Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of the EHD Family of Endocytic Recycling Regulators for TCR Recycling and T Cell Function

T cells use the endocytic pathway for key cell biological functions, including receptor turnover and maintenance of the immunological synapse. Some of the established players include the Rab GTPases, the SNARE complex proteins, and others, which function together with EPS-15 homology domain–containing (EHD) proteins in non–T cell systems. To date, the role of the EHD protein family in T cell function remains unexplored. We generated conditional EHD1/3/4 knockout mice using CD4-Cre and crossed these with mice bearing a myelin oligodendrocyte glycoprotein–specific TCR transgene. We found that CD4+ T cells from these mice exhibited reduced Ag-driven proliferation and IL-2 secretion in vitro. In vivo, these mice exhibited reduced severity of experimental autoimmune encephalomyelitis. Further analyses showed that recycling of the TCR-CD3 complex was impaired, leading to increased lysosomal targeting and reduced surface levels on CD4+ T cells of EHD1/3/4 knockout mice. Our studies reveal a novel role of the EHD family of endocytic recycling regulatory proteins in TCR-mediated T cell functions.
A simple sequence attention model for machine comprehension

Machine comprehension is an important NLP problem with a number of important applications. Particularly question answering has been attracting a lot of attention on the applied research area. This work explores a simple sequence attention architecture for question answering. In this work the Stanford Question and Answering Dataset (SQuAD) introduced by Rajpurkar et al. (2016) is employed.
Increasing big data front end processing efficiency via locality sensitive Bloom filter for elderly healthcare

In support of the increasing number of elderly population, wearable sensors and portable mobile devices capable of monitoring, recording, reporting and alerting are envisaged to enable them an independent lifestyle without relying on intrusive care programmes. However, the big data readings generated from the sensors are characterized as multidimensional, dynamic and non-linear with weak correlation with observable human behaviors and health conditions which challenges the information transmission, storing and processing. This paper proposes to use Locality Sensitive Bloom Filter to increase the Instance Based Learning efficiency for the front end sensor data pre-processing so that only relevant and meaningful information will be sent out for further processing aiming to relieve the burden of the above big data challenges. The approach is proven to optimize and enhance a popular instance-based learning method benefits from its faster speed, less space requirements and is adequate for the application.
Designing, Capturing and Validating History-Sensitive Security Policies for Distributed Systems

We consider the use of Aspect-oriented techniques as a flexible way to deal with security policies in distributed systems. We follow the approach of attaching security policies to the relevant locations that must be governed by them, and then combining them at runtime according to the interactions that happen. Recent work suggests using Aspects in this way to analyse the future behaviour of programs and to make access control decisions based on this; this gives the flavour of dealing with information flow rather than mere access control. We show in this paper that it is beneficial to augment this approach with history-based components, as is traditional in reference-monitorbased approaches to mandatory access control. Our developments are performed in an Aspect-oriented coordination language, aiming to describe the Bell-LaPadula policy as elegantly as possible. Furthermore, the resulting language has the capability of combining both historysensitive and future-sensitive policies, providing even more flexibility and power. Moreover, we propose a global Logic for reasoning about the systems designed with this language. We show how the Logic can be used to validate the combination of security policies in a distributed system, either with or without exploring the entire state space. 1 Department of Informatics, Technical University of Denmark, Richard Petersens Plads, Building 322, 2800 Kgs. Lyngby, Denmark. Email: {aher, nielson, riis}@imm.dtu. dk 108 A.M. Hernandez, F. Nielson, H. Riis-Nielson
Towards Social Touch Intelligence: Developing a Robust System for Automatic Touch Recognition

Touch behavior is of great importance during social interaction. Automatic recognition of social touch is necessary to transfer the touch modality from interpersonal interaction to other areas such as Human-Robot Interaction (HRI). This paper describes a PhD research program on the automatic detection, classification and interpretation of touch in social interaction between humans and artifacts. Progress thus far includes the recording of a Corpus of Social Touch (CoST) consisting of pressure sensor data of 14 different touch gestures and first classification results. Classification of these 14 gestures resulted in an overall accuracy of 53% using Bayesian classifiers. Further work includes the enhancement of the gesture recognition, building an embodied system for real-time classification and testing this system in a possible application scenario.
SLAP widgets: bridging the gap between virtual and physical controls on tabletops

