Miracle-dz/UniFAR-FTUs huggingface dataset:
You scratch my back, we'll scratch yours: Exploring "open entrepreneurship" in a private-collective community

While private-collective communities, such as open source communities engaging directly with firms, are argued to provide the “best of both worlds” for knowledge creation, little is known about how entrepreneurs, one of the driving forces of value creation in society, contribute to these communities. We conducted a multi-method exploratory case study using semi-structured interviews and archival data from a four-year period of the OpenSimulator project. We find that entrepreneurs connect themselves to diverse sets of ties, position themselves as bridges between disparate network groups, and are centrally located within the network across both time periods, making them an integral part of the success of the community.
Evidence that Herpes Simplex Virus VP16 Is Required for Viral Egress Downstream of the Initial Envelopment Event

ABSTRACT During infection with herpes simplex virus type 1 (HSV-1), VP16 serves multiple functions, including transcriptional activation of viral immediate early genes and downregulation of the virion host shutoff protein vhs. Furthermore, VP16 has been shown to be involved in some aspect of virus assembly and/or maturation. Experiments with a VP16 null virus, 8MA, suggested that VP16 plays a direct role in virion assembly, since removal of VP16 from the HSV-1 genome results in reduced levels of encapsidated DNA and a failure to produce extracellular enveloped particles. However, VP16 null mutants display a severe translational arrest due to unrestrained vhs activity, thus complicating interpretation of these data. We examine here the role of VP16 in virion assembly and egress in the context of a vhs null background, using the virus 8MA/ΔSma (VP16−vhs−). Comparison of 8MA and 8MA/ΔSma with respect to viral DNA accumulation and encapsidation and accumulation of the major capsid protein, VP5, revealed that the 8MA lethal phenotype is only partially due to uncontrolled vhs activity, indicating that VP16 is required in HSV-1 virion formation. Electron microscopy confirmed these results and further showed that VP16 is required for HSV-1 egress beyond the perinuclear space. In addition, we describe the isolation and characterization of an 8MA derivative capable of propagation on Vero cells, due to second site mutations in the vhs and UL53 (gK) genes. Taken together, these results show that VP16 is required for viral egress downstream of the initial envelopment step and further underscore the importance of VP16 in controlling vhs activity within an infected cell.
Flight Tour Planning with Recharging Optimization for Battery-operated Autonomous Drones

Autonomous drones (also known as unmanned aerial vehicles) have several advantages over ground vehicles, including agility, swiftness, and energy-efficiency, and hence are convenient for light-weight delivery and substitutions for manned missions in remote operations. It is expected that autonomous drones will be deployed in diverse applications in near future. Typical drones are electric vehicles, powered by on-board batteries. This paper presents several contributions for automated battery-operated drone management systems: (1) We conduct an empirical study to model the battery performance of drones, considering various flight scenarios. (2) We study a joint problem of flight tour planning with recharging optimization for drones with an objective to complete a tour mission for a set of sites of interest. This problem captures diverse applications of delivery and remote operations by drones. (3) We implemented our optimization algorithms in an intelligent drone management system.
Modeling Software Defect Introduction and Removal : COQUALMO ( COnstructive QUALity MOdel )

