a bridge between imagery and mental model research by positing the referent as the primary conceptual entity.


title: Kindness in the blood: A randomized controlled trial of the gene regulatory impact of prosocial behavior

abstract: Abstract Context Prosocial behavior is linked to longevity, but few studies have experimentally manipulated prosocial behavior to identify the causal mechanisms underlying this association. One possible mediating pathway involves changes in gene expression that may subsequently influence disease development or resistance. Design, setting, participants In the current study, we examined changes in a leukocyte gene expression profile known as the Conserved Transcriptional Response to Adversity (CTRA) in 159 adults who were randomly assigned for 4 weeks to engage in prosocial behavior directed towards specific others, prosocial behavior directed towards the world in general, self-focused kindness, or a neutral control task. Results Those randomized to prosocial behavior towards specific others demonstrated improvements (i.e., reductions) in leukocyte expression of CTRA indicator genes. No significant changes in CTRA gene expression were observed in the other 3 conditions. Conclusion These findings suggest that prosocial behavior can causally impact leukocyte gene expression profiles in ways that might potentially help explain the previously observed health advantages associated with social ties.


title: Accuracy of differential shift estimation by correlation and split-bandwidth interferometry for wideband and delta-k SAR systems

abstract: Estimation of differential shift of image elements between two synthetic aperture radar (SAR) images is the basis for many applications, like digital elevation model generation or ground motion mapping. The shift measurement can be done nonambiguously on the macro scale at an accuracy depending on the range resolution of the system or on the micro scale by employing interferometric methods. The latter suffers from phase cycle ambiguities and requires phase unwrapping. Modern wideband high-resolution SAR systems boast resolutions as small as a few tens of a wavelength. If sufficiently many samples are used for macro-scale shift estimation, the accuracy can be increased to a small fraction of a resolution cell and even in the order of a wavelength. Then, accurate absolute ranging becomes precise enough to support phase unwrapping or even make it obsolete. This letter establishes a few fundamental equations on the accuracy bounds of shift estimation accuracy for several algorithms: coherent speckle correlation, incoherent speckle correlation, split-band interferometry, a multifrequency approach, and correlation of point scatterers in clutter. It is shown that the performance of split-band interferometry is close to the Crame/spl acute/r-Rao bound for a broad variety of bandwidth ratios. Based on these findings, Delta-k systems are proposed to best take advantage of the available radar bandwidth.


title: A Chinese Named Entity Recognition System with Neural Networks

abstract: Named entity recognition (NER) is a typical sequential labeling problem that plays an important role in natural language processing (NLP) systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF) on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.


Toward a Theory of Game-Media Literacy: Playing and Building as Reading and Writing


Recurrent Residual Learning for Sequence Classification


title: An Ensemble Learning Approach for the Kaggle Taxi Travel Time Prediction Challenge

abstract: This paper describes the winning solution to the Taxi Trip Time Prediction Challenge run by Kaggle.com. The goal of the competition was to build a predictive framework that is able to predict the final destination and the total traveling time of taxi rides based on their (initial) partial trajectories. The available data consists of all taxi trips of 442 taxis running in the city of Porto within one year. The presented solution consists of an ensemble of expert models combined with a spatial clustering approach. The base classifiers consist of Random Forest Regressors where as the expert models for each test trip where based on a combination of gradient boosting and random forest. The paper shows how these models can be combined in order to generate accurate predictions of the remaining traveling time of a taxi.


title: Unsupervised cyber bullying detection in social networks

abstract: Modern young people (“digital natives”) have grown in an era dominated by new technologies where communications are pushed to quite a real-time level, and pose no limits in establishing relationships with other people or communities. However, the speed of evolution does not allow young people to split consciously acceptable behaviors from potentially harmful ones and a new phenomenon known as cyber bullying is emerging with increasing evidence, attracting the attention of educators, and media. Cyber bullying is defined as “willful and repeated harm inflicted through the use of electronic devices” [1]. In this paper we propose a possible solution for automatic detection of bully traces over a social network, using techniques derived from NLP (Natural Language Processing) and machine learning. Specifically, we shall design a model inspired by Growing Hierarchical SOMs, able to cluster efficiently documents containing bully traces, built upon semantic and syntactic features of textual sentences. We fine-tuned our model to work with the social network Twitter, but we also tested the model against other social networks such as YouTube and Formspring. Finally, we report our results, showing that the proposed unsupervised approach could be effectively used with good performances in some scenarios.


