Miracle-dz/UniFAR-FTUs huggingface dataset:
Central Bank Liquidity Provision, Risk-Taking and Economic Efficiency

After the Lehman default, but also during the euro area sovereign debt crisis, central banks have tended to extend the ability of banks to take recourse to central bank credit operations through changes of the collateral framework (e.g. CGFS, 2008 - in consistence with previous narratives, such as Bagehot, 1873). We provide a simple four sector model of the economy in which we illustrate the relevant trade-offs, derive optimal central bank collateral policies, and show why in a financial crisis, in which liquidity shocks become more erratic and the total costs of defaults increase, central banks may want to allow for greater potential recourse of banks to central bank credit. The model also illustrates that the credit riskiness of counterparties and issuers is endogenous to the central bank's credit policies and related risk control framework. Finally, the model allows identifying the circumstances under which the counterintuitive case arises in which a relaxation of the central bank collateral policy may reduce its expected losses. JEL Classification: E58, G32
Combining stroke-based and selection-based relevance feedback for content-based image retrieval

We propose a flexible interaction mechanism for CBIR by enabling relevance feedback inside images through drawling strokes. User's interest is obtained from an easy-to-use user interface, and fused seamlessly with traditional feedback information in a semi-supervised learning framework. Retrieval performance is boosted due to more precise description of the query concept. Region segmentation is also improved based on the collected strokes, and further enhances the retrieval precision. We implement our system Flexible Image Search Tool (FIST) based on the ideas above. Experiments on two real world data sets demonstrate the effectiveness of our approach.
Research on Scheduling Algorithm for Workflow-Based Grid Resource

In the research area of grid computing and workflow technology, workflow products based on Web-service technology can work as grid services in grid architecture. A new algorithm of scheduling grid resource with workflow engine is discussed in this paper. The QoS, executing time of process, cost and workload of grid resources are the key factors in grid resources scheduling. Dynamic feed back mechanism and executing time estimation are applied in this algorithm, which can keep the grid resources in a good balance and advance the whole performance of the grid application with workflow service.
MHD simulations of disk-star interaction

Abstract We discuss a number of topics relevant to disk-magnetosphere interaction and how numerical simulations illuminate them. The topics include: (1) disk-magnetosphere interaction and the problem of disk-locking; (2) the wind problem; (3) structure of the magnetospheric flow, hot spots at the star's surface, and the inner disk region; (4) modeling of spectra from 3D funnel streams; (5) accretion to a star with a complex magnetic field; (6) accretion through 3D instabilities; (7) magnetospheric gap and survival of protoplanets. Results of both 2D and 3D simulations are discussed.
Towards Open Distributed Information Systems By the Way of a Multi-Agent Conception Framework

At the heart of cooperation between agents we can find the notion of agent modeling. The representation one agent maintains of other agents is indeed essential for social reasoning. In order to cooperate, an agent must know how it may cooperate with each of its acquaintances and such knowledge is tightly connected to the known characteristics of another agent. Agent modeling is even more significant if we consider open systems. In such systems, agents have to update their representation of others in order to learn a description of any incoming agent. In this paper, we propose a model of representation of others based on the external description defined by Sichman. We make use of our model to allow incoming agents to introduce themselves to others and to classify their acquaintances into stereotypes. These two abilities permit us to conceive an open multi-agent system (MAS) keeping the agent integration task distributed among the agents. An application of such a MAS to the building of open distributed information systems is presented.
Optimal Fluctuations and Tail States of Non-Hermitian Operators

We develop a general variational approach to study the statistical properties of the tail states of a wide class of non-Hermitian operators. The utility of the method, which is a refinement of the instanton approach introduced by Zittartz and Langer, is illustrated in detail by reference to the problem of a quantum particle propagating in an imaginary scalar potential.
Multivariate Hawkes processes: an application to financial data

A Hawkes process is also known under the name of a self-exciting point process and has numerous applications throughout science and engineering. We derive the statistical estimation (maximum likelihood estimation) and goodness-of-fit (mainly graphical) for multivariate Hawkes processes with possibly dependent marks. As an application, we analyze two data sets from finance.
Text Clustering Based on Background Knowledge

