title: Update in Neurology

abstract: This Update in Neurology reviews important literature related to 6 main topics in the field: stroke, Parkinson disease, dementia, dizziness, epilepsy, and migraine headaches. The following papers represent the medical reports that have guided research during the past year. Stroke Mechanical Embolus Retrieval System Expanded the Window of Treatment Time in Acute Ischemic Stroke Gobin YP, Starkman S, Duckwiler GR, et al. MERCI 1: a phase 1 study of Mechanical Embolus Removal in Cerebral Ischemia. Stroke. 2004;35:2848-54. [PMID: 15514171] The Mechanical Embolus Removal in Cerebral Ischemia Retrieval System comprises a microcatheter and a retriever system consisting of a tapered wire with 5 helical loops of decreasing diameter. The catheter is guided into the occluded vessel and passed through the arterial thrombus. The outer catheter is retracted back through the thrombus, leaving the proximal loops of the retrieval system to be deployed and torqued within the thrombus. The ensnared thrombus is then removed. In this phase I study, the investigators enrolled 30 adult patients in 7 U.S. centers to evaluate the safety and technical efficacy of embolectomy of the cerebral arteries. A total of 28 patients were treated. Inclusion criteria were 1) an acute stroke with National Institutes of Health Stroke Scale score of 10 or higher, 2) presentation between 3 and 8 hours of symptom without contraindications to intravenous thrombolysis, 3) presentation within 3 hours of symptom onset with contraindications to intravenous thrombolysis, 4) angiographic evidence of occlusion of a major cerebral artery, and 5) no large hypodensity on computed tomography. Recanalization of the occluded artery occurred in 12 of 28 (43%) patients. The procedure was well tolerated, and patients with recanalized arteries had better 30-day outcomes (9 of 18) than those whose arteries did not recanalize (0 of 10). The U.S. Food and Drug Administration approved the Concentric Merci Retrieval System (Concentric Medical, Inc., Mountain View, California) in August 2004 (1). Although this embolus retrieval system requires significant technical expertise and questions remain about how it compares with intra-arterial thrombolysis, it is now available in many hospitals. One key advantage to the technique is its ability to expand the time window for treatment of acute ischemic stroke from 3 to 8 hours. It can also be offered to patients in whom intravenous thrombolysis fails or who are ineligible for intravenous thrombolysis, including those receiving anticoagulation, those who have had recent surgery, and those with left ventricular assist devices. Addition of Aspirin to Clopidogrel Therapy Increased Risk without Benefit in High-Risk Patients Diener HC, Bogousslavsky J, Brass LM, et al. Aspirin and clopidogrel compared with clopidogrel alone after recent ischaemic stroke or transient ischaemic attack in high-risk patients (MATCH): randomised, double-blind, placebo-controlled trial. Lancet. 2004;364:331-7. [PMID: 15276392] After a transient ischemic attack or an ischemic stroke, preventing a second vascular event is critical. Evidence of the benefits of aspirin for secondary prevention is abundant and unambiguous: Aspirin reduces the risk for stroke, myocardial infarction, or death by about 15%. In noncardioembolic ischemic stroke or transient ischemic attack, both clopidogrel therapy and combination therapy with extended-release dipyridamole and aspirin are marginally superior to aspirin therapy alone but cost more than U.S. $4.20 per day compared with pennies per day for aspirin (2). Warfarin therapy is generally not indicated for secondary stroke prevention except for patients with transient ischemic attack or stroke in the setting of persistent or paroxysmal atrial fibrillation and for some patients with a documented hypercoagulable state, left ventricular ejection fraction of 0.3 or less, carotid or vertebral artery dissection, or patent foramen ovale with an atrial septal defect. For patients already receiving aspirin therapy who have a first transient ischemic attack or stroke, an acceptable strategy is to change to an alternate antiplatelet agent. There is little evidence that adding aspirin provides additional benefit. Investigators in this study aimed to evaluate the effects of therapy with clopidogrel, alone and in combination with aspirin, for the secondary prevention of vascular events. The study included 7599 high-risk patients who had recent ischemic stroke or transient ischemic attack and at least 1 additional risk factor and who were taking 75 mg of clopidogrel daily. The participants were randomly assigned to receive placebo or additional treatment with aspirin (75 mg/d). Follow-up was 18 months. The combination therapy conferred no significant benefit over clopidogrel for reducing the composite outcome of ischemic stroke, myocardial infarction, vascular death, or rehospitalization secondary to ischemic events (15.7% vs. 16.7%); relative risk decreased by 6.4% (95% CI, 4.6% to 16.3%) and absolute risk decreased by 1% (CI, 0.6% to 2.7%). However, the risk for life-threatening hemorrhage was significantly increased in the group receiving combination therapy compared with the group receiving clopidogrel therapy alone (96 [2.6%] vs. 49 [1.3%]; absolute risk increase, 1.3 percentage points [CI, 0.6 to 1.9 percentage points]). In conclusion, combination therapy with clopidogrel and aspirin provided no benefit over clopidogrel alone in the secondary prevention of vascular events in patients with a recent ischemic stroke or transient ischemic attack (and 1 additional vascular risk factor) and increased the risk for serious bleeding. As a result, dual antiplatelet regimens with clopidogrel and aspirin can no longer be recommended as an option for secondary preventive therapy in this patient population. Simvastatin Therapy Reduced the Incidence of Stroke in High-Risk Patients Heart Protection Study Collaborative Group. Effects of cholesterol-lowering with simvastatin on stroke and other major vascular events in 20,536 people with cerebrovascular disease or other high-risk conditions. Lancet. 2004;363:757-67. [PMID: 15016485] In addition to antiplatelet therapy, secondary prevention of vascular events in patients with an ischemic stroke or transient ischemic attack should include blood pressure and lipid management and smoking cessation counseling. The investigators of this study randomly assigned 20536 men (65 years of age or older) with a history of cerebrovascular disease, other occlusive arterial disease, diabetes mellitus, or hypertension with other vascular risk factors to receive simvastatin therapy (40 mg/d) or placebo. During 5 years of treatment, serum low-density lipoprotein cholesterol levels were a mean of 1.05 mmol/L (39 mg/dL) lower in the simvastatin group than in the placebo group. The absolute risk for a primary vascular event (defined as a coronary event, stroke, or a revascularization procedure) was reduced by about 5 percentage points in the simvastatin group (20% risk in the simvastatin group vs. 25% in the placebo group). Benefits were also observed in the subgroup of patients with history of stroke, but the risk reduction was seen in coronary events and revascularization procedures and not in recurrent stroke. In summary, this study provides evidence to support an expanding role for statin agents in patients with a history of transient ischemic attack or stroke. The magnitude of benefit of simvastatin may be equal to or greater than that provided by antiplatelet therapy. The findings emphasize the need to think of vascular disease broadly: Patients with a history of stroke should be given statin therapy to reduce cardiac vascular events. Statin therapy should be considered in all patients with transient ischemic attacks or stroke with target lowering of serum low-density lipoprotein cholesterol levels to less than 2.60 mmol/L (<100 mg/dL) and possibly less than 1.81 mmol/L (<70 mg/dL) for very-high-risk patients with multiple risk factors. Questions about the role of simvastatin remain, however, for low-risk patients, women, and those with a life expectancy of less than 5 years. Carotid Artery Stenosis Carotid Endarterectomy Reduced Stroke Risk in Patients without Neurologic Symptoms Halliday A, Mansfield A, Marro J, et al. Prevention of disabling and fatal strokes by successful carotid endarterectomy in patients without recent neurological symptoms: randomised, controlled trial. Lancet. 2004;363:1491-502. [PMID: 15135594] This trial validated and extended the results of the Asymptomatic Carotid Artery Surgery trial published in 1995 (3). This study investigated the benefits of immediate versus delayed carotid endarterectomy in 3120 asymptomatic men with substantial carotid narrowing (diameter reduced by 70% or more as measured by ultrasonography). Immediate carotid endarterectomy reduced the risk for any stroke and perioperative death from 11.8% to 6.4% over the 5-year study period (P< 0.001). Beneficial effects, although less prominent, were also seen among women and for fatal or disabling strokes but were not seen in patients who were 75 years of age or older (many of whom died within 5 years of unrelated causes). These results only apply when surgical risk is low. The study did not address how best to screen patients for carotid stenosis. Benefits of Endarterectomy Were Greatest When Surgery Was Performed within 2 Weeks of an Ischemic Event Rothwell PM, Eliasziw M, Gutnikov SA, et al. Endarterectomy for symptomatic carotid stenosis in relation to clinical subgroups and timing of surgery. Lancet. 2004;363:915-24. [PMID: 15043958] For this study, the authors analyzed pooled data from the European Carotid Surgery Trial and North American Symptomatic Carotid Endarterectomy Trial to examine factors that might influence the beneficial effects of carotid endarterectomy in symptomatic patients (4, 5). The authors concluded from th


