Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Dimensional Visualization for Internet Resource

Traditional information retrieval systems return documents in a list, where documents are sorted according to their publication dates, titles, or similarities to the user query. Users select interested documents by searching them through the returned list. In a distributed environment, documents are returned from more than one information server. A simple document list is not eecient to present a large volume data to the users. In this paper, we propose a two-dimensional visualization scheme for Internet resource discovery. Our method displays data by clusters according to their cross-similarities and still retains the rankings with respect to the user query. In addition, it provides a customized view that arranges data in favor of user's preference terms.
A system for modeling and animating flexible dressed characters

We describe a system for the layered construction of flexible animated dressed characters. Bones and muscles are modeled as ellipsoids, while skin and clothes are simulated by triangle meshes attached to implicit surfaces generated by the ellipsoids. Muscles and cloth automatically change shape as the joints move. The system provides the necessary tools to easily create and manipulate the different components of a character. Several control panel windows are available to interactively modify parameters and record animations.
Detecting a Needle in Carm X-ray Images

This paper presents an algorithm to detect a rigid straight biopsy needle in multi-view C-arm X-ray images and reconstruct its tip and orientation in 3D-space. Several well known computer vision techniques are applied to achieve this goal. The processing pipeline that we describe consists of five stages containing a denoising and preprocessing stage with Gaussian filtering, computing the Hessian tensorfield and a Radon transform for ridge detection, needle tip detection, the reconstruction of the data into 3Dspace with Direct Linear Transform and improving robustness by backprojection. The processing steps are described in detail, a short overview is given about the surrounding application and finally we present the evaluation results of the experiments on real X-ray imaging data.
An Open-source Framework for Multi-level Semantic Similarity Measurement

We present an open source, freely available Java implementation of Align, Disambiguate, and Walk (ADW), a state-of-the-art approach for measuring semantic similarity based on the Personalized PageRank algorithm. A pair of linguistic items, such as phrases or sentences, are first disambiguated using an alignment-based disambiguation technique and then modeled using random walks on the WordNet graph. ADW provides three main advantages: (1) it is applicable to all types of linguistic items, from word senses to texts; (2) it is all-in-one, i.e., it does not need any additional resource, training or tuning; and (3) it has proven to be highly reliable at different lexical levels and multiple evaluation benchmarks. We are releasing the source code at https://github.com/pilehvar/adw/. We also provide at http://lcl.uniroma1. it/adw/ a Web interface and a Java API that can be seamlessly integrated into other NLP systems requiring semantic similarity measurement.
Sticky Expectations and Consumption Dynamics

To match aggregate consumption dynamics, macroeconomic models must generate “excess smoothness” in consumption expenditures. But microfounded models are calibrated to match micro data, which exhibit no “excess smoothness. ” So standard microfounded models fail to match the macro smoothness facts. We show that the micro and macro evidence are both consistent with a microfounded model where consumers know their personal circumstances but have “sticky expectations” about the macroeconomy. Aggregate consumption sluggishness reflects consumers’ imperfect attention to aggregate shocks. Our proposed degree of inattention has negligible utility costs because aggregate shocks constitute a tiny proportion of the uncertainty that consumers face. (JEL D83, D84, E21, E23, E24)
Prefetching with Adaptive Cache Culling for Striped Disk Arrays

Conventional prefetching schemes regard prediction accuracy as important because useless data prefetched by a faulty prediction may pollute the cache. If prefetching requires considerably low read cost but the prediction is not accurate, it may or may not be beneficial depending on the situation. However, the problem of low prediction accuracy can be dramatically reduced if we efficiently manage prefetched data by considering the total hit rate for both prefetched data and cached data. To achieve this goal, we propose an adaptive strip prefetching (ASP) scheme, which provides low prefetching cost and evicts prefetched data at the proper time by using differential feedback that maximizes the hit rate of both prefetched data and cached data in a given cache management scheme. Additionally, ASP controls prefetching by using an online disk simulation that investigates whether prefetching is beneficial for the current workloads and stops prefetching if it is not. Finally, ASP provides methods that resolve both independency loss and parallelism loss that may arise in striped disk arrays. We implemented a kernel module in Linux version 2.6. 18 as a RAID-5 driver with our scheme, which significantly outperforms the sequential prefetching of Linux from several times to an order of magnitude in a variety of realistic workloads.
Games on graphs that grow deterministically

