Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs

Linear programming (LP) is used in many machine learning applications, such as (cid:96) 1 -regularized SVMs, basis pursuit, nonnegative matrix factorization, etc. Interior Point Methods (IPMs) are one of the most popular methods to solve LPs both in theory and in practice. Their underlying complexity is dominated by the cost of solving a system of linear equations at each iteration. In this paper, we consider infeasible IPMs for the special case where the number of variables is much larger than the number of constraints (i.e., wide), or vice-versa (i.e., tall) by taking the dual. Using tools from Randomized Linear Algebra, we present a preconditioning technique that, when combined with the Conjugate Gradient iterative solver, provably guarantees that infeasible IPM algorithms (suitably modiﬁed to account for the error incurred by the approximate solver), converge to a feasible, approximately optimal solution, without increasing their iteration complexity. Our empirical evaluations verify our theoretical results on both real and synthetic data.
Deep Subspace Clustering with Data Augmentation

The idea behind data augmentation techniques is based on the fact that slight changes in the percept do not change the brain cognition. In classiﬁcation, neural networks use this fact by applying transformations to the inputs to learn to predict the same label. However, in deep subspace clustering (DSC), the ground-truth labels are not available, and as a result, one cannot easily use data augmentation techniques. We propose a technique to exploit the beneﬁts of data augmentation in DSC algorithms. We learn representations that have consistent subspaces for slightly transformed inputs. In particular, we introduce a temporal ensembling component to the objective function of DSC algorithms to enable the DSC networks to maintain consistent subspaces for random transformations in the input data. In addition, we provide a simple yet effective unsupervised procedure to ﬁnd efﬁcient data augmentation policies. An augmentation policy is deﬁned as an image processing transformation with a certain magnitude and probability of being applied to each image in each epoch. We search through the policies in a search space of the most common augmentation policies to ﬁnd the best policy such that the DSC network yields the highest mean Silhouette coefﬁcient in its clustering results on a target dataset. Our method achieves state-of-the-art performance on four standard subspace clustering datasets. The source code is available at: https://github.com/mahdiabavisani/DSCwithDA.git .
Evolution and competition between chiral spin textures in nanostripes with D2d symmetry

Chains of antiskyrmions and elliptical skyrmions are stabilized in nanostripes along several crystallographic directions. Chiral spin textures are of considerable interest for applications in spintronics. It has recently been shown that magnetic materials with D2d symmetry can sustain several distinct spin textures. Here, we show, using Lorentz transmission electron microscopy, that single and double chains of antiskyrmions can be generated at room temperature in nanostripes less than 0.5 μm in width formed from the D2d Heusler compound Mn1.4Pt0. 9Pd0.1Sn. Typically, truncated helical spin textures are formed in low magnetic fields, whose edges are terminated by half antiskyrmions. These evolve into chains of antiskyrmions with increasing magnetic field. Single chains of these objects are located in the middle of the nanostripes even when the stripes are much wider than the antiskyrmions. Moreover, the chains can even include elliptical Bloch skyrmions depending on details of the applied magnetic field history. These findings make D2d materials special and highly interesting for applications such as magnetic racetrack memory storage devices.
Intratumor Heterogeneity Correlates With Reduced Immune Activity and Worse Survival in Melanoma Patients

Background Human malignant melanoma is a highly aggressive, heterogeneous and drug-resistant cancer. Due to a high number of clones, harboring various mutations that affect key pathways, there is an exceptional level of phenotypic variation and intratumor heterogeneity (ITH) in melanoma. This poses a significant challenge to personalized cancer medicine. Hitherto, it remains unclear to what extent the heterogeneity of melanoma affects the immune microenvironment. Herein, we explore the interaction between the tumor heterogeneity and the host immune response in a melanoma cohort utilizing The Cancer Genome Atlas (TCGA). Methods Clonal Heterogeneity Analysis Tool (CHAT) was used to estimate intratumor heterogeneity, and immune cell composition was estimated using CIBERSORT. The Overall Survival (OS) among groups was analyzed using Kaplan–Meier curves with the log-rank test and multivariate cox regression. RNA-seq data were evaluated to identify differentially expressed immunomodulatory genes. The reverse phase protein array (RPPA) data platform was used to validate immune responses at protein level. Results Tumors with high heterogeneity were associated with decreased overall survival (p = 0.027). High CHAT tumors were correlated with less infiltration by anti-tumor CD8 T cells (p = 0.0049), T follicular cells (p = 0.00091), and M1 macrophages (p = 0.0028), whereas tumor-promoting M2 macrophages were increased (p = 0.02). High CHAT tumors correlated with a reduced expression of immunomodulatory genes, particularly Programmed Cell Death 1 (PD1) and its ligand PD-L1. In addition, high CHAT tumors exhibited lower immune Cytotoxic T lymphocytes (CTLs)-mediated toxicity pathway score (p = 2.9E−07) and cytotoxic pathway score (p = 2.9E−08). High CHAT tumors were also associated with a lower protein level of immune-regulatory kinases, such as lymphocyte-specific protein tyrosine kinase (LCK) (p = 3.4e−5) and spleen tyrosine kinase (SYK) (p = 0.0011). Conclusions Highly heterogeneous melanoma tumors are associated with reduced immune cell infiltration and immune response activation as well as decreased survival. Our results reveal that intratumor heterogeneity is an indicative factor for patient survival due to its impact on anti-tumor immune response.
Effect of Coating Method on the Surface Characteristics of NiTi Shape Memory Alloy

