Miracle-dz/UniFAR-FTUs huggingface dataset:
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy

When learning from a batch of logged bandit feedback, the discrepancy between the policy to be learned and the off-policy training data imposes statistical and computational challenges. Unlike classical supervised learning and online learning settings, in batch contextual bandit learning, one only has access to a collection of logged feedback from the actions taken by a historical policy, and expect to learn a policy that takes good actions in possibly unseen contexts. Such a batch learning setting is ubiquitous in online and interactive systems, such as ad platforms and recommendation systems. Existing approaches based on inverse propensity weights, such as Inverse Propensity Scoring (IPS) and Policy Optimizer for Exponential Models (POEM), enjoy unbiasedness but often suffer from large mean squared error. In this work, we introduce a new approach named Maximum Likelihood Inverse Propensity Scoring (MLIPS) for batch learning from logged bandit feedback. Instead of using the given historical policy as the proposal in inverse propensity weights, we estimate a maximum likelihood surrogate policy based on the logged action-context pairs, and then use this surrogate policy as the proposal. We prove that MLIPS is asymptotically unbiased, and moreover, has a smaller nonasymptotic mean squared error than IPS. Such an error reduction phenomenon is somewhat surprising as the estimated surrogate policy is less accurate than the given historical policy. Results on multi-label classification problems and a large- scale ad placement dataset demonstrate the empirical effectiveness of MLIPS. Furthermore, the proposed surrogate policy technique is complementary to existing error reduction techniques, and when combined, is able to consistently boost the performance of several widely used approaches.
Monolingual and Cross-lingual Zero-shot Style Transfer

We introduce the task of zero-shot style transfer between different languages. Our training data includes multilingual parallel corpora, but does not contain any parallel sentences between styles, similarly to the recent previous work. We propose a unified multilingual multi-style machine translation system design, that allows to perform zero-shot style conversions during inference; moreover, it does so both monolingually and cross-lingually. Our model allows to increase the presence of dissimilar styles in corpus by up to 3 times, easily learns to operate with various contractions, and provides reasonable lexicon swaps as we see from manual evaluation.
Physical-Model-Checking to Detect Switching-Related Attacks in Power Systems

Recent public disclosures on attacks targeting the power industry showed that savvy attackers are now capable of occulting themselves from conventional rule-based network intrusion detection systems (IDS), bringing about serious threats. In order to leverage the work of rule-based IDS, this paper presents an artificially intelligent physical-model-checking intrusion detection framework capable of detecting tampered-with control commands from control centers of power grids. Unlike the work presented in the literature, the work in this paper utilizes artificial intelligence (AI) to learn the load flow characteristics of the power system and benefits from the fast responses of the AI to decode and understand contents of network packets. The output of the AI is processed through an expert system to verify that incoming control commands do not violate the physical system operational constraints and do not put the power system in an insecure state. The proposed content-aware IDS is tested in simulation on a 14-bus IEEE benchmark system. Experimental verification on a small power system, with an IEC 61850 network architecture is also carried out. The results showed the accuracy of the proposed framework in successfully detecting malicious and/or erroneous control commands.
DNA barcoding of economically important freshwater fish species from north‐central Nigeria uncovers cryptic diversity

