Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of Image Data Processing Techniques for Sessile Drop Method

Both profile fitting and curvature plotting methods have previously been submitted as new image processing techniques in the sessile drop method by the authors． The purpose of this research is to elucidate the characteris− tics and the applicable Iimits of these data processing methods（including the traditional Bashforth and Adams method）and in tum submit a guide to their choice． Both theoretical sessile−drop profile as a solution of Laplace equation and sessile drop profile from the present experiment are processed by the above methods． As a result， the precision and the optimal applicable range of these methods are discussed． The results obtained are as follows．（1）Under the present processing conditions， the precision of the profile determination is estimated to be±1．3μm． This value is comparable to that obtained with an optical projecting magnifier which is usually used in the traditional Bashforth and Adams method．（2）The error in the data process− ing through the use of the profile fitting method is determined to be less than O．1％， independent of the number of profile data points（more than 130 points）．（3）The precision of the presumedβ一value， which was defined by Bashforth and Adams， by the curvature plotting method is dependent on the number of profile data points defi− nitely。 However， the curvature plotting method is optimal in order to inspect a locaHittle change in the sessile drop profile．（4）The traditional Bashforth and Adams method is very simple and requires only a few measuring points． However， it is apparent that the confirmation of fitting of an experilnental profile with a recalculated profile from the obtainedβ一value
Failure history data-based test case prioritization for effective regression test

For regression testing in the continuous integration environments, the time and cost should be considered; to satisfy these constraints, it is necessary to improve the test efficiency regarding the achievement of the test goal. It is especially important to identify the problem quickly by first executing a test case with a high probability of failure. This paper therefore proposes the FHD (Failure History Data)-Prioritization technique for the purpose of effective regression testing. This technique uses the failed test case history, the flipped result of method test case and the correlation data as the prioritization criteria, and the algorithm is designed to calculate the weight through the following two-step classification: 1) The FHD-Prioritization analyzes the failure history data statistically and sequentially arranges the test cases from the highest failure occurrence probability in the current session. 2) If a failure occurs during the test, the FHD-Prioritization reprioritizes in real time based on the correlation data. The performance of the FHD-Prioritization technique is evaluated with Tomcat and Camel, Apache open source software projects that were developed in the continuous integration environment. Because all of these projects are composed of the real faults of real-world projects, it is possible to practically evaluate the efficiency of the proposed approach. The FHD-Prioritization improved the efficiency of test case prioritization by about 5.62+ and 2.17+, respectively, compared to ROCKET and AFSAC.
Next-Generation Sequencing Reveals Significant Bacterial Diversity of Botrytized Wine

While wine fermentation has long been known to involve complex microbial communities, the composition and role of bacteria other than a select set of lactic acid bacteria (LAB) has often been assumed either negligible or detrimental. This study served as a pilot study for using barcoded amplicon next-generation sequencing to profile bacterial community structure in wines and grape musts, comparing the taxonomic depth achieved by sequencing two different domains of prokaryotic 16S rDNA (V4 and V5). This study was designed to serve two goals: 1) to empirically determine the most taxonomically informative 16S rDNA target region for barcoded amplicon sequencing of wine, comparing V4 and V5 domains of bacterial 16S rDNA to terminal restriction fragment length polymorphism (TRFLP) of LAB communities; and 2) to explore the bacterial communities of wine fermentation to better understand the biodiversity of wine at a depth previously unattainable using other techniques. Analysis of amplicons from the V4 and V5 provided similar views of the bacterial communities of botrytized wine fermentations, revealing a broad diversity of low-abundance taxa not traditionally associated with wine, as well as atypical LAB communities initially detected by TRFLP. The V4 domain was determined as the more suitable read for wine ecology studies, as it provided greater taxonomic depth for profiling LAB communities. In addition, targeted enrichment was used to isolate two species of Alphaproteobacteria from a finished fermentation. Significant differences in diversity between inoculated and uninoculated samples suggest that Saccharomyces inoculation exerts selective pressure on bacterial diversity in these fermentations, most notably suppressing abundance of acetic acid bacteria. These results determine the bacterial diversity of botrytized wines to be far higher than previously realized, providing further insight into the fermentation dynamics of these wines, and demonstrate the utility of next-generation sequencing for wine ecology studies.
GPU-Aware Non-contiguous Data Movement In Open MPI

