Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized Least Squares Policy Optimization

Policy Optimization (PO) methods with function approximation are one of the most popular classes of Reinforcement Learning (RL) algorithms. However, designing provably efﬁcient policy optimization algorithms remains a challenge. Recent work in this area has focused on incorporating upper conﬁdence bound (UCB)-style bonuses to drive exploration in policy optimization. In this paper, we present Randomized Least Squares Policy Optimization (RLSPO) which is inspired by Thompson Sampling. We prove that, in an episodic linear kernel MDP setting, RLSPO achieves (cid:101) O ( d 3 / 2 H 3 / 2 √ T ) worst-case (frequentist) regret, where H is the number of episodes, T is the total number of steps and d is the feature dimension. Finally, we evaluate RLSPO empirically and show that it is competitive with existing provably efﬁcient PO algorithms.
Weakly-Supervised Action Segmentation and Alignment via Transcript-Aware Union-of-Subspaces Learning

We address the problem of learning to segment actions from weakly-annotated videos, i.e., videos accompanied by transcripts (ordered list of actions). We propose a framework in which we model actions with a union of low-dimensional subspaces, learn the subspaces using transcripts and refine video features that lend themselves to action subspaces. To do so, we design an architecture consisting of a Union-of-Subspaces Network, which is an ensemble of autoencoders, each modeling a low-dimensional action subspace and can capture variations of an action within and across videos. For learning, at each iteration, we generate positive and negative soft alignment matrices using the segmentations from the previous iteration, which we use for discriminative training of our model. To regularize the learning, we introduce a constraint loss that prevents imbalanced segmentations and enforces relatively similar duration of each action across videos. To have a real-time inference, we develop a hierarchical segmentation framework that uses subset selection to find representative transcripts and hierarchically align a test video with increasingly refined representative transcripts. Our experiments on three datasets show that our method improves the state-of-the-art action segmentation and alignment, while speeding up the inference time by a factor of 4 to 13. 1
A high-speed digital correlator for radio astronomy

Design details for a high-speed digital cross correlator for the Owens Valley Millimeter Array are presented. The correlator uses ECL gate arrays operating at a 256-MHz clock rate to compute the real cross-correlation function for up to four independent frequency bands with bandwidths in the range of 2-128 MHz. For each baseline in the array the correlator provides 512 lags, giving a spectral resolution of 2 MHz at the maximum bandwidth. Two-bit digitization is used, and this results in a sensitivity of 0.88 relative to a perfect analog correlator. >
Spirituality as a Moderator Between PTSS and Cardiovascular Reactivity

Posttraumatic stress symptoms (PTSS) can be distressing and produce robust cardiovascular symptoms, such as increased heart rate and blood pressure, which have been implicated in higher risk of developing cardiovascular disease (CVD). Identifying factors that may reduce symptoms may suggest therapeutic strategies. One such potential factor is spirituality, given that spirituality is associated with both reducing PTSS and with preventing or improving CVD. We sampled 63 young college women who indicated being exposed to unwanted sexual contact. We asked them to write about their experience while we took heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) measures from baseline to pre-task, during task, and post-task. The participants were given a survey to assess spirituality. Results showed that PTSS was highly correlated with decreased DBP (blunted reactivity) but was not associated with increased reactivity. Spirituality was associated with decreases in HR at all points. Our findings further suggest that spirituality was not a significant moderator between PTSS and cardiovascular reactivity measures. We hope that this study will further shed light on the relationships between sexual trauma and cardiovascular health, as well as the role of spirituality as a therapeutic device.
Range-Free Localization Algorithm in Wireless Sensor Networks with Asymmetric Links

Good positioning accuracy and coverage are important evaluation criterion for Wireless Sensor Network localization algorithm. DV-HOP is one of the classical range-free localization algorithms, which has good adaptivity and flexibility when node density (ND) and anchor density (AD) are both smaller. However, DV-HOP doesn't consider asymmetric links' influence in heterogeneous network, which is analyzed and proved as the main reason for poor positioning accuracy in DV-HOP. Then a Range-Free Localization Algorithm in Wireless Sensor Network with Asymmetric Links (RLAAL) is proposed, whose kernel mechanisms are Neighbor Discovery Algorithm (NDA) and Least Hops Acquiring Mechanism (LHAM). Every Node uses NDA to find all neighbors and uses LHAM to find least hops to Anchor nodes. Simulation results show that RLAAL can reduce asymmetric links' influence and have better positioning accuracy and coverage than DV-HOP.
Research on a New Type of Plating Power Supply

