Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-organized construction of distributed access structures: A comparative evaluation of P-Grid and FreeNet

This paper provides a quantative comparison of the efficiency of the self-organized construction processes of the P-Grid and FreeNet peer-to-peer systems. Starting from a defined, realistic network topology we simulate the construction of their access structures and measure the incurred message load and memory usage for routing tables. Besides these results our experimental setup may also be used as a starting point for defining a standard test and evaluation suite for P2P systems.
HMM-Based singing voice synthesis and its application to Japanese and English

The present paper describes Japanese and English singing voice synthesis systems based on hidden Markov models (HMMs). In this approach, the spectrum, excitation, and vibrato of the singing voice are simultaneously modeled by context-dependent HMMs, and waveforms are generated by the HMMs themselves. Japanese singing voice synthesis systems have already been developed and used to create variable musical contents. To extend this system to English, language independent contexts are designed. Furthermore, methods for matching musical notes and pronunciation of English lyrics are presented and evaluated in subjective experiments. Then, Japanese and English singing voice synthesis systems are compared.
Extraction-Based Regression Test Selection

Frequent regression testing is a core activity in agile software development, but large test suites can lead to long test running times, hampering agility. By safe RTS (Regression Test Selection) techniques, a subset of the tests can be identified that cover all tests that can change result since the last run. To pay off in practice, the RTS overhead must be low. Most existing RTS techniques are based on dynamic coverage analysis, making the overhead related to the tests run. We present Extraction-Based RTS, a new safe RTS technique which uses a fast static analysis with very low overhead, related to the size of the modification rather than to the tests run. The method is suitable for program-driven testing, commonly used in agile development, where each test is a piece of code that uses parts of the system under test. We have implemented the method for Java, and bench-marked it on a number of open source projects, showing that it pays off substantially in practice.
Toward Precision Medicine: TBC1D4 Disruption Is Common Among the Inuit and Leads to Underdiagnosis of Type 2 Diabetes

OBJECTIVE A common nonsense mutation in TBC1D4 was recently found to substantially increase the odds of type 2 diabetes in Greenlandic Inuit, leading to exclusively increased postprandial glucose. We investigated the frequency and effect of the TBC1D4 mutation on glucose metabolism and type 2 diabetes diagnosis among Canadian and Alaskan Inuit. RESEARCH DESIGN AND METHODS Exome sequencing of the TBC1D4 variant was performed in 114 Inuit from Nunavik, Canada, and Sanger sequencing was undertaken in 1,027 Alaskan Inuit from the Genetics of Coronary Artery Disease in Alaskan Natives (GOCADAN) Study. Association testing evaluated the effect of the TBC1D4 variant on diabetes-related metabolic traits and diagnosis. RESULTS The TBC1D4 mutation was present in 27% of Canadian and Alaskan Inuit. It was strongly associated with higher glucose (effect size +3.3 mmol/L; P = 2.5 x 10−6) and insulin (effect size +175 pmol/L; P = 0.04) 2 h after an oral glucose load in homozygote carriers. TBC1D4 carriers with prediabetes and type 2 diabetes had an increased risk of remaining undiagnosed unless postprandial glucose values were tested (odds ratio 5.4 [95% CI 2.5–12]) compared with noncarriers. Of carriers with prediabetes or type 2 diabetes, 32% would remain undiagnosed without an oral glucose tolerance test (OGTT). CONCLUSIONS Disruption of TBC1D4 is common among North American Inuit, resulting in exclusively elevated postprandial glucose. This leads to underdiagnosis of type 2 diabetes, unless an OGTT is performed. Accounting for genetic factors in the care of Inuit with diabetes provides an opportunity to implement precision medicine in this population.
Phase Noise Estimation for M-QAM Constellations Using Gaussian Sum Particle Filtering

