Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Optimization of Neighbor Embedding for Visualization

Neighbor embedding (NE) methods have found their use in data visualization but are limited in big data analysis tasks due to their O(n2) complexity for n data samples. We demonstrate that the obvious approach of subsampling produces inferior results and propose a generic approximated optimization technique that reduces the NE optimization cost to O(n log n). The technique is based on realizing that in visualization the embedding space is necessarily very low-dimensional (2D or 3D), and hence efficient approximations developed for n-body force calculations can be applied. In gradient-based NE algorithms the gradient for an individual point decomposes into "forces" exerted by the other points. The contributions of close-by points need to be computed individually but far-away points can be approximated by their "center of mass", rapidly computable by applying a recursive decomposition of the visualization space into quadrants. The new algorithm brings a significant speed-up for medium-size data, and brings "big data" within reach of visualization.
A Method to Integrate Planning and Coordination

Multi-agent planning involves finding a plan for each agent in a group where the goals, the actions and the initial resources are distributed over these autonomous agents. An algorithm is given for multi-agent planning that constructs coordinated agent plans distributedly. Instead of designing a planning algorithm and a coordination method separately, this algorithm integrates planning and coordination. The presented multi-agent planning algorithm is based on the idea of several forward heuristic planners that run in parallel. These planners are coordinated by communicating side-products and services via a blackboard.
Plasmonic beam control in strongly coupled graphene sheet arrays

We investigate the propagation of surface plasmon polaritons (SPPs) in the graphene sheet arrays (GSA) consisting of periodically stacking graphene sheets. Thanks to the strong coupling, SPPs experience a linear diffraction relation in the GSAs. The unique feature is helpful in restrain diffraction of SPPs during propagation. By connecting two GSAs with different periods, negative refraction of SPPs in the heterointerface may occur. In addition, we demonstrate accelerated and decelerated beams in aperiodic GSAs, resulting in the beam routing effect.
International Journal of Bifurcation and Chaos Initiation of Hiv Therapy

In this paper, we numerically show that the dynamics of the HIV system is sensitive to both the initial condition and the system parameters. These phenomena imply that the system is chaotic and exhibits a bifurcation behavior. To control the system, we propose to initiate an HIV therapy based on both the concentration of the HIV-1 viral load and the ratio of the CD4 lymphocyte population to the CD8 lymphocyte population. If the concentration of the HIV-1 viral load is higher than a threshold, then the first type of therapy will be applied. If the concentration of the HIV-1 viral load is lower than or equal to the threshold and the ratio of the CD4 lymphocyte population to the CD8 lymphocyte population is greater than another threshold, then the second type of therapy will be applied. Otherwise, no therapy will be applied. The advantages of the proposed control strategy are that the therapy can be stopped under certain conditions, while the state variables of the overall system is asymptotically stable with fast convergent rate, the concentration of the controlled HIV-1 viral load is monotonic decreasing, as well as the positivity constraint of the system states and that of the dose concentration is guaranteed to be satisfied. Computer numerical simulation results are presented for an illustration.
Better cognitive control of emotional information is associated with reduced pro-inflammatory cytokine reactivity to emotional stress

Abstract Stress is strongly associated with several mental and physical health problems that involve inflammation, including asthma, cardiovascular disease, certain types of cancer, and depression. It has been hypothesized that better cognitive control of emotional information may lead to reduced inflammatory reactivity to stress and thus better health, but to date no studies have examined whether differences in cognitive control predict pro-inflammatory cytokine responses to stress. To address this issue, we conducted a laboratory-based experimental study in which we randomly assigned healthy young-adult females to either an acute emotional stress (emotionally evocative video) or no-stress (control video) condition. Salivary levels of the key pro-inflammatory cytokines IL-1β, IL-6, and IL-8 were measured before and after the experimental manipulation, and following the last cytokine sample, we assessed participants’ cognitive control of emotional information using an emotional Stroop task. We also assessed participants’ cortisol levels before and after the manipulation to verify that documented effects were specific to cytokines and not simply due to increased nonwater salivary output. As hypothesized, the emotional stressor triggered significant increases in IL-1β, IL-6, and IL-8. Moreover, even in fully adjusted models, better cognitive control following the emotional (but not control) video predicted less pronounced cytokine responses to that stressor. In contrast, no effects were observed for cortisol. These data thus indicate that better cognitive control specifically following an emotional stressor is uniquely associated with less pronounced pro-inflammatory cytokine reactivity to such stress. These findings may therefore help explain why superior cognitive control portends better health over the lifespan.
Bidirectional spreadsheet formulas