Cost, schedule and quality are highly correlated factors in software development. They basically form three sides of the same triangle. Beyond a certain point (the “Quality is Free” point), it is difficult to increase the quality without increasing eith er th cost or schedule or both for the software under development. Similarly, development schedule cannot be drastically compressed without hampering the quality of the software product and/or increasing the cost of development. Watts Humphrey, at the LA SPIN meeting in December '98, highlighted that "Measuring Productivity without caring about Quality has no meaning". Software estimation models can (and should) play an important role in facilitating the balance of cost/schedule and quality. Recognizing this important association, an attempt is being made to develop a quality model extension to COCOMO II; namely COQUALMO. An initial description of this model focusing on defect introduction was provided in [Chulani97a]. The model has evolved considerably since then and is now very well defined and calibrated to Delphi-gathered expert opinion. The data collection activity is underway and the aim is to have a statistically calibrated model by the onset of the next millennium. The many benefits of cost/quality modeling include:  Resource allocation: The primary but not the only important use of software estimation is budgeting for the development life cycle.  Tradeoff and risk analysis: An important capability is to enable ‘what-if’ analyses that demonstrate the impact of various defect removal techniques and the effects of personnel, project, product and platform characteristics on software quality. A related capability is to illuminate the cost/schedule/quality trade-offs and sensitivities of software project decisions such as scoping, staffing, tools, reuse, etc.  Time to Market initiatives: An important additional capability is to provide cost/schedule/quality planning and control by providing breakdowns by component, stage and activity to facilitate the Time To Market initiatives.  Software quality improvement investment analysis: A very important capability is to estimate the costs and defect densities and assess the return on investment of quality initiatives such as use of mature tools, peer reviews and disciplines methods. This paper presents the two sub-models, i.e. the Defect Introduction and the Defect Removal sub-models, of COQUALMO. It also illustrates the integration of COQUALMO with COCOMO II to achieve the benefits outlined above.
New Modalities in the Treatment of Ocular Allergy

Allergic conditions are common, irritating, and occasionally disabling. Allergic rhinitis, for example, has been estimated to occur in 20% of the general population in the United States. The economic impact has been on the rise with increasing occurrence of allergic conditions and increasing health care costs. Direct and indirect medical costs for allergic rhinitis are estimated at between $1.5 and $2 billion annually in the United States. In addition, allergic conditions significantly affect quality of life, cognitive and learning function, decision-making, and self-perception. Fortunately, our understanding of allergic conditions is improving. Increased understanding of the biology and immunology of the allergic response has fostered the development of new agents to control allergic inflammation. We will introduce basic immunology of the immune response and briefly discuss several new therapeutic modalities developed from our current understanding of that science.
Yin-Yang regulation of autoimmunity by DCs.

Department of Immunobiology, Biomedical Primate Research Center, P.O. Box 3306, 2280 GH Rijswijk, The Netherlands Department Immunology, Erasmus Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, The Netherlands Center for Research and Care in MS, Erasmus Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, The Netherlands Department of Molecular Cell Biology and Immunology, Vrije Universiteit Medical Center Amsterdam, P.O. Box 7057, 1081 BT Amsterdam, The Netherlands
ENGLISH-HINDI USING RNN’S

Recurrent Neural Networks are a type of Artificial Neural Networks which are adept at dealing with problems which have a temporal aspect to them. These networks exhibit dynamic properties due to their recurrent connections. Most of the advances in deep learning employ some form of Recurrent Neural Networks for their model architecture. RNN's have proven to be an effective technique in applications like computer vision and natural language processing. In this paper, we demonstrate the effectiveness of RNNs for the task of English to Hindi Machine Translation. We perform experiments using different neural network architectures employing Gated Recurrent Units, Long Short Term Memory Units and Attention Mechanism and report the results for each architecture. Our results show a substantial increase in translation quality over Rule-Based and Statistical Machine Translation approaches.
Deformed special relativity as an effective theory of measurements on quantum gravitational backgrounds

In this article we elaborate on a recently proposed interpretation of deformed special relativity (DSR) as an effective measurement theory in the presence of non-negligible (albeit small) quantum gravitational fluctuations. We provide several heuristic arguments to explain how such a new theory can emerge and discuss the possible observational consequences of this framework. Given that our discussion considers leading order corrections to the standard dispersion relations, our results apply to a very wide class of possible modifications of special relativity.
Boosting Statistical Word Alignment

