Miracle-dz/UniFAR-FTUs huggingface dataset:
High-performance geometric algorithms for sparse computation in big data analytics

Several leading supervised and unsupervised machine learning algorithms require as input similarities between objects in a data set. Since the number of pairwise similarities grows quadratically with the size of the data set, it is computationally prohibitive to compute all pairwise similarities for large-scale data sets. The recently introduced methodology of “sparse computation” resolves this issue by computing only the relevant similarities instead of all pairwise similarities. To identify the relevant similarities, sparse computation efficiently projects the data onto a low-dimensional space where a similarity is considered relevant if the corresponding objects are close in this space. The relevant similarities are then computed in the original space. Sparse computation identifies close pairs by partitioning the low-dimensional space into grid blocks, and considering objects close if they fall in the same or adjacent grid blocks. This guarantees that all pairs of objects that are within a specified L∞ distance are identified as well as some pairs that are within twice this distance. For very large data sets, sparse computation can have high runtime due to the enumeration of pairs of adjacent blocks. We propose here new geometric algorithms that eliminate the need to enumerate adjacent blocks. Our empirical results on data sets with up to 10 million objects show that the new algorithms achieve a significant reduction in runtime. The algorithms have applications in large-scale computational geometry and (approximate) nearest neighbor search. Python implementations of the proposed algorithms are publicly available.
Dietary intakes of starch and non-starch polysaccharides in a West African village

Dietary intakes of starch and non-starch polysaccharides (NSP) have been estimated for a rural West African community. These people eat directly from shared bowls of cooked food, and so measurement of any individual's food intake is not possible. Recently developed methodology for estimating food intake under these circumstances and the analysis of samples for dietary polysaccharides are combined to yield estimates of intakes of about 375 g starch and 25 g NSP/d for adult males, with lower intakes for women and children, related to their smaller body weights. These intakes are not direct measurements but are extremely plausible when compared with values obtained for other societies where the complication of the shared food bowl does not impinge upon the measurements.
On the design of hybrid peer-to-peer systems

In this paper, we consider hybrid peer-to-peer systems where users form an unstructured peer-to-peer network with the purpose of assisting a server in the distribution of data. We present a mathematical model that we use to analyze the scalability of hybrid peer-to-peer systems under two query propagation mechanisms: the random walk and the expanding ring. In particular, we characterize how the query load at the server, the load at peers as well as the query response time scale as the number of users in the peer-to-peer network increases. We show that, under a properly designed random walk propagation mechanism, hybrid peer-to-peer systems can support an unbounded number of users while requiring only bounded resources both at the server and at individual peers. This important result shows that hybrid peer-to-peer systems have excellent scalability properties. To the best of our knowledge, this is the first time that a theoretical study characterizing the scalability of such hybrid peer-to-peer systems has been presented. We illustrate our results through numerical studies.
Laser glass inspection system

The Megajoules laser works at high fluence.The damage threshold of slabs is limited by platinum inclusions inside the laserglass (Platinum is used for the melt inner sides). The light diffracted by theseinclusions can generate ghost focus points, and then induce heavy damage onthe optical components.The size of each slab is about 800 x 500 x 50 mm, and for the LMJ/NIF projet, totalquantity will be 6000.The size of the inclusions we need to detect is between I and 5 pm.Every single slab must be inspected, so there will be 6 slabs per day to inspectduring the 3 year production run.
DNA/Ag Nanoparticles as Antibacterial Agents against Gram-Negative Bacteria

Silver (Ag) nanoparticles were produced using DNA extracted from salmon milt as templates. Particles spherical in shape with an average diameter smaller than 10 nm were obtained. The nanoparticles consisted of Ag as the core with an outermost thin layer of DNA. The DNA/Ag hybrid nanoparticles were immobilized over the surface of cotton based fabrics and their antibacterial efficiency was evaluated using E. coli as the typical Gram-negative bacteria. The antibacterial experiments were performed according to the Antibacterial Standard of Japanese Association for the Functional Evaluation of Textiles. The fabrics modified with DNA/Ag nanoparticles showed a high enough inhibitory and killing efficiency against E. coli at a concentration of Ag ≥ 10 ppm.
Apical Hypertrophic Cardiomyopathy Mimicking Acute Coronary Syndrome: A Case Report and Review of the Literature

