Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking 3D Object using Flexible Models

This article proposes a flexible tracker which can estimate motion and deformations of 3D objects by considering their appearances as nonrigid surfaces. In this approach, a flexible model is built by matching features (key points) over training sequences and by learning the deformations of a spline based model. This statistical model captures the variations in the appearance of objects caused by 3D pose variations. Visual tracking is then possible, for each new frame, by matching local features of the model according to their local appearances as well as optimal optimization of the constraints provided by the flexible model. The approach is demonstrated on real-world images sequences.
Canonical community ordination. Part I: Basic theory and linear methods

AbstractCanonical community ordination comprises a collection of methods that relate species assemblages to their environment, in both observational studies and designed experiments. Canonical ordination differs from ordination sensu stricto in that species and environment data are analyzed simultaneously. Part I reviews the theory in a non-mathematical way with emphasis on new insights for the interpretation of ordination diagrams. The interpretation depends on the ordination method used to create the diagram. After the basic theory, Part I is focused on the ordination diagrams in linear methods of canonical community ordination, in particular principal component analysis, redundancy analysis and canonical correlation analysis. Special attention is devoted to the display of qualitative environmental variables.
A New Method for Fuzzy Information Retrieval Based on Fuzzy-Number Similarity Measure

Fuzzy similarity measure is very useful in fuzzy information retrieval. This study presents a new method based on fuzzy-number similarity measure to deal with fuzzy information retrieval problems. The proposed method can obtain the suitable retrieval results for the user because it translate the degree of satisfaction F~ into a linguistic term, and user can easily determine whether the retrieved document di satisfies his/her requirement by the linguistic term.
Risk Stratification after Biochemical Failure following Curative Treatment of Locally Advanced Prostate Cancer: Data from the TROG 96.01 Trial

Purpose. Survival following biochemical failure is highly variable. Using a randomized trial dataset, we sought to define a risk stratification scheme in men with locally advanced prostate cancer (LAPC). Methods. The TROG 96.01 trial randomized 802 men with LAPC to radiation ± neoadjuvant androgen suppression therapy (AST) between 1996 and 2000. Ten-year follow-up data was used to develop three-tier post-biochemical failure risk stratification schemes based on cutpoints of time to biochemical failure (TTBF) and PSA doubling time (PSADT). Schemes were evaluated in univariable, competing risk models for prostate cancer-specific mortality. The performance was assessed by c-indices and internally validated by the simple bootstrap method. Performance rankings were compared in sensitivity analyses using multivariable models and variations in PSADT calculation. Results. 485 men developed biochemical failure. c-indices ranged between 0.630 and 0.730. The most discriminatory scheme had a high risk category defined by PSADT < 4 months or TTBF < 1 year and low risk category by PSADT > 9 months or TTBF > 3 years. Conclusion. TTBF and PSADT can be combined to define risk stratification schemes after biochemical failure in men with LAPC treated with short-term AST and radiotherapy. External validation, particularly in long-term AST and radiotherapy datasets, is necessary.
An Opportunistic MAC in Multichannel Multiradio Wireless Ad Hoc Networks

Opportunistic scheduling has been known as one of the possible ways of exploiting multi-user diversity, which comes from the time-varying propagation characteristics and broadcast nature of wireless radios, to improve communication efficiency and reliability. Multi-channel multi-radio technology has offered great opportunity in designing wireless network MAC protocol to utilize all kinds of resource diversities. Other than multi-user diversity, we identify the unique multi-radio diversity which can be exploited in multi-radio wireless network to improve network throughput performance. The proposed opportunistic multi-radio MAC (OMMAC) takes advantage of multi-radio diversity to increase the spectrum usage as much as possible. Extensive NS2 simulation results demonstrate that OMMAC significantly improves the network throughput in both single and multi-hop wireless networks.
Structured multicategory support vector machines with analysis of variance decomposition