Metallic materials including stainless steel, cobalt-chromium alloys, titanium and its alloys, and nickel-titanium (NiTi) shape memory alloys have been used in biomedical applications since the 1940s due to their favorable mechanical properties. Classified as a smart material, NiTi alloy has gained noticeable popularity in biomedical fields such as orthopedic, dental, and cardiovascular applications as it retains reasonable corrosion resistance and biocompatibility along with unique shape memory properties. However, several studies have shown their limits in medical applications due to the risk of Ni ions release from the NiTi implant surface. In order to prevent Ni release, ceramic-based surface coatings such as hydroxyapatite, alumina, and titanium dioxide have been proposed applied by various methods such as electrophoretic deposition, sol-gel, biomimetic and dip coating. In this work, HA coatings on NiTi wire samples were obtained by using biomimetic and dip-coating methods. The biomimetic coating consisted of immersing the NiTi wire samples into simulated body fluid for 24 and 48 h periods, whereas dip coating was accomplished by placing the NiTi wire samples into the HA suspension and retracting the sample with a controlled speed. The effects of the two coating methods on coating quality, surface characteristics and corrosion behavior of NiTi wire samples were investigated. Dip coating method was shown to be a more favorable technique for the NiTi wire samples used throughout this study.
Optimal Reactive Power Dispatch Solution Using Enhanced Sine Cosine Optimization Algorithm with Selection Operator

In this paper, the sine and cosine algorithm is enhanced (ESCA) by selecting the best elements of the group in the next iterations to reduce active power loss. The sine and cosine algorithm (SCA) converges to an optimal solution through the sinus and cosine-based mathematical relations and the destination or the best global element in each iteration. Also, it uses the current elements for generating new elements without keeping the elements that have a better solution in the next generations. In ESCA this problem is avoided by applying the selection operator for keeping the best elements to the next generation. This method improves the optimal solution and is obtained in most implementations of the algorithm, which reduces the disturbances in obtaining the optimal solution. Also, one of the parameters of the standard method is fixed to the normal one value for the performance improvement of the algorithm. This method has been applied to a typical power network such as IEEE 30-bus and IEEE 118-bus in order to reduce the active power loss, reduce the power loss with the voltage deviation and with the stability of the voltage. Finally, this method proved effective than the standard method and with the previously applied metaheuristic methods.
Synthesis, Structure, Optic and Photocatalytic Properties of Anatase/Brookite Nanocomposites

Anatase/brookite nanocomposites were fabricated by the classical method of hydrolysis, additionally using hydrothermal treatment of preformed titanium dioxide sol with tetrabutyl orthotitanate. The influence of hydrothermal processing the buffer solution of TiO2 synthesis on the average particle sizes, specific surface area, pore sizes distributions, optical and photocatalytic properties investigated by X-ray diffraction, low-temperature nitrogen adsorption and UV-Vis spectroscopy. It has been determined that the hydrothermal treatment of pre-prepared titania sol as hydrolysis product leads to rutile formation after annealing at 400°C. Respective model of forming anatase/brookite/rutile nanocomposites was proposed. The changes of bang gap energy of TiO2 were observed and explained by effect of change phase composition and particles size of nanocomposite particles. Methylene blue (MB) photo-oxidation reactions using titanium dioxide nanocomposite were analyzed. Maximal photocatalytic activity of MB oxidation was detected for material with the ratio of the titania phases (anatase : brookite : rutile – 2 : 2 : 1). Synergistic effect between crystallinity, phase ratio, morphology of oxide material, band gap and photocatalytic activity in the anatase/brookite nanocomposites was established.
Synthetic high-density lipoproteins loaded with an antiplatelet drug for efficient inhibition of thrombosis in mice

