Miracle-dz/UniFAR-FTUs huggingface dataset:
Transferring Deep Reinforcement Learning from a Game Engine Simulation for Robots

Simulations are used to gather data for machine learning algorithms at low cost. However, many robot simulators can not render realistic graphics. Realistic image data is essential for several robots using reinforcement learning algorithms in fields such as self-driving cars, agricultural weed detection and grasping objects. In this report, we propose the use of modern game engines with highly realistic rendering for simulating robots and training them with deep reinforcement learning using image data. We successfully simulated and trained a Turtlebot2 robot in Unity3D with Deep Q-learning to find and drive into a blue ball. The resulting reinforcement learning model was used to control a real Turtlebot2 robot. The simulated and real robot are interchangeable by design, making it easy to use the reinforcement algorithm to control either. The real robot was controllable by the Q-learning algorithm, but not able to perform the task. The use of modern game engines for simulation of robots for reinforcement learning is shown to be promising. However, for future work, testing of more realistic simulation environments are needed to assess the usability of game engines for realistically simulating robots.
ERROR ESTIMATION OF CLOUD MASK RESULTS FROM MULTISPECTRAL THRESHOLDING SCHEMES

Cloud masking remains as one of the most fundamental steps for any attempt to retrieve geophysical parameters for Earth surfaces or for cloud-free portions of the atmosphere from satellite imagery. It also forms the basis for specific studies of clouds and their properties in climatological applications. Because of its fundamental importance it also means that potential errors in cloud masking may lead to serious errors in retrieved geophysical parameters. It is therefore important to try to minimise these errors and to make attempts to understand and describe the error propagation further downstream in the processing chain. Many of today’s operational cloud masking methods are non-parametric, i.e., make the decision of if a pixel is cloudy or cloud-free based on a multispectral sorting (or thresholding) of data rather than by a numerical calculation process directly estimating the likelihood of cloudiness. This means that there is no direct measure of how certain the cloud mask estimation is. On the other hand, methods that offer such uncertainty estimation (e.g., Bayesian Maximum Likelihood schemes and Optimal Estimation schemes) require extensive training and very accurate knowledge of cloudy properties in radiance space for being able to describe realistic error estimates. Furthermore, these methods are computationally very expensive, especially if handling information from many spectral channels. This presentation gives some ideas on how to extend results from thresholding schemes with parametric uncertainty estimations. The main concept is based on the fact that many current thresholding schemes have undergone extensive tuning and validation which means that the identification of cloudy regimes in radiance space is relatively well understood. Consequently, a method based on estimating the distance from chosen thresholds values in radiance space should have a potential of describing the uncertainty of the cloud mask decision. Previous attempts here have been restricted to defining some specific quality flags but here a more continuous quality parameter is considered. Examples and applications of this method are demonstrated using cloud mask results from the NWCSAF PPS cloud masking scheme applied to global AVHRR high-resolution (LAC) and coarse resolution (GAC) data.
Political competition and government responsiveness: Evidence from rural India

The effect of political competition on the provision of public goods is empirically quite robust but inconclusive. On the one hand, in two-party elections, political competition induces efficiency in policy-making through lower deadweight costs. On the other hand, in multi-party elections with plurality voting, increased political competition might favour targeted transfers instead of broad based public goods such as healthcare. Moreover, weak institutions create political market frictions that encourage incumbents to transfer targeted benefits at the cost of public goods. In this paper, we examine the effect of political competition, measured by the ‘effective number of parties’ (ENP), on the public provision of healthcare at the local level in India. After accounting for endogeneity, we find that a one standard deviation increase in ENP, results in a 10-22 percentage point decline in the provision of primary and secondary healthcare facilities. Our results are robust to several checks.
Multi-Step Decentralized Domain Adaptation

Despite the recent breakthroughs in unsupervised domain adaptation (uDA), no prior work has studied the challenges of applying these methods in practical machine learning scenarios. In this paper, we highlight two significant bottlenecks for uDA, namely excessive centralization and poor support for distributed domain datasets. Our proposed framework, MDDA, is powered by a novel collaborator selection algorithm and an effective distributed adversarial training method, and allows for uDA methods to work in a decentralized and privacy-preserving way.
Novel Therapeutic Game Controller for Telerehabilitation of Spastic Hands: Two Case Studies

Spasticity or increase in muscle tone presents challenges during post-stroke rehabilitation. The use of off-the-shelf game controllers is problematic for individuals with hand spasticity. The novel BrightBrainer Grasp (BBG) controller was designed to overcome these barriers when used in virtual rehabilitation of individuals post-stroke. The custom controller measures power grasp, finger extension, wrist position and orientation, as well as 3D hand position. It is designed to minimize friction when used by those with no gravity bearing. Two controllers were used in bilateral training. This paper presents a detailed description of the BBG controllers and their interaction with the BrightBrainer™ gaming system. Two case reports of individuals chronic post-stroke who participated in a 4-week telerehabilitation intervention are included.
Sustainability of current account deficit in Fiji Islands: An empirical analysis

