Miracle-dz/UniFAR-FTUs huggingface dataset:
Major Histocompatibility Complex Class II Dextramers : New Tools for the Detection of antigen-Specific , CD 4 T Cells in Basic and Clinical Research

The advent of major histocompatibility complex (MHC) tetramer technology has been a major contribution to T cell immunology, because tetramer reagents permit detection of antigen-specific T cells at the single-cell level in heterogeneous populations by flow cytometry. However, unlike MHC class I tetramers, the utility of MHC class II tetramers has been less frequently reported. MHC class II tetramers can be used successfully to enumerate the frequencies of antigen-specific CD4 T cells in cells activated in vitro, but their use for ex vivo analyses continues to be a problem, due in part to their activation dependency for binding with T cells. To circumvent this problem, we recently reported the creation of a new generation of reagents called MHC class II dextramers, which were found to be superior to their counterparts. In this review, we discuss the utility of class II dextramers vis-a-vis tetramers, with respect to their specificity and sensitivity, including potential applications and limitations.
A new two-stages PSO algorithm for railway trajectory planning on GIS system

Smart railway trajectory planning is one of the most interesting and important topics in the fields of intelligent railway transportation, computer science and evolutionary computation, etc. Several objectives and constraints of railway trajectory planning problem are closely related to the distance factor, the construction cost, the altitude factor and the comfortability, etc. More importantly, the new particle swarm optimization (PSO) algorithm, including two optimization substages, firstly optimize the coordinate in y-axis and secondly optimize the latitude in z-axis to design the optimal 3-D railway trajectory. Numerical results highlight that the new two-stages PSO algorithm can provide one realistic optimal trajectory from any two cities in the complicated three-dimensional GIS scenario, such as Chang Sha city and Nan Chang city.
Information Spreading in Dynamic Networks

We study the fundamental problem of information spreading (also known as gossip) in dynamic networks. In gossip, or more generally, $k$-gossip, there are $k$ pieces of information (or tokens) that are initially present in some nodes and the problem is to disseminate the $k$ tokens to all nodes. The goal is to accomplish the task in as few rounds of distributed computation as possible. The problem is especially challenging in dynamic networks where the network topology can change from round to round and can be controlled by an on-line adversary. The focus of this paper is on the power of token-forwarding algorithms, which do not manipulate tokens in any way other than storing and forwarding them. We first consider a worst-case adversarial model first studied by Kuhn, Lynch, and Oshman~\cite{kuhn+lo:dynamic} in which the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors for the current round are before they broadcast their messages. Our main result is an $\Omega(nk/\log n)$ lower bound on the number of rounds needed for any deterministic token-forwarding algorithm to solve $k$-gossip. This resolves an open problem raised in~\cite{kuhn+lo:dynamic}, improving their lower bound of $\Omega(n \log k)$, and matching their upper bound of $O(nk)$ to within a logarithmic factor. We next show that token-forwarding algorithms can achieve subquadratic time in the offline version of the problem where the adversary has to commit all the topology changes in advance at the beginning of the computation, and present two polynomial-time offline token-forwarding algorithms. Our results are a step towards understanding the power and limitation of token-forwarding algorithms in dynamic networks.
Measuring security requirements for software security

For the last decade's software security has gained attention by industries, experts and all other communities. Secure software is about mitigating risks from assets to achieve business goals. Security is highly depending on the context where software is deployed. But measuring software security even within a specific context is still not mature. This is because properties and metrics for measuring security are not properly defined and methods are lacking to provide a complete picture for measuring software security. Here we identify security requirements through asset based risk management process to describe soft ware security goal. Then based on the Goal-Question-Metric approach the identified security requirements are evaluated for measuring software security.
Mobile Application Market : A Developer ’ s Perspective