We introduce an adaptive graph model where strategy assigned vertices reproduce themselves (having off-spring with the same neighbourhood) and unfit vertices get removed. We study how different games cause different graphs to evolve. Under some games graphs grow and break into self replicating structures. Small initial graphs can lead to the generation of vast ‘ecosystems’ containing thousands of kinds of structures that change and make copies of one another. Understanding how local interactions induce self replication is important to biology. We examine self replicative processes under various games. We investigate how resilient these processes are to stochasticity and we introduce several modified growth models where analysis of the dynamics is easier.
Rapid eye movement sleep behavior disorder: devising controlled active treatment studies for symptomatic and neuroprotective therapy—a consensus statement from the International Rapid Eye Movement Sleep Behavior Disorder Study Group

Objectives: We aimed to provide a consensus statement by the International Rapid Eye Movement Sleep Behavior Disorder Study Group (IRBD-SG) on devising controlled active treatment studies in rapid eye movement sleep behavior disorder (RBD) and devising studies of neuroprotection against Parkinson disease (PD) and related neurodegeneration in RBD.
Promotions, Demotions, Halo Effects and Earnings Dynamics of American Executives

This paper explores the dynamics of wage growth in corporate hierarchies. Using panel data techniques, we estimate the causal effect of current and past transitions in reporting level and past earnings growth on components of current earnings and earnings growth using a large panel of US executives. After conditioning on unobserved heterogeneity, current compensation growth is positively correlated with past promotion outcomes but negatively correlated with past compensation growth. In a flexible model of wage growth, there is an important asymmetry between the effect of a promotion and a demotion. The effect of promotion is smaller in magnitude than the effect of a demotion. The causal effect of a promotion is positive on both growth in base pay and total cash compensation but is negative on bonus growth. The effect of a demotion is negative on growth in all pay components.
Resources for coordination in collaborative telelearning

We have analyzed data from a field study in collaborative telelearning (Wasson, 1999). The goal of the study was to investigate how the students used a set of groupware tools to coordinate learning activities. We have used concepts from collaborative telelearning and coordination mechanisms (Schmidt & Simone, 1996) as analytic framework. Our findings indicate that students coordinate many activities by implicit, locally adopted resources instead of fully developed mechanisms. Three implicit resources were identified and named (no initial discussion, asymmetry of knowledge, and different expectations). These resources were not associated with any specific groupware features, but referred to the students' background knowledge and subjective interpretation. We end the paper by suggesting how implicit resources in distributed learning environments can play a role similar to how non-verbal cues -- such as gesture and facial expression -- play a role in face-to-face interaction, but without imitating human body language.
Energy Management for Fuel-Cell Hybrid Vehicles Based on Specific Fuel Consumption Due to Load Shifting

Energy management is integral to the design of fuel-cell (FC) hybrid vehicles (FCHVs), as well as topology and component sizing. Therefore, an optimal energy management strategy is crucial for a successful FCHV concept. The goal in this optimization process is to coordinate the energy sources and power components on a vehicle in such a way that the total consumption is minimized. We defined a new strategy that quantifies the shifts in the operating point (OP) of the FC system by using the specific fuel consumption due to load shifting and adapts the conditions for a possible shifting of the OP of the FC system to reduce the total fuel consumption. This paper shows the simulation results of the proposed strategy in comparison with existing strategies. The main focus is given on the improvement in fuel consumption, the effect of the new approach on utilization of the power sources concerning their aging mechanisms, and the effect of varying conditions by time. The results indicate that the goal of reduction in total fuel consumption is achieved.
Inferring web communities through relaxed cocitation and dense bipartite graphs

Community forming is one of the important activity in the Web. The Web harbors a large number of communities. A community is a group of content creators that manifests itself as a set of interlinked pages. Given a large collection of pages our aim is to find potential communities in the Web. In the literature, Ravi Kumar et al. [18] proposed a trawling method to find potential communities by abstracting a core of the community as a group of pages that form a complete bipartite graph (CBG) (web-page as a node and link as an edge between two nodes). The trawling approach extracts a small group of pages that form a CBG, which is a signature of a potential community. In this paper we propose a different approach to find potential community patterns by analyzing linkage patterns in a large collection of pages. We mathematically abstract a community as a dense bipartite graph (DBG) pattern. Given a large collection of pages, we propose an algorithm to find all DBGs. It can be noted that a CBG pattern is an instance of a DBG pattern. By extracting potential DBG patterns, the proposed approach extract potential community patterns that exist in a page collection including CBG patterns. We report experimental results on 10 GB TREC (Text REtrieval Conference) data collection that contains 1.7 million pages and 21.5 million links. The results indicate that as compared to trawling approach, the proposed approach extracts community patterns of significantly large size. Also, the proposed approach has good scaleup properties and can be easily parallelized.
Demand function and its role in a business simulator

