title: Introducing database-centric support in AUTOSAR

abstract: We propose to integrate a real-time database management system into the basic software of the AUTOSAR component model. This integration can be performed without violating the fundamental principles of the component-based approach of AUTOSAR. Our database-centric approach allows developers to focus on application development instead of reinventing data management techniques or develop solutions using internal data structures. We use state-of-the-art database pointer techniques to achieve predictable timing, and database proxies to maintain component encapsulation and independence of data-management strategies. The paper illustrates the feasibility of our proposal when database proxies are used to manage the data communication between components and to perform run-time monitoring on the virtual function bus. Our implementation results show that the above benefits do not come at the expense of less accurate timing predictions while only introducing a total application CPU overhead, in the order of 4%.


title: Workplace bullying and the risk of cardiovascular disease and depression

abstract: AIMS ::: To examine exposure to workplace bullying as a risk factor for cardiovascular disease and depression in employees. ::: ::: ::: METHODS ::: Logistic regression models were related to prospective data from two surveys in a cohort of 5432 hospital employees (601 men and 4831 women), aged 18-63 years. Outcomes were new reports of doctor diagnosed cardiovascular disease and depression during the two year follow up among those who were free from these diseases at baseline. ::: ::: ::: RESULTS ::: The prevalence of bullying was 5% in the first survey and 6% in the second survey. Two per cent reported bullying experiences in both surveys, an indication of prolonged bullying. After adjustment for sex, age, and income, the odds ratio of incident cardiovascular disease for victims of prolonged bullying compared to non-bullied employees was 2.3 (95% CI 1.2 to 4.6). A further adjustment for overweight at baseline attenuated the odds ratio to 1.6 (95% CI 0.8 to 3.5). The association between prolonged bullying and incident depression was significant, even after these adjustments (odds ratio 4.2, 95% CI 2.0 to 8.6). ::: ::: ::: CONCLUSIONS ::: A strong association between workplace bullying and subsequent depression suggests that bullying is an aetiological factor for mental health problems. The victims of bullying also seem to be at greater risk of cardiovascular disease, but this risk may partly be attributable to overweight.


title: An influence diagram model for multi-agent negotiation

abstract: Bilateral negotiation is an important aspect of e-commerce to bring satisfactory agreement in business transactions. Though auction-based models are more common currently, they are unable to accommodate tradeoffs between multiple parameters. This makes them inappropriate when trading with intangible goods, like information, expertise and help. We approach e-commerce and negotiation in the context of a distributed multi-agent peer help system, I-Help, supporting students in a university course. Personal agents keep models of student preferences and negotiate on their behalf to acquire information resources (help) from other agents. Negotiation is modeled using an influence diagram, a decision theoretic tool. Agents create models of their opponents during negotiation, which help them predict better the opponent actions. The experiments show that the proposed approach brings better deals to the agent who uses it. However, if both negotiating agents use this approach, the negotiation fails more often.


Collaborative Partitioning for Coreference Resolution


title: Learning Effective RGB-D Representations for Scene Recognition

abstract: Deep convolutional networks can achieve impressive results on RGB scene recognition thanks to large data sets such as places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first limitation is the lack of depth data for training deep learning models. Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a two-step training approach that directly learns effective depth-specific features using weak supervision via patches. The resulting RGB-D model also benefits from more complementary multimodal features. Another limitation is the short range of depth sensors (typically 0.5 m to 5.5 m), resulting in depth images not capturing distant objects in the scenes that RGB images can. We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scenes. Focusing on this scenario, we introduce the ISIA RGB-D video data set to evaluate RGB-D scene recognition with videos. Our video recognition architecture combines convolutional and recurrent neural networks that are trained in three steps with increasingly complex data to learn effective features (i.e., patches, frames, and sequences). Our approach obtains the state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition.


Bacteriocinogenic potential and virulence traits of Enterococcus faecium and E. faecalis isolated from human milk


title: Effectiveness of occupational performance coaching in improving children's and mothers' performance and mothers' self-competence.

abstract: OBJECTIVE. This study examined the effectiveness of occupational performance coaching in improving children's and mothers' occupational performance and mothers' parenting self-competence. METHOD. A one-group time-series design was used to evaluate changes in children's (n 5 29) and mothers' (n 5 8) occupational performance at four time points: (1) pre-wait list, (2) preintervention, (3) postintervention, and (4) follow-up. RESULTS. Significant improvements in occupational performance occurred postintervention for children, F(1, 78) 5 153.72, p < .001, h2 5 .86, and mothers, F (1, 78) 5 153.72, p < .001, h2 5 .86, that were maintained 6 wk after intervention. Mothers' self-competence in parenting also improved, F(1, 72) 5 17.36, p < .001, h2 5 .42. CONCLUSION. Findings provide preliminary evidence supporting the effectiveness of occupational performance coaching in improving children's and mothers' occupational performance and mothers' parenting self-competence. Improvements were sustained and appeared to generalize to other areas of performance.


title: Correction of the Retracted Alar Base

abstract: Alar base retraction is a commonyet difficult problem faced by the rhinoplasty surgeon. It may be caused by weakened, overresected lateral crura, vestibular lining deficiencies, or congenital alar malpositioning. Methods of correction include soft tissue manipulation, auricular composite grafting, and cartilage grafting. We present the senior author's graded approach to alar retraction using auricular composite grafting, alar rim grafting, and lateral crural strut graft placement with caudal lateral crural repositioning.


title: Flexible and stretchable fabric-based tactile sensor

abstract: We introduce a novel, fabric-based, flexible, and stretchable tactile sensor, which is capable of seamlessly covering natural shapes. As humans and robots have curved body parts that move with respect to each other, the practical usage of traditional rigid tactile sensor arrays is limited. Rather, a flexible tactile skin is required. Our design allows for several tactile cells to be embedded in a single sensor patch. It can have an arbitrary perimeter and can cover free-form surfaces. In this article we discuss the construction of the sensor and evaluate its performance. Our flexible tactile sensor remains operational on top of soft padding such as a gel cushion, enabling the construction of a human-like soft tactile skin. The sensor allows pressure measurements to be read from a subtle less than 1?kPa up to high pressures of more than 500?kPa, which easily covers the common range for everyday human manual interactions. Due to a layered construction, the sensor is very robust and can withstand normal forces multiple magnitudes higher than what could be achieved by a human without sustaining damage.As an exciting application for the sensor, we describe the construction of a wearable tactile dataglove with 54 tactile cells and embedded data acquisition electronics. We also discuss the necessary implementation details to maintain long term sensor performance in the presence of moisture. A flexible and stretchable durable fabric-based tactile sensor capable of capturing typical human interaction forces was developed.We present elaborate measurement results of the sensor.A process of creating multiple sensor areas in a single fabric patch was developed.The measures against performance degradation due to moisture are presented.Using the developed technology, a tactile dataglove with 54 pressure sensitive regions was built.


title: Stimulation of lignocellulosic biomass hydrolysis by proteins of glycoside hydrolase family 61: structure and function of a large, enigmatic family.

abstract: Currently, the relatively high cost of enzymes such as glycoside hydrolases that catalyze cellulose hydrolysis represents a barrier to commercialization of a biorefinery capable of producing renewable transportable fuels such as ethanol from abundant lignocellulosic biomass. Among the many families of glycoside hydrolases that catalyze cellulose and hemicellulose hydrolysis, few are more enigmatic than family 61 (GH61), originally classified based on measurement of very weak endo-1,4-beta-d-glucanase activity in one family member. Here we show that certain GH61 proteins lack measurable hydrolytic activity by themselves but in the presence of various divalent metal ions can significantly reduce the total protein loading required to hydrolyze lignocellulosic biomass. We also solved the structure of one highly active GH61 protein and find that it is devoid of conserved, closely juxtaposed acidic side chains that could serve as general proton donor and nucleophile/base in a canonical hydrolytic reaction, and we conclude that the GH61 proteins are unlikely to be glycoside hydrolases. Structure-based mutagenesis shows the importance of several conserved residues for GH61 function. By incorporating the gene for one GH61 protein into a commercial Trichoderma reesei strain producing high levels of cellulolytic enzymes, we are able to reduce by 2-fold the total protein loading (and hence the cost) required to hydrolyze lignocellulosic biomass.


