Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural End-to-End Learning of Reach for Grasp Ability with a 6-DoF Robot Arm

—We present a neural end-to-end learning approach for a reach-for-grasp task on an industrial UR5 arm. Our approach combines the generation of suitable training samples by classical inverse kinematics (IK) solvers in a simulation environment in conjunction with real images taken from the grasping setup. Samples are generated in a safe and reliable way independent of real robotic hardware. The neural architecture is based on a pre-trained VGG16 network and trained on our collected images as input and motor joint values as output. The approach is evaluated by testing the performance on two test sets of different complexity levels. Based on our results, we outline challenges and solutions when combining classical and neural visuomotor approaches.
Predicting Adverse Outcomes in End Stage Renal Disease: Machine Learning Applied to the United States Renal Data System

We examined machine learning methods to predict death within six months using data derived from the United States Renal Data System (USRDS). We specifically evaluated a generalized linear model, a support vector machine, a decision tree and a random forest evaluated within the context of K-10 fold validation using the CARET package available within the open source architecture R program. We compared these models with the feed forward neural network strategy that we previously reported on with this data set.
Effect of Oil Paint Addition on Impact Strength of the Scleral Part of the Acrylic Ocular Prosthesis

For many dental and facial restorations, acrylic resins are the materials of choice because of their appropriate physical and mechanical properties. When making the ocular prosthesis from such materials, it is essential to add the perfect shade in order to match the colour of normal eye. This, however, might have a significant effect on the mechanical properties of acrylic resins. The purpose of this study was to assess the effect of adding the white oil paint (titanium dioxide) to clear acrylic resins on their impact strength. 20 samples were constructed from heat cured acrylic resins, and divided into two groups (control and experimental) and each group had 10 samples. The first group was made from clear acrylic resin without the addition of white oil paints, and the second group (experimental) comprised the addition of 1 ml of the white oil paints to acrylic samples. Such samples were prepared with dimensions of (80 mm X10 mm X4 mm) length, width, and thickness, respectively. All the samples were tested by the Charpy's impact strength test. They were exposed to the load till the fracture occurred. All the values were analyzed using SPSS version 20, and the independent T-test was used for comparison between the 2 groups. A statistically significant decrease (P-value < 0.001) was found in the impact strength of acrylic resins after the addition of titanium dioxide oil paints (experimental group: 5.97 + 1.11, control group: 9.42+1.32 KJ/M2). This study concluded that the addition of titanium dioxide oil paint significantly reduces the impact of strength of the acrylic resin. It is suggested to use different stains which will have no negative effect on impact strength of the acrylic resin.
Progressive Recurrent Learning for Visual Recognition

Computer vision is difficult, partly because the mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn. A currently popular solution is to design a deep neural network and optimize it on a large-scale dataset. However, as the number of parameters increases, the generalization ability is often not guaranteed, e.g., the model can over-fit due to the limited amount of training data, or fail to converge because the desired function is too difficult to learn. This paper presents an effective framework named progressive recurrent learning (PRL). The core idea is similar to curriculum learning which gradually increases the difficulty of training data. We generalize it to a wide range of vision problems that were previously considered less proper to apply curriculum learning. PRL starts with inserting a recurrent prediction scheme, based on the motivation of feeding the prediction of a vision model to the same model iteratively, so that the auxiliary cues contained in it can be exploited to improve the quality of itself. In order to better optimize this framework, we start with providing perfect prediction, i.e., ground-truth, to the second stage, but gradually replace it with the prediction of the first stage. In the final status, the ground-truth information is not needed any more, so that the entire model works on the real data distribution as in the testing process. We apply PRL to two challenging visual recognition tasks, namely, object localization and semantic segmentation, and demonstrate consistent accuracy gain compared to the baseline training strategy, especially in the scenarios of more difficult vision tasks.
DGBA: Delay-Guaranteed Bitrate Adaptation for Mobile Broadcaster in Interactive Live Video Streaming