title: A DTC-Based Subsampling PLL Capable of Self-Calibrated Fractional Synthesis and Two-Point Modulation

abstract: We present an analog subsampling PLL based on a digital-to-time converter (DTC), which operates with almost no performance gap (176/198 fs RMS jitter) between the integer and the worst case fractional operation, achieving −246.6 dB FOM in the worst case fractional mode. The PLL is capable of two-point, 10 Mbit/s GMSK modulation with −40.5 dB EVM around a 10.24 GHz fractional carrier. The analog nonidealities—DTC gain, DTC nonlinearity, modulating VCO bank gain, and nonlinearity—are calibrated in the background while the system operates normally. This results in ~15 dB fractional spur improvement (from −41 dBc to −56.5 dBc) during synthesis and ~15 dB EVM improvement (from −25 dB to −40.5 dB) during modulation. The paper provides an overview of the mechanisms that contribute to performance degradation in DTC-based PLL/phase modulators and presents ways to mitigate them. We demonstrate state-of-the-art performance in nanoscale CMOS for fractional-N synthesis and phase modulation.


title: Automatic Question Pattern Generation for Ontology-based Question Answering

abstract: This paper presents an automatic question pattern generation method for ontology-based question answering with the use of textual entailment. In this method, a set of question patterns, called predictive questions, which are predicted to be asked by users in a domain, were generated on the basis of a domain ontology. Their corresponding query templates, which can be used to extract answers to the predictive questions from a knowledge base, were generated as well. The process of producing these question patterns and query templates is described and discussed in the context of the “Movies & Cinemas” domain. An evaluation was carried out to assess the quality of the generated question patterns with the help of a textual entailment engine.


Memes: The Interaction Between Imagery and Subculture: An Analysis of Situation, Race, and Gender on the Pi Kappa Delta Social Media App


title: High repetition rate LC oscillator

abstract: LC oscillators have been built which can produce multikilowatt RF pulses in the megahertz frequency range with repetition rates of tens of kilohertz. The L and C for these oscillators can be determined from the frequency requirement and the high-Q requirements. The high repetition rates are achieved using a high-pressure spark-gap switch together with a dc to ac inverter power supply. Closely spaced antenna elements can be used to increase the number of cycles in the radiated waveforms (radiated Q).


Communicating Intent of Automated Vehicles to Pedestrians


title: Assembly HVDC Breaker for HVDC Grids With Modular Multilevel Converters

abstract: The modular multilevel converter (MMC) with half-bridge submodules (SMs) is the most promising technology for high-voltage direct current (HVDC) grids, but it lacks dc fault clearance capability. There are two main methods to handle the dc-side short-circuit fault. One is to employ the SMs that have dc fault clearance capability, but the power losses are high and the converter has to be blocked during the clearance. The other is to employ the hybrid HVDC breakers. The breaker is capable of interrupting fault current within 5 ms, but this technology is not cost effective, especially in meshed HVDC grids. In this paper, an assembly HVDC breaker and the corresponding control strategy are proposed to overcome these drawbacks. The assembly HVDC breaker consists of an active short-circuit breaker (ASCB), a main mechanical disconnector, a main breaker, and an accessory discharging switch (ADS). When a dc-side short-circuit fault occurs, the ASCB and the ADS close immediately to shunt the fault current. The main breaker opens after a short delay to isolate the faulted line from the system and then the mechanical disconnector opens. With the disconnector in open position, the ASCB opens and breaks the current. The proposed breaker can handle the dc-side fault with competitively low cost, and the operating speed is fast enough. A model of a four-terminal monopolar HVDC grid is developed in Power Systems Computer Aided Design / Electromagnetic Transients including DC, and the simulation result proves the validity and the feasibility of the proposed solution.


Impacts of Shunt Reactors on Transmission Line Protection


Explorations in Echo State Networks


title: Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice

abstract: In response to competitive pressures, firms are increasingly adopting revenue management opportunities afforded by advances in information and communication technologies. Motivated by these revenue management initiatives in industry, we consider a dynamic pricing problem facing a firm that sells given initial inventories of multiple substitutable and perishable products over a finite selling horizon. Because the products are substitutable, individual product demands are linked through consumer choice processes. Hence, the seller must formulate a joint dynamic pricing strategy while explicitly incorporating consumer behavior. For an integrative model of consumer choice based on linear random consumer utilities, we model this multiproduct dynamic pricing problem as a stochastic dynamic program and analyze its optimal prices. The consumer choice model allows us to capture the linkage between product differentiation and consumer choice, and readily specializes to the cases of vertically and horizontally differentiated assortments. When products are vertically differentiated, our results show monotonicity properties (with respect to quality, inventory, and time) of the optimal prices and reveal that the optimal price of a product depends on higher quality product inventories only through their aggregate inventory rather than individual availabilities. Furthermore, we show that the price of a product can be decomposed into the price of its adjacent lower quality product and a markup over this price, with the markup depending solely on the aggregate inventory. We exploit these properties to develop a polynomial-time, exact algorithm for determining the optimal prices and the profit. For a horizontally differentiated assortment, we show that the profit function is unimodal in prices. We also show that individual, rather than aggregate, product inventory availability drives pricing. However, we find that monotonicity properties observed in vertically differentiated assortments do not hold.