This study examines the sustainability of the current account deficit in the Fiji Islands. The empirical analysis is conducted utilizing the recent time series quantitative technique and annual data over the 1980 to 2013 period. This is of a significant concern because the convergence of national export and national import in the long run would ensure that the current account deficit is sustainable. It is found that while exports and imports for the Fiji Islands are cointegrated, the estimated coefficient of export is less than unity. These findings demonstrate that Fiji Islands only fulfills the necessary condition of intertemporal budget constraint and that the current account deficit is weakly sustainable. We propose that government shall take apposite policy actions to achieve sustainable current account position.
Ensuring Community-Level Research Protections

Community-Campus Partnerships for Health (CCPH) and the Tuskegee University National Center for Bioethics in Research and Health Care (the Bioethics Center) sponsored an Educational Conference Call Series on Institutional Review Boards (IRBs) and Ethical Issues in Research that took place between February 2007 and June 2007, which advanced their shared goal of meaningfully involving communities in decisions made about every aspect of research. The call series was intended to increase understanding of the role of IRBs and other mechanisms for assuring that human subjects research is ethical and appropriate - both at individual and community levels.
Stromal Cell-Derived Factor 1 Protects Brain Vascular Endothelial Cells from Radiation-Induced Brain Damage

Stromal cell-derived factor 1 (SDF-1) and its main receptor, CXC chemokine receptor 4 (CXCR4), play a critical role in endothelial cell function regulation during cardiogenesis, angiogenesis, and reendothelialization after injury. The expression of CXCR4 and SDF-1 in brain endothelial cells decreases due to ionizing radiation treatment and aging. SDF-1 protein treatment in the senescent and radiation-damaged cells reduced several senescence phenotypes, such as decreased cell proliferation, upregulated p53 and p21 expression, and increased senescence-associated beta-galactosidase (SA-β-gal) activity, through CXCR4-dependent signaling. By inhibiting extracellular signal-regulated kinase (ERK) and signal transducer and activator of transcription protein 3 (STAT3), we confirmed that activation of both is important in recovery by SDF-1-related mechanisms. A CXCR4 agonist, ATI2341, protected brain endothelial cells from radiation-induced damage. In irradiation-damaged tissue, ATI2341 treatment inhibited cell death in the villi of the small intestine and decreased SA-β-gal activity in arterial tissue. An ischemic injury experiment revealed no decrease in blood flow by irradiation in ATI2341-administrated mice. ATI2341 treatment specifically affected CXCR4 action in mouse brain vessels and partially restored normal cognitive ability in irradiated mice. These results demonstrate that SDF-1 and ATI2341 may offer potential therapeutic approaches to recover tissues damaged during chemotherapy or radiotherapy, particularly by protecting vascular endothelial cells.
VUSAA: AN AUGMENTED REALITY MOBILE APP FOR URBAN SOUNDWALKS

This paper presents VUSAA, an augmented reality soundwalking application for Apple iOS Devices. The application is based on the idea of Urban Sonic Acupuncture, providing site-aware generative audio content aligned with the present sonic environment. The sound-generating algorithm was implemented in Kronos, a declarative programming language for musical signal processing. We discuss the conceptual framework and implementation of the application, along with the practical considerations of deploying it via a commercial platform. We present results from a number of soundwalks so far organized and outline an approach to develop new models for urban dwelling.
Key Points : 12 Broadband ocean bottom seismic observation is conducted in the NW Pacific Ocean

31 We conducted broadband dispersion survey by deploying two arrays of broadband ocean 32 bottom seismometers in the northwestern Pacific Ocean at seafloor ages of 130 and 140 Ma. By 33 combining ambient noise and teleseismic surface waves analyses, dispersion curves of Rayleigh 34 waves were obtained at a period range of 5—100 s, and then used to invert for one-dimensional 35 isotropic and azimuthally anisotropic βV (VSV ) profiles beneath each array. The obtained 36 profiles show ~2% difference in isotropic βV in the low-velocity zone (LVZ) at a depth range 37 of 80—150 km in spite of the small difference in seafloor ages and the horizontal distance of 38 ~1000 km. Forward dispersion-curve calculation for thermal models indicates that simple 39 cooling models cannot explain the observed difference and an additional mechanism, such as 40 sub-lithospheric small-scale convection (SSC), is required. In addition, the fastest azimuths of 41 azimuthal anisotropy in the LVZ significantly deviate from the current plate motion direction. 42 We infer that these observations are consistent with the presence of SSC beneath the study area. 43 As for azimuthal anisotropy in the Lid, the peak-to-peak intensity is 3—4% at the depth from 44 Moho to ~40 km. The fastest direction is almost perpendicular to magnetic lineation in area A at 45 130 Ma, and oblique to magnetic lineations in area B at 140 Ma, suggesting complex mantle 46 flow beneath the infant Pacific Plate surrounded by three ridge axes. The intensity of azimuthal 47 anisotropy in the LVZ is ~2%, indicating radial anisotropy is stronger than azimuthal anisotropy 48 therein. 49
Robot Reinforcement Learning using Crowdsourced Rewards

