Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact 3D seismic data reconstruction using Tubal-Alt-Min algorithm

Data missing is an common issue in seismic data, and many methods have been proposed to solve it. In this paper, we present the low-tubal-rank tensor model and a novel tensor completion algorithm to recover 3D seismic data. This is a fast iterative algorithm, called Tubal-Alt-Min which completes our 3D seismic data by exploiting the low-tubal-rank property expressed as the product of two much smaller tensors. TubalAlt-Min alternates between estimating those two tensor using least squares minimization. We evaluate its reconstruction performance both on synthetic seismic data and land data survey. The experimental results show that compared with the tensor nuclear norm minimization algorithm, Tubal-Alt-Min improves the reconstruction error by orders of magnitude.
The Externalities Associated with the Common Property Exploitation of Groundwater

Abstract In this paper the rate of groundwater extraction under the common property arrangement is the outcome of a dynamic game played with feedback strategies. The analysis clarifies the externalities associated with the common property extraction of groundwater and identifies an risk externality that arises when firms are risk averse. Identifying the various externalities bears on the development of appropriate forms of groundwater management. In particular, the risk externality would be unknown to the "watermaster" of a central control agency, suggesting the need for creative, decentralized forms of groundwater management.
NBTI-Aware Dynamic Instruction Scheduling

is an important emerging silicon reliability problem. In this paper we explore a microarchitecture-level approach to mitigate NBTI related failures in the functional units of a superscalar processor. We analyze the impact of dynamic instruction scheduling on NBTI and show that the conventional approach to instruction scheduling can accelerate the wear out of certain functional units. We then propose and evaluate two different NBTI-aware instruction scheduling policies and quantify the tradeoffs between performance and reliability of each policy.
The quasi-parallel lives of satellite cells and atrophying muscle

Skeletal muscle atrophy or wasting accompanies various chronic illnesses and the aging process, thereby reducing muscle function. One of the most important components contributing to effective muscle repair in postnatal organisms, the satellite cells (SCs), have recently become the focus of several studies examining factors participating in the atrophic process. We critically examine here the experimental evidence linking SC function with muscle loss in connection with various diseases as well as aging, and in the subsequent recovery process. Several recent reports have investigated the changes in SCs in terms of their differentiation and proliferative capacity in response to various atrophic stimuli. In this regard, we review the molecular changes within SCs that contribute to their dysfunctional status in atrophy, with the intention of shedding light on novel potential pharmacological targets to counteract the loss of muscle mass.
Recoverable values for independent sets

The notion of recoverable value was advocated in the work of Feige, Immorlica, Mirrokni and Nazerzadeh (APPROX 2009) as a measure of quality for approximation algorithms. There, this concept was applied to facility location problems. In the current work we apply a similar framework to the maximum independent set problem (MIS). We say that an approximation algorithm has recoverable factor ρ, if for every graph it recovers an independent set of size at least maxI∑v∈Imin[1,ρd(v)+1] where d(v) is the degree of vertex v, and I ranges over all independent sets in G. Hence, in a sense, from every vertex v in the maximum independent set the algorithm recovers a value of at least ρ/(d(v)+1) toward the solution. This quality measure is most effective in graphs in which the maximum independent set is composed of low degree vertices. A simple greedy algorithm achieves ρ≥1 . We design a new randomized algorithm for MIS that ensures an expected recoverable factor of at least ρ≥7/3 . In passing, we prove that approximating MIS in graphs with a given k‐coloring within a ratio larger than 2/ k is unique‐games hard. This rules out an alternative approach for obtaining ρ≥2 . © 2013 Wiley Periodicals, Inc. Random Struct. Alg. , 46, 142–159, 2015
Trends in Health Information Technology Safety: From Technology-Induced Errors to Current Approaches for Ensuring Technology Safety

Objectives Health information technology (HIT) research findings suggested that new healthcare technologies could reduce some types of medical errors while at the same time introducing classes of medical errors (i.e., technology-induced errors). Technology-induced errors have their origins in HIT, and/or HIT contribute to their occurrence. The objective of this paper is to review current trends in the published literature on HIT safety. Methods A review and synthesis of the medical and life sciences literature focusing on the area of technology-induced error was conducted. Results There were four main trends in the literature on technology-induced error. The following areas were addressed in the literature: definitions of technology-induced errors; models, frameworks and evidence for understanding how technology-induced errors occur; a discussion of monitoring; and methods for preventing and learning about technology-induced errors. Conclusions The literature focusing on technology-induced errors continues to grow. Research has focused on the defining what an error is, models and frameworks used to understand these new types of errors, monitoring of such errors and methods that can be used to prevent these errors. More research will be needed to better understand and mitigate these types of errors.
Slow-light vortices in periodic waveguides

