Miracle-dz/UniFAR-FTUs huggingface dataset:
EVM as generic QoS trigger for heterogeneous wieless overlay network

Fourth Generation (4G) Wireless System will integrate heterogeneous wireless overlay systems i.e. interworking of WLAN/ GSM/ CDMA/ WiMAX/ LTE/ etc with guaranteed Quality of Service (QoS) and Experience (QoE). QoS(E) vary from network to network and is application sensitive. User needs an optimal mobility solution while roaming in Overlaid wireless environment i.e. user could seamlessly transfer his session/ call to a best available network bearing guaranteed Quality of Experience. And If this Seamless transfer of session is executed between two networks having different access standards then it is called Vertical Handover (VHO). Contemporary VHO decision algorithms are based on generic QoS metrics viz. SNR, bandwidth, jitter, BER and delay. In this paper, Error Vector Magnitude (EVM) is proposed to be a generic QoS trigger for VHO execution. EVM is defined as the deviation of inphase/ quadrature (I/Q) values from ideal signal states and thus provides a measure of signal quality. In 4G Interoperable environment, OFDM is the leading Modulation scheme (more prone to multi-path fading). EVM (modulation error) properly characterises the wireless link/ channel for accurate VHO decision. EVM depends on the inherent transmission impairments viz. frequency offset, phase noise, non-linearimpairment, skewness etc. for a given wireless link. Paper provides an insight to the analytical aspect of EVM & measures EVM (%) for key management subframes like association/re-association/disassociation/ probe request/response frames. EVM relation is explored for different possible NAV-Network Allocation Vectors (frame duration). Finally EVM is compared with SNR, BER and investigation concludes EVM as a promising QoS trigger for OFDM based emerging wireless standards.
Stabilization of networked control systems via non-monotone control ^lyapunov functions

This paper deals with stabilization of networked control systems (NCS) affected by uncertain time-varying delays and data packet dropouts. We point out that such network effects are likely to render the classical control Lyapunov function (CLF) method unfeasible, mainly due to the monotonic decreasing condition. To solve this problem we make use of a discrete-time equivalent of a control Lyapunov-Razumikhin function (CLRF), which is allowed to be non-monotone. The corresponding stabilizing control law is obtained by solving an optimization problem on-line, in a receding horizon manner, which incorporates the available knowledge about the past state/input trajectory. Furthermore, we provide extra flexibility to the CLRF via a relaxation variable, which is needed to handle hard state/input constraints. We also propose an effective method for dealing with delays larger than the sampling period by means of an on-line Minkowski set addition. This makes it possible to guarantee stability even in the presence of data packet dropouts, under certain assumptions.
Liberalizing protocols for argumentation in multi-agent systems

This publication summarizes research on the the design and implementation of liberalized version of existing truth-finding protocols for argumentation, such as the standard two-agent immediate-response protocol for computing the credulous acceptance of conclusions in an argument system. In the new setup agents decide autonomously which issues need to be discussed, when to query other agents, when to keep on querying other agents, and when to settle for an answer. In this way, inter-agent disputes are regulated by the agents themselves, rather than that they follow an outlined protocol. The paper concludes with a prototype implementation and with a comparison of related work on conversation analysis and computational dialectic.
Formation of a Unique Cluster of G-Quadruplex Structures in the HIV-1 nef Coding Region: Implications for Antiviral Activity

G-quadruplexes are tetraplex structures of nucleic acids that can form in G-rich sequences. Their presence and functional role have been established in telomeres, oncogene promoters and coding regions of the human chromosome. In particular, they have been proposed to be directly involved in gene regulation at the level of transcription. Because the HIV-1 Nef protein is a fundamental factor for efficient viral replication, infectivity and pathogenesis in vitro and in vivo, we investigated G-quadruplex formation in the HIV-1 nef gene to assess the potential for viral inhibition through G-quadruplex stabilization. A comprehensive computational analysis of the nef coding region of available strains showed the presence of three conserved sequences that were uniquely clustered. Biophysical testing proved that G-quadruplex conformations were efficiently stabilized or induced by G-quadruplex ligands in all three sequences. Upon incubation with a G-quadruplex ligand, Nef expression was reduced in a reporter gene assay and Nef-dependent enhancement of HIV-1 infectivity was significantly repressed in an antiviral assay. These data constitute the first evidence of the possibility to regulate HIV-1 gene expression and infectivity through G-quadruplex targeting and therefore open a new avenue for viral treatment.
The Dopaminergic Reward System and Leisure Time Exercise Behavior: A Candidate Allele Study