The support vector machine has been a popular choice of classification method for many applications in machine learning. While it often outperforms other methods in terms of classification accuracy, the implicit nature of its solution renders the support vector machine less attractive in providing insights into the relationship between covariates and classes. Use of structured kernels can remedy the drawback. Borrowing the flexible model-building idea of functional analysis of variance decomposition, we consider multicategory support vector machines with analysis of variance kernels in this paper. An additional penalty is imposed on the sum of weights of functional subspaces, which encourages a sparse representation of the solution. Incorporation of the additional penalty enhances the interpretability of a resulting classifier with often improved accuracy. The proposed method is demonstrated through simulation studies and an application to real data. Copyright 2006, Oxford University Press.
Efficient user Scheduling under Low Rate Feedback for Correlated MIMO Broadcast Channels

This paper investigates the scheduling of user signals in MIMO systems with spatially correlated channels. By maximizing an upper bound of the sum capacity, we propose a scheduling scheme which requires only a single scalar feedback from each receiving user. To further reduce the need for user information feedback and better explore the spatial correlation information, a more efficient scheduling method is also developed. This new approach only requires a 1-bit indicator from each user and selects users according to the slowly varying MIMO channel correlation information. Numerical results verify the effectiveness of our proposed schemes.
Lifelong Reduction of LDL-Cholesterol Related to a Common Variant in the LDL-Receptor Gene Decreases the Risk of Coronary Artery Disease—A Mendelian Randomisation Study

Background Rare mutations of the low-density lipoprotein receptor gene (LDLR) cause familial hypercholesterolemia, which increases the risk for coronary artery disease (CAD). Less is known about the implications of common genetic variation in the LDLR gene regarding the variability of cholesterol levels and risk of CAD. Methods Imputed genotype data at the LDLR locus on 1 644 individuals of a population-based sample were explored for association with LDL-C level. Replication of association with LDL-C level was sought for the most significant single nucleotide polymorphism (SNP) within the LDLR gene in three European samples comprising 6 642 adults and 533 children. Association of this SNP with CAD was examined in six case-control studies involving more than 15 000 individuals. Findings Each copy of the minor T allele of SNP rs2228671 within LDLR (frequency 11%) was related to a decrease of LDL-C levels by 0.19 mmol/L (95% confidence interval (CI) [0.13–0.24] mmol/L, p = 1.5×10−10). This association with LDL-C was uniformly found in children, men, and women of all samples studied. In parallel, the T allele of rs2228671 was associated with a significantly lower risk of CAD (Odds Ratio per copy of the T allele: 0.82, 95% CI [0.76–0.89], p = 2.1×10−7). Adjustment for LDL-C levels by logistic regression or Mendelian Randomisation models abolished the significant association between rs2228671 with CAD completely, indicating a functional link between the genetic variant at the LDLR gene locus, change in LDL-C and risk of CAD. Conclusion A common variant at the LDLR gene locus affects LDL-C levels and, thereby, the risk for CAD.
The dsRNA binding protein family: critical roles, diverse cellular functions

The dsRNA binding proteins (DRBPs) comprise a growing family of eukaryotic, prokaryotic, and viral‐encoded products that share a common evolutionarily conserved motif specifically facilitating interaction with dsRNA. Proteins harboring dsRNA binding domains (DRBDs) have been reported to interact with as little as 11 bp of dsRNA, an event that is independent of nucleotide sequence arrangement. More than 20 DRBPs have been identified and reportedly function in a diverse range of critically important roles in the cell. Examples include the dsRNA‐dependent protein kinase PKR that functions in dsRNA signaling and host defense against virus infection and DICER, which is implicated in RNA interference (RNAi) ‐mediated gene silencing. Other DRBPs such as Staufen, adenosine deaminase acting on RNA (ADAR), and spermatid perinuclear RNA binding protein (SPNR) are known to play essential roles in development, translation, RNA editing, and stability. In many cases, homozygous and even heterozygous disruption of DRBPs in animal models results in embryonic lethality. These results implicate the recognition of dsRNA as an evolutionarily conserved mechanism important in the regulation of gene expression and in host defense and underscore the diversity of essential biological tasks performed by dsRNA‐related processes in the cell. —Saunders, L. R. , Barber, G. N. The dsRNA binding protein family: critical roles, diverse cellular functionsFASEB J. 17, 961–983 (2003)
A new cumulant-based active contour model with wavelet energy for segmentation of SAR images

