Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Hemodialysis on lipid profile among chronic renal failure patients (Regular and Non–Regular Haemodialysis)

Chronic Renal Failure (CRF) patients are at risk of cardiovascular diseases due to the elevation of various forms of lipids. Many a time CRF patients live on hemodialysis on regular basis. Chronic renal failure (CRF) is complicated by characteristic dyslipidemias. We sought to evaluate the pattern of lipid profile in CRF patients with and without hemodialysis. Study were divided into 2 groups, Group-I: CRF patients who never undergone hemodialysis (24) and Group-II: CRF patients on hemodialysis (24). We obtained serum samples from patients in the morning after an overnight fast and were analyzed for total cholesterol (TC), triglycerides (TGs), HDL, LDL and VLDL. Significant change (p
The Vulnerability Analysis Framework for Java Bytecode

Since Java web applications are used widely in Internet today, the security of it becomes an outstanding problem. The attacks, including SQL injection attack, XSS attack, and etc, are great challenges for the Java application. This paper presents the vulnerability analysis framework to detect the security hole in the Java web applications. The framework combines the techniques of the static points-to dataflow analysis, the dynamical instrument and the fuzzing test. With the cooperation of these static and dynamical analysis techniques, it can improve the efficiency and accuracy of the analysis and lower the false positive rate.
A 10Gb/s receiver with equalizer and on-chip ISI monitor in 0.11μm CMOS

This paper presents a IOGbis receiver that consists of an equalizer, an inter-symbol interference (ISI) monitor, and a clock and data recovety (CDR) unit. The Cherry-Hooper topology was employed to realize an adjustable highbandwidth equalizer with reduced area and power consumption, without using on-chip inductors. The IS1 monitor measures the post-cursor and pre-cursor IS1 in the equalizer output. The IS1 measurement is achieved using a switched-capacitor correlator. A test chip was fabricated in O.llpm CMOS. The areas and power consumptions are 47pm x S5pm and 13.2mW for the equalizer and 145pm x 80pm and lOmW for the IS1 monitor. Keyword: CMOS, equalizer, ISI, receiver, CDR
Randomness and Recurrence in Dynamical Systems

Randomness and Recurrence in Dynamical Systems . The Carus Mathematical Monographs Number 31, Mathematical Association of America, Washington, DC, 2010, xviii + 357 pp., ISBN 978-088385-043-5, $52.95. by Rodney Nillsen Review by: James A. Walsh The American Mathematical Monthly, Vol. 119, No. 5 (May 2012), pp. 434-438 Published by: Mathematical Association of America Stable URL: http://www.jstor. org/stable/10.4169/amer.math.monthly.119. 05.434 . Accessed: 26/12/2014 19:20
The Re-improved GRAP Model of the Fourth Party Logistics Providers

With the fast development of the logistics industry, the fourth party logistics becomes a top issue in this line. The correct evaluation and selection of supply chain cooperation partners is a key problem in the development of the fourth party logistics. This paper constructs an evaluation model of the fourth party logistics provider selection by using analysis process of grey relation, GRAP together with analytic hierarchy process, AHP. This model is a re-improved one based on the traditional GRAP and its first improvement of the Entropy-GRAP model by the author. The grey nature of information is used fully on the basis of the weight defined by AHP. According to the analysis of a true example, it is clear that the model can successfully solve some complex problems such as incomplete information, numerous indices and correlations between indices.
An Assessment of Image Matching Algorithms in Depth Estimation

Computer vision is often used with mobile robot for feature tracking, landmark sensing, and obstacle detection. Almost all high-end robotics systems are now equipped with pairs of cameras arranged to provide depth perception. In stereo vision application, the disparity between the stereo images allows depth estimation within a scene. Detecting conjugate pair in stereo images is a challenging problem known as the correspondence problem. The goal of this research is to assess the performance of SIFT, MSER, and SURF, the well known matching algorithms, in solving the correspondence problem and then in estimating the depth within the scene. The results of each algorithm are evaluated and presented. The conclusion and recommendations for future works, lead towards the improvement of these powerful algorithms to achieve a higher level of efficiency within the scope of their performance.
Issues Affecting the Adoption and Continual usage of Internet Banking services in semi-Rural Areas of south Africa

