Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiresolution feature extraction for unstructured meshes

We present a framework to extract mesh features from unstructured two-manifold surfaces. Our method computes a collection of piecewise linear curves describing the salient features of surfaces, such as edges and ridge lines. We extend these basic techniques to a multiresolution setting which improves the quality of the results and accelerates the extraction process. The extraction process is semi-automatic, that is, the user is required to input a few control parameters and to select the operators to be applied to the input surface. Our mesh feature extraction algorithm can be used as a preprocessor for a variety of applications in geometric modeling including mesh fairing, subdivision and simplification.
Randomized self-stabilizing leader election in preference-based anonymous trees

The performance of processors in a distributed system can be measured by parameters such as bandwidth, storage capacity, work capability, reliability, manufacture technology, years of usage, among others. An algorithm using a preference-based approach uses these parameters to make decisions. In this paper we introduce a randomized self-stabilizing leader election algorithm for preference-based anonymous trees. Our algorithm uses the preference of the processors as criteria to select a leader under symmetric or non-symmetric configurations. It is partially inspired on Xu and Srimani's algorithm, but we use a distributed daemon and randomization to break symmetry. We prove that our algorithm has an optimal average complexity time and performed simulations to verify our results.
The Role of Category Accessibility in the Interpretation of Information About Persons: Some Determinants and Implications

Many personality trait terms can be thought of as summary labels for broad conceptual categories that are used to encode information about an individual's behavior into memory. The likelihood that a behavior is encoded in terms of a particular trait category is postulated to be a function of the relative accessibility of that category in memory. In addition, the trait category used to encode a particular behavior is thought to affect subsequent judgments of the person along dimensions to which it is directly or indirectly related. To test these hypotheses, subjects first performed a sentence construction task that activated concepts associated with either hostility (Experiment 1) or kindness (Experiment 2). As part of an ostensibly unrelated impression formation experiment, subjects later read a description of behaviors that were ambiguous with respect to hostility (kindness) and then rated the target person along a variety of trait dimensions. Ratings of the target along these dimensions increased with the number of times that the test concept had previously been activated in the sentence construction task and decreased with the time interval between these prior activations and presentation of the stimulus information to be encoded. Results suggest that category accessibility is a major determinant of the way in which social information is encoded into memory and subsequently used to make judgments. Implications of this for future research and theory development are discussed.
ECONOMIC VULNERABILITY AND RESILIENCE CONCEPTS AND MEASUREMENTS 1

In this paper, economic vulnerability is defined as the exposure of an economy to exogenous shocks, arising out of economic openness, while economic resilience is defined as the policy-induced ability of an economy to withstand or recover from the effects of such shocks. The paper briefly reviews the work already carried out on economic vulnerability and extends the research towards the development of a conceptual and methodological framework for the definition and measurement of economic resilience. Towards this end, the paper proposes an index of economic resilience gauging the adequacy of policy in four broad areas, namely macroeconomic stability, microeconomic market efficiency, good governance and social development. /.
Targeting Prolyl Endopeptidase with Valproic Acid as a Potential Modulator of Neutrophilic Inflammation

A novel neutrophil chemoattractant derived from collagen, proline-glycine-proline (PGP), has been recently characterized in chronic obstructive pulmonary disease (COPD). This peptide is derived via the proteolytic activity of matrix metalloproteases (MMP's)-8/9 and PE, enzymes produced by neutrophils and present in COPD serum and sputum. Valproic acid (VPA) is an inhibitor of PE and could possibly have an effect on the severity of chronic inflammation. Here the interaction site of VPA to PE and the resulting effect on the secondary structure of PE is investigated. Also, the potential inhibition of PGP-generation by VPA was examined in vitro and in vivo to improve our understanding of the biological role of VPA. UV- visible, fluorescence spectroscopy, CD and NMR were used to determine kinetic information and structural interactions between VPA and PE. In vitro, PGP generation was significantly inhibited by VPA. In vivo, VPA significantly reduced cigarette-smoke induced neutrophil influx. Investigating the molecular interaction between VPA and PE showed that VPA modified the secondary structure of PE, making substrate binding at the catalytic side of PE impossible. Revealing the molecular interaction VPA to PE may lead to a better understanding of the involvement of PE and PGP in inflammatory conditions. In addition, the model of VPA interaction with PE suggests that PE inhibitors have a great potential to serve as therapeutics in inflammatory disorders.
Molecular identification of the occurrence of magnetotactic bacteria in fresh water sediments (Czech Republic)

