Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy data preserving method based on fuzzy discretization

Protect data privacy is one of hot topic of database applications in recent years. This paper is concentrated on the issue of protecting the privacy attribute values when data publication and proposes a method based on fuzzy discretization. The method transforms sensitive attribute values to fuzzy values and publishes the data with fuzzy offset degree. This helps the end user of the data to make out the distinction between two attribute values, even though they are mapped to the same linguistic term. The experiments demonstrate that the method efficiently preserves privacy information and maintains the clustering model of primitive data wel1.
The Stochastic Matching Problem: Beating Half with a Non-Adaptive Algorithm

In the stochastic matching problem, we are given a general (not necessarily bipartite) graph G(V,E), where each edge in E is realized with some constant probability p > 0 and the goal is to compute a bounded-degree (bounded by a function depending only on p) subgraph H of G such that the expected maximum matching size in H is close to the expected maximum matching size in G. The algorithms in this setting are considered non-adaptive as they have to choose the subgraph H without knowing any information about the set of realized edges in G. Originally motivated by an application to kidney exchange, the stochastic matching problem and its variants have received significant attention in recent years. The state-of-the-art non-adaptive algorithms for stochastic matching achieve an approximation ratio of 1/2-ε for any ε > 0, naturally raising the question that if 1/2 is the limit of what can be achieved with a non-adaptive algorithm. In this work, we resolve this question by presenting the first algorithm for stochastic matching with an approximation guarantee that is strictly better than 1/2: the algorithm computes a subgraph H of G with the maximum degree O(log(1/p)/p such that the ratio of expected size of a maximum matching in realizations of H and G is at least 1/2 + δ0 for some absolute constant δ0 > 0. The degree bound on H achieved by our algorithm is essentially the best possible (up to an O(log(1/p)) factor) for any constant factor approximation algorithm, since an Ω(1/p) degree in H is necessary for a vertex to acquire at least one incident edge in a realization. Our result makes progress towards answering an open problem of Blum et al (EC 2015) regarding the possibility of achieving a (1 - ε)-approximation for the stochastic matching problem using non-adaptive algorithms. From the technical point of view, a key ingredient of our algorithm is a structural result showing that a graph whose expected maximum matching size is OPT always contains a b-matching of size (essentially) b . OPT, for b = 1/p.
Evaluation of Upper Indus Near-Surface Climate Representation by WRF in the High Asia Refined Analysis

Data paucity is a severe barrier to the characterization of Himalayan near-surface climates. Regional climate modeling can help to fill this gap, but the resulting data products need critical evaluation before use. This study aims to extend the appraisal of one such dataset, the High Asia Refined Analysis (HAR). Focusing on the upper Indus basin (UIB), the climatologies of variables needed for process-based hydrological and cryospheric modeling are evaluated, leading to three main conclusions. First, precipitation in the 10-km HAR product shows reasonable correspondence with most in situ measurements. It is also generally consistent with observed runoff, while additionally reproducing the UIB’s strong vertical precipitation gradients. Second, the HAR shows seasonally varying bias patterns. A cold bias in temperature peaks in spring but reduces in summer, at which time the high bias in relative humidity diminishes. These patterns are concurrent with summer overestimation (underestimation) of incoming shortwave (longwave) radiation. Finally, these seasonally varying biases are partly related to deficiencies in cloud, snow, and albedo representations. In particular, insufficient cloud cover in summer leads to the overestimation of incoming shortwave radiation. This contributes to the reduced cold bias in summer by enhancing surface warming. A persistent high bias in albedo also plays a critical role, particularly by suppressing surface heating in spring. Improving representations of cloud, snow cover, and albedo, and thus their coupling with seasonal climate transitions, would therefore help build upon the considerable potential shown by the HAR to fill a vital data gap in this immensely important basin.
A Mortgage Backed Securities Pricing Model and Its Implication of Trading Strategy

Mortgage-backed security (MBS) is a capital market innovation that gained popular acceptance in the 1980s and are even stronger in the 1990s in the states. In Taiwan since 2000 tech bubbles busted, stock market sunk, investors tended to invest their money into bond market that represents the features of having stable return than equity market. MBS as an instrument has the same credit rating as US treasury but get higher returns, become popular in the market. Since 2003 the US interest rate hit historical low level, said 1% through 2004 to now MBS attract lots of money to invest in. However, even in the states, MBS having longer history than in other countries, the pricing is still subject to uncertainty due to the existence of the mortgage prepayment option. This study describes the options-based model that can be used to price MBS and details possible prepayment functions that can be incorporated into the model. The Bloomberg prepayment model is suggested because the well-organized sub models are established and the data is completed to capture the prepayment behaviours. Also due to MBS is now the most common investment vehicle in the US fixed income market, trading MBS in the real world is also an important part to dig in. The Option adjusted spread method for trading MBS is selected in this study for studying the trading strategy of MBS to better understanding this blockbuster fixed income investment tool. Tel: 886-223519641 ext. 521 Fax: 886-223511826, e-mail:clin@ntu.edu. tw
Petri net versus modulo scheduling for software pipelining