title: An improved XGBoost based on weighted column subsampling for object classification

abstract: Object classification is one of main problems in computer vision and lots of state-of-the-art machine learning methods have been proposed recently. Traditional classifier may have the drawback of low effectiveness for image representations. In this paper, an improved eXtreme Gradient Boosting (XGBoost) based on weighted column (feature) subsampling is proposed to classify image representations for object classification. The contributions of the paper are as follows. First, Convolutional Neural Networks (CNN) pre-trained on large-scale image database ILSVRC and fine-tuned on PASCAL VOC 2012 dataset is used to extract features from object images. Besides, we concatenate multiple layers of learned features to obtain more information of image contents for determining their categories. Second, due to the high dimensional of extracted features, feature redundancy will unavoidably be incurred, so a weighted column subsampling method is proposed to applied to XGBoost algorithm, in order to draw the features by the importance during growing each tree. Futhermore, our proposed method is general, which can be easily extended to other methods which need column subsampling, when the feature of data is high dimensional and redundant. Finally, we validate performance of proposed method on the PASCAL VOC 2007 dataset. Compared with four typical methods, the proposed method has superior average precision (AP) on 5 out of 20 classes, e.g. the AP of person class we obtained is 92.1, 1% higher than that of best method mentioned above.


title: Shake, rattle, ‘n’ roll: the representation of motion in language and cognition

abstract: Languages vary strikingly in how they encode motion events. In some languages (e.g. English), manner of motion is typically encoded within the verb, while direction of motion information appears in modifiers. In other languages (e.g. Greek), the verb usually encodes the direction of motion, while the manner information is often omitted, or encoded in modifiers. We designed two studies to investigate whether these language-specific patterns affect speakers' reasoning about motion. We compared the performance of English and Greek children and adults (a) in nonlinguistic (memory and categorization) tasks involving motion events, and (b) in their linguistic descriptions of these same motion events. Even though the two linguistic groups differed significantly in terms of their linguistic preferences, their performance in the nonlinguistic tasks was identical. More surprisingly, the linguistic descriptions given by subjects within language also failed to correlate consistently with their memory and categorization performance in the relevant regards. For the domain studied, these results are consistent with the view that conceptual development and organization are largely independent of language-specific labeling practices. The discussion emphasizes that the necessarily sketchy nature of language use assures that it will be at best a crude index of thought.


title: Damage Localization Analysis to Spatial Steel Braced-Frame Structure

abstract: A vibration-based damage index localization method was proposed,and damage localization analysis of a complicated spatial model structure was conducted.The 7 known damaged cases including two kinds of damage states,brace damage cases and joint damage cases,were considered,and the different damage patterns were ana-lyzed according to the limited number of measurements from ambient excitations.Results indicate that reasonable estimation for the possible damage location of the complicated spatial structure can be derived by combining the dam-age index with the characteristics of the analytical model and the identified modal characteristics.


title: RNA-Seq methods for transcriptome analysis.

abstract: Deep sequencing has been revolutionizing biology and medicine in recent years, providing single base-level precision for our understanding of nucleic acid sequences in high throughput fashion. Sequencing of RNA, or RNA-Seq, is now a common method to analyze gene expression and to uncover novel RNA species. Aspects of RNA biogenesis and metabolism can be interrogated with specialized methods for cDNA library preparation. In this study, we review current RNA-Seq methods for general analysis of gene expression and several specific applications, including isoform and gene fusion detection, digital gene expression profiling, targeted sequencing and single-cell analysis. In addition, we discuss approaches to examine aspects of RNA in the cell, technical challenges of existing RNA-Seq methods, and future directions. WIREs RNA 2017, 8:e1364. doi: 10.1002/wrna.1364 For further resources related to this article, please visit the WIREs website.


title: Structural Balance Theory-Based E-Commerce Recommendation over Big Rating Data

abstract: Recommending appropriate product items to the target user is becoming the key to ensure continuous success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique, to realize product item recommendation. Overall, the present CF recommendation can perform very well, if the target user owns similar friends (user-based CF), or the product items purchased and preferred by target user own one or more similar product items (item-based CF). While due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be both absent from the user-product purchase network, which lead to a big challenge to recommend appropriate product items to the target user. Considering the challenge, we put forward a Structural Balance Theory-based Recommendation (i.e., SBT-Rec ) approach. In the concrete, (I) user-based recommendation: we look for target user's “enemy” (i.e., the users having opposite preference with target user); afterwards, we determine target user's “possible friends”, according to “enemy's enemy is a friend” rule of Structural Balance Theory, and recommend the product items preferred by “possible friends” of target user to the target user. (II) likewise, for the product items purchased and preferred by target user, we determine their “possibly similar product items” based on Structural Balance Theory and recommend them to the target user. At last, the feasibility of SBT-Rec is validated, through a set of experiments deployed on MovieLens-1M dataset.


title: High-Level Synthesis: Past, Present, and Future

abstract: This article presents the history and evolution of HLS from research to industry adoption. The authors offer insights on why earlier attempts to gain industry adoption were not successful, why current HLS tools are finally seeing adoption, and what to expect as HLS evolves toward system-level design.


title: Main Memory Database Systems : An Overview

abstract: Main memory database systems (MMDBs) store their data in main physical memory and provide very high-speed access. Conventional database systems are optimized for the particular characteristics of disk storage mechanisms. Memory resident systems, on the other hand, use different optimizations to structure and organize data, as well as to make it reliable. The authors survey the major memory residence optimizations and briefly discuss some of the MMDBs that have been designed or implemented. >


title: A First Algorithm to Calculate Force Histograms in the Case of 3D Vector Objects

abstract: In daily conversation, people use spatial prepositions to denote spatial relationships and describe relative positions. Various quantitative relative position descriptors can be found in the literature. However, they all have been designed with 2D objects in mind, most of them cannot be extended to handle 3D objects in vector form, and there is currently no implementation able to process such objects. In this paper, we build on a descriptor called the histogram of forces, and we present the first algorithm for quantitative relative position descriptor calculation in the case of 3D vector objects. Experiments validate the approach.


title: Perineal Urethrostomy: Surgical and Functional Evaluation of Two Techniques

abstract: INTRODUCTION ::: PU is an option to manage complex and/or recurrent urethral strictures and is necessary after urethrectomy and/or penectomy. PU is generally assumed to be the last option before abandoning the urethral outlet. ::: ::: ::: METHOD ::: Between 2001 and 2013, 51 patients underwent PU. Mean age (± standard deviation) was 60 ± 15 years. Only 13 patients (25.5%) did not undergo previous urethral interventions. PU was performed according to the Johanson (n = 35) or Blandy (n = 16) technique and these 2 groups were compared for surgical failure, maximum urinary flow (Qmax), urinary symptoms, and quality of life (according to the International Prostate Symptom Score). ::: ::: ::: RESULTS ::: Both groups were similar for patient's and stricture characteristics. Only follow-up duration was significantly longer after Johanson PU (47.9 months versus 11.1 months; P = 0.003). For the entire cohort, 11 patients (21.6%) were considered a failure (9 or 25.7% for Johanson group and 2 or 12.5% for Blandy group; P = 0.248). There was a significant improvement of Qmax in both groups. Quality of life after PU was comparable in both groups. ::: ::: ::: CONCLUSIONS ::: PU is associated with a 21.6% recurrence rate and the patient should be informed about this risk.


title: Evolving Fuzzy-Rule-Based Classifiers From Data Streams

abstract: A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (evolving) fuzzy-rule-based (FRB) classifier system of Takagi-Sugeno ( eTS) type. The proposed approach, called eClass (evolving class ifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1) eClass0, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order eTS fuzzy classifier, eClass1. An important property of eClass is that it can start learning ldquofrom scratch.rdquo Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for eClass (the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists, eClass can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources.


title: Advanced Driver Assistance Systems and Animals

abstract: Advanced driver assistance systems are widely used. Some support and inform the driver. Others relieve him or her of certain tasks—and transform the human-guided system into a semi-autonomous one. For some years also fully autonomous systems have been on the roads, so-called self-driving cars, as prototypes of companies and within research projects. From the perspective of ethics—both of the special fields of ethics like animal ethics, information ethics and technology ethics and of machine ethics which can be understood as a counterpart to human ethics—advanced driver assistance systems raise various questions. The aim of this paper is to derive suggestions from animal ethics and other disciplines for the improvement and development of the systems. The basis are literature analysis and own classifications and considerations. The result is that there are many possibilities to expand existing systems and to develop new functions in the context with the aim to reduce the number of animal victims.


title: German inflection: Single route or dual route

abstract: The German plural system has recently become a focal point for conflicting theories of language, both linguistic and cognitive. Marcus et al. (1995) highlight the German plural as support for the dual-route account of inflectional morphology first proposed by Pinker and colleagues (Pinker & Prince, 1988). On the dual-route account, inflectional morphology is universally subserved by a symbolic rule route which deals with regular inflection and an associative memory component which deals with irregular inflection. This contrasts with single-route connectionist systems. We seek to counter supposed evidence for the dual-route account through large-scale simulations as well as through experimental data. We argue that, in its current form, the dual-route account is incapable of generating experimental data provided by Marcus et al. (1995) as support. Finally, we provide direct quantitative comparisons between single-route and dual-route models of German plural inflection and find single-route performance superior on these tests.