We present Silicone iLluminated Active Peripherals (SLAP), a system of tangible, translucent widgets for use on multitouch tabletops. SLAP Widgets are cast from silicone or made of acrylic, and include sliders, knobs, keyboards, and buttons. They add tactile feedback to multi-touch tables, improving input accuracy. Using rear projection, SLAP Widgets can be relabeled dynamically, providing inexpensive, battery-free, and untethered augmentations. Furthermore, SLAP combines the flexibility of virtual objects with physical affordances. We evaluate how SLAP Widgets influence the user experience on tabletops compared to virtual controls. Empirical studies show that SLAPWidgets are easy to use and outperform virtual controls significantly in terms of accuracy and overall interaction time.
Posture control of redundant manipulators based on the task oriented stiffness regulation

This paper investigates the posture of a manipulator that has redundant DOF similar to human upper extremity. The human arm naturally takes a posture with no wandering although it has some redundant DOF. The authors consider that the posture will be determined task-oriented, which means that human unconsciously takes posture of his/her upper extremities, which is suitable for the task that the endpoint about to do. This study also assumes that all or some joints are capable to adjust the joint stiffness so that the stiffness of the endpoint in the task space is also adjustable. Hence our study aims to establish the way to make the manipulator take a posture that provides a stiffness of the endpoint suitable for the task. The new control formula for shaping the manipulator's posture to provide a desired stiffness of the endpoint is presented followed by the simulation study to verify it.
History of Guillain–Barré syndrome

Guillain–Barré syndrome (GBS) is an acute self‐limited polyneuropathy. The original paper was reported by Guillain, Barré and Strohl in 1916. GBS was considered to be a demyelinating disease of the peripheral nervous system, called acute inflammatory demyelinating polyneuropathy. In the 1980s, the acute axonal type of GBS was first reported. This subtype was later called acute motor axonal neuropathy. In addition to acute inflammatory demyelinating polyneuropathy and acute motor axonal neuropathy, several subtypes including Miller Fisher syndrome were reported. GBS was shown to be an immune‐mediated disease. The frequent presence of antibodies against various glycolipids have been reported in the acute‐phase sera from GBS patients since the end of the 1980s, including anti‐GM1 antibody in acute motor axonal neuropathy and anti‐GQ1b antibody in Miller Fisher syndrome. The efficacy of plasmapheresis and intravenous immunoglobulin therapy has been established since the mid 1980s. However, despite those therapies, there are still severe or refractory cases. Further investigation is necessary for the development of novel therapeutic strategies.
Capacitively-aided switching technique for high-frequency isolated bus converters

This paper presents a new capacitively-aided zero voltage switching (ZVS) technique for isolated bus converters. The proposed technique helps to achieve ZVS conditions for both inverter and rectifier switches with the aid of magnetizing current of the transformer and capacitors interconnecting primary and secondary side switching nodes. With the proposed topology, the ZVS conditions for inverter and rectifier switches occur at the same time, and (ideally) maintain constant required dead time independent of the load level of the converter. These features are particularly advantageous especially for designs operating at high frequencies where timing control becomes critical. The proposed approach is demonstrated in a 1.4 MHz prototype converter which operates from 36 Vdc and supplies a 12 Vdc output load (i.e., fixed input voltage and fixed voltage transformation ratio, 3 : 1 ) at a 36 W rating. Experimental results show ZVS operation of all devices independent of the load level, and the prototype converter achieves 94% peak efficiency with 300 W/ in3 power density.
Deciding type isomorphisms in a type-assignment framework

Abstract This paper provides a formal treatment of isomorphic types for languages equipped with an ML style polymorphic type inference mechanism. The results obtained make less justified the commonplace feeling that (the core of) ML is a subset of second order λ-calculus: we can provide an isomorphism of types that holds in the core ML language, but not in second order λ-calculus. This new isomorphism allows to provide a complete (and decidable) axiomatization of all the types isomorphic in ML style languages, a relevant issue for the type as specifications paradigm in library searches. This work is a very extended version of Di Cosmo (1992): we provide both a thorough theoretical treatment of the topic and describe a practical implementation of a library search system so that the paper can be used as a reference both by those interested in the formal theory of ML style languages, and by those simply concerned with implementation issues. The new isomorphism can also be used to extend the usual ML type-inference algorithm, as suggested by Di Cosmo (1992). Building on that proposal, we introduce a better type-inference algorithm that behaves well in the presence of non-functional primitives like references and exceptions. The algorithm described here has been implemented easily as a variation to the Caml-Light 0.4 system.
First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