Due to better parallel density and power efficiency, GPUs have become more popular for use in scientific applica- tions. Many of these applications are based on the ubiquitous Message Passing Interface (MPI) programming paradigm, and take advantage of non-contiguous memory layouts to exchange data between processes. However, support for efficient non- contiguous data movements for GPU-resident data is still in its infancy, imposing a negative impact on the overall application performance. To address this shortcoming, we present a solution where we take advantage of the inherent parallelism in the datatype pack- ing and unpacking operations. We developed a close integration between Open MPI's stack-based datatype engine, NVIDIA's Unified Memory Architecture and GPUDirect capabilities. In this design the datatype packing and unpacking operations are offloaded onto the GPU and handled by specialized GPU kernels, while the CPU remains the driver for data movements between nodes. By incorporating our design into the Open MPI library we have shown significantly better performance for non-contiguous GPU-resident data transfers on both shared and distributed memory machines.
Regulation of Pacing Rate by Variations of Mixed Venous Oxygen Saturation

La saturation d'oxygene du sang du ventricule droit (So2) serait un capteur idéal pour asservir la fréquence d'un stimulateur implanté. Afin de prouver cette hypothèse, des inesures de So2 au repos et à l'éxercice ont été réalisées chez des sujets normaux et chez des patients porteurs de stimulateurs; ensuite un algorithme a été développé pour faire varier la fréquence de stimulation en fonction de la So2 mesurée. Les résultats suggèrent que cette méthode pourrait être utile aux implants à long terme.
Power-saving mechanisms in emerging standards for wireless LANs: the MAC level perspective

This article provides an overview of mechanisms used for power saving in the upcoming standards for wireless LANs: IEEE 802.11 and ETSI RES 10 HIPERLAN. Power saving on the MAC level is addressed by these standards in a quite different way. We outline the main features of the mechanisms in both standards in terms of power saving. In addition we present simulation studies of the power-saving mechanism in ad hoc configurations of IEEE 802.11 networks, which demonstrate the optimization potential and some performance trade-offs quantitatively.
Cloning and identification of a two‐component signal‐transducing regulatory system from Bacteroides fragilis

A DNA fragment was cloned from Bacteroides fragilis that bestowed low‐level tetracycline resistance to Escherichia coli strains harbouring the cloned fragment on a multicopy plasmid. The tetracycline resistance determinant was localized to a 4.3kb BglII–Pstl subfragment of the original clone. DNA sequence analysis of this fragment revealed that it contained an operon encoding two proteins: one of 519 amino acids, RprX, and a second of 236 amino acids, RprY. Protein sequence analysis revealed that the two proteins shared sequence identity with a family of multicomponent signal‐transducing regulatory proteins identified from many diverse bacterial genera. RprX shared identity with the first component of the regulatory system, the histidine protein kinase receptor (for example EnvZ, PhoR, CheA, and VirA). RprY shared identity with the second member of the regulatory protein pair, the regulatory response protein (for example OmpR, PhoB, CheY, and VirG). Expression of these proteins from a multicopy plasmid vector in E. coli resulted in a decrease in the level of the outer membrane porin protein OmpF and an increase in the level of the outer membrane porin protein OmpC. The decrease in OmpF levels correlates with, and may be the cause of, the increased tetracycline resistance. Regulation of the levels of OmpF and OmpC is normally controlled by a multicomponent signal‐transducing regulatory pair of proteins, EnvZ and OmpR. The effect RprX and RprY have on OmpF expression is mediated at the level of transcription. Thus, RprX and RprY may be interfering with the normal regulation of OmpF by OmpR and EnvZ.
Parallel Orthomin Equation Solver on the Cell Broadband Engine

This paper presents our parallelization and implementation of the ORTHOMIN solver on the Cell Broadband Engine. The solution of linear systems of equation sis one of the most central processing unit-intensive steps in many engineering and simulation applications and can greatly benefit from the multitude of SIMD-capable synergistic processor element (SPE) cores in the Cell processor. We report the serial ORTHOMIN implementation on the Cell's PowerPCprocessor element (PPE), and the parallelization and performance analysis of ORTHOMIN across 8 SPEs forTridiagonal (1-D reservoir grid) and Heptadiagonal (3-Dreservoir grid) matrices. Our implementation is shown to scale well with data size, and grid dimensionality.
Steering a Virtual Crowd Based on a Semantically Augmented Navigation Graph

In this paper, we show our improvements in crowd behavior, rendering, and animation : based on our navigation graph [1], we are able to add, in real time, semantic information to the different zones of the virtual world. With an intuitive GUI, the user can steer the virtual humans through the environment with high-level instructions. The rendering of virtual humans is improved in 2 ways : firstly, the number of possible joints for the virtual humans is increased by compressing their matrices before sending them to the graphics card, and secondly, static meshes are used instead of billboards for distant people. We also present the way we generate and use many different walk and run animations in order to vary the crowd’s movements, based on the work of Glardon et. al. [2, 3]. Our results are illustrated with an application where a crowd of more than 5000 virtual humans are steered in an urban environment.
Securing data planes in software-defined networks