Business simulations are useful tools due to the fact that it eases management decision making. No doubt there are many processes which must be considered and simulated. Therefore, such business simulator is often composed of many processes and contains many agents and interrelations as well. Since the business simulator based on multi-agent system is characterized by many interrelations within, this article deals with a specific part of the business simulator only – a demand function and its modeling. The aim of this partial research is to suggest demand function which would be most suitable for the business simulation. In this paper a new approach for customer decision function in business process simulation was presented. The decision of the customer is based on Marshallian demand function and customer utility function using Cobb-Douglas preferences. The results obtained by means of the MAREA simulation environment proved that this approach yields correct simulation results.
Face hallucination based on stepwise sparse reconstruction

Face hallucination methods based on low-resolution (LR) and high-resolution (HR) dictionary pair scheme infer HR patches by directly reusing coding coefficients trained by LR patches over LR dictionary. This scheme implies that LR and HR patch manifolds share highly similar local geometric structure. However, latest preliminary studies argue that the manifold assumption does not hold well such that face hallucination performance inevitably suffers from inconsistency of coding coefficients between LR and HR patches. In this paper, we are the first to observe that coding coefficients of LR patches are more relevant to latent those of HR patches under conditions of involving small magnifying factor. On the basis of this finding, we suggest a stepwise reconstruction scheme to minimize inconsistency risk in solution space. In particular, this scheme divides face hallucination process into multiple cascaded incremental training-synthesis steps, in which each individual step allows smaller magnifying factor as well as the corresponding intermediate resolution (IR) dictionary rather than merely LR and HR dictionary based learning. Moreover, in order to keep sparse representation (SR) sufficiently sparse while favoring its locality, we introduce a weighted ℓ1/ℓ2 mixed norms minimization SR method and formulate a unified framework together with stepwise scheme. Experiments on commonly used face database demonstrate that our framework achieves state-of-the-art results.
Response to Wu et al. — Cost-effectiveness analysis of infant pneumococcal vaccination in Malaysia and Hong Kong

ABSTRACT A recently published paper that assessed the comparative cost-effectiveness of the 2 pneumococcal conjugate vaccines (PCVs) in Malaysia and Hong Kong reported that the 13-valent PCV vaccine (PCV13) is a better choice compared to the 10-valent pneumococcal non-typeable Haemophilus influenzae protein D conjugate vaccine (PHiD-CV or PCV10) from both a payer and societal perspective as well as under various scenarios. However, the analysis relied on a large number of assumptions that were either erroneous or did not take into account the most recent body of evidence available. A rigorous evaluation of the underlying assumptions is necessary to present a fair and balanced analysis for decision-making.
The Influence of Brain Tissue Anisotropy on Human EEG and MEG

The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were carried out with single dipoles or small extended sources in the cortical gray matter. The inclusion of anisotropic volume conduction in the brain was found to have a minor influence on the topology of EEG and MEG (and hence source localization). We found a major influence on the amplitude of EEG and MEG (and hence source strength estimation) due to the change in conductivity and the inclusion of anisotropy. We expect that inclusion of tissue anisotropy information will improve source estimation procedures.
Preliminary study-using RoboCup Rescue Simulations for Disasters Prevention

Activities of search and rescue of victims in large scale disasters are not only social problems, but also several research challenges. Evaluations of rescue agents behavior are affected from various standards such as human lives, properties, operation costs and so forth. The scheme of evaluation itself change with lapse of time. And earthquakes occur around the world, so standard evaluation methods that can be applied for various areas are necessary for rescue operations. In RoboCup community, rescue operations have been evaluated form a viewpoint of multi agent systems (MAS). They are efficiency, robustness and reliability of MAS, and the performances were analyzed by changing parameters of environments. In this paper, we present a preliminary study to use the evaluations for disaster prevention.
Portfolios of Estimated Portfolios : Combination Approaches to Estimating Optimal Portfolio Weights

In a canonical asset allocation problem with i.i. d. risky returns, the paper considers the problem of forming the optimal linear combination of a \basis set" of portfolio decision rules. This problem is shown to be formally equivalent to a transformed version of the original portfolio problem with the same risk-free asset and a new set of risky assets, each corresponding to a conceptual position in one of the basis estimators held over both the sample period and asset holding period. Using this fundamental equivalence, results regarding the optimal combination of estimators are derived in the general asset allocation setting as well as for the special case of mean-variance preferences. The optimal combining weights depend upon unknown properties of the returns distribution and are therefore infeasible. The paper discusses feasible approaches to forming portfolios of estimated portfolios and evaluates the practical merits of these approaches in a simulation environment. In parallel with the literature on forecast combination, the simulation evidence suggests that ad hoc combination schemes such as simple averaging across estimators often outperform more sophisticated combination approaches.
A Theoretical Framework For Understanding Aesthetic Experiences In Relation To Website Design And Utilitarian Outcomes