Application Placement on a Cluster of Servers (extended abstract)


title: A scalable content-addressable network

abstract: Hash tables - which map "keys" onto "values" - are an essential building block in modern software systems. We believe a similar functionality would be equally valuable to large distributed systems. In this paper, we introduce the concept of a Content-Addressable Network (CAN) as a distributed infrastructure that provides hash table-like functionality on Internet-like scales. The CAN is scalable, fault-tolerant and completely self-organizing, and we demonstrate its scalability, robustness and low-latency properties through simulation.


title: Survey on user interface programming

abstract: This paper reports on the results of a survey of user interface programming. The survey was widely distributed, and we received 74 responses. The results show that in today's applications, an average of 48% of the code is devoted to the user interface portion. The average time spent on the user interface portion is 45% during the design phase, 50% during the implementation phase, and 37% during the maintenance phase. 34% of the systems were implemented using a toolkit, 27% used a UIMS, 14% used an interface builder, and 26% used no tools. This appears to be because the toolkit systems had more sophisticated user interfaces. The projects using UIMSs or interface builders spent the least percent of time and code on the user interface (around 41%) suggesting that these tools are effective. In general, people were happy with the tools they used, especially the graphical interface builders. The most common problems people reported when developing a user interface included getting users' requirements, writing help text, achieving consistency, learning how to use the tools, getting acceptable performance, and communicating among various parts of the program.


Electric propulsion for satellites and spacecraft: established technologies and novel approaches


title: Sign Language Learning System with Image Sampling and Convolutional Neural Network

abstract: This paper proposes a novel sign language learning system based on 2D image sampling and concatenating to solve the problems of conventional sign recognition. The system constructs the training data by sampling and concatenating from a sign language demonstration video at a certain sampling rate. The learning process is implemented with a well-known network, convolutional neural network. 6 sign language actions are learned in 20 situations and then the system is tested in an uninformed situation. The results show that the system is accurate and robust even if only 2D images obtained with low-cost cameras are used.


title: Multi-path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"

abstract: Large-scale variations still pose a challenge in unconstrained face detection. To the best of our knowledge, no current face detection algorithm can detect a face as large as 800 x 800 pixels while simultaneously detecting another one as small as 8 x 8 pixels within a single image with equally high accuracy. We propose a two-stage cascaded face detection framework, Multi-Path Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a deep neural network with a classic learning strategy, to tackle this challenge. The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes faces at three different scales. It simultaneously utilizes three parallel outputs of the convolutional feature maps to predict multi-scale candidate face regions. The "atrous" convolution trick (convolution with up-sampled filters) and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN to further boost its performance. The second stage is a Boosted Forests classifier, which utilizes deep facial features pooled from inside the candidate face regions as well as deep contextual features pooled from a larger region surrounding the candidate face regions. This step is included to further remove hard negative samples. Experiments show that this approach achieves state-of-the-art face detection performance on the WIDER FACE dataset "hard" partition, outperforming the former best result by 9.6% for the Average Precision.


title: Design and Analysis of a Robust Broadcast Scheme for VANET Safety-Related Services

abstract: IEEE- and ASTM-adopted dedicated short-range communications (DSRC) standards are key enabling technologies for the next generation of vehicular safety communications. Vehicle-safety-related communication services, which require reliable and fast message delivery, usually demand broadcast communications in vehicular ad hoc networks (VANETs). In this paper, we propose and justify a distributive cross-layer scheme for the design of the control channel in DSRC with three levels of broadcast services that are critical to most potential vehicle-safety-related applications. The new scheme for enhancing broadcast reliability includes preemptive priority in safety services, dynamic receiver-oriented packet repetitions for one-hop emergency warning message dissemination, a multifrequency busy tone and minislot within the distributed interframe space (DIFS) in IEEE 802.11, and robust distance-based relay selection for multihop broadcast of emergency notification messages. Compared with a current draft of IEEE 802.11p and other schemes for DSRC safety-related services, the scheme proposed in this paper is more robust and scalable and easy to implement. Additionally, we investigate the reliability and performance of the proposed broadcast scheme for DSRC VANET safety-related services on the highway analytically and by simulations. The analytic model accounts for the impact of the hidden terminal problem, the fading channel conditions, varied message arrival intervals, and the backoff counter process on reliability and performance.


title: Core Self-Evaluations and Job and Life Satisfaction: The Role of Self-Concordance and Goal Attainment.

abstract: The present study tested a model explaining how the core self-evaluations (i.e., positive self-regard) concept is linked to job and life satisfaction. The self-concordance model, which focuses on motives underlying goal pursuit, was used as an explanatory framework. Data were collected from 2 samples: (a) 183 university students (longitudinal measures of goal attainment and life satisfaction were used) and (b) 251 employees (longitudinal measures of goal attainment and job satisfaction were utilized). In both studies, the core self-evaluations concept was positively related to goal self-concordance, meaning that individuals with positive self-regard were more likely to pursue goals for intrinsic and identified (value-congruent) reasons. Furthermore, in both studies, goal self-concordance was related to satisfaction (job satisfaction in Study 1 and life satisfaction in Study 2).


title: Walking control of the humanoid platform KHR-1 based on torque feedback control

abstract: This work presents three online controllers for maintaining dynamic stability of a humanoid robot using force/torque sensor. Those are damping controller, landing orientation controller and landing position controller. The legs of a humanoid robot are relatively long and serially connected with compliant force/torque sensor at the ankle. This architecture has the inherent characteristics of a lightly damped system. Most research on balance control overlook the deterministic vibration caused by structural compliance. In addition, the vibration was not positively considered to improve the characteristics of the system. Therefore, a simple inverted pendulum model with compliant joint is proposed. For this model, the damping controller that increases system damping is proposed as a balance controller. Furthermore, the performance of maintaining balance against external forces is experimentally shown. A landing orientation controller at the ankle joints is presented to manage fast and stable ground contact. A landing position controller is implemented in order to modify the prescribed trajectory of the swing foot and to reduce the landing impact during unexpected landing. The effectiveness of the proposed controllers is confirmed by walking experiments that has been applied on the KAIST humanoid robot platform KHR-1.


Complementary Learning Systems 1 Why there are Complementary Learning Systems in the Hippocampus and Neocortex : Insights from the Successes and Failures of Connectionist Models of Learning and Memory


title: Onto MODEL: Ontological mathematical modeling knowledge management

abstract: Abstract In this paper we describe OntoMODEL, an ontological mathematical model management tool that facilitates systematic, standardizable methods for model storage, use and solving. While the declarative knowledge in mathematical models has been captured using ontologies, the procedural knowledge required for solving these models has been handled by commercially available scientific computing software such as Mathematica and an execution engine written in Java. The interactions involved are well established and the approach is intuitive, therefore not requiring model user familiarity with any particular programming language or modeling software. Apart from this key benefit, the fact that OntoMODEL lends itself to more advanced applications such as model based fault diagnosis, model predictive control, process optimization, knowledge based decision making and process flowsheet simulation makes it an in dispensable tool in the intelligent automation of process operations. This paper also discusses the shortcomings of existing approaches that OntoMODEL addresses and also details its framework and use.


title: Ranking fraud detection for mobile apps: a holistic view

abstract: Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App develops to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we investigate two types of evidences, ranking based evidences and rating based evidences, by modeling Apps' ranking and rating behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the Apple's App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.


title: A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics

abstract: Facial micro-expressions are very brief, spontaneous facial expressions that appear on the face of humans when they either deliberately or unconsciously conceal an emotion. Micro-expression has shorter duration than macro-expression, which makes it more challenging for human and machine. Over the past ten years, automatic micro-expressions recognition has attracted increasing attention from researchers in psychology, computer science, security, neuroscience and other related disciplines. The aim of this paper is to provide the insights of automatic micro-expressions and recommendations for future research. There has been a lot of datasets released over the last decade that facilitated the rapid growth in this field. However, comparison across different datasets is difficult due to the inconsistency in experiment protocol, features used and evaluation methods. To address these issues, we review the datasets, features and the performance metrics deployed in the literature. Relevant challenges such as the spatial temporal settings during data collection, emotional classes versus objective classes in data labelling, face regions in data analysis, standardisation of metrics and the requirements for real-world implementation are discussed. We conclude by proposing some promising future directions to advancing micro-expressions research.