title: Automatic Generation of Natural Language Explanations

abstract: An interesting challenge for explainable recommender systems is to provide successful interpretation of recommendations using structured sentences. It is well known that user-generated reviews, have strong influence on the users' decision. Recent techniques exploit user reviews to generate natural language explanations. In this paper, we propose a character-level attention-enhanced long short-term memory model to generate natural language explanations. We empirically evaluated this network using two real-world review datasets. The generated text present readable and similar to a real user's writing, due to the ability of reproducing negation, misspellings, and domain-specific vocabulary.


title: An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet

abstract: This paper presents an adaptation of Lesk's dictionary-based word sense disambiguation algorithm. Rather than using a standard dictionary as the source of glosses for our approach, the lexical database WordNet is employed. This provides a rich hierarchy of semantic relations that our algorithm can exploit. This method is evaluated using the English lexical sample data from the SENSEVAL-2 word sense disambiguation exercise, and attains an overall accuracy of 32%. This represents a significant improvement over the 16% and 23% accuracy attained by variations of the Lesk algorithm used as benchmarks during the Senseval-2 comparative exercise among word sense disambiguation systems.


title: A Web-of-Things Gateway for KNX Networks

abstract: Smart buildings tend to democratize both in new and renovated constructions aiming at minimizing energy consumption and maximizing comfort. They rely on dedicated networks of sensors and actuators orchestrated by management systems. Building networks are often heterogeneous, leading to complex management systems having to implement all the available protocols and resulting in low system integration and heavy maintenance efforts. Typical building networks offer no common standardized application layer to build applications. We propose in this paper to leverage on the Web-of-Things (WoT) framework, using well-known technologies like HTTP and RESTful APIs to offer a simple and homogeneous application layer. We outline the implementation of a gateway using the principles of the WoT to expose capabilities of the KNX building network as Web services, allowing a fast integration in management systems.


Using CPU System Management Mode to Circumvent Operating System Security Functions


title: Plastics and microplastics in the oceans: From emerging pollutants to emerged threat

abstract: Plastic production has increased dramatically worldwide over the last 60 years and it is nowadays recognized as a serious threat to the marine environment. Plastic pollution is ubiquitous, but quantitative estimates on the global abundance and weight of floating plastics are still limited, particularly for the Southern Hemisphere and the more remote regions. Some large-scale convergence zones of plastic debris have been identified, but there is the urgency to standardize common methodologies to measure and quantify plastics in seawater and sediments. Investigations on temporal trends, geographical distribution and global cycle of plastics have management implications when defining the origin, possible drifting tracks and ecological consequences of such pollution. An elevated number of marine species is known to be affected by plastic contamination, and a more integrated ecological risk assessment of these materials has become a research priority. Beside entanglement and ingestion of macro debris by large vertebrates, microplastics are accumulated by planktonic and invertebrate organisms, being transferred along food chains. Negative consequences include loss of nutritional value of diet, physical damages, exposure to pathogens and transport of alien species. In addition, plastics contain chemical additives and efficiently adsorb several environmental contaminants, thus representing a potential source of exposure to such compounds after ingestion. Complex ecotoxicological effects are increasingly reported, but the fate and impact of microplastics in the marine environment are still far to be fully clarified.


SC2Net: Sparse LSTMs for Sparse Coding


Data Wrangling for Big Data: Towards a Lingua Franca for Data Wrangling


title: Clearing the RF smog: making 802.11n robust to cross-technology interference

abstract: Recent studies show that high-power cross-technology interference is becoming a major problem in today's 802.11 networks. Devices like baby monitors and cordless phones can cause a wireless LAN to lose connectivity. The existing approach for dealing with such high-power interferers makes the 802.11 network switch to a different channel; yet the ISM band is becoming increasingly crowded with diverse technologies, and hence many 802.11 access points may not find an interference-free channel. ::: This paper presents TIMO, a MIMO design that enables 802.11n to communicate in the presence of high-power cross-technology interference. Unlike existing MIMO designs, however, which require all concurrent transmissions to belong to the same technology, TIMO can exploit MIMO capabilities to decode in the presence of a signal from a different technology, hence enabling diverse technologies to share the same frequency band. We implement a prototype of TIMO in GNURadio-USRP2 and show that it enables 802.11n to communicate in the presence of interference from baby monitors, cordless phones, and microwave ovens, transforming scenarios with a complete loss of connectivity to operational networks.


title: Development of a control system for a stair-climbing inverted pendulum robot

abstract: In this paper, we develop the control system of a stair-climbing inverted pendulum robot using a planetary wheel mechanism. The purpose of the control system is to ensure that the robot adjusts its orientation towards a given step before climbing the step and keep its main body stable before and after performing the climbing maneuver. The proposed system operates as follows. We utilize a state feedback control with a reference speed input and additional torque in order to guarantee the stability while moving and performing climbing maneuver. We use a proportional-derivative controller to control the orientation of the robot towards the stairs before climbing and to maintain the same orientation after the ascent or descent. Experimental results confirmed that the robot successfully ascended and descended the stairs by using the proposed method, even though the angles of each step's riser surface varied.


title: Extracting semantic representations from word co-occurrence statistics: A computational study

abstract: The idea that at least some aspects of word meaning can be induced from patterns of word co-occurrence is becoming increasingly popular. However, there is less agreement about the precise computations involved, and the appropriate tests to distinguish between the various possibilities. It is important that the effect of the relevant design choices and parameter values are understood if psychological models using these methods are to be reliably evaluated and compared. In this article, we present a systematic exploration of the principal computational possibilities for formulating and validating representations of word meanings from word co-occurrence statistics. We find that, once we have identified the best procedures, a very simple approach is surprisingly successful and robust over a range of psychologically relevant evaluation measures.


title: A Decision Analysis Framework for Prioritizing a Portfolio of ICT Infrastructure Projects

abstract: The inadequacy of traditional quantitative cost-benefit analysis for evaluating information and communications technologies (ICT) infrastructure investments have led researchers to suggest real options (ROs) analysis for valuating ICT projects. However, ROs models are strictly quantitative and often, ICT investments may contain qualitative factors that cannot be quantified in monetary terms. In addition, ROs analysis results in some factors that can be treated more efficiently when taken qualitatively. This paper combines ROs and the analytic hierarchy process (AHP) into a common decision analysis framework, providing an integrated multiobjective, multicriteria model called ROAHP for prioritizing a portfolio of interdependent ICT investments. The proposed model is applied to an ICT case study showing how it can be formulated and solved.


title: Taming Subgraph Isomorphism for RDF Query Processing

abstract: RDF data are used to model knowledge in various areas such as life sciences, Semantic Web, bioinformatics, and social graphs. The size of real RDF data reaches billions of triples. This calls for a framework for efficiently processing RDF data. The core function of processing RDF data is subgraph pattern matching. There have been two completely different directions for supporting efficient subgraph pattern matching. One direction is to develop specialized RDF query processing engines exploiting the properties of RDF data for the last decade, while the other direction is to develop efficient subgraph isomorphism algorithms for general, labeled graphs for over 30 years. Although both directions have a similar goal (i.e., finding subgraphs in data graphs for a given query graph), they have been independently researched without clear reason. We argue that a subgraph isomorphism algorithm can be easily modified to handle the graph homomorphism, which is the RDF pattern matching semantics, by just removing the injectivity constraint. In this paper, based on the state-of-the-art subgraph isomorphism algorithm, we propose an in-memory solution, TurboHOM++, which is tamed for the RDF processing, and we compare it with the representative RDF processing engines for several RDF benchmarks in a server machine where billions of triples can be loaded in memory. In order to speed up TurboHOM++, we also provide a simple yet effective transformation and a series of optimization techniques. Extensive experiments using several RDF benchmarks show that TurboHOM++ consistently and significantly outperforms the representative RDF engines. Specifically, TurboHOM++ outperforms its competitors by up to five orders of magnitude.


title: A case for dynamic frequency tuning in on-chip networks

abstract: Performance and power are the first order design metrics for Network-on-Chips (NoCs) that have become the de-facto standard in providing scalable communication backbones for multicores/CMPs. However, NoCs can be plagued by higher power consumption and degraded throughput if the network and router are not designed properly. Towards this end, this paper proposes a novel router architecture, where we tune the frequency of a router in response to network load to manage both performance and power. We propose three dynamic frequency tuning techniques, FreqBoost, FreqThrtl and FreqTune, targeted at congestion and power management in NoCs. As enablers for these techniques, we exploit Dynamic Voltage and Frequency Scaling (DVFS) and the imbalance in a generic router pipeline through time stealing. Experiments using synthetic workloads on a 8x8 wormhole-switched mesh interconnect show that FreqBoost is a better choice for reducing average latency (maximum 40%) while, FreqThrtl provides the maximum benefits in terms of power saving and energy delay product (EDP). The FreqTune scheme is a better candidate for optimizing both performance and power, achieving on an average 36% reduction in latency, 13% savings in power (up to 24% at high load), and 40% savings (up to 70% at high load) in EDP. With application benchmarks, we observe IPC improvement up to 23% using our design. The performance and power benefits also scale for larger NoCs.