sHDL is a promising antithrombotic agent delivery system due to its antithrombotic effect itself and improved drug delivery. Antiplatelet agents offer a desirable approach to thrombosis prevention through the reduction of platelet reactivity. However, major bleeding events greatly attenuate the clinical outcomes of most antithrombotic agents. Therefore, the development of safer and more effective strategies to prevent vascular occlusion and avoid bleeding is urgently needed. A reconstituted nanoparticle, synthetic high-density lipoprotein (sHDL), which mimics the native HDL, has been established as clinically safe and is easily manufactured on a large scale. In this study, we propose that the delivery of the antiplatelet drug ML355, a selective inhibitor of 12(S)-lipoxygenase (12-LOX), by sHDL will efficiently inhibit thrombosis by targeting ML355 to the intended site of action, improving the pharmaceutical profile and harnessing the innate antithrombotic efficacy of the sHDL carrier. Our data show that ML355-sHDL exhibits more potent inhibition of thrombus formation in both small arterioles and larger arteries in mice without impairing the normal hemostasis in vivo.
Research on promoting the application of disease prediction system based on machine learnin

In order to solve the problem that the disease prediction system based on machine learning has more research and less clinical application, through the analysis of the training and application process of predictive disease model, it points out that the lack of interpretability of disease prediction model and the continuous optimization of disease prediction model are the reasons affecting doctors' use of the model, and proposes that doctors should be involved in the training through business parameters to improve the interpretability of the models and the process of model training and calling should be simplified to improve the experience of the system. Finally, experiments and data analysis prove that the above measures can promote the clinical application of the disease prediction model.
A Feature Extraction Method to Improve the Classification Effect of N1 and REM in Sleep Periods

Non-linear feature extraction is an effective method for processing EEG signal. Sample entropy, KC complexity and correlation dimension of EEG combined with the machine learning methods can get a good result in automatic sleep staging. However, due to the similar activity characteristics of EEG during light sleep N1 and REM, the discrimination of above methods in these two phases is reduced. That the brain is relatively active and sawtooth waves appear in REM will cause the amplitude's changes of EEG signal are dissimilar. This article proposes an improved feature extraction method based on the amplitude in order to improve the recognition effect of N1 and REM. Correlation dimension and sample entropy of EEG in N1 and REM are compared with the improved method so the effectiveness of the method introduced in this essay can be verified.
Retinal Blood Vessel Segmentation via Attention Gate Network

Automatic retinal vessel segmentation is a challenging problem in the clinical diagnosis of eye diseases. Accurate segmentation of retinal vessel can efficiently assist the physicians to make a more precise symptom detection. However, there are various shapes and sizes, complex backgrounds and noise in the retinal vessel images. To address these problems, in this paper, we design an attention gate network to model long-range dependencies and capture rich contextual information. Specifically, we adopt an attention gate module, which includes a spatial attention module to model spatial long-range contextual information. Moreover, to improve the contrast of original retinal fundus images, we employ green channel extraction and contrast limited adaptive histogram equalization as pre-processing steps. Experiments on the DRIVE and STARE show the proposed AGNET achieves the outstanding performance with 0.8247/0.8361 sensitivity, 0.9871/0.9899 specificity, 0.9764/0.9791 accuracy, and 0.9881/0.9928 AUC respectively.
Mechanism Research and Application of Brain-computer Interface

As a special human-computer interaction mode, brain-computer interface (BCI) has attracted more and more attention and become a research hotspot in the field of artificial intelligence and control, it has been widely studied and applied in many fields. This paper first introduces the formation and classification of Electroencephalogram (EEG) signals, the collection methods, the problems and the current solutions, then introduces several classical theories of core technologies and introduces some innovative algorithms. In addition, the application of BCI system in medical recovery and auxiliary work is introduced. Finally, the paper makes the expectation and prospect for the future development.
Data Mining of the Effect of Cold and Heat Properties of Traditional Chinese Medicine on Normal Rats

Objective: To explore the hidden law of the mechanism of "property and efficacy" of traditional Chinese medicine (TCM) by using the changes of drug effect indexes caused by cold and hot herbs in normal rats. Methods: 4 / 5 samples of each group were randomly selected from all high-dose groups of traditional Chinese medicine as training set, and the remaining samples were composed of test set. Bayesian network was used to mine the experimental data of changes in drug effect indexes of normal rats caused by cold and hot traditional Chinese medicine. Results: the Bayesian network model of the relationship between cold and heat properties of traditional Chinese medicine and drug effect indexes of normal rats was obtained; conclusion: the validity and reliability of the network structure was verified by literature research, in the next step, we need to expand the sample size of traditional Chinese medicine and the range of selected indicators.
Predictive Modeling of Diabetic Kidney Disease using Random Forest Algorithm along with Features Selection