title: Development and validation of the Delaying Gratification Inventory

abstract: Deficits in gratification delay are associated with a broad range of public health problems, such as obesity, risky sexual behavior, and substance abuse. However, six decades of research on the construct has progressed less quickly than might be hoped, largely due to measurement issues. Although past research implicates five domains of delay behavior, involving food, physical pleasures, social interactions, money, and achievement, no published measure to date has tapped all five components of the content domain. Existing measures have been criticized for limitations related to efficiency, reliability, and construct validity. Using an innovative Internet-mediated approach to survey construction, we developed the 35-item five-factor Delaying Gratification Inventory (DGI). Evidence from four studies and a large, diverse sample of respondents (N = 10,741) provided support for the psychometric properties of the measure. Specifically, scores on the DGI demonstrated strong internal consistency and test-retest reliability for the 35-item composite, each of the five domains, and a 10-item short-form. The five-factor structure fit the data well and had good measurement invariance across subgroups. Construct validity was supported by correlations with scores on closely-related self-control measures, behavioral ratings, Big Five personality trait measures, and measures of adjustment and psychopathology, including those on the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF). DGI scores also showed incremental validity in accounting for well-being and health-related variables. The present investigation holds implications for improving public health, accelerating future research on gratification delay, and facilitating survey construction research more generally by demonstrating the suitability of an Internet-mediated strategy.


title: Identification of Ten Additional Susceptibility Loci for Ulcerative Colitis Through Immunochip Analysis in Koreans.

abstract: BACKGROUND ::: Recent genetic association studies identified more than 160 susceptibility loci for inflammatory bowel disease in Caucasian populations, but studies in Asian populations are limited. We have previously reported 3 loci associated with Korean ulcerative colitis (UC). ::: ::: ::: METHODS ::: Using the Immunochip custom single nucleotide polymorphisms (SNP) array designed for dense genotyping of 186 known disease loci from 12 immune-mediated diseases, we analyzed 705 patients with UC and 1178 controls for 536,821 SNPs (89,057 genotyped and 447,764 imputed) in the discovery stage followed by replication in additional 980 affected individuals and 2694 controls in a Korean population. ::: ::: ::: RESULTS ::: We confirmed the associations of 10 known UC risk loci in Koreans: rs76418789 in IL23R (combined P = 1.25 × 10), rs4728142 in IRF5 (combined P = 3.17 × 10), rs1830610 near JAK2 (combined P = 2.28 × 10), rs1555791 near TNFRSF14 (combined P = 1.62 × 10), rs880790 between IL10-IL19 (combined P = 3.73 × 10), rs10185424 between IL1R2-IL1R1 (combined P = 1.54 × 10), rs6478108 in TNFSF15 (combined P = 9.28 × 10), rs861857 between UBE2L3-YDJC (combined P = 3.05 × 10), rs1801274 in FCGR2A (discovery P = 1.54 × 10), and rs17085007 between GPR12-USP12 (discovery P = 3.64 × 10). The percentage of phenotype variance explained by the 13 risk loci (including 3 previously reported loci) was 5.61% in Koreans (on the liability scale, population prevalence = 0.0308%). ::: ::: ::: CONCLUSIONS ::: Our study increased the number of UC susceptibility loci in Koreans to 13 and highlighted the extensive sharing of genetic risk across populations of UC.


title: An Adaptive Solar Photovoltaic Array Using Model-Based Reconfiguration Algorithm

abstract: This paper proposes an adaptive reconfiguration scheme to reduce the effect of shadows on solar panels. A switching matrix connects a solar adaptive bank to a fixed part of a solar photovoltaic (PV) array, according to a model-based control algorithm that increases the power output of the solar PV array. Control algorithms are implemented in real time. An experimental reconfiguration PV system with a resistive load is presented and is shown to verify the proposed reconfigurations.


title: Deep Learning for Stock Market Prediction Using Event Embedding and Technical Indicators

abstract: Recently, ability to handle tremendous amounts of information using increased computational capabilities has improved prediction of stock market behavior. Complex machine learning algorithms such as deep learning methods can analyze and detect complex data patterns. The recent prediction models use two types of inputs as (i) numerical information such as historical prices and technical indicators, and (ii) textual information including news contents or headlines. However, the use of textual data involves text representation construction. Traditional methods like word embedding may not be suitable for representing the semantics of financial news due to problems of word sparsity in datasets. In this paper, we aim to improve stock market predictions using a deep learning approach with event embedding vectors extracted from news headlines, historical price data, and a set of technical indicators as input. Our prediction model consists of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) architectures. We use accuracy and annualized return based on trading simulation as performance metrics, and then perform experiments on three datasets obtained from different news sources namely Reuters, Reddit, and Intrinio. Results show that enhancing text representation vectors and considering both numerical and textual information as input to a deep neural network can improve prediction performance.


title: 3D visual SLAM for an assistive robot in indoor environments using RGB-D cameras

abstract: With a growing global aging population, assistive robots are becoming increasingly important. This paper presents an integrated hardware and software architecture for assistive robots. This modular and reusable software framework incorporates capabilities of perception and navigation. The paper presents as well a system for three-dimensional (3D) vision-based simultaneous localization and mapping (SLAM) using a Red-Green-Blue and Depth (RGB-D) camera, and illustrates its application on an assistive robot. The ORB features and depth information are extracted for ego-motion estimation. Random Sample Consensus algorithm (RANSAC) is adopted for outlier removal, while the integration of RGB-D and iterated closest point (ICP) is used for alignment. Pose-graph optimization is completed by g2o. Finally, a 3D volumetric map is generated for further navigation.


title: Secure Queries on Encrypted Multi-writer Tables

abstract: Performing searches on encrypted data is a verycurrent and active area. Several efficient solutions have beenprovided for the single-writer scenario in which all sensitivedata originates with one party (the Data Owner) that encryptsit and uploads it to a public repository. Subsequently, theData Owner (or authorized clients, the Query Sources) perform queries on the encrypted data through a QueryProcessor which has direct access to the public repository. Motivated by the recent trend in pervasive data, we departfrom this model and consider a multi-writer scenario inwhich data originates with several and mutually untrustedparties. In this new scenario the Data Owner providespublic parameters so that each piece of the generated datastream can be put into an encrypted stream, moreover, the Data Owner keeps some related secret informationneeded to generate tokens so that different subscribers canaccess different subsets of the encrypted stream in clear. Weconsider the case in which each piece of the data streamconsists of a fixed number of cells, organized in columns, and the data owner can authorize subscribers to accessindividual data based on the content of the columns. Currentpublic-key functional encryption schemes provide a directand impractical implementation of this scenario. We thus propose a new public-key primitive, Amortized Or-thogonality Encryption or AOE, derived from Inner-ProductEncryption, that can be used to encrypt each piece ofdata stream so that ciphertexts have size proportional tothe un-encrypted data, moreover, encryption and decryptiontake time proportional to the number of columns. Previousschemes would give quadratic complexity. We provide aconstruction of AOE and prove its selective security understandard assumptions in a bilinear setting with prime ordergroup. Using AOE, we implement all the basic operations inour multi-writer scenario in one round of communication. Wedemonstrate the feasibility and effectiveness of our proposalby providing an implementation of our scenario in C++.


title: Hybrid spiking-based multi-layered self-learning neuromorphic system based on memristor crossbar arrays

abstract: Neuromorphic computing systems are under heavy investigation as a potential substitute for the traditional von Neumann systems in high-speed low-power applications. Recently, memristor crossbar arrays were utilized in realizing spiking-based neuromorphic system, where memristor conductance values correspond to synaptic weights. Most of these systems are composed of a single crossbar layer, in which system training is done off-chip, using computer based simulations, then the trained weights are pre-programmed to the memristor crossbar array. However, multi-layered, on-chip trained systems become crucial for handling massive amount of data and to overcome the resistance shift that occurs to memristors overtime. In this work, we propose a spiking-based multi-layered neuromorphic computing system capable of online training. The system performance is evaluated using three different datasets showing improved results versus previous work. In addition, studying the system accuracy versus memristor resistance shift shows promising results.


title: Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site

abstract: The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations from existing members, outbound WOM can be precisely tracked. Along with traditional marketing, WOM can then be linked to the number of new members subsequently joining the site (sign-ups). Because of the endogeneity among WOM, new sign-ups, and traditional marketing activity, the authors employ a vector autoregressive (VAR) modeling approach. Estimates from the VAR model show that WOM referrals have substantially longer carryover effects than traditional marketing actions and produce substantially higher response elasticities. Based on revenue from advertising impressions served to a new member, the monetary value of a WOM referral can be calculated; this yields an upper-bound estimate for the financial incentives the firm might offer to stimulate WOM.


