Miracle-dz/UniFAR-FTUs huggingface dataset:
Prominent users detection during specific events by learning On- and Off-topic features of user activities

Microblogs such as Twitter are characterized by the richness and recency of information shared by their users during major events. However, it is very challenging to automatically mine for information or for users sharing certain information due to the huge variety of unstructured stream of data shared in such microblogs. This work proposes a ranking and classification model for identifying users sharing useful information during a specified event. The model is based on a novel set of features that can be computed in real time. These features are designed such that they take into account both the on and off-topic activities of a user. Once users are characterized by a feature vector, supervised machine learning tool is trained to classify users as either prominent or not. Our model has been tested on data shared during a flooding disaster event and performed very well. The achieved results show the effectiveness of the proposed model for both the classification and ranking of prominent users in such events, and also the importance of the adjustment of the on-topic features by the off-topic ones when describing users' activities.
Cord Serum Lipidome in Prediction of Islet Autoimmunity and Type 1 Diabetes

Previous studies show that children who later progress to type 1 diabetes (T1D) have decreased preautoimmune concentrations of multiple phospholipids as compared with nonprogressors. It is still unclear whether these changes associate with development of β-cell autoimmunity or specifically with clinical T1D. Here, we studied umbilical cord serum lipidome in infants who later developed T1D (N = 33); infants who developed three or four (N = 31) islet autoantibodies, two (N = 31) islet autoantibodies, or one (N = 48) islet autoantibody during the follow-up; and controls (N = 143) matched for sex, HLA-DQB1 genotype, city of birth, and period of birth. The analyses of serum molecular lipids were performed using the established lipidomics platform based on ultra-performance liquid chromatography coupled to mass spectrometry. We found that T1D progressors are characterized by a distinct cord blood lipidomic profile that includes reduced major choline-containing phospholipids, including sphingomyelins and phosphatidylcholines. A molecular signature was developed comprising seven lipids that predicted high risk for progression to T1D with an odds ratio of 5.94 (95% CI, 1.07–17.50). Reduction in choline-containing phospholipids in cord blood therefore is specifically associated with progression to T1D but not with development of β-cell autoimmunity in general.
NIST Open Machine Translation 2009 Evaluation : Tel Aviv University ' s System Description

This document describes our first participation in the NIST MT evaluation project. We submitted the results of a non-structural Example-Based Machine Translation system that translates text from Arabic to English, using a parallel corpus aligned at the paragraph / sentence level. Each new input sentence is fragmented into phrases and those phrases are matched to example patterns, using various levels of morphological information. The system exploits a bilingual corpus to find examples that match fragments of the input source-language (Modern Standard Arabic-MSA, in this case) text, and imitates its translations. In the matching step, the system uses various levels of morphological information in order to broaden the amount of matched translation examples and to generate new translations based on morphologically similar fragments. In addition, we forced the matching algorithm to work on the phrase level only. The operant definition of a phrase for us is a combination of adjacent base-phrases of the input sentence. In the transfer step, those matched phrases are translated using the target-language (English, in our case) version of the parallel corpus, using the automatically created translation example alignment table. In the recombination step all the translated fragments are pasted together to form a complete target-language text, usually by preferring larger translated fragments since they use more context. 4.2. 1 Parallel Data The translation examples in our system were extracted from a collection of parallel unvocalized Arabic-English corpora, all provided by the LDC. In the current submission, we used the Arabic Eng-All the translation examples were morphologically analyzed using the Buckwalter morphological analyzer (version 1.0) (Buckwalter, 2002), and then part-of-speech tagged using AMIRA (Diab et al., 2004). For each Arabic word in the translation example, we look up its English equivalents in a lexicon, created using the Buckwalter glossaries, and then expand those English words with synonyms from WordNet. Then we search the English
Spreading behaviour of viruses into mobile network : A study

Viruses and malwares spread around mobile networks with the rapid growth of smart cell phone users. In a mobile network, viruses and malwares can cause privacy data leakage and remote listening. Additionally, they can jam wireless servers by sending thousands of spam messages or track user location through GPS. Because of the potential damage of mobile viruses, it is important for us to gain a profound understanding of the propagation mechanism of mobile viruses. The strong similarity between biological viruses and their computational counterparts have motivated people to adopt mathematical epidemiology in study of propagation of computer virus. But as the characteristics of mobile network are significantly different from computer network, a separate study of epidemic model of virus propagation in mobile phone is required.
Gamma Oscillations Distinguish True From False Memories

