Miracle-dz/UniFAR-FTUs huggingface dataset:
Overlay networks for task allocation and coordination in dynamic large-scale networks of cooperative agents

This work proposes a method for allocating temporally interdependent tasks to homogeneous or heterogeneous cooperative agents in dynamic large-scale networks. This method views searching, task allocation and scheduling as an integrated problem that has to be efficiently solved in such networks. Solving the general problem optimally in a decentralized way is very hard and can only be solved by a centralized method, be approximated by means of heuristics, or by relaxations of the original problem. Our method facilitates effective searching through the dynamic assignment of gateway roles to agents and the exploitation of routing indices. In combination to searching, it exploits distributed constraint satisfaction techniques and dynamic re-organization of agent teams to efficiently handle the allocation of complex tasks with interdependent subtasks.
Genetic Algorithms for Randomized Unit Testing

Randomized testing is an effective method for testing software units. The thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. In this paper, we describe Nighthawk, a system which uses a genetic algorithm (GA) to find parameters for randomized unit testing that optimize test coverage. Designing GAs is somewhat of a black art. We therefore use a feature subset selection (FSS) tool to assess the size and content of the representations within the GA. Using that tool, we can reduce the size of the representation substantially while still achieving most of the coverage found using the full representation. Our reduced GA achieves almost the same results as the full system, but in only 10 percent of the time. These results suggest that FSS could significantly optimize metaheuristic search-based software engineering tools.
An AIMD Distributed Control Law for Load Balancing in Content Delivery Networks

Content Delivery Networks (CDN) is the best for overcoming the inherited problems face by internet in modern days. The major idea at the basis of this technology is the delivery at edge points of the network, in proximity to the request areas, to advance the user’s perceived performance while off-putting the overheads. Literature shows that in Content Delivery Network how this demanding problem of defining and implementing an effective law for load balancing is model. But this system has some drawback, like even if the queue length of server is low they redirect loads to another server to only balance the overall load. Due to this request processing overhead and delay increases. Algorithm model in paper can help to reduce this delay by putting one equilibrium point to request queue. In CDN, the source adjusts its rate using a modified Additive Increase and Multiplicative Decrease (AIMD) algorithm. AIMD has been demonstrated to be sufficient and essential of efficiency and fairness under certain general conditions.
Licensed under Creative Commons Attribution Cc by a Novel Multiple View Clustering Using Tw-k- Means Algorithm

Multiple view clustering has become an active research area in the field of data mining. However, still there is a scope for improvement in the performance of clustering. This paper proposes TW-k-means algorithm which is an automated two-level variable weighting clustering algorithm for multiview data, which can simultaneously compute weights for views and individual variables. In this algorithm, a view weight is assigned to each view to identify the compactness of the view and a variable weight is also assigned to each variable in the view to identify the importance of the variable. Both view weights and variable weights are used in the distance function to determine the clusters of objects. In the new algorithm, two additional steps are added to the iterative k-means clustering process to automatically compute the view weights and the variable weights. Experimental results show that the TW-k-means algorithm outperforms the other existing clustering algorithms in effective manner.
Decoupled dq-Current Control of Grid-Tied Voltage Source Converters Using Nonparametric Models

This paper presents a vector control strategy for regulating the current of grid-tied voltage source converters (VSCs) in a rotating reference frame. The proposed approach is based on shaping the open-loop and closed-loop transfer matrices of the system. Solving a constrained convex optimization problem, the shaping is achieved, which guarantees the stability of the closed-loop system. The designed controller results in the desired dynamic performance and decouples the direct and quadrature (dq) current axes. The structure of the proposed controller is similar to that of its predecessors and consists of four proportional-integral controllers. The performance of the method is evaluated based on simulation and experimental results. It is confirmed that its dynamic performance is better than that of the previously proposed approaches, and it results in the decoupled current axes.
SeRViSO: a selective retransmission scheme for video streaming in overlay networks

Several works proposed methods to make video streaming scalable over the number of clients, avoiding the linear growth of bandwidth requirements for the media source node. Some are based on overlay networks built on top of the IP protocol and distribute content between overlay partners. In this way the clients share their bandwidth, reducing the burden on the source node. Similar to data-oriented proposals, this work breaks the media into segments which are requested from partners when available. Novel in this technique is the explicit handling of losses with a selective retransmission mechanism based on H.264 content, controlled by estimated decoding importance of packets.
UvA-DARE ( Digital Academic Repository ) Small steps in dynamics of information

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Selection of a Novel DNA Aptamer for Assay of Intracellular Interferon-Gamma