Magnetotactic bacteria (MTB) are of considerable interest because of their importance in the manufacture of various bioinspired materials. In order to find out the status of magnetotactic bacteria at three different sediment in Czech Republic, samples collected from both standing and running freshwaters were subjected to molecular diversity analysis by using 16S rRNA gene approach. Total community DNA from sediment sample was isolated and used for PCR, cloning and sequence analysis. Of the 24 analyzed sequences, six clones are closely related to Magnetobacterium sp. affiliated with the Nitrospira phylum which showed the dominance of Magnetobacterium phylotypes in the sample. This study will provide useful insight about the community structure of MTB in this particular geographical region. However more detailed and specific studies are warranted in order to properly assess the community structure of MTB’s in fresh water sediments.
An efficient eigenvector-node interchange approach for finding netlist partitions

A fast eigenvector technique for obtaining good initial node partitions of netlists for use in interchange heuristics is described. The method is based on approximating the netlist or hypergraph by a weighted graph G and applying the eigenvector technique of E.R. Barnes (1982) to partition G and k blocks of fixed module size. An efficient generalization of the Fiduccia-Mattheyses node-interchange heuristic is developed to further reduce the number of nets connecting k blocks. This node-interchange heuristic is tested on the one resulting netlist partition obtained by this eigenvector approach on a variety of small to large sized benchmark netlist partitioning problems (200 to 12000 modules and nets). The test results show that this eigenvector-node-interchange approach yields netlist partitions that are competitive with the best netlist partitions obtained by using node-interchange heuristics alone on many random initial netlist partitions. The running time of this method is a small fraction of that previous node-interchange methods. <<ETX>>
Biases in Voting Behaviour of the Ballon d'Or 2013-2015

We found supporting evidence for the existence of the home bias and the regional bias. Chances of being selected were higher when the candidate shared the same nationality as the voter. We also found these results considering the regional bias. Being teammates also had a positive effect on the chance of getting the vote. We saw the same effects in the ordered probit, where the chance of being selected went up with the number of points (sharing the same nationality, continent or team gave you a higher chance on a higher score). When we separated the different voter groups, we also saw home and regional bias present in the voting behaviour of the different groups. We thus have found supporting evidence to suggest that there are voting biases present in the voting behaviour of the Ballon d’Or. However, we did not find any evidence of strategic voting.
Speckle Noise Reduction and Edge-Enhancement of Coronary Plaque Tissue in Intravascular Ultrasound Image by Using Anisotropic Diffusion Filter

This paper describes a novel edge-preserved smoothing method with special consideration to an intravascular ultrasound (IVUS) image. An IVUS image, which is commonly used for a diagnosis of acute coronary syndromes (ACS), is very grainy due to heavy speckle noise. The speckle noise prevents not only the medical doctors’ interpretation of the IVUS image, but also the processing of medical images for computer-aided diagnoses (CADs). In order to reduce the speckle noise, in this study, we propose a modification of anisotropic diffusion filter in which a diffusion strength is locally and adaptively controlled by a weighted separability of an IVUS image. The weighted separability is a modification of separability for an edge detection with special consideration to an IVUS image. Furthermore, the proposed method not only reduces a speckle noise but also effectively enhances an edge of plaque tissue in an IVUS image. The effectiveness of the proposed method is verified by the experiments using the real IVUS images.
Suzaku Observation of the Ophiuchus Galaxy Cluster: One of the Hottest Cool Core Clusters

We present the analysis of a Suzaku observation of the Ophiuchus galaxy cluster. We confirmed that the cluster has a cool core. While the temperature of the intracluster medium (ICM) decreases toward the center, the metal abundance increases. Except for the core (r . 50 kpc), the cluster is hot (� 9–10 keV) and is almost isothermal for r . 1 Mpc; the latter contradicts a previous study. We do not detect any variation of the redshift of the ICM in the cluster; the upper limit of the velocity difference is 3000 km s � 1 . The iron-line ratios in X-ray spectra indicate that the ICM has reached the ionization equilibrium state. From these results, we conclude that the Ophiuchus cluster is not a major merger cluster, but one of the hottest clusters with a cool core. We obtain the upper limit of non-thermal emission from the cluster, which is consistent with both the recent claimed detection with INTEGRAL and the recent upper limits with the Swift/BAT. If the cluster has bright non-thermal emission, as suggested by the INTEGRAL measurement, it is probably not due to a recent major cluster merger.
An Adaptive Weighted Support Vector Machine

