Miracle-dz/UniFAR-FTUs huggingface dataset:
Interconnectedness in the CDS Market

Concentrated risks in the market for credit default swaps (CDSs) are widely considered to have contributed significantly to the 2007–08 financial crisis. We examine the structure of the CDS market using a network-based approach that allows us to capture the interconnectedness between dealers and nondealers of CDS contracts. We find a high degree of interconnectivity among major market participants. Our work helps assess the stability of the CDS market and the potential contagion among market participants. Our findings are of practical importance because even after central clearing becomes mandatory, counterparty risk will remain a relevant systemic consideration owing to the long-term nature of CDS contracts. Editor’s note: This article was reviewed and accepted by Robert Litterman, executive editor at the time the article was submitted. Authors’ note: All three authors worked at the US Securities and Exchange Commission when they wrote this article. As a matter of policy, the SEC disclaims responsibility for any private publication or statement of any of its employees. The views expressed herein are those of the authors and do not necessarily reflect the views of the SEC or of the authors’ colleagues on the staff of the SEC.
An Integrative Framework for Time-Varying Affective Agent Behavior

Recent findings suggest that humans treat computers in a social way. Affect lies at the core of our social behavior; therefore, to capture this social nature of human interaction with computers and to provide a systematic way of including affect into such systems, we propose TAME an integrative framework of affective agent behavior. This paper describes psychological and mathematical foundations for each of the TAME components: personality Traits, Attitudes, Moods and Emotions. An experiment to assess effectiveness of the proposed framework is also presented.
Variable Weighted BSVD-Based Privacy-Preserving Collaborative Filtering

Recommender systems typically use collaborative filtering to make sense of huge and growing volumes of data. However, sharing user-item preferential data for use in collaborative filtering poses significant privacy and security challenges. In recent years, privacy has attracted a lot of attention. There are many existing works on privacy-preserving collaborative filtering. However, while these schemes are theoretically feasible, there are many practical implementation difficulties on real world. In this paper, a privacy-preserving collaborative filtering algorithm based on weighted singular value decomposition is proposed. The users' needs are considered in the algorithm, and the user can disturb their original data with different weights according to their needs. At the privacy-preserving stage, the variable weighted-based BSVD scheme is used to protect the data privacy. At the prediction stage, the improved Slope One algorithm is used to get the prediction. Some experiments are performed using the proposed algorithm. The results indicate a good performance of the scheme in comparison with the Slope One algorithm. Meanwhile, it is shown that the algorithm can preserve the data privacy efficiently with high data usability.
RLWE-Based Homomorphic Encryption and Private Information Retrieval

A multi-bit somewhat homomorphic encryption scheme based on Learning With Errors over Rings (RLWE) assumption is provided, which use canonical embedding to transform elements over a ring into vectors, thus the operation of polynomial multiplication can be performed within O(nlog n) scalar operations, and cipher text size is reduced as well. CPA security of this scheme can be reduced into RLWE assumption. Moreover, an efficient private information retrieval protocol that employs this scheme is presented.
A supervised method for ranking reviews based on latent structure features

Previous research on reviews ranking has two branches: supervised method and unsupervised method. In this paper, we propose a supervised method to predict the review's importance, and the latent structure features are used to represent the review's writing styles. Our method mainly consists of three stages: Extracting lexical features to represent the review's lexical structure; Using lexical features as the input of a variant LDA topic model to learn latent structure features to represent review's writing styles; Obtaining the importance of a single review by a classifier. The experimental results show that our method performs better than the unsupervised baseline in accuracy, and our method is also more robust when compared with ordinary supervised baseline.
Heterogeneity of Astrocytes: From Development to Injury – Single Cell Gene Expression

Astrocytes perform control and regulatory functions in the central nervous system; heterogeneity among them is still a matter of debate due to limited knowledge of their gene expression profiles and functional diversity. To unravel astrocyte heterogeneity during postnatal development and after focal cerebral ischemia, we employed single-cell gene expression profiling in acutely isolated cortical GFAP/EGFP-positive cells. Using a microfluidic qPCR platform, we profiled 47 genes encoding glial markers and ion channels/transporters/receptors participating in maintaining K+ and glutamate homeostasis per cell. Self-organizing maps and principal component analyses revealed three subpopulations within 10–50 days of postnatal development (P10–P50). The first subpopulation, mainly immature glia from P10, was characterized by high transcriptional activity of all studied genes, including polydendrocytic markers. The second subpopulation (mostly from P20) was characterized by low gene transcript levels, while the third subpopulation encompassed mature astrocytes (mainly from P30, P50). Within 14 days after ischemia (D3, D7, D14), additional astrocytic subpopulations were identified: resting glia (mostly from P50 and D3), transcriptionally active early reactive glia (mainly from D7) and permanent reactive glia (solely from D14). Following focal cerebral ischemia, reactive astrocytes underwent pronounced changes in the expression of aquaporins, nonspecific cationic and potassium channels, glutamate receptors and reactive astrocyte markers.
How Does Correlation Affect the Capacity of MIMO Systems with Rate Constraints?

In this paper, we study the impact of antenna correlation at mobile units (MUs) on the capacity of multi-user multiple-input multiple-output (MIMO) systems with rate constraints. Both numerical results and asymptotic analyses show that antenna correlation can actually be regarded as a beneficial factor for such systems. It is also shown that the maximum eigenmode beamforming (MEB) based interleave-division multiple-access (IDMA) system is an effective platform to exploit the potential gain provided by antenna correlation at MUs.
Financial Prediction Using Neural Networks

From the Publisher: Many research articles have appeared on applying neural network techniques to prediction in the various financial markets, but few publications offer practical guidance for implementing these techniques in the real world. This book provides a step-by-step system for setting up and trading a market using a neural network as the prediction engine. The techniques and methods presented in this book can be applied to any market, anywhere in the world, so this book will appeal to anyone who wants to trade or predict financial markets, specifically institutional traders (futures, commodities, stock, bonds, currencies, etc.), private investors and brokerage houses. It should also be of interest to students of financial market timing and Artificial Intelligence.
Analysis of Discrete Shallow-Water Models on Geodesic Delaunay Grids with C-Type Staggering

The properties of C-grid staggered spatial discretizations of the shallow-water equations on regular Delaunay triangulations on the sphere are analyzed. Mass-conserving schemes that also conserve either energy or potential enstrophy are derived, and their features are analogous to those of the C-grid staggered schemes on quadrilateral grids. Results of numerical tests carried out with explicit and semi-implicit time discretizations show that the potential-enstrophy-conserving scheme is able to reproduce correctly the main features of large-scale atmospheric motion and that power spectra for energy and potential enstrophy obtained in long model integrations display a qualitative behavior similar to that predicted by the decaying turbulence theory for the continuous system.
Between Platform and APIs: Kachako API for Developers

Different types of users require different functions in NLP software. It is difficult for a single platform to cover all types of users. When a framework aims to provide more interoperability, users are required to learn more concepts; users’ application designs are restricted to be compliant with the framework. While an interoperability framework is useful in certain cases, some types of users will not select the framework due to the learning cost and design restrictions. We suggest a rather simple framework for the interoperability aiming at developers. Reusing an existing NLP platform Kachako, we created an API oriented NLP system. This system loosely couples rich high-end functions, including annotation visualizations, statistical evaluations, an-notation searching, etc. This API do not require users much learning cost, providing customization ability for power users while also allowing easy users to employ many GUI functions.
PNG graphics hardware decoding of High-speed Huffman decoder design

Change original PNG software decode, update decode algorithm, implement hardware huffman decoder, speed up PNG decode process. This paper discuss the implementation of Huffman decoder in ASIC based on the feature of table and compressed data. The result under the test of EDA tools and matlab shows the Huffman decoder for PNG decode without distortion.
The Neurobiological Basis of Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a common childhoodonset neuropsychiatric disorder characterized by cardinal features of inattention, locomotor hyperactivity, and poor impulse control. Research indicates that ADHD is associated with alterations in the higher cortical circuits that mediate attention and behavioral control. Given the prominent role of the prefrontal association cortex in regulating cognition and behavior through its extensive connections to sensory and motor cortices as well as subcortical structures, impairments in prefrontal function are believed to underlie many of the behavioral features of ADHD. Prefrontal cortex is sensitively modulated by numerous neurotransmitters including catecholamines, and changes in levels of dopamine-1 and norepinephrine α2A-adrenoceptor stimulation are associated with prominent effects on prefrontal function. Effective treatments for ADHD (including stimulants, atomoxetine, and guanfacine) influence catecholamine signaling in the prefrontal cortex and are believed to ameliorate ADHD symptoms via their effects on improved prefrontal cortical regulation of attention and impulse control.
Esophageal Speech enhancement using modified voicing source

This paper presented the modification to Esophageal Speech (ES) enhancement using Adaptive Gain Equalizer (AGE) for modifying the voicing source. However, the voicing source used previously with AGE, obtained using conventional Linear Predication (LP) vocal tract transfer function (AGE-LP), has produced low quality speech due to sensitivity to background noise. The better quality ES can be obtained by estimating voicing source through Iterative Adaptive Inverse Filtering utilized Weighted Linear Prediction (WLP) vocal tract transfer function (AGE-IAIF). The system performance evaluated through Harmonic to Noise Ratio (HNR), and system has shown 3 dB enhancement by AGE-IAIF over previously enhancement method AGE-LP.
Efficient Multiple Incremental and Decremental Ridge Support Vector Machines for Big Streams