Purpose. Twin studies provide evidence that genetic influences contribute strongly to individual differences in exercise behavior. We hypothesize that part of this heritability is explained by genetic variation in the dopaminergic reward system. Eight single nucleotide polymorphisms (SNPs in DRD1: rs265981, DRD2: rs6275, rs1800497, DRD3: rs6280, DRD4: rs1800955, DBH: rs1611115, rs2519152, and in COMT: rs4680) and three variable number of tandem repeats (VNTRs in DRD4, upstream of DRD5, and in DAT1) were investigated for an association with regular leisure time exercise behavior. Materials and Methods. Data on exercise activities and at least one SNP/VNTR were available for 8,768 individuals aged 7 to 50 years old that were part of the Netherlands Twin Register. Exercise behavior was quantified as weekly metabolic equivalents of task (MET) spent on exercise activities. Mixed models were fitted in SPSS with genetic relatedness as a random effect. Results. None of the genetic variants were associated with exercise behavior (P > .02), despite sufficient power to detect small effects. Discussion and Conclusions. We did not confirm that allelic variants involved in dopaminergic function play a role in creating individual differences in exercise behavior. A plea is made for large genome-wide association studies to unravel the genetic pathways that affect this health-enhancing behavior.
Axiomatizing Prefix Iteration with Silent Steps

Prefix iteration is a variation on the original binary version of the Kleene star operationP*Q, obtained by restricting the first argument to be an atomic action. The interaction of prefix iteration with silent steps is studied in the setting of Milner's basic CCS. Complete equational axiomatizations are given for four notions of behavioural congruence over basic CCS with prefix iteration, viz., branching congruence,? -congruence, delay congruence, and weak congruence. The completeness proofs for? -, delay, and weak congruence are obtained by reduction to the completeness theorem for branching congruence. It is also argued that the use of the completeness result for branching congruence in obtaining the completeness result for weak congruence leads to a considerable simplification with respect to the only direct proof presented in the literature. The preliminaries and the completeness proofs focus on open terms, i.e., terms that may contain process variables. As a by-product, the? -completeness of the axiomatizations is obtained, as well as their completeness for closed terms.
A Threshold Anonymous Authentication Protocol for VANETs

Vehicular ad hoc networks (VANETs) have recently received significant attention in improving traffic safety and efficiency. However, communication trust and user privacy still present practical concerns to the deployment of VANETs, as many existing authentication protocols for VANETs either suffer from the heavy workload of downloading the latest revocation list from a remote authority or cannot allow drivers on the road to decide the trustworthiness of a message when the authentication on messages is anonymous. In this paper, to cope with these challenging concerns, we propose a new authentication protocol for VANETs in a decentralized group model by using a new group signature scheme. With the assistance of the new group signature scheme, the proposed authentication protocol is featured with threshold authentication, efficient revocation, unforgeability, anonymity, and traceability. In addition, the assisting group signature scheme may also be of independent interest, as it is characterized by efficient traceability and message linkability at the same time. Extensive analyses indicate that our proposed threshold anonymous authentication protocol is secure, and the verification of messages among vehicles can be accelerated by using batch message processing techniques.
Matrix Factorization+ for Movie Recommendation

We present a novel model for movie recommendations using additional visual features extracted from pictural data like posters and still frames, to better understand movies. In particular, several context-based methods for recommendation are shown to be special cases of our proposed framework. Unlike existing context-based approaches, our method can be used to incorporate visual features - features that are lacking in existing context-based approaches for movie recommendations. In reality, movie posters and still frames provide us with rich knowledge for understanding movies as well as users' preferences. For instance, user may want to watch a movie at the minute when she/he finds some released posters or still frames attractive. Unfortunately, such unique features cannot be revealed from rating data or other forms of context being used in most of existing methods. In this paper, we take a step forward in this direction and investigate both low-level and high-level visual features from the movie posters and still frames for further improvement of recommendation methods. Extensive experiments on real world datasets show that our approach leads to significant improvement over several state-of-the-art methods.
NTT's Question Answering System for NTCIR-6 QAC-4

