Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized access control in distributed file systems

The Internet enables global sharing of data across organizational boundaries. Distributed file systems facilitate data sharing in the form of remote file access. However, traditional access control mechanisms used in distributed file systems are intended for machines under common administrative control, and rely on maintaining a centralized database of user identities. They fail to scale to a large user base distributed across multiple organizations. We provide a survey of decentralized access control mechanisms in distributed file systems intended for large scale, in both administrative domains and users. We identify essential properties of such access control mechanisms. We analyze both popular production and experimental distributed file systems in the context of our survey.
Remote Homology Detection of Protein Sequences

The classification of protein sequences using string kernels provides valuable insights for protein function prediction. Almost all string kernels are based on patterns that are not independent, and therefore the associated scores are obtained using a set of redundant features. In this talk we will discuss how a class of patterns, called Irredundant, is specifically designed to address this issue. Loosely speaking the set of Irredundant patterns is the smallest class of independent patterns that can describe all patterns in a string. We present a classification method based on the statistics of these patterns, named Irredundant Class. Results on benchmark data show that Irredundant Class outperforms most of the string kernel methods previously proposed, and it achieves results as good as the current state-of-the-art methods with a fewer number of patterns. Unfortunately we show that the information carried by the irredundant patterns can not be easily interpreted, thus alternative notions are needed.
A tetrahedra-based stream surface algorithm

This paper presents a new algorithm for the calculation of stream surfaces for tetrahedral grids. It propagates the surface through the tetrahedra, one at a time, calculating the intersections with the tetrahedral faces. The method allows us to incorporate topological information from the cells, e.g., critical points. The calculations are based on barycentric coordinates, since this simplifies theory and algorithm. The stream surfaces are ruled surfaces inside each cell, and their construction is started with line segments on the faces. Our method supports the analysis of velocity fields resulting from computational fluid dynamics (CFD) simulations.
A Fast and Compact Method for Unveiling Significant Patterns in High Speed Networks

Identification of significant patterns in network traffic, such as IPs or flows that contribute large volume (heavy hitters) or introduce large changes (heavy changers), has many applications in accounting and network anomaly detection. As network speed and the number of flows grow rapidly, tracking per-IP or per-flow statistics becomes infeasible due to both the computational overhead and memory requirements. In this paper, we propose a novel sequential hashing scheme that requires only O(H log N) both in memory and computational overhead that are close to being optimal, where N is the the number of all possible keys (e.g., flows, IPs) and H is the maximum number of heavy keys. Moreover, the generalized sequential hashing scheme makes it possible to trade off among memory, update cost, and detection cost in a large range that can be utilized by different computer architectures for optimizing the overall performance. In addition, we also propose statistically efficient algorithms for estimating the values of heavy hitters and heavy changers. Using both theoretical analysis and experimental studies of Internet traces, we demonstrate that our approach can achieve the same accuracy as the existing methods do but using much less memory and computational overhead.
Parallel control strategy of single-phase voltage-source inverter base on synchronous signal bus

Conventional droop-control strategy of parallel inverter regulates the phase and the amplitude of the output voltage according to output active power and reactive power respectively. But for a two-loop feedback control voltage-source inverter, active power and reactive power are related to both the amplitude and the phase of the output voltage. In this paper, the equivalent output impedance of the inverter is derived and the quantitative relationship between active/reactive power and amplitude/phase of output voltage is discussed. Based on which, a parallel inverter control strategy is proposed, with the phase synchronization via bus and output voltage amplitude regulated by the relation of output active power and reactive power. Based on this control technique, the model of parallel inverter system is established. Experimental results show the effectiveness of the control strategy.
Increasing the Transferability of Adversarial Attack by Depthwise Separation Convolution

Deep neural networks, susceptible to adversarial examples, can be misled by almost imperceptible alterations. In a black-box setting, however, adversarial examples created using the source model typically exhibit limited transferability for attacking other defended models. Therefore, obtaining adversarial examples with high transferability is highly desirable as it facilitates practical adversarial attacks. In our research, we introduce a novel approach termed DSC-Attack, designed to create adversarial examples with enhanced transferability that effectively bypass defense models. Differing from conventional attack algorithms, we employ depthwise separable convolutions to enhance computational efficiency, conserve computing resources, thereby achieving more transferable malicious adversaries. Experimental results on the widely-recognized ImageN et dataset show that although DSC-Attack under the single model setting performs consistently with existing attack methods, it significantly outperforms current methods in terms of transferability within an ensemble-model framework.
Resveratrol Induces Mitochondrial Apoptosis and Inhibits Epithelial-Mesenchymal Transition in Oral Squamous Cell Carcinoma Cells

