Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-based Face Synthesis for Pose-Robust Recognition from Single Image

Face recognition in real-world conditions requires the ability to deal with a number of conditions, such as variations in pose, illumination and expression. In this paper, we focus on variations in head pose and use a computationally efficient regression-based approach for synthesising face images in different poses, which are used to extend the face recognition training set. In this data-driven approach, the correspondences between facial landmark points in frontal and non-frontal views are learnt offline from manually annotated training data via Gaussian Process Regression. We then use this learner to synthesise non-frontal face images from any unseen frontal image. To demonstrate the utility of this approach, two frontal face recognition systems (the commonly used PCA and the recent Multi-Region Histograms) are augmented with synthesised non-frontal views for each person. This synthesis and augmentation approach is experimentally validated on the FERET dataset, showing a considerable improvement in recognition rates for ±40◦ and ±60◦ views, while maintaining high recognition rates for ±15◦ and ±25◦ views.
Established Clustering Procedures for Network Analysis

In light of the burgeoning interest in network analysis in the new millenium, we bring to the attention of contemporary network theorists, a two-stage double-standarization and hierarchical clustering (single-linkage-like) procedure devised in 1974. In its many applications over the next decade--primarily to the migration flows between geographic subdivisions within nations--the presence was often revealed of ``hubs''. These are, typically, ``cosmopolitan/non-provincial'' areas--such as the French capital, Paris--which send and receive people relatively broadly across their respective nations. Additionally, this two-stage procedure--which ``might very well be the most successful application of cluster analysis'' (R. C. Dubes)--has detected many (physically or socially) isolated groups (regions) of areas, such as those forming the southern islands, Shikoku and Kyushu, of Japan, the Italian islands of Sardinia and Sicily, and the New England region of the United States. Further, we discuss a (complementary) approach developed in 1976, involving the application of the max-flow/min-cut theorem to raw/non-standardized flows.
Comparative Evaluation of Direct Thrombin and Factor Xa Inhibitors with Antiplatelet Agents under Flow and Static Conditions: An In Vitro Flow Chamber Model

Dabigatran and rivaroxaban are novel oral anticoagulants that specifically inhibit thrombin and factor Xa, respectively. The aim of this study is to elucidate antithrombotic properties of these anticoagulant agents under arterial and venous shear conditions. Whole blood samples treated with dabigatran or rivaroxaban at 250, 500, and 1000 nM, with/without aspirin and AR-C66096, a P2Y12 antagonist, were perfused over a microchip coated with collagen and tissue thromboplastin at shear rates of 240 and 600 s−1. Fibrin-rich platelet thrombus formation was quantified by monitoring flow pressure changes. Dabigatran at higher concentrations (500 and 1000 nM) potently inhibited thrombus formation at both shear rates, whereas 1000 nM of rivaroxaban delayed, but did not completely inhibit, thrombus formation. Dual antiplatelet agents weakly suppressed thrombus formation at both shear rates, but intensified the anticoagulant effects of dabigatran and rivaroxaban. The anticoagulant effects of dabigatran and rivaroxaban were also evaluated under static conditions using thrombin generation (TG) assay. In platelet-poor plasma, dabigatran at 250 and 500 nM efficiently prolonged the lag time (LT) and moderately reduce peak height (PH) of TG, whereas rivaroxaban at 250 nM efficiently prolonged LT and reduced PH of TG. In platelet-rich plasma, however, both anticoagulants efficiently delayed LT and reduced PH of TG. Our results suggest that dabigatran and rivaroxaban may exert distinct antithrombotic effects under flow conditions, particularly in combination with dual antiplatelet therapy.
Something false about conceptual metaphors

ABSTRACT Although Lakoff and Johnson’s Conceptual Metaphor Theory has been influential across many disciplines, little research has tested the psychological reality of conceptual metaphors (CMs) using established experimental memory paradigms. Here we employ an episodic memory task based on the Deese-Roediger-McDermott (DRM) false memory paradigm to explore this possibility. We find that after reading lists of sentences based on underlying conceptual metaphors that participants are more likely to falsely remember the nonpresented conceptual metaphors themselves as well as new sentences consistent with the CM mapping than control items that do not share this mapping. This finding provides experimental support for conceptual metaphors and highlights the utility of using episodic tasks to explore the assumptions of this theory.
MAGIC J0616+225 as delayed TeV emission of cosmic rays diffusing from the supernova remnant IC 443