title: Weight-based canonical sparse cross-view correlation analysis

abstract: As a powerful method for multi-view feature extraction, canonical correlation analysis (CCA) can find linear correlation relationship between feature sets from two views. However, CCA has two disadvantages. One is CCA cannot find nonlinear correlation relationship and the local structure of features; the other is CCA does not consider the structure and cross-view information in feature extraction. Thus, Kernel CCA and locality preserving CCA are proposed to overcome the first disadvantage, while Canonical Sparse Cross-view Correlation Analysis (CSCCA) and its kernel version, Kernel CSCCA (KCSCCA), are proposed to overcome the second one. But CSCCA and KCSCCA ignore the weights of data so that the differences between data are not considered. Since globalized and localized CCA considers the weights of data so as to reflect the global and local structure and information of features, this paper introduces the weights of data into CSCCA and proposes a weight-based CSCCA (WCSCCA). Furthermore, a kernel version of WCSCCA (KWCSCCA) is also proposed to find the nonlinear correlation relationship between two sets of features. WCSCCA has the following contributions. First, on the base of CSCCA, it considers the structure and cross-view information in feature extraction. Second, with the weights of data introduced, WCSCCA can make full use of the differences between data for feature extraction according to the global and local structures and information of features. Moreover, on the base of WCSCCA, KWCSCCA can find the nonlinear correlation relationship of features. Finally, for a fair comparison, this paper adopts similar experimental settings and data sets which are used in the experiments of CSCCA and KCSCCA. The experimental results show that WCSCCA and KWCSCCA (1) can preserve much discriminant information; (2) have best recognition accuracies in average compared with other CCA-related methods; (3) have smaller Rademacher complexities; (4) save time compared with CSCCA and KCSCCA, respectively.


title: A Small and Lightweight Antenna for Handheld RFID Reader Applications

abstract: This letter presents a circularly polarized antenna suitable for handheld radio frequency identification (RFID) reader applications. The proposed antenna consists of four meandered monopole elements fed by a series feed network, each segment having an equal magnitude and a successive 90° phase difference. The monopole elements and the feed network are created on two separate printed circuit boards. Four coaxial lines are used to connect the feed network to the monopoles. Short-circuited tuning stubs are employed for impedance matching. A prototype antenna measuring 45 × 45 × 9 mm3 and weighing 13 g is designed, fabricated, and tested. Measurements show that the fabricated antenna has at 919 MHz a gain of 2.2 dBic, a 3-dB gain beamwidth of 150°, and a 3-dB axial-ratio beamwidth of 146°.


title: Inverse Synthetic Aperture LADAR for Geosynchronous Space Objects - Signal-to-Noise Analysis

abstract: Abstract : Inverse synthetic aperture LADAR (ISAL) provides high resolution images which are not limited in resolution by the diffraction limit of the telescope collecting the imaging data. This makes it an ideal method for imaging Geosynchronous Earth Orbit (GEO) space objects. Although LADAR is a well researched topic, little has been published on the suitability of ISAL for imaging GEO space objects and the associated SNR models. This paper derives an expression for the return signal detected by a coherent ISAL system. Using tomographic techniques common to synthetic aperture radar (SAR), a model is developed for the signal-to-noise ratio of the reconstructed image. Noise sources considered include photon noise and laser speckle noise.


title: Administration of Pure Ergothioneine to Healthy Human Subjects: Uptake, Metabolism, and Effects on Biomarkers of Oxidative Damage and Inflammation.

abstract: AIM ::: We investigated the uptake and pharmacokinetics of l-ergothioneine (ET), a dietary thione with free radical scavenging and cytoprotective capabilities, after oral administration to humans, and its effect on biomarkers of oxidative damage and inflammation. ::: ::: ::: RESULTS ::: After oral administration, ET is avidly absorbed and retained by the body with significant elevations in plasma and whole blood concentrations, and relatively low urinary excretion (<4% of administered ET). ET levels in whole blood were highly correlated to levels of hercynine and S-methyl-ergothioneine, suggesting that they may be metabolites. After ET administration, some decreasing trends were seen in biomarkers of oxidative damage and inflammation, including allantoin (urate oxidation), 8-hydroxy-2'-deoxyguanosine (DNA damage), 8-iso-PGF2α (lipid peroxidation), protein carbonylation, and C-reactive protein. However, most of the changes were non-significant. ::: ::: ::: INNOVATION ::: This is the first study investigating the administration of pure ET to healthy human volunteers and monitoring its uptake and pharmacokinetics. This compound is rapidly gaining attention due to its unique properties, and this study lays the foundation for future human studies. ::: ::: ::: CONCLUSION ::: The uptake and retention of ET by the body suggests an important physiological function. The decreasing trend of oxidative damage biomarkers is consistent with animal studies suggesting that ET may function as a major antioxidant but perhaps only under conditions of oxidative stress. Antioxid. Redox Signal. 26, 193-206.


title: Adaptive defogging with color correction in the HSV color space for consumer surveillance system

abstract: Consumer video surveillance systems often suffer from bad weather conditions, observed objects lose visibility and contrast due to the presence of atmospheric haze, fog, and smoke. In this paper, we present an image defogging algorithm with color correction in the HSV color space for video processing. We first generate a modified transmission map of the image segmentation using multiphase level set formulation from the intensity (V) values. We also estimate atmospheric light in the intensity (V) values. The proposed method can significantly enhance the visibility of foggy video frames using the estimated atmospheric light and the modified transmission map. Another contribution of the proposed work is the compensation of color distortion between consecutive frames using the temporal difference ratio of HSV color channels. Experimental results show that the proposed method can be applied to consumer video surveillance systems for removing atmospheric artifacts without color distortion.


title: Tracking source locations

abstract: Many programming tools require information to be associated with source locations. Current tools do this in different ways with different degrees of effectiveness. This paper is an investigation into the various approaches to maintaining source locations. It is based on an experiment that attempts to track a variety of locations over the evolution of a source file. The results demonstrate that relatively simple techniques can be very effective.


title: Formalizing the LLVM intermediate representation for verified program transformations

abstract: This paper presents Vellvm (verified LLVM), a framework for reasoning about programs expressed in LLVM's intermediate representation and transformations that operate on it. Vellvm provides a mechanized formal semantics of LLVM's intermediate representation, its type system, and properties of its SSA form. The framework is built using the Coq interactive theorem prover. It includes multiple operational semantics and proves relations among them to facilitate different reasoning styles and proof techniques. To validate Vellvm's design, we extract an interpreter from the Coq formal semantics that can execute programs from LLVM test suite and thus be compared against LLVM reference implementations. To demonstrate Vellvm's practicality, we formalize and verify a previously proposed transformation that hardens C programs against spatial memory safety violations. Vellvm's tools allow us to extract a new, verified implementation of the transformation pass that plugs into the real LLVM infrastructure; its performance is competitive with the non-verified, ad-hoc original.


title: Freight transport demand in the mechanics' sector of Friuli Venezia Giulia: the choice between intermodal and road transport

abstract: During recent years, freight transport has been experiencing an enormous growth, affecting in particular the road transport. In Italy as well as in most developed countries, this has called for appropriate policies aimed at a modal switch. A notable role in this context could be played by intermodal transport. This paper supplies a new transport attributes’ evaluation, estimating the possibility that firms rely on intermodal transport rather than on road transport. Consistently, the transport mode is threaded as a choice attribute. Differently from other studies (see for example Matear and Gray, 1993 and Lu, 2000), the modelling framework introduces attribute cut-offs (Swait, 2001), in order to account for a two-stage decision process. The dataset used for this study is the result of 30 interviews, which have been realised in the Italian region of Friuli Venezia Giulia. Results show that the transport mode does not represent a discriminatory choice variable, while attributes related to the quality level of the service (e.g. damages and losses) are as important as cost attributes.


Total Outer Independent Geodetic Number of a Graph


title: Digital-Based Interleaving Control for GaN-Based MHz CRM Totem-Pole PFC

abstract: In this paper, the performance of different interleaving control methods for gallium-nitride (GaN) devices based MHz critical conduction mode (CRM) totem-pole power factor correction (PFC) circuit is compared. Both closed-loop interleaving and open-loop interleaving are good for low frequency CRM PFC; but for MHz very high frequency CRM PFC with microcontroller (MCU) implementation, open-loop interleaving outperforms closed-loop interleaving with small and non-amplified phase error. After software optimization, the phase error of open-loop interleaving is smaller than 3 degree at 1MHz, when the control is implemented by 60MHz low cost MCU. Significant ripple cancellation effect and differential-mode (DM) filter size reduction is achieved with good interleaving. For a 1.2kW MHz totem-pole PFC, the DM filter size is reduced to one quarter compared to the counterpart of a 100kHz PFC. Last but not least, the stability of open-loop interleaving is also analyzed indicating that the MHz CRM totem-pole PFC with voltage-mode control, open-loop interleaving, and turn-on instant synchronization can maintain critical mode operation with better stability compared to low frequency CRM PFC.


title: Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

abstract: Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR) satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches.


title: Study of Green Supply Chain Management in the Indian Manufacturing Industries: A Literature Review cum an Analytical Approach for the measurement of performance

abstract: Green Supply Chain Management (GSCM) is one of the recent innovations for the enhancement of capabilities of Supply Chain Management. In this research, we aim to study the various activities of the Supply Chain processes of the various Indian Manufacturing Industries i.e. both SME‟s {Small Manufacturing Enterprises} & Large Scale Industries & finds how much eco-friendly they are (i.e. how much % of the green factor are involved in their supply chain activities from the procurement of the raw material to the transportation of the final product) for the purpose of metering the performance of the manufacturing sectors has been studied. The major six activities of the supply chain; namely Green Sourcing & Procurement, Green Manufacturing, Green Warehousing, Green Distribution, Green Packaging, Green Transportation are being covered throughout the research. From these above process activities we measured the performance of the various Indian manufacturing industries with the help of various crucial performance indicators & their sub-indicator‟s. The research outcome based on the survey will identify the important result that‟s causes impact on the environment caused by the manufacturing sectors based upon the appropriate methodology we applied for research purpose. We can also analyze by comparing various sectors with each other & finds out the leading and lagging sectors based upon the existing way of processes. In the end also discussed wide future scope for the further research. In this paper we discussed the various environment factors affecting in the manufacturing sectors .we designed a GSCM Index having 17 Indicators & 33 Sub-Indicators & with the help of survey questioner which has to be filled by the various multiple manufacturing sectors from that effort we can finds out the performance the various manufacturing sectors towards Green future.


Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles


Efficacy and safety of dienogest in patients with endometriosis: A single-center observational study over 12 months


Educational Software Applied in Teaching Electrocardiogram: A Systematic Review


title: Yoga for Multiple Sclerosis: A Systematic Review and Meta-Analysis

abstract: While yoga seems to be effective in a number of neuropsychiatric disorders, the evidence of efficacy in multiple sclerosis remains unclear. The aim of this review was to systematically assess and meta-analyze the available data on efficacy and safety of yoga in patients with multiple sclerosis. Medline/PubMed, Scopus, the Cochrane Central Register of Controlled Trials, PsycINFO, CAM-Quest, CAMbase, and IndMED were searched through March 2014. Randomized controlled trials (RCTs) of yoga for patients with multiple sclerosis were included if they assessed health-related quality of life, fatigue, and/or mobility. Mood, cognitive function, and safety were defined as secondary outcome measures. Risk of bias was assessed using the Cochrane tool. Seven RCTs with a total of 670 patients were included. Evidence for short-term effects of yoga compared to usual care were found for fatigue (standardized mean difference [SMD] = −0.52; 95% confidence intervals (CI) = −1.02 to −0.02; p = 0.04; heterogeneity: I2 = 60%; Chi2 = 7.43; p = 0.06) and mood (SMD = −0.55; 95%CI = −0.96 to −0.13; p = 0.01; heterogeneity: I2 = 0%; Chi2 = 1.25; p = 0.53), but not for health-related quality of life, muscle function, or cognitive function. The effects on fatigue and mood were not robust against bias. No short-term or longer term effects of yoga compared to exercise were found. Yoga was not associated with serious adverse events. In conclusion, since no methodological sound evidence was found, no recommendation can be made regarding yoga as a routine intervention for patients with multiple sclerosis. Yoga might be considered a treatment option for patients who are not adherent to recommended exercise regimens.


title: Tackling Area Coverage Problems in a Reconfigurable Floor Cleaning Robot Based on Polyomino Tiling Theory

abstract: Whilst Polyomino tiling theory has been extensively studied as a branch of research in mathematics, its application has been largely confined to multimedia, graphics and gaming domains. In this paper, we present a novel application of Tromino tiling theory, a class of Polyomino with three cells in the context of a reconfigurable floor cleaning robot, hTromo. The developed robot platform is able to automatically generate a global tiling set required to cover a defined space while leveraging on the Tromino tiling theory. Specifically, we validated the application of five Tromino tiling theorems with our hTromo robot. Experiments performed clearly demonstrate the efficacy of the proposed approach resulting in very high levels of area coverage performance in all considered experimental cases. This paper also presents the system architecture of our hTromo robot and a detailed description of the five tiling theorems applied in this study.


title: Elastic Load Balancing for Dynamic Virtual Machine Reconfiguration Based on Vertical and Horizontal Scaling

abstract: Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.


title: Substrate integrated waveguide based butterfly shaped slot antenna for WLAN/GIFI applications

abstract: In this paper, Proposes SIW based butterfly shaped slot antenna for 60GHz applications. This frequency is used for wireless communication applications. The proposed antenna is designed by using Rogers substrate with e r =2.2, height is 0.381 mm and the microstrip feed is used with the input impedance of 50ohms. The structure provides 3.05GHz impedance bandwidth in between 57–64 GHz band and matches with VSWR 2:1. The values of Reflection coefficient, Gain, transmission efficiency and radiation efficiency of proposed antenna at 60GHz are −25.383dB, 6.5dBi, 87%, 94% This frequency is also used for GIFI/WLAN Applications.


H.264/MPEG-4 Advanced Video Coding


title: Novel microstrip patch antenna design employing flexible PVC substrate suitable for defence and radio-determination applications

abstract: This paper propounds a new flexible material, which has been designed by employing flexible Poly Vinyl Chloride (PVC) having dielectric constant e r = 2.7 as substrate. The projected antenna design states that a circular shaped copper patch with flag shaped slot on it has been deployed over the hexagonal PVC substrate of thickness 1 mm. Then ground plane is partially minimized and slotted to revamp the antenna performance. The proposed antenna operates within frequency range of 7.2032GHz to 7.9035GHz while resonating at 7.55GHz. The proposed antenna design has minimal return loss of −55.226dB with the impedance bandwidth of 700 MHz, gain of 4.379dB and directivity of 4.111dBi. The antenna has VSWR less than maximum acceptable value of 2. The projected antenna design can be suitably employed for UWB, radio-determination applications (7.235GHz to 7.25GHz), naval, defence systems (7.25GHz – 7.3GHz) and weather satellite applications (7.75GHz to 7.90GHz). The antenna has been designed in CST Microwave Studio 2014. The proposed antenna has been fabricated and tested using E5071C Network Analyser and anechoic chamber. It has been observed that the stimulated results closely match with the experimental results.


title: SnapLink: Fast and Accurate Vision-Based Appliance Control in Large Commercial Buildings

abstract: As the number and heterogeneity of appliances in smart buildings increases, identifying and controlling them becomes challenging. Existing methods face various challenges when deployed in large commercial buildings. For example, voice command assistants require users to memorize many control commands. Attaching Bluetooth dongles or QR codes to appliances introduces considerable deployment overhead. In comparison, identifying an appliance by simply pointing a smartphone camera at it and controlling the appliance using a graphical overlay interface is more intuitive. We introduce SnapLink, a responsive and accurate vision-based system for mobile appliance identification and interaction using image localization. Compared to the image retrieval approaches used in previous vision-based appliance control systems, SnapLink exploits 3D models to improve identification accuracy and reduce deployment overhead via quick video captures and a simplified labeling process. We also introduce a feature sub-sampling mechanism to achieve low latency at the scale of a commercial building. To evaluate SnapLink, we collected training videos from 39 rooms to represent the scale of a modern commercial building. It achieves a 94% successful appliance identification rate among 1526 test images of 179 appliances within 120 ms average server processing time. Furthermore, we show that SnapLink is robust to viewing angle and distance differences, illumination changes, as well as daily changes in the environment. We believe the SnapLink use case is not limited to appliance control: it has the potential to enable various new smart building applications.


title: NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news

abstract: Abstract In this article, we describe a system that reads news articles in four different languages and detects what happened, who is involved, where and when. This event-centric information is represented as episodic situational knowledge on individuals in an interoperable RDF format that allows for reasoning on the implications of the events. Our system covers the complete path from unstructured text to structured knowledge, for which we defined a formal model that links interpreted textual mentions of things to their representation as instances. The model forms the skeleton for interoperable interpretation across different sources and languages. The real content, however, is defined using multilingual and cross-lingual knowledge resources, both semantic and episodic. We explain how these knowledge resources are used for the processing of text and ultimately define the actual content of the episodic situational knowledge that is reported in the news. The knowledge and model in our system can be seen as an example how the Semantic Web helps NLP. However, our systems also generate massive episodic knowledge of the same type as the Semantic Web is built on. We thus envision a cycle of knowledge acquisition and NLP improvement on a massive scale. This article reports on the details of the system but also on the performance of various high-level components. We demonstrate that our system performs at state-of-the-art level for various subtasks in the four languages of the project, but that we also consider the full integration of these tasks in an overall system with the purpose of reading text. We applied our system to millions of news articles, generating billions of triples expressing formal semantic properties. This shows the capacity of the system to perform at an unprecedented scale.


title: Common TCP Evaluation Suite for ns-3: Design, Implementation and Open Issues

abstract: This paper presents the design and implementation of a Common TCP Evaluation Suite for ns-3. The proposed evaluation suite uses Tmix to generate realistic synthetic TCP traffic, and is designed in line with the recommendations from Internet Congestion Control Research Group (ICCRG). We discuss the Tmix integration in ns-3, shuffling connection vectors of the real traces, architecture and validation of the proposed evaluation suite. The correctness of the evaluation suite is verified by comparing the results obtained from it to those from an existing implementation of such a suite in ns-2. Several open issues discovered with Tmix are also discussed in the paper.


