Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Diverse Solutions of High Quality to Binary Integer Programs

Typical output from an optimization solver is a single optimal solution. At the same time, a set of high-quality and diverse solutions could be beneficial in a variety of contexts, for example problems involving imperfect information, or those for which the structure of high-quality solution vectors can reveal meaningful insights. In view of this, we discuss a new method to obtain multiple high-quality yet diverse solutions to pure binary (0–1) integer programs, employing fractional programming techniques to manage these typically competing goals. Specifically, we develop a general approach that makes use of Dinkelbach’s algorithm to sequentially generate solutions that evaluate well with respect to both i) individual performance and, as a whole, ii) mutual variety. Experiments on a number of test instances yield encouraging computational results.
Simulation Performance of MMSE Iterative Equalization with Soft Boolean Value Propagation

The performance of MMSE Iterative Equalization based on MAP-SBVP and COD-MAP algorithms (for generating extrinsic information) are compared for fading and non-fading communication channels employing serial concatenated convolution codes. MAP-SBVP is a convolution decoder using a conventional soft-MAP decoder followed by a soft-convolution encoder using the soft-boolean value propagation (SBVP). From the simulations it is observed that for MMSE Iterative Equalization, MAP-SBVP performance is comparable to COD-MAP for fading and non-fading channels.
Performance Evaluation of a Flooding Detection Mechanism for VoIP Networks

The Internet based telephony services (IPTel) are mainly exposed to set of vulnerabilities that inherited from the employed protocols such as TCP/IP and proprietary VoIP protocols. One of the most critical threats in this sensitive environments is considered the denial of service (DoS) attacks. The main concern of a mechanism that focuses on detecting such attacks is the potential end-to-end delay between communicating parties. In this paper is described a hash based flooding detection mechanism and evaluated in an experimental test bed architecture. The outcomes demonstrate the potentiality of the mechanism as the end-to-end delay is negligible.
B-cell nuclear proteins binding in vitro to the human immunoglobulin kappa enhancer: localization by exonuclease protection

Proteins capable of interacting with the enhancer of the immunoglobulin kappa gene in vitro have been detected in extracts of nuclei from human B cells and from human, mouse, and rabbit spleens. The experiments, based on an exonuclease protection technique, demonstrate nuclear protein factors binding to a 30- to 35-base-pair domain containing both the simian virus 40 enhancer core element (TTTCCA) and the octamer CAGGTGGC that was previously identified as the consensus sequence for protein-binding sites in the murine immunoglobulin heavy-chain enhancer. This 30- to 35-base-pair domain in the human kappa enhancer is homologous to a site of protein binding detected in the murine kappa enhancer by other investigators using a gel retardation assay. Our results complement in vivo dimethyl sulfate footprinting studies of the human immunoglobulin kappa enhancer which demonstrated B cell-specific changes in guanine reactivity immediately 5' to the consensus octamer. Together, these findings suggest that DNA-binding proteins in B-cell nuclei interact with the 5' portion of the human kappa-gene enhancer. Such proteins could play a role in the B cell-specific transcription of the human immunoglobulin kappa gene.
Performance Analysis of NAND Flash Based Cache for Network Storage System

The high latency and low bandwidth nature of mobile network connection poses a great challenge in the use of network storage system in portable consumer electronics devices. To extend the use of network storage systems, such as cloud storage services in portable consumer electronics device, the built-in NAND flash device can be taken as a front-end cache for network storage system, so the efficiency of network storage system via mobile network can be boosted. This paper briefly introduces the structure of NAND flash cached network storage system, and analyzes the performance of NAND flash based cache in various important aspects. Performance evaluation is done on a NAND flash cache simulator, and a recommended structure of NAND flash based cache is given based on the results of evaluation experiments.
Analysis and Interpretation of the Human Microbiome

Abstract:Microbiome research has experienced an unprecedented level of growth over the last decade. This is largely due to revolutionary developments in, and accessibility to, DNA sequencing technologies that have enabled laboratories with even modest budgets to undertake projects. Study of the human microbiome in particular has seen a surge in interest, and although a lot of time and money has been focused on health and disease, the clinical interpretation of these data and the ability of clinicians to understand these studies in the context of disease are less straightforward. Conditions such as inflammatory bowel disease, asthma, and cancer have seen a huge increase in research focused on the role of microbiome in disease pathogenesis, but the ability of clinicians to appraise and use these data is largely lacking. The purpose of this article is to provide an introduction for clinicians and nonclinicians wishing to learn about and engage in microbiome research. It details the background of microbiome research and discusses the process of generating 16S rRNA sequencing data, the most commonly used method for microbiome analysis. We discuss the interpretation of results in a clinical context, commonly used metrics for analysis and discuss future impact and direction for microbiome research. The meteoric rise of genomic medicine to the brink of routine clinical use should be seen as a blueprint for the microbiome; the ability for physicians to understand and interpret these data is vital to this growth and aiding clinicians (and researchers) to participate in further microbiome research.
VChunkJoin: An Efficient Algorithm for Edit Similarity Joins