A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography


title: Empirical Observations on Software Testing Automation

abstract: This study explores the factors that affect the use of software testing automation in different types of organizations. A case study was conducted in five organizations that develop and test technical software for the automation or telecommunication domains. The data was collected from interviews with managers, testers, and developers in each organization. The grounded theory was used as the data analysis method. According to the results, the factors which emphasize the wide use of testing automation include that the tested products are generic and independent of third-party systems. Low human involvement in testing, steady underlying technology, and reusability support the use of testing automation.


title: Strategic Human Resource Management (SHRM) Practices and Its Effect on Financial Performance: Evidence from Some Selected Scheduled Private Commercial Banks in Bangladesh

abstract: Formulation and execution of Strategic Human Resource Management (SHRM) practices and its effect on perceived financial performance of any organization is a burning issue in the globalized competitive business era. This study aims to find out the relationship between SHRM and financial performance to ensure the sustainability and competitive advantage of the selected scheduled private commercial banks in Bangladesh. The research has been conducted on managers as sample of some private commercial banks during the period of January to November 2012 to collect the primary data. To evaluate the financial performance researchers have used annual reports of 2011 and 2012, journals, web sites etc. as secondary source. Survey research findings indicate that strategic integration and development of HRM were practiced to a full extent in the sampled firms. From the cross sectional analysis, the financial performance indicators show that the capital adequacy was mostly at satisfactory level compared with eh industry average. The quality of assets loan varies from bank to bank but most of them are performing at the desired level. Management efficiency was out-performing the standard in most of the cases. The profitability indicators ratio was also better than the average of private commercial banks. The result presented in this study suggests practicing intensive SHRM so that improved financial performance can be asserted to sustain in the competitive environment.


title: PLATO: Policy learning using adaptive trajectory optimization

abstract: Policy search can in principle acquire complex strategies for control of robots and other autonomous systems. When the policy is trained to process raw sensory inputs, such as images and depth maps, it can acquire a strategy that combines perception and control. However, effectively processing such complex inputs requires an expressive policy class, such as a large neural network. These high-dimensional policies are difficult to train, especially when training must be done for safety-critical systems. We propose PLATO, an algorithm that trains complex control policies with supervised learning, using model-predictive control (MPC) to generate the supervision. PLATO uses an adaptive training method to modify the behavior of MPC to gradually match the learned policy, in order to generate training samples at states that are likely to be visited by the policy while avoiding highly undesirable on-policy actions. We prove that this type of adaptive MPC expert produces supervision that leads to good long-horizon performance of the resulting policy. We also empirically demonstrate that MPC can still avoid dangerous on-policy actions in unexpected situations during training. Our empirical results on a set of challenging simulated aerial vehicle tasks demonstrate that, compared to prior methods, PLATO learns faster, experiences substantially fewer catastrophic failures (crashes) during training, and often converges to a better policy.


title: Assessing Workgroup Norms for Civility: The Development of the Civility Norms Questionnaire-Brief

abstract: Purpose ::: This research describes the development and validation of the Civility Norms Questionnaire-Brief (CNQ-B), a 4-item measure designed to assess workgroup climate for civility. Climate for civility is defined as employee perceptions of norms supporting respectful treatment among workgroup members.