In weighted support vector machines for regression, each training sample has different approximation error requirement and different penalty due to the effect of weighting factors on them. In order to solve the shortcoming of the weighting factors selection problems in weighted support vector machines, AWSVM, an adaptive selecting approach is proposed which can choose appropriate weighting factors adaptively by the new regression algorithm. Experimental results show that the proposed method has a better performance.
Predicting Stock Movements Using Market Correlation Networks

The goal for this project is to discern whether network properties of financial markets can be used to predict market dynamics. Building on previous work involving networks derived from market price correlations, we augment basic price correlation networks with additional information (revenue, sentiment, and newsflow). Our intuition is that these alternative networks will capture relationships beyond price correlations (e.g. business model exposures) that could eventually enhance downstream predictive models. The final insight we aim to provide is a prediction of future market behavior based on features that incorporate both standard trading information (price, volume, etc.) and market network characteristics (centrality, clustering coefficient, etc.). The project methodology composes of three components: structural, analytical, and predictive. In the structural component, we filter the data to find and visualize the underlying structural motifs of the network. In the analytical component, additional metrics are computed for graphs built from the full dataset, and we do statistical testing to see whether our graph features have predicting power for stock prices. These two components use correlations of both prices and the newly introduced news/sentiment variables when building networks. They also featurize properties of our market correlation networks for sub-periods of years or quarters to see how these networks change over time. Finally, the predictive component incorporates features/metrics generated by the structural and analytical components into a recurrent neural network (RNN) to predict binary market movements (up/down) over a future period of interest.
On broadcasting in heterogenous networks

In this paper we study a well known broadcasting heuristic for heterogenous networks of workstations, called fastest node first. We show that this heuristic produces an optimal solution for minimizing the sum of all completion times, and in addition produces a 1.5 approximation for the problem of minimizing the maximum completion time. We extend these results to show that the same bounds can be obtained for the multicast operation on such heterogenous networks. In addition we show that the problem of minimizing the maximum completion time is N P-hard, which settles the complexity of this open problem.
MapReduce : A Scalable Tool for Data-Processing and Search / Ensemble Applications on Large-Scale Supercomputers

K MapReduce (KMR) is a high-performance MapReduce system in the MPI environment, targeting large-scale supercomputers such as the K computer. Its objectives are to ease programming for data-processing and to achieve efficiency by utilizing the large amount of memory available in largescale supercomputers. In KMR, shuffling operation exchanges key-value pairs in a scalable way by collective communication algorithms utilizing the K’s interconnect. Mapping and reducing operations are multi-threaded to achieve even greater efficiency in modern multi-core machines. Sorting is optimized using fixedlength packed keys instead of variable-length raw keys, which is extensively used inside of shuffling and reducing operations. Besides the MapReduce operations, KMR provides routines for collective file reading for affinity-aware optimizations. This paper presents the results of experimental performance studies of KMR on the K computer. Affinity-aware file loading improves the performance by about 42% over a non-optimized implementation. We also show how KMR can be used to program real-world scientific applications such as meta-genome search and replicaexchange molecular dynamics.
Cynical neoliberalism or true agency? Israeli governmental discourse about entrepreneurship in the education system

Abstract This study examines and characterises the governmental discourse regarding entrepreneurship policy in the Israeli education system as a case study to expand existing knowledge regarding national entrepreneurship policy in education. The study is based on in-depth interviews with key governmental policymakers (holding the top of the pyramid position in the Ministry of Education) involved in the creation and formulation of the education policy in Israel for over a decade. We provide a unique glimpse into the mindset where decisions that shape the entire Israeli education system are made, and present new insights regarding the components of entrepreneurship policy in education in Israel, as well as the characteristics of such policy, its expressions and the construction of entrepreneurship as a field of policy in the education sector.
T cell development in B cell-deficient mice. II. Serological characterization of suppressor T cell factors (TsF1) produced in normal mice and in mice treated chronically with rabbit anti-mouse IgM antibodies

Serological analysis of idiotypic specificities present in azobenzenearsonate (ABA)-specific first-order suppressor T cell factors (TsF1) from C.AL-20 and BALB/c mice revealed a significant difference between TsF from these two strains of mice. The idiotypic composition of TsF1 from BALB/c mice appears to be more heterogeneous, and at least two different fractions can be readily identified. One bears the characteristic BALB/c-associated CRI(C) (crossreactive idiotype) determinants, and the other is non-CRI(C)-bearing. Analysis of ABA- specific TsF1 from animals lacking B cells uncovered a fundamental change in the expression of their idiotypic specificities. TsF from rabbit anti-mouse IgM (anti-mu)-treated C.AL-20 mice failed to express the characteristic CRI(A) determinants. Instead, they express CRI(C) specificities. Similarly, TsF1 from anti-mu-treated BALB/c mice did not express their characteristic CRI(C) specificities, but rather express CRI(A) determinants. These experiments provide strong evidence that the Igh restriction specificity of TsF is dictated by the particular idiotypic specificities expressed. They also clearly demonstrate that B cells and their products play an important role in establishing the idiotypic composition and repertoire of suppressor T cells.
Modular Product Design: Creating “Technologically Separable Interfaces”

