Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Visual-Locomotion Policies that Generalize to Diverse Environments

We address the problem of learning locomotion controllers which generalize to a diverse collection of terrains that are often found in the real world. Prior works optimize policies to succeed in a single type of terrain with limited variations and depend on observations gathered from the ground-truth simulation state (i.e., ground-truth height maps, which are difﬁcult to obtain in practice and noisy). We frame the challenge of learning generalizable locomotion controllers as a multi-task reinforcement learning problem and deﬁne each task as a type of terrain that the agent must traverse. We then propose an approach that trains an end-to-end visual locomotion policy to achieve high generalization performance. Our policies learn to navigate over many terrains that a legged robot may encounter in the real world, including stairs, rugged land, obstacles, ofﬁce environments, rooms with humans, all without requiring manual preprocessing of vision inputs.
The Impact of Youth Medicaid Eligibility on Adult Incarceration

This paper identifies an important spillover associated with public health insurance: reduced incarceration. In 1990, Congress passed legislation that increased Medicaid eligibility for individuals born after September 30, 1983. We show that Black children born just after the cutoff are 5 percent less likely to be incarcerated by age 28, driven primarily by a decrease in incarcerations connected to financially motivated offenses. Children of other races, who experienced almost no gain in Medicaid coverage as a result of the policy, demonstrate no such decline. We find that reduced incarceration in adulthood substantially offsets the initial costs of expanding eligibility. (JEL H51, I13, I18, I38, J15, K42)
Correspondence analysis approach to examine the Nobel Prize

The main goal of this study is to examine Nobel Prize data by exploring and studying the association between the Country of the nominated individual (or of the nominated team) and the Discipline in which the Nobel Prize was awarded. The sample studied comprises the eight the most developed countries that received at least one Nobel Prize in the period from 1901 to 2018; these being Canada, France, Germany, Italy, Japan, Russia, British Isles and the USA. The variables Country and Discipline are cross-classified to form a two-way contingency table. Simple correspondence analysis is performed to explore the nature of the association between these two variables.
Retention and the Monetization of Apps

Though free apps dominate mobile markets, firms struggle to monetize such products and make profits, relying on revenues from two sources: paying consumers, and paying advertisers. Which source is more attractive and when? In this paper, we present a framework that shed light on these issues and on additional questions relating to the monetization of mobile apps. When formulating such a model one needs to pay attention to three central empirical regularities about the retention of mobile apps. a) The process is inherently dynamic; b) consumers have some prior knowledge of their fit with the app, yet they remain uncertain about their exact matchutility until they are using it; and c) the match-utility of using the app in the lower in the second period than in the first. The resulting equilibrium in which user stickiness with the app plays a central role has a few insights on the monetization of apps. We find that in some cases it might be optimal for the firm to offer only one version of the app (i.e., free or paid). Specifically, when the drop in the match utility after the first period is low, it might be optimal to offer only the paid version. We discuss how this implication relates to hedonic products such as games, versus utilitarian products such as finance and business. Furthermore, we also demonstrate that in a dynamic setting, a firm can profit from offering a free version with ads even if advertisers are not paying for these ads.
Electrical and environmental stress and the hydrophobic stability of SIR, EVA and their blends

This paper treats the effects of electrical and environmental stresses on the hydrophobicity of polymers intended for use in nonceramic insulators. We investigated the effect of UV radiation, corona discharges, dry-band arcing, acid rain and water absorption on hydrophobic transfers in, and the aging of, room temperature vulcanized (RTV)-silicone rubber (SIR), high temperature vulcanized (HTV)-silicone rubber (SIR), ethylene vinyl acetate (EVA) rubber and the blends made from HTV-SIR and EVA. Acid rain had little influence on hydrophobicity in any of the samples. EVA and the blends with low silicone ratios to EVA allowed much larger reduction in hydrophobicity after or while subjected to the above stresses (except for acid rain) than did the silicone rubbers and a blend with a high silicone ratio to EVA. Salt-fog tests were conducted to correlate the hydrophobic stabilities of materials with leakage current levels thereon. The hydrophobic stability of the silicone rubbers was attributed to low-molecular-weight silicone components that were able to migrate and finally cover the aged surface layers. Because EVA possessed less fluid components and less chemical stability under electrical and environmental stresses, its hydrophobicity was readily reduced and hardly recovered, which led to higher current leakage and early material aging. For each material, the critical hydrophobic level permitting leakage current to develop was determined by adding a non-ionic wetting agent to saline water.
Investment on HRM and Its Impact on Project Success