Ensuring correct data-plane operations is an integral part of securing software-defined networks (SDN). This paper explores practical solutions for localizing and mitigating malicious switches that disobey installed flow rules. Our main insight is that the flexible and proactive nature of SDNs enables efficient defense against realistic adversaries who can collude and report falsified information in addition to manipulating packet forwarding decisions. In contrast, previous proposals either assume a simple threat model or require expensive cryptographic operations even during peacetime. To systematically explore the design space, we study three complementary techniques for data-plane security, that is, active probing, statistics checking, and packet obfuscation. This paper presents the initial design and implementation of our data-plane security system and highlight potential research challenges.
Understanding Stress: Characteristics and Caveats

Exposure to stressful situations is among the most common human experiences. These types of situations can range from unexpected calamities to routine daily annoyances. In response to stressors, a series of behavioral, neurochemical, and immunological changes occur that ought to serve in an adaptive capacity. However, if those systems become overly taxed, the organism may become vulnerable to pathology. Likewise, the biological changes, if sufficiently sustained, may themselves adversely affect the organism’s well-being. Several factors may dictate an individual’s response to environmental stressors, including characteristics of the stressor (i.e., type of stressor and its controllability, predictability, and chronicity); biological factors (i.e., age, gender, and genetics); and the subject’s previous stressor history and early life experiences. Research on the physiological and psychological responses to different types of stressful stimuli is presented, focusing particularly on processes that may be relevant to the development of alcohol use disorders. Stressful events may profoundly influence the use of alcohol or other drugs (AODs). For example, the resumption of AOD use after a lengthy period of abstinence may reflect a person’s attempt to self-medicate to attenuate the adverse psychological consequences of stressors (e.g., anxiety). Alternatively, stress may increase the reinforcing effects of AODs.
Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.
A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning

Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and use the knowledge to choose actions and acquire desired outcomes. It has been proposed that the orbitofrontal cortex (OFC) encodes the task state space during reinforcement learning. However, it is not well understood how the OFC acquires and stores task state information. Here, we propose a neural network model based on reservoir computing. Reservoir networks exhibit heterogeneous and dynamic activity patterns that are suitable to encode task states. The information can be extracted by a linear readout trained with reinforcement learning. We demonstrate how the network acquires and stores the task structures. The network exhibits reinforcement learning behavior and its aspects resemble experimental findings of the OFC. Our study provides a theoretical explanation of how the OFC may contribute to reinforcement learning and a new approach to understanding the neural mechanism underlying reinforcement learning.
Understanding the finite state projection and related methods for solving the chemical master equation

The finite state projection (FSP) method has enabled us to solve the chemical master equation of some biological models that were considered out of reach not long ago. Since the original FSP method, much effort has gone into transforming it into an adaptive time-stepping algorithm as well as studying its accuracy. Some of the improvements include the multiple time interval FSP, the sliding windows, and most notably the Krylov-FSP approach. Our goal in this tutorial is to give the reader an overview of the current methods that build on the FSP.
Indexing the Content of Multimedia Documents

As the concept of what constitutes a "content-based" search grows more mature, it becomes valuable for us to establish a clear sense of just what is meant by "content. " Recent multimedia document retrieval systems have dealt with this problem by indexing across multiple indexes; but it is important to identify how such multiple indexes are dealing with multiple dimensions of a description space, rather than simply providing the user with more descriptors. In this paper we consider a description space for multimedia documents based on three "dimensions" of a document, namely context, form, and content. We analyze the nature of this space with respect to three challenging examples of multimedia search tasks, and we address the nature of the index structures that would facilitate how these tasks may be achieved. These examples then lead us to some general conclusions on the nature of multimedia indexing and the intuitions we have inherited from the tradition of books and libraries.
Common structural features of cholesterol binding sites in crystallized soluble proteins

