Miracle-dz/UniFAR-FTUs huggingface dataset:
Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data

This paper explores the use of robots to autonomously assemble parts with variations in colors and textures. Specifically, we focus on peg-in-hole assembly with some initial position uncertainty and holes located on surfaces of different colors and textures. Two in-hand cameras and a force-torque sensor are used to account for the position uncertainty. A program sequence comprising learning-based visual servoing, spiral search, and impedance control is implemented to perform the peg-in-hole task with feedback from the above sensors. Contributions are mainly made in the learning-based visual servoing component of the sequence, where a deep neural network is trained with various sets of synthetic data generated using the concept of domain randomization to predict where a hole is. In the experiments and analysis section, the network is analyzed and compared, and a real-world robotic system to insert pegs to holes using the proposed method is implemented. The results show that the implemented peg-in-hole assembly system can perform successful peg-in-hole insertions on surfaces with various colors and textures. It can generally speed up the entire peg-in-hole process, especially when the initial position uncertainty is large.
Convolutional neural networks for automatic meter reading

Abstract. We tackle automatic meter reading (AMR) by leveraging the high capability of convolutional neural networks (CNNs). We design a two-stage approach that employs the Fast-YOLO object detector for counter detection and evaluates three different CNN-based approaches for counter recognition. In the AMR literature, most datasets are not available to the research community since the images belong to a service company. In this sense, we introduce a public dataset, called Federal University of Paraná-AMR dataset, with 2000 fully and manually annotated images. This dataset is, to the best of our knowledge, three times larger than the largest public dataset found in the literature and contains a well-defined evaluation protocol to assist the development and evaluation of AMR methods. Furthermore, we propose the use of a data augmentation technique to generate a balanced training set with many more examples to train the CNN models for counter recognition. In the proposed dataset, impressive results were obtained and a detailed speed/accuracy trade-off evaluation of each model was performed. In a public dataset, state-of-the-art results were achieved using <200 images for training.
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning

In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment. First, unlike traditional aggregated web search that merely presents multi-sourced results in the first page, this new task may present aggregated results in all pages and has to dynamically decide which source should be presented in the current page. Second, as pointed out by many existing studies, it is not trivial to rank items from heterogeneous sources because the relevance scores from different source systems are not directly comparable. To address these two issues, we decompose the task into two subtasks in a hierarchical structure: a high-level task for source selection where we model the sequential patterns of user behaviors onto aggregated results in different pages so as to understand user intents and select the relevant sources properly; and a low-level task for item presentation where we formulate a slot filling process to sequentially present the items instead of giving each item a relevance score when deciding the presentation order of heterogeneous items. Since both subtasks can be naturally formulated as sequential decision problems and learn from the future user feedback on search results, we build our model with hierarchical reinforcement learning. Extensive experiments demonstrate that our model obtains remarkable improvements in search performance metrics, and achieves a higher user satisfaction.
No Padding Please: Efficient Neural Handwriting Recognition

Neural handwriting recognition (NHR) is the recognition of handwritten text with deep learning models, such as multi-dimensional long short-term memory (MDLSTM) recurrent neural networks. Models with MDLSTM layers have achieved state-of-the art results on handwritten text recognition tasks. While multi-directional MDLSTM-layers have an unbeaten ability to capture the complete context in all directions, this strength limits the possibilities for parallelization, and therefore comes at a high computational cost. In this work we develop methods to create efficient MDLSTM-based models for NHR, particularly a method aimed at eliminating computation waste that results from padding. This proposed method, called example packing, replaces wasteful stacking of padded examples with efficient tiling in a 2-dimensional grid. For word-based NHR this yields a speed improvement of factor 6.6 over an already efficient baseline of minimal padding for each batch separately. For line-based NHR the savings are more modest, but still significant. In addition to example packing, we propose: 1) a technique to optimize parallelization for dynamic graph definition frameworks including PyTorch, using convolutions with grouping, 2) a method for parallelization across GPUs for variable-length example batches. All our techniques are thoroughly tested on our own PyTorch re-implementation of MDLSTM-based NHR models. A thorough evaluation on the IAM dataset shows that our models are performing similar to earlier implementations of state-of-the art models. Our efficient NHR model and some of the reusable techniques discussed with it offer ways to realize relatively efficient models for the omnipresent scenario of variable-length inputs in deep learning.
Snowboot: Bootstrap Methods for Network Inference

Complex networks are used to describe a broad range of disparate social systems and natural phenomena, from power grids to customer segmentation to human brain connectome. Challenges of parametric model specification and validation inspire a search for more data-driven and flexible nonparametric approaches for inference of complex networks. In this paper we discuss methodology and R implementation of two bootstrap procedures on random networks, that is, patchwork bootstrap of Thompson et al. (2016) and Gel et al. (2017) and vertex bootstrap of Snijders and Borgatti (1999). To our knowledge, the new R package snowboot is the first implementation of the vertex and patchwork bootstrap inference on networks in R. Our new package is accompanied with a detailed user's manual, and is compatible with the popular R package on network studies igraph. We evaluate the patchwork bootstrap and vertex bootstrap with extensive simulation studies and illustrate their utility in application to analysis of real world networks.
Learning to Plan via Neural Exploration-Exploitation Trees

Sampling-based planning algorithms such as RRT and its variants are powerful tools for path planning problems in high-dimensional continuous state and action spaces. While these algorithms perform systematic exploration of the state space, they do not fully exploit past planning experiences from similar environments. In this paper, we design a meta path planning algorithm, called Neural Exploration-Exploitation Trees (NEXT), which can utilize prior experience to drastically reduce the sample requirement for solving new path planning problems. More specifically, NEXT contains a novel neural architecture which can learn from experiences the dependency between task structures and promising path search directions. Then this learned prior is integrated with a UCB-type algorithm to achieve an online balance between exploration and exploitation when solving a new problem. Empirically, we show that NEXT can complete the planning tasks with very small search trees and significantly outperforms previous state-of-the-arts on several benchmark problems.
Improving Robustness of Machine Translation with Synthetic Noise

Modern Machine Translation (MT) systems perform remarkably well on clean, in-domain text. However most of the human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of MT. In this paper we propose methods to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system more resilient to naturally occurring noise, partially mitigating loss in accuracy resulting therefrom.
A Behavioral Approach to Visual Navigation with Graph Localization Networks

Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual input and the topological map of the environment. We propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen environments.
OCCUPATIONAL ACCIDENTS IN MUNICIPAL SOLID WASTE MANAGEMENT (MSW) COMPANIES

The objective of this research was to analyze the factors present in occupational accidents occurred at work among Municipal Solid Waste (MSW) workers. Garbage, and rubbish from household waste were considered as MSW. The study was based on a total of 11,935 accidents occurring and reported in the MSW sector during the period 2009-2012 in Andalusia (Spain), as well as a survey of 572 workers in the above mentioned sector conducted in 2009. All the variables included in the accident reports were analyzed and the possible existence of statistically significant differences between the percentages of each variable category and the severity of the accident were determined. In addition, the results of the accidents reported were compared with the results of the survey for certain variables. Unskilled (Risk Ratio= 7.6) male workers (Risk Ratio= 1.4), aged 25 or below (Risk Ratio= 3.0), with experience of less than one year (Risk Ratio= 12.1), were the ones most susceptible to suffering an accident. In addition, accidents for male workers over 55, occurred in companies with 10 to 24 employees, during the initial hours of the work shift, and caused by loss of control, had the highest probability of being severe or fatal. If the companies considered these risk factors during the design of preventive measures the accident rate in the MSW sector could be reduced.
What we have learned from the Methadone Maintenance Treatment of Dual Disorder Heroin Use Disorder patients

Mental Disorders and Heroin Use Disorder (HUD) often co-occur and constitute correlated risk factors that the authors believe are best considered from a unitary perspective. In this article we review and discuss data collected by the V.P. Dole Research Group in Dual Disorder (V.P. Dole DD-RG) patients according to the following six discussion points: (1) Treatment of personality disorders during Methadone Maintenance Treatment (MMT); (2) Treatment of Mood Disorders during MMT; (3) Treatment of Anxiety Disorders during MMT; (4) Treatment of Psychotic Disorders during MMT; (5) Treatment of violence during MMT; (6) Treatment of Alcohol Use Disorder (AUD) during MMT. In treating Mood Disorder in HUD patients, we must bear in mind the interactions (potentiation and side effects) between psychopharmacology, used substances and agonist opioid medications; the use of psychiatric medications as an anti-craving drug, and the possible use of agonist and antagonist opioid medications in treating the other mental disorders. In treating chronic psychosis in HUD patients, we must consider the potentiation and side effects of antipsychotic drugs consequent on HUD treatment, worsening addiction hypophoria and inducing a more severe reward deficiency syndrome (RDS) in hypophoric patients. Violence and AUD during MMT can benefit from adequate dosages of methadone and co-medication with Sodium gamma-hydroxybutyrate (GHB). The experience of our V.P. Dole DD-RG suggests the following: (a) DD is the new paradigm in neuroscience in deepening our understanding of mental health; (b) To successfully treat DD patients a double competence is needed; (c) In managing DD patients priority must be given to Substance Use Disorder (SUD) treatment (stabilizing patients); (d) Antidepressant use is ancillary to SUD treatment; antipsychotic use must be restricted to acute phases; mood stabilizers must be preferred; any use of Benzodiazepines (BDZs) must be avoided.
A specialized MreB-dependent cell wall biosynthetic complex mediates the formation of stalk-specific peptidoglycan in Caulobacter crescentus

Many bacteria have complex cell shapes, but the mechanisms producing their distinctive morphologies are still poorly understood. Caulobacter crescentus, for instance, exhibits a stalk-like extension that carries an adhesive holdfast mediating surface attachment. This structure forms through zonal peptidoglycan biosynthesis at the old cell pole and elongates extensively under phosphate-limiting conditions. We analyzed the composition of cell body and stalk peptidoglycan and identified significant differences in the nature and proportion of peptide crosslinks, indicating that the stalk represents a distinct subcellular domain with specific mechanical properties. To identify factors that participate in stalk formation, we systematically inactivated and localized predicted components of the cell wall biosynthetic machinery of C. crescentus. Our results show that the biosynthesis of stalk peptidoglycan involves a dedicated peptidoglycan biosynthetic complex that combines specific components of the divisome and elongasome, suggesting that the repurposing of preexisting machinery provides a straightforward means to evolve new morphological traits.
Real-life experience of lusutrombopag for cirrhotic patients with low platelet counts being prepared for invasive procedures

Background and aims The present study aimed to report our real-life experience of the TPO receptor agonist lusutrombopag for cirrhotic patients with low platelet counts. Methods We studied platelet counts in 1,760 cirrhotic patients undergoing invasive procedures at our hospital between January 2014 and December 2017. In addition, we studied 25 patients who were administered lusutrombopag before invasive procedures between June 2017 and January 2018. Effectiveness of lusutrombopag to raise platelet counts and to avoid transfusion and treatment-related adverse events were analyzed. Results In 1,760 cirrhotic patients without lusutrombopag prior to invasive procedures, proportion of patients whose platelet counts <50,000/μL and needed platelet transfusions were 66% (n = 27/41) for radiofrequency ablation, 43% (n = 21/49) for transarterial chemoembolization, and 55% (n = 21/38) for endoscopic injection sclerotherapy / endoscopic variceal ligation, respectively. In 25 cirrhotic patients treated by lusutrombopag prior to the invasive procedures, platelet counts significantly increased compared with baseline (82,000 ± 26,000 vs. 41,000 ± 11,000/μL) (p < 0.01). Out of 25 patients, only 4 patients (16%) needed platelet transfusion before the invasive procedures. The proportion of patients with low platelet count and who needed platelet transfusions was significantly low in patients treated with lusutrombopag compared to those not treated with lusutrombopag (16% (4/25) vs. 54% (69/128), p = 0.001). Platelet counts after lusutrombopag treatment and prior to invasive procedures were lower in patients with a baseline platelet count ≤30,000/μL (n = 8) compared with those with a baseline platelet count >30,000/μL (n = 17) (50,000 ± 20,000 vs 86,000 ± 26,000/μL, p = 0.002). Patients with a baseline platelet count ≤30,000/μL with spleen index (calculated by multiplying the transverse diameter by the vertical diameter measured by ultrasonography) ≥40 cm2 (n = 3) had a lower response rate to lusutrombopag compared to those with spleen index <40 cm2 (n = 5) (0% vs. 100%, p = 0.02). Hemorrhagic complication was not observed. Recurrence of portal thrombosis was observed and thrombolysis therapy was required in one patient who had prior history of thrombosis. Conclusions Lusutrombopag is an effective and safe drug for thrombocytopenia in cirrhotic patients, and can reduce the frequency of platelet transfusions.
Phytochemical Characterization, Antioxidant and In Vitro Cytotoxic Activity Evaluation of Juniperus oxycedrus Subsp. oxycedrus Needles and Berries

In order to evaluate the antioxidant properties of aqueous and methanol extracts of needles and berries of Juniperus oxycedrus subsp. oxycedrus (Joo) species, various antioxidant capacity assessment tests (free radical scavenging assays (DPPH• and ABTS•+ tests), ferrous ions (Fe2+) chelating activity and reducing power assay (FRAP) were conducted. In all of the tests, the extracts exhibited strong antioxidant activity. Furthermore, in-vitro cytotoxic activity assays of the methanolic extracts showed potent cytotoxic effects against two breast cancer cell lines (MDA-MB-468 and MCF-7), with no cytotoxicity towards normal cells (PBMCs). Reactive oxygen species generation was presumed to be a potential reason for the observed cytotoxic effects. According to all the above, and considering its appropriate composition of mineral elements and phenolic compounds, Joo could offer a beneficial and natural source of bioactive compounds that can be either used on the preventive side as it could potentially be used in the clinic without toxicity.
Participation in Physical Activity is Associated with Sexual Activity in Older English Adults

Physical activity (PA) is a potential modifiable correlate of the age-related decline in sexual function, but no studies have explicitly tested this. This study aimed to examine associations between PA, television viewing (TV) time and sexual activity, problems, and concerns. Data were from 7038 men and women aged ≥50 years participating in the English Longitudinal Study of Ageing. PA and TV viewing time were self-reported. Sexual behaviour and concerns were assessed by self-completion questionnaire. Covariates included age, partnership status, socio-economic status, limiting long-standing illness, smoking status, alcohol intake and depressive symptoms. The odds of reporting any sexual activity were increased among individuals who participated in moderate (OR = 1.64, 95% CI: 1.24–2.15 in men) or vigorous (OR = 2.06, 95% CI: 1.50–2.84 in men, OR = 1.42, 95% CI: 1.09–1.85 in women) PA at least once a week. Erectile difficulties were less common among men who were active (OR = 0.58, 95% CI: 0.44–0.77 for vigorous PA). Women who watched ≥6 h of TV/day had lower odds of thinking about sex frequently (OR = 0.69, 95% CI: 0.50–0.96) or, if they did not live with a partner, being sexually active (OR = 0.40, 95% CI: 0.22–0.72). Encouraging older adults to be more physically active could help to improve sexual relationships and, as a result, mental health and wellbeing.
Defining the representativeness heuristic in trauma triage: A retrospective observational cohort study

Background Under-triage of severely injured patients presenting to non-trauma centers (failure to transfer to a trauma center) remains problematic despite quality improvement efforts. Insights from the behavioral science literature suggest that physician heuristics (intuitive judgments), and in particular the representativeness heuristic (pattern recognition), may contribute to under-triage. However, little is known about how the representativeness heuristic is instantiated in practice. Methods A multi-disciplinary group of experts identified candidate characteristics of “representative” severe trauma cases (e.g., hypotension). We then reviewed the charts of patients with moderate-to-severe injuries who presented to nine non-trauma centers in western Pennsylvania from 2010–2014 to assess the association between the presence of those characteristics and triage decisions. We tested bivariate associations using χ2 and Fisher’s Exact method and multivariate associations using random effects logistic regression. Results We identified 235,605 injured patients with 3,199 patients (1%) having moderate-to-severe injuries. Patients had a median age of 78 years (SD 20.1) and mean Injury Severity Score of 10.9 (SD 3.3). Only 759 of these patients (24%) were transferred to a trauma center as recommended by the American College of Surgeons clinical practice guidelines. Representative characteristics occurred in 704 patients (22%). The adjusted odds of transfer were higher in the presence of representative characteristics compared to when they were absent (aOR 1.7, 95% CI: 1.4–2.0, p < 0.001). Conclusions Most moderate-to-severely injured patients present without the characteristics representative of severe trauma. Presence of these characteristics is associated with appropriate transfer, suggesting that modifying physicians’ heuristics in trauma may improve triage patterns.
Heterogeneous Nuclear Ribonucleoproteins Involved in the Functioning of Telomeres in Malignant Cells

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are structurally and functionally distinct proteins containing specific domains and motifs that enable the proteins to bind certain nucleotide sequences, particularly those found in human telomeres. In human malignant cells (HMCs), hnRNP-A1—the most studied hnRNP—is an abundant multifunctional protein that interacts with telomeric DNA and affects telomerase function. In addition, it is believed that other hnRNPs in HMCs may also be involved in the maintenance of telomere length. Accordingly, these proteins are considered possible participants in the processes associated with HMC immortalization. In our review, we discuss the results of studies on different hnRNPs that may be crucial to solving molecular oncological problems and relevant to further investigations of these proteins in HMCs.
Impact of Regional Cardiocerebrovascular Centers on Myocardial Infarction Patients in Korea: A Fixed-effects Model

Objectives The Regional Cardiocerebrovascular Center (RCCVC) Project designated local teaching hospitals as RCCVCs, in order to improve patient outcomes of acute cardiocerebrovascular emergencies by founding a regional system that can adequately transfer and manage patients within 3 hours. We investigated the effects of RCCVC establishment on treatment volume and 30-day mortality. Methods We constructed a panel dataset by extracting all acute myocardial infarction cases that occurred from 2007 to 2016 from the Health Insurance Review and Assessment Service claims data, a national and representative source. We then used a panel fixed-effect model to estimate the impacts of RCCVC establishment on patient outcomes. Results We found that the number of cases of acute myocardial infarction that were treated increased chronologically, but when the time effect and other related covariates were controlled for, RCCVCs only significantly increased the number of treatment cases of female in large catchment areas. There was no statistically significant impact on 30-day mortality. Conclusions The establishment of RCCVCs increased the number of treatment cases of female, without increasing the mortality rate. Therefore, the RCCVCs might have prevented potential untreated deaths by increasing the preparedness and capacity of hospitals to treat acute myocardial infarction patients.