Miracle-dz/UniFAR-FTUs huggingface dataset:
BigDataBench: A big data benchmark suite from internet services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architecture, the pressure of benchmarking and evaluating these systems rises. However, the complexity, diversity, frequently changed workloads, and rapid evolution of big data systems raise great challenges in big data benchmarking. Considering the broad use of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads, which is the prerequisite for evaluating big data systems and architecture. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite-BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. Currently, we choose 19 big data benchmarks from dimensions of application scenarios, operations/ algorithms, data types, data sources, software stacks, and application types, and they are comprehensive for fairly measuring and evaluating big data systems and architecture. BigDataBench is publicly available from the project home page http://prof.ict. ac.cn/BigDataBench. Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity, which measures the ratio of the total number of instructions divided by the total byte number of memory accesses; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache (L1I) misses per 1000 instructions (in short, MPKI) of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.
Volatility skews and extensions of the Libor market model

The paper considers extensions of the Libor market model to markets with volatility skews in observable option prices. The family of forward rate processes is expanded to include diffusions with non-linear forward rate dependence, and efficient techniques for calibration to quoted prices of caps and swaptions are discussed. Special emphasis is put on generalized CEV processes for which closed-form expressions for cap and swaption prices are derived. Modifications of the CEV process which exhibit more appealing growth and boundary characteristics are also discussed. The proposed models are investigated numerically through Crank–Nicholson finite difference schemes and Monte Carlo simulations.
Changes in collagen orientation and distribution in keratoconus corneas.

PURPOSE To map the collagen orientation and relative distribution of collagen fibrillar mass in keratoconus corneal buttons. METHODS Structural analysis was performed by obtaining synchrotron x-ray scattering patterns across the samples at 0.25-mm intervals. The patterns were analyzed to produce two-dimensional maps of the orientation of the lamellae and of the distribution of total and preferentially aligned lamellae. RESULTS Compared with normal corneas, in keratoconus the gross organization of the stromal lamellae was dramatically changed, and the collagen fibrillar mass was unevenly distributed, particularly around the presumed apex of the cone. CONCLUSIONS The development of keratoconus involves a high degree of inter- and probably intralamellar displacement and slippage that leads to thinning of the central cornea and associated changes in corneal curvature. This slippage may be promoted by a loss of cohesive forces and mechanical failure in regions where lamellae bifurcate.
Design issues in heterogeneous 3D/2.5D integration

Efficient processing of fine-pitched Through Silicon Vias, micro-bumps and back-side re-distribution layers enable face-to-back or face-to-face integration of heterogeneous ICs using 3D stacking and/or Silicon Interposers. While these technology features are extremely compelling, they considerably stress the existing design practices and EDA tool flows typically conceived for 2D systems. With all system, technology and implementation level options brought with these features, the design space increases to an extent where traditional 2D tools cannot be used any more for efficient exploration. Therefore, the cost-effective design of future 3D ICs products will require new planning and co-optimisation techniques and tools that are fast and accurate enough to cope with these challenges. In this paper we present design methodology and the practical EDA tool chain that covers different aspects of the design flow and is specific to efficient design of 3D-ICs. Flow features include: fast synthesis and 3D design partitioning at gate level, TSV/micro-bump array planning, 3D floor planning, placement and routing, congestion analysis, fast thermal and mechanical modeling, easy technology vs. implementation trade-off analysis, 3D device models generations and Design-for-Test (DfT). The application of the tool chain is illustrated using concrete example of a real-world design, showing not only the applicability of the tool chain, but also the benefits of heterogeneous 2.5 and 3D integration technologies.
On the singular problem in linear algorithm for the magnetic dipole positioning

Magnetic dipole positioning technique has attracted more and more attention recently, for its increasing applications in medical, traffic, and the tracking of motion objective, because it provides high accuracy and fast velocity. Typically, a nonlinear optimization algorithm is applied to such a problem, but a linear algorithm is preferable for faster, more reliable computation, and lower complexity. We proposed a linear algorithm to determine the 5D dipole's position and orientation parameters. However, there exist several singular problems in the linear algorithm that cause failure computation and discontinuous results. These singular problems are related to 1 or 2 zero orientation parameters, lower than 4 rank of sensing matrix for computation, and singularity in solution vector. In this paper, we present these singular problems and propose some methods to handle them. Simulations were done and results show that by solving these problems, the linear positioning algorithm is much more stable and accurate.
Performance of Polar Codes on wireless communications Channel