In modern society, Information communication technology (ICT) diffusion is very rapid. However, ICT infusion is very slow. Several issues contribute to ICT infusion. They range from social, economic, demographic and political reasons. Internet banking is one such ICT contribution that is facing limited acceptance in some parts of South African society. This research looks at issues affecting the adoption and continual usage of Internet banking in a South African semi rural area, Mafikeng region. Although Internet banking has been receiving a lot of interest worldwide, very little methodical study has been done to determine reasons why many people in semi rural areas like Mafikeng do not adopt and make use of Internet banking services. Technology Acceptance Model (TAM) was used to guide the enquiry process into factors affecting Internet banking infusion. Internet Security was found to be a major factor contributing to Internet banking adoption and continual usage in Mafikeng.
Packet Types: Abstract specifications of network protocol messages

In writing networking code, one is often faced with the task of interpreting a raw buffer according to a standardized packet format. This is needed, for example, when monitoring network traffic for specific kinds of packets, or when unmarshaling an incoming packet for protocol processing. In such cases, a programmer typically writes C code that understands the grammar of a packet and that also performs any necessary byte-order and alignment adjustments. Because of the complexity of certain protocol formats, and because of the low-level of programming involved, writing such code is usually a cumbersome and error-prone process. Furthermore, code written in this style loses the domain-specific information, viz. the packet format, in its details, making it difficult to maintain.
An Improved Collaborative Filtering Algorithm Based on User Clustering

Collaborative filtering algorithms have been successfully applied to the network personalizationrecommendation system, but data sparseness affects the recommendation quality of collaborative filteringalgorithms seriously. To solve this problem, this paper introduces the user activity and proposes animproved collaborative filtering algorithm based on user clustering. It extends user-item scoring matrixand improves user similarity computing method to alleviate the impact of data sparseness inrecommendation algorithm. So it can significantly improve the accuracy of the collaborativerecommendation algorithm. The experimental results show that it can describe the user similarityaccurately and improve the accuracy of recommendation algorithm.
Multivariate regression of mixed responses for evaluation of visualization designs

Abstract Information visualization significantly enhances human perception by graphically representing complex data sets. The variety of visualization designs makes it challenging to efficiently evaluate all possible designs catering to users’ preferences and characteristics. Most existing evaluation methods perform user studies to obtain multivariate qualitative responses from users via questionnaires and interviews. However, these methods cannot support online evaluation of designs, as they are often time-consuming. A statistical model is desired to predict users’ preferences on visualization designs based on non-interference measurements (i.e., wearable sensor signals). In this work, we propose a Multivariate Regression of Mixed Responses (MRMR) to facilitate quantitative evaluation of visualization designs. The proposed MRMR method is able to provide accurate model prediction with meaningful variable selection. A simulation study and a user study of evaluating visualization designs with 14 effective participants are conducted to illustrate the merits of the proposed model.
Comparing the Binding Interactions in the Receptor Binding Domains of SARS-CoV-2 and SARS-CoV

SARS-CoV-2, since emerging in Wuhan, China, has been a major concern because of its high infection rate and has left more than six million infected people around the world. Many studies endeavored to reveal the structure of the SARS-CoV-2 compared to the SARS-CoV, in order to find solutions to suppress this high infection rate. Some of these studies showed that the mutations in the SARS-CoV spike (S) protein might be responsible for its higher affinity to the ACE2 human cell receptor. In this work, we used molecular dynamics simulations and Monte Carlo sampling to compare the binding affinities of the S proteins of SARS-CoV and SARS-CoV-2 to the ACE2. Our results show that the protein surface of the ACE2 at the receptor binding domain (RBD) exhibits negative electrostatic potential, while a positive potential is observed for the S proteins of SARS-CoV/SARS-CoV-2. In addition, the binding energies at the interface are slightly higher for SARS-CoV-2 because of enhanced electrostatic interactions. The major contributions to the electrostatic binding energies result from the salt bridges forming between R426 and ACE-2-E329 in the case of SARS-CoV and K417 and ACE2-D30 in the SARS-CoV-2. In addition, our results indicate that the enhancement in the binding energy is not due to a single mutant but rather because of the sophisticated structural changes induced by all these mutations together. This finding suggests that it is implausible for the SARS-CoV-2 to be a lab-engineered virus.
Investigating EEG-based functional connectivity patterns for multimodal emotion recognition

Objective. Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore the functional connectivity between brain regions. Hence, in this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. Approach. After constructing the brain networks by the correlations between pairs of EEG signals, we calculated critical subnetworks through the average of brain network matrices with the same emotion label to eliminate the weak associations. Then, three network features were conveyed to a multimodal emotion recognition model using deep canonical correlation analysis along with eye movement features. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public datasets: SEED, SEED-V, and DEAP. Main results. The experimental results reveal that the strength feature outperforms the state-of-the-art features based on single-channel analysis. The classification accuracies of multimodal emotion recognition are 95.08±6.42% on the SEED dataset, 84.51±5.11% on the SEED-V dataset, and 85.34±2.90% and 86.61±3.76% for arousal and valence on the DEAP dataset, respectively, which all achieved the best performance. In addition, the brain networks constructed with 18 channels achieve comparable performance with that of the 62-channel network and enable easier setups in real scenarios. Significance. The EEG functional connectivity networks combined with emotion-relevant critical subnetworks selection algorithm we proposed is a successful exploration to excavate the information between channels.
Detect axial gauge fields with a calorimeter