Abstract This study examines the utility of morphology and DNA barcoding in species identification of freshwater fishes from north‐central Nigeria. We compared molecular data (mitochondrial cytochrome c oxidase subunit I (COI) sequences) of 136 de novo samples from 53 morphologically identified species alongside others in GenBank and BOLD databases. Using DNA sequence similarity‐based (≥97% cutoff) identification technique, 50 (94.30%) and 24 (45.30%) species were identified to species level using GenBank and BOLD databases, respectively. Furthermore, we identified cases of taxonomic problems in 26 (49.00%) morphologically identified species. There were also four (7.10%) cases of mismatch in DNA barcoding in which our query sequence in GenBank and BOLD showed a sequence match with different species names. Using DNA barcode reference data, we also identified four unknown fish samples collected from fishermen to species level. Our Neighbor‐joining (NJ) tree analysis recovers several intraspecific species clusters with strong bootstrap support (≥95%). Analysis uncovers two well‐supported lineages within Schilbe intermedius. The Bayesian phylogenetic analyses of Nigerian S. intermedius with others from GenBank recover four lineages. Evidence of genetic structuring is consistent with geographic regions of sub‐Saharan Africa. Thus, cryptic lineage diversity may illustrate species’ adaptive responses to local environmental conditions. Finally, our study underscores the importance of incorporating morphology and DNA barcoding in species identification. Although developing a complete DNA barcode reference library for Nigerian ichthyofauna will facilitate species identification and diversity studies, taxonomic revisions of DNA sequences submitted in databases alongside voucher specimens are necessary for a reliable taxonomic and diversity inventory.
Eccentric flywheel post-activation potentiation influences swimming start performance kinetics

ABSTRACT This study aimed to assess the effects of post-activation potentiation in the strength related variables of a kick start. Thirteen competitive swimmers performed three kick starts after a standardized warm up (denoted USUAL) and another after inducing post-activation through five isotonic repetitions on an eccentric flywheel (denoted PAP). A T-test was used to quantify differences between USUAL and PAP warm up. The best trial of each subject achieved by natural conditions (denoted PEAK) was compared with data obtained after PAP. An instrumented starting block with independent triaxial force plates, collected the strength variables related with the impulse at take off. Improvements in the vertical components of force were observed after PAP compared with USUAL, meanwhile no differences were detected on the horizontal components of it. The velocity at take off was higher after PAP compared with USUAL (4.32 ± 0.88 vs 3.93 ± 0.60 m*s-1; p = 0.02). No differences in force or velocity were detected comparing PAP with PEAK (4.13 ± 0.62 m*s-1, p = 0.11). The PAP warm-up increased vertical force and it was transferred to a higher resultant velocity at take-off. This improvement would equal the best result possible obtained in natural conditions after some trials.
Bipolar androgen therapy in the treatment of prostate cancer.

SD We know that testosterone can initially stimulate the growth of prostate cancer when the cancer is still hormone-sensitive, which is why castration is used to inhibit prostate cancer growth. For many years, however, published research has described a paradox in which supraphysiologic levels of androgens also seem to inhibit prostate cancer growth—particularly in prostate cancer cells that have adapted to growing in low levels of testosterone—and cause the tumor cells to die. How can this be? The answer may lie with the androgen receptor. Prostate cancer cells are highly dependent on or perhaps even addicted to the activity of the androgen receptor—the cells must regulate and degrade the androgen receptor if they are to proliferate. As a strategy for survival in the low-testosterone environment of ADT, prostate cancer cells adapt to produce far more androgen receptor, which allows them to capture every possible molecule of testosterone. Androgen receptors also may mutate so that they function even the absence of testosterone. The result is that prostate cancer is highly responsive to ADT initially but over time becomes resistant and continues to grow. In BAT, a very high dose of testosterone saturates the androgen receptors, making them more resistant to degradation by the prostate cancer cells. This degradation is required for each cell to go through its normal cycle and produce daughter cells. In other words, we are taking advantage of the cell’s adaptive increase in androgen receptor, which makes it vulnerable to the shock of a high level of testosterone. The other beneficial aspect of this approach is that when the testosterone level drops again, some of the prostate cancer cells will be resensitized to the low level. This occurs in prostate cancer cells that were able to survive the spike in testosterone by downregulating their androgen receptor. We want to be able to target all the prostate cancer cells—the ones with very high levels of androgen receptor, which will be sensitive to high levels of testosterone, and the ones with very low levels of androgen receptor, which will be sensitive to the rapid return of low levels of testosterone.
Comparison of quantitative real‐time PCR and short‐term (18‐hour) microculture in diagnosis of fetal cytomegalovirus infection: Impact of hyperimmunoglobulin treatment