This paper proposes an approach to improve statistical word alignment with the boosting method. Applying boosting to word alignment must solve two problems. The first is how to build the reference set for the training data. We propose an approach to automatically build a pseudo reference set, which can avoid manual annotation of the training set. The second is how to calculate the error rate of each individual word aligner. We solve this by calculating the error rate of a manually annotated held-out data set instead of the entire training set. In addition, the final ensemble takes into account the weights of the alignment links produced by the individual word aligners. Experimental results indicate that the boosting method proposed in this paper performs much better than the original word aligner, achieving a large error rate reduction.
A preliminary experimental study on haptic teleoperation of mobile robot with variable force feedback gain

In this paper, new force feedback rendering scheme for mobile robot teleoperation is presented. Previous research indicated the problem of the low quality of mobile robot's motion control during the teleoperation with feedback force based on obstacle range information. Human-operator's commands were distorted by the feedback force, as a result, mobile robot could not follow human-operator's intention. To solve this problem, a new force rendering approach with variable feedback gain is proposed. Force feedback gain is variable based on measured distances to the obstacle and derivatives of the distances. Simulation and experimental study showed that the variable haptic feedback improves the quality of mobile robot teleoperation. variable feedback force improved the quality of mobile robot teleoperation by making robot's trajectory smooth and accurate.
Distributed Local Multi-Aggregation and Centrality Approximation

We study local aggregation and graph analysis in distributed environments using the message passing model. We provide a flexible framework, where each of the nodes in a set $S$--which is a subset of all nodes in the network--can perform a large range of common aggregation functions in its $k$-neighborhood. We study this problem in the CONGEST model, where in each synchronous round, every node can transmit a different (but short) message to each of its neighbors. While the $k$-neighborhoods of nodes in $S$ might overlap and aggregation could cause congestion in this model, we present an algorithm that needs time $O(|S|+k)$ even when each of the nodes in $S$ performs a different aggregation on its $k$-neighborhood. The framework is not restricted to aggregation-trees such that it can be used for more advanced graph analysis. We demonstrate this by providing efficient approximations of centrality measures and approximation of minimum routing cost trees.
CoSMIC: An MDA Generative Tool for Distributed Real-time and Embedded Component Middleware and Applications

This paper presents three contributions to the challenges of applying the OMG Model Driven Architecture (MDA) to develop and deploy distributed real-time and embedded (DRE) applications. First, we motivate our MDA tool called CoSMIC, which is based on the Model Integrated Computing (MIC) paradigm that provides the intellectual foundation for MDA. Second, we describe how CoSMIC’s generative abilities can be used to configure and assemble DRE component middleware required to deploy DRE applications. Third, we delineate the challenges involved in developing CoSMIC. Based on our collective experience developing MIC tools and DRE middleware, we are confident that combining these two paradigms will yield significant advantages in developing model based DRE applications.
A temporal coding hardware implementation for spiking neural networks

Spiking Neural Networks (SNNs) models have been explored in recent years due to its biological plausibility where temporal coding plays an important role. Biological arguments and computational experiments suggest than some perceptual tasks (vision and olfaction for instance) are well performed by these models. Moreover, some other applications such as machine learning might be benefited from this approach. However, efficient simulation and implementation of SNNs still remain an open challenge. There are several issues that must be addressed, being one of them the temporal coding of real-value data itself. In order to study the possibilities of embedded real-time implementations of large scale SNNs, we have first chosen to implement a well-known coding scheme based on Gaussian Receptive Fields (GRFs) to map real-value data into spike trains. This paper proposes a configurable parallel FPGA based accelerator for GRF-based temporal coding. The proposed architecture of the hardware implementation is described in detail and implementation results, both performance and resource utilization, when mapped to a Virtex-II Pro FPGA device are reported.
Carotid chemoreceptor modulation of sympathetic vasoconstrictor outflow during exercise in healthy humans