Interferon-gamma (IFN-γ) is a glycoprotein generated by lymphocytes that possesses anti-tumor, antiviral and immunomodulatory functions. IFN-γ assays are broadly employed in immunological research and clinical diagnostic tests. Intracellular IFN-γ staining, in particular, is an important immune assay that allows simultaneous determination of cellular phenotype and antigen-specific T cell response. Aptamers have great potential for molecule detection and can bind to target molecules with high affinity and specificity. In this study, a novel 59-mer DNA aptamer (B1–4) was developed for assay of intracellular IFN-γ. The selected aptamer bound to IFN-γ with a Kd of 74.5 nM, with minimal cross-reactivity to albumin. The aptamer was also found capable of binding with paraformaldehyde-fixed IFN-γ. Moreover, B1–4 could enter permeated and paraformaldehyde-fixed lymphocytes, and bound to intracellular IFN-γ produced by these cells. When FITC-labeled B1–4 was used to stain a group of lymphocytes, the average fluorescence of the cells was positively correlated with the number of PMA-stimulated lymphocytes within the group. A standard curve could thus be established for assessing the fraction of IFN-γ-producing cells in a cluster of lymphocytes. The selected aptamer hence provides a novel approach for assaying intracellular IFN-γ generated by a group of lymphocytes, and may have application potential in both scientific research and clinical laboratory test.
Association of a Novel PDZ Domain-containing Peripheral Golgi Protein with the Q-SNARE (Q-solubleN-Ethylmaleimide-sensitive Fusion Protein (NSF) Attachment Protein Receptor) Protein Syntaxin 6*

PDZ domains are involved in the scaffolding and assembly of multi-protein complexes at various subcellular sites. We describe here the isolation and characterization of a novel PDZ domain-containing protein that localizes to the Golgi apparatus. Using an in silico cloning approach, we have identified and isolated a cDNA encoding a ubiquitously expressed 59-kDa protein that we call FIG. It is composed of two coiled coil regions, a leucine zipper, and a single PDZ domain. Cytological studies using indirect immunofluorescence microscopy revealed that FIG is a peripheral protein that uses one of its coiled coil domains to localize to the Golgi apparatus. To ascertain the modalities of this Golgi localization, the same coiled coil region was tested for its ability to interact with a panel of coiled coil domain-containing integral membrane Golgi proteins. Using a series of GST fusion protein binding assays, co-immunofluorescence and co-immunoprecipitation experiments, we show that FIG specifically binds to the coiled coil domain-containing Q-SNARE (Q-soluble NSF attachment protein receptor) protein syntaxin 6 both in vitro and in vivo. The structural features of FIG and its interaction with a SNARE protein suggest that FIG may play a role in membrane vesicle trafficking. This is the first example of a PDZ domain-containing peripheral protein that localizes to the Golgi through a coiled coil-mediated interaction with a resident membrane protein. Our results broaden the scope of PDZ domain-mediated functions.
Physically-based Deformation of High-Resolution 3D Lung Models for Augmented Reality based Medical Visualization

Visualization tools using Augmented Reality Environments are effective in applications related to medical training, prognosis and expert interaction. Such medical visualization tools can also provide key visual insights on the physiology of deformable anatomical organs (e.g. lungs). In this paper we propose a deformation method that facilitates physically-based elastostatic deformations of 3D highresolution polygonal models. The implementation of the deformation method as a pre-computation approach is shown for a 3D high-resolution lung model. The deformation is represented as an integration of the applied force and the local elastic property assigned to the 3D lung model. The proposed deformation method shows faster convergence to equilibrium as compared to other physically-based simulation methods. The proposed method also accounts for the anisotropic tissue elastic properties. The transfer functions are formulated in such a way that they overcome stiffness effects during deformations.
Integrating connectionist, statistical and symbolic approaches for continuous spoken Korean processing

This paper presents a multi-strategic and hybrid approach for large-scale integrated speech and natural language processing, employing connectionist, statistical and symbolic techniques. The developed spoken Korean processing engine (SKOPE) integrates connectionist TDNN-based phoneme recognition technique with statistical Viterbi-based lexical decoding and symbolic morphological/phonological analysis techniques. The modular large-scale TDNNs are organized to recognize all 41 Korean phonemes using 10 component networks combined through 3 glue networks. In performance phase, continuously shifted TDNN outputs are integrated with HMM-based Viterbi decoding using a tree-structured lexicon. The Viterbi beam search is integrated with Korean morphotactics and phonological modeling, and produces a morpheme-graph for high-level parsing module. Currently. SKOPE shows average 76.2% phoneme spotting performance for all 41 Korean phonemes (including silence) from continuous speech signals and exhibits average 92.6% morpheme spotting performance from erroneous TDNN outputs after morphological analysis. Other extensive experiments verify that the multi-strategic approaches are promising for complex integrated speech and natural language processing, and the approaches can be extended to other morphologically-complex agglutinative languages such as Japanese.
Kinetic device: designing interactions with a deformable mobile interface