This study was aimed to assess the impact of four major HR practices on the performance of projects of educational institutes in Gilgit Baltistan. In this regard, response of staff members in educational institutional projects has been collected through an adopted questionnaire survey. After recording the collected response in SPSS and processing the study related test, it has been found that three main HR practices are keeping the significant positive impact on the project performance of educational institutes in Gilgit Baltistan. These effective practices are compensation and rewards, training and development and employee involvement that have been assessed as they effectively been practices in their efficient practices performances of educational institutes have been seen as enhances. The one main HR practice of employee recruitment and selection have been seen as negatively associated, this has shown that the recruitment practices in educational institutes are notinlined with the HR fundamental practices. This requires serious attention for streamlining the recruitment and selection process for enhancing the functions of HRM for efficient project performance in the institutes. This study has come up with appropriate recommendations for betterment of educational institutional projects along with the directions for future research.
Sylo Network: An incentivised peer-to-peer network

With the invention of blockchain, many traditional centralised services can now be decentralised. But with this comes a change in the relationship be-tween consumers and producers, and how value is exchanged for these services. In a perfect altruistic world, producers would give up what they produce for free, but that is not always the case. We present the Sylo Network as a secure, scalable, incentivised P2P network where producers are rewarded for the services they offer.
Application of computational tools for ADME and target modeling of bioactive compounds from hydroalcoholic extracts of Erodium glaucophyllum flowers

Computational approaches based on predictive software used for computer-aided drug design to improve the quality control of drugs become a key tool in the selection and prioritization of drug targets. Seventeen identified compounds isolated from hydroalcoholic extracts of Erodium glaucophyllum flowers were subjected to in silico ADME and target prediction to evaluate their pharmacokinetics, drug-likeness and coupled target classes using Swiss ADME and Swiss Target Prediction programs. Results revealed that most of the tested compounds displayed good oral bioavailability and skin permeation suggesting that they are easily absorbed. They are also able to penetrate the blood-brain barrier and therefore to affect the central nervous system (CNS). Target REPORT 24(106), November December, 2020 Medical Science ISSN 2321–7359 EISSN 2321–7367
Satellite-Based Food Market Detection for Micronutrient Deficiency Prediction

“Hidden Hunger,” or micronutrient deficiency (MND), is a nutritional disorder caused by a lack of essential vitamin and mineral intake, often leading to severe physical and mental developmental impairment. MND is highly prevalent worldwide, particularly in South Asia, South America, and subSaharan Africa, affecting an estimated 2 billion people. While intervention programs exist to treat MND, identifying susceptible regions at a large scale may require time-intensive and costly blood draws and/or household surveys. Public health research in MND indicates that food markets are often correlated with improved dietary diversity, a proxy for MND prediction. However, data on market locations is limited. We propose an approach to automatically detect regional food markets from publicly available geographic infrastructure data and satellite imagery, which may be used towards developing a cost-effective, noninvasive MND prediction model.
Proximity, Communities, and Attributes in Social Network Visualisation

The identification of groups in social networks drawn as graphs is an important task for social scientists who wish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: that is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout.
PII: S1389-9457(01)00131-9

Objectives: Sleep disorders remain largely undiagnosed in the general population. The current study assess7ed whether the Global Sleep Assessment Questionnaire (GSAQ) could: (1), distinguish between sleep disorders (including no sleep disorder); (2), be a reliable and valid sleep disorder screener; and (3), serve as a practical, user-friendly screening tool for primary care and sleep centers. Methods: Two hundred and twelve adults from five sleep centers and two primary care clinics completed the GSAQ and received confirmed diagnoses from a sleep specialist. Of the 212 patients, 139 (65.6%) had at least one sleep disorder, 60 (28.3%) had two or more sleep disorders, and 13 (6.1%) had no confirmed sleep disorder. Ninety-one (43%) individuals completed the GSAQ a second time for reliability testing. Scores for each sleep disorder including, but not limited to, primary insomnia (I), insomnia associated with a mental disorder (IME), obstructive sleep apnea (OSA), periodic limb movement (PLM), and parasomnia (P) were computed. The sensitivity and specificity were estimated using comprehensive clinical diagnosis as the gold standard and mean domain scores as a cutpoint. Results: The mean participant age was 45 years, 52% were female. Observed frequencies were: 36 (I), 14 (IME), 31 (OSA), 7 (PLM) and 4% (P). Test–retest reliability ranged from 0.51 to 0.92. Pearson correlation coefficients suggested that the GSAQ discriminated between diagnoses. The sensitivities and specificities were 79/57, 83/51, 93/58, 93/52, and 100/49 for I, IME, OSA, PLM, and P, respectively. Conclusions: Our findings suggest that the GSAQ can aid in recognizing sleep disorders. Future studies should focus on characterizing its predictive values in primary care settings. q 2002 Elsevier Science B.V. All rights reserved.
Reconstructing Marxian Theory of Ground Rent: Based on Japanese Development of Marxian Political Economy