Recently, we have shown that specific, transient carotid chemoreceptor (CC) inhibition in exercising dogs causes vasodilatation in limb muscle. The purpose of the present investigation was to determine if CC suppression reduces muscle sympathetic nerve activity (MSNA) in exercising humans. Healthy subjects (N= 7) breathed hyperoxic gas (FIO2∼1.0) for 60 s at rest and during rhythmic handgrip exercise (50% maximal voluntary contraction, 20 r.p.m.). Microneurography was used to record MSNA in the peroneal nerve. End‐tidal PCO2 was maintained at resting eupnoeic levels throughout and breathing rate was voluntarily fixed. Exercise increased heart rate (67 versus 77 beats min−1), mean blood pressure (81 versus 97 mmHg), MSNA burst frequency (28 versus 37 bursts min−1) and MSNA total minute activity (5.7 versus 9.3 units), but did not change blood lactate (0.7 versus 0.7 mm). Transient hyperoxia had no significant effect on MSNA at rest. In contrast, during exercise both MSNA burst frequency and total minute activity were significantly reduced with hyperoxia. MSNA burst frequency was reduced within 9–23 s of end‐tidal PO2 exceeding 250 mmHg. The average nadir in MSNA burst frequency and total minute activity was −28 ± 2% and −39 ± 7%, respectively, below steady state normoxic values. Blood pressure was unchanged with hyperoxia at rest or during exercise. CC stimulation with transient hypoxia increased MSNA with a similar time delay to that obtained with CC inhibition via hyperoxia. Consistent with previous animal work, these data indicate that the CC contributes to exercise‐induced increases in sympathetic vasoconstrictor outflow.
AI for dynamic team-mate adaptation in games

There is a long tradition of developing games in which the difficulty level is dynamically adapted to the performance of human players. However, there has been less work on the creation of game systems that perform dynamic team-mate adaption - and even less on developing team-mate NPCs (Non Player Characters) that adaptively support players in the face of opponents that adaptively increase the difficulty for the player. This paper is based on preliminary research to identify the key elements involved in developing “buddy” NPC team-mates that dynamically adapt to the needs and behaviors of human players while cooperating to compete against adaptive AI opponents. We discuss the computational and design challenges involved in developing such agents in the context of a simple test game called Capture the Gunner (CTG). The main contributions of the paper include: a proposed vocabulary and framework for understanding/modeling team-mate systems with adaptive difficulty, a particular technique for adaptive team-mate cooperation in the face of an adaptive opponent, and the identification of several significant new issues that arise in the process of developing computer games that involve adaptive NPC team-mates that cooperate with the player in the face of adaptive opponents.
Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study.

CONTEXT There is consensus that autism spectrum disorder (ASD) is accompanied by differences in neuroanatomy. However, the neural substrates of ASD during adulthood, as well as how these relate to behavioral variation, remain poorly understood. OBJECTIVE To identify brain regions and systems associated with ASD in a large, well-characterized sample of adults. DESIGN Multicenter case-control design using quantitative magnetic resonance imaging. SETTING Medical Research Council UK Autism Imaging Multicentre Study (MRC AIMS), with sites comprising the Institute of Psychiatry, Kings College London; the Autism Research Centre, University of Cambridge; and the Autism Research Group, University of Oxford. PARTICIPANTS Eighty-nine men with ASD and 89 male control participants who did not differ significantly in mean age (26 and 28 years, respectively) and full-scale IQ (110 and 113, respectively). MAIN OUTCOME MEASURES (1) Between-group differences in regional neuroanatomy assessed by voxel-based morphometry and (2) distributed neural systems maximally correlated with ASD, as identified by partial least-squares analysis. RESULTS Adults with ASD did not differ significantly from the controls in overall brain volume, confirming the results of smaller studies of individuals in this age group without intellectual disability. However, voxelwise comparison between groups revealed that individuals with ASD had significantly increased gray matter volume in the anterior temporal and dorsolateral prefrontal regions and significant reductions in the occipital and medial parietal regions compared with controls. These regional differences in neuroanatomy were significantly correlated with the severity of specific autistic symptoms. The large-scale neuroanatomic networks maximally correlated with ASD identified by partial least-squares analysis included the regions identified by voxel-based analysis, as well as the cerebellum, basal ganglia, amygdala, inferior parietal lobe, cingulate cortex, and various medial, orbital, and lateral prefrontal regions. We also observed spatially distributed reductions in white matter volume in participants with ASD. CONCLUSIONS Adults with ASD have distributed differences in brain anatomy and connectivity that are associated with specific autistic features and traits. These results are compatible with the concept of autism as a syndrome characterized by atypical neural "connectivity. "
Age, Sex, and Socio-Economic Status Affect the Incidence of Pediatric Spinal Cord Injury: An Eleven-Year National Cohort Study