title: Cognitive Theories of Consciousness

abstract: Connectionism – Connectionism is a framework in cognitive science, according to which all of the processes achieved by the mind can be modeled by parallel and distributed processing among simple operational units. It is mostly based on the development of artificial neural networks, and it has been traditionally opposed to the position that mental processes are based on symbolic computations. Functionalism – Functionalism is a doctrine in cognitive science, according to which a mental state is defined by its functional role, rather than by its intrinsic structure and its implementation. In other words, a functional model of the mind (or of an operation that is achieved by the mind) involves mental states that are causally related to sensory inputs and other mental states, and behavior. Homunculus – Literally, ‘little man,’ in Latin, that is in the context of cognitive theories of consciousness, a conscious observer, which is at the top of the cognitive system. A Homunculus is a hypothetical construct that operates (i.e., with no further explanation) the very operation that is supposed to be explained. Thus, a theory that relies on a homunculus at some point is incomplete in the same extend.


title: Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities

abstract: Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive—new applications are being used in nearly every conceivable environment, which leads to the adoption of device-based interaction and automation. However, IoT has also raised issues about the security and privacy of these digitally augmented spaces. Program analysis is crucial in identifying those issues, yet the application and scope of program analysis in IoT remains largely unexplored by the technical community. In this article, we study privacy and security issues in IoT that require program-analysis techniques with an emphasis on identified attacks against these systems and defenses implemented so far. Based on a study of five IoT programming platforms, we identify the key insights that result from research efforts in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues. We conclude by studying recent IoT analysis systems and exploring their implementations. Through these explorations, we highlight key challenges and opportunities in calibrating for the environments in which IoT systems will be used.


title: Wafer Map Defect Detection and Recognition Using Joint Local and Nonlocal Linear Discriminant Analysis

abstract: In semiconductor manufacturing processes, defect detection and recognition in wafer maps have received increasing attention from semiconductor industry. The various defect patterns in wafer maps provide crucial information for assisting engineers in recognizing the root causes of the fabrication problems and solving them eventually. This paper develops a manifold learning-based wafer map defect detection and recognition system. In this system, a joint local and nonlocal linear discriminant analysis (JLNDA) is proposed to discover intrinsic manifold information that provides the discriminant characteristics of the defect patterns. An unsupervised version of JLNDA (called local and nonlocal preserving projection) is further developed to provide a monitoring chart for defect detection of wafer maps. A JLNDA-based Fisher discriminant is further put forward for online defect recognition without the modeling procedure of recognizers. Comparisons with other regular methods, principal component analysis, local preserving projection, linear discriminant analysis (LDA) and local LDA, illustrate the superiority of JLNDA in wafer map defect recognition. The effectiveness of the proposed system has been verified by experimental results from a real-world data set of wafer maps (WM-811K).


title: Mapping discrete emotions into the dimensional space: An empirical approach

abstract: A critical task in Affective Computing is the reliable assessment of emotional states. The two most prominent approaches to classify emotions are categorical concepts of discrete emotions (e.g. OCC) and dimensional models typically using the pleasure — arousal — dominance space (PAD). In current research and applications, however, there is little overlap between these two concepts. A mapping of discrete categories into the dimensional space would offer new possibilities to model the emotional states of users and artificial agents, though. We hence let N=70 healthy subjects place the labels of discrete OCC emotions into PAD space according to their subjective knowledge with a simple visual tool. There was a high inter-subject consistency regarding the positioning of OCC emotions for the dimension of pleasure. However, arousal and dominance ratings showed considerably greater variance. We conclude that global and reliable mappings of OCC emotions into the PAD space can best be provided for the pleasure dimension. The exact positioning of discrete emotions regarding arousal and dominance can only be gained by individual calibration of a given user in a strict within-subject approach.


Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling.


title: Exploring passive user interaction for adaptive narratives

abstract: Previous Interactive Storytelling systems have been designed to allow active user intervention in an unfolding story, using established multi-modal interactive techniques to influence narrative development. In this paper we instead explore the use of a form of passive interaction where users' affective responses, measured by physiological proxies, drive a process of narrative adaptation. We introduce a system that implements a passive interaction loop as part of narrative generation, monitoring users' physiological responses to an on-going narrative visualization and using these to adapt the subsequent development of character relationships, narrative focus and pacing. Idiomatic cinematographic techniques applied to the visualization utilize existing theories of establishing characteristic emotional tone and viewer expectations to foster additional user response. Experimental results support the applicability of filmic emotional theories in a non-film visual realization, demonstrating significant appropriate user physiological response to narrative events and "emotional cues". The subsequent narrative adaptation provides a variation of viewing experience with no loss of narrative comprehension.


title: Fault-Tolerant Control for Quadrotor UAV via Backstepping Approach

abstract: Different from the previous work for quadrotor which mostly discusses application of different controllers to quadrotor control under normal flight conditions, this paper introduces a new design strategy which considers fault tolerant capability of the quadrotor control problems. By utilizing Passive Fault Tolerant Control Systems (PFTCS) concept with backstepping control approach, the trajectory tracking control of the quadrotor UAV has been achieved and simulation results have shown the effectiveness of the proposed backstepping control with higher controller gains when different levels of actuator faults occur in the quadrotor UAV.


title: Impact of Innovation on the Performance of Indian Pharmaceutical Industry Using Data Envelopment Analysis

abstract: In this paper we have used data envelopment analysis (DEA) and econometric models to analyse the impact of research and development and innovation on relative efficiency and productivity change and firm performance in Indian pharmaceutical industry (IPI) between 1998 and 2007 which covers the post-TRIPS (1995) and post Indian Patent Act Amendment (2005) period. Output oriented BCC DEA model and Malmquist productivity index are used to estimate the relative efficiency and productivity change of Indian pharmaceutical companies over the 10 year period. Using econometric models, we have proposed and tested several hypotheses for the IPI and found a positive impact of innovation represented by R&D investment and patents on productivity (sales), market share, exports and ability to attract contract manufacturing among Indian pharmaceutical companies. We also found that the sales growth is additionally driven by DEA efficiency, size, age which have a positive impact on productivity (sales). Export revenue is additionally driven by sales. Within the limitations of the model discussed, contract manufacturing was additionally driven by innovation, size and sales. The company sales growth was additionally driven by export growth and DEA efficiency. The DEA efficiency having a positive impact on sales and sales growth is a new finding as there appears to be no previous investigation to explore this relationship.


title: A New Stochastic Computing Methodology for Efficient Neural Network Implementation

abstract: This paper presents a new methodology for the hardware implementation of neural networks (NNs) based on probabilistic laws. The proposed encoding scheme circumvents the limitations of classical stochastic computing (based on unipolar or bipolar encoding) extending the representation range to any real number using the ratio of two bipolar-encoded pulsed signals. Furthermore, the novel approach presents practically a total noise-immunity capability due to its specific codification. We introduce different designs for building the fundamental blocks needed to implement NNs. The validity of the present approach is demonstrated through a regression and a pattern recognition task. The low cost of the methodology in terms of hardware, along with its capacity to implement complex mathematical functions (such as the hyperbolic tangent), allows its use for building highly reliable systems and parallel computing.


title: MATCHING IN-CAR VOICE WITH DRIVER STATE: IMPACT ON ATTITUDE AND DRIVING PERFORMANCE

abstract: This study examines whether characteristics of a car voice can influence driver behavior and attitudes, and furthermore, if the driver’s emotional state will influence the perception of the car voice. Participants in a 2 (driver emotion: happy or upset) x 2 (car voice emotion: energetic or subdued) experimental study, had emotion induced by video clips and then spent 20 minutes using a driving simulator. While they were driving, a voice in the car spoke 36 questions (e.g., “Have we passed the sign for Lucia yet?”) and comments (“My favorite part of this drive is the lighthouse.”) in either an energetic or subdued voice. Participants were invited to interact with the car voice. Matching the car voice to the drivers’ emotions had enormous consequences. Drivers who interacted with voices that matched their own emotional state had less than half as many accidents on average as drivers who interacted with mismatched voices! Drivers paired with matched voices also communicated much more with the voice (the voice said exactly the same thing in all conditions). The effects of matching emotion versus mismatching emotion were so powerful that neither driver emotion nor gender had an expected effect on driving performance. There was a slight tendency for happy drivers and female drivers to be better drivers, even though this effect was minimal compared to the effects of matching. In other words, finding the appropriate in-car voice for the driver’s emotion stood out as the most critical factor in enabling a safe driving experience.