To test whether distinct patterns of electrophysiological activity prior to a response can distinguish true from false memories, we analyzed intracranial electroencephalographic recordings while 52 patients undergoing treatment for epilepsy performed a verbal free-recall task. These analyses revealed that the same pattern of gamma-band (28–100 Hz) oscillatory activity that predicts successful memory formation at item encoding—increased gamma power in the hippocampus, prefrontal cortex, and left temporal lobe—reemerges at retrieval to distinguish correct from incorrect responses. The timing of these oscillatory effects suggests that self-cued memory retrieval begins in the hippocampus and then spreads to the cortex. Thus, retrieval of true, as compared with false, memories induces a distinct pattern of gamma oscillations, possibly reflecting recollection of contextual information associated with past experience.
Genetic engineering of mouse embryonic stem cells by Nurr1 enhances differentiation and maturation into dopaminergic neurons

Nurr1 is a transcription factor critical for the development of midbrain dopaminergic (DA) neurons. This study modified mouse embryonic stem (ES) cells to constitutively express Nurr1 under the elongation factor‐1α promoter. The Nurr1‐expression in ES cells lead to up‐regulation of all DA neuronal markers tested, resulting in about a 4‐ to 5‐fold increase in the proportion of DA neurons. In contrast, other neuronal and glial markers were not significantly changed by Nurr1 expression. It was also observed that there was an additional 4‐fold increase in the number of DA neurons in Nurr1‐expressing clones following treatment with Shh, FGF8 and ascorbic acid. Several lines of evidence suggest that these neurons may represent midbrain DA neuronal phenotypes; firstly, they coexpress midbrain DA markers such as aromatic l‐amino acid decarboxylase, calretinin, and dopamine transporter, in addition to tyrosine hydroxylase and secondly, they do not coexpress other neurotransmitters such as GABA or serotonin. Finally, consistent with an increased number of DA neurons, the Nurr1 transduction enhanced the ability of these neurons to produce and release DA in response to membrane depolarization. This study demonstrates an efficient genetic manipulation of ES cells that facilitates differentiation to midbrain DA neurons, and it will serve as a framework of genetic engineering of ES cells by key transcription factor to regulate their cell fate.
Accented Handwritten Character Recognition Using SVM - Application to French

This paper deals with the problem of recognizing accented and non-accented characters in French handwriting. Accented characters increase the number of classes to be recognized. The performances of powerful classifier such as SVM are declined by the presence of accents. In this paper, an accented character is segmented into two parts: the root character or letter and the accent. These two parts are recognized separately, and the results are combined to rebuild the accented character. This approach avoids the combination of characters and accents that causes an increase in the number of classes to be considered. For handwritten character recognition, the combination of on-line and off-line features is used. The paper illustrates that French accented and non-accented characters and digits can be described by a combination of this kind of data. Moreover, the number of features of the combination is not necessarily very high. The experimental investigations show that the handwritten character recognition built on 45 selected features can compete with recognition rate and response time of other well known tested on standard databases such as UNIPEN and IRONOFF.
Sparse $\ell _{1}$-Optimal Multiloudspeaker Panning and Its Relation to Vector Base Amplitude Panning

Panning techniques, such as vector base amplitude panning (VBAP), are a widely used practical approach for spatial sound reproduction using multiple loudspeakers. Although limited to a relatively small listening area, they are very efficient and offer good localization accuracy, timbral quality, as well as a graceful degradation of quality outside the sweet spot. The aim of this paper is to investigate optimal sound reproduction techniques that adopt some of the advantageous properties of VBAP, such as the sparsity and the locality of the active loudspeakers for the reproduction of a single audio object. To this end, we state the task of multiloudspeaker panning as an <inline-formula> <tex-math notation="LaTeX">${\ell _{1}}$</tex-math></inline-formula> optimization problem. We demonstrate and prove that the resulting solutions are exactly sparse. Moreover, we show the effect of adding a nonnegativity constraint on the loudspeaker gains in order to preserve the locality of the panning solution. Adding this constraint, <inline-formula><tex-math notation="LaTeX">${\ell _{1}}$</tex-math></inline-formula>-optimal panning can be formulated as a linear program. Using this representation, we prove that unique <inline-formula><tex-math notation="LaTeX">${\ell _{1}}$</tex-math></inline-formula>-optimal panning solutions incorporating a nonnegativity constraint are identical to VBAP using a Delaunay triangulation for the loudspeaker setup. Using results from linear programming and duality theory, we describe properties and special cases, such as solution ambiguity, of the VBAP solution.
In Vivo Effect of Quaternized Chitosan-Loaded Polymethylmethacrylate Bone Cement on Methicillin-Resistant Staphylococcus epidermidis Infection of the Tibial Metaphysis in a Rabbit Model