Major software companies, such as Apple and Google, are disturbing the relatively safe and established actors of the mobile application business. These newcomers have caused significant structural changes in the market by imposing and enforcing their own rules for the future of mobile application developments. The implications of these changes do not only concern the mobile network operators and mobile phone manufacturers. This changed environment also brings additional opportunities and constraints for current mobile application developers. Therefore, developers need to assess what their options are and how they can take advantages of these current trends. In this paper, we take a developer’s perspective in order to explore how the identified trends will impact the mobile application development markets. Our preliminary analysis lead us to suggest eight propositions which summarize our findings and can be the starting points for future empirical research.
Differential Semantics of Intervention in Bayesian Networks

Differentiation is an important inference method in Bayesian networks and intervention is a basic notion in causal Bayesian networks. In this paper, we reveal the connection between differentiation and intervention in Bayesian networks. We first encode an intervention as changing a conditional probabilistic table into a partial intervention table. We next introduce a jointree algorithm to compute the full atomic interventions of all nodes with respect to evidence in a Bayesian network. We further discover that an intervention has differential semantics if the intervention variables can reach the evidence in Bayesian networks and the output of the state-of-the-art algorithm is not the differentiation but the intervention of a Bayesian network if the differential nodes cannot reach any one of the evidence nodes. Finally, we present experimental results to demonstrate the efficiency of our algorithm to infer the causal effect in Bayesian networks.
A frequency-domain approach for all-digital CMOS ultra wideband receivers

In this work, we propose a new approach to process received signals in the frequency-domain, which opens the possibility for CMOS implementation of all-digital ultra wideband receivers. The key idea for the proposed method is to extract the frequency components of the received signal and to perform signal processing in the frequency domain. The proposed receiver architecture relaxes the speed requirement of analog-to-digital converters and is highly suitable for a multipath rich environment such as UWB. Our simulation results indicate that the proposed receiver improves the SNR by about 3 dB at BER=10/sup -1/ when compared with an analog receiver under multi-path channel conditions.
Surface EMG model for Tibialis Anterior muscle with experimentally based simulation parameters

An Electromyogram model for the Tibialis Anterior muscle with novel firing rate equation, recruitment threshold, and spatial localisation of muscle fibres has been designed. This model has used a parallel-fibred volume conductor equation for this pennate muscle. Surface EMG was simulated using the experimental based parameters. Eight healthy subjects performed isometric dorsiflexion at 10, 20, 30, 50, 75 and 100% maximal voluntary contractions. To validate this model, the root mean square (RMS) and median frequency (MNF) were computed for both the experimental and simulated EMG. The rate of change in normalised EMG RMS and median frequency with MVC was statistically analysed using ANOCOVA between experimental and simulated signal. The gradients were found to be similar (p> 0.05), suggesting that the parallel fibred TA muscle model is suitable for analysing changes in the EMG amplitude and median frequency with MVC.
Clinical Effectiveness and Cost-Effectiveness of HIV Pre-Exposure Prophylaxis in Men Who Have Sex with Men: Risk Calculators for Real-World Decision-Making

Background Oral pre-exposure prophylaxis (PrEP) can be clinically effective and cost-effective for HIV prevention in high-risk men who have sex with men (MSM). However, individual patients have different risk profiles, real-world populations vary, and no practical tools exist to guide clinical decisions or public health strategies. We introduce a practical model of HIV acquisition, including both a personalized risk calculator for clinical management and a cost-effectiveness calculator for population-level decisions. Methods We developed a decision-analytic model of PrEP for MSM. The primary clinical effectiveness and cost-effectiveness outcomes were the number needed to treat (NNT) to prevent one HIV infection, and the cost per quality-adjusted life-year (QALY) gained. We characterized patients according to risk factors including PrEP adherence, condom use, sexual frequency, background HIV prevalence and antiretroviral therapy use. Results With standard PrEP adherence and national epidemiologic parameters, the estimated NNT was 64 (95% uncertainty range: 26, 176) at a cost of $160,000 (cost saving, $740,000) per QALY – comparable to other published models. With high (35%) HIV prevalence, the NNT was 35 (21, 57), and cost per QALY was $27,000 (cost saving, $160,000), and with high PrEP adherence, the NNT was 30 (14, 69), and cost per QALY was $3,000 (cost saving, $200,000). In contrast, for monogamous, serodiscordant relationships with partner antiretroviral therapy use, the NNT was 90 (39, 157) and cost per QALY was $280,000 ($14,000, $670,000). Conclusions PrEP results vary widely across individuals and populations. Risk calculators may aid in patient education, clinical decision-making, and cost-effectiveness evaluation.
Classifying text documents using unconventional representation