In this paper, a new algorithm for segmentation of Synthetic Aperture Radar images using the skewness wavelet energy has been presented. The skewness is the 3rd order cumulant which extracts the statistical properties of each region of a SAR image. SAR images have Nonlinearity in intensity inhomogeneities because of the speckle noise. The algorithm which we proposed in this paper is a region-based active contour model that is able to use the intensity information in local regions. This algorithm also is able to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use the wavelet energy to analyze each sub-band of a SAR image. The results of the proposed algorithm on the test SAR images of agricultural and urban regions show a good performance of this method.
Building Maximum Lifetime Shortest Path Data Aggregation Trees in Wireless Sensor Networks

In wireless sensor networks, the spanning tree is usually used as a routing structure to collect data. In some situations, nodes do in-network aggregation to reduce transmissions, save energy, and maximize network lifetime. Because of the restricted energy of sensor nodes, how to build an aggregation tree of maximum lifetime is an important issue. It has been proved to be NP-complete in previous works. As shortest path spanning trees intuitively have short delay, it is imperative to find an energy-efficient shortest path tree for time-critical applications. In this article, we first study the problem of building maximum lifetime shortest path aggregation trees in wireless sensor networks. We show that when restricted to shortest path trees, building maximum lifetime aggregation trees can be solved in polynomial time. We present a centralized algorithm and design a distributed protocol for building such trees. Simulation results show that our approaches greatly improve the lifetime of the network and are very effective compared to other solutions. We extend our discussion to networks without aggregation and present interesting results.
An Algorithm for SAR Image Embedded Compression Based on Wavelet Transform

Synthetic aperture radar (SAR) image compression is important in image transmission and archiving. In this paper, SAR image compression using embedded zerotree wavelets algorithm, based on discrete wavelet transform (DWT), is researched. Aiming at special characteristics of SAR image such as speckle noise, the author added a denoise step in the flow of compression, trying to do some meaningful attempt in SAR image compression. Experiments carried out show that the improvement can reduce the speckle noise and improve the computation precision and time.
Image Quality Assessment Measure based on Visual Region and displacement with distorted pixels for compressed Images

Image quality assessment (QA) plays a major role in a broad range of applications. Evaluating the adequacy of a quality image for a given application (image or video processing applications) is a requisite. However, objective quality assessment is far from being a solved problem. In this paper, a measure for image quality assessment is introduced after a brief review of previous work. A metric based on the distance between blocks of distorted and original image is proposed to overcome some of the limitations of existing approaches. Distorted pixel and visual regions of interest are computed. Experimental results confirm the potential of the proposed measure.
Fast implementation for non-linear time-scaling of stereo signals

In this paper we present an improved implementation of Duxbury’s adaptive phase-vocoder approach for audio time-stretching using non-linear time-scaling and temporal masked phase locking at transients [1]. We show that the previous algorithm has some limitations, notably its slow implementation and its incapacity to deal with stereo signals. We propose solutions to this problems including: an improved transient detection, a much faster implementation using the IFFT for re-synthesis and a method for stretching stereo signals without artifacts. Finally, we provide some graphical results and quantitative measures to illustrate our improvements.
The Parameters of Cross-Validation