This work is inspired by the idea that robots should possess the ability to learn new skills with minimum guidance from a dedicated human teacher and they should enhance their abilities by utilizing external resources such as the Internet. In this paper we describe our approach for achieving robot learning by combining basic skill transfer through physical demonstrations, reinforcement learning and web-based crowdsource rewards obtained via remote laboratory. We demonstrate how a robot that is taught very basic motion skills through kinesthetic teaching (i.e. lift, push, etc.), can learn to manipulate several objects through reinforcement learning by taking advantage of the feedback from multiple non-expert crowd workers. Although reinforcement learning is commonly intractable for real robot applications due to high exploration times, we demonstrate that by transferring the training to the crowd, it is possible to achieve robot learning while minimizing the effort of a dedicated teacher. This paper describes the system architecture, as well as the integration of our application to a current remote laboratory framework (Robot Management System). Experimental results with a real robot performing a time consuming learning task show a successful learning performance using crowdsouce feedback.
Plant growth promoting traits of rhizobacteria isolated from potato (Solanum tuberosum L.) and their antifungal activity against Fusarium oxysporum.

Rhizosphere bacteria are known to augment plant growth and suppress plant diseases. Current study was designed to evaluate the plant growth promoting and disease suppressing traits of indigenous bacteria from the rhizosphere of potato, Solanum tuberosum. For this purpose, out of 160 isolates, 20were further investigated to determine their ability for producing hydrogen cyanide (HCN), ammonia (NH3), indole 3-acetic acid (IAA), lytic enzymes, solubilize phosphate and antagonize Fusarium oxysporum. Results revealed that 12 isolates were able to solubilize phosphate thereby creating halo zone between 1.00-2.40 mm, where PRP-6 caused maximum solubilization. Three strains, PRP-6, PRS-17 and PRS24 were able to produce IAA, but the concentration was significantly different (p<0.01) from each other. Majority of the isolates exhibited strong potential (++) for producing NH3 and HCN. Lytic enzymes production trait was observed in 83% of isolates but with varying potentials (weak, medium, strong). Overall, the percentage growth inhibition of F. oxysporum by bacterial isolates ranged from 13-46%, where PRP-6, PRP-13displayed maximum and minimum antagonistic potential respectively. Our results indicate that novel strains from the potato rhizosphere exhibit the plant growth promoting rhizobacteria (PGPR) traits which could be used for the development of potato biofertilizers and as biocontrol agents to antagonize F. oxysporum.
Potential impacts of climate and environmental change

11 The changing climatic patterns and increasing human population within 12 the Lake Victoria Basin (LVB), together with overexploitation of water for 13 economic activities call for assessment of water management for the entire 14 basin. This study focused on the analysis of a combination of available 15 in-situ climate data, Gravity And Climate Experiment (GRACE), Tropical 16 Rainfall Measuring Mission (TRMM) observations, and high resolution Re17 gional Climate simulations during recent decade(s) to assess the water storage 18 changes within LVB that may be linked to recent climatic variability/changes 19 and anomalies. We employed trend analysis, principal component analysis 20 (PCA), and temporal/spatial correlations to explore the associations and 21 co-variability among LVB stored water, rainfall variability, and large scale 22 forcings associated with El-Niño/Southern Oscillation (ENSO) and Indian 23 Ocean Dipole (IOD). Potential economic impacts of human and climate24 induced changes in LVB stored water are also explored. 25 Email address: J.Awange@curtin.edu. au (J.L. Awange) Preprint submitted to Water Resources Research October 2, 2013 Journal of Research Resources; How to Cite Awange, J. L. , R. Anyah, N. Agola, E. Forootan, and P. Omondi (2013), Potential impacts of climate and environmental change on the stored water of Lake Victoria Basin and economic implications, Water Resour. Res., 49, 8160–8173, doi:10.1002/2013WR014350. Find the final version from: URL: http://onlinelibrary.wiley. com/doi/10.1002/2013WR014350/abstract Overall observed in-situ rainfall from lake-shore stations showed a modest 26 increasing trend during the recent decades. The dominant patterns of rainfall 27 data from the TRMM satellite estimates suggest that the spatial and tem28 poral distribution of precipitation have not changed much during the period 29 of 1998-2012 over the basin consistent with in-situ observations. However, 30 GRACE-derived water storage changes over LVB indicate an average de31 cline of 38.2 mm/yr for 2003-2006, likely due to the extension of the Owen 32 Fall/Nalubale dam, and an increase of 4.5 mm/yr over 2007-2013, likely due 33 to two massive rainfalls in 2006-2007 and 2010-2011. The temporal correla34 tions between rainfall and ENSO/IOD indices during the study period, based 35 on TRMM and model simulations, suggest significant influence of large scale 36 forcing on LVB rainfall, and thus stored water. The contributions of ENSO 37 and IOD on the amplitude of TRMM-rainfall and GRACE-derived water 38 storage changes, for the period of 2003-2013, are estimated to be ∼ 2.5 cm 39 and ∼ 1.5 cm, respectively. 40
Quantized Transformer

