Miracle-dz/UniFAR-FTUs huggingface dataset:
A free Kazakh speech database and a speech recognition baseline

Automatic speech recognition (ASR) has gained significant improvement for major languages such as English and Chinese, partly due to the emergence of deep neural networks (DNN) and large amount of training data. For minority languages, however, the progress is largely behind the main stream. A particularly obstacle is that there are almost no large-scale speech databases for minority languages, and the only few databases are held by some institutes as private properties, far from open and standard, and very few are free. Besides the speech database, phonetic and linguistic resources are also scarce, including phone set, lexicon, and language model. In this paper, we publish a speech database in Kazakh, a major minority language in the western China. Accompanying this database, a full set of phonetic and linguistic resources are also published, by which a full-fledged Kazakh ASR system can be constructed. We will describe the recipe for constructing a baseline system, and report our present results. The resources are free for research institutes and can be obtained by request. The publication is supported by the M2ASR project supported by NSFC, which aims to build multilingual ASR systems for minority languages in China.
Systematic Structure-Activity Relationship (SAR) Exploration of Diarylmethane Backbone and Discovery of A Highly Potent Novel Uric Acid Transporter 1 (URAT1) Inhibitor

In order to systematically explore and better understand the structure-activity relationship (SAR) of a diarylmethane backbone in the design of potent uric acid transporter 1 (URAT1) inhibitors, 33 compounds (1a–1x and 1ha–1hi) were designed and synthesized, and their in vitro URAT1 inhibitory activities (IC50) were determined. The three-round systematic SAR exploration led to the discovery of a highly potent novel URAT1 inhibitor, 1h, which was 200- and 8-fold more potent than parent lesinurad and benzbromarone, respectively (IC50 = 0.035 μM against human URAT1 for 1h vs. 7.18 μM and 0.28 μM for lesinurad and benzbromarone, respectively). Compound 1h is the most potent URAT1 inhibitor discovered in our laboratories so far and also comparable to the most potent ones currently under development in clinical trials. The present study demonstrates that the diarylmethane backbone represents a very promising molecular scaffold for the design of potent URAT1 inhibitors.
System Description for a Scalable, Fault-Tolerant, Distributed Garbage Collector

We describe an efficient and fault-tolerant algorithm for distributed cyclic garbage collection. The algorithm imposes few requirements on the local machines and allows for flexibility in the choice of local collector and distributed acyclic garbage collector to use with it. We have emphasized reducing the number and size of network messages without sacrificing the promptness of collection throughout the algorithm. Our proposed collector is a variant of back tracing to avoid extensive synchronization between machines. We have added an explicit forward tracing stage to the standard back tracing stage and designed a tuned heuristic to reduce the total amount of work done by the collector. Of particular note is the development of fault-tolerant cooperation between traces and a heuristic that aggressively reduces the set of suspect objects.
Enhancement of Noisy Speech with Low Speech Distortion Based on Probabilistic Geometric Spectral Subtraction

A speech enhancement method based on probabilistic geometric approach to spectral subtraction (PGA) performed on short time magnitude spectrum is presented in this paper. A confidence parameter of noise estimation is introduced in the gain function of the proposed method to prevent subtraction of the overestimated and underestimated noise, which not only removes the noise efficiently but also prevents the speech distortion. The noise compensated magnitude spectrum is then recombined with the unchanged phase spectrum to produce a modified complex spectrum prior to synthesize an enhanced frame. Extensive simulations are carried out using the speech files available in the NOIZEUS database in order to evaluate the performance of the proposed method.
The Influence of Policy Regimes on the Development and Social Implications of Privacy Enhancing Technologies

As privacy issues have gained social salience, entrepreneurs have begun to offer privacy enhancing technologies (PETs) and the U.S. has begun to enact privacy legislation. But "privacy" is an ambiguous notion. In the liberal tradition, it is an individualistic value protecting citizens from intrusion into a realm of autonomy. A feminist critique suggests that the social utility of privacy is to exclude certain issues from the public realm. Sociologists suggest that privacy is about identity management, while political economists suggest that the most salient privacy issue is the use of personal information to normalize and rationalize populations according to the needs of capital. While PETs have been developed for use by individual consumers, recently developers are focusing on the business to business market, where demand is stoked by the existence of new privacy regulations. These new laws tend to operationalize privacy in terms of "personally identifiable information. " The new generation of PETs reflect and reify that definition. This, in turn, has implications for the everyday understandings of privacy and the constitution of identity and social life. In particular, this socio-technical practice may strengthen the ability of data holders to rationalize populations and create self-serving social categories. At the same time, they may permit individuals to negotiate these categories outside of panoptic vision. They may also encourage public discussion and awareness of these created social categories.
Preferred well-founded semantics for logic programming by alternating fixpoints: preliminary report

