Miracle-dz/UniFAR-FTUs huggingface dataset:
A Serial Based Encryption for Enhanced Access Control in Cloud Computing

Cloud storage allows us to enjoy the on demand cloud application without any hardware implementation. Cloud provides the service a required by the cloud user in a rental basis. Even though the cloud issues the cloud application without any physical implementation results in a security risk since the cloud data can be accessed by everyone. To avoid security issue in the outsourced data a prevention measure is needed to secure the data from unauthenticated users or intruders. A flexible distributed storage integrity mechanism utilising homomorphic tokens and is proposed in this paper to provide security in the outsourced cloud data. This mechanism include the techniques such In order to address security for outsourced data and secure cloud storage, we propose in this paper a flexible distributed storage integrity checking mechanism, utilizing the homomorphism token and distributed erasure-coded data. The proposed design allows users to audit the cloud storage with very low computation cost and lightweight communication. The auditing result not only guarantees strong cloud storage correctness, but also simultaneously identifies fast data error localization, i.e., the identification of misbehaving server. Considering the cloud data are dynamic in nature, the proposed design further supports secure and efficient dynamic operations on outsourced data, including block modification, deletion, and append. Analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack.
Agreement between DSM-IV and DSM-5 measures of substance use disorders in a sample of adult substance users

Aim: In DSM-5, definitions of substance use disorders (SUD) were changed considerably from DSM-IV, yet little is known about how well DSM-IV and DSM-5 SUD diagnoses agree with each other within a series of individuals with substance use problems. Methods: Prevalences and chance-corrected agreement of DSM-5 SUD and DSM-IV substance dependence were evaluated in 588 adult substance users, interviewed by clinician interviewers using the semi-structured Psychiatric Research Interview for Substance and Mental Disorders (PRISM-5). Alcohol, tobacco, cannabis, cocaine, heroin, opioid, sedative, and stimulant use disorders were examined. Cohen's kappa was used to assess agreement between DSM-5 and DSM-IV SUD (including abuse or dependence), DSM-5 SUD and DSM-IV dependence, and DSM-5 moderate-to-severe SUD and DSM-IV dependence. Results: Agreement between DSM-5 and DSM-IV SUD was excellent for alcohol, cocaine, heroin, opioids, sedatives, and stimulants ({kappa}=0.84; 0.91; 0.99; 0.96; 0.92; 0.97; respectively) and substantial for alcohol and tobacco ({kappa}=0.75; 0.80, respectively). Agreement between DSM-5 SUD and DSM-IV substance dependence was excellent for cocaine, heroin, opioids, sedatives, and stimulants ({kappa}=0.89; 0.97; 0.90; 0.88; 0.94, respectively) and substantial for alcohol, tobacco, and cannabis ({kappa}=0.75; 0.69; 0.63, respectively). Agreement between moderate and severe DSM-5 SUD and DSM-IV dependence was excellent across all substances. Conclusion: Findings suggest that while care should always be used in interpreting the results of studies using different methods, studies relying on DSM-IV or DSM-5 SUD diagnostic criteria can be considered to offer similar information and thus can be compared when accumulating a body of evidence on a particular issue regarding substance use.
Profiling of circulating chromosome 21-encoded microRNAs, miR-155 and Let-7c, in Down Syndrome People

Down syndrome (DS), or Trisomy 21 (Ts21), is the most common chromosomal survival aneuploidy. Nevertheless, people with DS have compromised health, and the increase in their life expectancy further heightens the risk of developing chronic degenerative diseases such as obesity, dyslipidemias and diabetes mellitus associated with higher morbidity, and mortality for cardiovascular disease from an early age. DS is also accompanied by a higher risk of neurodegeneration. The extra genetic material that characterizes DS causes an imbalance in the genetic dosage, including overexpression of miR-155 and Let-7c miRNAs, both associated with cognitive impairment and dementia in adults. The dynamics of expression of their putative target genes in the early stages of the development of DS and their clinical associations, however, remain to be ascertained. This study aimed to evaluate the relative expression of miR-155 and Let-7c in young and adult individuals with DS and its possible association with biochemical indicators of lipid metabolism. The anthropometric, clinical, biochemical, and gene expression features of miR-155 and Let-7c were analyzed in a population of 52 control and 50 DS subjects divided into groups of 20 years of age or younger and 21 years or older. Expression changes for miR-155 were not significant. Nevertheless, a negative correlation for HDL-Cholesterol concentrations and miR-155 expression was identified. Notably, Let-7c was overexpressed in DS from young and old ages. Overall, our results suggest that Let-7c is related from early stages to cognitive impairment in DS, while a similar role of miR-155 in late stages could be mediated by alterations in lipid metabolism. Further studies with both miRNAs will shed light on their potential as therapeutic targets to prevent or delay cognitive impairment in DS.
Locally finite representations over Noetherian Hopf algebras