Improving the broadcaster-side performance is crucial for ensuring the video quality experience (QoE) for mobile interactive live streaming. But it is challenging in the presence of mobile network’s bandwidth fluctuation, as we need not only to ensure a high video bitrate but also to meet the broadcaster-side constraint. This paper presents Delay-Guaranteed Bitrate Adaptation (DGBA), an algorithm based on the idea of controllable bitrate adaptation used to optimize the broadcaster-side performance. We compare the performance of DGBA with a commercial bitrate adaptation algorithm by implementing these two algorithms in a prototype system. The experiment results show that DGBA can ensure 95.2% broadcaster-side delay constraint satisfaction compared to commercial algorithm’s 84.7%, while being more effective in bitrate adaptation.
Party System of Slovak Republic and its Stability after 2010 and 2012 Elections in Comparative Perspective

Abstract The main aim of this paper is to evaluate the changes in the Slovak party system after the elections to the National Council in 2012. This study is divided into two chapters. The first one deals with theoretical constructions of the classification of the party system with special focus on the party system theory by Giovanni Sartori. After the theoretical introduction, the author is trying to apply this concept on the Slovak party system. The second part of the paper is dedicated to the evaluation of volatility applying the concepts of Sarah Birch, Maurice N. Pedersen, Richard Rose, and Neil Munro on the election results. In the conclusion, the presented calculations are used in comparison with the data from the 2010 elections to verify the hypothesis that the rising volatility constitutes a new milestone in the party system of the Slovak Republic. Abstraktné Hlavný cieľom tejto štúdie je zhodnotiť zmeny v slovenskom straníckom systéme po voľbách do NR SR konaných v roku 2012. Táto štúdia je rozdelená do dvoch kapitol. Prvá sa zaoberá teoretickými konštrukciami klasifikácie straníckych systémov so špeciálnym zameraním na teóriu Giovanniho Sartoriho. Po teoretickom úvode sa autor pokúša tieto koncepty na slovenský stranícky systém. Druhá časť práce je určená na zhodnotenie volatility aplikáciou konceptov Sarach Birch, Maurica N. Pedersena, Richarda Rosa a Neila Munra na volebné výsledky. Na záver sú predložené výpočty použité v komparácii s údajmi z volieb 2010 na overenie hypotézy, že zvýšená volatilita predstavuje nový míľnik v straníckom systéme Slovenskej republiky.
River Areas Loss in Andalusia (1956-2007). A Scalar Approach.

Since the middle of the last century the river areas of Andalusia have undergone a process of degradation. This paper presents a historical and multiscale approach of the spatial distribution of river areas by using the land cover maps. At regional level, evidence of that Andalusia has lost a tenth of river areas over the last six decades. Secondly, analyzed of two local paradigmatic cases (Guadalmina river and Guadalquivir river) that can be extended to the rest of territory. The effort for the management of river areas has not been reflected in the results, being the interrelationships between water and land management (regional, sub regional and municipal) still an unresolved issue.
Genotoxicity evaluation of tilapia (Oreochromis niloticus) exposed to waters from two sites of Itajaí-Açu River (SC, Brazil)

The relationship between the genes and the effects attributed to them has been object of many studies, especially those seeking to establish the response of genes to environmental prod. The aim of this work was to establish a standard system to monitor effluents by using juvenile Nile tilapia ( Oreochromis niloticus ) as a bioindicator of genotoxicity, utilizing micronuclei test and comet assay. For this, the fish were exposed during 24, 48, 72 and 240 hours (10 days), to water samples collected at two sites of the Itajai-Acu River: Ilhota and Blumenau, in the Santa Catarina State, Brazil. For positive control the herbicide 2,4-D (75 ppm) was utilized, and the negative control of each fish were the values obtained before the exposure to the river water, termed time zero (T 0 ). Water samples from both sites of the Itajai-Acu River showed significant genotoxic effects in erythrocytes of the exposed fishes. The comet assay was a more sensitive test to detect genotoxic damage in shorter exposure times (24 and 48 hours) than the micronuclei test. Key words : genotoxicity, comet assay, micronuclei test, Oreochromis niloticus , bioindicator
Lithium Ion Conduction in PVdC-co-AN Based Polymer Blend Electrolytes Doped with Different Lithium Salts