ABSTRACT The aim of this paper is to reconstruct Marxian theory of ground rent in order to refurbish it as a fundamental theoretical framework of ecological Marxism. We make this attempt by the close examination of longstanding Japanese discussions on the rent theory among the Koza school, Rono school, and the Uno school, which have never been made available in English so far. By critically integrating Japanese preceding studies, Marxian theory of rent would be reinforced more effectively in analysing the class relation between the capitalist and the landowner, which is reflected in the formation of absolute rent. In addition, we use the price equations to formulate the rent formation and theoretically examine how the landowner performs as the third class. It is noteworthy that there is a case in which the conflict between the capitalist and the landowner takes the form of a confrontation about rent-seeking activities. In our two-sector model analysis, an intellectual property right in one sector can undermine the economic position of the landowners in the other sector, thereby fostering further destructive development of nature. Capitalist IP rent-seeking harms the environment not only directly but also through indirect causalities in the globally interrelated economy.
Attribute Driven Temporal Active Local Online Community Detection

This research investigates how the behavior of online social users' and topical activeness vary with time and how these parameters can be employed in order to improve the quality of the detected online local community. For a given input query, comprising a query node (user) and a set of attributes, this paper intends to find a densely connected community in which community members are temporally similar in terms of their activities related to the query attributes. To address the proposed problem, we develop a temporal activity biased weight model which assigns higher weight to users' recent activities and develop an algorithm to search effective community. The effectiveness of the proposed methodology is justified using three benchmark datasets.
Administrative organization and state formation. Siena in the 14th century

This paper investigates the institutional process that characterized political change and state capacity in the late medieval independent commune of Siena. After the mid14th century demographic crisis, Italian city-states were either turning to oligarchic forms of power or they were losing independence under the pressure of external threats. Siena, instead, developed an institutional framework based on extended participation of social groups to administrative and political power. In the following two centuries the city remained independent and preserved its capacity to collect and use financial resources. Combining historical and theoretical analysis, the paper underlines the mechanisms through which reforms of administrative organization were selecting and supporting coalitional governments with high popular representation. The use of data from original registers will show that increased cooperation maintained the capacity of the commune to collect resources from the community and to provide public services to the city and the controlled territory.
A Point Cloud Registration Pipeline using Gaussian Process Regression for Bathymetric SLAM*

Point cloud registration is a means of achieving loop closure correction within a simultaneous localization and mapping (SLAM) algorithm. Data association is a critical component in point cloud registration, and can be very challenging in feature-depleted environments such as seabed. This paper presents a point cloud registration pipeline for performing loop closure correction in feature-depleted subsea environments using data collected from an optical scanner. The pipeline uses Gaussian process regression to extract keypoint sets, and a weighted network alignment algorithm to propose point correspondences. A variant of the iterative closest point (ICP) registration algorithm is used to perform fine alignment, with point correspondences informed by the mappings determined following the network alignment step. The developed registration pipeline is deployed with success on a challenging section of field data containing topography that cannot be resolved using conventional imaging sonar.
Supplement Material of Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition

We discuss the setting of K in spatial DropGraph and temproal DropGraph respectively. In the spatial graph, the total number of nodes are typically less than 30 (25 nodes for NTU-RGBD, 18 nodes for Skeleton-Kinetics). Hence, we discuss K = 0, 1, 2 in Table 1. Note that when K = 0, the spatial DropGraph degenerates into dropping isolated joints, which is not effective. By expanding the drop area to 1 neighbor nodes, spatial DropGraph provides efficient regularization. Expanding drop area to 2 neighbor may cause too strong regularization. In the temporal graph, the total number of nodes are typically more than 100 (300 frames for NTU-RGBD, 150 frames for Skeleton-Kinetics). Hence, we discuss K = 0, 5, 10, 20, 30 in Table 2. When K = 0, the temporal DropGraph degenerated to drop isolated frames, which is not efficient. Temporal DropGraph can provide efficient regularization when K = 5, 10, 20. When K = 30, the regularization is too strong. We use this setting for NTU-RGBD and NTU-RGBD-120. For NW-UCLA, we set temporal K = 5 due to the small number of frames in NW-UCLA action samples.
Categorizing Flight Paths using Data Visualization and Clustering Methodologies

This work leverages the U.S. Federal Aviation Administration's Traffic Flow Management System dataset and DV8, a recently developed tool for highly interactive visualization of air traffic data, to develop clustering algorithms for categorizing air traffic by their varying flight paths. Two clustering methodologies, a spatial-based geographic distance model, and a vector-based cosine similarity model, are demonstrated and compared for their clustering effectiveness. Examples of their applications reveal successful, realistic clustering based on automated clustering result determination and human-in-the-loop processes, with geographic distance algorithms performing better for enroute portions of flight paths and cosine similarity algorithms performing better for near-terminal operations, such as arrival paths. A point extraction technique is applied to improve computation efficiency.
Functional Analysis of the 2020 U.S. Elections on Twitter and Facebook using Machine Learning