We study finite dimensional representations over some Noetherian algebras over a field of characteristic zero. More precisely, we give necessary and sufficient conditions for the category of locally finite dimensional representations to be closed under taking injective hulls and extend results known for group rings and enveloping algebras to Ore extensions, Hopf crossed products and affine Hopf algebras of low Gelfand-Kirillov dimension.
hXDP

The network interface cards (NICs) of modern computers are changing to adapt to faster data rates and to help with the scaling issues of general-purpose CPU technologies. Among the ongoing innovations, the inclusion of programmable accelerators on the NIC's data path is particularly interesting, since it provides the opportunity to offload some of the CPU's network packet processing tasks to the accelerator. Given the strict latency constraints of packet processing tasks, accelerators are often implemented leveraging platforms such as Field-Programmable Gate Arrays (FPGAs). FPGAs can be re-programmed after deployment, to adapt to changing application requirements, and can achieve both high throughput and low latency when implementing packet processing tasks. However, they have limited resources that may need to be shared among diverse applications, and programming them is difficult and requires hardware design expertise. We present hXDP, a solution to run on FPGAs software packet processing tasks described with the eBPF technology and targeting the Linux's eXpress Data Path. hXDP uses only a fraction of the available FPGA resources, while matching the performance of high-end CPUs. The iterative execution model of eBPF is not a good fit for FPGA accelerators. Nonetheless, we show that many of the instructions of an eBPF program can be compressed, parallelized, or completely removed, when targeting a purpose-built FPGA design, thereby significantly improving performance. We implement hXDP on an FPGA NIC and evaluate it running real-world unmodified eBPF programs. Our implementation runs at 156.25MHz and uses about 15% of the FPGA resources. Despite these modest requirements, it can run dynamically loaded programs, achieves the packet processing throughput of a high-end CPU core, and provides a 10X lower packet forwarding latency.
KFC: A Scalable Approximation Algorithm for k-center Fair Clustering

In this paper, we study the problem of fair clustering on the $k-$center objective. In fair clustering, the input is $N$ points, each belonging to at least one of $l$ protected groups, e.g. male, female, Asian, Hispanic. The objective is to cluster the $N$ points into $k$ clusters to minimize a classical clustering objective function. However, there is an additional constraint that each cluster needs to be fair, under some notion of fairness. This ensures that no group is either "over-represented" or "under-represented" in any cluster. Our work builds on the work of Chierichetti et al. (NIPS 2017), Bera et al. (NeurIPS 2019), Ahmadian et al. (KDD 2019), and Bercea et al. (APPROX 2019). We obtain a randomized $3-$approximation algorithm for the $k-$center objective function, beating the previous state of the art ($4-$approximation). We test our algorithm on real datasets, and show that our algorithm is effective in finding good clusters without over-representation or under-representation, surpassing the current state of the art in runtime speed, clustering cost, while achieving similar fairness violations.
Precision cosmology in modified and extended theories of gravity: An insightful test

In this work we present a brief discussion about modified and extended cosmological models using current observational tests. We show that according to these astrophysical samples based in late universe measurements, theories like $f(R)$ and $f(T,B)$ can provide useful interpretation to a dynamical dark energy. At this stage, precision cosmostatistics has also become a well-motivated endeavour by itself to test gravitational physics at cosmic scales and these analyses can be employed to test the viability and future constrains over specific cosmological models of these theories of gravity, making them a good approach to propose an alternative path from the standard $\Lambda$CDM scenario.
Interpretation of NLP Models through Input Marginalization

To demystify the “black box” property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input. Since existing methods replace each token with a predefined value (i.e., zero), the resulting sentence lies out of the training data distribution, yielding misleading interpretations. In this study, we raise the out-of-distribution problem induced by the existing interpretation methods and present a remedy; we propose to marginalize each token out. We interpret various NLP models trained for sentiment analysis and natural language inference using the proposed method.
Enhancing road signs segmentation using photometric invariants

