Miracle-dz/UniFAR-FTUs huggingface dataset:
Are We Missing Behçet's Disease?

Case Report Behçet's disease is already famous with classical triad of recurrent oral ulcers, genital ulcers and uveitis. It is also known for its multisystemic involvement. However, an accidental detection of this disease is unusual. We will report here the story of a gentleman who had presented with urinary tract infection and was found to have underlying Behçet's disease for years. He had peripheral neuropathy as the only neurological manifestation without involvement of other neuro-axis as commonly seen in Neuro-Behçet's disease. He also lacked prominent systemic involvement. This incident underscores the necessity of careful clinical examination, keeping suspicion high for this disease especially for late presenters as we may miss and expose patients to unnecessary sufferings and even to risk of serious complications. This case also shows the importance of inter-departmental coordination while approaching unusual cases or common cases with unusual presentation.
METODY PRO REDUKCI DIMENZE V MNOHOROZMĚRNÉ STATISTICE

The paper is devoted to standard multivariate statistical methods for dimension reduction. Their common basis is the eigendecomposition (i.e. computation of eigenvalues and eigenvectors) or, more generally, the singular value decomposition of specific matrices. These matrix decompositions possess excellent properties from the point of view of numerical mathematics. The paper overviews some results of numerical linear algebra on the numerical stability of methods for the computation of these decompositions. After that, it discusses various dimension reduction methods based on the decompositions, like principal component analysis, correspondence analysis, multidimensional scaling, factor analysis, and linear discriminant analysis for high-dimensional data.
Sequential Recommendation with Recurrent Convolutional Model

Personalized sequential recommendation refers to making recommendation based on users’ historical consumption behaviors. Most works based on RNN only model long-term patterns, which fail to capture skip behaviors. Contrarily, the CNN-based model whose target is to handle this problem can only leverage part of sequential behaviors and ignores global patterns, which limits its performance. In this paper, we propose a Recurrent Convolutional Recommendation Model (RCRM) to simultaneously catch global and lo-cal patterns. Specifically, we employ a recurrent layer to capture global patterns and a convolutional layer to extract local patterns. An attention mechanism is then introduced to generate the final attentive local pattern, which can further concatenate with global patterns to predict next item. We conduct extensive experiments on two benchmark datasets and the results demonstrate that RCRM outperforms state-of-the-art baselines by a large margin over a variety of common evaluation metrics.
[Frontiers in Bioscience 8, c14-24, January 1, 2003] 14 MOLECULAR EPIDEMIOLOGICAL TECHNIQUES FOR SALMONELLA STRAIN DISCRIMINATION

1. Abstract 2. Introduction 3. Phenotypic techniques 3.1. Serotype analysis 3.2. Biotype and phage type analysis 3.3. Antibiotic susceptibility patterns 4. Genotypic techniques 4.1. Plasmid analysis 4.2. Chromosomal restriction fragment analysis and pulsed-field gel electrophoresis (PFGE) 4.3. Chromosomal restriction-hybridization techniques 4.3.1. Ribotype analysis 4.3.2. IS200 4.4. Locus-specific PCR 4.4.1. PCR based is200 and ribotype analysis 4.4.2. Integron analysis 4.5. Arbitrarily primed PCR (AP PCR) 4.6. Repetitive sequence PCR (REP PCR) 4.7. Amplified fragment length polymorphisms (AFLP) 4.8. Multilocus sequence typing (MLST) 5. Perspective 6. Acknowledgments 7. References
RELATIVE STRENGTH INDEX AS INVESTMENT STRATEGY IN INDIAN STOCK MARKET

Received 16 , February, 2016, Received in revised form 29 , March, 2016, Accepted 18 , April, 2016, Published online 28,May , 2016 This paper examines the use of RSI as an investment strategies in Indian stock market on the basis of a data of 18 major indices over a period of 10 years. The analysis and findings reveal that investors use both fundamental as well as technical analysis while investing in Indian stock market. This paper examines that RSI can be used as the effective investment strategy to get high returns and outperform the market. Five most widely used investment strategies in Indian equitymarket are size based strategies, momentum strategies, following FIIs investmentbehaviour, buying stocks on the basis of 30 days moving average and buying stocks onthe basis of relative strength index. There has been substantial change in investmentstrategies used by active investors in Indian stock market over the past few years.
LncRNA HOXD-AS1 Is Upregulated in Ovarian Cancer