ABSTRACT OSCC is the most common malignant cancer of the head and neck. EMT is an essential cellular process critical to the morphogenesis and homeostasis of solid tissues. It is also involved in the initial stage of cancer metastasis and invasion in which cells lose epithelial characteristics. While cancer therapy protocols such as surgery, radiation, and chemotherapy are effective and useful, the drug tolerance and toxicity of OSCC patients remain a problem. Resveratrol is mainly produced in red grape skin and exhibits anti-oxidative, anti-inflammatory, anti-proliferative, and anti-cancer properties. This study was undertaken to investigate the underlying mechanisms giving rise to the induction of apoptosis by resveratrol in the human tongue squamous cell carcinoma cell line. Resveratrol treatment resulted in a time- and dose-dependent decrease in cell viability and increased the apoptotic cell ratio in CAL-27, SCC15, and SCC25 cells. Resveratrol treatment of CAL-27 cells showed that several lines of apoptotic manifestation and decreased cell migration, invasion, and EMT-inducing transcription factor. Taken together, our findings demonstrate the inhibitory effect of resveratrol in human OSCC cells via the mitochondrial pathway and that resveratrol is able to inhibit cell invasion and migration by inhibiting the EMT-inducing transcription factors.
Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.
On Associative Confounder Bias

Conditioning on some set of confounders that causally affect both treatment and outcome variables can be sufficient for eliminating bias introduced by all such confounders when estimating causal effect of the treatment on the outcome from observational data. It is done by including them in propensity score model in so-called potential outcome framework for causal inference whereas in causal graphical modeling framework usual conditioning on them is done. However in the former framework, it is confusing when modeler finds a variable that is non-causally associated with both the treatment and the outcome. Some argue that such variables should also be included in the analysis for removing bias. But others argue that they introduce no bias so they should be excluded and conditioning on them introduces spurious dependence between the treatment and the outcome, thus resulting extra bias in the estimation. We show that there may be errors in both the arguments in different contexts. When such a variable is found neither of the actions may give the correct causal effect estimate. Selecting one action over the other is needed in order to be less wrong. We discuss how to select the better action.
Mapping landslide surface fissures with matched filtering and object-oriented image analysis

Surface fractures are important indicators for geomechanical processes and mapping and monitoring of landslide surface fissures provide valuable information about the slope activity and related hazards. Mappings of surface fissures are traditionally carried out during field surveys, whereas recently aerial photography with sufficient spatial resolution to picture such fine structures is becoming more commonly available. This study targeted the development of a semi-automatic image processing chain to efficiently extract fissure maps from very-high resolution aerial images recorded at the Super Sauze landslide in the Southern French Alps. The developed processing chain combines a Gaussian filters, morphological operators and object-oriented image analysis to exploit higher level scene context. The technique was tested with a heterogeneous set of multi-temporal images recorded at five different dates and the resulting maps were compared quantitatively with expert maps based on field surveys and image interpretation. * corresponding author
Rational Radial Distortion Models of Camera Lenses with Analytical Solution for Distortion Correction

The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as well as satisfactory accuracy. This paper presents a new class of rational radial distortion models with easy analytical undistortion formulae. Experimental results are presented to show that with this class of rational radial distortion models, satisfactory and comparable accuracy can be achieved.
TaxMan: a server to trim rRNA reference databases and inspect taxonomic coverage