Untolerated load instruction latencies often have a significant impact on overall program performance. As one means of mitigating this effect we present an aggressive hardware-based mechanism that provides effective support for reducing the latency of load instructions. Through the judicious use of instruction predecode, base register caching, and fast address calculation, it becomes possible to complete load instructions up to two cycles earlier than traditional pipeline designs. For a pipeline with one cycle data cache access, this results in what we term a zero-cycle load. A zero-cycle load produces a result prior to reaching the execute stage of the pipeline, allowing subsequent dependent instructions to issue unfettered by load dependencies. Programs executing on processors with support for zero-cycle loads experience significantly fewer pipeline stalls due to load instructions and increased overall performance. We present two pipeline designs supporting zero-cycle loads: one for pipelines with a single stage of instruction decode, and another for pipelines with multiple decode stages. We evaluate these designs in a number of contexts: with and without software support, in-order vs. out-of-order issue, and on architectures with many and few registers. We find that our approach is quite effective at reducing the impact of load latency, even more so on architectures with in-order issue and few registers.
Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network

This study presents a new approach for classification of building type in complex urban scene. The approach consists of two parts: extended multiresolution segmentation (EMRS) and soft classification using BP network (SBP). The technology scheme is referred to here as EMRS-SBP. EMRS is used to guide the design of descriptor. A descriptor is a feature expression or a symbolized algorithm to systematically promote the expressing capability of image features. A classifier can perform far better to discern complex pattern of combining pixels working in an EMRS-based feature space constructed by a number of such descriptors. SBP serves as a classifier model to generate natural clusters of member which refers to here as both pixels and image patches. Class-mark ensured member is denoted as sure member and the rest as unsure (fuzzy) members. The latter can be relabeled through recursive defuzzifying according to the information carried by the gradually increased sure members. By using EMRS-SBP, three building types, i.e., old-fashioned courtyard dwellings, multistorey residential buildings, and high-rise buildings, can be accurately classified from high spatial resolution imagery in a feature space constructed with fifteen descriptors including nine EMRS-based ones. There is evidence that the mean overall accuracy using SBP in the EMRS-based feature space is 19.8% higher than that using the hard classification with BP network in a single resolution segmentation space and meanwhile, the mean kappa statistic value (κ) is 25.1% higher.
Variance of the turbo-code performance bound over the interleavers

We evaluate the variance of the union performance bound for a turbo-code over all possible interleavers of length N. The asymptotic analysis (for large N) shows that the ratio of the standard deviation over the mean value of the bound remains constant with respect to N. Numerical results corresponding to turbo-codes with two-memory component codes indicate that this ratio increases with the signal to noise ratio. Theoretical and simulation results demonstrate that the majority of the interleavers result in performance bounds very close to the average value of the bound. This effect is stronger for larger interleaver lengths.
D2D-based V2V communications with latency and reliability constraints

Direct device-to-device (D2D) communication has been proposed as a possible enabler for vehicle-to-vehicle (V2V) applications, where the incurred intra-cell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communications. Firstly, we analyze and mathematically model the actual requirements for vehicular communications and traditional cellular links. Secondly, we propose a problem formulation to fulfill these requirements, and then a Separate Resource Block allocation and Power control (SRBP) algorithm to solve this problem. Finally, simulations are presented to illustrate the improved performance of the proposed SRBP scheme compared to some other existing methods.
Flagellar Motility and Structure in the Hyperthermoacidophilic Archaeon Sulfolobus solfataricus

ABSTRACT Flagellation in archaea is widespread and is involved in swimming motility. Here, we demonstrate that the structural flagellin gene from the crenarchaeaon Sulfolobus solfataricus is highly expressed in stationary-phase-grown cells and under unfavorable nutritional conditions. A mutant in a flagellar auxiliary gene, flaJ, was found to be nonmotile. Electron microscopic imaging of the flagellum indicates that the filaments are composed of right-handed helices.
Fludarabine Downregulates Indoleamine 2,3-Dioxygenase in Tumors via a Proteasome-Mediated Degradation Mechanism