The prognostic value of human cytomegalovirus detection (HCMV) DNA levels from amniotic fluid (AF) for the outcome of the infected newborn is still a matter of debate, especially if the onset of maternal primary infection at amniocentesis is unknown. The objective of this study was to investigate the analytical performance in short‐term (18‐hour) microculture from preconcentrated samples and quantitative real‐time PCR (rtPCR) for diagnosis of fetal HCMV infection.
Linear Compartmental Models: Input-Output Equations and Operations That Preserve Identifiability

This work focuses on the question of how identifiability of a mathematical model, that is, whether parameters can be recovered from data, is related to identifiability of its submodels. We look specifically at linear compartmental models and investigate when identifiability is preserved after adding or removing model components. In particular, we examine whether identifiability is preserved when an input, output, edge, or leak is added or deleted. Our approach, via differential algebra, is to analyze specific input-output equations of a model and the Jacobian of the associated coefficient map. We clarify a prior determinantal formula for these equations, and then use it to prove that, under some hypotheses, a model's input-output equations can be understood in terms of certain submodels we call "output-reachable". Our proofs use algebraic and combinatorial techniques.
Genome‐wide variability and selection signatures in Italian island cattle breeds

In the present study, a sample of 88 animals belonging to four local (Modicana, Sarda, Sardo-Bruna and Sardo-Modicana) and one cosmopolitan (Italian Brown Swiss) cattle breeds were genotyped with a medium density SNP beadchip and compared to investigate their genetic diversity and the existence of selection signatures. A total of 43 012 SNPs distributed across all 29 autosomal chromosomes were retained after data quality control. Basic population statistics, Wright fixation index and runs of homozygosity (ROH) analyses confirmed that the Italian Brown Swiss genome was shaped mainly by selection, as underlined by the low values of heterozygosity and minor allele frequency. As expected, local cattle exhibited a large within-breed genetic heterogeneity. The FST comparison revealing the largest number of significant SNPs was Sardo-Bruna vs. Sardo-Modicana, whereas the smallest was observed for Italian Brown Swiss vs. Sardo-Modicana. Modicana exhibited the largest number of detected ROHs, whereas the smallest was observed for Sardo-Modicana. Signatures of selection were detected in genomic regions that harbor genes involved in milk production traits for Italian Brown Swiss and fitness traits for local breeds. According to the results of multi-dimensional scaling and the admixture analysis the Sardo-Bruna is more similar to the Sarda than to the Italian Brown Swiss breed. Moreover, the Sardo-Modicana is genetically closer to the Modicana than to the Sarda breed. Results of the present work confirm the usefulness of single nucleotide polymorphisms in deciphering the genetic architecture of livestock breeds.
National and state patterns of concept-flavoured cigar sales, USA, 2012–2016

Introduction Cigar sales have increased in the USA in recent years. A growing proportion of cigar sales are of flavoured varieties, many bearing ambiguous or ‘concept’ flavour descriptions (eg, Jazz). This study assessed US cigar sales by flavour category (ie, concept flavoured, characterising flavoured and tobacco), at national, regional and state levels. Methods Sales of cigarillos, large cigars and little cigars from chain, franchise and convenience stores, mass merchandisers, supermarkets, drug, dollar and club stores, and military commissaries during 2012–2016 were acquired from the Nielsen Company. US national-level and state-level sales, including District of Columbia, were analysed by flavour category. Flavour descriptors were classified as ‘tobacco', ‘characterising’ or ‘concept', based on Universal Product Code (UPC)-linked characteristics and brand website and consumer review descriptions. Results Cigar sales increased by 29% during 2012–2016, driven by a 78% increase in cigarillo sales. The proportion of concept-flavoured sales increased from 9% to 15%, while the proportion of sales decreased for tobacco (50% to 49%) and characterising flavours (eg, cherry) (41% to 36%). Cigarillos had the greatest increase in unique concept flavour descriptions (17 to 46 unique UPCs), with most sales occurring among Sweet, Jazz and Green Sweets concept flavours. By US region, total and concept-flavoured cigarillo sales were highest in the South. Conclusions Flavoured cigars are increasingly labelled with concept flavours, including in areas with flavoured tobacco sales restrictions. Cigarillos are driving recent increases in US cigar and concept-flavoured cigar sales. It is important to consider concept flavours when addressing flavoured tobacco product sales and use.
Geometry of energy landscapes and the optimizability of deep neural networks