title: The study on feature selection in customer churn prediction modeling

abstract: When the customer churn prediction model is built, a large number of features bring heavy burdens to the model and even decrease the accuracy. This paper is aimed to review the feature selection, to compare the algorithms from different fields and to design a framework of feature selection for customer churn prediction. Based on the framework, the author experiment on the structured module with some telecom operator's marketing data to verify the efficiency of the feature selection framework.


title: Automotive big data: Applications, workloads and infrastructures

abstract: Data is increasingly affecting the automotive industry, from vehicle development, to manufacturing and service processes, to online services centered around the connected vehicle. Connected, mobile and Internet of Things devices and machines generate immense amounts of sensor data. The ability to process and analyze this data to extract insights and knowledge that enable intelligent services, new ways to understand business problems, improvements of processes and decisions, is a critical capability. Hadoop is a scalable platform for compute and storage and emerged as de-facto standard for Big Data processing at Internet companies and in the scientific community. However, there is a lack of understanding of how and for what use cases these new Hadoop capabilities can be efficiently used to augment automotive applications and systems. This paper surveys use cases and applications for deploying Hadoop in the automotive industry. Over the years a rich ecosystem emerged around Hadoop comprising tools for parallel, in-memory and stream processing (most notable MapReduce and Spark), SQL and NOSQL engines (Hive, HBase), and machine learning (Mahout, MLlib). It is critical to develop an understanding of automotive applications and their characteristics and requirements for data discovery, integration, exploration and analytics. We then map these requirements to a confined technical architecture consisting of core Hadoop services and libraries for data ingest, processing and analytics. The objective of this paper is to address questions, such as: What applications and datasets are suitable for Hadoop? How can a diverse set of frameworks and tools be managed on multi-tenant Hadoop cluster? How do these tools integrate with existing relational data management systems? How can enterprise security requirements be addressed? What are the performance characteristics of these tools for real-world automotive applications? To address the last question, we utilize a standard benchmark (TPCx-HS), and two application benchmarks (SQL and machine learning) that operate on a dataset of multiple Terabytes and billions of rows.


title: The Role of Advanced Sensing in Smart Cities

abstract: In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.


SENTIMENT CLASSIFICATION ON STEAM REVIEWS


title: A randomized controlled trial of the effects of brief mindfulness meditation on anxiety symptoms and systolic blood pressure in Chinese nursing students

abstract: Summary Background Previous studies suggested that mindfulness meditation effectively reduced stress-related anxiety and depression symptoms, but no research has evaluated the efficacy of mindfulness meditation in nurses and nursing students in China. Objectives To evaluate the effects of brief mindfulness meditation on the anxiety and depression symptoms and autonomic nervous system activity in Chinese nursing students. Design A randomized controlled trial. Setting A medical university in Guangzhou, China. Participants One hundred and five nursing students were randomly approached by email and seventy-two responded. Sixty recruited students were randomized into meditation and control group (n = 30 each) after screening and exclusion due to factors known to influence mood ratings and autonomic nervous system measures. Methods The meditation group performed mindfulness meditation 30 min daily for 7 consecutive days. The control group received no intervention except pre–post treatment measurements. The Self-Rating Anxiety Scale and Self-Rating Depression Scale were administered to participants, and heart rate and blood pressure were measured. Pre- and post-treatment data were analyzed using repeated-measures analysis of variance. Results Differences between pre- and post-treatment Self-Rating Anxiety Scale scores were significantly larger in the meditation group than in the control group, but no similar effect was observed for Self-Rating Depression Scale scores. Systolic blood pressure was reduced more after the intervention in the meditation group than in the control group, with an average reduction of 2.2 mm Hg. A moderate level of anxiety was associated with the maximum meditation effect. Conclusions Brief mindfulness meditation was beneficial for Chinese nursing students in reducing anxiety symptoms and lowering systolic blood pressure. Individuals with moderate anxiety are most likely to benefit from a short-term mindfulness meditation program.


title: #Emotional Tweets

abstract: Detecting emotions in microblogs and social media posts has applications for industry, health, and security. However, there exists no microblog corpus with instances labeled for emotions for developing supervised systems. In this paper, we describe how we created such a corpus from Twitter posts using emotion-word hashtags. We conduct experiments to show that the self-labeled hashtag annotations are consistent and match with the annotations of trained judges. We also show how the Twitter emotion corpus can be used to improve emotion classification accuracy in a different domain. Finally, we extract a word-emotion association lexicon from this Twitter corpus, and show that it leads to significantly better results than the manually crafted WordNet Affect lexicon in an emotion classification task.


title: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes

abstract: Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies. Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task. Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.


title: Development of verification envioronment for SPI master interface using SystemVerilog

abstract: System-level verification with scalable and reusable components provides a solution for current complex SOC verification and SystemVerilog with OOP is one of the most promising language to develop a complete verification environment with constrained random testing, functional coverage and assertions. In this paper, a uniform verification environment for SPI master interface is developed using SystemVerilog after a comprehensive analysis of the verification plan. The proposed multi-layer testbench is comprised of APB driver, SPI slave, scoreboard, checker, coverage analysis and assertions, which are implemented with different properties of SystemVerilog. Furthermore, constrained random testing vectors are generated automatically and driven into the DUT for higher functional coverage. The verification result shows the effectiveness of the proposed verification environment, which is of great feasibility for further extension and reuse.


title: Muscle force redistributes segmental power for body progression during walking.

abstract: Abstract The ankle plantar flexors were previously shown to support the body in single-leg stance to ensure its forward progression [J. Biomech. 34 (2001) 1387]. The uni- (SOL) and biarticular (GAS) plantar flexors accelerated the trunk and leg forward, respectively, with each opposing the effect of the other. Around mid-stance their net effect on the trunk and the leg was negligible, consistent with the body acting as an inverted pendulum. In late stance, their net effect was to accelerate the leg and trunk forward, consistent with an active push-off. Because other muscles are active in the beginning and end of stance, we hypothesized that their active concentric and eccentric force generation also supports the body and redistributes segmental power to enable body forward progression. Muscle-actuated forward dynamical simulations that emulated observed walking kinematics and kinetics of young adult subjects were analyzed to quantify muscle contributions to the vertical and horizontal ground reaction force, and to the acceleration and mechanical power of the leg and trunk. The eccentric uniarticular knee extensors (vasti, VAS) and concentric uniarticular hip extensors (gluteus maximus, GMAX) were found to provide critical support to the body in the beginning of stance, before the plantar flexors became active. VAS also decelerated the forward motion of both the trunk and the leg. Afterwards when VAS shortens in mid-stance, it delivered the power produced to accelerate the trunk and also redistributed segmental power to the trunk by continuing to decelerate the leg. When present, rectus femoris (RF) activity in the beginning of stance had a minimal effect. But in late stance the lengthening RF accelerated the knee and hip into extension, which opposed swing initiation. Though RF was lengthening, it still accelerated the trunk forward by decelerating the leg and redistributing the leg segmental power to the trunk, as SOL does though it is shortening instead of lengthening. Force developed from highly stretched passive hip structures and active force produced by the uniarticular hip flexors assisted GAS in swing initiation. Hamstrings (HAM) decelerated the leg in late swing while lengthening and accelerated the leg in the beginning of stance while shortening. We conclude that the uniarticular knee and hip extensor muscles are critical to body support in the beginning of stance and redistribution of segmental power by muscles throughout the gait cycle is critical to forward progression of the trunk and legs.


title: A Deep Learning Method for Braille Recognition

abstract: This paper mainly proposes a deep learning method-Stacked Denoising Auto Encoder (SDAE) to solve the problems of automatic feature extraction and dimension reduction in Braille recognition. In the construction of a network with deep architecture, a feature extractor was trained with unsupervised greedy layer-wise training algorithm to initialize the weights for extracting features from Braille images, and then a following classifier was set up for recognition. The experimental results show that by comparing to traditional methods, the constructed network based on the deep learning method can easily recognize Braille images with satisfied performance. The deep learning model can effectively solve the Braille recognition problem in automatic feature extraction and dimension reduction with a reduced preprocessing.