We reveal that the reduction of the group velocity of light in periodic waveguides is generically associated with the presence of vortex energy flows. We show that the energy flows are gradually frozen for slow-light at the Brillouin zone edge, whereas vortices persist for slow-light states having non-vanishing phase velocity inside the Brillouin zone. We also demonstrate that presence of vortices can be linked to the absence of slow-light at the zone edge, and present calculations illustrating these general results.
Integration of Handwriting Recognition in Butterfly Net

ButterflyNet allows field biologists to organize and share their multimedia data easily. By integrating handwriting recognition into the system, a lot of additional functionalities can be implemented to further facilitate the process of data organization and sharing. In this paper, the search functionality is investigated and a prototype that enables searching through handwritten notes is implemented and tested.
An Ex Ante Assessment of Investments in Texas Grapefruit under Uncertainty

Abstract The Dixit-Pindyck model was applied to examine the hypothesis that uncertainty associated with grapefruit production costs and returns is an important determinant of Texas grapefruit growers' investment behavior. Freezes, price variability, and the effects of expanded trade were analyzed as risk factors. An investment decision rule based on a net-present value calculation would approve a 25-year commitment to a 20-acre grapefruit grove, given a 6-percent discount rate. The modified hurdle rate, calculated using an ex ante version of the Dixit-Pindyck model, is 24 percent. The major source of the risk borne by Texas grapefruit investors is from freezes, rather than from expanded trade.
Sentence Compression For Automatic Subtitling

This paper investigates sentence compression for automatic subtitle generation using supervised machine learning. We present a method for sentence compression as well as discuss generation of training data from compressed Finnish sentences, and different approaches to the problem. The method we present outperforms state-of-the-art baseline in both automatic and human evaluation. On real data, 44.9% of the sentences produced by the compression algorithm have been judged to be useable as-is or after minor edits.
Computer Simulation of Bound Component Washing To Minimize Processing Costs

In this paper we focused on the optimization of the washing processes because many technological processes are characterized by large consumption of water, electrical energy and auxiliary chemicals mainly. For this reason it is very important to deal with them. For the optimization of process of washing it is possible to set up an access of the indirect modeling that is based on make-up of mathematical models coming out of study of the physical operation mechanism. The process is diffusion character it is characterized by the value of diffusion effective coefficient and so called structure power of the removing item to the solid phase. The mentioned parameters belong to input data that are appropriate for the automatic control of washing process.
Diagnostic Accuracy of a Loop-Mediated Isothermal PCR Assay for Detection of Orientia tsutsugamushi during Acute Scrub Typhus Infection

Background There is an urgent need to develop rapid and accurate point-of-care (POC) technologies for acute scrub typhus diagnosis in low-resource, primary health care settings to guide clinical therapy. Methodology/Principal Findings In this study we present the clinical evaluation of loop-mediated isothermal PCR assay (LAMP) in the context of a prospective fever study, including 161 patients from scrub typhus-endemic Chiang Rai, northern Thailand. A robust reference comparator set comprising following ‘scrub typhus infection criteria’ (STIC) was used: a) positive cell culture isolate and/or b) an admission IgM titer ≥1∶12,800 using the ‘gold standard’ indirect immunofluorescence assay (IFA) and/or c) a 4-fold rising IFA IgM titer and/or d) a positive result in at least two out of three PCR assays. Compared to the STIC criteria, all PCR assays (including LAMP) demonstrated high specificity ranging from 96–99%, with sensitivities varying from 40% to 56%, similar to the antibody based rapid test, which had a sensitivity of 47% and a specificity of 95%. Conclusions/Significance The diagnostic accuracy of the LAMP assay was similar to realtime and nested conventional PCR assays, but superior to the antibody-based rapid test in the early disease course. The combination of DNA- and antibody-based detection methods increased sensitivity with minimal reduction of specificity, and expanded the timeframe of adequate diagnostic coverage throughout the acute phase of scrub typhus.
Changes in contractile proteins during differentiation of myeloid leukemia cells. II. Purification and characterization of actin