ABSTRACT Infection of open tibial fractures with contamination remains a challenge for orthopedic surgeons. Local use of antibiotic-impregnated polymethylmethacrylate (PMMA) beads and blocks is a widely used procedure to reduce the risk of infection. However, the development of antibiotic-resistant organisms make the management of infection more difficult. Our in vitro study demonstrated that quaternized chitosan (hydroxypropyltrimethyl ammonium chloride chitosan [HACC])-loaded PMMA bone cement exhibits strong antibacterial activity toward antibiotic-resistant bacteria. Therefore, the present study aimed to investigate the in vivo antibacterial activity of quaternized chitosan-loaded PMMA. Twenty-four adult female New Zealand White rabbits were used in this study. The right proximal tibial metaphyseal cavity was prepared, 107 CFU of methicillin-resistant Staphylococcus epidermidis was inoculated, and PMMA-only, gentamicin-loaded PMMA (PMMA-G), chitosan-loaded PMMA (PMMA-C), or HACC-loaded PMMA (PMMA-H) bone cement cylinders were inserted. During the follow-up period, the infections were evaluated using X rays on days 21 and 42 and histopathological and microbiological analyses on day 42 after surgery. Radiographic indications of bone infections, including bone lysis, periosteal reactions, cyst formation, and sequestral bone formation, were evident in the PMMA, PMMA-G, and PMMA-C groups but not in the PMMA-H group. The radiographic scores and gross bone pathological and histopathological scores were significantly lower in the PMMA-H group than in the PMMA, PMMA-G, and PMMA-C groups (P < 0.05). Explant cultures also indicated significantly less bacterial growth in the PMMA-H group than in the PMMA, PMMA-G, and PMMA-C groups (P < 0.01). We concluded that PMMA-H bone cement can inhibit the development of bone infections in this animal model inoculated with antibiotic-resistant bacteria, thereby demonstrating its potential application for treatment of local infections in open fractures.
Integrated circuit implementation of a cortical neuron

This paper presents an analogue integrated circuit implementation of a cortical neuron model. The VLSI chip prototype has been implemented in a 0.35 mum CMOS technology. The single neuron cell has a compact layout and very low energy consumption, in the range of 9 pJ per spike. Experimental results demonstrate the capability of the circuit to generate a realistic spike shape and a variety of spiking and bursting firing patterns. The models of various cortical neuron types are obtained in a single circuit, through the adjustment of two biasing voltages, making the circuit suitable for applications in reconfigurable neuromorphic devices that implement biologically plausible spiking neural networks.
A meta-analysis of the association between glutathione S-transferase P1 gene polymorphism and the risk of adenocarcinomas of lung cancer.

BACKGROUND Results of the published reports on the relationship between glutathione S-transferase P1 (GSTP1) gene polymorphism and the adenocarcinomas of lung cancer are still debated. OBJECTIVE This meta-analysis was performed to evaluate the association between GSTP1 A/G gene polymorphism and the risk of adenocarcinomas of lung cancer. METHODS The association investigations were identified from PubMed and Cochrane Library, and eligible studies were included and synthesized using meta-analysis method. RESULTS 16 reports were included into this meta-analysis for the association of GSTP1 A/G gene polymorphism and the risk of adenocarcinomas of lung cancer. The G allele and GG genotype were not associated with the susceptibility of risk of adenocarcinomas. Furthermore, in the sensitivity analysis, the results were similar with those from the non-sensitivity analysis. CONCLUSIONS GSTP1 G allele or GG genotype is not a biomarker to be associated with the susceptibility of adenocarcinomas of lung cancer.
Peripheral Inflammatory Parameters in Late-Life Depression: A Systematic Review

Depressive disorders appear relatively frequently in older patients, and therefore represent an important disease burden worldwide. Given the high levels of inflammatory parameters found in depressed elderly patients, the “inflammaging” hypothesis is gaining strength. In this systematic review, we summarize current evidence regarding the relationship between inflammatory parameters and late-life depression, with a unique focus on longitudinal studies to guarantee temporality. According to the data summarized in this review, the levels of some proinflammatory parameters—especially interleukin (IL)-8, IL-6, and tumor necrosis factor (TNF)-α—could serve as biomarkers for the future development of depressive symptoms in elderly patients. Proinflammatory cytokines seem to be associated with the future development of clinically significant depression, irrespective of baseline scores, thus indicating that inflammation temporally precedes and increases depression risk. As insufficient research has been conducted in this field, further prospective studies are clearly warranted.
Decision Analysis of Merchant Transmission Investment by Perpetual Options Theory

