Miracle-dz/UniFAR-FTUs huggingface dataset:
CPP, a selective N-methyl-D-aspartate (NMDA)-type receptor antagonist: characterization in vitro and in vivo.

3-(2-Carboxypiperazin-4-yl)propyl-1-phosphonic acid (CPP) was synthesized as a rigid analog of 2-amino-7-phosphonoheptanoate, a previously known antagonist at the N-methyl-D-aspartate (NMDA) preferring, or NMDA-type, of excitatory amino acid receptor. CPP was found to be a potent, selective and competitive antagonist of NMDA-type receptors. CPP antagonized with an IC50 of 8 muM [3H]ACh release which was evoked from rat striatal brain slices by NMDA (50 muM). In contrast, the release of [3H]ACh evoked by elevated KCI was not inhibited by CPP even at a concentration of 100 muM. The antagonism by CPP of NMDA-evoked [3H]ACh release was competitive, with a pA2 of 5.66 for CPP, compared with a pA2 value of 5.22 for 2-amino-7-phosphonoheptanoate. CPP affected neither the uptake of L-[3H]glutamate nor the inhibition by aconitine of L-[3H]glutamate uptake, suggesting a lack of membrane-stabilizing or local anesthetic effects, and also suggesting that CPP itself may not be taken up through the L-glutamate membrane transporter. Moreover, [3H] CPP was not accumulated by synaptosomes (P2 fraction) which avidly accumulate L-[3H]glutamate, supporting the concept that this NMDA-type receptor antagonist acts at an NMDA-type receptor on the external surface of the plasma membrane. CPP (10 muM) failed to interact with any of 21 other putative neurotransmitter receptors including alpha-[3H]amino-3-hydroxy-5-methylisoxazole-4-propionic acid binding (quisqualate-type receptor) and [3H]kainate binding (kainate-type receptor). Audiogenic convulsions in DBA/2 mice were blocked by CPP (ED50 = 1.5 mg/kg i.p.) as were NMDA-induced seizures in CF-1 mice (ED50 = 1.9 mg/kg i.p.). In both strains, CPP impaired the traction reflex at higher doses (ED50 = 6.8 mg/kg and 6.1 mg/kg and 6.1 mg/kg i.p. for DBA/2 and CF-1, respectively). The traction reflex impairment by CPP may be due to muscle relaxant effects of the compound, an explanation supported by the finding that CPP reduced muscle tone as assessed by electromyogram measurement in animals whose muscle tone had been increased by opiate administration. Finally, cerebellar cyclic GMP levels, known to be sensitive to neurotransmission via NMDA-type receptors, were decreased by CPP (ED50 = 4.7 mg/kg i.p.) in mice. In conclusion, based upon the competitive antagonism by CPP of NMDA-evoked [3H] ACh release in vitro and the antagonism of NMDA-induced convulsions in vivo, the data presented are consistent with competitive antagonism of NMDA-type receptors.
Video viewing system Optimization of Video Viewing System with Real-time Video Transcoding

In the past years the rapid development of smartphones and mobile telecommunications technologies (3G, 4G) enabled users to easily access multimedia content using mobile devices. High quality videos which are streamed from the Internet are power consuming and demand significant amounts of network resources. Viewers access video content with various devices which differ in screen resolution and screen size. Current video streaming systems don't provide the optimal video quality which depends on the device that is accessing the video and is limited with the network conditions at the time of streaming. In this paper, the overview of current video viewing system is made. In order to provide the best video quality based on network conditions and device properties while saving power and network resources, video transcoding system is proposed. Keywords—transcoding; quality of experience; mobile video; power consumption; video streaming
Multi-Engine Machine Translation as a Lifelong Machine Learning Problem

We describe an approach for multi-engine machine translation that uses machine learning methods to train one or several classifiers for a given set of candidate translations. Contrary to existing approaches in quality estimation which only consider a single translation at a time, we explicitly model pairwise comparison with our feature vectors. We discuss several challenges our method is facing and discuss how lifelong machine learning could be applied to resolve these. We also show how the proposed architecture can be extended to allow human feedback to be included into the training process, improving the system’s selection process over time.
Gate voltage dependence of 1/f noise in carbon nanotubes with the different metal contacts