Bidirectional transformations have potential applications in a vast number of computer science domains. Spread-sheets, on the other hand, are widely used for developing business applications, but their formulas are unidirectional, in the sense that their result can not be edited and propagated back to their input cells. In this paper, we interpret such formulas as a well-known class of bidirectional transformations that go by the name of lenses. Being aimed at users that are not proficient with programming languages, we devote particular attention to the seamless embedding of the proposed bidirectional mechanism with the typical workflow of spreadsheet environments, allowing users to have a fine control and understanding of the behavior of the derived backward transformations.
Exploiting SAR and VHR Optical Images to Quantify Damage Caused by the 2003 Bam Earthquake

Using satellite sensors to detect urban damage and other surface changes due to earthquakes is gaining increasing interest. Optical images at different resolutions and radar images represent useful tools for this application, particularly when more frequent revisit times will be available with the implementation of new missions and future possible constellations of satellites. Very high resolution (VHR) images (on the order of 1 m or less) may provide information at the scale of a single building, whereas images at resolutions on the order of tens of meters may give indications of damage levels at a district scale. Both types of information may be extremely important if provided with sufficient timeliness to rescue teams. The earthquake that hit the city of Bam, Iran, has been taken as a test case, where QuickBird VHR optical images and advanced synthetic aperture radar data were available both before and after the event. Methods to process these data in order to detect damage and to extract features used to estimate damage levels are investigated in this paper, pointing out the significant potential of these satellite data and their possible synergy.
Investigating the Effect of Fixing the Subset Length Using Ant Colony Optimization Algorithms for Feature Subset Selection Problems

The issue of studying the effect of fixing the length of the selected feature subsets using ant colony optimization (ACO) has not yet been studied. This paper addresses this concern by demonstrating four points that are: 1) determining the optimal feature subset, 2) determining the length of the subsets in ACO for subset selection problems, 3) different stopping criteria when solving feature selection by ACO, and 4) experiments on an ACO algorithm for feature selection problems using artificial and real-world datasets in two cases fixing and not fixing the length of the selected feature subsets with the use of a support vector machine (SVM) classifier. The results showed that not fixing the length of the selected feature subsets is better than fixing the length of the selected feature subsets in terms of the classifier accuracy in seven datasets out of ten.
SCMon: Leveraging segment routing to improve network monitoring

To guarantee correct operation of their networks, operators have to promptly detect and diagnose data-plane issues, like broken interface cards or link failures. Networks are becoming more complex, with a growing number of Equal Cost MultiPath (ECMP) and link bundles. Hence, some data-plane problems (e.g. silent packet dropping at one router) can hardly be detected with control-plane protocols or simple monitoring tools like ping or traceroute. In this paper, we propose a new technique, called SCMon, that enables continuous monitoring of the data-plane, in order to track the health of all routers and links. SCMon leverages the recently proposed Segment Routing (SR) architecture to monitor the entire network with a single box (and no additional monitoring protocol). In particular, SCMon uses SR to (i) force monitoring probes to travel over cycles; and (ii) test parallel links and bundles at a per-link granularity. We present original algorithms to compute cycles that cover all network links with a limited number of SR segments. Further, we prototype and evaluate SCMon both with simulations and Linux-based emulations. Our experiments show that SCMon quickly detects and precisely pinpoints data-plane problems, with a limited overhead.
Distributed Intrusion Detection Systems of Computer Communication Networks

The aim of the contribution is to present an attempt to utilize some data about network topology and performance to increase capabilities of intrusion detection system. Some simple example presents an approach to design topology (location and the needed number of intrusion detection devices) which allows efficiently detecting intrusions and limiting the overall overhead caused by intrusion detection devices performance.
Building systems that flexibly control downloaded executable context

Downloading executable content, which enables principals to run programs from remote sites, is a key technology in a number of emerging applications, including collaborative systems, electronic commerce, and web information services. However, the use of downloaded executable content also presents serious security problems because it enables remote principals to execute programs on behalf of the downloading principal. Unless downloaded executable content is properly controlled, a malicious remote principal may obtain unauthorized access to the downloading principal's resources. Current solutions either attempt to strictly limit the capabilities of downloaded content or require complete trust in the remote principal, so applications which require intermediate amounts of sharing, such as collaborative applications, cannot be constructed over insecure networks. In this paper, we describe an architecture that flexibly controls the access rights of downloaded content by: (1) authenticating content sources; (2) determining content access rights based on its source and the application that it is implementing; and (3) enforcing these access rights over a wide variety of objects and for the entire computation, even if external software is used. We describe the architecture in the context of an infrastructure for supporting collaborative applications.
Material Dependence of NBTI Physical Mechanism in Silicon Oxynitride (SiON) p-MOSFETs: A Comprehensive Study by Ultra-Fast On-The-Fly (UF-OTF) IDLIN Technique