Torsional strain in Weyl semimetals excites a unidirectional chiral density wave propagating in the direction of the torsional vector. This gapless excitation, named the chiral sound wave, is generated by a particular realization of the axial anomaly via the triple-axial (AAA) anomalous diagram. We show that the presence of the torsion-generated chiral sound leads to a linear behavior of the specific heat of a Weyl semimetal and to an enhancement of the thermal conductivty at experimentally accessible temperatures. We also demonstrate that such an elastic twist lowers the temperature of the sample, thus generating a new, anomalous type of elasto-calorific effect. Measurements of these thermodynamical effects will provide experimental verification of the exotic triple-axial anomaly as well as the reality of the elastic pseudomagnetic fields in Weyl semimetals.
Image-based phenotyping of diverse Rice (Oryza Sativa L.) Genotypes

Development of either drought-resistant or drought-tolerant varieties in rice (Oryza sativa L.), especially for high yield in the context of climate change, is a crucial task across the world. The need for high yielding rice varieties is a prime concern for developing nations like India, China, and other Asian-African countries where rice is a primary staple food. The present investigation is carried out for discriminating drought tolerant, and susceptible genotypes. A total of 150 genotypes were grown under controlled conditions to evaluate at High Throughput Plant Phenomics facility, Nanaji Deshmukh Plant Phenomics Centre, Indian Council of Agricultural Research-Indian Agricultural Research Institute, New Delhi. A subset of 10 genotypes is taken out of 150 for the current investigation. To discriminate against the genotypes, we considered features such as the number of leaves per plant, the convex hull and convex hull area of a plant-convex hull formed by joining the tips of the leaves, the number of leaves per unit convex hull of a plant, canopy spread - vertical spread, and horizontal spread of a plant. We trained You Only Look Once (YOLO) deep learning algorithm for leaves tips detection and to estimate the number of leaves in a rice plant. With this proposed framework, we screened the genotypes based on selected traits. These genotypes were further grouped among different groupings of drought-tolerant and drought susceptible genotypes using the Ward method of clustering.
Magnetic moments of short-lived nuclei with part-per-million accuracy: Paving the way for applications of $\beta$-detected NMR in chemistry and biology

We determine the magnetic dipole moment of a short-lived nucleus with part-per-million (ppm) accuracy for the first time. To achieve this two orders of magnitude improvement over previous studies, we implement a number of innovations into our $\beta$-detected Nuclear Magnetic Resonance ($\beta$-NMR) setup at ISOLDE/CERN. Using liquid samples as hosts leads to narrow, sub kHz linewidth, resonances. A simultaneous in-situ stable-isotope NMR measurement allows to calibrate and stabilize the magnetic field to ppm precision. To eliminate a large systematic error, we improve the accuracy of the reference magnetic moment using {\it ab initio} calculations of NMR shielding constants. We demonstrate the potential of this combined approach for the 1.1 s half-life radioactive nucleus $^{26}$Na. Our technique can be extended to other isotopic chains, providing accurate magnetic moments for many short-lived nuclei. This will open the path towards interdisciplinary applications of $\beta$-NMR in biochemistry, where ppm shifts in signals need to be detected.
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces

Facial Expressions Recognition(FER) on low-resolution images is necessary for applications like group expression recognition in crowd scenarios(station, classroom etc.). Classifying a small size facial image into the right expression category is still a challenging task. The main cause of this problem is the loss of discriminative feature due to reduced resolution. Super-resolution method is often used to enhance low-resolution images, but the performance on FER task is limited when on images of very low resolution. In this work, inspired by feature super-resolution methods for object detection, we proposed a novel generative adversary network-based feature level super-resolution method for robust facial expression recognition(FSR-FER). In particular, a pre-trained FER model was employed as feature extractor, and a generator network G and a discriminator network D are trained with features extracted from images of low resolution and original high resolution. Generator network G tries to transform features of low-resolution images to more discriminative ones by making them closer to the ones of corresponding high-resolution images. For better classification performance, we also proposed an effective classification-aware loss re-weighting strategy based on the classification probability calculated by a fixed FER model to make our model focus more on samples that are easily misclassified. Experiment results on Real-World Affective Faces (RAF) Database demonstrate that our method achieves satisfying results on various down-sample factors with a single model and has better performance on low-resolution images compared with methods using image super-resolution and expression recognition separately.
Intrinsic Exploration as Multi-Objective RL

Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy would typically fail. However, intrinsic exploration is generally handled in an ad-hoc manner, where exploration is not treated as a core objective of the learning process; this weak formulation leads to sub-optimal exploration performance. To overcome this problem, we propose a framework based on multi-objective RL where both exploration and exploitation are being optimized as separate objectives. This formulation brings the balance between exploration and exploitation at a policy level, resulting in advantages over traditional methods. This also allows for controlling exploration while learning, at no extra cost. Such strategies achieve a degree of control over agent exploration that was previously unattainable with classic or intrinsic rewards. We demonstrate scalability to continuous state-action spaces by presenting a method (EMU-Q) based on our framework, guiding exploration towards regions of higher value-function uncertainty. EMU-Q is experimentally shown to outperform classic exploration techniques and other intrinsic RL methods on a continuous control benchmark and on a robotic manipulator.
Blazar variability power spectra from radio up to TeV photon energies: Mrk 421 and PKS 2155−304

We present the results of the power spectral density (PSD) analysis for the blazars Mrk\,421 and PKS\,2155$-$304, using good-quality, densely sampled light curves at multiple frequencies, covering 17 decades of the electromagnetic spectrum, and variability timescales from weeks up to a decade. The data were collected from publicly available archives of observatories at radio from OVRO, optical and infrared (B, V, R, I, J, H, and K-bands), X-rays from the {\it Swift} and the {\it Rossi} X-ray Timing Explorer, high and very high energy $\gamma-$rays from the {\it Fermi} and Very Energetic Radiation Imaging Telescope Array System as well as the High Energy Stereoscopic System. Our results are: (1) the power-law form of the variability power spectra at radio, infra-red and optical frequencies have slopes $\sim$1.8, indicative of random-walk type noise processes; (2) the power-law form of the variability power spectra at higher frequencies, from X-rays to very high energy \,$\gamma$-rays, however, have slopes $\sim$1.2, suggesting a flicker noise type process; (3) there is significantly more variability power at X-rays, high and very high energy $\gamma$-rays on timescales $\lesssim$ 100 days, as compared to lower energies. Our results do not easily fit into a simple model, in which a single compact emission zone is dominating the radiative output of the blazars across all the timescales probed in our analysis. Instead, we argue that the frequency-dependent shape of the variability power spectra points out a more complex picture, with highly inhomogeneous outflow producing non-thermal emission over an extended, stratified volume.
TraDE: Transformers for Density Estimation

We present TraDE, an attention-based architecture for auto-regressive density estimation. In addition to a Maximum Likelihood loss we employ a Maximum Mean Discrepancy (MMD) two-sample loss to ensure that samples from the estimate resemble the training data. The use of attention means that the model need not retain conditional sufficient statistics during the process beyond what is needed for each covariate. TraDE performs significantly better than existing approaches such differentiable flow based estimators on standard tabular and image-based benchmarks in terms of the log-likelihood on held out data. TraDE works well wide range of tasks that includes classification methods to ascertain the quality of generated samples, out of distribution sample detection, and handling outliers in the training data.
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation

Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora. These methods suffer from issues associated with a domain information gap, which leads to translation errors for low frequency and out-of-vocabulary terminology. This paper proposes a dictionary-based data augmentation (DDA) method for cross-domain NMT. DDA synthesizes a domain-specific dictionary with general domain corpora to automatically generate a large-scale pseudo-IND parallel corpus. The generated pseudo-IND data can be used to enhance a general domain trained baseline. The experiments show that the DDA-enhanced NMT models demonstrate consistent significant improvements, outperforming the baseline models by 3.75-11.53 BLEU. The proposed method is also able to further improve the performance of the back-translation based and IND-finetuned NMT models. The improvement is associated with the enhanced domain coverage produced by DDA.
Equivalence testing for standardized effect sizes in linear regression

In this paper, we introduce equivalence testing procedures for standardized effect sizes in a linear regression. We show how to define valid hypotheses and calculate p-values for these tests. Such tests are necessary to confirm the lack of a meaningful association between an outcome and predictors. A simulation study is conducted to examine type I error rates and statistical power. We also compare using equivalence testing as part of a frequentist testing scheme with an alternative Bayesian testing approach. The results indicate that the proposed equivalence test is a potentially useful tool for ''testing the null. ''