Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk

Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation. Most of the existing methods for safe reinforcement learning can only handle transition disturbance or observation disturbance since these two kinds of disturbance affect different parts of the agent; besides, the popular worst-case return may lead to overly pessimistic policies. To address these issues, we first theoretically prove that the performance degradation under transition disturbance and observation disturbance depends on a novel metric of Value Function Range (VFR), which corresponds to the gap in the value function between the best state and the worst state. Based on the analysis, we adopt conditional value-at-risk (CVaR) as an assessment of risk and propose a novel reinforcement learning algorithm of CVaR-Proximal-Policy-Optimization (CPPO) which formalizes the risk-sensitive constrained optimization problem by keeping its CVaR under a given threshold. Experimental results show that CPPO achieves a higher cumulative reward and is more robust against both observation and transition disturbances on a series of continuous control tasks in MuJoCo.
Generalised Task Planning with First-Order Function Approximation

: Real world robotics often operates in uncertain and dynamic environ-1 ments. Therefore, generalisation over different planning problems is of practical 2 interest. In the absence of a model, value-based reinforcement learning can be 3 used to learn a goal-directed policy. Typically, the interaction between robots and 4 the objects in the environment exhibit a ﬁrst-order structure. We propose to use 5 ﬁrst-order features to approximate the Q-function that generates a generalised pol-6 icy. Empirical results for a service robot domain show that our method of online 7 relational reinforcement learning is scalable to large-scale problems and enables 8 transfer learning across different problems and simulation environments with dis-9 similar transition dynamics. 10
Efficacy of Several Serological Tests and Antigens for Diagnosis of Bovine Brucellosis in the Presence of False-Positive Serological Results Due to <italic toggle='yes'>Yersinia enterocolitica</italic> O:9

Efficacy of Several Serological Tests and Antigens for Diagnosis of Bovine Brucellosis in the Presence of False-Positive Serological Results Due to Yersinia enterocolitica O:9 P. M. Muñoz, C. M. Marı́n, D. Monreal, D. González, B. Garin-Bastuji, R. Dı́az, R. C. Mainar-Jaime, I. Moriyón, and J. M. Blasco* Centro de Investigación y Tecnologı́a Agroalimentaria, Gobierno de Aragón, Zaragoza, and Departamento de Microbiologı́a, Universidad de Navarra, Pamplona, Spain; OIE/FAO Reference Laboratory for Brucellosis, Agence Française de Sécurité Sanitaire des Aliments, Maisons-Alfort, France; and Department of Veterinary Microbiology, WCVM, University of Saskatchewan, Saskatoon, Canada
A Review on 5G Sub-6 GHz Base Station Antenna Design Challenges

Modern wireless networks such as 5G require multiband MIMO-supported Base Station Antennas. As a result, antennas have multiple ports to support a range of frequency bands leading to multiple arrays within one compact antenna enclosure. The close proximity of the arrays results in significant scattering degrading pattern performance of each band while coupling between arrays leads to degradation in return loss and port-to-port isolations. Different design techniques are adopted in the literature to overcome such challenges. This paper provides a classification of challenges in BSA design and a cohesive list of design techniques adopted in the literature to overcome such challenges.
Examining the empirical relationship between subjective fatigue and employee work engagement in a heavy workload manufacturing environment

The purpose of this study was to examine the empirical relationship between Subjective Fatigue and Employee work engagement among employees working under heavy workload conditions in Tennessee, USA. The questionnaire was developed by reviewing extant literature and the factors that were chosen were subjected to exploratory factor analysis. Post elimination of the weakly loaded factors, a questionnaire based on the selected factors was designed to measure the subjective levels of fatigue. The data was collected from two manufacturing company sites from East Tennessee and was subjected to analysis using IBM SPSS and SmartPLS softwares. As a part of the SEM analysis, a causal model was built to determine the existence of the relationship between subjective fatigue and employee work engagement. The model aimed at determining whether a correlation and causality were present between the two constructs. The results suggested that a correlation between subjective fatigue and employee engagement does exist, but a unidirectional causality between the two does not. In other words, high levels of subjective fatigue do not directly impact the engagement levels of employees.
A 5,000-year vegetation and fire history for tierra firme forests in the Medio Putumayo-Algodón watersheds, northeastern Peru

Significance The extent to which pre-Columbian societies in Amazonia occupied and significantly altered the tierra firme (nonflooded, nonriverine) forest environment is an enduring question and the subject of a current debate. Our research addresses these issues in tierra firme forests of northeastern Peru. We present phytolith and charcoal data indicating the forests were not cleared, farmed, or otherwise significantly altered in prehistory. Frequencies of hyperdominant palm species did not increase through time, indicating prehistoric human exploitation contributed little to the species’ disproportionate abundance in the modern flora. Our data indicate that forest resurgence and fire decrease upon the tragic consequences of European contact were not so widespread as to have been principal contributors to the onset of the “Little Ice Age. ” This paper addresses an important debate in Amazonian studies; namely, the scale, intensity, and nature of human modification of the forests in prehistory. Phytolith and charcoal analysis of terrestrial soils underneath mature tierra firme (nonflooded, nonriverine) forests in the remote Medio Putumayo-Algodón watersheds, northeastern Peru, provide a vegetation and fire history spanning at least the past 5,000 y. A tree inventory carried out in the region enables calibration of ancient phytolith records with standing vegetation and estimates of palm species densities on the landscape through time. Phytolith records show no evidence for forest clearing or agriculture with major annual seed and root crops. Frequencies of important economic palms such as Oenocarpus, Euterpe, Bactris, and Astrocaryum spp. , some of which contain hyperdominant species in the modern flora, do not increase through prehistoric time. This indicates pre-Columbian occupations, if documented in the region with future research, did not significantly increase the abundance of those species through management or cultivation. Phytoliths from other arboreal and woody species similarly reflect a stable forest structure and diversity throughout the records. Charcoal 14C dates evidence local forest burning between ca. 2,800 and 1,400 y ago. Our data support previous research indicating that considerable areas of some Amazonian tierra firme forests were not significantly impacted by human activities during the prehistoric era. Rather, it appears that over the last 5,000 y, indigenous populations in this region coexisted with, and helped maintain, large expanses of relatively unmodified forest, as they continue to do today.
Financial Incentives in Crowdsourced Content Curation

The amount of available content on the Internet vastly outweighs the attention span of online users. In the last few years, crowdsourced content curation has emerged as a novel approach to the aggregation and filtering of content in social media platforms. As opposed to traditional curation techniques, it avoids the reliance on a central party to curate content, allowing online communities to self-regulate. The advent of blockchains and cryptocurrencies has created new ways for the distribution and ownership of financial value over the Internet. Steemit is a social media platform which establishes financial rewards for the crowdsourced curation of content using the cryptocurrencies of the Steem blockchain. In this work we present an analysis of the Steem blockchain, focusing on the economic and incentives model of the platform. Then we identify and evaluate the main issues of the incentives model of Steemit. Namely, we analyze the self-voting pattern and voting rings, moderation and censorship abuse in Steemit, and the types of voting bots in the platform. To better understand how self-voting can affect financially incentivized crowdsourced curation, we construct a formal model inspired by Steemit. Our model provides a formal framework which may be used in the evaluation of the incentive compatibility of Steemit or other decentralized content curation systems with monetary rewards. To conclude, we design and implement a solution for the moderation and censorship issues of Steemit in the form of a smart contract.
GLACIATION OF THE BLANCA MASSIF, SANGRE DE CRISTO MOUNTAINS, COLORADO, DURING THE LAST GLACIAL MAXIMUM: IMPLICATIONS FOR CLIMATE

Temperature-index modeling is used to determine the magnitude of temperature depression on the Blanca Massif, Colorado, required to maintain steady-state mass balances of nine reconstructed glaciers at their extent during the Last Glacial Maximum (LGM). The mean temperature depression thus determined is ~8.6 +0.7/–0.9 °C where the uncertainties account for those inherent in the glacier reconstructions, in model parameters (e.g. melt factors), and possible modest changes in LGM precipitation. Associated equilibrium-line altitudes (ELAs) exhibit a statistically significant directional dependency being lower toward the north and east. Under the assumption that regional temperature change was uniform, required changes in precipitation vary systematically – also exhibiting a directional dependency coinciding with that in ELAs – and indicate increases (over modern) occurred on the eastern side of the massif while decreases occurred on the western side. This disparity represents a strengthening of a precipitation asymmetry, particularly winter precipitation, that exists today. The modern precipitation asymmetry may be a consequence of snow being blown over to the eastern side of the massif (advective transport) by southwesterly flow. Intensification of this flow during the LGM would have enhanced advection, and augmented snow accumulation on glaciers, thus explaining the lower ELAs and increased precipitation on that side of the massif.
Experimental Model for Studying the Prefrontal EEG Biomarkers in Correlation with Food Intake Behavior: Neurofeedback Platform

Background: This study aims to investigate the effects of visual neurofeedback stimulation on the brain activity in overweight cases. The neuroscience studies indicated the personal decision about eating under the impact of environmental factors such as (visually, smelling, tasting) is related to neural activity of the prefrontal lobe of the brain. Therefore, there were many attempts to modify the food intake behavior in overweight cases through the stimulation of the prefrontal cortex. However, the empirical viewing of EEG-neurofeedback experiments has not explicated the details about the effect of the EEG-NF, the specificity of positive treatment effects remains in a challenging scope.Methods: This study is a cue-exposure EEG-NF experiment to verify the hypothesis of effecting the EEG-NF on the electrical activity of PFC and modifying the general symptoms of food intake behavior in overweight cases. Twenty-four individuals were recruited as participants for this study. These participants were assigned randomly into two groups; the EX-Group (N=12) enrolled in 8 sessions of the EEG-NF experiment, and the C-Group (N=12) was listed in no EEG-NF sessions. The participants provided researchers with a self-report questionnaire relating to their observation of general symptoms of food intake behavior, and EEG signals recordings into the pre and posts stimulation phase. The power spectral density (PSD) method was applied for EEG parameters extraction.Results: The results of a two-way analysis of variance (ANOVA) explained that a significant variation in variables between the two groups after the EEG-NF experiment. The analysis of the quantitative variables indicated that the effect of EEG-NF experiment was a significant decrement in EEG power bands which significantly influenced changing the median of self-report questionnaire responses that is related to general symptoms of food intake behavior.Conclusions: This study provides preliminary support for the therapeutic potential of EEG-NF experiment that targets the prefrontal cortex, to influence neural processes underlying food intake behavior in overweight cases.
A Differentiable Recipe for Learning Visual Dexterous Planar Manipulation

: Specifying tasks with videos is a powerful technique towards acquiring novel 1 and general robot skills. However, reasoning over mechanics and dexterous interactions 2 can make it challenging to scale visual learning for contact-rich manipulation. In this 3 work, we focus on the problem of visual dexterous planar manipulation: given a video 4 of an object in planar motion, find contact-aware robot actions that reproduce the 5 same object motion. We propose a novel learning architecture that combines video 6 decoding neural models with priors from contact mechanics by leveraging differentiable 7 optimization and differentiable simulation. Through extensive simulated experiments, 8 we investigate the interplay between traditional model-based techniques and modern 9 deep learning approaches. We find that our modular and fully differentiable architecture 10 outperforms learning-only methods on unseen objects and motions. 11
InitNet: Learning Initialisations for Robotic Pick-and-Place Planning

: Non-linear optimisation can be a powerful tool when used in applica-1 tions such as robotic pick-and-place. It scales well to high-dimensional spaces 2 and produces locally-optimal solutions. However, its performance is strongly de-3 pendent on the initialisation. In this work, we present a novel approach to predict 4 a good initialisation for the optimisation, which simultaneously ﬁnds the robot’s 5 conﬁguration for both the grasping and placing ends of a trajectory. We achieve 6 this by learning the distribution of the optimiser’s basin of attraction, using data 7 generated ofﬂine from previous optimisation attempts. We further reﬁne the pre-8 dictions using a learnt discriminator model, resulting in the predictions of high-9 quality initialisations. Evaluation of our method shows that using the predicted 10 initialisations increases the efﬁciency of the optimisation and the quality of the 11 produced solutions increases signiﬁcantly, and we also show how the trained mod-12 els can transfer to entirely novel environments. 13
Joint Inference for Informed End-to-End Knowledge Base Population

Knowledge Base Population is the problem of populating a structured database with facts from a document corpus. This breaks down into several tasks such as the recognition of named entities, co-reference resolution, relation extraction and entity linking. Traditional systems solve this task set in a pipeline fashion, where modular components are chained in row, each solving one task. These approaches suﬀer from propagation of errors when information is passed from one pipeline component to the next and they are restricted to information ﬂow in one direction. In addition, black box models are usually chained, which results in a low interpretability. In this paper, we propose a probabilistic model structure and framework for joint inference for end-to-end knowledge base population, which enables to make globally consistent decisions based on local component feedback and additional background knowledge. A decisive advantage of the approach is that knowledge about the pipeline components, the underlying knowledge base ontology, linguistics and the text corpus can be easily and transparently integrated. We evaluate our approach regarding two aspects, exhaustive relation extraction and entity linking to the knowledge base.
ICT resources and use: examining differences in pathways to improved small firm performance

PurposeThe paper shows how small firms perceive the pathways through which access to and adoption of superfast broadband-enabled resources strengthen business performance. Improvements to broadband infrastructure do not automatically lead to adoption of opportunities made available through the broadband resource. Then, interventions can be used to alert small firms to new opportunities. However, the quality of interventions in terms of education and digital audits can be better targeted with information available on how small firms perceive the benefits from broadband access and whether these perceptions are reflected in business performance outcomes.Design/methodology/approachData are used from the Digital Maturity Survey from Wales. The study uses principal component analysis and a dual stage cluster approach to show how SMEs believe they are benefitting from broadband access. These belief-based perceptions of broadband inferred business benefits are tested against business performance variables.FindingsThe analysis shows variation in SME perceptions of the benefits of broadband-enabled services. This study reveals a cluster of firms which perceived routes to business value in terms of variables linked to security and risk management, and then more commonly held notions linked to communication, competition enhancement and productivity.Originality/valueWhile the research literature points to Information and Communication Technology (ICT) resources (ICT investment and skills) and use (digital applications), leading to new to business value improvements, this study suggests less work has sought to identify the critical themes identified by business owners in explaining how ICT resources and use tie to observed business performance. The study identifies these critical themes. The analysis suggests that these critical themes in terms of business value benefits as perceived by business owners can be summarised in terms of communication and competition benefits, and security and risk related benefits. The findings have a series of implications for interventions in the space.
Guided Imitation of Task and Motion Planning

While modern policy optimization methods can do complex manipulation from sensory data, they struggle on problems with extended time horizons and multiple sub-goals. On the other hand, task and motion planning (TAMP) methods scale to long horizons but they are computationally expensive and need to precisely track world state. We propose a method that draws on the strength of both methods: we train a policy to imitate a TAMP solver's output. This produces a feed-forward policy that can accomplish multi-step tasks from sensory data. First, we build an asynchronous distributed TAMP solver that can produce supervision data fast enough for imitation learning. Then, we propose a hierarchical policy architecture that lets us use partially trained control policies to speed up the TAMP solver. In robotic manipulation tasks with 7-DoF joint control, the partially trained policies reduce the time needed for planning by a factor of up to 2.6. Among these tasks, we can learn a policy that solves the RoboSuite 4-object pick-place task 88% of the time from object pose observations and a policy that solves the RoboDesk 9-goal benchmark 79% of the time from RGB images (averaged across the 9 disparate tasks).
Act, Delegate or Learn: A Framework for Shared Autonomy to Complete Tasks with Minimum Effort

: We consider the problem of completing a set of n tasks with mini-1 mum human and robot effort in a shared autonomy setting. In many domains, 2 teaching a robot to be fully autonomous can be counterproductive if there are 3 ﬁnitely many tasks to be done. Rather, the optimal strategy is to weigh the cost 4 of teaching a robot and its beneﬁt - how many new tasks it allows the robot to 5 solve autonomously. We formulate this as a planning problem where the goal is to 6 decide what tasks the robot should do autonomously (act), what tasks should be 7 delegated to a human (delegate) and what tasks the robot should be taught (learn) 8 so as to complete all the given tasks with minimum effort. This planning prob-9 lem results in a search tree that grows exponentially with n – making standard 10 graph search algorithms intractable. We address this by converting the problem 11 into a mixed integer program that can be solved efﬁciently using off-the-shelf 12 solvers with bounds on solution quality. To predict the beneﬁt of learning, we 13 use an approximate simulation model of the tasks to train a precondition model 14 that is parameterized by the training task. Finally, we evaluate our approach on 15 peg insertion and Lego stacking tasks-both in simulation and real-world, showing 16 substantial savings in human effort. 17
Previously titled: Characterization of CTX-M-15- Klebsiella pneumoniae from the inpatients and outpatients of a teaching hospital

Background The presence of Extended-spectrum β-lactamase (ESBL) positive bacteria in hospital setting is an aggravating influential factor for hospitalized patients, and its consequences may be hazardous. Therefore, there is a need for rapid detection methods for newly emerging drug-resistant bacteria. This study was aimed at the molecular characterization of ESBL-positive Klebsiella pneumoniae isolates recovered from clinical samples. Methods A total of 513 K. pneumoniae isolates were obtained from various clinical samples during June 2019 to May 2020. The collected isolates were investigated for antimicrobial susceptibility (antibiogram), and PCR and DNA sequencing were performed to analyse the ESBL genes. Results Among the 513 isolates, as many as 359 (69.9%) were ESBL producers and 87.5% were multi-drug resistant, while none had resistance to imipenem. PCR scored 3% blaTEM, 3% blaSHV, and 60% blaCTX-M-15 Open Peer Review
Dialogue Acts in Design Conversations

In the studies of design brainstorming meetings, researchers have identiﬁed twelve common Dialogue Act (DA) types that occur. These include things like “Ques-tions” and “Yes-And” Statements, among others. The distribution of dialogue acts within a meeting may help determine whether the meeting was productive. In this paper, we set out to explore these distributions, and their impact on productivity. We further build a classiﬁer to predict dialogue acts given a transcript, with the goal that this clas-siﬁer will help in the intensive and expensive labelling process of design dialogue acts. This paper explores what features are most predictive of a certain dialogue act, and also provides researchers with a tool to label data at scale.