Cholesterol-protein interactions are essential for the architectural organization of cell membranes and for lipid metabolism. While cholesterol-sensing motifs in transmembrane proteins have been identified, little is known about cholesterol recognition by soluble proteins. We reviewed the structural characteristics of binding sites for cholesterol and cholesterol sulfate from crystallographic structures available in the Protein Data Bank. This analysis unveiled key features of cholesterol-binding sites that are present in either all or the majority of sites: i) the cholesterol molecule is generally positioned between protein domains that have an organized secondary structure; ii) the cholesterol hydroxyl/sulfo group is often partnered by Asn, Gln, and/or Tyr, while the hydrophobic part of cholesterol interacts with Leu, Ile, Val, and/or Phe; iii) cholesterol hydrogen-bonding partners are often found on α-helices, while amino acids that interact with cholesterol’s hydrophobic core have a slight preference for β-strands and secondary structure-lacking protein areas; iv) the steroid’s C21 and C26 constitute the “hot spots” most often seen for steroid-protein hydrophobic interactions; v) common “cold spots” are C8–C10, C13, and C17, at which contacts with the proteins were not detected. Several common features we identified for soluble protein-steroid interaction appear evolutionarily conserved.
Integration of the VSC-based HVDC model into an OPF Matlab-based analysis

This paper presents the integration of the Voltage Source Converted-High Voltage Direct Current model| (VSCHVDC) into a conventional Optimal Power Flow (OPF) formulation. The assembled VSC-HVDC-OPF model is computationally implemented and solved by using the Matlab ® optimization toolbox. In this sense, the development complexity degree of the proposed implementation is equivalent to that required by commercial optimization packages for solving optimization models. Therefore, this proposal hugely reduces the time to the implementation and for obtaining results of an OPF analysis of power systems with VSC-HVDC devices. In order to illustrate numerically the prowess of the proposed Matlab based implementation to solve the VSC-HVDC-OPF model, two numerical examples with the IEEE two-area benchmark power system and the 46-machine, 190-bus model of Mexican system are presented.
Proceedings of the National Academy of Sciences

The structure of hyaluronic acid has been elucidated largely by the work of Meyer and collaborators,1' 2, I who have shown it to be a linear polymer of alternating units of N-acetyl-D-glucosamine and D-glucuronic acid. The monosaccharide components appear to be combined in alternating f8-1,3and p-1,4linkages, the former being those involving C1 of GA4 and the latter those involving Cl of AG. Hyaluronic acid isolated from different tissues appears to have constant chemical composition3' L 6 but varies in average molecular weight from 300,000 to 1,500,000.7 No enzymatic synthesis of hyaluronic acid by cell-free preparations has been reported. We have now been able to demonstrate the synthesis of oligosaccharide chains having the structure of hyaluronic acid by using cell-free homogenates of the Rous chicken sarcoma,8 a tissue which has been known for a long time to produce large quantities of the mucopolysaccharide6 9, 10 and to have very little if any active hyaluronidase.10 Synthesis of the oligosaccharides has been shown by incubating sarcoma homogenates with uridine diphospho-N-acetyl-glucosamine labeled with C14 in the N-acetyl group, together with uridine diphosphoglucuronic acid or with uridine diphosphoglucose and diphosphopyridine nucleotide. In some experiments the homogenates were incubated with uridine triphosphate and N-acetyl-glucosamine-6-phosphate labeled with C14 in the N-acetyl group. The preparation of the latter material with a specific yeast N-acetylase has been described.11 UDPAg was discovered by Leloir and co-workers,12' 13 but no coenzymatic function for it was demonstrated. UDPGA has been isolated and found to be the substrate for glucuronide synthesis by liver extracts. '4' 15 The formation of UDPGA by enzymatic oxidation of UDPG in the presence of DPN has been described by Kalckar and coworkers.16 Evidence discussed below indicates that UDPAg and UDPGA are the substrates from which hyaluronic acid chains are synthesized.
Constrained LMS for dynamic flow networks

In this era of climate change, there is a growing need to offer adaptive learning algorithms in the optimisation of natural resources. These resources are typically optimised by evolutionary algorithms. However, evolutionary algorithms (EAs) are no longer adequate due to the ‘drift’ component introduced by environmental factors such as flash flooding. We therefore propose a novel constrained Least Mean Squares (LMS) algorithm for the optimisation of flow networks. For rigor, we provide a stability analysis of our adaptive algorithm, which enables us to interpret the physical meaning of the network at equilibrium. We evaluate our proposed method against genetic algorithm (GA), the most common evolutionary algorithm. The results are promising: not only the proposed constrained LMS has a performance advantage over GA, but its computational cost is significantly lower making it more suitable for real-time applications.
Ensembles of extremely randomized trees and some generic applications

In this paper we present a new tree-based en- semble method called "Extra-Trees". This algorithm aver- ages predictions of trees obtained by partitioning the input- space with randomly generated splits, leading to significant improvements of precision, and various algorithmic advan- tages, in particular reduced computational complexity and scalability. We also discuss two generic applications of this algorithm, namely for time-series classification and for the automatic inference of near-optimal sequential decision poli- cies from experimental data.