title: Comparison of Sociodemographic and Nutritional Characteristics between Self-Reported Vegetarians, Vegans, and Meat-Eaters from the NutriNet-Santé Study

abstract: Background: There is a growing trend for vegetarian and vegan diets in many Western countries. Epidemiological evidence suggesting that such diets may help in maintaining good health is rising. However, dietary and sociodemographic characteristics of vegetarians and vegans are not well known. The aim of this cross-sectional study was to describe sociodemographic and nutritional characteristics of self-reported, adult vegetarians and vegans, compared to meat-eaters, from the French NutriNet-Sante study. Methods: Participants were asked if they were following a specific diet. They were then classified into three self-reported diet groups: 90,664 meat-eaters, 2370 vegetarians, and 789 vegans. Dietary data were collected using three repeated 24-h dietary records. Multivariable polytomic logistic regression models were perfomed to assess the association between the sociodemographic characteristics and type of diet. The prevalence of nutrient intake inadequacy was estimated, by sex and age for micronutrients, as well as by type of self-reported diet. Results: Compared with meat-eaters, vegetarians were more likely to have a higher educational level, whereas vegans had a lower education level. Compared with meat-eaters, vegetarians were more likely to be women, younger individuals, and to be self-employed or never employed rather than managerial staff. Vegetarians and vegans substituted animal protein-dense products with a higher consumption of plant protein-dense products (e.g., soy-based products or legumes). Vegetarians had the most balanced diets in terms of macronutrients, but also had a better adherence to French dietary guidelines. Vegetarians exhibited a lower estimated prevalence of inadequacies for micronutrients such as antioxidant vitamins (e.g., for vitamin E, 28.9% for vegetarian women <55 years of age vs. 41.6% in meat-eaters) while vegans exhibited a higher estimated prevalence of inadequacies for some nutrients, in particular vitamin B12 (69.9% in men and 83.4% in women <55 years of age), compared to meat-eaters. Conclusions: Our study highlighted that, overall, self-reported vegetarians and vegans may meet nutritional recommendations.


title: Computational geometry on the OTIS-Mesh optoelectronic computer

abstract: We develop efficient algorithms for problems in computational geometry-convex hull, smallest enclosing box, ECDF two-set dominance, maximal points, all-nearest neighbor and closest-pair-on the OTIS-Mesh optoelectronic computer We also demonstrate the algorithms for computing convex hull and prefix sum with condition on a multi-dimensional mesh, which are used to compute convex hull and ECDF respectively. We show that all these problems can be solved in O(/spl radic/N) time even with N/sup 2/ inputs.


title: Transverse slot antenna array in the broad wall of a rectangular waveguide partially filled with a dielectric slab

abstract: A compact and wide-band transverse slot antenna array in the broad wall of a rectangular waveguide partially filled with an H-plane -dielectric slab is presented in this paper. By partially filling an H-plane dielectric slab in the waveguide, the inter-element spacing between slots is reduced to be about 0.8 free-space wavelengths to avoid grating lobes. In addition, the partially filled material provides extra flexibility to adjust the slot impedance as desired within a relatively wide frequency range. A new feeding network is designed for this antenna array, which can prevent the shift in the pointing angle of the array's main radiation beam with the change of frequency. Experimental data for a 2/spl times/4 antenna array operating at X-band show that stable radiation pattern over a wide frequency range can be obtained. The measured gain is 16.9 dB at 10 GHz and the fluctuation is less than 2 dB over a frequency band of 1.5 GHz. The slot array's 10-dB return loss bandwidth is 13% and the cross-polarization level is better than -25 dB.


title: Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks

abstract: Recognising human actions in untrimmed videos is an important challenging task. An effective three-dimensional (3D) motion representation and a powerful learning model are two key factors influencing recognition performance. In this study, the authors introduce a new skeleton-based representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a colour encoding process. By normalising the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the colour-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. They then design and train different deep convolutional neural networks based on the residual network architecture on the obtained image-based representations to learn 3D motion features and classify them into classes. Their proposed method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches while requiring less computation for training and prediction.


title: The Association Between Psychological Well-Being and Problematic Use of Internet Communicative Services Among Young People

abstract: ABSTRACTPrevious studies on problematic Internet use have focused almost exclusively on the fact that presence of negative functioning, such as social anxiety, depressive symptoms, or loneliness, represents a risk factor for unhealthy use of the web. For this reason the aim of the present study was to investigate the association between psychological well-being dimensions and problematic use of Internet communicative services. In the current study 495 undergraduate students were recruited. The Italian adaptations of the Psychological Well-being Scales and the Generalized Problematic Internet Use Scale 2 (GPIUS2) were used to assess psychological well-being dimensions and generalized problematic Internet use, respectively. Psychological well-being dimensions explained a significant portion of variance for the GPIUS2 total score levels, after controlling for sex, age, and occupational status. The levels of Autonomy, Environmental Mastery, and Positive Relations with Others acted as significant negative pred...


title: A muscle model for animation three-dimensional facial expression

abstract: The development of a parameterized facial muscle process, that incorporates the use of a model to create realistic facial animation is described.Existing methods of facial parameterization have the inherent problem of hard-wiring performable actions. The development of a muscle process that is controllable by a limited number of parameters and is non-specific to facial topology allows a richer vocabulary and a more general approach to the modelling of the primary facial expressions.A brief discussion of facial structure is given, from which a method for a simple modelling of a muscle process that is suitable for the animation of a number of divergent facial types is described.