We discuss the performance of polar codes, the capacity-achieving channel codes, on wireless communication channel in this paper. By generalizing the definition of Bhattacharyya Parameter in discrete memoryless channel, we present the special expression of the parameter for Gaussian and Rayleigh fading the two continuous channels, including the recursive formulas and the initial values. We analyze the applications of polar codes with the defined parameter over Rayleigh fading channel by transmitting image and speech. By comparing with low density parity-check codes(LDPC) at the same cases, our simulation results show that polar codes have better performance than that of LDPC codes. Polar codes will be good candidate for wireless communication channel.
Genome-wide Association Study Using Pooled DNA to Identify Candidate Markers Mediating Susceptibility to Postoperative Nausea and Vomiting

Background:A family history has been established as a risk factor for postoperative nausea and vomiting (PONV), but the identities of susceptibility genes remain unknown. The goal of this study was to identify the genetic loci that may contribute to PONV susceptibility in an adult population. Methods:The authors performed a genome-wide association study involving pooling of DNA obtained from 122 patients with severe PONV and 129 matched controls. Each pool was hybridized to a single nucleotide polymorphism (SNP) microarray, and probe intensity was used to predict allele frequency. Differences in allele frequency between SNP in the PONV and control groups were ranked after accounting for the pooling error. The highest ranking SNPs were selected for individual genotyping in the subjects from whom the DNA pool was comprised and in the new verification cohort consisting of 208 subjects (104 PONV patients and 104 controls). Results:The authors identified 41 SNP targets showing substantial difference in allelic frequency between pools. These markers were first genotyped in the individual DNA samples from which the pools were comprised. The authors observed evidence for an association between PONV and 19 different loci in the genome. In the separate verification cohort, the association with PONV was observed for four SNPs. This association remained significant after correcting for multiple testing (P < 0.0023) for one SNP (rs2165870), which is located upstream of the promoter for the muscarinic acetylcholine receptor 3 subtype (CHRM3) gene. Conclusions:The authors performed the genome-wide association study for PONV using pooled DNA samples. Through individual genotyping, they confirmed association of at least one SNP that is predictive of PONV susceptibility.
A Ranking Approach to Persian Pronoun Resolution

Coreference resolution is an essential step toward understanding dis- courses, and it is needed by many NLP tasks such as machine translation, ques- tion answering, and summarization. Pronoun resolution is a major and challeng- ing subpart of coreference resolution, in which only the resolution of pronouns is considered. Classification approaches have been widely used for corefer- ence/pronoun resolution, but it has been shown that ranking approaches outper- form classification approaches in a variety of fields such as English pronoun resolution (Denis and Baldridge, 2007), question answering (Ravichandran, 2003), and tagging/parsing (Collins and Duffy, 2002; Charniak and Johnson, 2005). The strength of ranking is in its ability to consider all candidates at once and selecting the best one based on the model, while existing classification methods consider at most two candidate responses at a time. Persian and its va- rieties are spoken by more than 71 million people, and it has some characteristic that make parsing and other related processing of Persian more difficult than those of English. In this paper, we have evaluated maximum entropy ranker on Persian pronoun resolution and compared the results with that of four base clas- sifiers.
First-Order System Least Squares for the Stokes Equations, with Application to Linear Elasticity

Following our earlier work on general second-order scalar equations, here we develop a least-squares functional for the two- and three-dimensional Stokes equations, generalized slightly by allowing a pressure term in the continuity equation. By introducing a velocity flux variable and associated curl and trace equations, we are able to establish ellipticity in an H1 product norm appropriately weighted by the Reynolds number. This immediately yields optimal discretization error estimates for finite element spaces in this norm and optimal algebraic convergence estimates for multiplicative and additive multigrid methods applied to the resulting discrete systems. Both estimates are naturally uniform in the Reynolds number. Moreover, our pressure-perturbed form of the generalized Stokes equations allows us to develop an analogous result for the Dirichlet problem for linear elasticity, where we obtain the more substantive result that the estimates are uniform in the Poisson ratio.
Privacy-Preserving SimRank over Distributed Information Network