Abstract Polymer electrolytes prepared by the complexation of lithium salts with poly(ethylene oxide) (PEO) and poly(vinylidene chloride-co-acrylonitrile) (PVdC-co-AN) will be of great use as separators in lithium polymer batteries. The amorphous nature of the blend electrolyte shows that the conductivity increases by the addition of lithium salts. The presence of C≡N and C=N in PVdC-co-AN are confirmed from the Fourier transform infrared studies. Among the various lithium salts studied, lithium trifluoro methane sulfonoimide [LiN(CF3SO2)2] based electrolyte exhibits the highest ionic conductivity of the order of 0.265 × 10−5 Scm−1 at room temperature. The sample having a maximum ionic conductivity PEO(80 wt%)/PVdC-co-AN(20 wt%)/LiN(CF3SO2)2(8 wt%) is supported by the lower optical band gap in UV-Visible analysis and low intensity in luminescence studies. Two and three dimensional topographic images of the above sample reveal the presence of micropores. Thermal stability of the prepared electrolytes is studied by thermo gravimetric/differential thermal analysis. Using differential scanning calorimetric analysis, the minimum glass transition temperature (30°C) is observed for the sample doped with LiN(CF3SO2)2. The cyclic voltammetric studies reveal the strong capacitive behavior of the prepared polymer electrolytes. The electrochemical stability windows for the prepared samples are observed using linear sweep voltammetry.
Paper Physics: HOCS Fibre: CFD-based software for fibre orientation profile optimization for conventional and dilution headboxes

This paper describes a novel fibre orientation profile optimization software. The software, called HOCS Fibre, is used to ensure fulfilment of given fibre orientation profile guarantees as well as to solve runnability problems caused by a poor fibre orientation profile, both on dilution and conventional headboxes. HOCS Fibre software has been used in more than 50 optimization cases during the last ten years. Based on end user experiences HOCS Fibre has been capable of correcting both local fibre orientation defects and large scale profile errors. Fibre orientation ranges below 4° have been achieved in most cases after the optimization.
MR video fusion: interactive 3D modeling and stitching on wide-baseline videos

A major challenge facing camera networks today is how to effectively organizing and visualizing videos in the presence of complicated network connection and overwhelming and even increasing amount of data. Previous works focus on 2D stitching or dynamic projection to 3D models, such as panorama and Augmented Virtual Environment (AVE), and haven't given an ideal solution. We present a novel method of multiple video fusion in 3D environment, which produces a highly comprehensive imagery and yields a spatio-temporal consistent scene. User initially interact with a newly designed background model named video model to register and stitch videos' background frames offline. The method then fuses the offline results to render videos in a real time manner. We demonstrate our system on 3 real scenes, each of which contains dozens of wide-baseline videos. The experimental results show that, our 3D modeling interface developed with the our presented model and method can efficiently assist the users to seamlessly integrate videos by comparing to commercial-off-the-shelf software with less operating complexity and more accurate 3D environment. The stitching method proposed by us is much more robust against the position, orientation, attribute differences among videos than the start-of-the-art methods. More importantly, this study sheds light on how to use the 3D techniques to solve 2D problems in realistic and we validate its feasibility.
Cities as Catalysts for Climate Action: The Rise of Cities as Catalysts for Effective Climate Action in a Post-Westphalian Landscape