Abstract : The validation of predictor weights, derived in one sample, by computing the correlation of the weighted sum of the predictors with the criterion in new samples is called cross-validation. The technique applies to any method of calculating the predictor weights. In this study three prediction methods were compared by cross-validation--multiple regression on the predictors, on the principal components of the predictors, and on the principal predictors. Prediction from the principal predictors is only possible when there are several criterion variables. In order to discover the parameters of the multivariate distribution which affect the choice of prediction method and the number of principal components or principal predictors to include in the regression, a large number of distributions were simulated on a computer and samples generated from these distributions. The population distributions varied in the following parameters: n, the number of predictors, m, the number of criteria, rho sq, the squared multiple correlation in the case of one criterion or the average squared multiple correlation of m criteria when m > 1; and pi sq, the average predictor variance related to the criteria. A typical calculation consisted of the following steps: generation of a population distribution for a set of values of the parameters; generation of two samples of size N from this population; calculation, in one sample, of the predictor weights for one or more prediction methods; and validation of these weights in the second sample. A large number of populations were generated, varying in the values of the parameters.
PowerBench: A Scalable Testbed Infrastructure for Benchmarking Power Consumption

— The focus of the sensor network community on energy-efﬁciency has produced a string of novel MAC, routing, and data-aggregation protocols. Their power consumption has mainly been assessed through simulations, i.e. by counting the fraction of time spent in sending, receiving, and computing, and multiplying that by ﬁgures taken from data sheets or isolated (single-node) power measurements. In contrast we present PowerBench, a 24-node testbed capable of recording the power consumption of all nodes in parallel with a 5 kHz sampling rate and 30 µA resolution. This is accomplished by means of a low-cost interface board featuring a shunt resistor and an A/D converter, whose output is collectively sampled by an embedded Linux platform (Linksys NSLU2). The experience with the PowerBench testbed so far is twofold. First, we have determined that –much to our surprise– timer-based estimations can match true, measured power consumption values within a few percent. Second, we have experienced that a graphical display of the power traces is an effective means to study (and debug) protocol behavior; in particular, inter-node related timing issues can be easily viewed from the state (IDLE/COMPUTE/RX/TX) changes embodied in the power data.
Silicon Debug for Timing Errors

Due to various sources of noise and process variations, assuring a circuit to operate correctly at its desired operational frequency has become a major challenge. In this paper, we propose a timing-reasoning-based algorithm and an adaptive test-generation algorithm for diagnosing timing errors in the silicon-debug phase. We first derive three metrics that are strongly correlated to the probability of a candidate's being an actual error source. We analyze the problem of circuit timing uncertainties caused by delay variations and test sampling. Then, we propose a candidate-ranking heuristic, which is robust with respect to such sources of timing uncertainty. Based on the initial ranking result and the timing information, we further propose an adaptive path-selection and test-generation algorithm to generate additional diagnostic patterns for further improvement of the first-hit-rate. The experimental results demonstrate that combining the ranking heuristic and the adaptive test-generation method would result in a very high resolution for timing diagnosis.
Activation of the Interferon Pathway is Dependent Upon Autoantibodies in African-American SLE Patients, but Not in European-American SLE Patients