At present, the number of diabetes mellitus patients in China ranks first in the world, and diabetic kidney disease is the most common disease in complications. Therefore, it is necessary to establish a predictive model for early diagnosis of diabetic kidney disease. The model predicts the risk of diabetic kidney disease in the general Asian population, and recognizes high-risk groups, then warns the onset of diabetes. The data were obtained from the electronic medical record of patients in Beijing Pinggu Hospital. Twenty-nine initial candidate indicators including age, ALB, and A/C were selected. The random forest algorithm was used to predict diabetic kidney disease, and the classification accuracy was 89.831%. The importance weight ratio of each factor index was also given, Microalbuminuria (ALB), albumin-to-creatinine ratio (A/C), serum creatinine (SCr), Serum albumin (umALB), and blood urea nitrogen (BUN) accounted for a relatively high proportion of the weight of the characteristic variables. So these five indicators can be the primary indicators of our classification prediction, and the accuracy can reach 87.453%. Some other typical classification algorithms, liking KNN, logistic regression, and decision tree, were compared to classify and predict diabetic kidney disease, and precision recall f1-score and area AUC under ROC curve were used to evaluate these models. By experiments, random forest model was better than other algorithm models on both the classification accuracy and the evaluation indicators. The results can be applied to the screening of patients with high risk of diabetic kidney disease and the guidance of risk intervention measures. Consequently, the detection rate of undiagnosed diabetic kidney disease in the population can be improved, and the prevention effect of diabetic kidney disease can be enhanced as well.
Regional and total body active heating and cooling of a resting diver in water of varied temperatures

Passive insulations alone are not sufficient for maintaining underwater divers in thermal balance or comfort. The purpose of this study was to experimentally determine the active heating and cooling requirements to keep a diver at rest in thermal balance and comfort in water temperatures between 10 and 40 °C. A diver wearing a prototype tubesuit and a wetsuit (3 or 6.5 mm foam neoprene) was fully submersed (0.6 m) in water at a specified temperature (10, 20, 30 and 40 °C). During immersion, the tubesuit was perfused with 30 °C water at a flow rate of 0.5 L min−1 to six individual body regions. An attempt was made to keep skin temperatures below 42 °C in hot water (>30 °C) and elevated but below 32 °C in cold water (<20 °C). A skin temperature of 32 °C is the threshold for maximal body thermal resistance due to vasoconstriction. Skin temperatures and core temperature were monitored during immersion to ensure they remained within set thermal limits. In addition skin heat flux, oxygen consumption and the thermal exchange of the tubesuit were measured. In both wetsuit thicknesses there was a linear correlation between the thermal exchange of the tubesuit and ambient water temperature. In the 6.5 mm wetsuit −214 W to 242 W of heating (−) and cooling (+) was necessary in 10 °C to 40 °C water, respectively. In the 3 mm wetsuit −462 to 342 W was necessary in 10 °C to 40 °C water, respectively. It was therefore concluded that a diver at rest can be kept in thermal balance in 10–40 °C water with active heating and cooling.
India’s Relations with China from the Doklam Crisis to the Galwan Tragedy

No other bilateral relationship for India is as complex and challenging as the one with China. The intractable border dispute is at the root of their rivalry. The military stand-off at Doklam in 2017 had poisoned the bilateral relationship until steps were taken to remove mistrust and misunderstanding through two ‘informal’ summits between Prime Minister Narendra Modi and President Xi Jinping in 2018 and 2019. Nonetheless, the sense of general improvement in Sino-Indian ties generated at Wuhan and Mamallapuram was shattered by China’s aggressive behaviour in June 2020 at the Galwan Valley in Ladakh along the Line of Actual Control (LAC). The article traces key events during the Doklam stand-off and the violent military clashes at Galwan, as well as the steps taken by India to enhance political trust at the highest level. It contends that since there has been no change in China’s policies on a range of issues that are critical for India, it is not possible for India to remain ambivalent on how to deal with China. The article concludes that there seems to be greater recognition of the challenge from China and the need to recalibrate the Indian response.
Perceived normalization of radical ideologies and its effect on political tolerance and support for freedom of speech

Two studies tested the idea that perceived normalization of radical political ideologies (right and left) reduces support for freedom of speech of the opponents and political tolerance. In Study 1 (N = 633), Americans were primed with the normalization of the radical right or left. Primed with the normalization of radical outgroup ideologies, both liberals and conservatives were more willing to restrict their opponents’ freedom of speech and were more politically intolerant – effects that were mediated by collective angst. Study 2 (N = 632) replicated the results of Study 1 and extended them by showing that both conservatives and liberals worried about the image of their party not when they were exposed to the normalization of radical ingroup ideologies, but when they were exposed to the normalization of radical outgroup ideologies. These results suggest that perceived normalization of radical ideologies affects people’s attitudes towards freedom of speech and political (in)tolerance.