Apical hypertrophic cardiomyopathy is a form of hypertrophic cardiomyopathy localized to the left ventricular apex. It is common in Japanese and other Asian populations, where it is generally considered relatively benign. However, its presence has also been recognized, though less commonly, in non-Asian patients. In these patients, the electrocardiographic changes and symptoms associated with apical hypertrophic cardiomyopathy often mimic acute coronary syndromes. Invasive or noninvasive evaluation of the left ventricular cavity confirms the diagnosis, with the “ace-of-spades” sign on left ventriculography being pathognomonic. Its prognosis is relatively benign in terms of cardiovascular mortality; however, morbid sequelae, such as diastolic dysfunction, left atrial enlargement, apical thrombi, ventricular aneurysms, and myocardial infarction, are not uncommon. The authors present a case of apical hypertrophic cardiomyopathy in a Caucasian patient who presented with findings suggestive of acute coronary syndrome and review the literature on apical hypertrophic cardiomyopathy.
Molecular dynamics simulations of strained and defective carbon nabotubes

Abstract Carbon nanotubes are tubular molecules of pure carbon with typical diameters of1 nm – 100 nm and lengths from 100 nm up to several cm. The nanotubes haveoutstanding electronical and mechanical properties which has resulted in remark-able scientiﬁc interest and in proporsals of various applications. For example,their ability to be either metals or semiconductors enables the usage of nanotubesas components of electronic devices, while excellent mechanical characteristicsmotivate the use of nanotubes as reinforcement agents in composite structuresand in nanoelectromechanical devices.This thesis aims to contribute to the understanding of the mechanical proper-ties of carbon nanotubes and it contains two parts. The ﬁrst part concentrates oninitially defect-free but strained nanotubes and on the deformations and defectsinduced by the strain. The employed methods are empirical and tight bindingmolecular dynamics simulations. As results the criteria for uniform and discon-tinuous buckling deformations are reported. In addition, defect formation andstrain relaxation are discussed and the stability of various strained and deformedstructures is assessed.The second part of the thesis evaluates defects as a means to improve thebulk mechanical properties of a nanotube sample. Defects, and irradiation asa method of inducing them, are proposed to improve mechanical load transferbetween a nanotube and its surroundings. These proposals are veriﬁed by analyt-ics and molecular dynamics simulations based on classical empirical potential.The load transfer between nanotubes is found to improve signiﬁcantly in thepresence of defects. This concept is extended to bundles of nanotubes wherethe improved tube-tube load transfer is predicted to increase shear and stiffen thebundle at moderate irradiation doses. The load transfer has great signiﬁcancefor reinforcement of polymer composites in which the nanotube bundles may actas reinforcement ﬁbers. Furthermore, the mechanical degradation of individualtubes as a result of the defects is also assessed. Point defects have little effect onthe axial stiffness of an individual tube but the tensile strength may decrease to afraction of the strength for a perfect tube. Although individual tubes deterioratein strength because of the defects, the results indicate that the overall mechanicalproperties of a nanotube sample can be signiﬁcantly improved by imperfections
Efficient Skyline Computation in MapReduce

