Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Optimization and Learning in Uncertain Dynamical Environments with Performance Guarantees

We propose a new framework to solve online optimization and learning problems in unknown and uncertain dynamical environments. This framework enables us to simultaneously learn the uncertain dynamical environment while making online decisions in a quantifiably robust manner. The main technical approach relies on the theory of distributional robust optimization that leverages adaptive probabilistic ambiguity sets. However, as defined, the ambiguity set usually leads to online intractable problems, and the first part of our work is directed to find reformulations in the form of online convex problems for two sub-classes of objective functions. To solve the resulting problems in the proposed framework, we further introduce an online version of the Nesterov accelerated-gradient algorithm. We determine how the proposed solution system achieves a probabilistic regret bound under certain conditions. Two applications illustrate the applicability of the proposed framework.
Low Resource Audio-To-Lyrics Alignment from Polyphonic Music Recordings

Lyrics alignment in long music recordings can be memory exhaustive when performed in a single pass. In this study, we present a novel method that performs audio-to-lyrics alignment with a low memory consumption footprint regardless of the duration of the music recording. The proposed system first spots the anchoring words within the audio signal. With respect to these anchors, the recording is then segmented and a second-pass alignment is performed to obtain the word timings. We show that our audio-to-lyrics alignment system performs competitively with the state-of-the-art, while requiring much less computational resources. In addition, we utilize our lyrics alignment system to segment the music recordings into sentence-level chunks. Notably on the segmented recordings, we report the lyrics transcription scores on a number of benchmark test sets. Finally, our experiments highlight the importance of the source separation step for good performance on the transcription and alignment tasks. For reproducibility, we publicly share our code with the research community.
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations

Precise user modeling is critical for online personalized recommendation services. Generally, users' interests are diverse and are not limited to a single aspect, which is particularly evident when their behaviors are observed for a longer time. For example, a user may demonstrate interests in cats/dogs, dancing and food \&delights when browsing short videos on Tik Tok; the same user may show interests in real estate and women's wear in her web browsing behaviors. Traditional models tend to encode a user's behaviors into a single embedding vector, which do not have enough capacity to effectively capture her diverse interests. This paper proposes a Sequential User Matrix (SUM) to accurately and efficiently capture users' diverse interests. SUM models user behavior with a multi-channel network, with each channel representing a different aspect of the user's interests. User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention. We further propose a local proximity debuff component and a highway connection component to make the model more robust and accurate. SUM can be maintained and updated incrementally, making it feasible to be deployed for large-scale online serving. We conduct extensive experiments on two datasets. Results demonstrate that SUM consistently outperforms state-of-the-art baselines.
Deep Miner: A Deep and Multi-branch Network which Mines Rich and Diverse Features for Person Re-identification

Most recent person re-identification approaches are based on the use of deep convolutional neural networks (CNNs). These networks, although effective in multiple tasks such as classification or object detection, tend to focus on the most discriminative part of an object rather than retrieving all its relevant features. This behavior penalizes the performance of a CNN for the re-identification task, since it should identify diverse and fine grained features. It is then essential to make the network learn a wide variety of finer characteristics in order to make the re-identification process of people effective and robust to finer changes. In this article, we introduce Deep Miner, a method that allows CNNs to"mine"richer and more diverse features about people for their re-identification. Deep Miner is specifically composed of three types of branches: a Global branch (G-branch), a Local branch (L-branch) and an Input-Erased branch (IE-branch). G-branch corresponds to the initial backbone which predicts global characteristics, while L-branch retrieves part level resolution features. The IE-branch for its part, receives partially suppressed feature maps as input thereby allowing the network to"mine"new features (those ignored by G-branch) as output. For this special purpose, a dedicated suppression procedure for identifying and removing features within a given CNN is introduced. This suppression procedure has the major benefit of being simple, while it produces a model that significantly outperforms state-of-the-art (SOTA) re-identification methods. Specifically, we conduct experiments on four standard person re-identification benchmarks and witness an absolute performance gain up to 6.5% mAP compared to SOTA.
Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley

The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and industrial applications demand. This new curriculum is already reshaping the undergraduate experience at Berkeley, where these courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.
Probing black hole microstructure with the kinetic turnover of phase transition

By treating black hole as the macroscopic stable state on the free energy landscape, we propose that the stochastic dynamics of the black hole phase transition can be effectively described by the Langevin equation or equivalently by the Fokker-Planck equation in phase space. We demonstrate the turnover of the kinetics for the charged anti-de Sitter black hole phase transition, which shows that the mean first passage time is linear with the friction in the high damping regime and inversely proportional to the friction in the low damping regime. The fluctuations in the kinetics are shown to be large/small in the high/low damping regime and the switching behavior from the small fluctuations to the large fluctuations takes place at the kinetic turnover point. Because the friction is a reflection of the microscopic degrees of freedom acting on the order parameter of the black hole, the turnover and the corresponding fluctuations of the phase transition kinetics can be used to probe the black hole microstructure. ∗Equal contributions, liran@htu.edu. cn †Equal contributions, zhangkun@ciac.ac. cn ‡Corresponding author, jin.wang. 1@stonybrook.edu 1 ar X iv :2 10 2. 09 43 9v 1 [ gr -q c] 1 8 Fe b 20 21
Maximum weighted induced forests and trees: New formulations and a computational comparative review

Given a graph G = (V, E ) with a weight wv associated with each vertex v ∈ V , the maximum weighted induced forest problem (MWIF) consists of encountering a maximum weighted subset V ′ ⊆ V of the vertices such that V ′ induces a forest. This NP-hard problem is known to be equivalent to the minimum weighted feedback vertex set problem, which has applicability in a variety of domains. The closely related maximum weighted induced tree problem (MWIT), on the other hand, requires that the subset V ′ ⊆ V induces a tree. We propose two new integer programming formulations with an exponential number of constraints and branch-and-cut procedures for MWIF. Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when solved by a standard commercial mixed integer programming solver. More specifically, five formulations are compared, two compact ones (i.e., with a polynomial number of variables and constraints) and the three others with an exponential number of constraints. The experiments show that a new formulation for the problem based on directed cutset inequalities for eliminating cycles (DCUT) offers stronger linear relaxation bounds earlier in the search process. The results also indicate that the other new formulation, denoted tree with cycle elimination (TCYC), outperforms those available in the literature when it comes to the average times for proving optimality for the benchmark instances with up to 81 vertices. Additionally, this formulation can achieve much lower average times for solving the larger random instances that can be optimally solved. Furthermore, we show how the formulations for MWIF can be easily extended for MWIT. Such extension allowed us to analyze the difference between the optimal solution values of the two problems when considering different classes of graphs.
Recombinant immunotoxin targeting GPC3 is cytotoxic to H446 small cell lung cancer cells

Glypican-3 (GPC3) is a cell membrane glycoprotein that regulates cell growth and proliferation. Aberrant expression or distribution of GPC3 underlies developmental abnormalities and the development of solid tumours. The strongest evidence for the participation of GPC3 in carcinogenesis stems from studies on hepatocellular carcinoma and lung squamous cell carcinoma. To the best of our knowledge, the role of the GPC3 protein and its potential therapeutic application have never been studied in small cell lung carcinoma (SCLC), despite the known involvement of associated pathways and the high mortality caused by this disease. Therefore, the aim of the present study was to examine GPC3 targeting for SCLC immunotherapy. An immunotoxin carrying an anti-GPC3 antibody (hGC33) and Pseudomonas aeruginosa exotoxin A 38 (PE38) was generated. This hGC33-PE38 protein was overexpressed in E. coli and purified. ADP-ribosylation activity was tested in vitro against eukaryotic translation elongation factor 2. Cell internalisation ability was confirmed by confocal microscopy. Cytotoxicity was analysed by treating liver cancer (HepG2, SNU-398 and SNU-449) and lung cancer (NCI-H510A, NCI-H446, A549 and SK-MES1) cell lines with hGC33-PE38 and estimating viable cells number. A BrdU assay was employed to verify anti-proliferative activity of hGC33-PE38 on treated cells. Fluorescence-activated cell sorting was used for the detection of cell membrane-bound GPC3. The hGC33-PE38 immunotoxin displayed enzymatic activity comparable to native PE38. The protein was efficiently internalised by GPC3-positive cells. Moreover, hGC33-PE38 was cytotoxic to HepG2 cells but had no effect on known GPC3-negative cell lines. The H446 cells were sensitive to hGC33-PE38 (IC50, 70.6±4.6 ng/ml), whereas H510A cells were resistant. Cell surface-bound GPC3 was abundant on the membranes of H446 cells, but absent on H510A. Altogether, the present findings suggested that GPC3 could be considered as a potential therapeutic target for SCLC immunotherapy.
Size-controlled nitrogen-containing mesoporous carbon nanospheres by one-step aqueous self-assembly strategy

Nitrogen-containing mesoporous carbon nanospheres with tunable sizes have been prepared through an aqueous self-assembly process with F127 as a template and morphological control agent, and 3-aminophenol as carbon and nitrogen sources. The sphere sizes could be simply tuned by optimizing the concentration of F127 and ammonia. Compared with a traditional nanocasting method and a hydrothermal process, the present one-step method has the striking features of a simple synthesis process and easy introduction of the nitrogen atom into the carbon nanospheres. The feasibility of the fabrication method may open up a new way for the synthesis of heteroatom-containing carbon nanospheres with mesoporous structure. The prepared nitrogen-containing mesoporous carbon nanospheres exhibit good catalytic performance in the direct dehydrogenation of ethylbenzene due to their unique mesostructure and abundant oxygen and nitrogen functional groups.
Hypertension and its physio-psychosocial risks factors in elderly people: a cross-sectional study in north-eastern region of Bangladesh

1. Department of Statistics, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh; 2. Biomedical Research Foundation, Dhaka-1230, Bangladesh; 3. Flinders University Rural Health SA, College of Medicine & Public Health, Flinders University, South Australia, Australia; 4. Bangladesh Bank-The Central Bank of Bangladesh, Bangladesh; 5. Department of Statistics, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh; 6. Asian University of Bangladesh, Dhaka, Bangladesh; 7. Department of Statistics, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh; 8. Department of Statistics, Shahjalal University of Science and Technology, Sylhet, Bangladesh ✉ Correspondence to: meshbahur.rahman@brfbd.org https://doi.org/10.11909/j.issn. 1671-5411.2021. 01.011
Health problems in agricultural workers occupationally exposed to pesticides

Agricultural workers are susceptible to several health problems as a result of occupational exposure to toxic substances, especially pesticides. The aim of this review was to describe the health issues associated with occupational exposure to pesticides in agricultural workers. A descriptive study, in the form of an integrative literature review, was conducted based on articles retrieved from the LILACS, SciELO and PubMed databases, published between January 2015 and October 2018. The searches were conducted using the keywords “pesticides,” “workers’ health,” “occupational exposure” and “agricultural workers. ” The study was guided by the following research question: what health problems do agricultural workers experience as a result of occupational exposure to pesticides? The screening process led to the selection of 35 studies performed in several countries and continents, all of which shed light on the vulnerability of agricultural workers, especially due to the misuse of personal protective equipment and lack of knowledge about the correct use of these devices. The studies investigated a variety of health issues, and most reported a positive association between these conditions, which include cancer, and the use of pesticides. Educational and preventive measures must be implemented to promote the health of rural workers. Furthermore, it is crucial that governments play an active role in these initiatives and provide alternatives to pesticides for pest control.
High burden of infections caused by ESBL-producing MDR Escherichia coli in paediatric patients, Yangon, Myanmar

Abstract Background There is mounting evidence of a high burden of antimicrobial-resistant infections in children in low- and middle-income countries (LMICs). Objectives To detect the frequency of ESBL-producing Escherichia coli in clinical specimens from paediatric patients attending Yangon Children’s Hospital in Myanmar. Methods All children attending Yangon Children’s Hospital who had clinical specimens submitted to the hospital diagnostic microbiology laboratory from June 2019 to December 2019 were included in the study. Specimens were processed routinely using standard methods with BD Phoenix used for pathogen identification and susceptibility testing. Presence of ESBLs was determined using the cephalosporin/clavulanate combination disc method with confirmation by PCR. Results From 3462 specimens submitted to the Microbiology Laboratory, a total of 123 E. coli were isolated. Among them, 100 isolates were phenotypically ESBL producers, 94 (76.4%) of which were confirmed by PCR [82/94 (87%) CTX-M, 72/94 (77%) TEM, 1/94 (1%) SHV]. Most of the ESBL-producing E. coli were isolated from urine samples (52.1%, 49/94) and the majority were from the surgical unit (61.7%, 58/94). Only 34/94 (36%) isolates were susceptible to meropenem. Conclusions This study confirms a high proportion of infections caused by ESBL-producing and MDR E. coli in children hospitalized in Yangon, where access to effective second-line antimicrobials is limited.
Modeling and Investigation of Carbon Loss on the Cathode Electrode during PEMFC Operation

Severe carbon loss phenomena, such as partial hydrogen starvation and start-up, have been investigated in previous works. Even for short times, these acute situations can be fatal, and some methods have been suggested to mitigate carbon oxidation and improve proton exchange membrane fuel cell (PEMFC) durability. By comparison, the rate of carbon loss is low during an ordinary PEMFC operation. Although less severe than for hydrogen starvation and start-up, the impact is still large enough so that the cell performance degrades markedly after a long-term operation. In this work, carbon loss during a PEMFC operation is investigated with a physical model. These results quantify the effects that cell voltage, activity for oxygen reduction, humidity, and temperature have on the rate of carbon loss. More rapid kinetics for oxygen reduction promotes carbon loss by lowering the solution potential at the fuel cell cathode.
Blunt Injury Forearm Debridement Under Ultrasound-Guided Regional Anesthesia for a Marfan Syndrome Patient

Marfan syndrome is an autosomal dominant connective tissue disorder with anomalies involving the musculoskeletal system, cardiovascular system, skin, eyes, and teeth. Patients with Marfan syndrome are especially prone to cardiovascular complications, which increases the risk multifold under general anesthesia. This is a case of a 37-year-old Marfan syndrome male patient with cardiac manifestations and his anesthesia course during emergency wound debridement.
Endocrine-Disrupting Chemicals in Industrial Wastewater Samples in Toronto, Ontario

The occurrence of endocrine-disrupting chemicals (EDCs) such as bisphenol A (BPA), 4-tert-octylphenol (OP), nonylphenol (NP) and its ethoxylates (NPEO) in wastewater generated in the Toronto area has been studied. In all,97 samples from 40 facilities in ten different industry classes have been collected and analyzed. Widely divergent concentrations have been observed in these samples. They ranged from <0.01 to 195 μg/L for OP, from <0.1 to 253 μg/L for NP, from <2 to 117,570 μg/L for NPEO, and from <0.01 to 149 μg/L for BPA. The results show that the concentrations of NP and NPEO in these samples generally exceeded City of Toronto By-law (No. 457-2000) limits. The results also suggest that detergents based on NPEO are still extensively used by the commercial laundries, and also by the textile products and clothing industries. These facilities, together with several sources in the chemical and chemical products industries and the fabricated metal products industries are believed to be the major sources of NP and NPEO input into the sewer system in Toronto. In addition to the two facilities in the chemicals and chemical products sector, several commercial laundries also had significant on-site releases of BPA. Except for those collected from three facilities in the chemicals and chemical products industries, the levels of OP in these samples were generally low. Many industries in the Toronto area would have to take drastic actions to reduce releases of NPEO and NP if full compliance with the most recent City By-law regarding wastewater quality were to be achieved.
Multi‐compartment metabolomics and metagenomics reveal major hepatic and intestinal disturbances in cancer cachectic mice

Cancer cachexia is a multifactorial syndrome characterized by multiple metabolic dysfunctions. Besides the muscle, other organs such as the liver and the gut microbiota may also contribute to this syndrome. Indeed, the gut microbiota, an important regulator of the host metabolism, is altered in the C26 preclinical model of cancer cachexia. Interventions targeting the gut microbiota have shown benefits, but mechanisms underlying the host–microbiota crosstalk in this context are still poorly understood.
Enhancing Research Through the Use of the Genotype-Tissue Expression (GTEx) Database

Despite a growing interest in multi-omic research, individual investigators may struggle to collect large-scale omic data, particularly from human subjects. Publicly available datasets can help to address this problem, including those sponsored by the NIH Common Fund, such as the Genotype-Tissue Expression (GTEx) database. This database contains genotype and expression data obtained from 54 non-diseased tissues in human subjects. But these data are often underutilized, because users may find the browsing tools to be counterintuitive or have difficulty navigating the procedures to request controlled data access. Furthermore, there is limited knowledge of these resources among nurse scientists interested in incorporating such information into their programs of research. This article outlines the procedures for using the GTEx database. Next, we provide one exemplar of using this resource to enhance existing research by investigating expression of dopamine receptor type 2 (DRD2) across brain tissues in human subjects.
Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy

Abstract Background The placental pathological changes in hypertensive disorders of pregnancy (HDP) starts early in pregnancy, the deep convolutional neural networks (CNN) can identify these changes before its clinical manifestation. Objective To compare the placental quantitative ultrasound image texture of women with HDP to those with the normal outcome. Methods The cases were enrolled in the first trimester of pregnancy, good quality images of the placenta were taken serially in the first, second, and third trimester of pregnancy. The women were followed till delivery, those with normal outcomes were controls, and those with HDP were cases. The images were processed and classified using validated deep learning tools. Results Total of 429 cases were fully followed till delivery, 58 of them had HDP (13.5%). In the first trimester, there was a significant difference in the placental length (p = .033), uterine artery PI (p = .019), biomarkers PAPP-A (p = .001) PlGF (p = .013) and placental image texture (p = .001) between the cases and controls. In the second trimester the uterine artery PI, serum PAPP-A (p = .010) and PlGF (p = .005) levels were significantly low among women who developed hypertension later on pregnancy. The image texture disparity between the two groups was highly significant (p < .001). The model “resnext 101_32x8d” had Cohen kappa score of 0.413 (moderate) and the accuracy score of 0.710 (good). In the first trimester the best sensitivity and specificity was observed for abnormal placental image texture (70.6% and 76.6%, respectively) followed by PlGF (64% and 50%, respectively), in the second trimester the abnormal image texture had the highest sensitivity and specificity (60.4% and 73.3%, respectively) followed by uterine artery PI (58.6% and 54.7%, respectively). Similarly in the third trimester, uterine artery PI had sensitivity and specificity of 60.3% and specificity of 50.7%, whereas the abnormal image texture had sensitivity and specificity of 83.5%. Conclusion Ultrasound placental analysis using artificial intelligence (UPAAI) is a promising technique, would open avenues for more research in this field.