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. Standard partitional or agglomerative clustering methods efficiently compute results to this end. However, the bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning has been achieved, because it is only specified extensionally. In order to deal with the two problems, we integrate background knowledge into the process of clustering text documents. First, we preprocess the texts, enriching their representations by background knowledge provided in a core ontology — in our application Wordnet. Then, we cluster the documents by a partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks. Second, the clustering partitions the large number of documents to a relatively small number of clusters, which may then be analyzed by conceptual clustering. In our approach, we applied Formal Concept Analysis. Conceptual clustering techniques are known to be too slow for directly clustering several hundreds of documents, but they give an intensional account of cluster results. They allow for a concise description of commonalities and distinctions of different clusters. With background knowledge they even find abstractions like “food” (vs. specializations like “beef” or “corn”). Thus, in our approach, partitional clustering reduces first the size of the problem such that it becomes tractable for conceptual clustering, which then facilitates the understanding of the results.
A simple framework to justify linear response theory

The use of linear response theory for forced dissipative stochastic dynamical systems through the fluctuation dissipation theorem is an attractive way to study climate change systematically among other applications. Here, a mathematically rigorous justification of linear response theory for forced dissipative stochastic dynamical systems is developed. The main results are formulated in an abstract setting and apply to suitable systems, in finite and infinite dimensions, that are of interest in climate change science and other applications.
Psychotherapy with mentally disabled persons: the importance of non-verbal qualities in music therapy with adults with severe autism

Background: Adults with severe autism or mental disability in general have limited abilities in both understanding and usage of verbal language. Music Therapy is the psychotherapeutic approach that is addressing their emotional needs. The study describes the significance of the tonal/musical interaction on the client’s difficult journey towards intensive social interaction. At the end of the process, first moments of jointly making music has become a reality with the assistance of the Music Therapist.Methods: Twenty-two Individual Music Therapy sessions with an adult with ASD including a qualitative evaluation.Discussion: Music Therapy is the appropriate psychotherapeutic treatment for adults with ASD. Especially the non-verbal qualities of Music Therapy empower these clients to respond to this kind of treatment. An increasing emotional participation by using their musical and interactive potential can be observed and will be proved by evidence-based secondary studies.Conclusions: Adults with ASD who have.
LineAO&#x2014;Improved Three-Dimensional Line Rendering

Rendering large numbers of dense line bundles in three dimensions is a common need for many visualization techniques, including streamlines and fiber tractography. Unfortunately, depiction of spatial relations inside these line bundles is often difficult but critical for understanding the represented structures. Many approaches evolved for solving this problem by providing special illumination models or tube-like renderings. Although these methods improve spatial perception of individual lines or related sets of lines, they do not solve the problem for complex spatial relations between dense bundles of lines. In this paper, we present a novel approach that improves spatial and structural perception of line renderings by providing a novel ambient occlusion approach suited for line rendering in real time.
Insights into recently fragmented vole populations from combined genetic and demographic data

We combined demographic and genetic data to evaluate the effects of habitat fragmentation on the population structure of the California red‐backed vole (Clethrionomys californicus). We analysed variation in the mitochondrial DNA (mtDNA) control region and five nuclear microsatellite loci in small samples collected from two forest fragments and an unfragmented control site in 1990–91. We intensively sampled the same forest fragments and two different control sites in 1998 and 1999. Vole abundances fluctuated greatly at sizes below 50 individuals per fragment. Fragment populations had significantly lower mtDNA allelic diversity than controls, but not nuclear heterozygosity or numbers of alleles. The use of only trapping and/or mtDNA marker data would imply that fragment populations are at least partially isolated and vulnerable to inbreeding depression. In contrast, the abundance estimates combined with microsatellite data show that small fragment populations must be linked to nearby forests by high rates of migration. These results provide evidence for the usefulness of combining genetic and demographic data to understand nonequilibrium population structure in recently fragmented habitats.
Learning to play Pac-Man: an evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.
An Experimental Evaluation of Nearest Neighbour Time Series Classification

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC~\cite{lines14elastic}, we perform a series of experiments to test whether this standard practice is valid. Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW. Based on experiments on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to beat but 1-NN with DTW is not, if window size is set through cross validation.
Traffic-aware compatible controller deployment

Multi-controller architecture(flat or hierarchical) has been proposed for Software Defined Networking(SDN). The placement of these controllers is needed to be considered, as which influences the optimization of network. To optimize network by placing controllers, we develop our controller placement framework to solve this issue, which is compatible for both flat and hierarchical control plane. Our algorithm is evaluated at some topologies and the simulation results show that: in networks with varied traffic, (1) load balance between controllers(domains) is maintained well and (2) fewer inter-domain flows are generated, which helps to eliminate the impact of control state inconsistency.
An efficient linearly convergent semismooth Netwon-CG augmented Lagrangian method for Lasso problems

We develop a fast and robust algorithm for solving large-scale convex composite optimization models with an emphasis on the $\ell_1$-regularized least square regression (the Lasso) problems. Although there exist a large amount of solvers in the literature for Lasso problems, so far no solver can handle difficult real large scale regression problems. By relying on the piecewise linear-quadratic structure of the problems to realize the remarkable fast linear convergence property of the augmented Lagrangian algorithm, and by exploiting the superlinear convergence of the semismooth Newton-CG method, we are able to design a new algorithm, called {\sc Ssnal}, to efficiently solve the aforementioned difficult problems. Global convergence and local linear convergence results for {\sc Ssnal} are established. Numerical results, including the comparison between our approach and several state-of-the-art solvers, on real data sets, are presented to demonstrate the high efficiency and robustness of our proposed algorithm in solving large-scale difficult problems.
Efficacy and Cardiotoxicity of Liposomal Doxorubicin-Based Chemotherapy in Advanced Breast Cancer: A Meta-Analysis of Ten Randomized Controlled Trials

Background Various trials have compared the efficacy and toxicity of liposomal doxorubicin-based chemotherapy with the conventional formulation of doxorubicin although arriving at inconsistent conclusions. To derive a conclusive assessment of the efficacy and cardiotoxicity associated with chemotherapy, we performed a meta-analysis by combining data from all eligible randomized controlled trials. Methods We used the PubMed database to identify relevant studies published through December 28, 2014. Eligible studies included randomized controlled trials directly comparing the efficacy and cardiotoxicity of liposomal doxorubicin-based chemotherapy with conventional doxorubicin in advanced breast cancer with adequate data. Odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs) were used to assess the efficacy and cardiotoxicity in a fixed-effects or random-effects model. Results Ten randomized controlled trials containing efficacy and data from a total of 2,889 advanced breast cancer patients were included in this report. Liposomal doxorubicin-based chemotherapy was associated with a significant reduction in the risk of cardiotoxicity (OR = 0.46, 95% CI 0.23 to 0.92, p = 0.03) and a significant improvement in the overall response rate (ORR) (OR = 1.25, 95% CI 1.02 to 1.52, p=0.03) compared with conventional doxorubicin. An apparent improvement in progression-free survival (PFS) for patients treated with liposomal doxorubicin-based chemotherapy was noted; however, this difference was not significant (HR = 1.14, 95% CI 0.96 to 1.34, p = 0.12). In terms of overall survival (OS), no significant difference between the two chemotherapy regimens was noted (HR = 1.00, 95% CI 0.91 to 1.10, p = 0.93). Conclusion The results of this meta-analysis suggest that liposomal doxorubicin-based chemotherapy is associated with a significant improvement in the ORR and a significant reduction in the risk of cardiotoxicity.
Cramer-Rao bounds for semi-blind, blind and training sequence based channel estimation

Two channel estimation techniques are often opposed: training sequence based estimation in which a sequence of symbols known by the receiver is used and blind equalization in which the channel and/or the symbols are determined from the received signal only. The purpose of semi-blind techniques would be to adapt blind techniques in order to profit from the existence of a training sequence. A first approach to assess the performance of semi-blind methods is proposed. We study Cramer-Rao Bounds for blind, semi-blind and training-sequence based channel estimates for a deterministic as well as a Gaussian symbol model, and compare them theoretically as well as in numerical evaluations. Furthermore, an example of comparison between the corresponding maximum likelihood estimation methods is given.
On semi-supervised learning of Dirichlet Mixture Models for Web content classification

This paper presents a method for designing semi-supervised classifier trained on labeled and unlabeled instances. We explore the trade-off between maximizing a discriminative likelihood of labeled data and a generative likelihood of labeled and unlabeled data. Moreover, mixture models are an interesting and flexible model family. The different uses of mixture models include for example generative models and density estimation. This paper investigates semi-supervised learning of mixture models using a unified objective function taking both labeled and unlabeled data into account. We conducted experiments on the WebKB and 20NEWSGROUPS. The results show that unlabeled data results in improvement in classification accuracy over the supervised model.
Data Clustering using Artificial Neural Network , Rough set Theory and Optimization Techniques