In this paper we show that Gaussian sum particle filters (GSPFs) present an effective solution for the problem of estimating phase noise in digital communications. We start by describing the problem of estimating the phase noise in terms of its general Bayesian formulation and then present the solution offered by the GSPF. Our filter is based in two central features. On the one hand, the system model comprising both the dynamics model and the observations model and on the other hand the sensor factor, a periodic function with respect to the current observation of the state. Although phase noise estimation algorithms are widely represented in the literature to our knowledge it has never been considered to use GSPFs for this purpose. Furthermore, other solutions using conventional particle filters assumed the modulation symbols to be PSK, i.e., phase-defined, while we assume phase- and amplitude-defined symbols, e.g., M-QAM.
ism: Improvisation Supporting System based on Melody Correction

In this paper, we describe a novel improvisation supporting system based on correcting musically unnatural melodies. Since improvisation is the musical performance style that involves creating melodies while playing, it is not easy even for the people who can play musical instruments. However, previous studies have not dealt with improvisation support for the people who can play musical instruments but cannot improvise. In this study, to support such players' improvisation, we propose a novel improvisation supporting system called ism, which corrects musically unnatural melodies automatically. The main issue in realizing this system is how to detect notes to be corrected (i.e., musically unnatural or inappropriate). We propose a method for detecting notes to be corrected based on the N-gram model. This method first calculates N-gram probabilities of played notes, and then judges notes with low N-gram probabilities to be corrected. Experimental results show that the N-gram-based melody correction and the proposed system are useful for supporting improvisation.
Silibinin Regulates Lipid Metabolism and Differentiation in Functional Human Adipocytes

Silibinin, a natural plant flavonolignan is the main active constituent found in milk thistle (Silybum marianum). It is known to have hepatoprotective, anti-neoplastic effect, and suppresses lipid accumulation in adipocytes. Objective of this study was to investigate the effect of silibinin on adipogenic differentiation and thermogenic capacity of human adipose tissue derived mesenchymal stem cells. Silibinin (10 μM) treatment, either at the beginning or at the end of adipogenic differentiation, resulted in an increase of SIRT-1, PPARα, Pgc-1α, and UCPs gene expression. Moreover, silibinin administration resulted in a decrease of PPARγ, FABP4, FAS, and MEST/PEG1 gene expression during the differentiation, confirming that this compound is able to reduce fatty acid accumulation and adipocyte size. Our data showed that silibinin regulated adipocyte lipid metabolism, inducing thermogenesis and promoting a brown remodeling in adipocyte. Taken together, our findings suggest that silibinin increases UCPs expression by stimulation of SIRT1, PPARα, and Pgc-1α, improved metabolic parameters, decreased lipid mass leading to the formation of functional adipocytes.
Virus-encoded toxin of Ustilago maydis: two polypeptides are essential for activity

Cells of Ustilago maydis containing double-stranded RNA viruses secrete a virus-encoded toxin to which other cells of the same species and related species are sensitive. Mutants affected in the expression of the KP6 toxin were characterized, and all were viral mutants. A temperature-sensitive nonkiller mutant indicated that the toxin consists of two polypeptides, 12.5K and 10K, that are essential for the toxic activity. The temperature-sensitive nonkiller mutant was affected in the expression of the 10K polypeptide, and its toxic activity was restored by the addition of the 10K polypeptide to its secreted inactive toxin. These results led to the reexamination of other mutants that were known to complement in vitro. Each was found to secrete one of the two polypeptides. Here we show for the first time that P6 toxin consists of two polypeptides that do not interact in solution, but both are essential for the toxic effect. Studies on the interaction between the two polypeptides indicated that there are no covalent or hydrogen bonds between the polypeptides. Toxin activity is not affected by the presence of 0.3 M NaCl in the toxin preparations and in the medium, suggesting that no electrostatic forces are involved in this interaction. Also, the two polypeptides do not share common antigenic determinants. The activity of the two polypeptides appears to be dependent on a sequential interaction with the target cell, and it is the 10K polypeptide that initiates the toxic effect. The similarity of the U. maydis virus-encoded toxin to that of Saccharomyces cerevisiae is discussed.
Renal survival in proteinase 3 and myeloperoxidase ANCA-associated systemic vasculitis.