Globalization is a dynamic, transboundary force challenging the Westphalian model of state-dominated geopolitics that has dominated world affairs for nearly 400 years. Equally problematic to state-centric international relations is global climate change, an environmental calamity that is increasingly being recognized as a threat to state security, yet cannot be solved by traditional diplomatic or military means. Consequently, an array of sub-national actors are becoming more influential in all areas of global governance, including the management of the planet’s ecological commons. This paper explores how cities are following the trajectory of this trend to establish themselves as world leaders in formulating climate change agendas. Inconsistent efforts to draft effective climate strategies at the state and international levels are contributing to this power shift, along with the ability of metro-regions to establish global networks dedicated to sound emissions reduction and climate planning strategies. Analysis further shows that urban areas are important loci of economic production and commodities output, as well as key entry points for domestic and international trade, the combination of which suggests metro-regions have the necessary capital and political wherewithal to serve as initiators of green diplomacy. Discussion follows concerning the specific intra-state and transnational efforts cities are taking to become catalysts for international climate action, as well as what unique challenges they face. CITIES AS CATALYSTS FOR CLIMATE ACTION THE RISE OF CITIES AS CATALYSTS FOR EFFECTIVE CLIMATE ACTION IN A POST-WESTPHALIAN LANDSCAPE Chris Lower Urban Studies Student University of Washington, Tacoma Urban Studies Program University of Washington Tacoma
Prospects for detecting stellar tidal disruptions with LOFAR

A star that passes too close to a super-massive black hole is shredded and an electromagnetic flare is emitted as the bound fraction of the stellar debris falls back onto the black hole. Candidate examples of such accretion flares have been observed at optical to X-ray frequencies. If a jet is launched from the accretion disk, tidal flares might be detectable at radio frequencies too. Here we estimate the luminosity of this jet using the observed properties of a sample of (candidate) tidal flares that have been found in SDSS and GALEX data. The scaling between disk and jet luminosity as observed for radio-loud quasars is used to predict a jet flux of ∼ 0.1 mJy. This flux is well observable with LOFAR and other radio telescopes.
Energy-saving attitudes and behavioural influences in large organisations : the case of Network Rail

Railways in the UK account for 1% of all electricity consumed nationally, and Network Rail consumed in excess of 400GWh in 2013/14. Large organisations need to reduce their energy consumption in order to prevent adverse climatic effects driven by anthropogenic greenhouse gas emissions. This doctoral thesis investigates attitudes and behaviours of individual Network Rail employees, relating to the adoption of energy-efficient practices and technologies, and their likely impacts on the organisation’s energy consumption. The research programme consists of two principal stages. Firstly, an exploratory approach is taken with managers of railway energy infrastructure to establish factors which may be influencing these behaviours, based on a commonly-used research framework for semi-structured interviews. Secondly, a staff survey is developed based on findings from the exploratory interviews. This is designed to test commonly-used behavioural frameworks from the sociological and psychological literature on pro-environmental behaviours in a variety of contexts, including the Theory of Planned Behaviour (Ajzen, 1991), and Theory of Interpersonal Behaviour (Triandis, 1977). This survey also tests the salience of a set of barriers to energy efficiency in organisations proposed by Sorrell et al (2000; 2004). However, analysis of collected survey data suggests that no single theory provided a strong fit with observed results. Principal components analysis and structural equation modelling suggest an array of 6 alternative factors governing energy-efficient behaviours, management practices, and technology adoption, and an alternative causal model is proposed, based on these new factors. Cluster analysis then categorises groups of employees based on their personal approaches to energy consumption. Although a combination of technological- and behavioural interventions are required to mitigate global greenhouse gas emissions (IPCC, 2014b), the findings presented here highlight how understanding the attitudinal and behavioural context in which energy consumption behaviours take place can help large organisations such as Network Rail lower their electricity consumption.
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