Social Networking Sites (SNS), such as Facebook and Twitter, are important tools for political campaigns. A line of related work analyzed political campaigns online. The initial efforts in analyzing campaign discourse functions relied on human analysis, which is time consuming and does not scale well with big data. To address these gaps, we propose a model to detect the type of campaign topics: Policy vs. Character, and how the public (commentators) responded to these messages. The proposed model yielded an accuracy of 78% (F-measure) in detecting post type. Moreover, experimental results show the analysis of commentators linguistic and psychological characteristics.
EXPLORING INFORMATION PROCESSING AND DECISION MAKING FOR SUSTAINABLE SUPPLY MANAGEMENT

Since supply networks are highly complex, buying firms face information deficits regarding the actual environmental and social conduct at supplier premises. We investigate sustainable supply management from an information processing perspective to discover (1) how firms reduce sustainability-related information deficits, and (2) how a strategy to achieve that goal is chosen. Building on four in-depth case studies including 20 interviews we found that firms lower their sustainability-related information deficits by (a) reducing their information processing needs using supply chain (re)-design, standardization and supplier development, and (b) by creating information processing capacity using employee management, information exchange, IT support, and supplier evaluation. We hypothesize that decisions on which strategy to pursue are derived from idiosyncratic assessments of costs and benefits associated with the available measures.
The Determinants of Foreign Currency Debt Financing: Evidence from Korea

a Bae is Professor at the Department of Finance, College of Business Administration, Bowling Green State University in Bowling Green, OH, USA; Tel) 419-372-8714; E-mail) bae@bgsu.edu. b Kim is a doctoral student at the School of Business, Chungnam National University, Daejon, Korea and a Researcher at Public Procurement Service, Government Complex, Daejon, Korea; Tel) 82-70-40567475; E-mail) khsvega@korea.kr. c Kwon is Professor at the School of Business, Chungnam National University in Daejon, Korea; Tel) 8242-821-5533; E-mail) thk5556@cnu.ac. kr.
Approval ) Proposal P 1044 – Plain English Allergen Labelling Safety assessment Executive summary

FSANZ is preparing a proposal to consider potential changes to the Australia New Zealand Food Standards Code (the Code) that introduce requirements to use plain English allergen labelling (PEAL). As part of this proposal, consideration has been given to the clinical significance of mollusc allergy in Australia and New Zealand, which tree nuts are important in food allergy, and whether all cereals that contain gluten are also of concern for food allergy.
Characteristics of Blended Geopolymer Concrete Using Ultrafine Ground Granulated Blast Furnace Slag and Copper Slag

This paper presents the properties of blended geopolymer concrete manufactured using fly ash and ultrafine Ground Granulated Blast Furnace Slag (UFGGBFS), along with the copper slag (CPS) as replacement of fine aggregate (crushed stone sand). Various parameters considered in this study include different sodium hydroxide concentrations (10M, 12M and 14M); 0.35 as alkaline liquid to binder ratio; 2.5 as sodium silicate to sodium hydroxide ratio and cured in ambient curing condition. Further, geopolymer concrete was manufactured using fly ash as the prime source material which is replaced with UFGGBFS (0%, 5%, 10% and 15%). Copper slag has been used as replacement of fine aggregate in this study. Properties of the fresh manufactured geopolymer concrete were studied by slump test. Compressive strength of the manufactured geopolymer concrete was tested and recorded after curing for 3, 7 and 28 days. Microstructure Characterization of Geopolymer concrete specimens was done by Scanning Electron Microscope (SEM) analysis. Experimental results revealed that the addition of UFGGBFS resulted in an increased strength performance of geopolymer concrete. Also, this study demonstrated that the strength of geopolymer concrete increased with an increase in sodium hydroxide concentration. SEM results revealed that the addition of UFGGBFS resulted in a dense structure.
Multimodal hybrid and theranostic nanoparticles as smart Trojan horses to fight cancer

Adobe Stock © Jezper and Mars Lewis Cancer, together with circulatory diseases, is one of the primary causes of mortality worldwide and its incidence is expected to rise by about 70 per cent (from 14 million to 22 million new cases/year) over the next two decades. There is a continuous growth of scientific studies on the genetic and molecular bases of this severe disease on one side and on the development of new smart and nano-sized materials as theranostic (i.e. therapeutic and diagnostic) platforms on the other.