Similarity joins play an important role in many application areas, such as data integration and cleaning, record linkage, and pattern recognition. In this paper, we study efficient algorithms for similarity joins with an edit distance constraint. Currently, the most prevalent approach is based on extracting overlapping grams from strings and considering only strings that share a certain number of grams as candidates. Unlike these existing approaches, we propose a novel approach to edit similarity join based on extracting nonoverlapping substrings, or chunks, from strings. We propose a class of chunking schemes based on the notion of tail-restricted chunk boundary dictionary. A new algorithm, VChunkJoin, is designed by integrating existing filtering methods and several new filters unique to our chunk-based method. We also design a greedy algorithm to automatically select a good chunking scheme for a given data set. We demonstrate experimentally that the new algorithm is faster than alternative methods yet occupies less space.
Ensuring Architecture Conventions in Multi-site Development

In a multi-site software development setting, creational and validational activities can be distributed and carried out by separate teams. An example of such a distributed development setup is when a system implemented in site A needs to be validated in a remote site B with regard to the component interaction rules dictated by the system architecture. In the current software engineering practice, support has mainly focused on the software creation paying less attention to the needs of the validation phase, which however is becoming increasingly important due to global software engineering practices. This paper argues that systematic support should be provided to the software validation based on the assumptions used in the software creation using the example of component interactions. Furthermore, the paper presents validational tool support for such a systematic process applied to an industrial project. The findings and experiences of the case study are also reported.
Interleukin-12p35 Deletion Promotes CD4 T-Cell–Dependent Macrophage Differentiation and Enhances Angiotensin II–Induced Cardiac Fibrosis

Objective—Interleukin-12 is essential for the differentiation of naïve T cells into interferon-&ggr;–producing T cells, which regulate inflammatory responses. We investigated this process of regulating hypertension-induced cardiac fibrosis. Methods and Results—Mice infused with angiotensin II showed a marked increase in interleukin-12p35 expression in cardiac macrophages. The degree of cardiac fibrosis was significantly enhanced in interleukin-12p35 knockout (p35-KO) mice compared with wild-type (WT) littermates in response to angiotensin II. Fibrotic hearts of p35-KO mice showed increased accumulation of alternatively activated (M2) macrophages and expression of M2 genes such as Arg-1 and Fizz1. Bone marrow–derived macrophages from WT or p35-KO mice did not differ in differentiation in response to angiotensin II treatment; however, in the presence of CD4+ T cells, macrophages from p35-KO mice differentiated into M2 macrophages and showed elevated expression of transforming growth factor-&bgr;. Moreover, CD4+ T-cell–treated p35-KO macrophages could stimulate cardiac fibroblasts to differentiate into &agr;-smooth muscle actin–positive and collagen I–positive myofibroblasts in 3-dimensional nanofiber gels. Neutralizing antibodies against transforming growth factor-&bgr; inhibited myofibroblast formation induced by M2 macrophages. Conclusion—Deficiency in interleukin-12p35 regulates angiotensin II–induced cardiac fibrosis by promoting CD4+ T-cell–dependent differentiation of M2 macrophages and production of transforming growth factor-&bgr;.
Modal parameters identification of power transformer winding based on Hilbert-Huang Transform

Modal parameters are inherent properties of power transformer winding and closely related to its variations of mechanical condition. To identify the modal parameter of winding more accurately, a modal experiment on a real transformer winding is made in this paper and the vibration frequency function is obtained. Then Hilbert-Huang Transform is introduced to identify the winding modal parameters from the experiment data. The general frequency-domain method PolyMax is also used to extract modal parameters of the winding as a comparison to the proposed method. The first four ranks of modal parameters of the winding are obtained by proposed method and PolyMax method. The good agreements of two methods illustrate the effectiveness of proposed HHT method. Furthermore, the proposed HHT method has better accuracy on the identification of damping ratio of transformer winding. The results can provide theoretical foundation for transformer manufacturing and winding deformation detection based on vibration.
An N-ary view integration method using conceptual dependencies

The purpose of this paper is to describe a methodology that can integrate n database views simultaneously. The methodology consists of transforming a database view into an intermediate representation, based on the conceptual dependency theory. The conceptual representations corresponding to the views are then combined to form a "global" representation, which is subsequently converted back to a data model that represents the global, integrated schema. Our methodology makes use of the semantic content of a database view in the integration process, unlike other view integration methodologies proposed in the literature. We show, by examples, how this approach can eliminate multiple restructuring of constituent views in the integration process. <<ETX>>
Benefits and Challenges of System Prognostics