Three types of organizational knowledge: Implications for the tacit-explicit & knowledge creation debates. Paper presented at the Organizational Learning and Knowledge Management: New Directions


title: A novel monolithic integrated phased array antenna using 4-bit distributed MEMS phase shifter and triangular patch antenna

abstract: This paper presents the design of a monolithically integrated micro strip equilateral triangular patch antenna array with a digital (distributed MEMS transmission line) DMTL 4-bit phase shifter having an area of 3.1×1.9cm2 only. The phase shifter constitutes of 130 capacitive switches which are loaded on a (coplanar waveguide) CPW line with dimensions of 90-70-90. The design is made basically for the wireless communication purposes and its best optimization is one for the Ku band applications at 15GHz. The simulations are performed in HFSS (version 13.0). The parallel inset feeding technique is applied in the design where only one input port leads to various branches and no external feeding is required for the matching purposes. The calculated return loss is 22.57dB and overall gain of the system is found to be 7.81dB. The 4-bit phase shifter employs a phase shift of 22.5°/45°/90°/180° respectively.


title: A SURVEY OF METHODS FOR COLOUR IMAGE INDEXING AND RETRIEVAL IN IMAGE DATABASES

abstract: Color is a feature of the great majority of content-based image retrieval systems. However the robustness, effectiveness, and efficiency of its use in image indexing are still open issues. This paper provides a comprehensive survey of the methods for color image indexing and retrieval described in the literature. In particular, image preprocessing, the features used to represent color information, and the measures adopted to compute the similarity between the features of two images are critically analyzed.


title: Opinion mining in legal blogs

abstract: We perform a survey into the scope and utility of opinion mining in legal Weblogs (a.k.a. blawgs). The number of 'blogs' in the legal domain is growing at a rapid pace and many potential applications for opinion detection and monitoring are arising as a result. We summarize current approaches to opinion mining before describing different categories of blawgs and their potential impact on the law and the legal profession. In addition to educating the community on recent developments in the legal blog space, we also conduct some introductory opinion mining trials. We first construct a Weblog test collection containing blog entries that discuss legal search tools. We subsequently examine the performance of a language modeling approach deployed for both subjectivity analysis (i.e., is the text subjective or objective?) and polarity analysis (i.e., is the text affirmative or negative towards its subject?). This work may thus help establish early baselines for these core opinion mining tasks.


GLAD: Groningen Lightweight Authorship Detection


title: IcoRating: A Deep-Learning System for Scam ICO Identification

abstract: Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing astonishing profits to investors. Unlike other money and banking systems, most digital tokens do not require central authorities. Being decentralized poses significant challenges for credit rating. Most ICOs are currently not subject to government regulations, which makes a reliable credit rating system for ICO projects necessary and urgent. In this paper, we introduce IcoRating, the first learning--based cryptocurrency rating system. We exploit natural-language processing techniques to analyze various aspects of 2,251 digital currencies to date, such as white paper content, founding teams, Github repositories, websites, etc. Supervised learning models are used to correlate the life span and the price change of cryptocurrencies with these features. For the best setting, the proposed system is able to identify scam ICO projects with 0.83 precision. We hope this work will help investors identify scam ICOs and attract more efforts in automatically evaluating and analyzing ICO projects.


title: The Augmented Climbing Wall: High-Exertion Proximity Interaction on a Wall-Sized Interactive Surface

abstract: We present the design and evaluation of the Augmented Climbing Wall (ACW). The system combines computer vision and interactive projected graphics for motivating and instructing indoor wall climbing. We have installed the system in a commercial climbing center, where it has been successfully used by hundreds of climbers, including both children and adults. Our primary contribution is a novel movement-based game system that can inform the design of future games and augmented sports. We evaluate ACW based on three user studies (N=50, N=10, N=10) and further observations and interviews. We highlight three central themes of how digital augmentation can contribute to a sport: increasing diversity of movement and challenges, enabling user-created content in an otherwise risky environment, and enabling procedurally generated content. We further discuss how ACW represents an underexplored class of interactive systems, i.e., proximity interaction on wall-sized interactive surfaces, which presents novel human-computer interaction challenges.


title: A Two-Step Methodology for Human Pose Estimation Increasing the Accuracy and Reducing the Amount of Learning Samples Dramatically

abstract: In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we first estimate the orientation of the standing person seen by the camera and then use this information to dynamically select a pose estimation model suited for this particular orientation. We evaluated our method on a public dataset of realistic depth images with precise ground truth joints location. Our experiments show that our method decreases the error of a state-of-the-art pose estimation method by \(30\%\), or reduces the size of the needed learning set by a factor larger than 10.


title: CAWA: coordinated warp scheduling and cache prioritization for critical warp acceleration of GPGPU workloads

abstract: The ubiquity of graphics processing unit (GPU) architectures has made them efficient alternatives to chip-multiprocessors for parallel workloads. GPUs achieve superior performance by making use of massive multi-threading and fast context-switching to hide pipeline stalls and memory access latency. However, recent characterization results have shown that general purpose GPU (GPGPU) applications commonly encounter long stall latencies that cannot be easily hidden with the large number of concurrent threads/warps. This results in varying execution time disparity between different parallel warps, hurting the overall performance of GPUs -- the warp criticality problem. To tackle the warp criticality problem, we propose a coordinated solution, criticality-aware warp acceleration (CAWA), that efficiently manages compute and memory resources to accelerate the critical warp execution. Specifically, we design (1) an instruction-based and stall-based criticality predictor to identify the critical warp in a thread-block, (2) a criticality-aware warp scheduler that preferentially allocates more time resources to the critical warp, and (3) a criticality-aware cache reuse predictor that assists critical warp acceleration by retaining latency-critical and useful cache blocks in the L1 data cache. CAWA targets to remove the significant execution time disparity in order to improve resource utilization for GPGPU workloads. Our evaluation results show that, under the proposed coordinated scheduler and cache prioritization management scheme, the performance of the GPGPU workloads can be improved by 23% while other state-of-the-art schedulers, GTO and 2-level schedulers, improve performance by 16% and -2% respectively.


title: A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification

abstract: In this study we have proposed and compared use of CWT (Continues Wavelet Transform) with two powerful data transformation techniques DWT (Discrete Wavelet Transform), and DCT (Discrete Cosine Transform) which have already been in use, in order to improve the capability of two pattern classifiers in ECG arrhythmias classification. The classifiers under examination are MLP (Multi-Layered Perceptron, a conventional neural network) and SVM (Support Vector Machine). The training or learning algorithms used in MLP and SVM are BackPropagation (BP) and Kernel-Adatron (K-A), respectively. The ECG signals taken from MIT-BIH arrhythmia database are used to classify four different arrhythmias together with normal ECG. The output of MLP and SVM classifiers in terms of training performance, testing performance or generalization ability and training time are compared. MLP and SVM training and testing stages have been carried out twice. At first, only one lead (II) is used, and then a second ECG lead (V1) has been added to the training and testing datasets. Three feature extraction techniques are applied separately to datasets before classification. The results show that selection of the best feature extraction method will depend on the substantial value considered for training time, training and testing performance. This is stated because when applying MLP or SVM, addition of CWT and DCT will show the advantage only when training performance and testing performance are important, respectively. Generally speaking only testing performance with single lead for MLP shows superiority over SVM.


title: On the interaction between exemplar-based concepts and a response scaling process

abstract: Abstract An analysis of the “response scaling” parameter in the Generalized Context Model is presented. In light of the existing debate over the behavior of the model when this parameter is included, three different interpretations are discussed, in order to illustrate the effect of the parameter at the decision level, the category similarity level, and the representational structure level.


title: Towards Formula Translation using Recursive Neural Networks

abstract: While it has become common to perform automated translations on natural language, performing translations between different representations of mathematical formulae has thus far not been possible. We implemented the first translator for mathematical formulae based on recursive neural networks. We chose recursive neural networks because mathematical formulae inherently include a structural encoding. In our implementation, we developed new techniques and topologies for recursive tree-to-tree neural networks based on multi-variate multi-valued Long Short-Term Memory cells. We propose a novel approach for mini-batch training that utilizes clustering and tree traversal. We evaluate our translator and analyze the behavior of our proposed topologies and techniques based on a translation from generic LaTeX to the semantic LaTeX notation. We use the semantic LaTeX notation from the Digital Library for Mathematical Formulae and the Digital Repository for Mathematical Formulae at the National Institute for Standards and Technology. We find that a simple heuristics-based clustering algorithm outperforms the conventional clustering algorithms on the task of clustering binary trees of mathematical formulae with respect to their topology. Furthermore, we find a mask for the loss function, which can prevent the neural network from finding a local minimum of the loss function. Given our preliminary results, a complete translation from formula to formula is not yet possible. However, we achieved a prediction accuracy of 47.05% for predicting symbols at the correct position and an accuracy of 92.3% when ignoring the predicted position. Concluding, our work advances the field of recursive neural networks by improving the training speed and quality of training. In the future, we will work towards a complete translation allowing a machine-interpretation of LaTeX formulae.


title: Traffic sign detection and recognition using fully convolutional network guided proposals

abstract: Detecting and recognizing traffic signs is a hot topic in the field of computer vision with lots of applications, e.g., safe driving, path planning, robot navigation etc. We propose a novel framework with two deep learning components including fully convolutional network (FCN) guided traffic sign proposals and deep convolutional neural network (CNN) for object classification. Our core idea is to use CNN to classify traffic sign proposals to perform fast and accurate traffic sign detection and recognition. Due to the complexity of the traffic scene, we improve the state-of-the-art object proposal method, EdgeBox, by incorporating with a trained FCN. The FCN guided object proposals can produce more discriminative candidates, which help to make the whole detection system fast and accurate. In the experiments, we have evaluated the proposed method on publicly available traffic sign benchmark, Swedish Traffic Signs Dataset (STSD), and achieved the state-of-the-art results.


