Miracle-dz/UniFAR-FTUs huggingface dataset:
Base station searching for manipulators with huge objective workpieces

Although industrial manipulators are widely utilized on transport and assembly, applying paint to large objects, such as ships or aircraft, is challenging because of the limited workspace available. We made a trial to place a manipulator on a mobile coordinative platform to enable its workspace to be greatly extended. Then, a difficult problem is put forward that it generally takes a long time to find the optimal stop position for the manipulator based on the experience of the operator. To address this problem, we present a method for determining the appropriate stop position of the platform under the manipulator based on the observation that the manipulator could obtain greater flexibility when its joints are closer to the original configuration. In this paper, a search algorithm for initial stations is first presented, followed by optimization with consideration of the manipulator's reachability. The results of the experimental simulation are then discussed, and an evaluation of the planning quality and efficiency is performed.
D2D broadcast communications for 4G PMR networks

As professional mobile radio (PMR) networks evolve to a new generation based on Long Term Evolution (LTE), a new direct communication mode needs to be developed. Not only in PMR networks, but the increase of proximity services due to the explosion of the internet of things bring us to a new communication paradigm. The PMR users have very specific requirements and one of those is the ability to communicate in a direct way with all the group members, even in out-of-coverage scenarios. In the current PMR standards, communication degradations due to collisions can occur when there is no infrastructure available, especially in dense scenarios. In this article, we propose a mechanism that allows device-to-device (D2D) broadcast communications, even in out-of-coverage scenarios. The mechanism presented hereafter improves the data rate compared to the current PMR mechanism, by reducing the number of collisions using the LTE physical resource block (RB) structure.
Event Ordering and Congestion Control for Distributed Multiplayer Games

Abstract—Peer-to-peer, multiplayer games require secureevent ordering, bounded latency between any two players, anda real-time transport protocol that includes congestion control.In the past, research has focused on solving one of these threeissues, but has yet to address all of them in conjunction. In thispaper, we design the NEO protocol to provide all three of thesefeatures in a fully-distributed, peer-to-peer game. We ﬁrst prov ethat NEO can prevent ﬁve common, protocol-level cheats. Wethen use simulation to demonstrate that NEO can adhere to thelatency restraints of a real-time, interactive application. Finally,we conduct an extensive simulation study to show that NEO hasa stable congestion control algorithm that can react to changingnetwork conditions and maintain fairness with TCP trafﬁc. Tothe best of our knowledge, NEO is the ﬁrst protocol that canmeet all of these requirements. I. I NTRODUCTION Secure event ordering, bounded latency, and congestion con-trol are three fundamental requirements for fully distributed,multi-player game network protocols. Secure event orderingprevents a number of cheats that can be performed by playersthrough the simple modiﬁcation of packet streams. Boundedlatency ensures that updates are delivered in a timely manner,allowing the game to retain its interactive nature. With theaddition of congestion control, we can ensure that the evergrowing stream of multi-player games on the Internet sharebandwidth fairly with TCP applications.Traditionally, multi-player games have used a client/servercommunication architecture. This architecture has the advan-tage that a single authority orders events, resolves conﬂic tsin the simulation, acts as a central repository for data, and iseasy to secure. On the other hand, this architecture has severaldisadvantages. First, it introduces delay because messagesbetween players are always forwarded through the server.Second, trafﬁc at the server increases with the number ofplayers, creating localized congestion. Third, players musttrust that the server is not tainted. Last, this architecture islimited by the computational power of the server. While wecan throw technology at most of these problems in the form ofmore servers and higher bandwidth lines, this solution incurssigniﬁcant cost and is not a viable option for the average pla yerwho desires to host a game.Researchers have proposed distributed architectures to ad-dress the shortcomings of the client/server architecture [1]–[4]. These architectures allow peers to send messages directlyto each other, reducing delay for messages and eliminating
Competitive Screening in Insurance Markets with Endogenous Labor Supply

We examine equilibria in competitive insurance markets when individuals take unobservable labor supply decisions. Precautionary labor motives intro-duce countervailing incentives in the insurance market, and equilibria with positive profits can occur even in the standard case in which individuals exogenously differ in risk only. We then extend the model to allow for both privately known risks and labor productivities. This endogenously introduces two-dimensional heterogeneity in the insurance market since precautionary labor effects lead to differences in income and hence risk aversion. Under these circumstances, separating and pooling equilibria exist, which generally differ from those with exogenous two-dimensional heterogeneity considered by the existing literature. Notably, in contrast to standard screening models, profits may be increasing with insurance coverage, and the correlation between risk and coverage can be zero or negative in equilibrium, a phenomenon frequently observed in empirical studies.
Predicting world coordinates of pixels in RGB images using Convolutional Neural Network for camera relocalization