The development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques.
Identification, annotation and visualisation of extreme changes in splicing from RNA-seq experiments with SwitchSeq

In the past years, RNA sequencing has become the method of choice for the study of transcriptome composition. When working with this type of data, several tools exist to quantify differences in splicing across conditions and to address the significance of those changes. However, the number of genes predicted to undergo differential splicing is often high, and further interpretation of the results becomes a challenging task. Here we present SwitchSeq, a novel set of tools designed to help the users in the interpretation of differential splicing events that affect protein coding genes. More specifically, we provide a framework to identify switch events, i.e., cases where, for a given gene, the identity of the most abundant transcript changes across conditions. The identified events are then annotated by incorporating information from several public databases and third-party tools, and are further visualised in an intuitive manner with the independent R package tviz. All the results are displayed in a self-contained HTML document, and are also stored in txt and json format to facilitate the integration with any further downstream analysis tools. Such analysis approach can be used complementarily to Gene Ontology and pathway enrichment analysis, and can also serve as an aid in the validation of predicted changes in mRNA and protein abundance. Availability: The latest version of SwitchSeq, including installation instructions and use cases, can be found at https://github.com/mgonzalezporta/SwitchSeq. Additionally, the plot capabilities are provided as an independent R package at https://github.com/mgonzalezporta/tviz.
A QR Decomposition Approach for Improved Anomaly Detection over Non-linear Data

Anomalies are patterns that lack normal behavior. Anomaly detection process can be used to predict changes in input patterns and can prevent different malicious activities. Kernel Principal Component Analysis is an effective, anomaly detection technique. This is an effective technique for nonlinear data set. In this, kernel Eigenspace splitting and merging approach is used for predicting the anomalies effectively. Kernel splitting process, can extract smaller KES from larger KES. Here a QR decomposition technique can be used for, KES splitting. This is done to remove the data patterns that no longer fit to the current data distribution.KES splitting is combined with KES update. An adaptive update takes an appropriate sliding window size into consideration and reduces the number of updates required, when a change in the data distribution occurs. Thus an adaptive split-merge KES with QR decomposition, shows a superior performance in predicting anomalies that have a non-linear behavior. General Terms Anomaly detection, KPCA, Non-linear, QR decomposition,
Identification of Myosin Heavy Chain I, IIa and IIx in Canine Skeletal Muscles by an Electrophoretic and Immunoblotting Study

To determine which myosin heavy chain (MHC) isoforms are expressed in canine skeletal muscles, different muscle samples of five mixed-breed dogs were analysed by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). The separated MHC isoforms were identified by immunoblotting technique using a set of specific monoclonal antibodies. To compare the results of the electrophoretic and immunoblotting study, the pattern of MHC isoform expression and histochemical profiles of canine fibres were additionally demonstrated on serial muscle sections by immunohistochemistry and myofibrillar adenosine triphosphatase (mATPase) histochemistry. Not more than three MHC isoforms were demonstrated by SDS-PAGE in the analysed canine muscles. By the immunoblotting technique, the fastest migrating MHC band was identified as slow or MHC-I, the intermediate one as MHC-IIx and the slowest migrating band as MHC-IIa isoform. Since none of the three MHC bands and none of the analysed fibres were recognized by the antibody specific to MHC-IIb of rats, we concluded that MHC-IIb is not expressed in large skeletal muscles of dogs. Similarly, only three major fibre types, i.e. I, IIA and IIX, were revealed according to the pattern of MHC immunohistochemistry and mATPase reaction. Type IIA fibres were more alkali- and acid-stable than type IIX fibres after mATPase histochemistry; hence, the latter corresponded to type IIDog fibres. However, beside the three major fibre types, scarce hybrid fibres co-expressing two MHC isoforms (I/IIA and IIA/IIX) were demonstrated by immunohistochemistry.
Nonparametric Ordinal Classification with Monotonicity Constraints

In many applications of ordinal classification we know that the class label must be increasing (or decreasing) in the attributes. Such relations are called monotone. We discuss two nonparametric approaches to monotone classification: osdl and moca. Our conjecture is that both methods have a tendency to overfit on the training sample, because their basic class probability estimates are often computed on a few observations only. Therefore, we propose to smooth these basic probability estimates by using weighted k nearest neighbour. Through substantial experiments we show how this adjustment improves the classification performance of osdl considerably. The effect on moca on the other hand is less conclusive.
Simple Layout Algorithms To Maintain Network Connectivity Under Faults