Indoleamine 2,3-dioxygenase (IDO) is found in multiple malignancies and exerts immunosuppressive effects that are central in protecting tumors from host T lymphocyte rejection. IDO is an enzyme involved in the catabolism of tryptophan resulting in inhibition of T lymphocyte function. While inhibition of IDO enzymatic activity results in tumor rejection, it is still unknown how we can directly target IDO expression within tumors using drugs. We have chosen to interfere with IDO expression by targeting the key-signaling event signal transducer and activator of transcription 1 (STAT1). We evaluated the efficacy of fludarabine, previously described to inhibit STAT1 phosphorylation. Interestingly, fludarabine was efficient in suppressing protein expression and consequently IDO activity in two different cell lines derived from breast cancer and melanoma when IDO was activated with interferon-gamma (IFN-γ) or supernatants prepared from activated T lymphocytes. However, fludarabine had no inhibitory effect on STAT1 phosphorylation. Other IFN-γ-responsive genes were only marginally inhibited by fludarabine. The level of IDO transcript was unaffected by this inhibitor, suggesting the involvement of post-transcriptional control. Strikingly, we have found that the inhibition of proteasome partially protected IDO from fludarabine-induced degradation, indicating that fludarabine induces IDO degradation through a proteasome-dependent pathway. Currently used in the clinic to treat some malignancies, fludarabine has the potential for use in the treatment of human tumors through induction of IDO degradation and consequently, for the promotion of T cell-mediated anti-tumor response.
IRP SPECIAL REPORT UTILIZATION AND EFFECTS ON PAYMENTS OF PERCENTAGE- EXPRESSED CHILD SUPPORT ORDERS

This paper has been prepared under a contractual agreement between the Wisconsin Department of Health and Social Services and the Institute for Research on Poverty. Any views expressed in this paper are those of the authors and not necessarily those of the sponsoring institutions. The authors thank Dan Meyer and Pat Brown for their assistance. Abstract This report examines the use of child support orders expressed as a percentage of noncustodial parents' incomes and the effects of percentage-expressed orders on child support payments. Data are from court records in twenty-one Wisconsin counties and the Wisconsin Department of Revenue. The investigators discover that the use of percentage-expressed orders, as opposed to orders expressed as a fixed sum of money, results in substantially more money collected in child support payments, after controlling for differences between child support cases. Compliance rates, however, are lower in cases with percentage-expressed orders than in cases with fixed-sum orders. Even so, there are large increases over time in the dollar amounts of support orders when they are expressed as a percentage of income, and these increases offset the adverse effects of low compliance rates.
Pulp fibre recognition expert system based on neural network

The characteristics of pulp fibre influence the final qualities of paper production, but the phenomenon of intersectant, curled and bifurcate of the pulp fibre is very familiar, as a result the routine methods can hardly achieve the analysis and recognition of complex pulp fibre image preferably. The paper introduces the basic principle, structure and recognition tactics of the pulp fibre recognition artificial intelligence expert system based on neural network. According to the characters of pulp fibre, it distinguishes from different levels, The system start the fleet repository to analyze the input simple fibre character, then the system will start the deep repository of expert system to get the result of complex fiber character. From the parallel processing and oneself learning of neural network, it overcomes some insufficiency of traditional recognition methods, obtaining preferable recognition effect.
Optimizing Urine Processing Protocols for Protein and Metabolite Detection.

BACKGROUND In urine, factors such as timing of voids, and duration at room temperature (RT) may affect the quality of recovered protein and metabolite data. Additives may aid with detection, but can add more complexity in sample collection or analysis. We aimed to identify the optimal urine processing protocol for clinically-obtained urine samples that allows for the highest protein and metabolite yields with minimal degradation. METHODS Healthy women provided multiple urine samples during the same day. Women collected their first morning (1st AM) void and another "random void". Random voids were aliquotted with: 1) no additive; 2) boric acid (BA); 3) protease inhibitor (PI); or 4) both BA + PI. Of these aliquots, some were immediately stored at 4°C, and some were left at RT for 4 hours. Proteins and individual metabolites were quantified, normalized to creatinine concentrations, and compared across processing conditions. Sample pools corresponding to each processing condition were analyzed using mass spectrometry to assess protein degradation. RESULTS Ten Caucasian women between 35-65 years of age provided paired 1st morning and random voided urine samples. Normalized protein concentrations were slightly higher in 1st AM compared to random "spot" voids. The addition of BA did not significantly change proteins, while PI significantly improved normalized protein concentrations, regardless of whether samples were immediately cooled or left at RT for 4 hours. In pooled samples, there were minimal differences in protein degradation under the various conditions we tested. In metabolite analyses, there were significant differences in individual amino acids based on the timing of the void. CONCLUSIONS For comparative translational research using urine, information about void timing should be collected and standardized. For urine samples processed in the same day, BA does not appear to be necessary while the addition of PI enhances protein yields, regardless of 4°C or RT storage temperature.
The effect of age-correction on IQ scores among school-aged children born preterm