Convolutional Neural Networks (CNNs) have achieved great successes in many computer vision tasks and have been applied to pose regression for camera relocalization. Traditional Simultaneously Localization and Mapping (SLAM) approaches use correspondences between camera coordinates and world coordinates to estimate camera pose. In this paper, we present a new camera relocalization method including pixels' world coordinates regression with CNNs and camera pose optimization. We also explore the different characteristics of CNNs and SCoRe Forests on world coordinates regression. Experiments show that our approach has larger camera relocalization error but better performance on predicting world coordinates of pixels compared to SCoRe Forests.
Intra-Firm Retail Contracting: Survey Evidence from the UK'

This paper looks at intra-firm contractual practices in retailing. It examines existing explanations, illustrates some limitations, and proposes a new hypothesis to explain the adoption of both vertical integration and delegation within firms. This focuses on the human capital of potential retailers i.e., on the existence of a ‘workers’ selection mechanism’ to determine the (upstream and downstream) contract choice. This hypothesis is supported by evidence from a survey of UK companies conducted for the purpose of this study. Further hypotheses are tested by means of econometric analysis.
Flow-split: An approach to reduce flow establish time and invoking of controller in OpenFlow networks

In OpenFlow networks, the controller configures the forwarding of packets and gets states and statistics of the network from operating on flow entries in flow tables of switches. This centralized controlling brings such a problem. If flow entries matching a wide range of packets is installed, the controller cannot get statistics of precise flows. If flow entries to match a precise range are used, lots of misses are happened, which invokes the controller frequently and affects the performance of packet forwarding. In this paper, we propose and implement a new OpenFlow action: flow-split to solve this problem. This action can generate new flow entries in the switch according to its configurations and the header fields of the matched packets. By installing flows using flow-split action that match a wide range, the controller can still get the statistics of more precise flows from the generated precise flow entries, and reduce the invoking of the controller. Experiments on emulated networks shows that flow entries with flow-split action achieves a significant reduction on flow establish time and involvement of the controller.
Integration of a Physics Engine into an Adventure Game Platform

This paper presents the challenges and possibilities that arise from the inte gration of a 2D physics engine into an adventure game platform (i.e. engine and editor). This integration allows the use of a complex physics model within a narrative environment, thus increasing expressiveness and educational potential (through re-usability and increased challenge). In this work eAdventure is used as the adventure game platform and Box2D is used as the physics engine. While adventure games have been successfully applied in educational contexts from language learning to medicine, including a physics engine broadens the number of subjects that can be covered, thus increasing the scope and potential of the resulting games. This paper shows how such integration was implemented, and shows several mini-games that use the physics engine to provide visualization of physical experiments and attempt to increase player engagement. The mini-game approach allows for the easy integration of these new physics-based interactive scenarios within an adventure game narrative, where each mini-game can represent a puzzle or challenge to be solved.
Image Compression Based on Spatial Redundancy Removal and Reconstruction using Image Inpainting

In any digital communication system, the efficient transmission of an image over a channel involves source coding which represents the image in the compression form at the transmitter side. An information-carrying signal may be compressed by removing redundancy from the signal. In a lossless compression system statistical redundancy is removed so that the original signal can be perfectly reconstructed at the receiver. We present an image compression algorithm that integrates a novel technique for filling-in the missing information in an image. Firstly, the information in image regions is removed to reduce the data. The remaining data is transmitted and is used at the receiver’s side for recovering the removed regions. At the decoder, to retrieve removed regions, an inpainting method is applied using the information extracted from the received image.
Collaborative online learning of user generated content

We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last but not least, when learning a classification model from a group of users, there is a dilemma: A single classification model trained on the entire corpus may fail to capture personalized characteristics such as language and writing styles unique to each user. On the other hand, a personalized model dedicated to each user may be inaccurate due to the scarcity of training data, especially at the very beginning; when users have written just a few articles. To overcome these challenges, we propose learning a global model over all users' data, which is then leveraged to continuously refine the individual models through a collaborative online learning approach. The class imbalance problem is addressed via a cost-sensitive learning approach. Experimental results show that our method is effective and scalable for timely classification of user generated content.
Sequence logos: a powerful, yet simple, tool