A myeloid leukemia cell line, M1, differentiates to macrophage and gains locomotive and phagocytic activity when incubated with conditioned medium (CM) from a fibroblast culture and bacterial endotoxin. To characterize the actin molecules before and after differentiation, the actin was purified through three sequential steps: DEAE-sephadex A- 50, polymerization/depolymerization, and sephadex G-150 chromatography. There were no essential differences between the inhibitory activity of actins from control M1 cells and CM-treated M1 cells on both DNase I and heavy meromyosin (HMMM) K(+)-EDTA-ATPase; the same dose response as with skeletal muscle actin took place. After the treatment with CM, however, the specific activity for the activation of HMMM Mg(2+)- ATPase by actin became two-fold that of untreated M1 actin, which was one third of the value for skeletal muscle actin. The V(max) for the control and the CM-treated M1 cell, as well as the skeletal muscle actins, proved to be the same. By contrast, the K(app) values for the control and CM-treated M1-cell actins were 3- and 1.5-fold the value for skeletal-muscle actin. This means that CM treatment of the M1 actin produced a twofold affinity for the Mg(2+)-ATPase of skeletal-muscle myosin. The critical concentrations for polymerization were compared under different salt concentrations and temperatures. Although no marked difference was found for the presence of 2 mM MgCl(2), 0.1 M KCl in place of MgCl(2) at 5 degrees C gave the following values: 0.1 mg/ml for skeletal-muscle actin, 0.7 mg/ml for control M1 actin, 0,5 mg/ml for CM- treated M1 actin, and 1.0 mg/ml for the D(-) subline that is insensitive to CM. Although the critical concentration of D(-) actin is extraordinarily high, this actin showed normal polymerization above the critical concentration. This together with the data presented in our previous paper, that the D(-) actin in the crude extract did not polymerize, suggests that an inhibitor for actin polymerization is present in the subline. The kinetics experiment at 0.1 M KCl and 25 degrees C revealed a slower polymerization of untreated M1- and D(-)-cell actins as compared with CM-treated M1 actin. This delayed polymerization was due to a delay during the nucleation stage, not during the elongation stage. By isoelectric focusing, the ratios of β- to γ-actin showed a marked difference depending on the states of cells: about 4.9 for control M1, 2.8 for CM-treated M1, and 7.6 for D(-)-subline actins. Tryptic peptide maps also revealed the presence of different peptides. Thus, the functional differences of actin before and after the differentiation was accompanied by some chemical changes in actin molecules.
Speech enhancement via energy separation

This work presents a novel technique to enhance speech signals in the presence of interfering noise. In this paper, the amplitude and frequency (AM-FM) modulation model [7] and a multi-band analysis scheme [5] are applied to extract the speech signal parameters. The enhancement process is performed using a time-warping function (cid:12)(n) that is used to warp the speech signal. (cid:12)(n) is extracted from the speech signal using the Smoothed Energy Operator Separation Algorithm (SEOSA) [4]. This warping is capable of increasing the SNR of the high frequency harmonics of a voiced signal by forcing the the quasiperiodic nature of the voiced component to be more periodic, and consequently is useful for extracting more robust parameters of the signal in the presence of noise.
Automatic structure recovery and visualization

We describe an automated pipeline for the reconstruction and rendering of three dimensional objects, with particular emphasys for urban environments. Our system can robustly recover 3D points and cameras from uncalibrated views, without manual assistance. The reconstructed structure is augmented by fitting geometrical primitives such as planes and cylinders to the sparse point cloud obtained. Such information is the key to obtain a higher level understanding of the scene; we use this knowledge to efficiently render the recovered environment, capturing its global appearance while preserving scalability. Several examples display our system in action.
Dualities and hidden supersymmetry in string quantum cosmology

A supersymmetric approach to string quantum cosmology based on the non-compact, global duality symmetries of the effective action is developed. An N=2 supersymmetric action is derived whose bosonic component is the Neveu-Schwarz/Neveu-Schwarz sector of the $(d+1)$--dimensional effective action compactified on a $d$--torus. A representation for the supercharges is found and the form of the zero-and one-fermion quantum states is determined. The purely bosonic component of the wavefunction is unique and manifestly invariant under the symmetry of the action. The formalism applies to a wide class of non-linear sigma-models.
MEDICAL TEXTS CLASSIFICATION BASED ON KEYWORDS USING SEMANTIC INFORMATION

: This paper presents a method to classify medical texts based on keywords with the support of additional semantic information. The classification is performed in two phases. In the first phase, keyword sets are extracted for each type of disease presented in the training set. Keywords are ranked according to their semantic relatedness. In the second phase, medical texts are classified basing on the resulting keyword lists. The experimental results proved to be encouraging.
Configurable range memory for effective data reuse on programmable accelerators

While programmable accelerators such as application-specific processors and reconfigurable architectures can dramatically speed up compute-intensive kernels of an application, application performance can still be severely limited by the communication between processors. To minimize the communication overhead, a shared memory such as a scratchpad memory may be employed between the main processor and the accelerator coprocessor. However, this setup poses a significant challenge to the main processor, which now must manage data on the scratchpad explicitly, resulting in superfluous data copying due to the inflexibility of a scratchpad. In this article, we present an enhancement of a scratchpad, Configurable Range Memory (CRM), whose address range can be reprogrammed to minimize unnecessary data copying between processors and therefore promote data reuse on the accelerator, and also present a software management algorithm for the CRM. Our experimental results involving detailed simulation of full multimedia applications demonstrate that our CRM architecture can reduce the communication overhead quite effectively, reducing the kernel execution time by up to 28% and the application runtime by up to 12.8%, in addition to considerable system energy reduction, compared to the conventional architecture based on a scratchpad.
Large-Scale Wi-Fi Hotspot Classification via Deep Learning