title: Can Individual Investors Beat the Market?

abstract: We document strong persistence in the performance of trades of individual investors. Investors classified in the top 10 percent place other trades that on average earn excess returns of 15 basis points per day. A rolling-forward strategy of going long firms purchased by previously successful investors and shorting firms purchased by previously unsuccessful investors results in excess returns of 5 basis points per day. These returns are not confined to small stocks nor to stocks in which the investors are likely to have inside information. Our results suggest that skillful individual investors exploit market inefficiencies to earn abnormal profits, above and beyond any profits available from well-known strategies based upon size, value, or momentum.


title: IEEE 802.15.4e LLDN: Superframe configuration for networked control systems

abstract: Wireless networked control systems have gained significant popularity due to commissioning and maintenance ease. Currently there are various wireless communication protocols available to accomplish the control networks. Recently, IEEE 802.15.4e protocol for low latency deterministic network has been introduced that shows prominent theoretical characteristics under effective frequency planning to minimize mutual interference. Factory automation, highly exacting from low latency point of view, requires sensors refresh rate not more than of order 10 milliseconds. This new protocol meets such stringent requirement of industrial automation systems. In this paper, we compare the new protocol with its previous version and observe its suitability for industrial networked control systems as a communication protocol. We introduce different LLDN (low latency deterministic network) superframe configurations that show trade-offs among various network parameters. It gives an insight of relationship among various network parameters like sensors refresh rate, number of devices accommodated in network and data payload exchanged between coordinator and end devices with emphasis on different levels of security incorporated.


title: Development of elements for an adaptive optics system for solar telescope

abstract: The history of the development of works on the creation of the elemental base of adaptive optics for a solar Russian telescope is briefly described. We consider separate issues of the development of the wavefront sensor (WFS) based on the Shack–Hartmann correlation sensor to provide measurements under conditions of strong turbulence. The parameters of the WFS are calculated on the basis of long-term observations of meteorological characteristics and optical measurements of the Fried parameter. A special adjusting device with a block of fast rasters changing is developed. The use of rasters of various dimensions is provided in the WFS. The sensor uses the original algorithm for determining the maximum of the correlation function. The effect on the accuracy of measuring the phase distortions of turbulence occurring in the rooms of the telescope itself is analyzed.


title: Design of single-ridge SIW power dividers with over 75% bandwidth

abstract: Abstract Waveguide structures have a constrained bandwidth due to their cutoff frequencies and presence of higher order modes. Ridged substrate integrated waveguides (RSIW) are low profile, low cost and planar that can be designed for wider bandwidth. In this paper, a two-way power divider based on RSIW is designed to extend bandwidth beyond that of conventional guides. The design utilizes a developed low loss GCPW to RSIW transition to feed the structure. Design was successfully extended to eight way splitter as well. The developed divider has almost a 75% bandwidth with better than 15dB return loss from 5 to 11 GHz. In particular, the splitter demonstrated excellent amplitude and phase imbalance of less than ±O.35 dB and ±7° within 5.5 to 9 GHz, respectively. Theoretical and experimental results are in good agreement. Index Terms - GCPW to SIW transition, RSIW, SIW, Tjunction. I. INTRODUCTION Various SIW power dividers have been studied over the years [1-3], but they suffer from a limited bandwidth and high loss due to the substrate dielectric loss, and the pronounced metallization loss due to the reduced waveguide height compared to the air-filled full height conventional waveguides. Typically, for conventional waveguide components, a longitudinal metal ridge can be introduced to significantly widen its bandwidth without obvious deterioration of RF performance. This ridged waveguide has a lower cutoff frequency of its dominant mode and wider separation between the dominant mode and its first higher order mode, resulting in a wider operating bandwidth than the conventional rectangular waveguide with the same dimensions for single mode operation. A previous implementation of a ridge SIW structure was confined to a design in which a single ridge is formed from a single row of vias [1]. Demonstrated structures were constructed using 10 substrate layers with a total height of 2.54 mm, where the depth of each via was controlled by the number of layers penetrated. This process obviously is complicated but could be compatible, for example, with L TCC technology. Bozzi et a1. in [2] introduced a novel class of SIWs based on the concept of the classical ridge rectangular waveguide, which permits the design of broadband, compact and low-loss integrated components and interconnects. In [2] a ridge SIW was presented, where the straight structure is integrated in a dielectric substrate with top and bottom metal layers, and include two rows of full-height metal cylinders to emulate the side walls, and a central row of partial-height metal posts connected at their bottom by a metal strip. The design and experimental verification of their proposed prototypes cover the frequency band from 6.8 to 25 GHz. Demonstrated structures exhibited a mono-modal bandwidth three times broader than the classical rectangular waveguides or SIWs. However, no microwave components were presented. Subsequently, Wu's group introduced a slot antenna array fed by ridge guide structure [3]. Various miniaturization techniques based on SIW structures were also discussed which utilize vertical metal walls implemented using rows o


title: A CMOS DC offset cancellation (DOC) circuit for PGA of low IF wireless receivers

abstract: In this paper, the sources of DC offset and the corresponding DC offset cancellation techniques are analyzed. A DC negative feedback technique based DC offset canceller (DOC) is designed in details and implemented by SMIC 0.18μm CMOS process. DC offset detector is an important part of DOC. In this paper, an on chip DC offset detection circuit is proposed for the DC negative feedback canceller which can be used in the CMOS programmable gain amplifier (PGA) of a low intermediate frequency (IF) receiver such as IEEE802.15.4/ZigBee wireless sensor network. Post-simulation and chip measurement results show that the DC offset canceller performs well.


title: A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity

abstract: Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. ::: In this paper, inspired by features of human recognition process and their biological mechanisms, a new integrated and dynamic framework is proposed to mimic the semantic extraction, concept formation and feature re-selection in human visual processing. The main contributions of the proposed model are as follows: ::: (1) Semantic feature extraction: Local semantic features are learnt from episodic features that are extracted from raw images through a deep neural network; ::: (2) Integrated concept formation: Concepts are formed with local semantic information and structural information learnt through network. ::: (3) Feature re-selection: When ambiguity is detected during recognition process, distinctive features according to the difference between ambiguous candidates are re-selected for recognition. ::: Experimental results on hand-written digits and facial shape dataset show that, compared with other methods, the new proposed model exhibits higher robustness and precision for visual recognition, especially in the condition when input samples are smantic ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.


title: Memristive devices for computing

abstract: This Review looks at recent progress in the development and understanding of memristive devices, and examines the performance requirements for computing with such devices.


title: Knowing One’s Place: Parental Educational Background Influences Social Identification with Academia, Test Anxiety, and Satisfaction with Studying at University

abstract: First-generation students (i.e., students whose parents did not attend university) often experience difficulties fitting in with the social environment at universities. This experience of personal misfit is supposedly associated with an impaired social identification with their aspired in-group of academics compared to continuing-generation students (i.e., students with at least one parent with an academic degree. In this article, we investigate how the postulated differences in social identification with the group of academics affect first-generation students’ satisfaction with studying and test anxiety over time. We assume that first-generation students’ impaired social identification with the group of academics leads to decreased satisfaction with studying and aggravated test anxiety over the course of the first academic year. In a longitudinal study covering students’ first year at a German university, we found that continuing-generation students consistently identified more strongly with their new in-group of academics than first-generation students. The influence of social identification on test anxiety and satisfaction with studying differed between groups. For continuing-generation students, social identification with the group of academics buffered test anxiety and helped them maintain satisfaction with studying over time. We could not find these direct effects within the group of first-generation students. Instead, first-generation students were more sensitive to effects of test anxiety on satisfaction with studying and vice versa over time. The results suggest that first-generation students might be more sensitive to the anticipation of academic failure. Furthermore, continuing-generation students’ social identification with the group of academics might have buffered them against the impact of negative experiences during the entry phase at university. Taken together, our findings underscore that deficit-driven approaches focusing solely on first-generation status may not be sufficient to fully understand the importance of parental educational background for students’ well-being. More specifically, continuing-generation students might reap benefits from their parental educational background. These benefits widen the social gap in academia in addition to the disadvantages of students with first-generation status. In sum, understanding the benefits of continuing-generation status has important implications for interventions aiming to reduce social class gaps in academia.


title: Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach

abstract: We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase ‘sick of’ and the word ‘depressed’), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive ‘my’ when mentioning their ‘wife’ or ‘girlfriend’ more often than females use ‘my’ with ‘husband’ or 'boyfriend’). To date, this represents the largest study, by an order of magnitude, of language and personality.