Deep neural networks are workhorse models in machine learning with multiple layers of nonlinear functions composed in series. Their loss function is highly nonconvex, yet empirically even gradient descent minimization is sufficient to arrive at accurate and predictive models. It is hitherto unknown why deep neural networks are easily optimizable. We analyze the energy landscape of a spin glass model of deep neural networks using random matrix theory and algebraic geometry. We analytically show that the multilayered structure holds the key to optimizability: Fixing the number of parameters and increasing network depth, the number of stationary points in the loss function decreases, minima become more clustered in parameter space, and the trade-off between the depth and width of minima becomes less severe. Our analytical results are numerically verified through comparison with neural networks trained on a set of classical benchmark datasets. Our model uncovers generic design principles of machine learning models.
Experience, Imitation and Reflection; Confucius' Conjecture and Machine Learning

Artificial intelligence recently had a great advancements caused by the emergence of new processing power and machine learning methods. Having said that, the learning capability of artificial intelligence is still at its infancy comparing to the learning capability of human and many animals. Many of the current artificial intelligence applications can only operate in a very orchestrated, specific environments with an extensive training set that exactly describes the conditions that will occur during execution time. Having that in mind, and considering the several existing machine learning methods this question rises that 'What are some of the best ways for a machine to learn? ' Regarding the learning methods of human, Confucius' point of view is that they are by experience, imitation and reflection. This paper tries to explore and discuss regarding these three ways of learning and their implementations in machines by having a look at how they happen in minds.
Approximate Inference-Based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models

This work presents an efficient framework to generate a motion plan of a robot with high degrees of freedom (e.g., a humanoid robot). High dimensionality of the robot configuration space often leads to difficulties in utilizing the widely used motion planning algorithms, since the volume of the decision space increases exponentially with the number of dimensions. To handle complications arising from the large decision space, and to solve a corresponding motion planning problem efficiently, two key concepts are adopted in this work: First, the Gaussian process latent variable model (GP-LVM) is utilized for low-dimensional representation of the original configuration space. Second, an approximate inference algorithm is used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot. Utilizing the GP-LVM and the duality between control and estimation, we construct a fully probabilistic generative model with which a high-dimensional motion planning problem is transformed into a tractable inference problem. Finally, we compute the motion trajectory via an approximate inference algorithm based on a variant of the particle filter.
Molecular characterization and efficacy evaluation of a transgenic corn event for insect resistance and glyphosate tolerance

A transgenic maize event ZD12-6 expressing a Bacillus thuringiensis (Bt) fusion protein Cry1Ab/Cry2Aj and a modified 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) protein G10 was characterized and evaluated. Southern blot analysis indicated that ZD12-6 is a single copy integration event. The insert site was determined to be at chromosome 1 by border sequence analysis. Expression analyses of Bt fusion protein Cry1Ab/Cry2Aj and the EPSPS protein G10 suggested that they are both expressed stably in different generations. Insect bioassays demonstrated that the transgenic plants are highly resistant to Asian corn borer (Ostrinia furnacalis), cotton boll worm (Helicoverpa armigera), and armyworm (Mythimna separata). This study suggested that ZD12-6 has the potential to be developed into a commercial transgenic line.
PABED – A Tool for Big Education Data Analysis