title: A Fraud Detection Model Based on Feature Selection and Undersampling Applied to Web Payment Systems

abstract: The volume of electronic transactions has raised a lot in last years, mainly due to the popularization of e-commerce. Since this popularization, we have observed a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore, it is important and necessary to develop and apply techniques that can assist in fraud detection in Web transactions. Due to the large amount of data generated in electronic transactions, to find the best set of features is an essential task to identify frauds. Fraud detection is a specific application of anomaly detection, characterized by a large imbalance between the classes (e.g., fraud or non fraud), which can be a detrimental factor for feature selection techniques. In this work we evaluate the behavior and impact of feature selection techniques to detect fraud in a Web Transaction scenario, applying feature selection techniques and performing undersampling in this step. To measure the effectiveness of the feature selection approach we use some state-of-the-art classification techniques to identify frauds, using real data from one of the largest electronic payment system in Latin America. Thus, the fraud detection models comprises a feature selection and classification techniques. To evaluate our results we use metrics of F-Measure and Economic Efficiency. Our results show that the imbalance between the classes reduces the effectiveness of feature selection and the undersampling strategy applied in this task improves the final results. We achieve a very good performance in fraud detection using the proposed methodology, reducing the number of features and presenting financial gains of up to 61% compared to the actual scenario of the company.


title: Arabic Sentiment Analysis: A Survey

abstract: Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.


title: Getting to Know ArcGIS ModelBuilder

abstract: Getting to Know ArcGIS ModelBuilder teaches readers how to develop and run reusable geoprocessing workflows and models in ModelBuilder, a visual programming technology available in ArcGIS. Getting to Know ArcGIS ModelBuilder presents basic and more complex concepts and demonstrates best practices through hands-on exercises.Divided into seven chapters addressing model basics interactive models, flow control, multiple inputs, model iterations, model environment, and building documentation, this book fosters a comprehensive knowledge of ModelBuilder for the classroom and forself-learners. All the data needed for the exercises, as well as examples of models, Python scripts, and custom applications are included in a companion DVD.


title: Exploring the effects of trust, task interdependence and virtualness on knowledge sharing in teams

abstract: The sharing of knowledge within teams is critical to team functioning. However, working with team members who are in different locations (i.e. in virtual teams) may introduce communication challenges and reduce opportunities for rich interactions, potentially affecting knowledge sharing and its outcomes. Therefore, using questionnaire-based data, this study examined the potential effects of dif- ferent aspects of virtuality on a knowledge-sharing model. Social exchange theory was used to develop a model relating trust to knowledge sharing and knowledge sharing to team effectiveness. The moderating effects of virtuality and task inter- dependence on these relationships were examined. A strong positive relationship was found between trust and knowledge sharing for all types of teams (local, hybrid and distributed), but the relationship was stronger when task interdepen- dence was low, supporting the position that trust is more critical in weak structural situations. Knowledge sharing was positively associated with team effectiveness outcomes; however, this relationship was moderated by team imbalance and hybrid structures, such that the relationship between sharing and effectiveness was weaker. Organizations should therefore avoid creating unbalanced or hybrid virtual teams.


title: Current Concepts Differential Diagnosis of Pain Around the Hip Joint

abstract: The differential diagnosis of hip pain is broad and includes intra-articular pathology, extra-articular pathology, and mimickers, including the joints of the pelvic ring. With the current advancements in hip arthroscopy, more patients are being evaluated for hip pain. In recent years, our understanding of the functional anatomy around the hip has improved. In addition, because of advancements in magnetic resonance imaging, the diagnosis of soft tissue causes of hip pain has improved. All of these advances have broadened the differential diagnosis of pain around the hip joint and improved the treatment of these problems. In this review, we discuss the causes of intra-articular hip pain that can be addressed arthroscopically: labral tears, loose bodies, femoroacetabular impinge- ment, capsular laxity, tears of the ligamentum teres, and chondral damage. Extra-articular diagnoses that can be managed arthroscopically are also discussed, including: iliopsoas tendonitis, "internal" snapping hip, "external" snapping hip, iliotibial band and greater trochanteric bursitis, and gluteal tendon injury. Finally, we discuss extra-articular causes of hip pain that are often managed nonop- eratively or in an open fashion: femoral neck stress fracture, adductor strain, piriformis syndrome, sacroiliac joint pain, athletic pubalgia, "sports hernia," "Gilmore's groin," and osteitis pubis. Key Words: Athletic groin injury—Chronic groin pain—Greater trochanteric pain syndrome—Hip arthroscopy—Hip pain—Snapping hip.


abstract: Biometric is an automated recognition process that measures the unique human characteristics, such as iris recognition. Of late, different kinds of iris recognition techniques are available that can be used in different platforms. Daugman algorithm is one of the iris recognition techniques that provide high percentage of accuracy. This study aims to develop a device that performs Iris Recognition using Daugman algorithm on Raspberry Pi. Through the implementation of Daugman algorithm on Raspberry Pi, more applications may come up for iris recognition. Also to make the system adapt with the limitations of Raspberry Pi and the existing application, this work created an image processing function that preprocesses the image before it is passed to the application. Based on the tests implemented, the False Acceptance Rate (FAR) and False Rejection Rate (FRR), the system is mostly to reject registered actors than to accept impostors due to the hardware limitations.


title: Caelus: Verifying the Consistency of Cloud Services with Battery-Powered Devices

abstract: Cloud storage services such as Amazon S3, Drop Box, Google Drive and Microsoft One Drive have become increasingly popular. However, users may be reluctant to completely trust a cloud service. Current proposals in the literature to protect the confidentiality, integrity and consistency of data stored in the cloud all have shortcomings when used on battery-powered devices -- they either require devices to be on longer so they can communicate directly with each other, rely on a trusted service to relay messages, or cannot provide timely detection of attacks. We propose Caelus, which addresses these shortcoming. The key insight that enables Caelus to do this is having the cloud service declare the timing and order of operations on the cloud service. This relieves Caelus devices from having to record and send the timing and order of operations to each other -- instead, they need to only ensure that the timing and order of operations both conforms to the cloud's promised consistency model and that it is perceived identically on all devices. In addition, we show that Caelus is general enough to support popular consistency models such as strong, eventual and causal consistency. Our experiments show that Caelus can detect consistency violations on Amazon's S3 service when the desired consistency requirements set by the user are stricter than what S3 provides. Caelus achieves this with a roughly 12.6% increase in CPU utilization on clients, 1.3% of network bandwidth overhead and negligible impact on the battery life of devices.


title: Periodic U-Slot-Loaded Dual-Band Half-Width Microstrip Leaky-Wave Antennas for Forward and Backward Beam Scanning

abstract: Half-width microstrip leaky-wave antennas (HW-MLWAs) are generally single band. Here, we present a new method to achieve dual-band operation from an HW-MLWA by periodically loading the antenna with U-shaped slots. These dual-band MLWAs are able to steer the beam in forward directions in one band and in backward directions in the other band. One of the antenna designs was prototyped and tested, and excellent agreement between the predicted and measured results were observed. The measured 10-dB return loss bandwidth of the first and second bands are $19.5 \% $ (5.24–6.37 GHz) and $13.2 \% $ (7.9–9.02 GHz), respectively. The antenna can steer the main beam from $30^\circ $ to $65^\circ $ in the first band and from $-46^\circ $ to $-10^\circ$ in the second band by sweeping the frequency from 5.25 to 6.25 GHz and 7.75 to 9 GHz, respectively. The measured peak gain of the antenna is 12.2 and 14.1 dBi in the first and second bands, respectively. Although the antenna parameters are optimized for dual-band operation, the radiation properties in another higher frequency band (third band) are also explored. In the third band, the antenna beam continuously scans from backward to forward direction as frequency increases. Moreover, this U-slot loaded single-layer half-width LWA is easy to fabricate.


title: 2L-DWTS — Steganography technique based on second level DWT

abstract: Steganography is a technique used to communicate a secret message secretly hidden in an innocuous cover. In this paper, a new image steganography technique 2L-DWTS is proposed. This technique, 2L-DWTS embeds secret message in the higher frequency components among the second level DWT components of the cover image. Coefficients in the low frequency components (LL), at both DWT levels, are left untouched improving the image quality. The stego images obtained using 2L-DWTS are very similar to the corresponding cover images, thereby making this technique secure. The message of size as big as 28.12% of the cover image size can be embedded using this technique. The extraction method does not need the original cover image or any separate key for extracting the message. The experiments conducted on monochrome as well as colored BMP and JPEG images show that using this technique a high capacity, secure, less perceptible stego image is formed. Various metrics — MSE, PSNR, NC, NCC, NAE and IF are used to establish this fact.