Patients with ovarian cancer are mostly diagnosed at advanced stages which leads to poor prognosis and high mortality rate. Deregulation of lncRNA HOXD-AS1 expression associates with cancer development and metastasis. However, the expression level of this lncRNA in ovarian cancer is not determined.50paired ovarian tumors and their adjusted normal tissues were included in the study. Total RNA was extracted by TRIzol® Reagent and reverse-transcribed to cDNA using PrimeScript II cDNA synthesis kit. The expression levels of HOXD-AS1 were quantified by qRT-PCR and compared. The Roc curve analysis was used to evaluate the capacity of HOXD-AS1 as a biomarker for ovarian cancer. We observed that lncRNA HOXD-AS1 was significantly upregulated in ovarian tumors compared to their adjusted normal tissues (p <0.003). Moreover, the ROC curve analysis revealed that the lncRNA HOXD-AS1 expression level could discriminate tumoral and non-tumoral tissues with 85% sensitivity and 88% specificity. The lncRNA HOXD-AS1 expression level might be considered as a potential biomarker for ovarian cancer development.
Examples from agriculture and mining

Satellite earth observation data are a key source of information to improve sustainability assessments of global supply chains. Earth observation is the only viable and cost-effective data source capable of providing regular and consistent information about a wide range of resource extraction activities and their related impacts across countries worldwide. In this Brief, we discuss the potentials of remote sensing data to analyse spatiotemporal patterns of resource extraction and impacts and present examples from the agricultural andmining sectors.
Health, Welfare and Immunity CORRECTING SAMPLING BIAS IN MICROSATELLITE MARKER TESTING FOR POLLEDNESS

The poll microsatellite test has been available to Australia’s beef industry for approximately 7 years and in that time, the bias in polled phenotyped animals submitted for testing from industry has influenced the accuracy of polled probability assignment to observed haplotypes. This article describes examples of observed mis-assigned haplotypes and their respective phenotypic observations, and the steps taken to correct the poll probabilities and resulting genotype estimations.
Neutrophil and Macrophage Response to Hypoxia in the Context of Inflammatory and Infectious Diseases

Neutrophils and macrophages are the first line of defense against pathogens. They have the ability to eradicate pathogens in an environment with inflammation and hypoxia. How these cells adapt to that environment is something that has been studied for several years now. HIF-1α is the main orchestrator to maintain this cells functional in hypoxia, and more recently is have established that HIF-1α can be stabilized by bacterial antigens in the absence of hypoxia via NF-κB, making HIF-1α a key protein in the innate immune response. Using HIF-1 as a therapeutic target requires knowing the pathogen that is causing the infection and whether this infection is local or systemic. In this review we gathered information about this issue to understand how these cells remain functional in this type of environment where you have a pathogen, inflammation and hypoxia.
The Coexistence and Interference Evaluation of Bluetooth with IEEE 802.11 Networks

This paper discerns key ideas and themes of Bluetooth and WiFi system used in mobile communication. In this article the coexistence of WiFi and Bluetooth are shown with simulink model in MATLAB and it also shows that the interference issues of these two individual systems. Almost 75% of the devices in the mobile computing world are equipped with either one of these or both. These technologies use the Radio Frequency (RF) for communication. The Bluetooth operates in 2.4GHz Industrial Science Medical (ISM) band. Unfortunately IEEE 802.11 also operates in the same 2.4GHz ISM band. There are different versions of IEEE 802.11 like 802.11a, 802.11b, 802.11g, and 802.11n to name a few. Some of them operate in a different frequency range. This paper reflects the coexistence and interference of both technology and shows the existence of each technology with same band.
GENETIC CORRELATIONS BETWEEN DAYS TO CALVING AND OTHER MALE AND FEMALE REPRODUCTION TRAITS IN BRAHMAN CATTLE

Heritabilities and genetic correlations for male and female reproduction traits were estimated for Brahman cattle raised in northern Australia. The traits included the female reproduction traits of days to calving (DC), age at puberty (AP) and lactation anoestrous interval (LAI). Days to calving using repeat records (DCr) was further considered as separate DC traits for first (DC1) and second parity (DC2) records, as well as a simple binary trait for calving rate (CR). Male reproduction traits included scrotal circumference (SC) and percent normal sperm (PNS) measured in young bulls. The heritability estimates for DCr, CR, DC1, DC2, AP, LAI, SC and PNS, were 0.09, 0.10, 0.09, 0.15, 0.47, 0.40, 0.44 and 0.15, respectively. Genetic correlations between DC1 and AP, LAI, SC and PNS were 0.62, 0.52, -0.32 and -0.66, respectively. For DC2, the genetic correlation with DC1, AP, LAI, SC and PNS were 0.46, 0.56, 1.0, -0.29 and -0.71, respectively. The study has shown that the various reproduction traits were heritable. The 0.46 genetic correlation between DC1 and DC2 suggests they should be considered as separate traits in genetic evaluation and this would allow fitting different genetic correlations with important component traits. Improvement of the genetic evaluation will increase accuracies of female reproduction EBVs and allow more genetic progress in tropical beef breeds in northern Australia.
Influence of a non-biodegradable porous structure on bone repair

Pore and interconnection size are two key structural parameters for bone tissue engineering scaffolds. In the present study, four hydroxyapatite (HA) bioceramic scaffolds with similar pore size (500–600 μm) but different interconnection sizes (100, 120, 150 and 200 μm), and three HA scaffolds with similar interconnection size (120 μm) but different pore sizes (400–500, 500–600 and 600–700 μm) were implanted into distal femoral condyle defects for 72 rabbits. The results showed that volume of new bone increased proportionally to the interconnection size. However, significant differences between interconnection size groups were only observed at week 24. Smaller pore size showed significantly more new bone formation at week 4, whereas no significant differences between the three pore size groups were found at weeks 12 and 24. In conclusion, large interconnection size facilitates new bone formation and a minimum size of 120 μm is recommended. Although small pore size improves bone ingrowth at early time points, pore size has limited impact on bone ingrowth later.
Stability analysis of a diverging Kelvin-Helmholtz slamming flow

When a liquid slams into a solid, the intermediate gas is squeezed out at a speed that diverges when approaching the moment of impact. Although there is mounting experimental evidence that instabilities form on the liquid interface during such an event, understanding of the nature of these instabilities is limited. This study therefore addresses the stability of a liquid-gas interface subject to a diverging flow in the gas phase. We perform a linear modal stability analysis of the surface to obtain an amplitude equation that is subsequently analysed in detail and applied to two cases of interest for impact problems, namely, the parallel impact of a wave onto a vertical wall, and the impact of a horizontal plate onto a liquid surface. In both cases we find that long wavelengths are stabilised considerably in comparison to what may be expected based upon classical knowledge of the stability of interfaces subject to a constant gas flow. In the former case, this leads to the prediction of a marginally stable wavelength that is completely absent in the classical analysis. For the latter we find much resemblance to the classical case, with the connotation that the instability is suppressed for smaller disk sizes. The study ends with a discussion of the influence of gas viscosity and gas compressibility on the respective stability diagrams.
Approximating optimal feedback controllers of finite horizon control problems using hierarchical tensor formats

Controlling systems of ordinary differential equations (ODEs) is ubiquitous in science and engineering. For finding an optimal feedback controller, the value function and associated fundamental equations such as the Bellman equation and the Hamilton-Jacobi-Bellman (HJB) equation are essential. The numerical treatment of these equations poses formidable challenges due to their non-linearity and their (possibly) high-dimensionality. In this paper we consider a finite horizon control system with associated Bellman equation. After a time-discretization, we obtain a sequence of short time horizon problems which we call local optimal control problems. For solving the local optimal control problems we apply two different methods, one being the well-known policy iteration, where a fixed-point iteration is required for every time step. The other algorithm borrows ideas from Model Predictive Control (MPC), by solving the local optimal control problem via open-loop control methods on a short time horizon, allowing us to replace the fixed-point iteration by an adjoint method. For high-dimensional systems we apply low rank hierarchical tensor product approximation/tree-based tensor formats, in particular tensor trains (TT tensors) and multi-polynomials, together with high-dimensional quadrature, e.g. Monte-Carlo. We prove a linear error propagation with respect to the time discretization and give numerical evidence by controlling a diffusion equation with unstable reaction term and an Allen-Kahn equation.
Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles

Widespread adoption of autonomous cars will require greater confidence in their safety than is currently possible. Certified control is a new safety architecture whose goal is two-fold: to achieve a very high level of safety, and to provide a framework for justifiable confidence in that safety. The key idea is a runtime monitor that acts, along with sensor hardware and low-level control and actuators, as a small trusted base, ensuring the safety of the system as a whole. Unfortunately, in current systems complex perception makes the verification even of a runtime monitor challenging. Unlike traditional runtime monitoring, therefore, a certified control monitor does not perform perception and analysis itself. Instead, the main controller assembles evidence that the proposed action is safe into a certificate that is then checked independently by the monitor. This exploits the classic gap between the costs of finding and checking. The controller is assigned the task of finding the certificate, and can thus use the most sophisticated algorithms available (including learning-enabled software); the monitor is assigned only the task of checking, and can thus run quickly and be smaller and formally verifiable. This paper explains the key ideas of certified control and illustrates them with a certificate for LiDAR data and its formal verification. It shows how the architecture dramatically reduces the amount of code to be verified, providing an end-to-end safety analysis that would likely not be achievable in a traditional architecture.
Epidemics on critical random graphs with heavy-tailed degree distribution