Viewing the problems of lower power factor, high harmonic content, lots of switch losses in tubes of lagging bridge arm when using FB-ZVS-PWM converter in plating power supply, this paper applied the three-level PWM rectifier technology to electroplating power supply, A new converter has been researched. Both different converters have also been compared and analyzed. Phase shift pulse generator and simulation models have been established in Matlab/Simulink. The results show that the PS-FB-ZVZCS PWM converter is more suitable for plating power supply, phases of current and voltage are synchronous, it has the advantages of higher power factor, low harmonic content. All the switch tubes have achieved soft switch, reducing the switch losses, it can improve the efficiency and its performance effectively.
Proteomics of Streptococcus mutans to Reveal the Antibiofilm Formation Mechanism of Ag/ZnO Nanocomposites with Light-Emitting Diode Radiation

Introduction As a biofilm-associated disease, dental caries benefits from nanoparticle (NP)-based therapies. Streptococcus mutans (S. mutans) is a primary aetiologic agent for dental caries development. We successfully applied a synergistic therapy of Ag/ZnO nanocomposites combined with light-emitting diode (LED) radiation to inhibit S. mutans biofilms. However, the antibiofilm mechanism has not been fully elucidated, and little is known about the biofilm formation ability of bacteria that survive NP-based therapies. Methods This study explored the antibiofilm formation mechanism of this synergistic therapy by an integrated approach based upon proteomics. Results Synergistic therapy killed 99.8% of bacteria, while the biofilm formation ability of 0.2% surviving bacteria was inhibited. The proteomic responses of S. mutans to synergistic therapy were comprehensively characterized to unveil the mechanism of bacterial death and biofilm formation inhibition of the surviving bacteria. In total, 55 differentially expressed proteins (12 upregulated and 43 downregulated) were recorded. The bioinformatic analysis demonstrated that cellular integrity damage and regulated expression of structure-associated proteins were the main reasons for bacterial death. In addition, the proteomic study indicated the potential inhibition of metabolism in surviving bacteria and provided a biofilm-related network consisting of 17 differentially expressed proteins, explaining the multiantibiofilm formation actions. Finally, we reported and verified the inhibitory effects of synergistic therapy on sucrose metabolism and D-alanine metabolism, which disturbed the biofilm formation of surviving bacteria. Conclusion Our findings demonstrated that synergistic therapy killed most bacteria and inhibited the surviving bacteria from forming biofilms. Furthermore, the antibiofilm formation mechanism was revealed by proteomics analysis of S. mutans after synergistic therapy and subsequent metabolic studies. Our success may provide a showcase to explore the antibiofilm formation mechanism of NP-based therapies using proteomic studies.
Two worst pandemics- Spanish Flu and COVID-19: A review

1918 Spanish flu and COVID-19 can be considered as most hazardous pandemics earth has ever witnessed. They are results of what mayhems an organism not even visible with naked eye can cause. Causative agent for both the diseases is a virus but, they are not same. 1918 Spanish flu gave major indications that it has the existence of the new strain of flu and lasted for longer duration affecting lives in various ways. COVID-19 is again an indication of another emerging worst pandemic. Here, we have reviewed the overall factors that contributed to the severity of these two pandemics and comparison of the same. The main motive of this paper is to put together, compare and analyze versatility of both the pandemics and to discuss implications of the same.
Economic Assessment of the Possibility of Using Different Types of Wind Turbine in Turkey

Abstract Some of Turkey's region has vast open land. Therefore, she has great potential for using solar and wind energy sources for domestic and industrial use. This research is going to propose and assess about the wind power cost per kWh of electricity produced using three types of wind electric conversion systems at 22 locations in Turkey. Wind duration curves were developed and utilized to calculate the cost per kWh of electricity generated from three chosen wind turbines. According to the research findings. Kumköy is the most advantageous station from the point of view of economical and production of wind power.
Consistent Instance Classiﬁcation for Unsupervised Representation Learning

In this paper, we address the problem of learning the representations from images without human annotations. We study the instance classiﬁcation solution, which regards each instance as a category, and improve the optimization and feature quality. The proposed consistent instance clas-siﬁcation (ConIC) approach simultaneously optimizes the classiﬁcation loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance. The beneﬁt of optimizing the consistency loss is that the learned features for augmented views from the same instance are more compact and accordingly the classiﬁcation loss optimization becomes easier, thus boosting the quality of the learned representations. This differs from InstDisc and MoCo that use an estimated prototype as the classiﬁer weight to ease the optimization. Different from SimCLR that directly compares different instances, our approach does not require large batch size. Experimental results demonstrate competitive performance for linear evaluation and better performance than InstDisc, MoCo and SimCLR at downstream tasks, as well as competitive or superior performance compared to other methods with stronger training setting.
Stereotypes and disparate criminal sentencing of Native Hawaiians