Classification of text documents is one of the most common themes in the field of machine learning. Although a text document expresses a wide range of information, but it lacks the imposed structure of tradition database. Thus, unstructured data, particularly free running text data has to be transferred into a structured data. Hence, in this paper we represent the text document unconventionally by making use of symbolic data analysis concepts. We propose a new method of representing documents based on clustering of term frequency vectors. Term frequency vectors of each cluster are used to form a symbolic representation by the use of Mean and Standard Deviation. Further, term frequency vectors are used in the form a interval valued features. To cluster the term frequency vectors, we make use of Single Linkage, Complete Linkage, Average Linkage, K-Means and Fuzzy C-Means clustering algorithms. To corroborate the efficacy of the proposed model we conducted extensive experimentations on standard datasets like 20 Newsgroup Large, 20 Mini Newsgroup, Vehicles Wikipedia datasets and our own created datasets like Google Newsgroup and Research Article Abstracts. Experimental results reveal that the proposed model gives better results when compared to the state of the art techniques. In addition, as the method is based on a simple matching scheme, it requires a negligible time.
ESTIMATING APP DEMAND FROM PUBLICLY AVAILABLE DATA

With the abundance in variety of products available online, many online retailers provide sales rankings for available products to make it easier for the consumer to find popular products. Successful implementation of product rankings on online platform was done a decade ago by Amazon and more recently by Apple’s App store. However, none of these market providers provide actual downloads data, a very useful statistics for both practitioners and researchers. To address similar issues, researchers in the past developed strategies to estimate sales from product rank. Almost all of that work is based on either doing some experiments to shift sales or partnering with a vendor to get actual sales data. In this research, we present an innovative method to use purely public data to infer this relationship for Apple’s iTunes App store. We provide various validations to show our method provides highly accurate estimates on downloads if rank data in available for a given app.
How to Improve Multi-Agent Recommendations Using Data from Social Networks?

User profiles have an important role in multi-agent recommender systems. The information stored in them improves the system’s generated recommendations. Multi-agent recommender systems learn from previous recommendations to update users’ profiles and improve next recommendations according to the user feedback. However, when the user does not evaluate the recommendations the system may deliver poor recommendations in the future. This paper presents a mechanism that explores user information from social networks to update the user profile and to generate implicit evaluations on behalf of the user. The mechanism was validated with travel packages recommendations and some preliminary results illustrate how user information gathered from social networks may help to improve recommendations in multiagent recommender systems.
Single stream parallelization of generalized LSTM-like RNNs on a GPU

Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data. However, they suffer from long training time, which demands parallel implementations of the training procedure. Parallelization of the training algorithms for RNNs are very challenging because internal recurrent paths form dependencies between two different time frames. In this paper, we first propose a generalized graph-based RNN structure that covers the most popular long short-term memory (LSTM) network. Then, we present a parallelization approach that automatically explores parallelisms of arbitrary RNNs by analyzing the graph structure. The experimental results show that the proposed approach shows great speed-up even with a single training stream, and further accelerates the training when combined with multiple parallel training streams.
Graduated automation for humanoid manipulation

