Miracle-dz/UniFAR-FTUs huggingface dataset:
Virus infection triggers widespread silencing of host genes by a distinct class of endogenous siRNAs in Arabidopsis

Significance RNAi-mediated antiviral immunity directs specific virus resistance by virus-derived siRNAs in contrast to broad-spectrum resistance triggered in innate immunity by host pattern recognition receptors. Here we show that induction of antiviral RNAi in Arabidopsis is associated with production of a genetically distinct class of virus-activated siRNAs (vasiRNAs) by RNA-dependent RNA polymerase-1 to target hundreds of host genes for RNA silencing by Argonaute-2. Production of vasiRNAs is induced by viruses from two different supergroups of RNA virus families, targeted for inhibition by Cucumber mosaic virus, and correlated with virus resistance independently of viral siRNAs. We propose that antiviral RNAi activates broad-spectrum antiviral activity via widespread silencing of host genes directed by vasiRNAs in addition to specific antiviral defense by viral siRNAs. Antiviral immunity controlled by RNA interference (RNAi) in plants and animals is thought to specifically target only viral RNAs by the virus-derived small interfering RNAs (siRNAs). Here we show that activation of antiviral RNAi in Arabidopsis plants is accompanied by the production of an abundant class of endogenous siRNAs mapped to the exon regions of more than 1,000 host genes and rRNA. These virus-activated siRNAs (vasiRNAs) are predominantly 21 nucleotides long with an approximately equal ratio of sense and antisense strands. Genetically, vasiRNAs are distinct from the known plant endogenous siRNAs characterized to date and instead resemble viral siRNAs by requiring Dicer-like 4 and RNA-dependent RNA polymerase 1 (RDR1) for biogenesis. However, loss of EXORIBONUCLEASE4/THYLENE-INSENSITIVE5 enhances vasiRNA biogenesis and virus resistance without altering the biogenesis of viral siRNAs. We show that vasiRNAs are active in directing widespread silencing of the target host genes and that Argonaute-2 binds to and is essential for the silencing activity of vasiRNAs. Production of vasiRNAs is readily detectable in Arabidopsis after infection by viruses from two distinct supergroups of plant RNA virus families and is targeted for inhibition by the silencing suppressor protein 2b of Cucumber mosaic virus. These findings reveal RDR1 production of Arabidopsis endogenous siRNAs and identify production of vasiRNAs to direct widespread silencing of host genes as a conserved response of plants to infection by diverse viruses. A possible function for vasiRNAs to confer broad-spectrum antiviral activity distinct to the virus-specific antiviral RNAi by viral siRNAs is discussed.
The Effects of Fiscal Policy and its Interactions with Monetary Policy in Brazil

This paper analyzes the effects of fiscal policy shocks on the dynamics of the economy and the interaction between fiscal and monetary policy using structural vector autoregressions (SVARs). We test the Fiscal Theory of the Price Level for Brazil, analyzing the response of public sector liabilities to primary surplus shocks. For the hybrid identification we find that it is not possible to distinguish empirically between Ricardian (Monetary Dominance) and non-Ricardian (Fiscal Dominance) regimes. However, using sign restrictions there is some evidence that the government followed a Ricardian (Monetary Dominance) regime from January 2000 to June 2008.
Comparison of Approaches for Self-Improvement in Self-Adaptive Systems

Various trends such as mobility of devices, Cloud Computing, or Cyber-Physical Systems lead to a higher degree of distribution. These systems-of-systems need to be integrated. The integration of various subsystems still remains a challenge. Self-improvement within self-adaptive systems can help to shift integration tasks from the static design time to the runtime, which fits the dynamic needs of these systems. Thus, it can enable the integration of system parts at runtime. In this paper, we define self-improvement as an adaptation of an Autonomic Computing system's adaptation logic. We present an overview of approaches for self-improvement in the domains of Autonomic Computing and self-adaptive systems. Based on a taxonomy for self-adaptation, we compare the approaches and categorize them. The categorization shows that the approaches focus either on structural or parameter adaptation but seldomly combine both. Based on the categorization, we elaborate challenges, that need to be addressed by future approaches for offering self-improving system integration at runtime.
Randomness Evaluation and Hardware Implementation of Nonadditive CA-Based Stream Cipher

We shall review the cellular automaton(CA)-based pseudorandom-number generators (PRNGs), and show that one of these PRNGs can generate high-quality random numbers which can pass all of the statistical tests provided by the National Institute of Standards and Technology (NIST). A CA is suitable for hardware implementation. We demonstrate that the CA-based stream cipher, which is implemented in the field-programmable gate arrays (FPGA), has a high encryption speed in a real-time video encryption and decryption system.
Teaching theoretical computer science using a cognitive apprenticeship approach