We analyze the problem of defining well-founded semantics for ordered logic programs within a general framework based on alternating fixpoint theory. We start by showing that generalizations of existing answer set approaches to preference are too weak in the setting of well-founded semantics. We then specify some informal yet intuitive criteria and propose a semantical framework for preference handling that is more suitable for defining well-founded semantics for ordered logic programs. The suitability of the new approach is convinced by the fact that many attractive properties are satisfied by our semantics. In particular, our semantics is still correct with respect to various existing answer sets semantics while it successfully overcomes the weakness of their generalization to well-founded semantics. Finally, we indicate how an existing preferred well-founded semantics can be captured within our semantical framework.
Integrating online i-vector into GMM-UBM for text-dependent speaker verification

GMM-UBM is widely used for the text-dependent task for its simplicity and effectiveness, while i-vector provides a compact representation for speaker information. Thus it is promising to fuse these two frameworks. In this paper, a variation of traditional i-vector extracted at frame level is appended with MFCC as tandem features. Incorporating this feature into GMM-UBM system achieves 26% and 41% performance gain compared with DNN /-vector baseline on the RSR2015 and RedDots evaluation set, respectively. Moreover, the performance of the proposed system that trained on 86 hours data is on par with that of the DNN i-vector baseline trained on a much larger dataset (5000 hours).
Modeling Microscopic Chemical Sensors in Capillaries

Nanotechnology-based microscopic robots could provide accurate in vivo measurement of chemicals in the bloodstream for detailed biological research and as an aid to medical treatment. Quantitative performance estimates of such devices require models of how chemicals in the blood diffuse to the devices. This paper models microscopic robots and red blood cells (erythrocytes) in capillaries using realistic distorted cell shapes. The models evaluate two sensing scenarios: robots moving with the cells past a chemical source on the vessel wall, and robots attached to the wall for longer-term chemical monitoring. Using axial symmetric geometry with realistic flow speeds and diffusion coefficients, we compare detection performance with a simpler model that does not include the cells. The average chemical absorption is quantitatively similar in both models, indicating the simpler model is an adequate design guide to sensor performance in capillaries. However, determining the variation in forces and absorption as cells move requires the full model.
Failure strength assessment of pipes with local wall thinning under combined loading based on finite element analyses

Failure assessment of a pipe with local wall thinning draws increasing attention in the nuclear power plant industry. Although many guidelines have been developed and are used for assessing the integrity of a wall-thinned pipeline, most of these guidelines consider only pressure loading and thus neglect bending loading. As most pipelines in nuclear power plants are subjected to internal pressure and bending moment, an assessment procedure for locally wall-thinned pipeline subjected to combined loading is urgently needed. In this paper, three-dimensional finite element (FE) analyses are carried out to simulate full-scale pipe tests conducted for various shapes of wall-thinned area under internal pressure and bending moment. Maximum moments based on ultimate tensile stress were obtained from FE results to predict the failure of the pipe. These results are compared with test results, showing good agreement. Additional finite element analyses are then performed to investigate the effect of key parameters, such as wall-thinned depth, wall-thinned angle and wall-thinned length, on maximum moment. Moreover, the effect of internal pressure on maximum moment was investigated. Change of internal pressure did not show significant effect on the maximum moment.
Quantitative, time-resolved detection of CH 4 concentrations in flows for injection analysis in CNG engines using IR absorption

Abstract. The reduction of CO2 and other greenhouse gas emissions is an important driving force for the development of modern engines. Especially in the transport sector, the use of alternative fuels like methane, the main component of compressed natural gas (CNG), is an applied measure to achieve this goal. This work describes the development of an optical measurement system for accurate quantification of CH4 densities in gas flows based on broadband absorption of infrared light, i.e. non-dispersive IR absorption spectroscopy (NDIR). We demonstrate the capability of the system to achieve high time resolution as well as high measurement accuracy and precision. The optical set-up of the system is designed for usage at the inlet manifold of CNG-fuelled spark ignition engines. It allows for detailed analysis of the mixture formation during single engine cycles. CH4 densities can be determined by monitoring the infrared light attenuation around 3.3 µm caused by the ν3 anti-symmetric C–H-stretch vibration. We calculate the nonlinear relation between transmittance and CH4 density from absorption cross sections calculated from the HITRAN database. The theoretical transmittance signals are corrected for spectral influences of the bandpass filter, the detector sensitivity, the fibre transmittance and the emission spectrum of the light source in order to calculate CH4 densities directly from the measured transmittance. A calibration function corrects remaining differences between experimental and simulated values and improves the accuracy. We show that the sensor system is capable for determination of air–fuel ratios (λ-values) and demonstrate the dynamic quantification of a CH4 injection into a flow channel under various flow conditions. Furthermore, we present the first measurements with a prototype probe capable of measurements inside the inlet manifold of a four-stroke spark ignition engine. We validate the detection strategy in experiments with premixed gases using a modified inlet geometry and demonstrate its application under standard engine operation with port fuel injection while varying the injection parameters.
The antihypertensive MTHFR gene polymorphism rs17367504-G is a possible novel protective locus for preeclampsia