This study examined the effect of age-correction on IQ scores among preterm school-aged children. Data from the Flinders Medical Centre Neonatal Unit Follow-up Program for 81 children aged five years and assessed with the WPPSI-III, and 177 children aged eight years and assessed with the WISC-IV, were analysed. Corrected IQ scores were significantly higher than not-corrected IQ scores (Full Scale IQ and all indices) for both the WPPSI-III and WISC-IV. The use of age-corrected IQ scores has the potential to exclude some children from support services.
Determination of Aspect-Ratio Distribution in Gold Nanowires Using Absorption Spectra and Transmission Electron Microscopy Techniques

The surface plasmon excitation energies of noble metal nanowires are strongly dependent on the dimensions of the nanowire and the local environment, which allows for tunable optical characteristics of nanowire samples (Noguez, 2007; Nehl, C. L. & Hafner, J. H. 2008). This has great advantages for the use of nanowires in surface-enhanced Raman spectroscopy (Orendorff et al. 2006; Sztainbuch, 2006; Suzuki et al. 2004, 2005), medical imaging, disease treatment, and other applications such as highly sensitive sensors (Agarwal et al. 2007; Gibbons et al. 2006; Yu & Irudayaraj 2007; Chen et al. 2007). For this reason, the fabrication processes for nanowires and the characterization of their optical properties are of great interest in current research (Murphy et al. 2006; Gulati et al. 2006; Miranda & Ahmadi 2005; Zweifel & Wei 2005; Hu et al. 2003; Sonnichsen et al. 2002). It would be useful to have a quick and easy way to accurately determine the size distribution of nanoparticles in solution in order to analyze nanowire synthesis processes or characterize samples for applications.
The TREC 2002 Filtering Track Report

The TREC–11 ﬁltering track measures the ability of systems to build persistent user proﬁles which successfully separate relevant and non-relevant documents in an incoming stream. It consists of three major subtasks: adaptive ﬁltering, batch ﬁltering, and routing. In adaptive ﬁltering, the system begins with only a topic statement and a small number of positive examples, and must learn a better proﬁle from on-line feedback. Batch ﬁltering and routing are more traditional machine learning tasks where the system begins with a large sample of evaluated training documents. This report describes the track, presents some evaluation results, and provides a general commentary on lessons learned from this year’s track.
Endothelin Receptor B Inhibition Triggers Apoptosis and Enhances Angiogenesis in Melanomas

Endothelin receptor B (ETRB or EDNRB) is overexpressed in most human melanomas and is proposed to provide a marker of melanoma progression. We have shown previously that inhibition of ETRB leads to increased human melanoma cell death in vitro and in vivo, resulting in shrinkage of tumors grown in immunocompromised mice. In the present work, we analyzed the effects of ETRB inhibition on 10 human melanoma cell lines derived from tumors at distinct stages of progression. Our observations suggest that the ETRB antagonist BQ788 induces apoptosis most effectively in metastatic melanoma cells. Microarray analysis shows that BQ788 treatment leads to a reduction in the expression of the survival factor BCL-2A1 and the DNA repair factor poly(ADP-ribose) polymerase 3 that is more pronounced in cells derived from metastatic than primary melanoma. Decreased cell viability was observed to correlate with reduction in ETRB expression, and reduction in ETRB protein levels by small interfering RNA led to an increase in cell death. Interestingly, reduction of ETRB expression by BQ788 was accompanied by a strong induction of VEGF expression and repression of the angiogenic suppressor gravin. These changes in gene expression correlated with increased angiogenesis in tumors injected with ETRB antagonist in vivo. Taken together, our observations suggest that ETRB may provide a potential therapeutic target in high-grade melanomas and identify candidate pathways that may be implicated in the regulation of cell survival and tumor progression associated with ETRB signaling.
Persistent Topology and Metastable State in Conformational Dynamics

The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain.