Miracle-dz/UniFAR-FTUs huggingface dataset:
An Autonomous Approach to Measure Social Distances and Hygienic Practices during COVID-19 Pandemic in Public Open Spaces

Coronavirus has been spreading around the world since the end of 2019. The virus can cause acute respiratory syndrome, which can be lethal, and is easily transmitted between hosts. Most states have issued state-at-home executive orders, however, parks and other public open spaces have largely remained open and are seeing sharp increases in public use. Therefore, in order to ensure public safety, it is imperative for patrons of public open spaces to practice safe hygiene and take preventative measures. This work provides a scalable sensing approach to detect physical activities within public open spaces and monitor adherence to social distancing guidelines suggested by the US Centers for Disease Control and Prevention (CDC). A deep learning-based computer vision sensing framework is designed to investigate the careful and proper utilization of parks and park facilities with hard surfaces (e.g. benches, fence poles, and trash cans) using video feeds from a pre-installed surveillance camera network. The sensing framework consists of a CNN-based object detector, a multi-target tracker, a mapping module, and a group reasoning module. The experiments are carried out during the COVID-19 pandemic between March 2020 and May 2020 across several key locations at the Detroit Riverfront Parks in Detroit, Michigan. The sensing framework is validated by comparing automatic sensing results with manually labeled ground-truth results. The proposed approach significantly improves the efficiency of providing spatial and temporal statistics of users in public open spaces by creating straightforward data visualizations for federal and state agencies. The results can also provide on-time triggering information for an alarming or actuator system which can later be added to intervene inappropriate behavior during this pandemic.
Analysis and Forecasting of Financial Time Series Using CNN and LSTM-Based Deep Learning Models

Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes interval of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.
Enforcing robust control guarantees within neural network policies

When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rigorous guarantees on system stability under certain worst-case disturbances, they often result in simple controllers that perform poorly in the average (non-worst) case. In contrast, nonlinear control methods trained using deep learning have achieved state-of-the-art performance on many control tasks, but often lack robustness guarantees. We propose a technique that combines the strengths of these two approaches: a generic nonlinear control policy class, parameterized by neural networks, that nonetheless enforces the same provable robustness criteria as robust control. Specifically, we show that by integrating custom convex-optimization-based projection layers into a nonlinear policy, we can construct a provably robust neural network policy class that outperforms robust control methods in the average (non-adversarial) setting. We demonstrate the power of this approach on several domains, improving in performance over existing robust control methods and in stability over (non-robust) RL methods.
TenFor: A Tensor-Based Tool to Extract Interesting Events from Security Forums

How can we get a security forum to “tell” us its activities and events of interest? We take a unique angle: we want to identify these activities without any a priori knowledge, which is a key difference compared to most of the previous problem formulations. Despite some recent efforts, mining security forums to extract useful information has received relatively little attention, while most of them are usually searching for specific information. We propose TenFor, an unsupervised tensor-based approach, to systematically identify important events in a three-dimensional space: (a) user, (b) thread, and (c) time. Our method consists of three high-level steps: (a) a tensor-based clustering across the three dimensions, (b) an extensive cluster profiling that uses both content and behavioral features, and (c) a deeper investigation, where we identify key users and threads within the events of interest. In addition, we implement our approach as a powerful and easy-to-use platform for practitioners. In our evaluation, we find that 83% of our clusters capture meaningful events and we find more meaningful clusters compared to previous approaches. Our approach and our platform constitute an important step towards detecting activities of interest from a forum in an unsupervised learning fashion in practice.
The autophagic response to Staphylococcus aureus provides an intracellular niche in neutrophils

Staphylococcus aureus is a major human pathogen causing multiple pathologies, from cutaneous lesions to life-threatening sepsis. Although neutrophils contribute to immunity against S. aureus, multiple lines of evidence suggest that these phagocytes can provide an intracellular niche for staphylococcal dissemination. However, the mechanism of neutrophil subversion by intracellular S. aureus remains unknown. Targeting of intracellular pathogens by autophagy is recognised as an important component of host innate immunity, but whether autophagy is beneficial or detrimental to S. aureus-infected hosts remains controversial. Here, using larval zebrafish we show that S. aureus is rapidly decorated by the autophagy marker Lc3 following engulfment by macrophages and neutrophils. Upon phagocytosis by neutrophils, Lc3-positive, non-acidified spacious phagosomes are formed. This response is dependent on phagocyte NADPH oxidase as both cyba knockdown and diphenyleneiodonium (DPI) treatment inhibited Lc3 decoration of phagosomes. Importantly, NADPH oxidase inhibition diverted neutrophil S. aureus processing into tight acidified vesicles, which resulted in increased host resistance to the infection. Some intracellular bacteria within neutrophils were also tagged by p62-GFP fusion protein and loss of p62 impaired host defence. Taken together, we have shown that intracellular handling of S. aureus by neutrophils is best explained by Lc3-associated phagocytosis (LAP), which appears to provide an intracellular niche for bacterial pathogenesis, while the selective autophagy receptor p62 is host-protective. The antagonistic roles of LAP and p62-mediated pathways in S. aureus-infected neutrophils may explain the conflicting reports relating to anti-staphylococcal autophagy and provide new insights for therapeutic strategies against antimicrobial resistant staphylococci.
Efficient nonlinear manifold reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, such as advection-dominated flow phenomena, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed an efficient nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models (FOMs). The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 2D Burgers' equations with a high Reynolds number. A speed-up of up to 11.7 for 2D Burgers' equations is achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique.
Cluster-Specific Predictions with Multi-Task Gaussian Processes

A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The model is instantiated as a mixture of multi-task GPs with common mean processes. A variational EM algorithm is derived for dealing with the optimisation of the hyper-parameters along with the hyper-posteriors' estimation of latent variables and processes. We establish explicit formulas for integrating the mean processes and the latent clustering variables within a predictive distribution, accounting for uncertainty on both aspects. This distribution is defined as a mixture of cluster-specific GP predictions, which enhances the performances when dealing with group-structured data. The model handles irregular grid of observations and offers different hypotheses on the covariance structure for sharing additional information across tasks. The performances on both clustering and prediction tasks are assessed through various simulated scenarios and real datasets. The overall algorithm, called MagmaClust, is publicly available as an R package.
Temperature Control for Langevin Diffusions

We study the temperature control problem for Langevin diffusions in the context of non-convex optimization. The classical optimal control of such a problem is of the bang-bang type, which is overly sensitive to any errors. A remedy is to allow the diffusions to explore other temperature values and hence smooth out the bang-bang control. We accomplish this by a stochastic relaxed control formulation incorporating randomization of the temperature control and regularizing its entropy. We derive a state-dependent, truncated exponential distribution, which can be used to sample temperatures in a Langevin algorithm. We carry out a numerical experiment to compare the performance of the algorithm with two other available algorithms in search of a global optimum.
iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering

Most prior art in visual understanding relies solely on analyzing the "what" (e.g., event recognition) and "where" (e.g., event localization), which in some cases, fails to describe correct contextual relationships between events or leads to incorrect underlying visual attention. Part of what defines us as human and fundamentally different from machines is our instinct to seek causality behind any association, say an event Y that happened as a direct result of event X. To this end, we propose iPerceive, a framework capable of understanding the "why" between events in a video by building a common-sense knowledge base using contextual cues to infer causal relationships between objects in the video. We demonstrate the effectiveness of our technique using the dense video captioning (DVC) and video question answering (VideoQA) tasks. Furthermore, while most prior work in DVC and VideoQA relies solely on visual information, other modalities such as audio and speech are vital for a human observer's perception of an environment. We formulate DVC and VideoQA tasks as machine translation problems that utilize multiple modalities. By evaluating the performance of iPerceive DVC and iPerceive VideoQA on the ActivityNet Captions and TVQA datasets respectively, we show that our approach furthers the state-of-the-art. Code and samples are available at: this http URL.
Nonlinear stability of phase transition steady states to a hyperbolic–parabolic system modeling vascular networks

This paper is concerned with the existence and stability of phase transition steady states to a quasi‐linear hyperbolic–parabolic system of chemotactic aggregation, which was proposed in [Ambrosi, Bussolino and Preziosi, J. Theoret. Med. 6 (2005) 1–19; Gamba et al., Phys. Rev. Lett. 90 (2003) 118101. ] to describe the coherent vascular network formation observed in vitro experiment. Considering the system in the half line R+=(0,∞) with Dirichlet boundary conditions, we first prove the existence and uniqueness of non‐constant phase transition steady states under some structure conditions on the pressure function. Then we prove that this unique phase transition steady state is nonlinearly asymptotically stable against a small perturbation. We prove our results by the method of energy estimates, the technique of a priori assumption and a weighted Hardy‐type inequality.
Enhanced Photocatalytic Activation of Methane C-H Bond by Incorporate Cerium into ETS-10

Ce-ETS-10 was prepared and their ability on photocatalysis coupling of methane has been studied in detail. Ce modification enhanced the photocatalysis performance of ETS-10 on methane conversion reaction, and the methane conversion can be carried out under irradiation of visible light. To study the various rare earth modification to ETS-10, various rare earth elements were introduced to ETS-10 molecular sieve, and the relative photocatalytic methane conversion performance were studied. Various characterizations were applied to examine rare earth modification behavior. The SPS results show that the Cerium modification induce visible light photovoltage, which means the strong electron-hole separation which enhance the photocatalytic performance. The charge-hole separation is non-traditional n-n heterojunction for the charge-hole separation is highly oriented and efficient at atomic scale. This mechanism shows significant enhancement for methane conversion reaction.
Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets

Objective: We aim to learn potential novel cures for diseases from unstructured text sources. More specifically, we seek to extract drug-disease pairs of potential cures to diseases by a simple reasoning over the structure of spoken text. Materials and Methods: We use Google Cloud to transcribe podcast episodes of an NPR radio show. We then build a pipeline for systematically pre-processing the text to ensure quality input to the core classification model, which feeds to a series of post-processing steps for obtaining filtered results. Our classification model itself uses a language model pre-trained on PubMed text. The modular nature of our pipeline allows for ease of future developments in this area by substituting higher quality components at each stage of the pipeline. As a validation measure, we use ROBOKOP, an engine over a medical knowledge graph with only validated pathways, as a ground truth source for checking the existence of the proposed pairs. For the proposed pairs not found in ROBOKOP, we provide further verification using Chemotext. Results: We found 30.4% of our proposed pairs in the ROBOKOP database. For example, our model successfully identified that Omeprazole can help treat heartburn.We discuss the significance of this result, showing some examples of the proposed pairs. Discussion and Conclusion: The agreement of our results with the existing knowledge source indicates a step in the right direction. Given the plug-and-play nature of our framework, it is easy to add, remove, or modify parts to improve the model as necessary. We discuss the results showing some examples, and note that this is a potentially new line of research that has further scope to be explored. Although our approach was originally oriented on radio podcast transcripts, it is input-agnostic and could be applied to any source of textual data and to any problem of interest.
Experimental and Analytical Study on Pool Scrubbing Under JET Injection Regime

During some pressurized water reactor risk-dominant sequences, most of the radioactivity is discharged at very high velocities into nearly saturated pools. An experimental plan for pool scrubbing and its associated hydrodynamics under representative boundary conditions is carried out in the PECA facility. The retention tests show that a substantial fraction of particle absorption takes place at the pool entrance because of inertial removal mechanisms. This submergence-independent component of the decontamination factor (DF) becomes dominant for small submergences (S ≤ 1.25 m). The behavior of the gas at the pool entrance is investigated experimentally, and a close relation between primary bubble size and inlet gas flow is observed. In addition, the retention tests are modeled with the SPARC90 and BUSCA-AUG92 codes. SPARC90 shows fairly good agreement with the experimental data and indicates the importance of the entrance region in particle absorption. Nonetheless, the approximations and drawbacks of the aerosol removal models used in SPARC90 at the injection zone suggest the need for further separate-effects tests to validate, improve, and/or develop specific models for the entrance region and the need for additional hydrodynamic tests to better describe primary bubble behavior under a jet injection regime.
Deep Shallow Fusion for RNN-T Personalization

End-to-end models in general, and Recurrent Neural Network Transducer (RNN-T) in particular, have gained significant traction in the automatic speech recognition community in the last few years due to their simplicity, compactness, and excellent performance on generic transcription tasks. However, these models are more challenging to personalize compared to traditional hybrid systems due to the lack of external language models and difficulties in recognizing rare long-tail words, specifically entity names. In this work, we present novel techniques to improve RNN-T’s ability to model rare WordPieces, infuse extra information into the encoder, enable the use of alternative graphemic pronunciations, and perform deep fusion with personalized language models for more robust biasing. We show that these combined techniques result in 15.4%–34.5% relative Word Error Rate improvement compared to a strong RNN-T baseline which uses shallow fusion and text-to-speech augmentation. Our work helps push the boundary of RNN-T personalization and close the gap with hybrid systems on use cases where biasing and entity recognition are crucial.
Comparison of Safety, Efficacy and Outcomes of Robot Assisted Laparoscopic Pyeloplasty vs Conventional Laparoscopy

Abstract Pyeloplasty is considered the gold standard for the management of ureteropelvic junction obstruction in cases of flank pain, recurrent stone formation or infection, and deteriorating renal function. Over the last two decades, minimally invasive techniques such as robotic (RALP) and laparoscopic pyeloplasty (LP) have become increasingly popular and have been moderately replacing the open approach. This paper aims to provide a comprehensive up-to-date review on safety, efficacy and outcomes regarding robotic repair of UPJO compared to the conventional laparoscopic procedure. RALP represents a viable and innovative alternative to conventional LP with a comparable success and complication rate both in adult and in paediatric fields. The robotic approach seems to add further technical advantages when compared to conventional LP but sustains a higher costs. Currently, the choice to adopt one of the different minimally invasive approaches depends on the surgeon’s preference or experience, and on institutional availability.
Probabilistic enumerative geometry over p-adic numbers: linear spaces on complete intersections

We compute the expectation of the number of linear spaces on a random complete intersection in $p$-adic projective space. Here "random" means that the coefficients of the polynomials defining the complete intersections are sampled uniformly form the $p$-adic integers. We show that as the prime $p$ tends to infinity the expected number of linear spaces on a random complete intersection tends to $1$. In the case of the number of lines on a random cubic in three-space and on the intersection of two random quadrics in four-space, we give an explicit formula for this expectation.
Prognostic Value of Long Noncoding RNA SPRY4-IT1 on Survival Outcomes in Human Carcinomas: A Systematic Review and Meta-Analysis with TCGA Database

Background Emerging evidences have shown that long noncoding RNA SPRY4-IT1 can be aberrantly expressed in human cancers, and it could be an unfavorable prognostic factor in cancer patients. However, the prognostic mechanism of SPRY4-IT1 is still unclear. This study is aimed at evaluating its potential predictive value for cancer prognosis. Methods We thoroughly searched PubMed, Embase, Web of Science, and MEDLINE databases so as to explore the relationship between SPRY4-IT1 expression and cancer prognosis value. Then, TCGA datasets were used to validate the results of our meta-analysis. Results In all, seventeen studies involving 1650 patients were included in this meta-analysis. Pooled results showed that high expression of SPRY4-IT1 was significantly correlated with poor OS (HR = 1.96, 95% confidence interval (CI) = 1.47‐2.62, P < 0.001) in cancer patients. Furthermore, exploration of TCGA dataset further validated that SPRY4-IT1 was aberrantly expressed in various cancers, which partially confirmed our results in this meta-analysis. Conclusions The present systematic review and meta-analysis implicated that the aberrant expressions of lncRNA SPRY4-IT1 were strongly associated with clinical survival outcomes in various cancers and therefore might serve as a promising biomarker for predicting prognosis of human cancers.
Generalized hydrodynamics in complete box-ball system for $U_q(\widehat{sl}_n)$

We introduce the complete box-ball system (cBBS), which is an integrable cellular automaton on 1D lattice associated with the quantum group $U_q(\widehat{sl}_n)$. Compared with the conventional $(n-1)$-color BBS, it enjoys a remarkable simplification that scattering of solitons is totally diagonal. We also introduce a randomized cBBS and study its non-equilibrium behavior by thermodynamic Bethe ansatz and generalized hydrodynamics. Excellent agreement is demonstrated between theoretical predictions and numerical simulation on the density plateaux generated from domain wall initial conditions including their diffusive broadening.