High failure rates in introductory courses on theoretical computer science are a common problem at universities in Germany, Europe, and North America, as students often have difficulties coping with the contents of such courses due to their abstract and theoretical nature. This paper describes modifications to the pedagogy of a theory course held at the University of Potsdam, Germany that are motivated by a cognitive apprenticeship approach and have led to a significant reduction of the course's failure rates. Since our approach is based on the typical infrastructure for teaching introductory computer science courses and does not require additional expenses or special resources, it can be replicated by other institutions. We believe that it is a serious contribution to better support teaching as well as student learning success in this field.
Automatically Associating Documents with Concept Map Knowledge Models

Concept-map-based knowledge models are widely used for knowledge capture and sharing. When libraries of concept maps are available, those concept maps can provide a useful context for understanding new documents, and the new documents can provide useful annotations to the knowledge models—if the right documents can be associated to the right concept maps. This paper presents ongoing research on a method for classifying documents according the most relevant concept maps in a concept map library, using natural language processing techniques. It presents an algorithm for extracting concept map fragments from a document, introduces a matching procedure, and compares performance of the overall algorithm with a baseline method, with encouraging results.
Relation Between Randic Index and Average Distance of Trees 1

The Randic index R(G) of a graph G is the sum of weights (deg(u )d eg(v)) −0.5 over all edges uv of G , where deg(v) denotes the degree of a vertex v . W e prove that for any tree T with n1 leaves R(T ) ≥ ad(T )+m ax(0, √ n1 − 2) , where ad(T ) is the average distance between vertices of T. As a consequence we resolve the conjecture R(G) ≥ ad(G) given by Fajtlowicz in 1988 for the case when G is a tree.
Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation

In this paper, we consider the asymptotic form of the generalization error for the restricted Boltzmann machine in Bayesian estimation. It has been shown that obtaining the maximum pole of zeta functions is related to the asymptotic form of the generalization error for hierarchical learning models (Watanabe, 2001a,b). The zeta function is defined by using a Kullback function. We use two methods to obtain the maximum pole: a new eigenvalue analysis method and a recursive blowing up process. We show that these methods are effective for obtaining the asymptotic form of the generalization error of hierarchical learning models.
CVIEW : A Real-Time Interactive Conferencing System

Computer-based tools for supporting collaboration, from informal conversations to structured meetings, are becoming commonplace. Different technologies support various techniques for computer conferencing. On the one extreme is asynchronous communication where the participants do not need to be available at the same time in order to communicate. Traditionally, this has taken the form of computer conferences, bulletin boards, mail and forms management. In contrast, video conferencing provides the ability to have a conference where the participants can see each other and possibly make impromptu presentations. Video conferencing is progressing from freeze frame to full motion technology. An emerging area in computer supported cooperative work is real-time computer conferencing. This technology allows two or more people in the privacy of their own offices to see a shared view of a computer application. That is, when any person moves a mouse, uses the keyboard, or uses other input devices, the result of these actions are immediately shown to all people in the conference (limited by network bandwidth). This technology is especially useful in consulting, teaching, programming, meetings, marketing technical writing and a variety of other applications. This paper discusses a system called CVIEW that implements a real-time interactive conferencing system. The architecture of the system is described along with its implications and directions for future systems based on User Interface Management Systems (UIMS).
DT-MRI interpolation: At what level?

This paper studies two levels of interpolation techniques for improving spatial resolution of diffusion tensor magnetic resonance imaging (DTI): a) at the level of scalar gray-level images using anisotropic interpolation and b) at the level of diffusion tensor fields using Log-Euclidean interpolation and quaternion interpolation. The performance of these different level interpolations is evaluated both qualitatively and quantitatively using criteria such as fractional anisotropy (FA), mean diffusivity (MD), fiber length, etc. We conclude that tensor field level interpolations avoids undesirable swelling effect in DTI, which is not the case with scalar gray-level interpolation, and that scalar gray-level image interpolation and Log-Euclidean tensor field interpolation suffer from decreases in FA and MD, which is however avoided by the quaternion tensor field interpolation.
Fault Diagnosis on Hermetic Compressors Based on Sound Measurements

A fault identification study is made to identify five common faults in hermetic compressors manufactured in a large plant. Sound power level is used as raw data. Sound measurements were made in a room where microphones were located at different places of a virtual hemi-sphere, designed according to international standards. Obtained data is analyzed using the artificial neural networks method, where the multilayer perceptron model is used. Two different analysis approaches are carried out. In the first approach, only the summary data that emanated from the information coming from all microphones are used. In the second approach, all data coming from all microphones are used. The results indicate that the first approach is partially successful and the second is successful.
Performance Analysis of Image Encryption Using Block Based Technique

In digital communication and multimedia application, security is must for communication and storage of images. Image Encryption is one of the best methods to ensure high security images which are generally used in medical science, military applications. In this paper, an efficient technique is proposed with the help of cipher block chaining (CBC) operation, which is used for image encryption. We test our proposed technique using MATLAB R2013a, it can achieve various image quality parameters such as the mean square error(MSE), peak signal to noise ratio (PSNR) and the overall time taken by the algorithm to perform the process is also computed.
Time-domain performance bound analysis for analog and interconnect circuits considering process variations