Skyline queries are useful for nding interesting tuples from a large data set according to multiple criteria. The sizes of data sets are constantly increasing and the architecture of back-ends are switching from single-node environments to non-conventional paradigms like MapReduce. Despite the usefulness of skyline queries, existing works on skyline computation in MapReduce do not take full advantage of parallelism but still run signicant parts serially. In this paper, we propose a novel approach to compute skylines eciently in MapReduce. We design a grid partitioning scheme to divide the data space into partitions, and employ a bitstring to represent the partitions. The bitstring is eciently obtained in MapReduce, and it clearly helps prune partitions (and tuples) that cannot have skyline tuples. Based on the grid partitioning, we propose two MapReduce algorithms to compute skylines. Both algorithms utilize the bitstring and distribute the original tuples to multiple mappers and make use of them to compute local skylines in parallel. In particular, MapReduce Grid Partitioning based Single-Reducer Skyline Computation (MR-GPSRS) employs a single reducer to assemble the local skylines appropriately to compute the global skyline. In contrast, MapReduce Grid Partitioning based Multiple Reducer Skyline Computation (MR-GPMRS) further divides local skylines and distributes them to multiple reducers that compute the global skyline in an independent and parallel manner. The proposed algorithms are evaluated through extensive experiments, and the results show that MR-GPMRS signicantly outperforms the alternatives in various settings.
Extracellular Vesicles in Alzheimer’s Disease: Friends or Foes? Focus on Aβ-Vesicle Interaction

The intercellular transfer of amyloid-β (Aβ) and tau proteins has received increasing attention in Alzheimer’s disease (AD). Among other transfer modes, Aβ and tau dissemination has been suggested to occur through release of Extracellular Vesicles (EVs), which may facilitate delivery of pathogenic proteins over large distances. Recent evidence indicates that EVs carry on their surface, specific molecules which bind to extracellular Aβ, opening the possibility that EVs may also influence Aβ assembly and synaptotoxicity. In this review we focus on studies which investigated the impact of EVs in Aβ-mediated neurodegeneration and showed either detrimental or protective role for EVs in the pathology.
Tactile-Vision Integration for Task-Compatible Fine-Part Manipulation

We propose to integrate tactile and visual sensing to predict task-compatible grasp regions for manipulation. We address the problem of fine-part assembly, by leveraging vision to observe scene-level information, then touch to perceive fine local details necessary for task completion. We directly target the end goal of task success, by predicting grasp regions that are simultaneously geometrically stable and task-compatible. We represent grasp regions with 2D probabilistic maps, which we first coarsely estimate with vision, and then refine by making contacts with the scene. We show preliminary results of probabilistic grasp regions generated by vision, and demonstrate the impact of tactile sensors for disambiguating task-compatible regions.
Finite model reasoning over existential rules*

Abstract Ontology-based query answering asks whether a Boolean conjunctive query is satisfied by all models of a logical theory consisting of a relational database paired with an ontology. The introduction of existential rules (i.e., Datalog rules extended with existential quantifiers in rule heads) as a means to specify the ontology gave birth to Datalog+/-, a framework that has received increasing attention in the last decade, with focus also on decidability and finite controllability to support effective reasoning. Five basic decidable fragments have been singled out: linear, weakly acyclic, guarded, sticky, and shy. Moreover, for all these fragments, except shy, the important property of finite controllability has been proved, ensuring that a query is satisfied by all models of the theory iff it is satisfied by all its finite models. In this paper, we complete the picture by demonstrating that finite controllability of ontology-based query answering holds also for shy ontologies, and it therefore applies to all basic decidable Datalog+/- classes. To make the demonstration, we devise a general technique to facilitate the process of (dis)proving finite controllability of an arbitrary ontological fragment.
A comparison of supervised and unsupervised cross-lingual speaker adaptation approaches for HMM-based speech synthesis

The EMIME project aims to build a personalized speech-to-speech translator, such that spoken input of a user in one language is used to produce spoken output that still sounds like the user's voice however in another language. This distinctiveness makes unsupervised cross-lingual speaker adaptation one key to the project's success. So far, research has been conducted into unsupervised and cross-lingual cases separately by means of decision tree marginalization and HMM state mapping respectively. In this paper we combine the two techniques to perform unsupervised cross-lingual speaker adaptation. The performance of eight speaker adaptation systems (supervised vs. unsupervised, intra-lingual vs. cross-lingual) is compared using objective and subjective evaluations. Experimental results show the performance of unsupervised cross-lingual speaker adaptation is comparable to that of the supervised case in terms of spectrum adaptation in the EMIME scenario, even though automatically obtained transcriptions have a very high phoneme error rate.
Mission Statement: ToleranceZone A Self-Stabilizing