We study the susceptible-infected-recovered (SIR) epidemic on a random graph chosen uniformly over all graphs with certain critical, heavy-tailed degree distributions. For this model, each vertex infects all its susceptible neighbors and recovers the day after it was infected. When a single individual is initially infected, the total proportion of individuals who are eventually infected approaches zero as the size of the graph grows towards infinity. Using different scaling, we prove process level scaling limits for the number of individuals infected on day h on the largest connected components of the graph. The scaling limits are contain non-negative jumps corresponding to some vertices of large degree, that is these vertices are super-spreaders. Using weak convergence techniques, we can describe the height profile of the α-stable continuum random graph [42, 34], extending results known in the Brownian case [57]. We also prove abstract results that can be used on other critical random graph models.
Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to exploit end-to-end trainable Hough voting for generating object proposals. However, the current voting strategy can only receive partial votes from the surfaces of potential objects together with severe outlier votes from the cluttered backgrounds, which hampers full utilization of the information from the input point clouds. Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds. Therefore, this bottom-up and then top-down strategy in our BRNet enforces mutual consistency between the predicted vote centers and the raw surface points and thus achieves more reliable and flexible object localization and class prediction results. Our BRNet is simple but effective, which significantly outperforms the state-of-the-art methods on two large-scale point cloud datasets, ScanNet V2 (+7.5% in terms of mAP@0.50) and SUN RGB-D (+4.7% in terms of mAP@0.50), while it is still lightweight and efficient.
On the Efficiency of Polar-Like Decoding for Symmetric Codes

The recently introduced polar codes constitute a breakthrough in coding theory due to their capacity-achieving property. This goes hand in hand with a quasilinear construction, encoding, and successive cancellation list decoding procedures based on the Plotkin construction. The decoding algorithm can be applied with slight modifications to Reed-Muller or eBCH codes, that both achieve the capacity of erasure channels, although the list size needed for good performance grows too fast to make the decoding practical even for moderate block lengths. The key ingredient for proving the capacity-achieving property of Reed-Muller and eBCH codes is their group of symmetries. It can be plugged into the concept of Plotkin decomposition to design various permutation decoding algorithms. Although such techniques allow to outperform the straightforward polar-like decoding, the complexity stays impractical. In this paper, we show that although invariance under a large automorphism group is valuable in a theoretical sense, it also ensures that the list size needed for good performance grows exponentially. We further establish the bounds that arise if we sacrifice some of the symmetries. Although the theoretical analysis of the list decoding algorithm remains an open problem, our result provides an insight into the factors that impact the decoding complexity.
Sequential Ski Rental Problem

The classical 'buy or rent' ski-rental problem was recently considered in the setting where multiple experts (such as Machine Learning algorithms) advice on the length of the ski season. Here, robust algorithms were developed with improved theoretical performance over adversarial scenarios where such expert predictions were unavailable. We consider a variant of this problem which we call the 'sequential ski-rental' problem. Here, a sequence of ski-rental problems has to be solved in an online fashion where both the buy cost and the length of the ski season are unknown to the learner. The learner has access to two sets of experts, one set who advise on the true cost of buying the ski and another set who advise on the length of the ski season. Under certain stochastic assumptions on the experts who predict the buy costs, we develop online algorithms and prove regret bounds for the same. Our experimental evaluations confirm our theoretical results.
Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation

We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism. Our contribution is four-fold. Firstly, we rank 124 source languages empirically to determine their closeness to the low resource language and select the top few. We call the linguistic definition of language family Family of Origin (FAMO), and we call the empirical definition of higher-ranked languages using our metrics Family of Choice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual Order-preserving Lexiconized Transformer (IPML) to train on ∼ 1,000 lines ( ∼ 3.5%) of low resource data from the Bible dataset and the medical EMEA dataset. Using English as a hypothetical low resource language to translate from Spanish, we obtain a +24.7 BLEU increase over a multilingual baseline, and a +10.2 BLEU increase over our asymmetric baseline. Thirdly, we also use a real severely low resource Mayan language, Eastern Pokom-chi. Finally, we add an order-preserving lex-iconized component to translate named entities accurately. We build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages. Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.
The Ruble Collapse in an Online Marketplace: Some Lessons for Market Designers

The sharp devaluation of the ruble in 2014 increased the real returns to Russians from working in a global online labor marketplace, as con- tracts in this market are dollar-denominated. Russians clearly noticed the opportunity, with Russian hours-worked increasing substantially, primarily on the extensive margin -- incumbent Russians already active were fairly inelastic. Contrary to the predictions of bargaining models, there was little to no pass-through of the ruble price changes in to wages. There was also no evidence of a demand-side response, with buyers not posting more "Russian friendly" jobs, suggesting limited cross-side externalities. The key findings -- a high extensive margin elasticity but low intensive margin elasticity; little pass-through into wages; and little evidence of a cross-side externality -- have implications for market designers with respect to pricing and supply acquisition.