The challenges posed by humanoid robotic manipulation in complex, remote environments have shown that fully-autonomous and fully-teleoperated approaches are impractical. In real-world environments, one encounters rapidly changing situations, localization drift, and other errors that break autonomy. Pure teleoperation is impractical due to the sheer number of degrees-of-freedom, real-time constraints such as balancing, and potentially low-bandwidth or unreliable communications between operator and platform. To address these issues, the operator must be able to seamlessly interact with the platform across multiple levels of abstraction. In this paper we present a manipulation framework underpinned by a novel system for applying graduated levels of automation to humanoid control. The level of automation used by the operator is dictated both by their preference as well as the system's own confidence in succeeding autonomously. We present experimental results using the Boston Dynamics Atlas humanoid platform.
Devising Strain Hardening Models Using Kocks–Mecking Plots—A Comparison of Model Development for Titanium Aluminides and Case Hardening Steel

The present study focuses on the development of strain hardening models taking into account the peculiarities of titanium aluminides. In comparison to steels, whose behavior has been studied extensively in the past, titanium aluminides possess a much larger initial work hardening rate, a sharp peak stress and pronounced softening. The work hardening behavior of a TNB-V4 (Ti–44.5Al–6.25Nb–0.8Mo–0.1B) alloy is studied using isothermal hot compression tests conducted on a Gleeble 3500 simulator, and compared to the typical case hardening steel 25MoCrS4. The behavior is analyzed with the help of the Kocks-Mecking plots. In contrast to steel the TNB-V4 alloy shows a non-linear course of θ (i.e., no stage-III hardening) initially and exhibits neither a plateau (stage IV hardening) nor an inflection point at all deformation conditions. The present paper describes the development and application of a methodology for the design of strain hardening models for the TNB-V4 alloy and the 25CrMoS4 steel by taking the course of the Kocks-Mecking plots into account. Both models use different approaches for the hardening and softening mechanisms and accurately predict the flow stress over a wide range of deformation conditions. The methodology may hence assist in further developments of more sophisticated physically-based strain hardening models for TiAl-alloys.
A Verifiable Multiple-Players Enrollment Protocol for Threshold Schemes

In this paper a verifiable multiple-players enrollment protocol for threshold schemes is presented. The protocol allows distributing new shares to a group of new enrollment players, but old shareholders need not change their shares. The dishonest behavior of old shareholders can be detected and the new enrollment players can assure to get the right shares. The new protocol is very efficient compared with other schemes. It only needs 4t + n' - 2 times broadcast when all players are honest, where t is the threshold value and n' is the number of enrolling players. Furthermore, the broadcasting data are very few in this protocol. Finally, we give the security and performance analysis of the presented protocol
Pipeline Exploration for Reconfigurable Targets

The ability to compile software into an efficient, synthesisable hardware description can dramatically reduce the design times for embedded systems that require the acceleration offered by hardware coprocessors. Most hardware compilers apply loop pipelining to increase the parallelism achieved, but pipelining is restricted to the only innermost level in a nested loop. Methods have been developed for software compilers that extend loop pipelining to levels above the innermost loop but they have yet to be extended for hardware. In this work we extend and adapt an existing outer loop pipelining approach known as Single Dimension Software Pipelining to generate schedules for FPGA hardware coprocessors. Each loop level in four test loops is pipelined and the resulting schedules are implemented in VHDL and targeted to an Altera Stratix II FPGA. The results show that the fastest solution for each loop occurs when pipelining is applied one or two levels above the innermost loop. Across the four test loops we achieve an acceleration over the innermost loop solution of between 1.15 and 7 times, with a mean speedup of 2.56 times. The results suggest that inclusion of outer loop pipelining in future hardware compilers may be worthwhile as it can allow significantly improved results to be achieved at the cost of a small increase in compile time.
THUNLP at TAC KBP 2011 in Entity Linking