—Wireless sensor networks (WSN) can be used in a wide range of monitoring and controlling applications. These networks consist of nodes with sparse resources, which makes application implementation challenging. Therefore, many middleware systems were developed in the last decade. Furthermore, unattended and long-living deployments of WSNs need fault-tolerant software architectures. The goal of the T OLERANCE Z ONE project is to design a self-stabilizing middleware, which supports the development of autonomously recovering and highly fault-tolerant WSN applications.
Randomized Evaluation of Institutions: Theory with Applications to Voting and Deliberation Experiments

We study causal inference in randomized experiments where the treatment is a decision making process or an institution such as voting, deliberation or decentralized governance. We provide a statistical framework for the estimation of the intrinsic eect of the institution. The proposed framework builds on a standard set-up for estimating causal eects in randomized experiments with noncompliance (Hirano-Imbens-Rubin-Zhou (2000)). We use the model to re- analyze the eect of deliberation on voting for programmatic platforms in Benin (Wantchekon (2008)), and provide practical suggestions for the implementation and analysis of experiments involving institutions.
Chemical Analysis through CL-Detection Assisted by Periodate Oxidation

The progress of the research work of the author and his colleagues on the field of CL-emission generated by pyrogallol oxidation and further application for the direct determination of periodate and indirect or direct determination of other compounds through flow-injection manifold/CL-detection set up is described. The instrumentation used for these studies was a simple flow-injection manifold that provides good reproducibility, coupled to a red sensitive photomultiplier that gives sensitive CL-detection. In addition, recent reports on studies and analytical methods based on CL-emission generated by periodate oxidation by other authors are included.
The Asymptotics of Selecting the Shortest of Two, Improved

We investigate variations of a novel, recently proposed load balancing scheme based on small amounts of choice. The static (hashing) setting is modeled as a balls-and-bins process. The balls are sequentially placed into bins, with each ball selecting d bins randomly and going to the bin with the fewest balls. A similar dynamic setting is modeled as a scenario where tasks arrive as a Poisson process at a bank of FIFO servers and queue at one for service. Tasks probe a small random sample of servers in the bank and queue at the server with the fewest tasks. Recently it has been shown that breaking ties in a xed, asymmetric fashion, actually improves performance, whereas in all previous analyses, ties were broken randomly. We demonstrate the nature of this improvement using uid limit models, suggest further improvements, and verify and quantify the improvement through simulations.
Investigation of architecture, key technology and application strategy for the internet of things

The article firstly analyzes the history and concept of internet of things (IOT) and discusses in depth the contents of IOT. Secondly it introduces the system architecture of China Communications Standards Association for IOT and offers the model. It introduces the key technologies in the development of IOT mainly— wireless sensor network technology, core bearer network communication technology and Intelligent Data Processing. Finally, this article proposes several suggestions to application strategy and design according to the current state of IOT.
Natural compound methyl protodioscin protects against intestinal inflammation through modulation of intestinal immune responses

Dioscoreaceae, a kind of yam plant, has been recommended for treatment of chronic inflammatory conditions. However, the mechanisms are poorly defined. Methyl protodioscin (MPD) is one of the main bioactive components in Dioscoreaceae. Here, we aim to determine the mechanisms by which MPD ameliorates intestinal inflammation. Surgical intestinal specimens were collected from inflammatory bowel diseases (IBD) patients to perform organ culture. Experimental colitis was induced in mice by dextran sulfate sodium (DSS) or Citrobacter rodentium, and was then treated with MPD. NF‐κB activation, expression of mucosal pro‐inflammatory cytokines, disease severity, and epithelial proliferation/apoptosis were determined. Mouse crypts and Caco‐2 monolayers were cultured to observe the effect of MPD upon intestinal epithelial differentiation and barrier function. We found that MPD increased the percentage of survival from high‐dose DSS‐(4%) treated mice, and accelerated mucosal healing and epithelial proliferation in low‐dose DSS‐(2.5%) treated mice characterized by marked reduction in NF‐κB activation, pro‐inflammatory cytokines expression and bacterial translocation. Consistently, MPD protected colonic mucosa from C. rodentium‐induced colonic inflammation and bacterial colonization. In vitro studies showed that MPD significantly increased crypt formation and restored intestinal barrier dysfunction induced by pro‐inflammatory cytokines. In conclusion, MPD ameliorates the intestinal mucosal inflammation by modulating the intestinal immunity to enhance intestinal barrier differentiation. MPD could be an alternative for treating chronic intestinal inflammatory diseases.
Phase 2, Dose-Ranging Study of Relebactam with Imipenem-Cilastatin in Subjects with Complicated Intra-abdominal Infection