This paper discus the relationship between a physical product design and the definition of tasks in integral and modular product development strategies. It is argued that there are different criteria for defining tasks depending on the types of advantages of specialization one tries to realize. Moreover, task definition is also influenced by the costs of coordinating tasks. The physical product design strongly influences the trade-off between benefits of specialization and costs of coordination. This paper represents work in progress and is still very preliminary. Comments from Ron Sanchez on an earlier draft of the paper are gratefully acknowledged.
Over-the-Counter Marketmaking

We study how intermediation and asset prices are affected by illiquidity associated with search and bargaining. We compute explicitly marketmakersr bid and ask prices in a dynamic model with strategic agents. Bid-ask spreads are lower if investors can more easily find other investors or have more easy access to multiple marketmakers. This distinguishes our theory from the information-based intermediation , which implies higher spreads in connection with higher investor sophistication. With a monopolistic marketmaker, bid-ask spreads are higher if investors have easier access to the marketmaker. We discuss several empirical implications and study endogenous search and welfare.
Improving TCP/IP Performance over Last-hop Wireless Networks for Streaming Video Delivery

This paper discusses a protocol for delivery of streaming video with a short play-out deadline over a last-hop wireless network, which trades reliability for latency. This protocol could be implemented in the wireless access point to improve the quality of the received streaming video over an Internet connection to an office or home. The main ideas of the protocols are two-fold: 1) across the wireless link: perform local retransmissions and drop any stale packets; 2) across the entire source-destination path: hide duplicated acknowledgements from the sender to avoid retransmissions. In addition, the protocol takes advantage of application level framing and acknowledges missing transport layer segments with expired play-out deadlines. Although several existing protocols partially solve the problem of streaming video delivery in this particular context, either by improving the performance of the wireless link or by dropping the stale packets, to our best knowledge none of the protocols solves the complete problem. The performance of our protocol is compared against the performance of the snoop, TCP RTM, combined with snoop and TCP RTM protocols, as well as TCP Reno. The results of simulations show a manyfold improvement of the goodput across a wide range of packet-error rates.
A maximum correntropy criterion for robust multidimensional scaling

Multidimensional Scaling (MDS) refers to a class of dimensionality reduction techniques applied to pairwise dissimilarities between objects, so that the interpoint distances in the space of reduced dimensions approximate the initial pairwise dissimilarities as closely as possible. Here, a unified framework is proposed, where the MDS is treated as maximization of a correntropy criterion, which is solved by half-quadratic optimization in a multiplicative formulation. The proposed algorithm is coined as Multiplicative Half-Quadratic MDS (MHQMDS). Its performance is assessed for potential functions associated to various M-estimators, because the correntropy criterion is closely related to the Welsch M-estimator. Three state-of-the-art MDS techniques, namely the Scaling by Majorizing a Complicated Function (SMACOF), the Robust Euclidean Embedding (REE), and the Robust MDS (RMDS), are implemented under the same conditions. The experimental results indicate that the MHQMDS, relying on the M-estimators, performs better than the aforementioned state-of-the-art competing techniques.
CORC Report 2008-03 A behavioral finance based tick-by-tick model for price and volume ∗

We propose a model for jointly predicting stock price and volume at the tick-by-tick level. We model the investors’ preferences by a random utility model that incorporates several important behavioral biases such as the status quo bias, the disposition effect, and loss-aversion. Our model is a logistic regression model with incomplete information; consequently, we are unable to use the maximum likelihood estimation method and have to resort to Markov Chain Monte Carlo (MCMC) to estimate the model parameters. Moreover, the constraint that the volume predicted by the MCMC model exactly match the observed volume vt introduces serial correlation in the stock price. Thus, the standard MCMC methods for calibrating parameters do not work. We develop new modifications of the Metropolis-within-Gibbs method to estimate the parameters in our model. Our primary goal in developing this model is to predict the market impact function and VWAP (volume weighted average price) of individual stocks.