We introduce the user-centered research that we are conducting using functional deformable research prototypes. This work has recently crystallized in the demonstration of the Nokia Kinetic Device (figure 1). In the large design space that opens before us around deformable user interfaces (DUIs), we have chosen to focus on mobile personal interfaces. We aim to investigate how human factors should influence the transition from rigid to deformable hardware. In this paper, we propose the topics that a research agenda should cover, and we discuss our research methodology. We also describe the functional deformable research prototype (called Kinetic DUI-RP) that we are using to conduct our research. Finally, we present an initial set of design guidelines that future research will develop further.
Influences of Indoor Air Temperatures on Empathy and Positive Affect

The consequences of climate change are already visible, and yet, its effect on psychosocial factors, including the expression of empathy, affect, and social disconnection, is widely unknown. Outdoor conditions are expected to influence indoor conditions. Therefore, the aim of this study was to investigate the effect of indoor air temperature during work hours on empathy, positive and negative affect, and social disconnection. Participants (N = 31) were exposed, in a cross-over design, to two thermal conditions in a simulated office environment. Questions on empathy and social disconnection were administered before and after the exposure to each condition, while affect was measured throughout the day. Subjective thermal sensation and objective measures of mean skin temperature were considered. The results indicated a significant difference in empathy (F(1, 24) = 5.37, p = 0.03, with an η2 = 0.126) between conditions. Participants reported increases in empathy after exposure to the warm condition compared to the cool condition, in which reductions in empathy were reported. Although the same pattern was observed for positive affect, the difference was smaller and the results were not significant. Thermal sensation had a significant effect on changes in empathy too (F(1, 54) = 7.015, p = 0.01, with an R2 = 0.115), while mean skin temperature had no effect on empathy (F(1, 6) = 0.53, p = 0.89, with an R2 = 0.81). No effects were observed for positive and negative affect and social disconnection. Longitudinal studies are needed to support these findings.
Evaluating Environmental Programs: The Perspective of Modern Evaluation Research

Large-scale environmental programs generally commit substantial societal resources, making the evaluation of their actual effects on the relevant outcomes imperative. As the example of the subsidization of energy-saving appliances illustrates, much of the applied environmental economics literature has yet to confront the problem of proper attribution of effects to underlying causes on a convincing methodological basis. This paper argues that recent results in the econometrics and statistics literature on program evaluation could be utilized to advance considerably in this context. In particular, the construction of a credible counterfactual situation is at the heart of the formal statistical evaluation problem. Even when controlled experiments are not a viable option, appropriate approaches might succeed where traditional empirical strategies fail to uncover the effects of environmental interventions.
A Model-Theoretic Semantics for Defeasible Logic

Department of Computer ScienceLoyola University Chicagomjm@cs.luc. eduCIT, Griﬃth UniversityNathan, QLD 4111, AustraliaAbstract. Defeasible logic is an eﬃcient logic for defeasible reasoning.It is deﬁned through a proof theory and, until now, has had no modeltheory. In this paper a model-theoretic semantics is given for defeasiblelogic. The logic is sound and complete with respect to the semantics.We also brieﬂy outline how this approach extends to a wide range ofdefeasible logics.
The Dynamic Web Pages Information Extraction Algorithm Based on Sequence Alignment

In this paper, 'common framework' is defined as the information which is irrelative to the kernel contents of web pages and common in web pages from the same source, such as headers, tails, advertisements, orientations of browsers and flash etc. Sequence alignment is adopted in the information extraction algorithm to detect the common framework. After eliminating the common frameworks from web pages, the data fields obtained will be more suitable for information extraction. On data-intensive web pages from real-world websites, the effects of the alignment parameter on extraction results and the phase of common framework detection on decreasing data quantity and increasing extraction accuracy were tested and evaluated. The experimental results proved the validity of this approach convincingly.
Prevalence and characteristics of phantom limb pain and residual limb pain in the long term after upper limb amputation