Artificial Neural Network in particular Self Organizing Map (SOM) has been widely used in clustering analysis operations. SOM maps high dimensional data space into two dimensions colored grid. The crisp clustering which employs one threshold to determine cluster boundaries has poor performance in many complex and high dimension data sets. In this paper to improve the performance of clustering analysis, SOM is used to reduce the data dimensionality and to have optimized cluster boundaries Rough set theory and Optimization techniques has been employed in the second stage. In this proposed approach, first using SOM to cluster and reduce the dimensionality of the data set and using Rough set theory and optimization problems to reduce uncertainty that involved in cluster analysis, in the second stage, seems that has much better performance in compare with crisp clustering methods.
Towards microsecond biological molecular dynamics simulations on hybrid processors

Biomolecular simulations continue to become an increasingly important component of molecular biochemistry and biophysics investigations. Performance improvements in the simulations based on molecular dynamics (MD) codes are widely desired. This is particularly driven by the rapid growth of biological data due to improvements in experimental techniques. Unfortunately, the factors, which allowed past performance improvements of MD simulations, particularly the increase in microprocessor clock frequencies, are no longer improving. Hence, novel software and hardware solutions are being explored for accelerating the performance of popular MD codes. In this paper, we describe our efforts to port and optimize LAMMPS, a popular MD framework, on hybrid processors: graphical processing units (GPUs) accelerated multi-core processors. Our implementation is based on porting the computationally expensive, non-bonded interaction terms on the GPUs, and overlapping the computation on the CPU and GPUs. This functionality is built on top of message passing interface (MPI) that allows multi-level parallelism to be extracted even at the workstation level with the multi-core CPUs as well as extend the implementation on GPU clusters. The results from a number of typically sized biomolecular systems are provided and analysis is performed on 3 generations of GPUs from NVIDIA. Our implementation allows up to 30–40 ns/day throughput on a single workstation as well as significant speedup over Cray XT5, a high-end supercomputing platform. Moreover, detailed analysis of the implementation indicates that further code optimization and improvements in GPUs will allow ∼100 ns/day throughput on workstations and inexpensive GPU clusters, putting the widely-desired microsecond simulation time-scale within reach to a large user community.
The Relationship between Hip Extension Force Measured by Using a Hand-held Dynamometer and the Effect of Proprioceptive Neuromuscular Facilitation Stretching on Hamstring Flexibility

본 연구의 목적은 고유수용성 신경근 촉진법을 이용한 뒤넙다리근의 스트레칭 동안 휴대용 근력계를 이용하여 측정된 고관절 신전력과 스트레칭으로 인해 증가된 뒤넙다리근 유연성 사이의 관계를 보는 것이었다. 스트레칭은 1세트 (총 6회, 회당 5초) 수행되었다. 스트레칭 전, 후 능동 무릎 폄 검사가 즉청되었으며, 뒤넙다리근의 최대 신전력은 휴대용 근력계로 측정되었다. 신전력과 유연성 사이의 상관관계는 없었으나, 6회 동안 측정된 평균 신전력의 최소값과 최대값은 유의한 차이를 보였다. 일정하지 않은 신전력의 적용은 스트레칭 효과를 감소할지 모른다. 스트레칭 동안 휴대용 근력계를 이용하여 피험자의 스트레칭 수행정도를 관찰하고 실시간으로 피드백을 줄 수 있다면, 임상 및 스포츠물리치료현장에서 매우 유용할 것이다.
An MDP-based approach oriented optimal policy for path planning

In this article we present an approach to improve the execution time of the Markov decision process (MDP) used in robotics for path planning. We've improved it for both value iteration algorithms (value iteration) and Policy Iteration (policy iteration). Unlike the conventional approach which initializes the algorithms with random values and explores all the accessible states at each iteration, our approach modifies the classical algorithms to improve their performance. These changes are reflected mainly in two points: non-random initialization of the algorithms, and a significant reduction of calculations at each iteration. The results are promising; they are presented at the end of the paper.