Online affective experiences and their relationships to utilitarian and non-utilitarian outcomes are of growing interest. This study investigates one affective experience – aesthetics – and its impact on online utilitarian values. As the concept of web aesthetic experience has not been well studied or understood, this study proposes a novel theoretical model connecting web aesthetic experiences to online utilitarian values. Based on the prior literature from other disciplines, the study treats the aesthetic experience as a complex and multi-dimensional construct, which contains three distinct subdimensions: aesthetic appraisal, aesthetic judgment, and aesthetic emotion. The research process will include making the selection of diverse level of aesthetic websites and a cross-over design experiment. The structural equation modelling technique will be employed to evaluate the relationships between the web aesthetic experience and utilitarian outcome. The expected contributions of this study are: (i) to construct a novel theoretical model based on the psychological and philosophical concepts of aesthetic experience and utilitarian value; (ii) to identify the multi-dimensional evidence of web aesthetic experiences; (iii) to provide theoretical and practical knowledge for predicting the utilitarian outcomes through online users’ aesthetic experiences. The study also points to the need for more research in this complex area.
Effect of Graphene-Graphene Oxide Modified Anode on the Performance of Microbial Fuel Cell

The inferior hydrophilicity of graphene is an adverse factor to the performance of the graphene modified anodes (G anodes) in microbial fuel cells (MFCs). In this paper, different amounts of hydrophilic graphene oxide (GO) were doped into the modification layers to elevate the hydrophilicity of the G anodes so as to further improve their performance. Increasing the GO doped ratio from 0.15 mg·mg−1 to 0.2 mg·mg−1 and 0.25 mg·mg−1, the static water contact angle (θc) of the G-GO anodes decreased from 74.2 ± 0.52° to 64.6 ± 2.75° and 41.7 ± 3.69°, respectively. The G-GO0.2 anode with GO doped ratio of 0.2 mg·mg−1 exhibited the optimal performance and the maximum power density (Pmax) of the corresponding MFC was 1100.18 mW·m−2, 1.51 times higher than that of the MFC with the G anode.
Inhibition of Histone Deacetylase Activity Aggravates Coxsackievirus B3-Induced Myocarditis by Promoting Viral Replication and Myocardial Apoptosis

ABSTRACT Viral myocarditis, which is most prevalently caused by coxsackievirus B3 (CVB3), is a serious clinical condition characterized by excessive myocardial inflammation. Recent studies suggest that regulation of protein acetylation levels by inhibiting histone deacetylase (HDAC) activity modulates inflammatory response and shows promise as a therapy for several inflammatory diseases. However, the role of HDAC activity in viral myocarditis is still not fully understood. Here, we aim to investigate the role of HDAC activity in viral myocarditis and its underlying mechanism. CVB3-infected BALB/c mice were treated with the HDAC inhibitor (HDACI) suberoylanilide hydroxamic acid (SAHA) or trichostatin A (TSA). We found inhibition of HDAC activity aggravated rather than ameliorated the severity of CVB3-induced myocarditis, which was contrary to our expectations. The aggravated myocarditis by HDACI treatment seemed not to be caused by an elevated inflammatory response but by the increased CVB3 replication. Further, it was revealed that the increased CVB3 replication was closely associated with the HDACI-enhanced autophagosome formation. Inhibition of autophagosome formation by wortmannin or ATG5 short hairpin RNA dramatically suppressed the HDACI-increased CVB3 replication. The increased viral replication subsequently elevated CVB3-induced myocardial apoptosis. Conversely, inhibition of CVB3 replication and ensuing myocardial apoptosis by the antiviral drug ribavirin significantly reversed the HDACI-aggravated viral myocarditis. In conclusion, we elucidate that the inhibition of HDAC activity increases CVB3 replication and ensuing myocardial apoptosis, resulting in aggravated viral myocarditis. Possible adverse consequences of administering HDACI should be considered in patients infected (or coinfected) with CVB3. IMPORTANCE Viral myocarditis, which is most prevalently caused by CVB3, is characterized by excessive myocardial inflammation. Inhibition of HDAC activity was originally identified as a powerful anti-cancer therapeutic strategy and was recently found to be implicated in the regulation of inflammatory response. HDACI has been demonstrated to be efficacious in animal models of several inflammatory diseases. Thus, we hypothesize that inhibition of HDAC activity also protects against CVB3-induced viral myocarditis. Surprisingly, we found inhibition of HDAC activity enhanced myocardial autophagosome formation, which led to the elevated CVB3 viral replication and ensuing increased myocardial apoptosis. Viral myocarditis was eventually aggravated rather than ameliorated by HDAC inhibition. In conclusion, we elucidate the role of HDAC activity in viral myocarditis. Moreover, given the importance of HDACI in preclinical and clinical treatments, the possible unfavorable effect of HDACI should be carefully evaluated in patients infected with viruses, including CVB3.