Entity Linking is to link a name string from plain-text documents to the corresponding entry in given knowledge base. In this paper we demonstrate our entity linking system for TAC KBP 2011 Track. Our system implements pairwise and listwise learning to rank methods to create a ranking list of candidates with several kinds of features, including context similarity, term frequency, key entity extraction and WikiPage information. We participate in entity linking and cross-lingual entity linking task. We use random forest to validate the top 1 candidate recommended by our system. We achieve a performance of 72.9% F1 measure for both two tasks.
A Power Consumption Benchmark Framework for Ontology Reasoning on Android Devices

Ontology reasoning, in particular query answering with Description Logics-based ontologies, is a power-consuming task, especially in mobile settings where such a resource is limited and shared by other processes. To be able to determine whether a reasoning task will be consuming significant amount of power, a benchmark framework will be needed for ontology designers and reasoner developers. In this paper, we report our work on a power consumption benchmark framework that can be used to evaluate and compare the level of battery consumption of different Description Logics reasoners on Android devices. To calculate the power consumption of the reasoners, we measured the current flow and the voltage of the integrated battery. We focus on Android-based devices and evaluate the framework using 3 popular DL reasoners.
Quantitative Trait Loci for Salinity Tolerance Identified under Drained and Waterlogged Conditions and Their Association with Flowering Time in Barley (Hordeum vulgare. L)

Introduction Salinity is one of the major abiotic stresses affecting crop production via adverse effects of osmotic stress, specific ion toxicity, and stress-related nutritional disorders. Detrimental effects of salinity are also often exacerbated by low oxygen availability when plants are grown under waterlogged conditions. Developing salinity-tolerant varieties is critical to overcome these problems, and molecular marker assisted selection can make breeding programs more effective. Methods In this study, a double haploid (DH) population consisting of 175 lines, derived from a cross between a Chinese barley variety Yangsimai 1 (YSM1) and an Australian malting barley variety Gairdner, was used to construct a high density molecular map which contained more than 8,000 Diversity Arrays Technology (DArT) markers and single nucleotide polymorphism (SNP) markers. Salinity tolerance of parental and DH lines was evaluated under drained (SalinityD) and waterlogged (SalinityW) conditions at two different sowing times. Results Three quantitative trait loci (QTL) located on chromosome 1H, single QTL located on chromosomes 1H, 2H, 4H, 5H and 7H, were identified to be responsible for salinity tolerance under different environments. Waterlogging stress, daylight length and temperature showed significant effects on barley salinity tolerance. The QTL for salinity tolerance mapped on chromosomes 4H and 7H, QSlwd.YG. 4H, QSlwd.YG. 7H and QSlww.YG. 7H were only identified in winter trials, while the QTL on chromosome 2H QSlsd.YG. 2H and QSlsw.YG. 2H were only detected in summer trials. Genes associated with flowering time were found to pose significant effects on the salinity QTL mapped on chromosomes 2H and 5H in summer trials. Given the fact that the QTL for salinity tolerance QSlsd.YG. 1H and QSlww.YG. 1H-1 reported here have never been considered in the literature, this warrants further investigation and evaluation for suitability to be used in breeding programs.
Class Outliers Mining: Distance-Based Approach

In large datasets, identifying exceptional or rare cases with respect to a group of similar cases is considered very significant problem. The traditional problem (Outlier Mining) is to find exception or rare cases in a dataset irrespective of the class label of these cases, they are considered rare events with respect to the whole dataset. In this research, we pose the problem that is Class Outliers Mining and a method to find out those outliers. The general definition of this problem is "given a set of observations with class labels, find those that arouse suspicions, taking into account the class labels". We introduce a novel definition of Outlier that is Class Outlier, and propose the Class Outlier Factor (COF) which measures the degree of being a Class Outlier for a data object. Our work includes a proposal of a new algorithm towards mining of the Class Outliers, presenting experimental results applied on various domains of real world datasets and finally a comparison study with other related methods is performed. Keywords—Class Outliers, Distance-Based Approach, Outliers Mining.