A problem of survivable layout of an IP network topology on a wavelength division multiplexed network is considered. Simple layout algorithms are given that have low time complexities. The algorithms are variations of a basic algorithm, that lays out IP links as lightpaths sequentially according to some order and with respect to link costs. Simulations show that the ordering and link costs can significantly affect the performance of the algorithm.
Optimal two‐stage genotyping designs for genome‐wide association scans

The much‐anticipated fixed‐array, genome‐wide SNP genotyping technologies make large‐scale genome‐wide association scans now possible for large numbers of subjects. In this paper we reconsider the problem (Satagopan and Elston [2003] Genet Epidemiol 25:149–157) of optimizing a two‐stage genotyping design to deal with important new issues that are relevant when studies are expanded from candidate gene size to a genome‐wide scale. We investigate how the basic two‐stage genotyping approach, in which all markers are genotyped in an initial group of subjects (stage I) and only the promising markers are genotyped in additional subjects (stage II), can be used to reduce genotyping cost in a genome‐wide case‐control association study even after allowing for much higher per genotype costs using specially designed assays in stage II, compared to the fixed array of SNPs used in stage I. In addition, we consider the problem of using measured SNPs to make (imperfect) prediction of unmeasured SNPs for association tests of all SNPs (measured or unmeasured) genome wide and the utility of expanding genotyping densities in stage II in the regions where significant associations were detected in stage I. Under a set of reasonable but conservative assumptions, we derive optimal two‐stage design configurations (sample sizes and the thresholds of significance in both stages) with these optimal designs depending both on the total number of markers tested and upon the ratios of cost in stage II versus stage I. In addition we show how existing software for power and sample size calculations can be used for the purpose of designing two‐stage studies, for a wide range of assumptions about the number of markers genotyped and the costs of genotyping in each stage of the study. Genet. Epidemiol. 2006. © 2006 Wiley‐Liss, Inc.
Automated Instruction-Set Randomization for Web Applications in Diversified Redundant Systems

The use of diversity and redundancy in the security domain is an interesting approach to prevent or detect intrusions. Many researchers have proposed architectures based on those concepts where diversity is either natural or artificial. These architectures are based on the architecture of N-version programming and were often instantiated for web servers without taking into account the web application(s) running on those. In this article, we present a solution to protect the web applications running on this kind of architectures in order to detect and tolerate code injection intrusions. Our solution consists in creating diversity in the web application scripts by randomizing the language understood by the interpreter so that an injected code can not be executed by all the servers. We also present the issues related to the automatization of our solution and present some solutions to tackle these issues.
Effect of Age, Country, and Gender on Music Listening Preferences.

We tested the music listening preference opinions of 2,042 participants in Greece, Korea, and the United States using an 18 item listening test composed of art music, traditional jazz, and rock music examples. Listener ages ranged from 8 to 18 years, and listener gender was approximately equally distributed in each country. Age, country, and gender were all significant influences on music listening preference, but every variable was involved in significant two and three way interactions. We concluded that considerable caution must be exercised in attempting to apply research findings from one culture to predict music listening preference in another culture.
Rapid quantitation of cardiovascular flow using slice‐selective fourier velocity encoding with spiral readouts

Accurate flow visualization and quantitation is important for the assessment of many cardiovascular conditions such as valvular stenosis and regurgitation. Phase contrast based methods experience partial volume artifacts when flow is highly localized, complex and/or turbulent. Fourier velocity encoding (FVE) avoids such problems by resolving the full velocity distribution within each voxel. This work proposes the use of slice selective FVE with spiral readouts to acquire fully localized velocity distributions in a short breath‐hold. Scan‐plane prescription is performed using classic protocols, and an automatic algorithm is used for in‐plane localization of the flow. Time and spatially‐resolved aortic valve velocity distributions with 26‐msec temporal resolution and 25 cm/sec velocity resolution over a 600 cm/sec field‐of‐view were acquired in a 12‐heartbeat breath‐hold. In carotid studies, scan time was extended to achieve higher spatial resolution. The method was demonstrated in healthy volunteers and patients, and the results compared qualitatively well with Doppler ultrasound. Acquisition time could be reduced to 7 heartbeats (a 42% reduction) using partial Fourier reconstruction along the velocity dimension. Magn Reson Med 57:639–646, 2007. © 2007 Wiley‐Liss, Inc.