ABSTRACT Relebactam (REL [MK-7655]) is a novel class A/C β-lactamase inhibitor intended for use with imipenem for the treatment of Gram-negative bacterial infections. REL restores imipenem activity against some resistant strains of Klebsiella and Pseudomonas. In this multicenter, double-blind, controlled trial (NCT01506271), subjects who were ≥18 years of age with complicated intra-abdominal infection were randomly assigned (1:1:1) to receive 250 mg REL, 125 mg REL, or placebo, each given intravenously (i.v.) with 500 mg imipenem-cilastatin (IMI) every 6 h (q6h) for 4 to 14 days. The primary efficacy endpoint was the proportion of microbiologically evaluable (ME) subjects with a favorable clinical response at discontinuation of i.v. therapy (DCIV). A total of 351 subjects were randomized, 347 (99%) were treated, and 255 (73%) were ME at DCIV (55% male; mean age, 49 years). The most common diagnoses were complicated appendicitis (53%) and complicated cholecystitis (17%). Thirty-six subjects (13%) had imipenem-resistant Gram-negative infections at baseline. Both REL doses plus IMI were generally well tolerated and demonstrated safety profiles similar to that of IMI alone. Clinical response rates at DCIV were similar in subjects who received 250 mg REL plus IMI (96.3%) or 125 mg REL plus IMI (98.8%), and both were noninferior to IMI alone (95.2%; one-sided P < 0.001). The treatment groups were also similar with respect to clinical response at early and late follow-up and microbiological response at all visits. Pharmacokinetic/pharmacodynamic simulations show that imipenem exposure at the proposed dose of 500 mg IMI with 250 mg REL q6h provides coverage of >90% of carbapenem-resistant bacterial strains.
An Approach for Solving the Permutation Problem of Convolutive Blind Source Separation Based on Statistical Signal Models

In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. Transformed to the frequency domain, existing algorithms can efficiently solve the reduction of the source separation problem into independent instantaneous separation in each frequency bin. However, this independency leads to the problem of correctly aligning these single bins. The new algorithm models the frequency-domain separated signals by means of the generalized Gaussian distribution and employs the small deviation of the parameters between neighboring bins for the detection of correct permutations. The performance of the algorithm will be demonstrated on synthetic and real-world data.
Sensor planning for mobile robot localization using Bayesian network inference

We propose a new method of sensor planning for mobile robot localization using Bayesian network inference. Since we can model causal relations between situations of the robot's behavior and sensing events as nodes of a Bayesian network, we can use the inference of the network for dealing with uncertainty in sensor planning and thus derive appropriate sensing actions. In this system we employ a multi-layered-behavior architecture for navigation and localization. This architecture effectively combines mapping of local sensor information and the inference via a Bayesian network for sensor planning. The mobile robot recognizes the local sensor patterns for localization and navigation using a learned regression function. Since the environment may change during the navigation and the sensor capability has limitations in the real world, the mobile robot actively gathers sensor information to construct and reconstruct a Bayesian network, and then derives an appropriate sensing action which maximizes a utility function based on inference of the reconstructed network. The utility function takes into account belief of the localization and the sensing cost. We have conducted some simulation and real robot experiments to validate the sensor planning system.