Objective: Preeclampsia is a complex heterogeneous disease commonly defined by new-onset hypertension and proteinuria in pregnancy. Women experiencing preeclampsia have increased risk for cardiovascular diseases (CVD) later in life. Preeclampsia and CVD share risk factors and pathophysiologic mechanisms, including dysregulated inflammation and raised blood pressure. Despite commonalities, little is known about the contribution of shared genes (pleiotropy) to these diseases. This study aimed to investigate whether genetic risk factors for hypertension or inflammation are pleiotropic by also being associated with preeclampsia. Methods: We genotyped 122 single nucleotide polymorphisms (SNPs) in women with preeclampsia (n = 1006) and nonpreeclamptic controls (n = 816) from the Norwegian HUNT Study. SNPs were chosen on the basis of previously reported associations with either nongestational hypertension or inflammation in genome-wide association studies. The SNPs were tested for association with preeclampsia in a multiple logistic regression model. Results: The minor (G) allele of the intronic SNP rs17367504 in the gene methylenetetrahydrofolate reductase (MTHFR) was associated with a protective effect on preeclampsia (odds ratio 0.65, 95% confidence interval 0.53–0.80) in the Norwegian cohort. This association did not replicate in an Australian preeclampsia case–control cohort (P = 0.68, odds ratio 1.05, 95% confidence interval 0.83–1.32, minor allele frequency = 0.15). Conclusion: MTHFR is important for regulating transmethylation processes and is involved in regulation of folate metabolism. The G allele of rs17367504 has previously been shown to protect against nongestational hypertension. Our study suggests a novel association between this allele and reduced risk for preeclampsia. This is the first study associating the minor (G) allele of a SNP within the MTHFR gene with a protective effect on preeclampsia, and in doing so identifying a possible pleiotropic protective effect on preeclampsia and hypertension.
Lithographing of Biomolecules on a Substrate Surface Using an Enzyme-Immobilized AFM Tip

A method of enzymatic lithography was successfully performed in a buffered solution using Staphylococcal serine V8 protease and atomic force microscopy (AFM). The retained activity of the protease immobilized on the AFM tip was confirmed by force measurement to rupture the enzyme−substrate complex. After contact scanning using the enzyme-immobilized tip to the substrate peptide layer, a square shape of the lithographed area was clearly observed by subsequent AFM imaging.
Better than the real thing?: iterative pseudo-query processing using cluster-based language models

We present a novel approach to pseudo-feedback-based ad hoc retrieval that uses language models induced from both documents and clusters. First, we treat the pseudo-feedback documents produced in response to the original query as a set of pseudo-query that themselves can serve as input to the retrieval process. Observing that the documents returned in response to the pseudo-query can then act as pseudo-query for subsequent rounds, we arrive at a formulation of pseudo-query-based retrieval as an iterative process. Experiments show that several concrete instantiations of this idea, when applied in conjunction with techniques designed to heighten precision, yield performance results rivaling those of a number of previously-proposed algorithms, including the standard language-modeling approach. The use of cluster-based language models is a key contributing factor to our algorithms' success.
Ramsey partitions and proximity data structures

This paper addresses the non-linear isomorphic Dvoretzky theorem and the design of good approximate distance oracles for large distortion. We introduce and construct optimal Ramsey partitions, and use them to show that for every epsiv isin (0,1), any n-point metric space has a subset of size n1-epsiv which embeds into Hilbert space with distortion O(1/epsiv). This result is best possible and improves part of the metric Ramsey theorem of Bartal et al. (2005), in addition to considerably simplifying its proof. We use our new Ramsey partitions to design approximate distance oracles with a universal constant query time, closing a gap left open by Thorup and Zwick (2005). Namely, we show that for any n point metric space X, and k ges 1, there exists an O(k)-approximate distance oracle whose storage requirement is O(n1+1k/), and whose query time is a universal constant. We also discuss applications to various other geometric data structures, and the relation to well separated pair decompositions
Laboratory Characterization and Evaluation of Durability Performance of New Polyester and Vinylester E-glass GFRP Dowels for Jointed Concrete Pavement