An ultra-fast on-the-fly (UF-OTF) IDLIN technique having 1 mus resolution is developed and used to study gate insulator process dependence of NBTI in silicon oxynitride (SiON) p- MOSFETs. The nitrogen density at the Si-SiON interface and the thickness of SiON layer are shown to impact temperature, time, and field dependencies of NBTI. The plausible material dependence of NBTI physical mechanism is explored.
Constructivist Pedagogy Meets Agile Development – A Case Study

This paper introduces a structured agile software engineering methodology that we use at NITLE to guide teams of inexperienced software engineering interns through complex engineering tasks. We have employed the method successfully in classroom settings, for grant funded extracurricular projects, and in industry. Most recently, we employed it to complete a Tetris-variant for an accelerometer-enabled mobile phone. The task was accomplished by two undergraduates who had no prior knowledge of game programming, of the Android platform, or of mobile development in general -- yet they completed the project within two weeks. Here, we present their case study and discuss our method's possible incorporation into undergraduate curricula.
Re-expression of elafÃ ¬ nin 21 MT 2 Breast Carcinomas by Phorbol 12-Myristate 13-Acetate Is Mediated by the Api Site in the elafÃ ¬ nPromoter

is a unique elastase inhibitor. It is differentially expressed at the transcriptional level in human normal mammary epithelial cells and carcinomas. The elafÃ¬ngene is induced by PMA in 21MT2 breast tumor cells. By deletion analysis and mutagenesis, we have identified the Api site in the promoter as the cis element mediating transcriptional activation of elafÃ¬nin 70N normal breast cells and its induction by phorbol 12-myristate 13-acetate (PMA) in 21MT2 breast tumors. PMA treatment induces API factor expression, which binds to the Api site of the elafÃ¬npromoter. Mutation of this Api abolishes the capability of induction by PMA. Furthermore, our data provide a basis for therapeutic manipulation of proteinase inhibitors such as elafÃ¬n.
ERROR-RELATED BRAIN ACTIVITY, ANXIETY AND COGNITIVE PERFORMANCE

Research strongly suggests that the negative thoughts and ruminations brought on by anxiety, i.e. worry, occupy working memory (WM) resources that would otherwise be applied to cognitive performance. Although worry decreased cognitive performance for individuals overall, the effects seem to particularly target those higher in trait levels of WM, as they rely on their expanded WM for more advanced problem solving strategies. Furthermore, heightened amplitudes of ACC-generated error monitoring known as error-related negativity (ERN) have been repeatedly shown to relate to higher cognitive performance. A recent hypothesis argues that as worry depletes cognitive resources, this ERN signaling represents a compensatory response in order to prevent cognitive deficits. Across three experiments, we replicate and expand these findings, while further being the first group to analyze cognitive performance in terms of WM, attentional control, anxiety and ERN within one study. In Experiment 1, we showed that the executive function of attentional control prevented worry-induced cognitive deficits in those higher in WM when levels of attentional control were high. In Experiment 2, we supported the claim that the ERN is a response to worry’s depletion of cognitive resources by showing that the relationship of worry and ERN is altered by levels of both WM and attentional control. In Experiment 3, we married these two findings by predicting real-world cognitive performance in the future (GPA) using worry, WM, attentional control and ERN as predictors. Although we replicated Experiment 1 by predicting GPA, ERN failed to interact with worry to account for GPA scores. However, we did replicate findings that ERN generally relates to GPA scores, and that this relationship was altered by individuals’ levels of WM and attentional control. These findings strengthen support for attentional control theory, and produce a number of questions regarding the relationship of ERN to cognitive performance.
Social Sources of Community Solidarity in U.S. Congregations

ABSTRACT Community solidarity, or a collective sense of belonging, plays a vital role in the health and survival of many organizations. Consequently, identifying the elements that contribute to a strong sense of solidarity within communities has long been a topic of inquiry for scholars. In this study, we draw upon prior theorizing to develop and test four hypotheses regarding the organizational characteristics associated with community solidarity in religious congregations. Multivariate models are estimated using national data on religious congregations from the 2001 U.S. Congregational Life Survey (n = 357). Organizations with greater community solidarity tend to feature higher levels of social capital (operationalized with measures of friendship networks and participation in organizational activities), higher levels of official membership within the organization, and inspiring congregational leadership. Higher levels of commitment are associated with greater community solidarity, but the effect is wholly mediated by social capital. Congregations that engender higher levels of community solidarity share certain organizational features, including higher levels of social capital, higher rates of membership, and inspiring leaders.
Robust discrete-time consensus of multi-agent systems with uncertain interaction