title: 3D eWLB (embedded wafer level BGA) technology for 3D-packaging/3D-SiP (Systems-in-Package) applications

abstract: Demand for wafer level packaging (WLP) is being driven by the need to shrink package size and height, simplify the supply chain and provide a lower overall cost by using the infrastructure of a batch process. There are some restrictions in possible applications for Fan-In WLPs since global chip trends tend toward smaller chip areas with an increasing number of interconnects. The shrinkage of the pitches and pads at the chip to package interface is happening much faster than the shrinkage at the package to board interface. This interconnection gap requires fan-out packaging, where the package size is larger than the chip size in order to provide a sufficient area to accommodate the 2nd level interconnects. eWLB (embedded Wafer Level BGA) is a type of fan-out WLP that has the potential to realize any number of interconnects with standard pitches at any shrink stage of the wafer node technology. Furthermore, 3D eWLB technology enables 3D IC and 3D SiP packaging with vertical interconnection. 3D eWLB can be implemented with through silicon via (TSV) applications as well as discrete component embedding. In this paper, there will be discussion of the recent advancements in 3D eWLB packaging and integration as well as what is being envisioned and developed to address future technology requirements in 3D packaging and 3D SIP. The advantage of 3D eWLB technology and applications of 3D packaging will be presented with several examples. The process flow of 3D eWLB fabrication, assembly and packaging challenges, and performance characteristics will be also discussed.


title: The Prevention And Reduction Of Postpartum Complications

abstract: This study was conducted using Orem's self-care model to investigate its impact on the reduction and prevention of women's postpartum complications. One group of 63 women was included in the study in which a pretest and posttest quasi-experimental method was used. For analysis of the data, descriptive statistics, dependent groups t test, and McNemar test were applied, 24 different nursing diagnoses were made. Self-care agency pretest mean score was 97.13 ± 17.20, posttest mean score was 114.44 ± 13.72, and the difference was statistically significant (p < .001). The study found that the care given to women in the postpartum period using Orem's self-care model prevented postpartum complications and increased the self-care agency of postpartum women.


title: Anaplastic carcinoma of the lacrimal gland presenting with recurrent subconjunctival hemorrhages and displaying incipient sebaceous differentiation

abstract: Primary adenocarcinomas of the lacrimal gland rarely display sebaceous differentiation. An anaplastic lacrimal gland neoplasm manifesting this feature was excised from a 64-year-old man who initially appeared with recurrent painless subconjunctival hemorrhages. Globe displacement and tumor-induced hyperopia were later findings. Electron microscopic studies performed on the widely excised tumor documented prominent lipid vacuoles, desmosomes, scattered cytoplasmic filaments, and linear segments of basement membrane material. The patient refused radical surgery and instead opted for 6,000 rads of adjuvant radiotherapy. Eleven months postoperatively liver and bone metastases developed, but he was free of local orbital recurrence. Twenty-two months postoperatively he died from complications of his metastatic disease. This most unusual lacrimal neoplasm is discussed in terms of its initial signs and symptoms and its histopathology. When the current case is analyzed in combination with three earlier reports, there is evidence that primary lacrimal gland sebaceous carcinoma is probably among the most malignant orbital tumors due to its uniform early fatality.


title: General-Purpose Deep Point Cloud Feature Extractor

abstract: Depth sensors used in autonomous driving and gaming systems often report back 3D point clouds. The lack of structure from these sensors does not allow these systems to take advantage of recent advances in convolutional neural networks which are dependent upon traditional filtering and pooling operations. Analogous to image based convolutional architectures, recently introduced graph based architectures afford similar filtering and pooling operations on arbitrary graphs. We adopt these graph based methods to 3D point clouds to introduce a generic vector representation of 3D graphs, we call graph 3D (G3D). We believe we are the first to use large scale transfer learning on 3D point cloud data and demonstrate the discriminant power of our salient latent representation of 3D point clouds on unforeseen test sets. By using our G3D network (G3DNet) as a feature extractor, and then pairing G3D feature vectors with a standard classifier, we achieve the best accuracy on ModelNet10 (93.1%) and ModelNet 40 (91.7%) for a graph network, and comparable performance on the Sydney Urban Objects dataset to other methods. This general-purpose feature extractor can be used as an off-the-shelf component in other 3D scene understanding or object tracking works.


title: A 151 dB High Dynamic Range CMOS Image Sensor Chip Architecture With Tone Mapping Compression Embedded In-Pixel

abstract:  Abstract—This paper presents a high dynamic range CMOS image sensor that implements an in-pixel content-aware adaptive global tone mapping algorithm during image capture operation. The histogram of the previous frame of an auxiliary image, which contains time stamp information, is employed as an estimation of the probability of illuminations impinging pixels at the present frame. The compression function of illuminations, namely Tone Mapping Curve (TMC), is calculated using this histogram. A QCIF resolution proof-of-concept prototype has been fabricated using a 0.35µm opto-flavored standard technology. The sensor is capable of mapping scenes with a maximum intra-frame dynamic range of 151dB (25-bits/pixel in linear representation) by compressing them to only 7-bits/pixel, while keeping visual quality in details and contrast. The in-pixel on-the-fly fully- parallel tone mapping achieves high frame rate allowing real- time HDR video (120dB@30fps).


title: PACES: A Partition-Centering-Based Symmetry Placement for Binary-Weighted Unit Capacitor Arrays

abstract: Capacitor matching influences linearity performance, which is a critical measure of analog-to-digital converters (ADCs). Various placement techniques have been proposed to eliminate both systematic and random mismatches of capacitor pairs. However, a placement technique that eliminates capacitor mismatches may not result in good linearity performance for successive-approximation-register ADCs because their linearity performance is related to the accuracy of their binary-weighted continued ratio. This paper addresses the critical problem of placement estimation based on ratio mismatch ${M}$ , overall correlation coefficient ${L}$ , and performance metrics. A low ${M}$ and a high ${L}$ value do not imply higher linearity performance. Therefore, we propose a partition-centering-based symmetry placement algorithm for the layout considering parasitic capacitance matching. The experimental results show that the proposed placement approach can achieve higher linearity performance and a shorter placement generation time compared with the conventional approach.


title: Grammar as a Foreign Language

abstract: Syntactic parsing is a fundamental problem in computational linguistics and Natural Language Processing. Traditional approaches to parsing are highly complex and problem specific. Recently, Sutskever et al. (2014) presented a domain-independent method for learning to map input sequences to output sequences that achieved strong results on a large scale machine translation problem. In this work, we show that precisely the same sequence-to-sequence method achieves results that are close to state-of-the-art on syntactic constituency parsing, whilst making almost no assumptions about the structure of the problem. To achieve these results we need to mitigate the lack of domain knowledge in the model by providing it with a large amount of automatically parsed data.


title: Personalized Age Progression with Aging Dictionary

abstract: In this paper, we aim to automatically render aging faces in a personalized way. Basically, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each subject may have extra personalized facial characteristics, e.g. mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups. Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.


What Papers Should I Cite from my Reading List? User Evaluation of a Manuscript Preparatory Assistive Task


title: Strategic Programming of Detection and Therapy Parameters in Implantable Cardioverter-Defibrillators Reduces Shocks in Primary Prevention Patients

abstract: OBJECTIVES ::: Our purpose was to demonstrate that strategically chosen implantable cardioverter-defibrillator (ICD) ventricular tachycardia (VT) or ventricular fibrillation (VF) detection and therapy parameters can reduce the combined incidence of device-delivered shocks, arrhythmic syncope, and untreated sustained symptomatic VT/VF (morbidity index). ::: ::: ::: BACKGROUND ::: Strategically chosen ICD VT/VF detection and therapy parameters have been shown in previous studies to reduce the number of shocked episodes. In the PREPARE (Primary Prevention Parameters Evaluation) study, these prior strategies were combined with additional strategies specific to primary prevention patients. ::: ::: ::: METHODS ::: The PREPARE study was a prospective, cohort-controlled study that analyzed 700 patients (biventricular [Bi-V] ICD and non-Bi-V ICD) with primary prevention indications for an ICD from 38 centers followed for 1 year. VT/VF was detected for rates > or =182 beats/min that were maintained for at least 30 of 40 beats. Antitachycardia pacing was programmed as the first therapy for regular rhythms with rates of 182 to 250 beats/min, and supraventricular tachycardia discriminators were used for rhythms < or =200 beats/min. The control cohort consisted of 689 primary prevention patients from the EMPIRIC (Comparison of Empiric to Physician-Tailored Programming of Implantable Cardioverter Defibrillators Trial) (non-Bi-V ICD, physician arm only) and MIRACLE ICD (Multicenter InSync Implantable Cardioversion Defibrillation Randomized Clinical Evaluation) (Bi-V ICD) trials for whom VT/VF detection and therapy programming were not controlled. ::: ::: ::: RESULTS ::: The PREPARE programming significantly reduced the morbidity index incidence density (0.26 events/patient-year for PREPARE study patients vs. 0.69 control cohort, p = 0.003). The PREPARE study patients were less likely to receive a shock in the first year compared with control patients (9% vs. 17%, p < 0.01). The incidence of untreated VT and arrhythmic syncope was similar between the PREPARE study patients and the control cohort. ::: ::: ::: CONCLUSIONS ::: Strategically chosen VT/VF detection and therapy parameters can safely reduce shocks and other morbidities associated with ICD therapy in patients receiving an ICD for primary prevention indications. (PREPARE-Primary Prevention Parameters Evaluation; NCT00279279).