In this work, we consider the distributed optimization of non-smooth convex functions using a network of computing units. We investigate this problem under two regularity assumptions: (1) the Lipschitz continuity of the global objective function, and (2) the Lipschitz continuity of local individual functions. Under the local regularity assumption, we provide the first optimal first-order decentralized algorithm called multi-step primal-dual (MSPD) and its corresponding optimal convergence rate. A notable aspect of this result is that, for non-smooth functions, while the dominant term of the error is in $O(1/\sqrt{t})$, the structure of the communication network only impacts a second-order term in $O(1/t)$, where $t$ is time. In other words, the error due to limits in communication resources decreases at a fast rate even in the case of non-strongly-convex objective functions. Under the global regularity assumption, we provide a simple yet efficient algorithm called distributed randomized smoothing (DRS) based on a local smoothing of the objective function, and show that DRS is within a $d^{1/4}$ multiplicative factor of the optimal convergence rate, where $d$ is the underlying dimension.
Advanced Memory Buoyancy for Forgetful Information Systems

Knowledge workers face an ever increasing flood of informa- tion in their daily lives. To counter this and provide better support for information management and knowledge work in general, we have been investigating solutions inspired by human forgetting since 2013. These solutions are based on Semantic Desktop (SD) and Managed Forgetting(MF) technology. A key concept of the latter is the so-called Memory Buoyancy (MB), which is intended to represent an information item’s current value for the user and allows to employ forgetting mechanisms. The SD thus continuously performs information value assessment up- dating MB and triggering respective MF measures. We extended an SD- based organizational memory system, which we have been using in daily work for over seven years now, with MF mechanisms directly embedding them in daily activities, too, and enabling us to test and optimize them in real-world scenarios. In this paper, we first present our initial version of MB and discuss success and failure stories we have been experiencing with it during three years of practical usage. We learned from cognitive psychology that our previous research on context can be beneficial for MF. Thus, we created an advanced MB version especially taking user context, and in particular context switches, into account. These enhancements as well as a first prototypical implementation are presented, too.
Diversity of marine gliding bacteria in Thailand and their cytotoxicity

Eighty-four marine gliding bacteria were isolated from specimens collected in the Gulf of Thailand and the Andaman Sea. All exhibited gliding motility and swarm colonies on cultivation plates and they were purified by subculturing and micromanipulator techniques. Their 16S rRNA genes were amplified by the polymerase chain reaction (PCR) technique. The phylogenetic analysis indicated that the represented isolates can be separated into six different clads (gr 1 - gr 6) within the Cytophaga-Flavobacterium-Bacteriodes (CFB) group. Group 1 formed a remote linear, with only 90% sequence similarity, from Flavobacteriaceae bacterium which indicated a potentially novel taxonomic group. Groups 2 and 3 were identified as the recently proposed Tenacibaculum mesophilum and Fulvivirga kasyanovii respectively. Groups 4, 5 and 6, consisting of the largest number of the members, were identified as Rapidithrix thailandica , Aureispira marina and Aureispira maritima respectively. The isolates were cultivated in four different cultivation media (Vy/2, RL 1, CY and SK) and the crude extracts were submitted to screen cytotoxicity using a sulphorodamine B (SRB) assay. The results from cytotoxic screening showed that groups 2, 4 and 6 were capable of producing the cytotoxic metabolites against selected human cell lines (breast adenocarcinoma (MCF-7), colon cancer (HT-29), cervical cancer (HeLa) and oral cancer (KB)). However, groups 1, 3 and 5 did not produce metabolites with cytotoxicity when cultivated in the same cultivation media as the previous groups. CY medium was the only cultivation medium which could yield the cytotoxic metabolites against MCF-7.
Matrix-based Kernel Method for Large-scale Data Set

In the computation process of many kernel methods, one of the important step is the formation of the kernel matrix. But the size of kernel matrix scales with the number of data set, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set. To overcome computational and storage problem for large- scale data set, a new framework, matrix-based kernel method, is proposed. By initially dividing the large scale data set into small subsets, we could treat the autocorrelation matrix of each subset as the special computational unit. A novel polynomial-matrix kernel function is then adopted to compute the similarity between the data matrices in place of vectors. The proposed method can greatly reduce the size of kernel matrix, which makes its computation possible. The effectiveness is demonstrated by the experimental results on the artificial and real data set.