We present a theoretical model that explains the high-energy phenomenology of the neighbourhood of the supernova remnant IC 443, as observed with the Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope and the Energetic Gamma-Ray Experiment Telescope (EGRET). We interpret MAGIC J0616+225 as delayed TeV emission of cosmic rays diffusing from IC 443 and interacting with a known cloud located at a distance of about 20 pc in the foreground of the remnant. This scenario naturally explains the displacement between EGRET and MAGIC sources, their fluxes, and their spectra. We compare this model with others recently presented, and discuss how it can be tested with observations by the Gamma-ray Large Area Space Telescope.
Genetic Identification Is Critical for the Diagnosis of Parkinsonism: A Chinese Pedigree with Early Onset of Parkinsonism

Background A number of hereditary neurological diseases display indistinguishable features at the early disease stage. Parkinsonian symptoms can be found in numerous diseases, making it difficult to get a definitive early diagnosis of primary causes for patients with onset of parkinsonism. The accurate and early diagnosis of the causes of parkinsonian patients is important for effective treatments of these patients. Methods We have identified a Chinese family (82 family members over four generations with 21 affected individuals) that manifested the characterized symptoms of parkinsonism and was initially diagnosed as Parkinson’s disease. We followed up with the family for two years, during which we carried out clinical observations, Positron Emission Tomography-Computed Tomography neuroimaging analysis, and exome sequencing to correctly diagnose the case. Results During the two-year follow-up period, we performed comprehensive medical history collection, physical examination, and structural and functional neuroimaging studies of this Chinese family. We found that the patient exhibited progressive deteriorated parkinsonism with Parkinson disease-like neuropathology and also had a good response to the initial levodopa treatment. However, exome sequencing identified a missense mutation, N279K, in exon 10 of MAPT gene, verifying that the early parkinsonian symptoms in this family are caused by the genetic mutation for hereditary frontotemporal lobar dementia. Conclusions For the inherited parkinsonian patients who even show the neuropathology similar to that in Parkinson’s disease and have initial response to levodopa treatment, genetic identification of the molecular basis for the disease is still required for defining the early diagnosis and correct treatment.
The Toxicity of Bullying by Teachers and Other School Staff

Abstract Although the toxic effects of peer bullying among middle school students are widely recognized, bullying by teachers and other school staff has received little attention. This study compared the prevalence and school adjustment of students bullied by teachers/staff, students bullied by peers, and students who were not bullied. The sample consisted of 56,508 students in Grades 7 and 8 who completed a statewide school climate survey. Students were classified into four groups: (a) not bullied (87.2%); (b) bullied only by peers (9.3%); (c) bullied only by teachers/staff (1.2%); and (d) bullied by peers and teachers/staff (1.5%). In comparison to students who reported no bullying, students bullied by teachers and other school staff were significantly more likely to report lower school engagement and self-reported grades and more negative perceptions of school climate. Students bullied only by peers reported more distress symptoms than those bullied by teachers and other school staff. These findings call for more attention to the problem of teacher and other school staff bullying.
SVMpAUCtight: a new support vector method for optimizing partial AUC based on a tight convex upper bound

The area under the ROC curve (AUC) is a well known performance measure in machine learning and data mining. In an increasing number of applications, however, ranging from ranking applications to a variety of important bioinformatics applications, performance is measured in terms of the partial area under the ROC curve between two specified false positive rates. In recent work, we proposed a structural SVM based approach for optimizing this performance measure (Narasimhan and Agarwal, 2013). In this paper, we develop a new support vector method, SVMpAUCtight, that optimizes a tighter convex upper bound on the partial AUC loss, which leads to both improved accuracy and reduced computational complexity. In particular, by rewriting the empirical partial AUC risk as a maximum over subsets of negative instances, we derive a new formulation, where a modified form of the earlier optimization objective is evaluated on each of these subsets, leading to a tighter hinge relaxation on the partial AUC loss. As with our previous method, the resulting optimization problem can be solved using a cutting-plane algorithm, but the new method has better run time guarantees. We also discuss a projected subgradient method for solving this problem, which offers additional computational savings in certain settings. We demonstrate on a wide variety of bioinformatics tasks, ranging from protein-protein interaction prediction to drug discovery tasks, that the proposed method does, in many cases, perform significantly better on the partial AUC measure than the previous structural SVM approach. In addition, we also develop extensions of our method to learn sparse and group sparse models, often of interest in biological applications.
Changing views at Banaras Hindu University on the Academic Study of Religion: A first report from an on-going research project