Road signs detection and recognition in natural scenes is one of the most important tasksin the design of Intelligent Transport Systems (ITS). However, illumination changes remain a major problem. In this paper, an efficient ap-proach of road signs segmentation based on photometric invariants is proposed. This method is based on color in-formation using a hybrid distance, by exploiting the chro-matic distance and the red and blue ratio, on l Theta Phi color space which is invariant to highlight, shading and shadow changes. A comparative study is performed to demonstrate the robustness of this approach over the most frequently used methods for road sign segmentation. The experimental results and the detailed analysis show the high performance of the algorithm described in this paper.
On some tensor tubal-Krylov subspace methods via the T-product

In the present paper, we introduce new tensor Krylov subspace methods for solving linear tensor equations. The proposed methods use the well known T-product for tensors and tensor subspaces related to tube fibers. We introduce some new tensor products and the related algebraic properties. These new products will enable us to develop third-order the tensor tubal GMRES and the tensor tubal Golub Kahan methods. We give some properties related to these methods and proopse some numerical experiments.
Linear Predictive Coding as a Valid Approximation of a Mass Spring Damper Model for Acute Stress Prediction from Computer Mouse Movement.

Prior work demonstrated the potential of using Linear Predictive Coding (LPC) to approximate muscle stiffness and damping from computer mouse (a.k.a. mouse) movement to predict stress levels of users. Theoretically, muscle stiffness in the arm can be estimated using a mass-spring-damper (MSD) biomechanical model of the arm. However, the damping frequency and damping ratio values derived using LPC have not yet been compared with those from the theoretical MSD model. In this work, we demonstrate the damping frequency and damping ratio from LPC are significantly correlated with those from MSD model, thus confirming the validity of using LPC to infer muscle stiffness and damping. We also compare the stress level binary classification performance using the values from LPC and MSD with each other and with neural network-based baselines. We found comparable performance across all conditions demonstrating the efficacy of LPC and MSD model-based stress prediction.
A Method of Generating Measurable Panoramic Image for Indoor Mobile Measurement System

This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching. For the fusion of 3D points and image data, since a sparse depth map can be firstly generated by projecting LiDAR point onto the RGB image plane based on our reliable calibrated and synchronized sensors, we adopt a parameter self-adaptive framework to produce 2D dense depth map. For image stitching, optimal seamline for the overlapping area is searched using a graph-cuts-based method to alleviate the geometric influence and image blending based on the pyramid multi-band is utilized to eliminate the photometric effects near the stitching line. Since each pixel is associated with a depth value, we design this depth value as a radius in the spherical projection which can further project the panoramic image to the world coordinate and consequently produces a high-quality measurable panoramic image. The purposed method is tested on the data from our data collection platform and presents a satisfactory application prospects.
Numerical analysis of the strain distribution in skin domes formed upon the application of hypobaric pressure

Suction cups are widely used in applications such as in measurement of mechanical properties of skin in vivo, in drug delivery devices or in acupuncture treatment. Understanding mechanical response of skin under hypobaric pressure is of great importance for users of suction cups. The aim of this work is to predict the hypobaric pressure induced 3D stretching of the skin.
Classifying Be Star Variability With TESS. I. The Southern Ecliptic

TESS photometry is analyzed for 430 classical Be stars observed in the first year of the mission. The often complex and diverse variability of each object in this sample is classified to obtain an understanding of the behavior of this class as a population. Ninety-seven percent of the systems are variable above the noise level, with timescales spanning nearly the entire range of what is accessible with TESS, from tens of minutes to tens of days. The variability seen with TESS is summarized as follows. Nearly every system contains multiple periodic signals in the frequency regime between about 0.5 and 4 day−1. One or more groups of closely spaced frequencies is the most common feature, present in 87% of the sample. Among the Be stars with brightening events that are characteristic of mass ejection episodes (18% of the full sample, or 31% of early-type stars), all have at least one frequency group, and the majority of these (83%) show a concurrent temporary amplitude enhancement in one or more frequency groups. About one-third (34%) of the sample is dominated by longer-term trends (timescales >2 day). Low-frequency stochastic signals are prominent in about 25% of the sample, with varying degrees of intensity. Higher-frequency signals (6 < f < 15 day−1) are sometimes seen (in 15% of the sample) and in most cases likely reflect p-mode pulsation. In rare cases (∼3%), even higher frequencies beyond the traditional p-mode regime (f > 15 day−1) are observed.
Airborne Pathogens in a Heterogeneous World: Superspreading & Mitigation

Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20 % infecting more than 80 %, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.
Contrastive Pre-training for Sequential Recommendation