Nano carbon devices are promising for the next generation devices. Recently, a single-layer graphene has also been developed as nano-devices. For the device applications, there are several reasons why we should control the noise. Of course, the reduction of noise is one of strategies for nano devices as the noise might be increases for nano size devices including Si devices. On the other hand, noise is sometimes useful for the enhancements of the small signal operation such as stochastic resonance. In this case, noise can help the operation of the device as transistors. However, for the stochastic resonance devices, noise control is also important for the actual operations. We also reported the gate voltage control of stochastic resonance in carbon nanotubes field effect transistor (CNT-FET). On the other hand, as the device structures, the electrode should fit to the total process of device fabrications. In this paper, we discuss about the metal contact dependence of noise control for CNT-FET. Usually, CNT-FET has special gate voltage dependence with carrier number control of noise in the channel. On the contrary, the thick electrode of Ti/Au can cause the reverse dependence of noise by the gate voltage. In this case, the crossover of the two different noises is important. Thus, the noise properties seem to be also useful for the evaluation of noise in the device structures.
Adaptive Algorithms in Accelerometer Biometrics

Nowadays, many services are available from mobile devices, like smartphones. A growing number of people are using these devices to access bank accounts, social networks and to store personal information. However, common authentication mechanisms already present in these devices may not provide enough security. Recently, a new authentication method, named accelerometer biometrics, has been proposed. This method allows the identification of users using accelerometer data. Accelerometers, usually present in modern smartphones, are devices that measure acceleration forces. In accelerometer biometrics, a model is induced for the user of the smartphone. However, as a behavioural biometric technology, user models may became outdated over time. This paper investigates the use of adaptation mechanisms to update biometric user models induced by accelerometer data along the time. The paper also proposes and evaluates a new adaptation mechanism with promising experimental results.
Subspace-based blind channel estimation in CP systems by repeated use of remodulated received blocks

Blind channel estimation has been studied in various types of communication systems since it has a better bandwidth efficiency than training-based counterpart. In communication systems employed with cyclic prefixes (CP), subspace-based (SS) methods are among the most popular categories of blind channel estimation schemes. Existing SS methods, however, either require a large amount of received data or possess a high computational complexity. In this paper, a new algorithm for blind channel estimation in CP systems that require few received blocks with a reasonable complexity is proposed. The idea is based on combing advantages from two previously reported SS methods, namely, remodulation and repeated use of each received block. The combination of the two separate ideas turns out to be superior to each of them alone in many aspects. Simulation results not only confirms the capability of the proposed method to work properly with very few received blocks, but also show that it outperforms all previously reported methods. An extension of the proposed algorithm to MIMO case is promising.
Using Long-Term Volunteer Records to Examine Dormouse ( Muscardinus avellanarius ) Nestbox Selection

Within ecology, there are unanswered questions about species-habitat interactions, which could potentially be resolved by a pragmatic analysis of a long-term volunteer-collected dataset. Here, we analysed 18 years of volunteer-collected data from a UK dormouse nestbox monitoring programme to determine the influence of habitat variables on nestbox choice by common dormice ( Muscardinus avellanarius ). We measured a range of habitat variables in a coppiced woodland in Gloucestershire, UK, and analysed these in relation to dormouse nestbox occupancy records (by dormice, other small mammals, and birds) collected by volunteers. While some characteristics of the woodland had changed over 18 years, simple transformation of the data and interpretation of the results indicated that the dataset was informative. Using stepwise regressions, multiple environmental and ecological factors were found to determine nestbox selection. Distance from the edge of the wood was the most influential (this did not change over 18 years), with boxes in the woodland interior being selected preferentially. There was a significant negative relationship with the presence of ferns (indicative of damp shady conditions). The presence of oak (a long-lived species), and the clumped structural complexity of the canopy were also important factors in the final model. There was no evidence of competition between dormice and birds or other mammals. The results provide greater understanding of artificial dormouse nest-site requirements and indicate that, in terms of habitat selection, long-term volunteer-collected datasets contribute usefully to understanding the requirements of species with an important conservation status.
SigMol: repertoire of quorum sensing signaling molecules in prokaryotes

Quorum sensing is a widespread phenomenon in prokaryotes that helps them to communicate among themselves and with eukaryotes. It is driven through quorum sensing signaling molecules (QSSMs) in a density dependent manner that assists in numerous biological functions like biofilm formation, virulence factors secretion, swarming motility, bioluminescence, etc. Despite immense implications, dedicated resources of QSSMs are lacking. Therefore, we have developed SigMol (http://bioinfo.imtech.res.in/manojk/sigmol), a specialized repository of these molecules in prokaryotes. SigMol harbors information on QSSMs pertaining to different quorum sensing signaling systems namely acylated homoserine lactones (AHLs), diketopiperazines (DKPs), 4-hydroxy-2-alkylquinolines (HAQs), diffusible signal factors (DSFs), autoinducer-2 (AI-2) and others. Database contains 1382 entries of 182 unique signaling molecules from 215 organisms. It encompasses biological as well as chemical aspects of signaling molecules. Biological information includes genes, preliminary bioassays, identification assays and applications, while chemical detail comprises of IUPAC name, SMILES and structure. We have provided user-friendly browsing and searching facilities for easy data retrieval and comparison. We have gleaned information of diverse QSSMs reported in literature at a single platform ‘SigMol’. This comprehensive resource will assist the scientific community in understanding intraspecies, interspecies or interkingdom networking and further help to unfold different facets of quorum sensing and related therapeutics.
Adaptive single-trial error/erasure decoding of binary codes