Background Few studies focus on pediatric spinal cord injury (SCI) and there is little information regarding the cause, anatomic level, and high risk population of SCI in children. This study aims to investigate the incidence and risk factors of pediatric SCI. Methods A nationwide cohort of 8.7 million children aged<18 years in an 11-year period was analyzed for causes, age at injury, anatomic sites, disability, and familial socio-economic factors. Incidence rates and Cox regression analysis were conducted. Results A total of 4949 SCI patients were analyzed. The incidence rates of cervical, thoracic, lumbar, and other SCI were 4.06, 0.34, 0.75, and 0.85 per 100,000 person-years, respectively. The proportional composition of gender, age, and socio-economic status of SCI patients were significantly different than those of non-SCI patients (all p<0.001). Male children were significantly more likely to have SCI than females in both the cervical and the other SCI groups [Incidence rate ratio (IRR) = 2.03 and 1.52; both p<0.001]. Young adults and teenagers were also significantly more likely to have SCI than pre-school age children in the cervical SCI (IRR = 28.55 and 10.50, both p<0.001) and other SCI groups (IRR = 18.8 and 7.47, both p<0.001). Children in families of lower socio-economic status were also significantly more likely to have SCI (p<0.05). Conclusions In the pediatric population, the overall SCI incidence rate is 5.99 per 100,000 person-years, with traumatic cervical SCI accounting for the majority. The incidence rate increases abruptly in male teenagers. Gender, age, and socio-economic status are independent risk factors that should be considered.
Generating Loop Invariants by Computing Vanishing Ideals of Sample Points

Loop invariants play a very important role in proving correctness of programs. In this paper, we address the problem of generating invariants of polynomial loop programs. We present a new approach, for generating polynomial equation invariants of polynomial loop programs through computing vanishing ideals of sample points. We apply rational function interpolation, based on early termination technique, to generate invariants of loop programs with symbolic initial values. Our approach avoids first-order quantifier elimination and cylindrical algebraic decomposition(CAD). An algorithm for generating polynomial invariants is proposed and some examples are given to illustrate the algorithm. Furthermore, we demonstrate on a set of loop programs with symbolic initial values that our algorithm can yield polynomial invariants with degrees high up to 15.
Relaxing the IIA Assumption in Locational Choice Models: A Comparison Between Conditional Logit, Mixed Logit, and Multinomial Probit Models

This paper estimates a locational choice model to assess the demand for local public services, using a data set where individuals chooses between 26 municipalities within a local labor market. We assess the importance of the IIA assumption by comparing the predictions of three difference models; the conditional logit (CL) model, the mixed logit (MXL) model, and the multinomial probit (MNP) model. Our main finding is that a MXL or a MNP estimator leads to exactly the same conclusions as the traditional CL estimator. That is, given the data used here, the IIA assumption, and hence the use of a CL estimator, seems to be valid when estimating Tiebout-related migration. The only instance when we get somewhat different results when using the MXL or MNP estimator compared with the CL estimator is when we have a too parsimonious model. One possible hypothesis explaining this result is that omitted variables are captured by the distribution parameters of the coeffcients of the included variables, leading to the false conclusion that the coefficients are not fixed. This hypothesis is supported by the results from a Monte Carlo investigation.