NTCIR-6 QAC-4 organizers announced that there would be no restriction (such as factoid) on QAC4 questions, but they plan to include many ‘definition’ questions and ‘why’ questions. Therefore, we focused on these two question types. For ‘definition’ questions, we used a simple pattern-based approach. For ‘why’ questions, hand-crafted rules were used in previous work for answer candidate extraction [5]. However, such rules greatly depend on developers’ intuition and are costly to make. We adopt a supervised machine learning approach. We collected causal expressions from the EDR corpus and trained a causal expression classifier, integrating lexical, syntactic, and semantic features. The experimental results show that our system is effective for ‘why’ and ‘definition’ questions.
Novel Structurally Designed Vaccine for S. aureus α-Hemolysin: Protection against Bacteremia and Pneumonia

Staphylococcus aureus (S. aureus) is a human pathogen associated with skin and soft tissue infections (SSTI) and life threatening sepsis and pneumonia. Efforts to develop effective vaccines against S. aureus have been largely unsuccessful, in part due to the variety of virulence factors produced by this organism. S. aureus alpha-hemolysin (Hla) is a pore-forming toxin expressed by most S. aureus strains and reported to play a key role in the pathogenesis of SSTI and pneumonia. Here we report a novel recombinant subunit vaccine candidate for Hla, rationally designed based on the heptameric crystal structure. This vaccine candidate, denoted AT-62aa, was tested in pneumonia and bacteremia infection models using S. aureus strain Newman and the pandemic strain USA300 (LAC). Significant protection from lethal bacteremia/sepsis and pneumonia was observed upon vaccination with AT-62aa along with a Glucopyranosyl Lipid Adjuvant-Stable Emulsion (GLA-SE) that is currently in clinical trials. Passive transfer of rabbit immunoglobulin against AT-62aa (AT62-IgG) protected mice against intraperitoneal and intranasal challenge with USA300 and produced significant reduction in bacterial burden in blood, spleen, kidney, and lungs. Our Hla-based vaccine is the first to be reported to reduce bacterial dissemination and to provide protection in a sepsis model of S. aureus infection. AT62-IgG and sera from vaccinated mice effectively neutralized the toxin in vitro and AT62-IgG inhibited the formation of Hla heptamers, suggesting antibody-mediated neutralization as the primary mechanism of action. This remarkable efficacy makes this Hla-based vaccine a prime candidate for inclusion in future multivalent S. aureus vaccine. Furthermore, identification of protective epitopes within AT-62aa could lead to novel immunotherapy for S. aureus infection.
Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmission requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.
Self-referral psychological treatment centre for young adults: a 2-year observational evaluation of routine practice before and after treatment

Objectives To examine a self-referral psychological service provided to young adults with regard to effects on anxiety, depression and psychological distress and to explore client factors predicting non-adherence and non-response. Design Observational study over a 2-year period. Setting Young Adults Centre providing psychological services by self-referral (preprimary care) to Linköping, Åtvidaberg, and Kinda municipalities (combined population 145 000) in Östergötland county, Sweden. Participants 607 young adults (16–25 years of age); 71% females (n=429). Intervention Individually scheduled cognitive behavioural therapy delivered in up to six 45 min sessions structured according to an assessment of the client's mental health problems: anxiety, depression, anxiety and depression combined, or decreased distress without specific anxiety or depression. Primary outcome measures Pre–post intervention changes in psychological distress (General Health Questionnaire-12, GHQ-12), Hospital Anxiety and Depression Scale Anxiety/Depression (HADS-A/D). Results 192 clients (32.5%) discontinued the intervention on their own initiative and 39 clients (6.6%) were referred to a psychiatric clinic during the course of the intervention. Intention-to-treat analyses including all clients showed a medium treatment effect size (d=0.64) with regard to psychological distress, and small effect sizes were observed with regard to anxiety (d=0.58) and depression (d=0.57). Restricting the analyses to clients who adhered to the agreed programme, a large effect size (d=1.26) was observed with regard to psychological distress, and medium effect sizes were observed with regard to anxiety (d=1.18) and depression (d=1.19). Lower age and a high initial HADS-A score were the strongest risk factors for non-adherence, and inability to concentrate and thinking of oneself as a worthless person increased the risk for discontinuation. Conclusions We conclude that provision of psychological services to young people through a self-referral centre has potential to improve long-term mental health in communities, but management of non-adherence remains a central challenge.
Stochastic Calculus for Dirichlet Processes