BACKGROUND AND OBJECTIVES This study evaluated predictors for patient and renal survival in patients with ANCA-associated vasculitis (AAV) with and without renal involvement. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS There were 273 consecutive AAV patients from January 1990 until December 2007 who were followed until death, loss to follow-up, or December 2010. Based on organ involvement, patients were divided into renal (n=212) and nonrenal groups (n=61). The primary end point was ESRD requiring renal replacement therapy (RRT) or renal transplantation or death. RESULTS Patient survival was significantly better in the nonrenal group compared with the renal group (hazard ratio, 0.55; 95% confidence interval, 0.33 to 0.92; P=0.02). In the renal group, renal survival was significantly worse in MPO-ANCA-positive patients (n=65) compared with PR3-ANCA-positive patients (n=138) (hazard ratio, 2.1; 95% confidence interval, 1.11 to 3.8; P=0.01). Of 48 patients who needed RRT at diagnosis, 11 patients (23%) died within 6 months and 14 patients (29%) did not regain renal function. Of all 23 patients who regained renal function after RRT, 7 patients (30%) were temporarily dialysis independent and needed dialysis later (range, 13-63 months). Five patients had a renal relapse in the 6 months before restart of RRT. Of all 203 PR3-ANCA-positive and MPO-ANCA-positive patients with renal involvement, 12 patients (6%) developed ESRD during follow-up. These patients were classified as CKD stage 4 or 5 after initial treatment and eight patients had a renal relapse before becoming dialysis dependent. CONCLUSIONS AAV patients with renal involvement who needed RRT had the worst survival probability. In multivariate analysis, the only major determinants for long-term renal survival were renal function at 6 months and renal relapses.
An Elimination Method for Polynomial Systems

We present an elimination method for polynomial systems, in the form of three main algorithms. For any given system P,Q] of two sets of multivariate polynomials, one of the algorithms computes a sequence of triangular forms T1,? ,Te and polynomial sets U1,? ,Ue such that Zero(P/Q) = ? ei=1 Zero(Ti/Ui), where Zero(P/Q) denotes the set of common zeros of the polynomials in P which are not zeros of any polynomial in Q, and similarly for Zero(Ti/Ui). The two other algorithms compute the same zero decomposition but with nicer properties such as Zero(Ti/Ui) ? &0slash; for each i. One of them, for which the computed triangular systems Ti, Ui] possess the projection property, provides a quantifier elimination procedure for algebraically closed fields. For the other, the computed triangular forms Ti are irreducible. The relationship between our method and some existing elimination methods is explained. Experimental data for a set of test examples by a draft implementation of the method are provided, and show that the efficiency of our method is comparable with that of some well-known methods. A few encouraging examples are given in detail for illustration.
Source-Side Discontinuous Phrases for Machine Translation: A Comparative Study on Phrase Extraction and Search

Abstract Standard phrase-based statistical machine translation systems generate translations based on an inventory of continuous bilingual phrases. In this work, we extend a phrase-based decoder with the ability to make use of phrases that are discontinuous in the source part. Our dynamic programming beam search algorithm supports separate pruning of coverage hypotheses per cardinality and of lexical hypotheses per coverage, as well as coverage constraints that impose restrictions on the possible reorderings. In addition to investigating these aspects, which are related to the decoding procedure, we also concentrate our attention on the question of how to obtain source-side discontinuous phrases from parallel training data. Two approaches (hierarchical and discontinuous extraction) are presented and compared. On a large-scale Chinese! English translation task, we conduct a thorough empirical evaluation in order to study a number of system configurations with source-side discontinuous phrases, and to compare them to setups which employ continuous phrases only.
Using a hierarchy of coordinators to overcome the frontier effect in social learning