Information network analysis has drawn a lot attention in recent years. Among all the aspects of network analysis, similarity measure of nodes has been shown useful in many applications, such as clustering, link prediction and community identification, to name a few. As linkage data in a large network is inherently sparse, it is noted that collecting more data can improve the quality of similarity measure. This gives different parties a motivation to cooperate. In this paper, we address the problem of link-based similarity measure of nodes in an information network distributed over different parties. Concerning the data privacy, we propose a privacy-preserving Sim Rank protocol based on fully-homomorphic encryption to provide cryptographic protection for the links.
Quality of Early Care and Childhood Trauma: A Prospective Study of Developmental Pathways to Dissociation

Kihlstrom (2005) has recently called attention to the need for prospective longitudinal studies of dissociation. The present study assesses quality of early care and childhood trauma as predictors of dissociation in a sample of 56 low-income young adults followed from infancy to age 19. Dissociation was assessed with the Dissociative Experiences Scale; quality of early care was assessed by observer ratings of mother-infant interaction at home and in the laboratory; and childhood trauma was indexed by state-documented maltreatment, self-report, and interviewer ratings of participants’ narratives. Regression analysis indicated that dissociation in young adulthood was significantly predicted by observed lack of parental responsiveness in infancy, while childhood verbal abuse was the only type of trauma that added to the prediction of dissociation. Implications are discussed in the context of previous prospective work also pointing to the important contribution of parental emotional unresponsiveness in the development of dissociation.
Image classification with user defined ontology

In this paper we are interested in classification of objects in images according to user defined scenarios. We show how the user-defined ontology with a specialisation by a concrete scenario / object of interest allows for an adapted choice of methods and their tuning through the whole framework: selection of the area of interest, descriptors choice, classification of objects. Particular attention here is payed to the classification. We use SVM classifiers for their good capacity of generalisation. We show that in an adapted descriptor space, the choice of a “light” linear kernel together with boosting of classifiers is interesting compared to more complex and computationally expensive RBF kernels. The results on real-life images are promising. The paper results from the research we conduct in the framework of X-Media EU-funded Integrated Project.
Features of Microsystems for Cultivation and Characterization of Stem Cells with the Aim of Regenerative Therapy

Stem cells have infinite potential for regenerative therapy thanks to their advantageous ability which is differentiable to requisite cell types for recovery and self-renewal. The microsystem has been proved to be more helpful to stem cell studies compared to the traditional methods, relying on its advantageous feature of mimicking in vivo cellular environments as well as other profitable features such as minimum sample consumption for analysis and multiprocedures. A wide variety of microsystems were developed for stem cell studies; however, regenerative therapy-targeted applications of microtechnology should be more emphasized and gain more attractions since the regenerative therapy is one of ultimate goals of biologists and bioengineers. In this review, we introduce stem cell researches harnessing well-known microtechniques (microwell, micropattern, and microfluidic channel) in view point of physical principles and how these systems and principles have been implemented appropriately for characterizing stem cells and finding possible regenerative therapies. Biologists may gain information on the principles of microsystems to apply them to find solutions for their current challenges, and engineers may understand limitations of the conventional microsystems and find new chances for further developing practical microsystems. Through the well combination of engineers and biologists, the regenerative therapy-targeted stem cell researches harnessing microtechnology will find better suitable treatments for human disorders.
Fast and accurate spike sorting of high-channel count probes with KiloSort

New silicon technology is enabling large-scale electrophysiological recordings in vivo from hundreds to thousands of channels. Interpreting these recordings requires scalable and accurate automated methods for spike sorting, which should minimize the time required for manual curation of the results. Here we introduce KiloSort, a new integrated spike sorting framework that uses template matching both during spike detection and during spike clustering. KiloSort models the electrical voltage as a sum of template waveforms triggered on the spike times, which allows overlapping spikes to be identified and resolved. Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms. Processing times are faster than in previous algorithms thanks to batch-based optimization on GPUs. We compare KiloSort to an established algorithm and show favorable performance, at much reduced processing times. A novel post-clustering merging step based on the continuity of the templates further reduced substantially the number of manual operations required on this data, for the neurons with near-zero error rates, paving the way for fully automated spike sorting of multichannel electrode recordings.
Social Network Analysis on Name Disambiguation and More