This paper introduces the concept of perpetual options theory to analyze merchant transmission investment through an approved rate. Since the electric usages and the associated revenue is stochastic in nature, applying the perpetual options theory allows an investor to determine the most opportunistic time to start a transmission project and obtain the maximum revenue returns or let the option expire when the economic incentive is not sufficient. In today's environment, this decision approach is more appropriate since it provides transmission investors a better projection of its return on investment and an exit strategy for an investment decision.
Kinematic/Dynamic analysis and optimization of a 2-PRU-PPRC 2T1R redundant parallel manipulator with specified workspace

This paper presents an improved performance chart based design methodology for the optimum design of the 2-PRU-PPRC 2T1R redundant parallel manipulator with specified workspace, here P, R, U, C denote the prismatic, revolute, universal and cylindrical joints, respectively. Conditioning index and dynamic dexterity are used to analysis the kinematic and dynamic performance, and a new workspace efficiency index is proposed to evaluate the manipulator efficiency. An improved performance chart based design methodology, which take both geometrical parameters and task workspace parameter into account, is applied to obtain the optimum result with specified workspace. This study brings valuable kinematic/dynamic optimization method for the 2T1R parallel manipulator, and it is also applicable for other parallel manipulator optimization with specified workspace.
A functional-logic library for wired

We develop a Haskell library for functional-logic programming, motivated by the implementation of Wired, a relational embedded domain-specific language for describing and analysing digital circuits at the VLSI-layout level. Compared to a previous library for logic programming by Claessen and Ljunglöf, we support residuation, easier creation of logical data types, and pattern matching. We discuss other applications of our library, including test-data generation, and various extensions, including lazy narrowing.
Protoplast fusion of Lactobacillus fermentum

Tetracycline-resistant (Tetr) erythromycin-resistant (Eryr) fusants of Lactobacillus fermentum 604 carrying a 10-megadalton Tetr plasmid and L. fermentum 605 carrying a 38-megadalton Eryr plasmid were obtained by means of polyethylene glycol-induced protoplast fusion.
Image segmentation based on complexity mining and mean-shift algorithm

Mean shift algorithm is a well established method for image segmentation. It is particularly popular technique due to non-parametric nature which enables efficient segmentation of complex arbitrary shapes. Despite such advantage, high computational complexity still makes it unsuitable for segmentation of high resolution images in time critical applications. This paper introduces a new approach which alleviates performance issues of mean shift using complexity reduction based on information theory. Proposed algorithm starts by calculating information potential field of the image in order to get insight into complexity of the regions. Afterwards, only complex regions are segmented by computationally expensive mean shift algorithm, while segmentation of simpler regions is performed by a cheaper method. Performance of our method is additionally improved with execution of the key code sections on the GPGPU platform. Experimental results have shown that our method produces comparable segmentation quality to regular parallel mean shift, but with significant reduction in overall execution time.
Efficient reinforcement learning using Gaussian processes

This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems. First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
Chebyshev Polynomials in Distributed Consensus Applications

In this paper we analyze the use of Chebyshev polynomials in distributed consensus applications. It is well known that the use of polynomials speeds up the convergence to the consensus in a significant way. However, existing solutions only work for low degree polynomials and require the topology of the network to be fixed and known. We propose a distributed algorithm based on the second order difference equation that describes the Chebyshev polynomials of first kind. The contributions of our algorithm are three: (i) Since the evaluation of Chebyshev polynomials is stable, there is no limitation in the degree of the polynomial. (ii) Instead of the knowledge of the whole network topology, it only requires a partial knowledge or an approximation to it. (iii) It can be applied to time varying topologies. In the paper we characterize the main properties of the algorithm for both fixed and time-varying communication topologies. Theoretical results, as well as experiments with synthetic data, show the benefits of using our algorithm compared to existing methods.
Characterizing stages of a multi-session complex search task through direct and indirect query modifications

Search systems use context to effectively satisfy a user's information need as expressed by a query. Tasks are important factors in determining user context during search and many studies have been conducted that identify tasks and task stages through users' interaction behavior with search systems. The type of interaction available to users, however, depends on the type of search interface features available. Queries are the most pervasive input from users to express their information need regardless of the input method, e.g., typing keywords or clicking facets. Instead of characterizing interaction behavior in terms of interface specific components, we propose to characterize users' search behavior in terms of two types of query modification: (i) direct modification, which refers to reformulations of queries; and (ii) indirect modification, which refers to user operations on additional input components provided by various search interfaces. We investigate the utility of characterizing task stages through direct and indirect query reformulations in a case study and find that it is possible to effectively differentiate subsequent stages of the search task. We found that describing user interaction behavior in such a generic form allowed us to relate user actions to search task stages independent from the specific search interface deployed. The next step will then be to validate this idea in a setting with a wider palette of search tasks and tools.