Amplicon sequencing of the hypervariable regions of the small subunit ribosomal RNA gene is a widely accepted method for identifying the members of complex bacterial communities. Several rRNA gene sequence reference databases can be used to assign taxonomic names to the sequencing reads using BLAST, USEARCH, GAST or the RDP classifier. Next-generation sequencing methods produce ample reads, but they are short, currently ∼100–450 nt (depending on the technology), as compared to the full rRNA gene of ∼1550 nt. It is important, therefore, to select the right rRNA gene region for sequencing. The primers should amplify the species of interest and the hypervariable regions should differentiate their taxonomy. Here, we introduce TaxMan: a web-based tool that trims reference sequences based on user-selected primer pairs and returns an assessment of the primer specificity by taxa. It allows interactive plotting of taxa, both amplified and missed in silico by the primers used. Additionally, using the trimmed sequences improves the speed of sequence matching algorithms. The smaller database greatly improves run times (up to 98%) and memory usage, not only of similarity searching (BLAST), but also of chimera checking (UCHIME) and of clustering the reads (UCLUST). TaxMan is available at http://www.ibi. vu.nl/programs/taxmanwww/.
Autocratic Leadership Around the Globe Do Climate and Wealth Drive Leadership Culture

Common autocratic leader characteristics in a country's organizations are conceptualized here as cultural adaptations to noncultural components of the national environment, such as the harshness of cold or hot climate and the level of wealth. A secondary analysis of managerial survey data gathered in 61 cultures is used to retest the recently formulated theory that climatic demands matched by wealth-based resources are positively linked to democratic leadership, whereas climatic demands unmatched by wealth-based resources are negatively linked to democratic leadership. In further support of this cultural leadership theory, autocratic leadership, as the opposite of democratic leadership, is seen as less effective in richer countries with more demanding climates but as more effective in poorer countries with more demanding climates.
Resource-Aware Wireless Sensor-Actuator Networks

Innovations in wireless sensor networks (WSNs) have dramatically expanded the applicability of control technology in day-to-day life, by enabling the cost-effective deployment of large scale sensor-actuator systems. In this paper, we discuss the issues and challenges involved in deploying control-oriented applications over unreliable, resource-constrained WSNs, and describe the design of our planned Sensor Control System (SCS) that can enable the rapid development and deployment of such applications.
Should Transfusion Trigger Thresholds Differ for Critical Care Versus Perioperative Patients? A Meta-Analysis of Randomized Trials

Objective: To address the significant uncertainty as to whether transfusion thresholds for critical care versus surgical patients should differ. Design: Meta-analysis of randomized controlled trials. Setting: Medline, EMBASE, and Cochrane Library searches were performed up to 15 June 2016. Patients: Trials had to enroll adult surgical or critically ill patients for inclusion. Interventions: Studies had to compare a liberal versus restrictive threshold for the transfusion of allogeneic packed RBCs. Measurements and Main Results: The primary outcome was 30-day all-cause mortality, sub-grouped by surgical and critical care patients. Secondary outcomes included myocardial infarction, stroke, renal failure, allogeneic blood exposure, and length of stay. Odds ratios and weighted mean differences were calculated using random effects meta-analysis. To assess whether subgroups were significantly different, tests for subgroup interaction were used. Subgroup analysis by trials enrolling critically ill versus surgical patients was performed. Twenty-seven randomized controlled trials (10,797 patients) were included. In critical care patients, restrictive transfusion resulted in significantly reduced 30-day mortality compared with liberal transfusion (odds ratio, 0.82; 95% CI, 0.70–0.97). In surgical patients, a restrictive transfusion strategy led to the opposite direction of effect for mortality (odds ratio, 1.31; 95% CI, 0.94–1.82). The subgroup interaction test was significant (p = 0.04), suggesting that the effect of restrictive transfusion on mortality is statistically different for critical care (decreased risk) versus surgical patients (potentially increased risk or no difference). Regarding secondary outcomes, for critically ill patients, a restrictive strategy resulted in reduced risk of stroke/transient ischemic attack, packed RBC exposure, transfusion reactions, and hospital length of stay. In surgical patients, restrictive transfusion resulted in reduced packed RBC exposure. Conclusions: The safety of restrictive transfusion strategies likely differs for critically ill patients versus perioperative patients. Further trials investigating transfusion strategies in the perioperative setting are necessary.
Combining Interprocedural Pointer Analysis and Conditional Constant Propagation