This study presents a rapid multiple incremental and decremental mechanism based on WECs for support-vector analysis. The proposed method examines the characteristics of Ridge SVMs and devises a recursion-free function for predicting the Lagrangian multipliers of new samples. Such a function is derived from WECs and Ridge SVMs. With this function, all of the new Lagrangian multipliers can be computed at once without using any gradual step sizes. Moreover, such a function can relax a constraint, where the increment of all the new Lagrangian multipliers should be the same, thereby easily satisfying the requirement of KKT conditions. The ridge parameter of Ridge SVMs stabilizes the computation of inverse matrices during the update of existing Lagrangian multipliers.
Facing peak loads in a P2P transaction system

We present in this article TranspeerToken, a new approach for managing transactions for WEB2.0 applications, based on a previous work, Transpeer. Contrarily to the latter, TranspeerToken is able to manage peak loads efficiently, i.e., when most of transactions try to access the same data concurrently. This bottleneck problem happens because Transpeer exploits a distributed system (DHT) for storing metadata used for transaction routing, which raises data contention in case of peak loads. We leverage it by replacing the DHT by a token ring-based mechanism, where each token carries a metadata, avoiding thus locking metadata. Furthermore, the token mechanism tolerates the failure of k consecutive nodes of the ring and guarantees that transactions requests are not lost in case of failure. Some preliminary experiments on top of SimJava simulator have given promising evaluation results.
Plasminogen Binding Proteins and Plasmin Generation on the Surface of Leptospira spp.: The Contribution to the Bacteria-Host Interactions

Leptospirosis is considered a neglected infectious disease of human and veterinary concern. Although extensive investigations on host-pathogen interactions have been pursued by several research groups, mechanisms of infection, invasion and persistence of pathogenic Leptospira spp. remain to be elucidated. We have reported the ability of leptospires to bind human plasminogen (PLG) and to generate enzimatically active plasmin (PLA) on the bacteria surface. PLA-coated Leptospira can degrade immobilized ECM molecules, an activity with implications in host tissue penetration. Moreover, we have identified and characterized several proteins that may act as PLG-binding receptors, each of them competent to generate active plasmin. The PLA activity associated to the outer surface of Leptospira could hamper the host immune attack by conferring the bacteria some benefit during infection. The PLA-coated leptospires obstruct complement C3b and IgG depositions on the bacterial surface, most probably through degradation. The decrease of leptospiral opsonization might be an important aspect of the immune evasion strategy. We believe that the presence of PLA on the leptospiral surface may (i) facilitate host tissue penetration, (ii) help the bacteria to evade the immune system and, as a consequence, (iii) permit Leptospira to reach secondary sites of infection.
Semantic indexing of sports program sequences by audio-visual analysis

Semantic indexing of sports videos is a subject of great interest to researchers working on multimedia content characterization. Sports programs appeal to large audiences and their efficient distribution over various networks should contribute to widespread usage of multimedia services. In this paper, we propose a semantic indexing algorithm for soccer programs, which uses both audio and visual information for content characterization. The video signal is processed first by extracting low-level visual descriptors from the MPEG compressed bit-stream. The temporal evolution of these descriptors during a semantic event is supposed to be governed by a controlled Markov chain. This allows to determine a list of those video segments where a semantic event of interest is likely to be found, based on the maximum likelihood criterion. The audio information is then used to refine the results of the video classification procedure by ranking the candidate video segments in the list so that the segments associated to the event of interest appear in the very first positions of the ordered list. The proposed method is applied to goal detection. Experimental results show the effectiveness of the proposed cross-modal approach.
A marking scheme using Huffman codes for IP traceback

In (distributed) denial of service attack ((D)DoS), attackers send a huge number of packets with spoofed source addresses to disguise themselves toward a target host or network Various IP traceback techniques such as link testing, marking, and logging to find out the real source of attacking packets have been proposed. We present a marking scheme (with marking and traceback algorithms) in which a router marks a packet with a link that the packet came through. Links of a router are represented by Huffman codes according to the traffic distribution among the links. If the packet runs out of space allotted for the marking field in the packet header, then the router stores the marking field in the router's local memory along with a message digest of the packet. We analyze the memory requirement of routers to store marking fields, compare the scheme with other existing techniques, and address practical issues to deploy the scheme in the Internet. The scheme marks every packet, therefore IP traceback can be accomplished with only a packet unlike in probabilistic markings; also it requires far less amount of memory compared to logging methods and is robust in case of DDoS.
Measurements about the quality of controlled-load service