Prognostics is an engineering discipline utilizing in-situ monitoring and analysis to assess system degradation trends, and determine remaining useful life. This paper discusses the benefits of prognostics in terms of system life-cycle processes, such as design and development, production, operations, logistics support, and maintenance. Challenges for prognostics technologies from the viewpoint of both system designers and users will be addressed. These challenges include implementing optimum sensor systems and settings, selecting applicable prognostics methods, addressing prognostic uncertainties, and estimating the cost-benefit implications of prognostics implementation. The research opportunities are summarized as well.
Parameter estimation for random amplitude chirp signals

We consider the problem of estimating the parameters of a chirp signal observed in multiplicative noise, i.e., whose amplitude is randomly time-varying. Two methods for solving this problem are presented. First, an unstructured nonlinear least-squares approach (NLS) is proposed. It is shown that by minimizing the NLS criterion with respect to all samples of the time-varying amplitude, the problem reduces to a two-dimensional (2-D) maximization problem. A theoretical analysis of the NLS estimator is presented, and an expression for its asymptotic variance is derived. It is shown that the NLS estimator has a variance that is very close to the Cramer-Rao bound. The second approach combines the principles behind the high-order ambiguity function (HBF) and the NLS approach. It provides a computationally simpler but suboptimum estimator. A statistical analysis of the HAF-based estimator is also carried out, and closed-form expressions are derived for the asymptotic variance of the HAF estimators based on the data and on the squared data. Numerical examples attest to the validity of the theoretical analyzes and establish a comparison between the two proposed methods.
Oxidation of Chloroform in a Gliding-Arc Plasma: Observation of Molecular Vibrations

This paper presents a direct comparison of the vibrational spectra between quantum mechanistic calculation and ultrashort laser pulses (USLP) spectrometer for chloroform oxidation. The chloroform decomposition was conducted in the presence of oxygen by using a gliding-arc plasma. In agreement with the prediction from both ab initio density functional theory calculation and USLP spectra analysis, it shows that the early step of the chloroform decomposition by oxygen follows a mechanism of CHCl3 + O2 rarr CCl3OOH rarr COCl2 + OH + Cl.
Grf40, A Novel Grb2 Family Member, Is Involved in T Cell Signaling through Interaction with SLP-76 and LAT

We molecularly cloned a new Grb2 family member, named Grf40, containing the common SH3-SH2-SH3 motif. Expression of Grf40 is predominant in hematopoietic cells, particularly T cells. Grf40 binds to the SH2 domain–containing leukocyte protein of 76 kD (SLP-76) via its SH3 domain more tightly than Grb2. Incidentally, Grf40 binds to linker for activation of T cells (LAT) possibly via its SH2 domain. Overexpression of wild-type Grf40 in Jurkat cells induced a significant increase of SLP-76–dependent interleukin (IL)-2 promoter and nuclear factor of activated T cell (NF-AT) activation upon T cell receptor (TCR) stimulation, whereas the COOH-terminal SH3-deleted Grf40 mutant lacked any recognizable increase in IL-2 promoter activity. Furthermore, the SH2-deleted Grf40 mutant led to a marked inhibition of these regulatory activities, the effect of which is apparently stronger than that of the SH2-deleted Grb2 mutant. Our data suggest that Grf40 is an adaptor molecule involved in TCR-mediated signaling through a more efficient interaction than Grb2 with SLP-76 and LAT.
Energy-Aware Scheduling of Embarrassingly Parallel Jobs and Resource Allocation in Cloud

In cloud computing, with full control of the underlying infrastructures, cloud providers can flexibly place user jobs on suitable physical servers and dynamically allocate computing resources to user jobs in the form of virtual machines. As a cloud provider, scheduling user jobs in a way that minimizes their completion time is important, as this can increase the utilization, productivity, or profit of a cloud. In this paper, we focus on the problem of scheduling embarrassingly parallel jobs composed of a set of independent tasks and consider energy consumption during scheduling. Our goal is to determine task placement plan and resource allocation plan for such jobs in a way that minimizes the Job Completion Time (JCT). We begin with proposing an analytical solution to the problem of optimal resource allocation with pre-determined task placement. In the following, we formulate the problem of scheduling a single job as a Non-linear Mixed Integer Programming problem and present a relaxation with an equivalent Linear Programming problem. We further propose an algorithm named TaPRA and its simplified version TaPRA-fast that solve the single job scheduling problem. Lastly, to address multiple jobs in online scheduling, we propose an online scheduler named OnTaPRA. By comparing with the start-of-the-art algorithms and schedulers via simulations, we demonstrate that TaPRA and TaPRA-fast reduce the JCT by 40-430 percent and the OnTaPRA scheduler reduces the average JCT by 60-280 percent. In addition, TaPRA-fast can be 10 times faster than TaPRA with around 5 percent performance degradation compared to TaPRA, which makes the use of TaPRA-fast very appropriate in practice.
I/O-efficient triangular range search and its application