We describe the problem of classifying hundreds of millions of Wi-Fi hotspots using only connection and user count characteristics. We use a combination of deep learning and frequency analysis. Specifically, Convolution Neural Networks (CNN) capture the spatio-temporal relationship between adjacent connection/user counts across a 24 hour×7 day matrix, while FFT (Fast Fourier Transforms) extract user and connection frequencies. Our production system has been deployed to classify 239 million hotspots in 12 hours on a SPARK 2.0 cluster, achieving close to 80% F1-score for binary classification.
Self-Adapating Maxflow Routing for Autonomous Wireless Sensor Networks

Energy-harvesting wireless sensor networks (EHWSNs) can be modeled as flow networks with channel capacities limited by the ratio between the environmental power available at the nodes and the energy they need to process each packet.The maximum workload that can be sustained by environmental power is the maxflow that can be routed across the network from sensor nodes to the sink.This paper presents a theoretically-optimum maxflow routing algorithm that makes EH-WSNs able to transparently adapt to time-varying environmental power conditions during their normal operation.
The Corporate Elite Community Structure of Global Capitalism

A key debate on the merits and consequences of globalisation asks to what extent we have moved to a multipolar global political economy. Here we investigate this issue through the properties and topologies of corporate elite networks and ask: what is the community structure of the global corporate elite? In order to answer this question, we analyse how the largest one million firms in the world are interconnected at the level of corporate governance through interlocking directorates. Community detection through modularity maximisation reveals that regional clusters play a fundamental role in the network architecture of the global political economy. Transatlantic connections remain particularly strong: Europe and North America remain interconnected in a dense network of shared directors. A distinct Asian cluster stands apart as separate and oriented more towards itself. While it develops and gains economic and political power, Asia remains by and large outside the scope of the networks of the incumbent global (that is, North Atlantic) corporate elite. We see this as a sign of the rise of competing corporate elites. But the corporate elites from the traditional core countries still form a powerful opponent for any competing faction in the global corporate elite.
The neural correlates of human vibrotactile working memory : Converging evidence from functional magnetic resonance imaging, electroencephalographic and behavioral studies

The present dissertation aimed to shed more light on the psychological mechanisms and the neural basis of tactile working memory (WM). For this purpose, a vibrotactile delayed discrimination task was studied using the methods of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), concurrent subliminal electrical stimulation and psychophysics. The fMRI study (Study I) showed that a broad network of brain regions much broader than known from previous studies in non-human primates supports the performance of a vibrotactile delayed discrimination task in the different task periods: encoding, maintenance, decision making. The analysis of oscillatory activity over the somatosensory cortex in the EEG study (Study II) and the experiment using subliminal electrical stimulation to locally inhibit the primary somatosensory cortex (S1) (Study III) suggest that S1 does not contribute to the active maintenance of the vibrotactile memory trace. The level of activity in S1 during the early delay period depends on the efficiency with which subjects encode the vibrotactile stimulus. Study II and III also showed that the activation level of S1 in the pre-trial period plays an important role. Study III suggests that, in this task period, S1 activity is up-regulated under WM demands probably reflecting the operation of top-down attentional control. Study III indicates that increasing local inhibition of S1 in the pre-trial period improves performance by facilitating bottom-up processing. Importantly, long-term memory representations of the average frequency of the stimulus set strongly influence performance giving rise to the time-order effect (Study IV). Additionally, the fMRI data (Study V) showed that a somatosensory network integrates information about the current vibrotactile stimulus and the representation of the average vibration frequency during stimulus encoding and maintenance.
Multiframe Auto White Balance

Automatic white balancing has the aim of ensuring color consistency of the captured image over a wide range of light sources with different color temperatures. Almost all available automatic white balance techniques contain two main processing steps. Firstly, the illuminant is identified and some gaining coefficients are computed for each color component. After that, the red, green, and blue color components are multiplied with the corresponding gains to obtain the result image. The difference between the majority of the existing solutions rely on the method for gain computation. In this paper we deal with the second step of the AWB procedure where we expose differently each color component instead of multiplying it with a constant.
Notice of RetractionDynamic modeling of multi-axis synchronous gantry machining center

The dynamic model of a multi-axis synchronous gantry machining center VX32-60 is developed based on the finite element method. Both the numerical simulation and the modal experiment are studied. The nature frequencies and mode shapes are obtained by theoretical analysis and experimental results. The effects of material parameters and section parameters on the dynamic characteristics of multi-axis synchronous gantry machining center are investigated. The design optimization method of these parameters is proposed, which increases the nature frequencies of machining center and reduces the motion error.