AbstractThis paper presents an experimental study that investigated the physical, mechanical, and durability characteristics of newly developed vinylester- and polyester-based glass-fiber-reinforced polymer (GFRP) dowels through a collaboration research project with the Ministry of Transportation of Quebec (MTQ). Durability performance was first evaluated with a preliminary set of conditionings in different solutions. The long-term performance was then assessed under harsh alkaline exposure simulating the concrete environment. The alkaline exposure was achieved by immersing the dowels in an alkaline solution at elevated temperatures to accelerate the effects. Thereafter, the properties were assessed and compared with the unconditioned reference values. The test parameters were (1) type of resin (vinylester and polyester), (2) diameter of GFRP dowels (25.4–44.8 mm), (3) temperature (23, 50, and 60°C), and (4) conditioning time (30, 60, and 180 days). The test results revealed that the vinylester-based GFRP.
Strategy Improvement for Concurrent Safety Games

We consider concurrent games played on graphs. At every round of the game, each player simultaneously and independently selects a move; the moves jointly determine the transition to a successor state. Two basic objectives are the safety objective: ``stay forever in a set F of states'', and its dual, the reachability objective, ``reach a set R of states''. We present in this paper a strategy improvement algorithm for computing the value of a concurrent safety game, that is, the maximal probability with which player 1 can enforce the safety objective. The algorithm yields a sequence of player-1 strategies which ensure probabilities of winning that converge monotonically to the value of the safety game. The significance of the result is twofold. First, while strategy improvement algorithms were known for Markov decision processes and turn-based games, as well as for concurrent reachability games, this is the first strategy improvement algorithm for concurrent safety games. Second, and most importantly, the improvement algorithm provides a way to approximate the value of a concurrent safety game from below (the known value-iteration algorithms approximate the value from above). Thus, when used together with value-iteration algorithms, or with strategy improvement algorithms for reachability games, our algorithm leads to the first practical algorithm for computing converging upper and lower bounds for the value of reachability and safety games.
In Search for Simplicity: A Self-Organizing Multi-Source Multicast Overlay

Group communication primitives have broad utility as building blocks for distributed applications. However, creating and maintaining the distributed structures that support these primitives is challenging, particularly when network and node characteristics are transient. We propose a solution for creating and maintaining multicast structures that uses an unstructured overlay and a novel approach to extract source-specific multicast trees. The strength of our solution comes from the following two properties: First, it decouples the overlay construction and maintenance mechanism from the tree-extraction mechanism, allowing for separate component optimization. Second, it relies on soft-state and passive data collection to adapt to the dynamics of the physical network, resulting in low protocol complexity. We implemented our solution and evaluated it on a 100-node PlanetLab testbed and on up to 1024-node emulated ModelNet networks. Experimental and analytical evaluations demonstrate low communication overhead (up to 1%), efficient network usage (low delay penalty and network stress) compared to alternative solutions, and ability to adapt quickly to network changes and recover from node failures. In addition, we have built a replica location service on top of this multicast layer to prove the practicality of our solution.
The evolution of navigable small-world networks

Small-world networks, which combine randomized and structured elements, are seen as prevalent in nature. Several random graph models have been given for small-world networks, with one of the most fruitful, introduced by Jon Kleinberg [10], showing in which type of graphs it is possible to route, or navigate, between vertices with very little knowledge of the graph itself. Kleinberg’s model is static, with random edges added to a fixed grid. In this paper we introduce, analyze and test a randomized algorithm which successively rewires a graph with every application. The resulting process gives a model for the evolution of small-world networks with properties similar to those studied by Kleinberg.
On learning thresholds of parities and unions of rectangles in random walk models

In a recent breakthrough, [Bshouty et al., J Comput Syst Sci 71 (2005), 250–265] obtained the first passive‐learning algorithm for DNFs under the uniform distribution. They showed that DNFs are learnable in the Random Walk and Noise Sensitivity models. We extend their results in several directions. We first show that thresholds of parities, a natural class encompassing DNFs, cannot be learned efficiently in the Noise Sensitivity model using only statistical queries. In contrast, we show that a cyclic version of the Random Walk model allows to learn efficiently polynomially weighted thresholds of parities. We also extend the algorithm of Bshouty et al. to the case of Unions of Rectangles, a natural generalization of DNFs to {0,…,b − 1}n. © 2007 Wiley Periodicals, Inc. Random Struct. Alg. , 2007
The Role of Conceptual Relations in Word Sense Disambiguation

We explore many ways of using conceptual distance measures in Word Sense Disambiguation, starting with the Agirre-Rigau conceptual density measure. We use a generalized form of this measure, introducing many (parameterized) refinements and performing an exhaustive evaluation of all meaningful combinations. We finally obtain a 42% improvement over the original algorithm, and show that measures of conceptual distance are not worse indicators for sense disambiguation than measures based on word-coocurrence (exemplified by the Lesk algorithm). Our results, however, reinforce the idea that only a combination of different sources of knowledge might eventually lead to accurate word sense disambiguation.