Causal Inference for Recommendation


title: Towards workload shift detection and prediction for autonomic databases

abstract: Due to the complexity of industry-scale database systems, the total cost of ownership for these systems is no longer dominated by hardware and software, but by administration expenses. Autonomic databases intend to reduce these costs by providing self-management features. Existing approaches towards this goal are supportive advisors for the database administrator and feedback control loops for online monitoring, analysis and re-configuration. But while advisors are too resource-consuming for continuous operation, feedback control loops suffer from overreaction, oscillation and interference. In this position paper we give a general analysis of the parameters that affect the self-management of a database. Out of these parameters, we identify that the workload has major influence on both physical design of data and DBMS configuration. Hence, we propose to employ a workload model for light-weight, continuous workload monitoring and analysis. This model can be used for the identification and prediction of significant workload shifts, which require autonomic re-configuration of the database.


title: Exploring a conceptual model, based on the combined effects of entrepreneurial leadership, market orientation and relationship marketing orientation on South Africa's small tourism business performance

abstract: This article explores the relationships between entrepreneurial leadership, market orientation and relationship marketing orientation and South Africa’s Small tourism business performance. The entrepreneurial leadership construct is a product of the fusion of entrepreneurship and leadership constructs. This article firstly explores the relationship of entrepreneurial leadership as an antecedent to the blend of market orientation and relationship marketing orientation on small tourism performance, and secondly the relationship of the combined effects of entrepreneurial leadership, market orientation and relationship marketing orientation on small tourism business performance. It is a descriptive and theoretical article, and thus secondary data from previous studies are used as comparative analysis for examination and discussion. The findings indicate that there is a relationship between the identified constructs. However, the exact nature and extent of these relationships need to be further investigated.


title: Challenges in the reproducibility of clinical studies with resting state fMRI: An example in early Parkinson's disease

abstract: Resting state fMRI (rfMRI) is gaining in popularity, being easy to acquire and with promising clinical applications. However, rfMRI studies, especially those involving clinical groups, still lack reproducibility, largely due to the different analysis settings. This is particularly important for the development of imaging biomarkers. The aim of this work was to evaluate the reproducibility of our recent study regarding the functional connectivity of the basal ganglia network in early Parkinson's disease (PD) (Szewczyk-Krolikowski et al., 2014). In particular, we systematically analysed the influence of two rfMRI analysis steps on the results: the individual cleaning (artefact removal) of fMRI data and the choice of the set of independent components (template) used for dual regression. ::: ::: Our experience suggests that the use of a cleaning approach based on single-subject independent component analysis, which removes non neural-related sources of inter-individual variability, can help to increase the reproducibility of clinical findings. A template generated using an independent set of healthy controls is recommended for studies where the aim is to detect differences from a “healthy” brain, rather than an “average” template, derived from an equal number of patients and controls. While, exploratory analyses (e.g. testing multiple resting state networks) should be used to formulate new hypotheses, careful validation is necessary before promising findings can be translated into useful biomarkers.


title: Analysis of the complex phase error introduced by the application of the Fourier transform method

abstract: The relation between modulus and phase errors introduced by the application of the Fourier transform method to fringe pattern analysis has been established. The theoretical results obtained are proposed for the reconstructed phase error correction. Numerical verification for a one-dimensional fringe pattern, comparison of corrected and non-corrected results obtained for different basic spatial frequencies and an example of application of the proposed correction for real data are given.


title: Adversarial Training Versus Weight Decay

abstract: Performance-critical machine learning models should be robust to input perturbations not seen during training. Adversarial training is a method for improving a model's robustness to some perturbations by including them in the training process, but this tends to exacerbate other vulnerabilities of the model. The adversarial training framework has the effect of translating the data with respect to the cost function, while weight decay has a scaling effect. Although weight decay could be considered a crude regularization technique, it appears superior to adversarial training as it remains stable over a broader range of regimes and reduces all generalization errors. Equipped with these abstractions, we provide key baseline results and methodology for characterizing robustness. The two approaches can be combined to yield one small model that demonstrates good robustness to several white-box attacks associated with different metrics.


Atomic Energy and World Peace