This thesis consists of two studies that attempt to understand the stereotypes and disparate treatment of Native Hawaiians within the criminal justice system, for which existing research is limited. In Study 1, participants (n = 154) selected adjectives that they believed to be stereotypes of Native Hawaiians, as well as of American Indians and Black Americans. It was hypothesized that because the groups have similar histories of colonization and oppression, they may also consequently share stereotypes of criminality and inferiority, with the exception that Native Hawaiians would be uniquely marked as friendly and welcoming because of the tourism industry. Results showed that Native Hawaiians and Americans Indians were frequently assigned to spiritual, traditional, ritualistic, and superstitious. Native Hawaiians alone were most frequently assigned to friendly and tropical. Study 2 (n = 52) examined the sentencing decisions of judges for Native Hawaiian defendants as compared to White defendants. In the 2 (race of defendant) x 2 (type of crime) design, it was predicted that Native Hawaiian defendants would be assigned longer sentences than White defendants, and that when the crime was violent, the disparity between Native Hawaiian and White sentences would be larger than when crime was nonviolent. The results were not significant, although together, these studies could still help suggest why Native Hawaiians are disproportionately incarcerated in the U.S. Once the stereotypes and their implications are understood, then plans for reform can be developed.
Probabilistically approaching a probabilistic nomic truth Option 3 . Probabilistically approaching a deterministic nomic truth

Theories of truth approximation in terms of truthlikeness (or verisimilitude) almost always deal with (non-probabilistically) approaching deterministic truths, either actual or nomic. This paper deals first with approaching a probabilistic nomic truth, viz. a true probability distribution. It assumes a multinomial probabilistic context, hence with a lawlike true, but usually unknown, probability distribution. We will first show that this true multinomial distribution can be approached by Carnapian inductive probabilities. Next we will deal with the corresponding deterministic nomic truth, that is, the set of conceptually possible outcomes with a positive true probability. We will introduce Hintikkian inductive probabilities, based on a prior distribution over the relevant deterministic nomic theories and on conditional Carnapian inductive probabilities, and first show that they enable again probabilistic approximation of the true distribution. Finally, we will show, in terms of a kind of success theorem, based on Niiniluoto’s estimated distance from the truth, in what sense Hintikkian inductive probabilities enable the probabilistic approximation of the relevant deterministic nomic truth. In sum, the (realist) truth approximation perspective on Carnapian and Hintikkian inductive probabilities leads to the unification of the inductive probability field and the field of truth approximation.
AN AUTOMATED GUNSHOT AUDIO CLASSIFICATION METHOD BASED ON FINGER PATTERN FEATURE GENERATOR AND ITERATIVE RELIEFF FEATURE SELECTOR

Audio forensics applications and methods are very crucial to clarify crimes. To accelerate audio analysis process and classify audios with high accuracy, machine learning (ML) methods must be used in audio forensics. An automated gunshot audios classification method is presented in this study. To implement our automated gunshot classification method, a novel gun audios dataset was collected from YouTube with 8 classes in the first phase. A novel ML method is presented in the second phase and the proposed ML method contains three fundamental phases. These phases are a novel finger pattern (finger - pat), statistical moments and discrete wavelet transform (DWT) based feature generation network, informative/distinctive feature selection with iterative ReliefF (IRF) feature selector and classification with a k nearest neighbors (kNN) classifier (shallow) to show success of the generated and selected features by using the proposed finger - pat based feature generation network and IRF feature selector. These methods and kNN achieved 94.48% classification accuracy. These results demonstrate that our proposed method can be used in gunshot audio analysis.
Discriminator Variance Regularization for Wasserstein GAN

In Wasserstein GAN, it is important to regularize the discriminator to have a not big Lipschitz constant. In this paper, I introduce discriminator variance regularization to regularize the discriminator of Wasserstein GAN to have a small Lipschitz constant. Discriminator variance regularization simply regularizes the variance of the discriminator's output to be small when input is real data distribution or generated data distribution. Intuitively, a low variance of discriminator output implies that the discriminator is more likely to have a low Lipschitz constant. Discriminator variance regularization does not explicitly regularize the Lipschitz constant of discriminator through differentiation on discriminator but lowers the probability that the Lipschitz constant of the discriminator is high. Discriminator variance regularization is used in Wasserstein GAN with R1 regularization, which reduces the vibration of GAN. Discriminator variance regularization requires very little additional computing.
Modelling stochastic time delay for regression analysis