This paper addresses robust discrete-time consensus problem of multiple agents with uncertain structure, where the network coupling weights are supposed polynomial functions of an uncertain vector constrained in a semialgebraic set. Based on the Lyapunov stability theory, a necessary and sufficient condition for robust discrete-time consensus is proposed. Then, we investigate the robust discrete-time consensus with positive weighted network, and a necessary and sufficient condition is also provided based on the property of an uncertain matrix. Corresponding sufficient conditions for robust discrete-time consensus are derived by solving a linear matrix inequality (LMI) problem built by exploiting sum-of-squares (SOS) polynomials. Some examples illustrate the proposed results.
Mobile base stations placement and energy aware routing in wireless sensor networks

Increasing network lifetime is important in wireless sensor/ad-hoc networks. In this paper, we are concerned with algorithms to increase network lifetime and amount of data delivered during the lifetime by deploying multiple mobile base stations in the sensor network field. Specifically, we allow multiple mobile base stations to be deployed along the periphery of the sensor network field and develop algorithms to dynamically choose the locations of these base stations so as to improve network lifetime. We propose energy efficient low-complexity algorithms to determine the locations of the base stations; they include i) top-Kmax algorithm, ii) maximizing the minimum residual energy (max-min-RE) algorithm, and iii) minimizing the residual energy difference (MinDiff-RE) algorithm. We show that the proposed base stations placement algorithms provide increased network lifetimes and amount of data delivered during the network lifetime compared to single base station scenario as well as multiple static base stations scenario, and close to those obtained by solving an integer linear program (ILP) to determine the locations of the mobile base stations. We also investigate the lifetime gain when an energy aware routing protocol is employed along with multiple base stations
How well do Yao graph and theta graph support Greedy forwarding?

Greedy Forwarding algorithm is a widely-used routing algorithm for wireless networks. However, it can fail if the wireless network topologies contain voids, where a packet cannot be moved closer to destination. Since Yao graph and Theta graph are two types of geometric graphs exploited to construct wireless network topologies, this paper firstly studied whether these two types of graphs can contain voids, showing that when the number of cones in a Yao graph or Theta graph is less than six, Yao graph and Theta graph can have voids, and when the number of cones equals or exceeds six, Yao graph and Theta graph are free of voids. Secondly, this paper experimented on how well Greedy Forwarding is supported on Yao graphs and Theta graphs in terms oí stretch, i.e., the ratio between the path length found by Greedy Forwarding and the shortest path length in a graph. The experiments also included comparison with the stretch on Delaunay triangulation, another well-known geometric graph exploited in constructing wireless networks. Overall, our experiments revealed several interesting results.
ScyPer: A Hybrid OLTP&OLAP Distributed Main Memory Database System for Scalable Real-Time Analytics

: ScyPer is an abbreviation for Sc aled-out H yPer , a version of the HyPer main memory hybrid OLTP&OLAP database system that horizontally scales out on shared-nothing commodity hardware. Our demo shows that ScyPer a) achieves a near-linear scale-out of OLAP query throughput with the number of active nodes, b) sustains a constant OLTP throughput, c) is resilient to node failures, and d) offers real-time analytical capabilities through market-leading query response times and periodically forked TX-consistent virtual memory snapshots with sub-second lifetime durations.
Challenges and insights in inter-organizational collaborative healthcare networks

Purpose Public sectors have responded to grand societal challenges by establishing collaboratives – new inter-organizational partnerships to secure better quality health services. In the UK, a proliferation of collaboration-based healthcare networks exists that could help to enhance the value of investments in quality improvement programs. The nature and organizational form of such improvements is still a subject of debate within the public-sector literature. Place-based collaboration has been proposed as a possible solution. In response, the purpose of this study is to present the results and findings of a place-based collaborative network, highlighting challenges and insights. Design/methodology/approach This study adopted a social constructionist epistemological approach, using a qualitative methodology. A single case study was used and data collected in three different stages over a two-year period. Findings The study finds that leadership, data-enabled learning through system-wide training and development, and the provision of an enabling environment that is facilitated by an academic partner, can go a long way in the managing of healthcare networks for improving quality. Research limitations/implications Regardless of the tensions and challenges with place-based networks, they could still be a solution in maximizing the public value required by government investments in the healthcare sector, as they offer a more innovative structure that can help to address complex issues beyond the remit of hierarchical structures. This study is limited by the use of a single case study. Practical implications Across countries health systems are moving away from markets to collaborative models for healthcare delivery and from individual services to population-based approaches. This study provides insights to inform leaders of collaborative health models in the design and delivery of these new collaborations. Social implications As demand rises (as a result of increasing complexity and demographics) in the western world, health systems are seeking to redefine the boundaries between health service provision and community self-reliance and resilience. This study provides insights into the new partnership between health institutions and communities, providing opportunities for more social- and solidarity-based healthcare models which place patients and the public at the heart of change. Originality/value The city place-based network is the first of such organizational form in healthcare collaboration in the UK.