Name variants are ubiquitous in real world due typographical errors (e.g., "Forschungszentrum Julich" vs. "Forschungszentrum Julich"), abbreviated, imcomplete, or missing information (e.g., "R. E. Ellis" vs. "Randy E. Ellis"), lack of standard name formatting convention (e.g., "Spike Jonze" vs. "Jones, Spike"), and their combinations. In this paper, we project this name disambiguation problem to graph representation, and then analyze graphs using social network analysis. In particular, we used real duplicate name entities that we manually verifed from ACM digital library. Then, using various string similarity metrics and additional information (i.e., co-author names, titles, and venues), we analyze the effectiveness of string similarity metrics and additional information based on social network analysis. Through our experimental validation, name disambiguation problem can be analyzed in graphical, visual manner.
2015 Ieee International Conference on Big Data (big Data) Fast Decentralized Gradient Descent Method and Applications to In-situ Seismic Tomography

—We consider the decentralized consensus optimization problem arising from in-situ seismic tomography in large-scale sensor networks. Unlike traditional seismic imaging performed in a centralized location, each node in this setting privately holds an objective function and partial data. The goal of each node is to obtain the optimal solution of the whole seismic image, while communicating only with its immediate neighbors. We present a fast decentralized gradient descent method and prove that this new method can reach optimal convergence rate of O(1/k 2) where k is the number of communication/iteration rounds. Extensive numerical experiments on synthetic and real-world sensor network seismic data demonstrate that the proposed algorithms significantly outperform existing methods.
Dynamic redeployment of control software in distributed industrial automation systems during runtime

Current research on the reconfiguration of automated industrial manufacturing systems focuses mainly on the reconfiguration of the production process. For uninterrupted operation also the distributed control system has to allow for reconfiguration during runtime. In this paper a method for dynamic redeployment of control software during runtime is presented, comprising a structural and behavioral concept. To enable this dynamic redeployment, control hardware faults are detected and the control software deployment is adjusted to the smaller number of controllers. Likewise, additional hardware resources are integrated to the system. An implementation and evaluation within a demonstration scenario shows the feasibility of the concept.
Integrating topological map for autonomous navigation in unstructured environments

To address the local path planning problem for autonomous land vehicle in unstructured environments, this paper will incorporate the topological representation of the local map and a fast A∗ search. Topological connection of the local map will serve as a navigation function for the computing of heuristic value. To extract the topological representation, polar histogram is constructed iteratively. Outlets of temporal expanded state can be found by wave peak of polar histogram. To improve the efficiency of computing the heuristic value based on topological map, a lazy computing process is adopted. Experimental results show that the proposed method can well resolve the local path planning problem for autonomous land vehicle in unstructured environments, even if the localization of the vehicle may not be very accurate.
Epistemology and the Role of Mathematics in Translational Science

The terminology “translational science” has recently beco me very popular; however, there has been little effort to give it an epistemological characterization and t hereby give it meaning as a scientific enterprise. This paper takes a step in that direction by recognizing that translational science must begin with the epistemology of science and, rooted in the scientific epistemology , extend that epistemology to encompass human actions in the physical world. The road taken here is to consi der the classical understanding of operators on random processes in terms of analysis and synthesis, and t o delineate their epistemological domains. The main focus of the paper is on synthesis as translational s cience, in which synthesis is characterized via operator optimization as opposed to being left to trial a nd error. Three translational settings are used as illustrations, the unity of these theories within the con text of translational science being emphasized: (1) the Wiener-Kolmogorov founding paradigm of optimal lin ear filters in the context of canonical signal representation, (2) the analogous optimal nonlinear filter th ory for images in the context of granulometric spectral representation, and (3) the determination of opti mal therapeutic strategies based on structural intervention in gene regulatory networks. The paper closes with s ome comments on the demands imposed by an epistemologically rigorous approach to translational sci en e.