Given India’s vibrant religious landscape, there is a somewhat surprising paucity of depart‐ ments, centres or even programs for the academic study of religion. This article discusses this issue based on the preliminary results of an interview study conducted at Banaras Hindu University (BHU), Varanasi, India, in 2014 and 2015. Its focus is on the views of university teachers and researchers concerning the place, role and function of religion and religious stud‐ ies at BHU. Twenty ‐eight semi ‐structured interviews were conducted. In the course of their analysis, six themes emerged: 1) the place and role of religion in society; 2) religion as ‘religi‐ osity/spirituality’ or sanatana dharma vs. political ideology/communitarianism; 3) religion vs. dharma; 4) secularization; 5) religion in education in general; and, 6) religion in the education at BHU. The informants agreed on the increasing importance of religion in India, and most of them viewed the meaning of secularization as being ‘equal respect for all religions’. Moreover, a majority distinguished between ‘religion’, in the Western sense, and the Indian conception of dharma, considering it regrettable that the latter, described as the common ground of all reli‐ gions, is not taught more extensively at BHU. They also considered the original ideal of BHU’s founder, Madan Mohan Malaviya, to be of significant importance. That ideal involved not only teaching students the knowledge and skill sets found in a standard modern university, but 108 Åke SANDER, Clemens CAVALLIN, Sushil KUMAR also equipping them with a value ‐based education, grounded upon sanatana ‐dharma. As our project progresses, further understanding of this turn toward dharma education is something we intend to pursue through the lens of multiple modernities, developed by Marian Burchardt et al. as multiple secularities.
Exploring the Connections and Tensions Between Sacrifice and Self-Care as Relational Processes in Religious Families

Exploring the Connections and Tensions Between Sacrifice and Self-Care as Relational Processes in Religious Families Hilary Dalton School of Family Life, BYU Master of Science The relational processes of sacrifice and self-care both influence every human relationship and as such, every human has to learn how to engage in them. Families are one of the many communities in which one must address sacrifice and self-care. This study provides a qualitative exploration of the relational processes of sacrifice and self-care among a sample of 198 highly religious (Abrahamic faiths) families. In-depth analyses explored motivations, types, and related family processes among family relationships. Five themes from the data about how families perceived and addressed the relational processes of sacrifice and self-care are discussed: (1) tensions between sacrifice and self-care, (2) motivations of sacrifice and/or self-care, (3) types of sacrifice, (4) types of self-care, and (5) processes in faith and family relationships. The ways that participants discussed struggling to address these processes are discussed along with why these ideas are important for marriages and families.
A System for the Specification and Execution of Conditional WoT Applications over Voice

This paper presents a novel system that supports the specification and execution of conditional Web of Things (WoT) applications over voice. We illustrate the architecture of our system that consists of the key components such as a Cloud-based automatic speech recognition (ASR), an application composition engine and an event-based platform for application execution. We demonstrate this system through a mobile voice-based personal assistance application. Through this demonstration, we highlight our technical contributions that address the shortcomings of the existing voice-based applications and introduce further challenges.
Sentence Factorization for Opinion Feature Mining

Opinion mining has tremendous potentials in extracting valuable information and experience from individuals on products and services. In particular, product features extraction and sentiment scoring on extracted features are fundamental steps. Opinion knowledge extraction often involves extensive application of natural language processing, manual labeling and machine learning methods.In this paper, we focus on developing fine-grained product feature extractions with minimal tailor build language models and labeling.A threshold-normalized sentence-level word model is proposed for opinion feature mining. The opinion feature extraction is then solved via matrix factorization technique. Evaluation on feature-entropies, sentence-entropies and human evaluation demonstrated the superiority of our approach. Highly relevant and fine-grained opinion features are extracted automatically.
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving

While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open field for scientific exploration. Broadly, there are two major developmental bottlenecks: the unavailability of comprehensively labeled datasets and of expressive evaluation strategies. Approaches for labeling datasets have relied on intensive hand-engineering, and strategies for evaluating learning systems have been unable to identify failure-case scenarios. Human intelligence offers an untapped approach for breaking through these bottlenecks. This paper introduces Driverseat, a technology for embedding crowds around learning systems for autonomous driving. Driverseat utilizes crowd contributions for (a) collecting complex 3D labels and (b) tagging diverse scenarios for ready evaluation of learning systems. We demonstrate how Driverseat can crowdstrap a convolutional neural network on the lane-detection task. More generally, crowdstrapping introduces a valuable paradigm for any technology that can benefit from leveraging the powerful combination of human and computer intelligence.
Neuro Fuzzy and Self Tunging Fuzzy Controller to Improve Pitch and Yaw Control Systems Resposes of Twin Rotor MIMO System

In this paper, Neuro-Fuzzy based Fuzzy Subtractive Clustering Method (FSCM) and Self Tuning Fuzzy PD-like Controller (STFPDC) were used to solve non-linearity and trajectory problems of pitch AND yaw angles of Twin Rotor MIMO system (TRMS). The control objective is to make the beams of TRMS reach a desired position quickly and accurately. The proposed method could achieve control objectives with simpler controller. To simplify the complexity of STFPDC, ANFIS based FSCM was used to simplify the controller and improve the response. The proposed controllers could achieve satisfactory objectives under different input signals. Simulation results under MATLAB/Simulink® proved the improvement of response and superiority of simplified STFPDC on Fuzzy Logic Controller (FLC).
A Utility-Aware and Holistic Approach for Privacy Preserving Distributed Mining with Worst Case Privacy Guarantee