A powerful technique for analyzing DNA and protein sequences, the sequence logo, is now available to researchers. The authors explain sequence logos, to show their usefulness for biological and linguistic research, and to encourage their use by others. A sequence logo is a graphic representation of the amount of information to be found in a set of DNA, RNA, or protein sequences. This method has advantages over the conventional method of creating a consensus. For example, a logo of DNA shows all the bases found in the binding site of a protein, indicates how much each base is conserved, and depicts the location of the conservation with respect to the major and minor grooves of the DNA. <<ETX>>
Fast events in single‐channel currents activated by acetylcholine and its analogues at the frog muscle end‐plate.

The fine structure of ion‐channel activations by junctional nicotinic receptors in adult frog muscle fibres has been investigated. The agonists used were acetylcholine (ACh), carbachol (CCh), suberyldicholine (SubCh) and decan‐1,10‐dicarboxylic acid dicholine ester (DecCh). Individual activations (bursts) were interrupted by short closed periods; the distribution of their durations showed a major fast component ('short gaps') and a minor slower component ('intermediate gaps'). The mean duration of both short and intermediate gaps was dependent on the nature of the agonist. For short gaps the mean durations (microseconds) were: ACh, 20; SubCh, 43; DecCh, 71; CCh, 13. The mean number of short gaps per burst were: ACh, 1.9; SubCh, 4.1; DecCh, 2.0. The mean number of short gaps per burst, and the mean number per unit open time, were dependent on the nature of the agonist, but showed little dependence on agonist concentration or membrane potential for ACh, SubCh and DecCh. The short gaps in CCh increased in frequency with agonist concentration and were mainly produced by channel blockages by CCh itself. Partially open channels (subconductance states) were clearly resolved rarely (0.4% of gaps within bursts) but regularly. Conductances of 18% (most commonly) and 71% of the main value were found. However, most short gaps were probably full closures. The distribution of burst lengths had two components. The faster component represented mainly isolated short openings that were much more common at low agonist concentrations. The slower component represented bursts of longer openings. Except at very low concentrations more than 85% of activations were of this type, which corresponds to the 'channel lifetime' found by noise analysis. The frequency of channel openings increased slightly with hyperpolarization. The short gaps during activations were little affected when (a) the [H+]o or [Ca2+]o were reduced to 1/10th of normal, (b) when extracellular Ca2+ was replaced by Mg2+, (c) when the [Cl‐]i was raised or (d) when, in one experiment on an isolated inside‐out patch, the normal intracellular constituents were replaced by KCl. Reduction of [Ca2+]O to 1/10 of normal increased the single‐channel conductance by 50%, and considerably increased the number of intermediate gaps. No temporal asymmetry was detectable in the bursts of openings. Positive correlations were found between the lengths of successive apparent open times at low SubCh concentrations, but no correlations between burst lengths were detectable. The component of brief openings behaves, at low concentrations, as though it originates from openings of singly occupied channels. (ABSTRACT TRUNCATED AT 400 WORDS)
Chronic gastroesophageal reflux disease shares genetic background with esophageal adenocarcinoma and Barrett's esophagus

Esophageal adenocarcinoma (EA) is a rapidly fatal cancer with rising incidence in the developed world. Most EAs arise in a metaplastic epithelium, Barrett's esophagus (BE), which is associated with greatly increased risk of EA. One of the key risk factors for both BE and EA is chronic gastroesophageal reflux disease (GERD). This study used the linkage disequilibrium (LD) score regression and genomic profile risk scoring approaches to investigate the contribution of multiple common single-nucleotide polymorphisms (SNPs) to the risk of GERD, and the extent of genetic overlap between GERD and BE or EA. Using LD score regression, we estimated an overall phenotypic variance of 7% (95% CI 3–11%) for GERD explained by all the genotyped SNPs. A genetic correlation of 77% (s.e. = 24%, P = 0.0012) between GERD and BE and 88% between GERD and EA (s.e. = 25%, P = 0.0004) was estimated using the LD score regression approach. Results from the genomic profile risk scoring approach, as a robustness check, were broadly similar to those from the LD score regression. This study provides the first evidence for a polygenic basis for GERD and supports for a polygenic overlap between GERD and BE, and GERD and EA.
Building and Exploiting a Corpus of Dialog Interactions between French Speaking Virtual and Human Agents