Cloud computing and big data have risen to become the most popular technologies of the modern world. Apparently, the reason behind their immense popularity is their wide range of applicability as far as the areas of interest are concerned. Education and research remain one of the most obvious and befitting application areas. This research paper introduces a big data analytics tool, PABED (Project - Analyzing Big Education Data), for the education sector that makes use of cloud-based technologies. This tool is implemented using Google BigQuery and R programming language and allows comparison of undergraduate enrollment data for different academic years. Although, there are many proposed applications of big data in education, there is a lack of tools that can actualize the concept into practice. PABED is an effort in this direction. The implementation and testing details of the project have been described in this paper. This tool validates the use of cloud computing and big data technologies in education and shall head start development of more sophisticated educational intelligence tools.
The cumulative effect of reporting and citation biases on the apparent efficacy of treatments: the case of depression

Evidence-based medicine is the cornerstone of clinical practice, but it is dependent on the quality of evidence upon which it is based. Unfortunately, up to half of all randomized controlled trials (RCTs) have never been published, and trials with statistically significant findings are more likely to be published than those without (Dwan et al., 2013). Importantly, negative trials face additional hurdles beyond study publication bias that can result in the disappearance of non-significant results (Boutron et al., 2010; Dwan et al., 2013; Duyx et al., 2017). Here, we analyze the cumulative impact of biases on apparent efficacy, and discuss possible remedies, using the evidence base for two effective treatments for depression: Antidepressants and psychotherapy.
Soyasaponins Reduce Inflammation and Improve Serum Lipid Profiles and Glucose Homeostasis in High Fat Diet‐Induced Obese Mice

SCOPE Obesity is linked to a chronic low-grade inflammatory state that contributes to the development of obesity-associated metabolic disorders. The anti-inflammatory activities and mechanisms of soyasaponin monomers (A1 , A2 , and I) have been recently demonstrated in cell models. However, their potential in vivo abilities to reduce chronic inflammation and alleviate metabolic disorders in obese status remain unclear. METHODS AND RESULTS High fat diet (HFD)-fed obese male C57BL/6J mice are intervened by aspirin (0.1 mg kg-1 body weight) or 20 mg kg-1 of soyasaponins A1 , A2 , or I for 8 weeks. Soyasaponins A1 , A2 , and I significantly reduce pro-inflammatory cytokines/mediators in serum, liver, and white adipose tissues (WATs), improve serum lipid profiles, decrease liver cholesterol, triglyceride and steatosis, and promote fecal excretion of cholesterol, triglycerides, and bile acids. Soyasaponins A1 , A2 , and I also decrease IKKα/β phosphorylation in liver and WATs and reduce NF-κB p65 phosphorylation and CD68 mRNA and protein expression in WATs. Soyasaponins A1 and A2 but not I decrease NF-κB p65 phosphorylation in liver and adipocytes hypertrophy in WATs. In addition, Soyasaponin A2 but not A1 nor I decreases fasting blood glucose and improved insulin resistance. CONCLUSION Soyasaponins reduce inflammation and improve serum lipid profiles and glucose homeostasis in HFD-induced obese mice.
DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation

Human auditory cortex excels at selectively suppressing background noise to focus on a target speaker. The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises. In this study, we propose a novel deep neural network (DNN) based audiovisual (AV) mask estimation model. The proposed AV mask estimation model contextually integrates the temporal dynamics of both audio and noise-immune visual features for improved mask estimation and speech separation. For optimal AV features extraction and ideal binary mask (IBM) estimation, a hybrid DNN architecture is exploited that leverages the complementary strengths of a stacked long short term memory (LSTM) and convolution LSTM network. The comparative simulation results in terms of speech quality and intelligibility demonstrate the significant performance improvement of our proposed AV mask estimation model as compared to audio-only and visual-only mask estimation approaches for both speaker dependent and independent scenarios.