This paper describes a series of measurements about the characteristics of a reserved controlled-load flow in a simple router network at the presence of excessive background traffic, which was forwarded on a best-effort basis via a common link between two routers. Results concerning the delays and packet loss of the controlled-load flow are discussed and compared to those of the best-effort flow. The tests were carried out according to the simple evaluation criteria given in the RFC specifying the controlled-load service. A quite reasonable performance was measured, roughly satisfying the goals of the controlled-load service. Yet, the presence of background traffic introduced a clear increase in the average level of the network delay of the reserved flow, and also resulted in a larger jitter, which could be intolerable to inelastic traffic.
Personnel Economics in Imperfect Labour Markets

Personnel economics, the use of economics for studying human resource issues, is becoming a standard course in business and economics departments around the world. Indeed, after being successfully introduced in North American business schools, the teaching of personnel economics is now growing in Europe and in the rest of the world. Yet, most of the traditional analysis of personnel economics assumes a perfectly competitive labour market, a situation in which wages are fully flexible and dismissals can take place at no cost. Such a setting is inappropriate for most European markets, where wage rigidity and wage compression are widespread phenomena, and where employment protection legislation is very stringent. Personnel Economics in Imperfect Labour Markets aims to describe key personnel issues when firms and human resource managers act in highly regulated labour markets. Written to be accessible to students, the book provides original answers to questions which have previously been left to specialized academic journals. Should hiring take place under temporary or permanent contracts? How can we provide compensation related incentives when minimum wages are binding? How de we solve the employment/hours trade-off? These questions and more are discussed within the text.
Optimal Design of Accelerating Structure with Nose Cone Shape Based on Genetic Algorithm

This paper analyzed the process of optimal design of accelerating structure and described the method of optimal design of accelerating structure based on genetic algorithm (GA). We performed the simulation experiment which is optimal design of the accelerating structure with a nose cone shape using the C++ programming language to write GA program. It proved that the algorithm was feasible and it gave a set of geometric parameter with higher shunt impedance for accelerating structure.
ChIP-Seq and RNA-Seq Reveal an AmrZ-Mediated Mechanism for Cyclic di-GMP Synthesis and Biofilm Development by Pseudomonas aeruginosa

The transcription factor AmrZ regulates genes important for P. aeruginosa virulence, including type IV pili, extracellular polysaccharides, and the flagellum; however, the global effect of AmrZ on gene expression remains unknown, and therefore, AmrZ may directly regulate many additional genes that are crucial for infection. Compared to the wild type strain, a ΔamrZ mutant exhibits a rugose colony phenotype, which is commonly observed in variants that accumulate the intracellular second messenger cyclic diguanylate (c-di-GMP). Cyclic di-GMP is produced by diguanylate cyclases (DGC) and degraded by phosphodiesterases (PDE). We hypothesized that AmrZ limits the intracellular accumulation of c-di-GMP through transcriptional repression of gene(s) encoding a DGC. In support of this, we observed elevated c-di-GMP in the ΔamrZ mutant compared to the wild type strain. Consistent with other strains that accumulate c-di-GMP, when grown as a biofilm, the ΔamrZ mutant formed larger microcolonies than the wild-type strain. This enhanced biofilm formation was abrogated by expression of a PDE. To identify potential target DGCs, a ChIP-Seq was performed and identified regions of the genome that are bound by AmrZ. RNA-Seq experiments revealed the entire AmrZ regulon, and characterized AmrZ as an activator or repressor at each binding site. We identified an AmrZ-repressed DGC-encoding gene (PA4843) from this cohort, which we named AmrZ dependent cyclase A (adcA). PAO1 overexpressing adcA accumulates 29-fold more c-di-GMP than the wild type strain, confirming the cyclase activity of AdcA. In biofilm reactors, a ΔamrZ ΔadcA double mutant formed smaller microcolonies than the single ΔamrZ mutant, indicating adcA is responsible for the hyper biofilm phenotype of the ΔamrZ mutant. This study combined the techniques of ChIP-Seq and RNA-Seq to define the comprehensive regulon of a bifunctional transcriptional regulator. Moreover, we identified a c-di-GMP mediated mechanism for AmrZ regulation of biofilm formation and chronicity.
Better Synchronous Binarization for Machine Translation

Binarization of Synchronous Context Free Grammars (SCFG) is essential for achieving polynomial time complexity of decoding for SCFG parsing based machine translation systems. In this paper, we first investigate the excess edge competition issue caused by a left-heavy binary SCFG derived with the method of Zhang et al. (2006). Then we propose a new binarization method to mitigate the problem by exploring other alternative equivalent binary SCFGs. We present an algorithm that iteratively improves the resulting binary SCFG, and empirically show that our method can improve a string-to-tree statistical machine translations system based on the synchronous binarization method in Zhang et al. (2006) on the NIST machine translation evaluation tasks.