Systems with stochastic time delay between the input and output present a number of unique challenges. Time domain noise leads to irregular alignments, obfuscates relationships and attenuates inferred coefficients. To handle these challenges, we introduce a maximum likelihood regression model that regards stochastic time delay as an"error"in the time domain. For a certain subset of problems, by modelling both prediction and time errors it is possible to outperform traditional models. Through a simulated experiment of a univariate problem, we demonstrate results that significantly improve upon Ordinary Least Squares (OLS) regression.
Knowledge and Attitude of Food Borne (E. coli)

Purpose: This research aim to contribute to greater awareness, identify the knowledge and attitude of food borne disease caused by E. coli (bacteria Escherichia coli) at Hail and highlighting the update methods to prevent food borne disease, make these methods more applicable of grate use for all family member. Design: This research study the knowledge and attitude of food borne disease caused by E. Coli at Hail. 2013 Method: 150 Questionnaire was set to collect information randomly from both male and female in hail, to identify their knowledge and attitude about the food borne disease caused by E. coli. Results: The results are set in tables and graphs to illustrate the knowledge of food borne disease in Hail. No cases of food borne disease reported from the respondents. The knowledge about the E. coli is poor. Conclusion: Personal hygiene in general is good. Increase the knowledge about disease is very important through nutrition education. Keyword: E. coli, Personal hygiene, Nutrition education
Occurrence of contaminated/carryover samples in automated milking systems during milk recording

During a genetic study about milk protein variants in different breeds, contaminated milk samples on one farm were observed, where an automated milking system (AMS) was used. In a following study about 1800 milk samples from 11 different farms using 5 different automated milking systems (AMS) within official milk recording were included. The samples were analysed using isoelectric focusing (IEF) and the milk protein variants were used for quality control. The occurrence of contaminated milk samples was up to 52% with variations between farms and AMS. The additional alleles/different concentrations as reason for the contaminated samples could in all cases traced back to the previously milked cow. Depending on the milk protein genotypes a minimum of about 3% - 10% contamination could be identified. In addition, in several cases the milk protein genotypes were not in agreement to the genotypes obtained during the other milkings of the same animal. One source of those misidentifications might be the management of the samples on farm during milk recording due to the limits of the autosampler. Therefore we conclude that milk samples from routine milk recording using an AMS are a source of incorrect data used for management and estimation of breeding values and are not suitable as DNA source for genomic evaluation and identification of health status. Therefore, it is necessary to improve milk recording using AMS.
Impact of clinical supervision on healthcare organisational outcomes: A mixed methods systematic review

Objective To review the impact of clinical supervision of post-registration/qualification healthcare professionals on healthcare organisational outcomes. Background Clinical supervision is a professional support mechanism that benefits patients, healthcare professionals and healthcare organisations. Whilst evidence is growing on the impact of clinical supervision on patient and healthcare professional outcomes, the evidence base for the impact of clinical supervision on organisational outcomes remains weak. Methods This review used a convergent segregated approach to synthesise and integrate quantitative and qualitative research findings, as per the Joanna Briggs Institute’s recommendations for mixed methods systematic reviews. Databases searched included CINAHL, Embase, PubMed, PschINFO, and Scopus. Whilst a narrative synthesis was performed to present the findings of the quantitative and qualitative studies, the evidence from both quantitative and qualitative studies was subsequently integrated for a combined presentation. The review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Results Thirty-two studies including 27 quantitative, two qualitative and three mixed methods studies, were included in the review. The results of the quantitative analysis showed that effective clinical supervision was associated with lower burnout and greater staff retention, and effective supervisor was associated with lower burnout and greater job satisfaction. Qualitative findings showed that healthcare professionals believed that adequate clinical supervision could mitigate the risk of burnout, facilitate staff retention, and improve the work environment, while inadequate clinical supervision can lead to stress and burnout. The evidence from quantitative and qualitative studies were complementary of each other. Conclusion Clinical supervision can have a variable effect on healthcare organisational outcomes. The direction of this effect appears to be influenced by the effectiveness of both the clinical supervision provided and that of the clinical supervisor. This highlights the need for organisations to invest in high quality supervision practices if maximal gains from clinical supervision are to be attained.