In this paper, we consider a special type of triangular range search for a planar point set S in the I/O-model. Here, two sides of the query triangle are parallel to the coordinate axes (x- and y-axis). We call such a triangle an axis parallel triangle. For the axis parallel triangular range search problem, we propose a data structure of size O( N ( log 1+ǫ B N logB logB N ) 2 ) disk blocks and a query algorithm of complexity O(log B N( log B N logB logB N ) 2 + K/B) I/Os, where 0 < ǫ < 1, N is the number of points in S, B is the number of points transferred in one I/O, and K is the number of points in the answer to the query. We also consider the application of this data structure and query algorithm to oriented rectangular range search.
Multi-level free-space dilation for sampling narrow passages in PRM planning

Free-space dilation is an effective approach for narrow passage sampling, a well-recognized difficulty in probabilistic roadmap (PRM) planning. Key to this approach are methods for dilating the free space and for determining the amount of dilation needed. This paper presents a new method of dilation by shrinking the geometric models of robots and obstacles. Compared with existing work, the new method is more efficient in both running time and memory usage. It is also integrated with collision checking, a key operation in PRM planning. The efficiency of the dilation method enables a new PRM planner which quickly constructs a series of dilated free spaces and automatically determine the amount of dilation needed. Experiments show that both the dilation method and the planner work well in complex geometric environments. In particular, the planner reliably solved the most difficult version of the alpha puzzle, a benchmark test for PRM planners
Adding Quality of Service Extensions to the Associativity Based Routing Protocol for Mobile Ad Hoc Networks (MANET)

This paper aims at adding quality of services (QoS) extensions to the associativity based routing (ABR). The performance analysis of the ABR with QOS support is the same as ABR except for more computation on the intermediate nodes to comply with the QOS requirements and the size of the control messages will be varying. But the total number of operations performed and total number of messages exchange will remain the same in terms of Operation and communication complexities analysis. There is only one additional case that in which the QOS-Route will be invoked, specifically when any intermediate node fails to provide the required QOS, in this case the source will be notified by a QOS-LOST message sent by that node and a new QOS-Route discovery will be initiated by the source node.
Decompositional Rule Extraction from Support Vector Machines by Active Learning

Support vector machines (SVMs) are currently state-of-the-art for the classification task and, generally speaking, exhibit good predictive performance due to their ability to model nonlinearities. However, their strength is also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. In this paper, we propose a new active learning-based approach (ALBA) to extract comprehensible rules from opaque SVM models. Through rule extraction, some insight is provided into the logics of the SVM model. ALBA extracts rules from the trained SVM model by explicitly making use of key concepts of the SVM: the support vectors, and the observation that these are typically close to the decision boundary. Active learning implies the focus on apparent problem areas, which for rule induction techniques are the regions close to the SVM decision boundary where most of the noise is found. By generating extra data close to these support vectors that are provided with a class label by the trained SVM model, rule induction techniques are better able to discover suitable discrimination rules. This performance increase, both in terms of predictive accuracy as comprehensibility, is confirmed in our experiments where we apply ALBA on several publicly available data sets.
Recent Changes Leading to Subsequent Changes: Extensions of Multivariate Latent Difference Score Models

Latent difference score models (e.g., McArdle & Hamagami, 2001) are extended to include effects from prior changes to subsequent changes. This extension of latent difference scores allows for testing hypotheses where recent changes, as opposed to recent levels, are a primary predictor of subsequent changes. These models are applied to bivariate longitudinal data collected as part of the Baltimore Longitudinal Study of Aging on memory performance, measured by the California Verbal Learning Test, and lateral ventricle size, measured by structural MRIs. Results indicate that recent increases in the lateral ventricle size were a leading indicator of subsequent declines in memory performance from age 60 to 90.
An Approach to Active Spatial Data Mining Based on Statistical Information

Spatial data mining presents new challenges due to the large size of spatial data, the complexity of spatial data types, and the special nature of spatial access methods. Most research in this area has focused on efficient query processing of static data. This paper introduces an active spatial data mining approach that extends the current spatial data mining algorithms to efficiently support user-defined triggers on dynamically evolving spatial data. To exploit the locality of the effect of an update and the nature of spatial data, we employ a hierarchical structure with associated statistical information at the various levels of the hierarchy and decompose the user-defined trigger into a set of subtriggers associated with cells in the hierarchy. Updates are suspended in the hierarchy until their cumulative effect might cause the trigger to fire. It is shown that this approach achieves three orders of magnitude improvement over the naive approach that reevaluate the condition over the database for each update, while both approaches produce the same result without any delay. Moreover, this scheme can support incremental query processing as well.