This study aims to describe the prevalence and characteristics of phantom limb pain and residual limb pain after upper limb amputation. One-hundred and forty-one participants (139 males; mean age 74.8 years; mean time since amputation 50.1 years) completed a self-report questionnaire assessing residual and phantom limb pain experience. Prevalence of phantom limb pain during the week preceding assessment was 42.6% (60 of 141). Prevalence of residual limb pain was 43.3% (61 of 141). More than one third of these had some pain constantly or most days. Phantom limb pain was commonly described as ‘discomforting’ (31 of 60) and associated with ‘a little bit’ of lifestyle interference (23 of 60). Residual limb pain was most often described as ‘discomforting’ (27 of 61) or ‘distressing’ (19 of 61) and was typically associated with low to moderate levels of lifestyle interference. Assessment of multiple dimensions of postamputation pain in the long term after upper limb amputation is warranted.
Remote Control of a Mobile Robot Subject to a Communication Delay

This paper addresses the remote tracking control of a mobile robot subject to a bilateral time-delay. The delay affects the system since the controller and the robot are linked via a delay inducing communication channel, such as the Internet, and consequently, the performance and stability of the system are compromised. Based on the notion of anticipating synchronization, a state estimator which stabilizes the system when it is affected by a bilateral time-delay is proposed. A stability analysis including the system, tracking controller and estimator is provided, and the applicability of the proposed delay compensation strategy is demonstrated by means ofexperiments between multi-robot platforms located in Eindhoven, The Netherlands and Tokyo, Japan.
USENIX Association

SNMP is the standard protocol used to manage IP networks. Service providers often analyze the utilization statistics available from SNMP-enabled devices to make informed engineering decisions, diagnose faults and perform billing. However collecting and efficiently storing large amounts of time-series data quickly, without impacting network or device performance, is challenging in very large installations. We identify three crucial requirements for an SNMP statistical solution: (i) support for hundreds of devices each with thousands of objects; (ii) the ability to retain the data indefinitely; and (iii) an abstract interface to the data. We then compare the applicability of several tools in a service provider environment. Finally, we detail Real Traffic Grabber (RTG), an application currently in use on our national IP backbone which we developed in lieu of existing packages to meet our requirements.
Cellular interactions in tumor microenvironment during breast cancer progression: new frontiers and implications for novel therapeutics

The breast cancer tumor microenvironment (TME) is dynamic, with various immune and non-immune cells interacting to regulate tumor progression and anti-tumor immunity. It is now evident that the cells within the TME significantly contribute to breast cancer progression and resistance to various conventional and newly developed anti-tumor therapies. Both immune and non-immune cells in the TME play critical roles in tumor onset, uncontrolled proliferation, metastasis, immune evasion, and resistance to anti-tumor therapies. Consequently, molecular and cellular components of breast TME have emerged as promising therapeutic targets for developing novel treatments. The breast TME primarily comprises cancer cells, stromal cells, vasculature, and infiltrating immune cells. Currently, numerous clinical trials targeting specific TME components of breast cancer are underway. However, the complexity of the TME and its impact on the evasion of anti-tumor immunity necessitate further research to develop novel and improved breast cancer therapies. The multifaceted nature of breast TME cells arises from their phenotypic and functional plasticity, which endows them with both pro and anti-tumor roles during tumor progression. In this review, we discuss current understanding and recent advances in the pro and anti-tumoral functions of TME cells and their implications for developing safe and effective therapies to control breast cancer progress.
What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations

The study of spontaneous fluctuations of brain activity, often referred as brain noise, is getting increasing attention in functional magnetic resonance imaging (fMRI) studies. Despite important efforts, much of the statistical properties of such fluctuations remain largely unknown. This work scrutinizes these fluctuations looking at specific statistical properties which are relevant to clarify its dynamical origins. Here, three statistical features which clearly differentiate brain data from naive expectations for random processes are uncovered: First, the variance of the fMRI mean signal as a function of the number of averaged voxels remains constant across a wide range of observed clusters sizes. Second, the anomalous behavior of the variance is originated by bursts of synchronized activity across regions, regardless of their widely different sizes. Finally, the correlation length (i.e., the length at which the correlation strength between two regions vanishes) as well as mutual information diverges with the cluster's size considered, such that arbitrarily large clusters exhibit the same collective dynamics than smaller ones. These three properties are known to be exclusive of complex systems exhibiting critical dynamics, where the spatio-temporal dynamics show these peculiar type of fluctuations. Thus, these findings are fully consistent with previous reports of brain critical dynamics, and are relevant for the interpretation of the role of fluctuations and variability in brain function in health and disease.