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture users' dynamic interests from their historical interactions. Despite their success, we argue that these approaches require huge amounts of parameters to learn a high-quality user representation model. However, they usually suffer from the data sparsity problem, which makes it difficult for them to collect sufficient supervised information to optimize the parameters. To tackle that, inspired by recent advances of pre-training techniques in the natural language processing area, we construct the training signal from unsupervised data and then pre-train the user representation model with this information. We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), which utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively. In addition, we propose three data augmentation approaches to construct pre-training tasks and exploit the effects of the composition of different augmentations. Comprehensive experiments on four public datasets demonstrate that CP4Rec achieves state-of-the-art performance over existing baselines especially when limited training data is available.
Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding

Negative sampling, which samples negative triplets from non-observed ones in knowledge graph (KG), is an essential step in KG embedding. Recently, generative adversarial network (GAN), has been introduced in negative sampling. By sampling negative triplets with large gradients, these methods avoid the problem of vanishing gradient and thus obtain better performance. However, they make the original model more complex and harder to train. In this paper, motivated by the observation that negative triplets with large gradients are important but rare, we propose to directly keep track of them with the cache. In this way, our method acts as a "distilled" version of previous GAN-based methods, which does not waste training time on additional parameters to fit the full distribution of negative triplets. However, how to sample from and update the cache are two critical questions. We propose to solve these issues by automated machine learning techniques. The automated version also covers GAN-based methods as special cases. Theoretical explanation of NSCaching is also provided, justifying the superior over fixed sampling scheme. Besides, we further extend NSCaching with skip-gram model for graph embedding. Finally, extensive experiments show that our method can gain significant improvements on various KG embedding models and the skip-gram model, and outperforms the state-of-the-art negative sampling methods.
Examining the Causal Structures of Deep Neural Networks Using Information Theory

Deep Neural Networks (DNNs) are often examined at the level of their response to input, such as analyzing the mutual information between nodes and data sets. Yet DNNs can also be examined at the level of causation, exploring “what does what” within the layers of the network itself. Historically, analyzing the causal structure of DNNs has received less attention than understanding their responses to input. Yet definitionally, generalizability must be a function of a DNN’s causal structure as it reflects how the DNN responds to unseen or even not-yet-defined future inputs. Here, we introduce a suite of metrics based on information theory to quantify and track changes in the causal structure of DNNs during training. Specifically, we introduce the effective information (EI) of a feedforward DNN, which is the mutual information between layer input and output following a maximum-entropy perturbation. The EI can be used to assess the degree of causal influence nodes and edges have over their downstream targets in each layer. We show that the EI can be further decomposed in order to examine the sensitivity of a layer (measured by how well edges transmit perturbations) and the degeneracy of a layer (measured by how edge overlap interferes with transmission), along with estimates of the amount of integrated information of a layer. Together, these properties define where each layer lies in the “causal plane”, which can be used to visualize how layer connectivity becomes more sensitive or degenerate over time, and how integration changes during training, revealing how the layer-by-layer causal structure differentiates. These results may help in understanding the generalization capabilities of DNNs and provide foundational tools for making DNNs both more generalizable and more explainable.
The mass of the Milky Way out to 100 kpc using halo stars

We use a distribution function analysis to estimate the mass of the Milky Way out to 100 kpc using a large sample of halo stars. These stars are compiled from the literature, and the majority (~95%) have 6D phase-space information. We pay particular attention to systematic effects, such as the dynamical influence of the Large Magellanic Cloud (LMC), and the effect of unrelaxed substructure. The LMC biases the (pre-LMC infall) halo mass estimates towards higher values, while realistic stellar halos from cosmological simulations tend to underestimate the true halo mass. After applying our method to the Milky Way data we find a mass within 100 kpc of M(< 100 kpc) = 6.31 +/- 0.32 (stat.) +/- 1.26 (sys.) x 10^11 M_Sun. For this estimate, we have approximately corrected for the reflex motion induced by the LMC using the Erkal et al. model, which assumes a rigid potential for the LMC and MW. Furthermore, stars that likely belong to the Sagittarius stream are removed, and we include a 5% systematic bias, and a 20% systematic uncertainty based on our tests with cosmological simulations. Assuming the mass-concentration relation for Navarro-Frenk-White haloes, our mass estimate favours a total (pre-LMC infall) Milky Way mass of M_200c = 1.05 +/- 0.25 x 10^12 M_Sun, or (post-LMC infall) mass of M_200c = 1.20 +/- 0.25 x 10^12 M_Sun when a 1.5 x 10^11 M_Sun mass of a rigid LMC is included.