We propose in this paper the use of a hierarchy of coordinators to improve the convergence of a network of agents to a global norm. A norm or a convention is an unwritten law that a society of agents agree on. Social norms are used by humans all the time. Choosing on which side of the road to drive a car and the right-of-way at an intersection are well-known examples. In a multi-agent setting, a convention may refer to a dominant coordination strategy, a common communication language, or the right of way among a group of robots. Upon establishing a norm, the overhead of coordination drops and the reliability of the multi-agent system increases [2]. When studying the emergence of norms and conventions, we typically assume the interaction between agents is random: a pair of agents are selected randomly to interact with one another. The process repeats both concurrently (several pairs interact at the same time) and consecutively (each agent collects history of interactions). When agents are adaptive, the process is then referred to as social learning. The coordination game is perhaps the most widely used game for studying social learning as it presents an agent community with two equally plausible norms to choose from (i.e. two Nash equilibriums). It was shown that in the absence of any restriction on agent interactions, a norm is guaranteed to emerge in the simple social learning setting where agents play the coordination game [1].
Cryptic Speciation and Chromosomal Repatterning in the South African Climbing Mice Dendromus (Rodentia, Nesomyidae)

We evaluate the intra- and interspecific diversity in the four South African rodent species of the genus Dendromus. The molecular phylogenetic analysis on twenty-three individuals have been conducted on a combined dataset of nuclear and mitochondrial markers. Moreover, the extent and processes underlying chromosomal variation, have been investigated on three species by mean of G-, C-bands, NORs and Zoo-FISH analysis. The molecular analysis shows the presence of six monophyletic lineages corresponding to D. mesomelas, D. mystacalis and four lineages within D. cfr. melanotis with high divergence values (ranges: 10.6% – 18.3%) that raises the question of the possible presence of cryptic species. The first description of the karyotype for D. mesomelas and D. mystacalis and C- and G- banding for one lineage of D. cfr. melanotis are reported highlighting an extended karyotype reorganization in the genus. Furthermore, the G-banding and Zoo-FISH evidenced an autosome-sex chromosome translocation characterizing all the species and our timing estimates this mutation date back 7.4 mya (Late Miocene). Finally, the molecular clock suggests that cladogenesis took place since the end of Miocene to Plio-Pleistocene, probably due to ecological factors, isolation in refugia followed by differential adaptation to the mesic or dry habitat.
5.5 A quadrature relaxation oscillator with a process-induced frequency-error compensation loop

With the emergence of wearable and implantable technologies, there has been growing demand on development of key enabling circuits for ultra-low-power sensor interface SoCs. As a reference-frequency generation block for clock management of the overall system, the relaxation oscillator has been widely adopted since it can provide a controllable and well-defined untrimmed frequency with low-cost circuits. In the past decade, the major goal in the design of the relaxation oscillators has been the improvement of phase-noise figure-of-merit (FOM) closer to the fundamental limit of 169dBc/Hz [1]. There have been feedback approaches to internally generate reference voltages for comparison, hence compensating the comparator circuit delay [2–4]. Since the delay compensation relies on the feedback operation, power consumption by analog circuits to meet the required bandwidth of the feedback loop eventually limits FOM. Recently, a swing-boosted differential scheme was proposed to reduce the effect of comparator noise by boosting the signal slope at the comparator input, demonstrating an FOM of over 160dBc/Hz [5]. However, the boosted voltage swing can increase stress on the input transistors of the comparator. In addition, a high-speed comparator is also needed to reduce the effect of the circuit delay on the output frequency. While most of previous works achieved good FOMs with MHz oscillators, implementation of low-frequency relaxation oscillators presents additional challenges since it requires excessive area for RC and power consumption by analog circuits with leakage not scaled down along with the output frequency.
Heuristic programmers and their gambling machines

Following our paper given at the IFIP Congress 71, another progres report is presented of our ongoing research efforts aimed at human decision making under uncertainty and risk. We have studied many aspects of human and machine learning processes, inductive and deductive inference making methods, how heuristic rules are formed and optimized by human players, and how similar results can be arrived at by machines. Although the investigations have been within the general framework of the game of Poker. our findings are considered to have a rather wide range of applicability. Also, the complex information processing system at hand incorporates both normative and descriptive theories of certain human behavior. Finally, the results of some empirical explorations are followed by an account of present and planned activities.
Output Range Analysis for Deep Neural Networks

Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs are also been deployed in high-assurance applications. Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied. In this paper, we study a specific verification problem of computing a guaranteed range for the output of a deep neural network given a set of inputs represented as a convex polyhedron. Range estimation is a key primitive for verifying deep NNs. We present an efficient range estimation algorithm that uses a combination of local search and linear programming problems to efficiently find the maximum and minimum values taken by the outputs of the NN over the given input set. In contrast to recently proposed "monolithic" optimization approaches, we use local gradient descent to repeatedly find and eliminate local minima of the function. The final global optimum is certified using a mixed integer programming instance. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate the effectiveness of the proposed approach for verification of NNs used in automated control as well as those used in classification.
Arabic Text Classification Framework Based on Latent Dirichlet Allocation

In this paper, we present a new algorithm based on the LDA (Latent Dirichlet Allocation) and the Support Vector Machine (SVM) used in the classification of Arabic texts. Current research usually adopts Vector Space Model to represent documents in Text Classification applications. In this way, document is coded as a vector of words; n-grams. These features cannot indicate semantic or textual content; it results in huge feature space and semantic loss. The proposed model in this work adopts a “topics” sampled by LDA model as text features. It effectively avoids the above problems. We extracted significant themes (topics) of all texts, each theme is described by a particular distribution of descriptors, then each text is represented on the vectors of these topics. Experiments are conducted using an in-house corpus of Arabic texts. Precision, recall and F-measure are used to quantify categorization effectiveness. The results show that the proposed LDA-SVM algorithm is able to achieve high effectiveness for Arabic text classification task (Macro-averaged F1 88.1% and Micro-averaged F1 91.4%).
P5G: A Bio-Inspired Algorithm for the Superfluid Management of 5G Networks

5G is expected to become the dominant technology in the forthcoming years. In this work, we consider a 5G Superfluid network, as an outcome of the H2020 project SUPERFLUIDITY. The project exploits the concept of Reusable Functional Block (RFB), a virtual resource that can be deployed on top of 5G physical nodes. Specifically, we focus on the management of the RFBs in a Superfluid network to deliver a high definition video to the users. We design an efficient algorithm, called P5G, which is based on Particle Swarm Optimization (PSO). Our solution targets different Key Performance Indicators (KPIs), including the maximization of user throughput, or the minimization of the number of used 5G nodes. Results, obtained over a representative scenario, show that P5G is able to wisely manage the RFBs, while always guaranteeing a large throughput to the users.
Effects of MicroRNA-23a on Differentiation and Gene Expression Profiles in 3T3-L1 Adipocytes

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate growth, development, and programmed death of cells. A newly-published study has shown that miRNA-23a could regulate 3T3-L1 adipocyte differentiation. Here, we identified miRNA-23a as a negative regulator of 3T3-L1 adipocyte differentiation again. Over-expression of miRNA-23a inhibited differentiation and decreased lipogenesis as well as down-regulated mRNA and protein expression of both peroxisome proliferator-activated receptor (PPAR) γ and fatty acid binding protein (FABP) 4, whereas knock down of miRNA-23a showed the opposite effects on differentiation as well as increasing the number of apoptotic cells. Additionally, digital gene expression profiling sequencing (DGE-Seq) was used to assay changes in gene expression profiles following alterations in the level of miR-23a. In total, over-expression or knock down of miRNA-23a significantly changed the expression of 313 and 425 genes, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that these genes were mainly involved in the stress response, immune system, metabolism, cell cycle, among other pathways. Additionally, the signal transducer and activator of transcription 1 (Stat1) was shown to be a target of miRNA-23a by computational and dual-luciferase reporter assays that indicated Janus Kinase (Jak)-Stat signal pathway was implicated in regulating adipogenesis mediated by miRNA-23a in adipocytes.