We investigate adaptive single-trial error/erasure decoding of binary codes whose decoder is able to correct ε errors and τ erasures if λε + ≤ < dmin −1. Thereby, dmin is the minimum Hamming distance and λ ∊ R, 1 < λ < 2, is the tradeoff parameter between errors and erasures. The error/erasure decoder allows to exploit soft information by treating a set of most unreliable received symbols as erasures. The obvious question here is, how this erasing should be performed, i.e. how the unreliable symbols that must be erased in order to obtain the smallest possible residual codeword error probability can be determined. This was answered before [1] for the case of fixed erasing, where only the channel state and not the individual symbol reliabilities of each received vector are taken into consideration. In this paper, we address the adaptive case, where the optimal erasing strategy is determined for every given received vector.
The Definition of Novelty in Recommendation System

With the development of information technology and application of the Internet, People gradually entered the time of information overload from information scarcity. User satisfaction with recommender systems is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. Novelty is one of the important metrics of customer satisfaction. There is an increasing realization in the Recommender Systems (RS) field that novelty is fundamental qualities of recommendation effectiveness and added-value. This paper combed research results about definition and algorithm of novel recommendation, and starting from the meaning of "novel", defined novelty of item in recommendation system. Experiment proved using the definition of novelty to recommend can effectively recognize the item that the user is familiar with and ensure certain accuracy.
A simple system for detecting non-entailment

In order for a text to entail a hypothesis, the text usually must mention all of the information in the hypothesis. We use this observation as a basis for a simple system for detecting non-entailment. Unlike many previous lexically-based systems, we do not measure the degree of overlap or similarity, and we do no machine learning. This simple system performs well on the Recognizing Textual Entailment (RTE) evaluation.
Protocol Design With Concolic Snippets

With the maturing of computer-aided verification technology, there is an emerging opportunity to develop design tools that can transform the way systems are designed. In this paper, we propose a new way to specify protocols using concolic snippets, that is, sample execution fragments that contain both concrete and symbolic values. While the purely symbolic extreme is simply an alternative representation of the traditional communicating extended finite-state-machines, and the purely concrete extreme is an instantiation of the “programming by examples” paradigm, our specification language allows the designer to specify the desired protocol using a mixture of symbolic state machines and concrete scenarios. Our synthesis engine generalizes the snippets into a transition function, which is then analyzed using a model checker with respect to high-level temporal-logic correctness requirements. We describe a prototype implementation for design of cache coherence protocols built using (1) a straightforward enumeration of all expressions for transition functions, (2) a check for consistency with respect to concolic snippets using the SMT solver CVC3, and (3) a check for correctness using the model checker Murφ. We discuss our experience in designing classical cache coherence protocols using the proposed methodology.
Scheduling and resource binding for low power

Decisions taken at the earliest steps of the design process may have a significant impact on the characteristics of the final implementation. This paper illustrates how power consumption issues can be tackled during the scheduling and resource-binding steps of high-level synthesis. Algorithms for these steps targeting at low-power data-paths and trading off, in some cases, speed and area for low power are presented. The algorithms focus on reducing the activity of the functional units (adders, multipliers) by minimizing the transitions of their input operands. The power consumption of the functional units accounts for a large fraction of the overall data-path power budget.
Using structural domains to predict obligate and non-obligate protein-protein interactions

The identification and prediction of particular types of protein-protein interactions (PPIs) based on knowledge of their interacting domains is a problem that has drawn the attention of researchers in the past few years. We focus on the prediction and analysis of obligate and non-obligate complexes by using structural domains from the CATH database. Our proposed prediction model uses desolvation energies of domain-domain interactions (DDIs) present in the interfaces of such complexes. The prediction is performed via linear dimensionality reduction (LDR) and support vector machines (SVMs). Our results on two well-known datasets show that DDI features of the first three levels of CATH, especially level 2, are more powerful and discriminative than features of other levels in predicting these types of complexes. Furthermore, a detailed analysis shows that different DDIs are present in obligate and non-obligate complexes, and that homo-DDIs are more likely to be present in obligate interactions.
Error-Resilient Routing for Supporting Multi-dimensional Range Query in HD Tree