Background: In systemic lupus erythematosus (SLE), antibodies directed at RNA-binding proteins (anti-RBP) are associated with high serum type I interferon (IFN), which plays an important role in SLE pathogenesis. African-Americans (AA) are more likely to develop SLE, and SLE is also more severe in this population. We hypothesized that peripheral blood gene expression patterns would differ between AA and European-American (EA) SLE patients, and between those with anti-RBP antibodies and those who lack these antibodies. Methods: Whole blood RNA from 33 female SLE patients and 16 matched female controls from AA and EA ancestral backgrounds was analyzed on Affymetrix Gene 1.0 ST gene expression arrays. Ingenuity Pathway Analysis was used to compare the top differentially expressed canonical pathways amongst the sample groups. An independent cohort of 116 SLE patients was used to replicate findings using quantitative real-time PCR (qPCR). Results: Both AA and EA patients with positive anti-RBP antibodies showed over-expression of similar IFN-related canonical pathways, such as IFN Signaling (P = 1.3 × 10−7 and 6.3 × 10−11 in AA vs. EA respectively), Antigen Presenting Pathway (P = 1.8 × 10−5 and 2.5 × 10−6), and a number of pattern recognition receptor pathways. In anti-RBP negative (RBP−) patients, EA subjects demonstrated similar IFN-related pathway activation, whereas no IFN-related pathways were detected in RBP−AA patients. qPCR validation confirmed similar results. Conclusion: Our data show that IFN-induced gene expression is completely dependent on the presence of autoantibodies in AA SLE patients but not in EA patients. This molecular heterogeneity suggests differences in IFN-pathway activation between ancestral backgrounds in SLE. This heterogeneity may be clinically important, as therapeutics targeting this pathway are being developed.
State Legislative Responses to Violent Juvenile Crime: 1996-97 Update. Juvenile Justice Bulletin.

Serious and violent juvenile offenders have precipitated unprecedented change in the juvenile justice system. In 1996, the Office of Juvenile Justice and Delinquency Prevention published State Responses to Serious and Violent Juvenile Crime. The Report summarized changes in jurisdictional authority, sentencing, corrections programming, confidentiality of records and court hearings, and victim involvement in juvenile proceedings, undertaken by States from 1992 through 1995.
Reining in Long Tails in Warehouse-Scale Computers with Quick Voltage Boosting Using Adrenaline

Reducing the long tail of the query latency distribution in modern warehouse scale computers is critical for improving performance and quality of service (QoS) of workloads such as Web Search and Memcached. Traditional turbo boost increases a processor’s voltage and frequency during a coarse-grained sliding window, boosting all queries that are processed during that window. However, the inability of such a technique to pinpoint tail queries for boosting limits its tail reduction benefit. In this work, we propose Adrenaline, an approach to leverage finer-granularity (tens of nanoseconds) voltage boosting to effectively rein in the tail latency with query-level precision. Two key insights underlie this work. First, emerging finer granularity voltage/frequency boosting is an enabling mechanism for intelligent allocation of the power budget to precisely boost only the queries that contribute to the tail latency; second, per-query characteristics can be used to design indicators for proactively pinpointing these queries, triggering boosting accordingly. Based on these insights, Adrenaline effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost. By evaluating under various workload configurations, we demonstrate the effectiveness of our methodology. We achieve up to a 2.50 × tail latency improvement for Memcached and up to a 3.03 × for Web Search over coarse-grained dynamic voltage and frequency scaling (DVFS) given a fixed boosting power budget. When optimizing for energy reduction, Adrenaline achieves up to a 1.81 × improvement for Memcached and up to a 1.99 × for Web Search over coarse-grained DVFS. By using the carefully chosen boost thresholds, Adrenaline further improves the tail latency reduction to 4.82 × over coarse-grained DVFS.
Road Traversability analysis using network properties of roadmaps

Traversability analysis is an important aspect of autonomous navigation in robotics. In this paper, we relate the idea of traversability to safety and ease of road usage by defining a novel sensor-data driven metric called Road Traversability Index (RTI). The RTI translate the geometric interaction of vehicle with road into a distance modulated index that can be used as advice for a human driver or an autonomous agent intending to traverse a particular road segment using a specific vehicle. We present a framework in which 3D sensor data is converted into a road model, which in turn is converted into a roadmap based motion planning graph to represent the underlying configuration space. The RTI is defined as a function of the roadmap by axiomatically satisfying all required properties of road traversability. We have tested our algorithmic framework on simulated scenarios to explore safety; and real-world data sets to discover aspects of traversability for vehicles of various types. Experimental results show that RTI is a practical tool that reveals information that may be hidden to human inspection or other methods of assessment that do not explicitly model a vehicle.