title: State power and elite autonomy: The board interlock network of Chinese non-profits

abstract: In response to failures of central planning, the Chinese government has experimented not only with free-market trade zones, but with allowing non-profit foundations to operate in a decentralized fashion. A network study shows how these foundations have connected together by sharing board members, in a structural parallel to what is seen in corporations in the United States. This board interlock leads to the emergence of an elite group with privileged network positions. While the presence of government officials on non-profit boards is widespread, state officials are much less common in a subgroup of foundations that control just over half of all revenue in the network. This subgroup, associated with business elites, not only enjoys higher levels of within-elite links, but even preferentially excludes government officials from the nodes with higher degree. The emergence of this structurally autonomous sphere is associated with major political and social events in the state-society relationship.


title: Results from deploying a participation incentive mechanism within the enterprise

abstract: Success and sustainability of social networking sites is highly dependent on user participation. To encourage contribution to an opt-in social networking site designed for employees, we have designed and implemented a feature that rewards contribution with points. In our evaluation of the impact of the system, we found that employees are initially motivated to add more content to the site. This paper presents the analysis and design of the point system, the results of our experiment, and our insights regarding future directions derived from our post-experiment user interviews.


title: Serum C-Reactive Protein and Self-Reported Stroke : Findings From the Third National Health and Nutrition Examination Survey

abstract: Abstract —C-reactive protein may predict the risk of coronary heart disease, but its association with stroke has not been well studied. We used data from the Third National Health and Nutrition Examination Survey, conducted from 1988 to 1994, to examine the association between serum C-reactive protein concentrations and self-reported past history of stroke among 8850 US men and women aged ≥40 years. The unadjusted geometric mean of C-reactive protein concentration was higher among participants with stroke than those without stroke (0.45±0.02 versus 0.32±0.01, P <0.001). After adjusting for age, sex, race or ethnicity, education, smoking status, systolic blood pressure, serum cholesterol, high density lipoprotein cholesterol, history of diabetes mellitus, body mass index, and physical activity, the odds ratio for stroke among participants with C-reactive protein concentrations ≥0.55 mg/dL compared with participants with concentrations ≤0.21 mg/dL was 1.71 (95% CI 1.11 to 2.64 [odds ratio per mg/dL 1.19, 95% CI 1.05 to 1.34]). These cross-sectional data support findings from other studies suggesting that C-reactive protein concentration may be a risk factor or marker for stroke in the US population.


title: CNTFET based Logic Circuits: A Brief Review

abstract: In this paper at first a brief review of carbon nanotubes (CNTs) is given and then an extensive survey of CNTFET (carbon nanotube field effect transistor) based logic circuits are discussed with the most recently reported CNTFET logic circuits.


title: Antenna with circular polarization composed of bent crossed dipoles in a cylindrical cavity

abstract: The paper presents cavity backed printed antenna with circular polarization (CBACP). The antenna radiating elements are two crossed double bent dipoles that are printed on the dielectric substrate and have complex conjugated impedances. In order to diminish environmental influences the antenna is placed in the metallic cylindrical cavity. By using double bent dipoles the overall antenna dimensions are significantly reduced. CBACP is simulated and realized for operation around 5.9 GHz. Simulated and measured results show good agreement. Furthermore, investigation of cavity parameters and their effect on antenna characteristics has been carried out in order to achieve (a) minimal axial ratio, (b) maximal gain and aperture efficiency and (c) broader axial ratio bandwidth (AR<;3 dB).


title: Facial expression morphing and animation with local warping methods

abstract: In this paper, we applied local warping techniques to construct a framework of image-based morphing for facial expression animation with low complexity. From the experience we obtained in various spatial transformations, the local warping methods are more adequate for facial expression morphing. We utilized two transformations that can deal with local deformations: the piecewise polynomial transformation and radial basis function transformation. The simulation results are satisfactory. The facial warping techniques could be applied to various applications, such as face recognition, authentication, dynamic imagery, speech (action) semantic animation, multimedia and virtual reality construction, low-bandwidth videoconferencing, image transmission, and intelligent man-machine interface.


title: The Mozart Effect: A quantitative EEG study

abstract: Abstract The aim of this study is to investigate the influence of Mozart’s music on brain activity through spectral analysis of the EEG in young healthy adults (Adults), in healthy elderly (Elderly) and in elderly with Mild Cognitive Impairment (MCI). EEG recording was performed at basal rest conditions and after listening to Mozart’s K448 or “Fur Elise” Beethoven’s sonatas. After listening to Mozart, an increase of alpha band and median frequency index of background alpha rhythm activity (a pattern of brain wave activity linked to memory, cognition and open mind to problem solving) was observed both in Adults and in Elderly. No changes were observed in MCI. After listening to Beethoven, no changes in EEG activity were detected. This results may be representative of the fact that said Mozart’s music is able to “activate” neuronal cortical circuits related to attentive and cognitive functions.


title: Neuro-memristive Circuits for Edge Computing: A review

abstract: The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.


title: Isolated Rheumatic Tricuspid Stenosis with Reverse Lutembacher's Physiology.

abstract: Tricuspid valve dysfunction occurs frequently in patients with rheumatic heart disease and is usually manifested as functional or organic tricuspid regurgitation. Rheumatic tricuspid stenosis is less common and occurs characteristically in the presence of concomitant mitral valve disease. In this report, we describe the clinical and echocardiographic findings in a patient with isolated rheumatic tricuspid stenosis and a right-to-left shunt across the interatrial septum, likely as a result of a patent foramen ovale, resulting in central cyanosis. This case illustrates an interesting association of tricuspid stenosis and an interatrial right-to-left shunt suggestive of a reverse Lutembacher's physiology.


title: C5: Cross-Cores Cache Covert Channel

abstract: Cloud computing relies on hypervisors to isolate virtual machines running on shared hardware. Since perfect isolation is difficult to achieve, sharing hardware induces threats. Covert channels were demonstrated to violate isolation and, typically, allow data exfiltration. Several covert channels have been proposed that rely on the processor's cache. However, these covert channels are either slow or impractical due to the addressing uncertainty. This uncertainty exists in particular in virtualized environments and with recent L3 caches which are using complex addressing. Using shared memory would elude addressing uncertainty, but shared memory is not available in most practical setups. ::: ::: We build C5, a covert channel that tackles addressing uncertainty without requiring any shared memory, making the covert channel fast and practical. We are able to transfer messages on modern hardware across any cores of the same processor. The covert channel targets the last level cache that is shared across all cores. It exploits the inclusive feature of caches, allowing a core to evict lines in the private first level cache of another core. We experimentally evaluate the covert channel in native and virtualized environments. In particular, we successfully establish a covert channel between virtual machines running on different cores. We measure a bitrate of 1291i¾?bps for a native setup, and 751i¾?bps for a virtualized setup. This is one order of magnitude above previous cache-based covert channels in the same setup.


title: Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

abstract: In this paper, a multiple classifier machine learning (ML) methodology for predictive maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating the so-called “health factors,” or quantitative indicators, of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management, and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance tradeoffs in terms of frequency of unexpected breaks and unexploited lifetime, and then employing this information in an operating cost-based maintenance decision system to minimize expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.


title: 3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

abstract: Important attributes of 3D brain cortex segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours which minimize image-based energies in a manner that yields flexibly global minimizers based on active regions. Region-based information and boundary-based information may be combined flexibly in the evolution potentials for accurate segmentation results. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the flexibly global nature of the dual-front evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods.


title: Deep Learning Phase Segregation

abstract: Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems. In this work, we present a data-driven approach for the learning, modeling, and prediction of phase segregation. A direct mapping between an initially dispersed, immiscible binary fluid and the equilibrium concentration field is learned by conditional generative convolutional neural networks. Concentration field predictions by the deep learning model conserve phase fraction, correctly predict phase transition, and reproduce area, perimeter, and total free energy distributions up to 98% accuracy.


title: A Li-Ion Battery Charger With Smooth Control Circuit and Built-In Resistance Compensator for Achieving Stable and Fast Charging

abstract: A built-in resistance compensator (BRC) technique is presented to speed up the charging time of a lithium-ion battery. A smooth control circuit (SCC) is proposed to ensure the stable transition from the constant-current (CC) to the constant-voltage (CV) stage. Due to the external parasitic resistance of the Li-ion battery-pack system, the charger circuit switches from the CC to the CV stage without fully charging the cell. The BRC technique dynamically estimates the external resistance to extend the CC stage. The experimental results show that the period of the CC stage can be extended to 40% of that of the original design. The charging time is effectively reduced.


title: Calibration in Eye Tracking Using Transfer Learning

abstract: This thesis empirically studies transfer learning as a calibration framework for Convolutional Neural Network (CNN) based appearance-based gaze estimation models. A dataset of approximately 1,900,0 ...


Tropical Underdevelopment


title: Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data

abstract: Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.


title: Visual Lisp/CLOS programming in OpenMusic

abstract: OpenMusic (OM) is a visual programming language developed on top of Common Lisp and CLOS, in which most of the functional and object-oriented programming concepts can be implemented and carried out graphically. Although this visual language was designed for musical applications, the focus in this paper is to describe and study OM as a complete general-purpose programming environment.


title: Enabling IOT services using WIFI - ZigBee gateway for a home automation system

abstract: Home automation system is a process of automating or adapting to basic household activities like control of Lighting, Heating, Ventilation and Air condition (HVAC) appliances on user command. A dedicated hand held device is ideal in providing a user interface in a home automation system, due to their portability and their wide range of capabilities. The hand held device runs on Linux OS which is an open source platform and can work on limited memory communication between the appliances a home automation network through low power communication protocols such as ZigBee. With the recent increase in the use of internet and cheaper components, large number of Home automation system with IOT capabilities are in demand. Home automation system prototype includes a gateway with user interactions capabilities, this experiment demonstrates that the proposed gateway work efficiently by sending and receiving instructions from different protocols, a Graphical User Interface (GUI) allows user to interact with the ambient environment settings.


title: The Data Deluge: An e-Science Perspective

abstract: This paper previews the imminent flood of scientific data expected from the next generation of experiments, simulations, sensors and satellites. In order to be exploited by search engines and data mining software tools, such experimental data needs to be annotated with relevant metadata giving information as to provenance, content, conditions and so on. The case for automating the process of going from raw data to information to knowledge is briefly discussed. The paper argues the case for creating new types of digital libraries for scientific data with the same sort of management services as conventional digital libraries in addition to other data-specific services. Some likely implications of both the Open Archives Initiative and e-Science data for the future role for university libraries are briefly mentioned. A substantial subset of this e-Science data needs to archived and curated for long-term preservation. Some of the issues involved in the digital preservation of both scientific data and of the programs needed to interpret the data are reviewed. Finally, the implications of this wealth of e-Science data for the Grid middleware infrastructure are highlighted. * Postal address: EPSRC, Polaris House, North Star Avenue, Swindon SN2 1 ET, UK + On secondment from the Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK


title: An avalanche rugged NPT trench IGBT used in single-ended quasi resonant topology for induction heating appliance

abstract: A 1000 V NPT (non-punch through) trench IGBT is developed for the single-ended quasi resonant circuit. Optimizing trench cell design and NPT technology, high avalanche ruggedness as well as the outstanding static and dynamic performances is obtained. Comparing the PT trench IGBT having the identical design and processes, it is also empirically shown that the developed NPT trench IGBT has significantly improved avalanche capability under the various test conditions for the IH application using the quasi resonant topology and reduces the instantaneous device failures resulting from inadequate avalanche ruggedness under abnormal operating condition


title: Evaluation of Talent Management on Employees Performance in Beverage Industry: A Case of Delmonte Kenya Limited

abstract: Talent management practice within organization is an international human resource strategy that seeks to identify, develop, deploy and retain talented and high potential employees. The objectives of the study were to determine the effect of talent retention on employees performance, assess how talent attraction impacts, effect of learning and development on employees performance in beverage industry in Kenya. The study adopted a descriptive research design in which the target population of 2,500 employees of Del Monte Kenya. The study used stratified sampling method to select 83 employees according to their job cadres. Descriptive statistics such as the standard deviation, percentages and frequency distribution were used. The study established that the job retention motivated the employees of Del Monte leading to ultimate performance. The study recommended that the management should ensure the work environment was attractive to the employees so as to motivate, thus leading to better performance.


title: Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

abstract: Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs.


title: Analyzing Longitudinal Data With the Linear Mixed Models Procedure in SPSS

abstract: Many applied researchers analyzing longitudinal data share a common misconception: that specialized statistical software is necessary to fit hierarchical linear models (also known as linear mixed models [LMMs], or multilevel models) to longitudinal data sets. Although several specialized statistical software programs of high quality are available that allow researchers to fit these models to longitudinal data sets (e.g., HLM), rapid advances in general purpose statistical software packages have recently enabled analysts to fit these same models when using preferred packages that also enable other more common analyses. One of these general purpose statistical packages is SPSS, which includes a very flexible and powerful procedure for fitting LMMs to longitudinal data sets with continuous outcomes. This article aims to present readers with a practical discussion of how to analyze longitudinal data using the LMMs procedure in the SPSS statistical software package.


title: Hughes , Jones Reflections on the use of Grounded Theory in IS Research Reflections on the use of Grounded Theory in Interpretive Information Systems Research

abstract: In Information Systems research there are a growing number of studies that must necessarily draw upon the contexts, experiences and narratives of practitioners. This calls for research approaches that are qualitative and may also be interpretive. These may include case studies or action research projects. For some researchers, particularly those with limited experience of interpretive qualitative research, there may be a lack of confidence when faced with the prospect of collecting and analysing the data from studies of this kind. In this paper we reflect on the lessons learned from using Grounded Theory in an interpretive case study based piece of research. The paper discusses the lessons and provides guidance for the use of the method in interpretive studies.


title: A view management method for mobile mixed reality systems

abstract: This paper describes a view management method for annotations on mobile mixed reality systems. In recent years, mobile devices, such as mobile phones and PDAs, have rapidly gained popularity. Thus, MR systems using mo- bile devices are expected to be a rich field. However, the mobile devices' components (screen and application executable memory) are very small compared to those of notebook computers. Also, the processor does not have enough computational power. Thus, it is difficult for such devices to apply existing view management techniques. We propose a view management method that consists of seven sub-functions. Each sub-function is implemented by a simple algorithm, while a system developer assembles required sub-functions into a customized view manage- ment component. We implemented some applications based on the proposed method and discussed the effectiveness of the view management algorithm.


title: Visual behaviour mediated by retinal projections directed to the auditory pathway.

abstract: An unresolved issue in cortical development concerns the relative contributions of intrinsic and extrinsic factors to the functional specification of different cortical areas1,2,3,4. Ferrets in which retinal projections are redirected neonatally to the auditory thalamus5 have visually responsive cells in auditory thalamus and cortex, form a retinotopic map in auditory cortex and have visual receptive field properties in auditory cortex that are typical of cells in visual cortex5,6,7,8. Here we report that this cross-modal projection and its representation in auditory cortex can mediate visual behaviour. When light stimuli are presented in the portion of the visual field that is ‘seen’ only by this projection, ‘rewired’ ferrets respond as though they perceive the stimuli to be visual rather than auditory. Thus the perceptual modality of a neocortical region is instructed to a significant extent by its extrinsic inputs. In addition, gratings of different spatial frequencies can be discriminated by the rewired pathway, although the grating acuity is lower than that of the normal visual pathway.


title: Economic Hardship and Depression Among Women in Latino Farmworker Families

abstract: Farmworker family members risk poor mental health due to stressors including poverty, relocation, and documentation status. This paper explores the relationship between farm-work related stressors and depressive symptoms in women of Latino farmworker families. 248 mothers of young children completed fixed-response interviews in Spanish. Measures included the Center for Epidemiologic Studies-Depression Scale, Migrant Farmworker Stress Inventory, and USDA Household Food Security Survey Module. Bivariate analyses indicated greater depressive symptoms with more economic hardship, more farm work-related stressors, greater age, and being unmarried. In multivariable logistic regression, economic hardship remained the only factor associated with depressive symptoms. Greater economic hardship, but not general farm work-related stress, is a main factor associated with depression in women of Latino farmworker families. Maternal depression can have consequences for both mothers and families. Mental health services for women in farmworker families should be targeted to those with the greatest economic challenges.