Transformer is a powerful architecture for various NLP tasks. However, it requires significant amount of computation and memory resources. In this work, we study how quantized transformer perform on machine translation, sentence classification, and question answering tasks. We show that transformer can be quantized to 8 bits with little loss of performance. When training data is scarce, 8 bits quantization can act as regularizer and improve performance. 8 bits quantized model is 4x smaller than 32 bits model. This will enable deployment of large transformer model on server. With optimized INT8 matrix kernel, 8 bits quantized transformer can also run efficiently on mobile devices, making it possible to translate offline.
Rational Contextual Choices under Imperfect Perception of Attributes

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Mechanistic modelling of cropland and grassland ecosystems: Focus on the carbon cycle and on plant phenology

One of the greatest challenges our society is currently facing is climate change, its causes and consequences. The agriculture sector is a major contributor to climate change and is increasingly significant as food demand steadily grows. Yet, the effects of climate change on the agroecosystems, the response of these complex ecosystems as well as the feedbacks between these ecosystems and the atmosphere are still not fully understood. In this regard, mechanistic models are a valuable tool for analysis, understanding and generating future predictions. As a result of a collaborative work, the Terrestrial Agroecosystems Dynamics Analysis (TADA) model was developed. This model simulates the carbon, water and nitrogen fluxes of a grassland or a cropland as well as the plant development. The objectives specific to this thesis were the implementation of processes related to the carbon cycle and to the plant phenology in this mechanistic model. Moreover, some of these processes (plant phenology, diffuse fraction of global radiation and gross assimilation) were calibrated and validated against data measured in experimental sites managed by Gembloux Agro-Bio Tech. At the end of this work, the gross primary productivity simulated for a winter wheat crop was clearly underestimated (RMSE = 7.3 gC m−2 day−1, r2 = 0.7) whereas the gross primary productivity was of a correct order of magnitude for a grazed grassland (RMSE = 4.57 gC m−2 day−1, r2 = 0.86) and the intra-day variability was well represented. The inaccuracy of the cropland results was attributed to the incorrectness of the model inputs, making the calibration and validation improper. Frequent measurements are recommended in order to better adjust this module. Furthermore, this work identifies several improvements that are essential to the development of the TADA model. Mechanistic modelling Two-leaf assimilation Winter wheat phenology Diffuse fraction of global radiation
Enzymatic Production of Biodiesel Using Immobilized Lipase on Core-Shell Structured Fe3O4@MIL-100(Fe) Composites

In this research, core–shell structured Fe3O4@MIL-100(Fe) composites were prepared by coating Fe3O4 magnetite with porous MIL-100(Fe) metal-organic framework (MOF) material, which were then utilized as magnetic supports for the covalent immobilization of the lipase from Candida rugosa through amide linkages. By using the carbodiimide/hydroxysulfosuccinimide (EDC/NHS) activation strategy, the lipase immobilization efficiency could reach 83.1%, with an activity recovery of 63.5%. The magnetic Fe3O4@MIL-100(Fe) composite and immobilized lipase were characterized by several techniques. The characterization results showed that the Fe3O4 core was coated with MIL-100(Fe) shell with the formation of perfect core–shell structured composites, and moreover, the lipase was covalently tethered on the magnetic carrier. The immobilized lipase displayed a strong magnetic response and could be facilely separated by an external magnetic field. With this magnetic biocatalyst, the maximum biodiesel conversion attained 92.3% at a methanol/oil molar ratio of 4:1, with a three-step methanol addition manner, and a reaction temperature of 40 °C. Moreover, the biocatalyst prepared in the present study was recycled easily by magnetic separation without significant mass loss, and displayed 83.6% of its initial activity as it was reused for five runs, thus allowing its potential application for the cleaner production of biodiesel.