We describe the acquisition of a dialog corpus for French based on multi-task human-machine interactions in a serious game setting. We present a tool for data collection that is configurable for multiple games; describe the data collected using this tool and the annotation schema used to annotate it; and report on the results obtained when training a classifier on the annotated data to associate each player turn with a dialog move usable by a rule based dialog manager. The collected data consists of approximately 1250 dialogs, 10454 utterances and 168509 words and will be made freely available to academic and nonprofit research.
Effects of multiple disturbances in seagrass meadows: shading decreases resilience to grazing

Ecosystem shifts are often associated with multiple disturbances, but limited knowledge on the mechanisms involved hampers management. This study investigated how short-term shading affected the resilience of the seagrass Halophila ovalis to grazing by black swans (Cygnus atratus) - a historically dominant grazer currently recovering from hunting and habitat loss - in south-western Australian estuaries, using field surveys and a grazing-shading experiment. Black swans were heterogeneously distributed and, in a high-density site, consumed 23% of seagrass production. Seagrasses recovered rapidly from a single disturbance (i.e. short-term shading or grazing), even though shading alone halved carbohydrate content. When seagrasses were exposed to both disturbances, recovery depended on the type of grazing; where grazing was confined to leaves, leaf densities recovered within 3 weeks, but where grazing was on both leaves and rhizomes there was no recovery. Shading increased the frequency of apex initiation, as did rhizome grazing, but only if the plants had not been shaded. This indicates that shading alters the flow of energy needed to produce apices and leaves following disturbance on rhizomes. Based on the historical swan densities and continuing recurring phytoplankton blooms, management actions reintroducing swans without controlling algal blooms could have an on impact seagrass resilience and associated organisms.
Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News

Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers reproduce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%.
A study on intravascular ultrasound image processing

Innovative applications of image processing techniques for analysis of intravascular ultrasound images are presented. This work has two main objectives: (1) to detect the luminal contour which is necessary to assess the degree of vessel stenosis, and (2) to detect presence of calcification, which is an important information for definition of the intervention method. These problems were addressed using a combination of mathematical morphology techniques, fuzzy systems, and a priori knowledge of the problems. Encouraging results were found when the results were compared with images manually segmented by expert medical doctors.
FairCPU: Architecture for Allocation of Virtual Machines Using Processing Features

This paper proposes an architecture to handle the allocation of virtual machines based on the processing power for heterogeneous Clouds, where there is a wide variety of CPU types. Our major contribution is a novel representation of the processing capacity in terms of the Processing Unit (PU) and the CPU usage limitation in order to isolate the processing capability from the Physical Machine (PM) where the Virtual Machine (VM) is allocated. The efficiency of the proposed architecture is validated by extensive replications of five experiments using a real private cloud. The results show that it is possible to use the proposed idea to define a PU, supported by the CPU usage limitation, to enable the VM's processing power remain at the same level regardless of the PM.
Henipavirus V Protein Association with Polo-Like Kinase Reveals Functional Overlap with STAT1 Binding and Interferon Evasion

ABSTRACT Emerging viruses in the paramyxovirus genus Henipavirus evade host antiviral responses via protein interactions between the viral V and W proteins and cellular STAT1 and STAT2 and the cytosolic RNA sensor MDA5. Polo-like kinase (PLK1) is identified as being an additional cellular partner that can bind to Nipah virus P, V, and W proteins. For both Nipah virus and Hendra virus, contact between the V protein and the PLK1 polo box domain is required for V protein phosphorylation. Results indicate that PLK1 is engaged by Nipah virus V protein amino acids 100 to 160, previously identified as being the STAT1 binding domain responsible for host interferon (IFN) signaling evasion, via a Thr-Ser-Ser-Pro motif surrounding residue 130. A distinct Ser-Thr-Pro motif surrounding residue 199 mediates the PLK1 interaction with Hendra virus V protein. Select mutations in the motif surrounding residue 130 also influenced STAT1 binding and innate immune interference, and data indicate that the V:PLK1 and V:STAT complexes are V mediated yet independent of one another. The effects of STAT1/PLK1 binding motif mutations on the function the Nipah virus P protein in directing RNA synthesis were tested. Remarkably, mutations that selectively disrupt the STAT or PLK1 interaction site have no effects on Nipah virus P protein-mediated viral RNA synthesis. Therefore, mutations targeting V protein-mediated IFN evasion will not alter the RNA synthetic capacity of the virus, supporting an attenuation strategy based on disrupting host protein interactions.