Using time-reversal, we introduce the stochastic integration for zero-energy additive functionals of symmetric Markov processes, which extends an early work of S. Nakao. Various properties of such stochastic integrals are discussed and a Ito formula for Dirichlet processes are obtained. AMS 2000 Mathematics Subject Classification: Primary 31C25; Secondary 60J57, 60J55, 60H05.
Quantification of Cell Adhesion Force with Atomic Force Microscope

Cell adhesion to cells or substrates is the basis for cell colonization and survival. Based on atomic force microscope (AFM), a single probe was used to quantitatively measure the adhesion of different cell types. The self-made pneumatic control system produces negative pressure to fix the cells, replacing the traditional chemical fixation method, reducing the fixation time from about 30 min to a few seconds, and a single probe can test about 15 cells, greatly improving single-cell force spectroscopy test speed. As a result, statistical data can be quickly recorded, the range of cells tested can be expanded, and the physiological activity of the cells during the test is preserved. In this paper, we studied the adhesion of three different mammalian cells to polystyrene substrates, and indicated that the adhesion forces depend on the probe retraction speed and the contact time: the adhesion forces are proportional to the logarithm of the probe retraction speed and increase with increasing contact time.
Optimal Asset Allocation using Adaptive Dynamic Programming

In recent years, the interest of investors has shifted to computerized asset allocation (portfolio management) to exploit the growing dynamics of the capital markets. In this paper, asset allocation is formalized as a Markovian Decision Problem which can be optimized by applying dynamic programming or reinforcement learning based algorithms. Using an artificial exchange rate, the asset allocation strategy optimized with reinforcement learning (Q-Learning) is shown to be equivalent to a policy computed by dynamic programming. The approach is then tested on the task to invest liquid capital in the German stock market. Here, neural networks are used as value function approximators. The resulting asset allocation strategy is superior to a heuristic benchmark policy. This is a further example which demonstrates the applicability of neural network based reinforcement learning to a problem setting with a high dimensional state space.
Kinomer v. 1.0: a database of systematically classified eukaryotic protein kinases

The regulation of protein function through reversible phosphorylation by protein kinases and phosphatases is a general mechanism controlling virtually every cellular activity. Eukaryotic protein kinases can be classified into distinct, well-characterized groups based on amino acid sequence similarity and function. We recently reported a highly sensitive and accurate hidden Markov model-based method for the automatic detection and classification of protein kinases into these specific groups. The Kinomer v. 1.0 database presented here contains annotated classifications for the protein kinase complements of 43 eukaryotic genomes. These span the taxonomic range and include fungi (16 species), plants (6), diatoms (1), amoebas (2), protists (1) and animals (17). The kinomes are stored in a relational database and are accessible through a web interface on the basis of species, kinase group or a combination of both. In addition, the Kinomer v. 1.0 HMM library is made available for users to perform classification on arbitrary sequences. The Kinomer v. 1.0 database is a continually updated resource where direct comparison of kinase sequences across kinase groups and across species can give insights into kinase function and evolution. Kinomer v. 1.0 is available at http://www.compbio. dundee.ac. uk/kinomer/.
Binary-tree-based high speed packet classification system on FPGA

In the network intrusion detection system (NIDS), there is a limitation on the speed of software-based packet classification because of the processor performance, the serial program execution and so on. It has become a great challenge to develop scalable solutions for next-generation packet classification that support higher throughput, larger rule sets and more packet header fields. For low-cost high performance embedded networking applications, the best solution could be doing packet classification by special designed hardware, which can effectively release the burden of system CPU. In order to improve the speed of packet classification, exhibit good memory performance and support quick rule update, a high-speed packet classification system based on FPGA is proposed in this paper. Taking advantage of parallel processing, pipeline and hardware circuit, the throughput has been improved greatly; defining the size of the tree nodes to be binary tree, the memory usage can be more efficient. The binary tree structure is generated through pre-processing on computer, which does not influence the searching speed of FPGA. During the packet header division, the division field is dynamic and selected according to the rules. The experimental results show that the pre-processing time for 50000 rules is shorter than 0.051s, the average speed of rule-header classification for Snort IDS is higher than 10 Gbps.