Organizations often want to predict some attribute values collaboratively. However, they are often unwilling or not allowed to directly share their private data. Thus there is great need for distributed privacy preserving techniques. There exists a rich body of work based on Secure MultiParty Computation techniques. However, most such techniques are tied to a specific mining algorithm and users have to run a different protocol for each mining algorithm. A holistic approach [11] was proposed in which all parties first use a SMC protocol to generate a synthetic data set and then share this data for different mining algorithms. However, this approach has two major drawbacks: 1) it provides no worst case privacy guarantee, 2) parties involved in the mining process often know what attribute to predict, but the holistic approach does not take this into account. In this paper, we propose a method that addresses these shortcomings. Experimental results demonstrate the benefits of the
Recognizing Textual Entailment Challenge Organizers Recognising Textual Entailment Challenge Workshop Program Application of the Bleu Algorithm for Recognising Textual Entailments What Syntax Can Contribute in Entailment Task Recognizing Textual Entailment with Edit Distance Algorithms Textual Entai

This paper describes the PASCAL Network of Excellence Recognising Textual Entailment (RTE) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This applicationindependent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.
Electrochemical Glucose Biosensor of Platinum Nanospheres Connected by Carbon Nanotubes

Background: Glucose biosensors comprised of nanomaterials such as carbon nanotubes (CNTs) and metallic nanoparticles offer enhanced electrochemical performance that produces highly sensitive glucose sensing. This article presents a facile biosensor fabrication and biofunctionalization procedure that utilizes CNTs electrochemically decorated with platinum (Pt) nanospheres to sense glucose amperometrically with high sensitivity. Method: Carbon nanotubes are grown in situ by microwave plasma chemical vapor deposition (MPCVD) and electrochemically decorated with Pt nanospheres to form a CNT/Pt nanosphere composite biosensor. Carbon nanotube electrodes are immobilized with fluorescently labeled bovine serum albumin (BSA) and analyzed with fluorescence microscopy to demonstrate their biocompatibility. The enzyme glucose oxidase (GOX) is immobilized onto the CNT/Pt nanosphere biosensor by a simple drop-coat method for amperometric glucose sensing. Results: Fluorescence microscopy demonstrates the biofunctionalization capability of the sensor by portraying adsorption of fluorescently labeled BSA unto MPCVD-grown CNT electrodes. The subsequent GOX-CNT/Pt nanosphere biosensor demonstrates a high sensitivity toward H2O2 (7.4 μA/mM/cm2) and glucose (70 μA/mM/cm2), with a glucose detection limit and response time of 380 nM (signal-to-noise ratio = 3) and 8 s (t90%), respectively. The apparent Michaelis-Menten constant (0.64 mM) of the biosensor also reflects the improved sensitivity of the immobilized GOX/nanomaterial complexes. Conclusions: The GOX-CNT/Pt nanosphere biosensor outperforms similar CNT, metallic nanoparticle, and more conventional carbon-based biosensors in terms of glucose sensitivity and detection limit. The biosensor fabrication and biofunctionalization scheme can easily be scaled and adapted for microsensors for physiological research applications that require highly sensitive glucose sensing.
i2WAP: Improving non-volatile cache lifetime by reducing inter- and intra-set write variations

Modern computers require large on-chip caches, but the scalability of traditional SRAM and eDRAM caches is constrained by leakage and cell density. Emerging non-volatile memory (NVM) is a promising alternative to build large on-chip caches. However, limited write endurance is a common problem for non-volatile memory technologies. In addition, today's cache management might result in unbalanced write traffic to cache blocks causing heavily-written cache blocks to fail much earlier than others. Unfortunately, existing wear-leveling techniques for NVM-based main memories cannot be simply applied to NVM-based on-chip caches because cache writes have intra-set variations as well as inter-set variations. To solve this problem, we propose i2WAP, a new cache management policy that can reduce both inter- and intra-set write variations. i2WAP has two features: (1) Swap-Shift, an enhancement based on previous main memory wear-leveling to reduce cache inter-set write variations; (2) Probabilistic Set Line Flush, a novel technique to reduce cache intra-set write variations. Implementing i2WAP only needs two global counters and two global registers. By adopting i2WAP, we can improve the lifetime of on-chip non-volatile caches by 75% on average and up to 224%.