The Hierarchically Distributed Tree (HD Tree) is a novel distributed data structure built over a complete tree. The purpose of proposing this new data structure is to better support multi-dimensional range query in the distributed environment. HD Tree doubles the number of neighbors at the cost of doubling total links of a tree. The routing operation in HD Tree is supposed to be highly error-resilient. In HD Tree, the routing table size is determined by the system parameter k, and the performance of all basic operations are bound by O(lg(n)). Multiple routing options can be found between any two nodes in the system. This paper explores fault tolerant routing strategies in HD Tree. The experimental results produce very limited and unnoticeable increases in routing cost when conducting range queries in an error-prone routing environment. The maximum failures we have tested are about 5 percent of routing nodes. The experimental results also indicate that higher fault tolerant capability requires finer consideration in the design of the error-resilient routing strategy.
Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R

Abstract The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow easy publication of datasets. So far, however, platforms for data sharing offer limited functions for distributing and interacting with evolving datasets— those that continue to grow with time as more records are added, errors fixed, and new data structures are created. In this article, we describe a workflow for maintaining and distributing successive versions of an evolving dataset, allowing users to retrieve and load different versions directly into the R platform. Our workflow utilizes tools and platforms used for development and distribution of successive versions of an open source software program, including version control, GitHub, and semantic versioning, and applies these to the analogous process of developing successive versions of an open source dataset. Moreover, we argue that this model allows for individual research groups to achieve a dynamic and versioned model of data delivery at no cost.
Shared decision making for thromboembolic prophylaxis in non-valvular atrial fibrillation: promise and problems with the American College of Cardiology’s AnticoagEvaluator (based on SPARCtool)

Background and methods People with atrial fibrillation face an increased risk of thromboembolic events, and deciding on an antithromboembolic strategy in non-valvular atrial fibrillation is a prime opportunity for shared decision making. Therefore, tools facilitating this process are highly desirable. The American College of Cardiology strives for such with its ‘AnticoagEvaluator’, which is based on Peter Loewen’s ‘SPARCtool’. However, it appears these tools were released without standard peer review. Therefore, an analysis of these tools was undertaken to evaluate their soundness, namely proper use of baseline risk and effect estimates from the available evidence. Results Despite Loewen’s laudable idea and the American College of Cardiology developing AnticoagEvaluator based on Loewen’s work, both tools have a flaw: they use relative effect estimates based on composite outcomes (thromboembolic and haemorrhagic) that do not match the baseline risk to which they are applied (thromboembolic). This can lead to importantly inaccurate impressions of therapeutic efficacy. This analysis explores this issue and offers potential solutions. Conclusions The American College of Cardiology releasing and promoting a tool that gives misleading impressions of therapeutic efficacy is of considerable importance, though SPARCtool should also be corrected as a matter of importance. Means to correct the tools are identified herein, and if corrected, these tools stand to better fulfil their intended purpose as important and useful additions for clinical and shared decision making. This article’s analysis of the tools has a directly practical purpose, but it also serves as an instructive example of key elements of evidence-based medicine and shared decision making.
A Unified Approach in Speech-to-Speech Translation: Integrating Features of Speech recognition and Machine Translation

Based upon a statistically trained speech translation system, in this study, we try to combine distinctive features derived from the two modules: speech recognition and statistical machine translation, in a loglinear model. The translation hypotheses are then rescored and translation performance is improved. The standard translation evaluation metrics, including BLEU, NIST, multiple reference word error rate and its position independent counterpart, were optimized to solve the weights of the features in the log-linear model. The experimental results have shown significant improvement over the baseline IBM model 4 in all automatic translation evaluation metrics. The largest was for BLEU, by 7.9% absolute.
Code clones detection using machine learning technique: Support vector machine

Code clones defined as sequence of source code that occur more than once in the same program or across different programs are undesirable as they increase the size of program and creates the problems of redundancy. Fixing of bugs detected in one clone require detection of all clones. Hence, it is imperative to identify and remove all code clones in a program. The focus of previous research work on the code clone detection was to find identical clones, or clones that are identical up to identifiers and literal values. But, detection of similar clones is often important. In the present paper it is proposed to generate the feature sets after parsing the given C program for code fragments and then match their similarity. On the basis of feature sets the classification of algorithm is being performed by using the Support Vector Machine (SVM) as a machine learning tool. The output of the machine tool would be the similarity ratio with which the two C programs are related to each other and also the class in which they would occur. It was observed that the test results of the tool implementation show detection of code clones in the program and its accuracy increases with the increase in number of instances.