LIMITED DISTRIBUTION NOTICE This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and speciic requests. After outside publication, requests should be lled only by reprints or legally obtained copies of the article (e.g., payment of royalties). Abstract. The information computed by a pointer alias analysis can be used to improve the precision of subsequent analyses, such as conditional constant propagation. Conditional constant propagation can, in turn, discover unexecutable code, which can improve the precision of pointer analysis. This improved alias precision, in turn, may increase the eeectiveness of the conditional constant propagation, and so on. One way to resolved this mutual dependence between conditional constant propagation and pointer alias analysis is to iterate over the two analyses until a xed point is reached. In addition to raising eeciency concerns, the resulting precision may also not be optimal, i.e., a more tightly coupled analysis can have better precision for both alias analysis and conditional constant propagation than iterating over the two analyses. In this work we explore three combinations of pointer alias analysis and conditional constant propagation: a one-pass-over-analyses approach, an iterate-over-analyses approach, and a new algorithm that synthesizes pointer alias analysis and conditional constant propagation. We empirically demonstrate the eeciency and precision of these three approaches on a benchmark suite of 21 C programs ranging in size from 200 { 4,600 LOC.
A constant-factor approximation for multi-covering with disks

We consider variants of the following multi-covering problem with disks. We are given two point sets Y (servers) and X (clients) in the plane, and a coverage function κ :X -> N. Centered at each server is a single disk whose radius we are free to set. The requirement is that each client x ∈ X be covered by at least κ(x) of the server disks. The objective function we wish to minimize is the sum of the areas of the disks. We present a polynomial time algorithm for this problem achieving an O(1) approximation.
End-to-End Evaluation in Simultaneous Translation

This paper presents the end-to-end evaluation of an automatic simultaneous translation system, built with state-of-the-art components. It shows whether, and for which situations, such a system might be advantageous when compared to a human interpreter. Using speeches in English translated into Spanish, we present the evaluation procedure and we discuss the results both for the recognition and translation components as well as for the overall system. Even if the translation process remains the Achilles' heel of the system, the results show that the system can keep at least half of the information, becoming potentially useful for final users.
MANAGEMENT OF GIANT RETINAL TEARS WITHOUT SCLERAL BUCKLING

Giant retina! tears have been repaired in six consecutive patients without the use of a scleral buckle. By using preoperative laser photocoagulation, pars plana vitrectomy with light choroidal cryopexy, pure C3F8 gas injection followed by postoperative positioning and supplemental laser, excellent anatomic and visual results have been achieved. The risk of proliferative vitreoretinopathy may be minimized using this technique.
A UWB IR timed-array radar using time-shifted direct-sampling architecture

A UWB impulse radio (IR) timed-array radar using time-shifted direct-sampling architecture is presented. The transmitter array can generate and send a variety of 10GS/s pulses towards targets. The receiver array samples the reflected signal in RF domain directly by time interleaved sampling with equivalent sampling rate of 20 GS/s. The radar system can determine time of arrival (TOA) and direction of arrival (DOA) through time-shifted sampling edges which are generated by on-chip digital-to-time converters (DTC). The proposed architecture has range and azimuth resolution of 0.75 cm and 3 degree respectively. This prototype is implemented in a 0. 18μm CMOS technology.
Coactivation of Lower Limb Muscles during Gait in Patients with Multiple Sclerosis

Background Coactivation of agonist and antagonist lower limb muscles during gait stiffens joints and ensures stability. In patients with multiple sclerosis, coactivation of lower limb muscles might be a compensatory mechanism to cope with impairments of balance and gait. Objective The aim of this study was to assess coactivation of agonist and antagonist muscles at the knee and ankle joints during gait in patients with multiple sclerosis, and to evaluate the relationship between muscle coactivation and disability, gait performance, dynamic ankle strength measured during gait, and postural stability. Methods The magnitude and duration of coactivation of agonist-antagonist muscle pairs at the knee and ankle were determined for both lower limbs (more and less-affected) in 14 patients with multiple sclerosis and 11 healthy subjects walking at a spontaneous speed, using 3D-gait analysis. Results In the patient group, coactivation was increased in the knee muscles during single support (proximal strategy) and in the ankle muscles during double support (distal strategy). The magnitude of coactivation was highest in the patients with the slowest gait, the greatest motor impairment and the most instability. Conclusion Increased muscle coactivation is likely a compensatory mechanism to limit the number of degrees of freedom during gait in patients with multiple sclerosis, particularly when postural stability is impaired.