Time-Domain worst case or performance bound estimation for analog integrated circuits and interconnect circuits are crucial for both analog and digital circuit design and optimization in the presence of process variations. In this paper, we present a novel non-Monte-Carlo (MC) performance bound analysis technique in time domain. The new method consists of several steps. First the symbolic transient modified nodal analysis (MNA) formulation of the circuit matrices of (linearized) analog and interconnect circuits at a time step is formed. Then the closed-form expressions of the interested performance in terms of variational parameters of the circuit matrices of (linearized) analog and interconnect circuits are derived via a graph-based symbolic analysis method. Then time-domain performance response bound of current time step are obtained by a nonlinear constrained optimization process subject to the parameter variations and variational circuit state bounds computed from the previous time step. We study the bounds computed by the proposed against the different sigma bounds by the standard MC method, which shows that the proposed method is more efficient for computing high sigma bounds than the MC method. Experimental results show that the new method can deliver order of magnitudes speedup over the standard Monte Carlo simulation on some typical analog circuits and interconnect circuits with high accuracy.
A Nonlinear Analysis of Driver-Vehicle Performance with Four Wheel Steering Using Time Delay Control Method

The authors have adopted a time delay control method to roundly analyze vehicle dynamic capability equipped with four wheel steering based on close-loop driver vehicle system, especially, driver's steering behavior tends to become more discontinuous while performing an activity during driving. The single-loop quasi-linear driver steering model can certainly be applied to modeling of a 5 DOF vehicle coupling system. To quantify vehicle performance, the steering control is obtained by step input as the steering angles of the front/rear wheels. The simulation result indicates that the proposed method is very effective in the performance of the vehicle maneuverability at low speed and stability at high speed.
Human Language Technology for Text-based Analysis of Psychotherapy Sessions in the Spanish Language

We present work in progress in the application of Natural Language Processing (NLP) technology to the analysis of textual transcriptions of psychotherapy sessions in the Spanish Language. We are developing a set of NLP tools as well as adapting an existing dictionary for the analysis of interviews framed on a psychoanalytic theory. We investigate the application of NLP techniques, including dictionary-based interpretation, and speech act identification and classification for the (semi) automatic identification in text of a set of psychoanalytical variables. The objective of the work is to provide a set of tools and resources to assist therapist during discourse analysis.
Model predictive control and reinforcement learning as two complementary frameworks

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulations on a benchmark control problem taken from the power system literature and discuss the results obtained.
An Efficient Greedy Algorithm on Optimizing the Architecture of Delay Limited Erasure Error Recovery

In this paper, we address the QoS issue of reliable real-time multimedia multicast services over packet-switched networks. Using a generalized architecture of delay limited erasure error recovery, we propose an efficient greedy algorithm for optimizing the parameters of the general architecture. Through the proposed greedy algorithm, the total needed redundant information can be minimized by choosing the best scheme automatically among those entire schemes integrated in the architecture. As a result, the greedy algorithm can be a good candidate technique for guaranteeing QoS requirements for real-time multimedia multicast services over packet-switched, especially over wireless networks.
Cultural Competency in Law Enforcement: Assessing Cultural Competence Levels among College Campus Police Departments

i DEDICATION ii LIST OF TABLES iv LIST OF FIGURES v CHAPTERS CHAPTER 1 – Introduction 1 CHAPTER 2 – Literature Review 4 CHAPTER 3 – Research Synthesis 9 CHAPTER 4 – Methodology 19 CHAPTER 5 – Discussion of Findings 23 CHAPTER 6 – Limitations 32 CHAPTER 7 – Recommendations 33 CHAPTER 8 – Conclusion 37 REFERENCES 39 APPENDIX 42 VITA 44
Distributed Graph Layout for Scalable Small-world Network Analysis

The in-memory graph layout or organization has a considerable impact on the time and energy efficiency of distributed memory graph computations. It affects memory locality, inter-task load balance, communication time, and overall memory utilization. Graph layout could refer to partitioning or replication of vertex and edge arrays, selective replication of data structures that hold meta-data, and reordering vertex and edge identifiers. In this work, we present DGL, a fast, parallel, and memory-efficient distributed graph layout strategy that is specifically designed for small-world networks (low-diameter graphs with skewed vertex degree distributions). Label propagation-based partitioning and a scalable BFS-based ordering are the main steps in the layout strategy. We show that the DGL layout can significantly improve end-to-end performance of five challenging graph analytics workloads: PageRank, a parallel subgraph enumeration program, tuned implementations of breadth-first search and single-source shortest paths, and RDF3X-MPI, a distributed SPARQL query processing engine. Using these benchmarks, we additionally offer a comprehensive analysis on how graph layout affects the performance of graph analytics with variable computation and communication characteristics.
Worker Viewpoints: Valuable Feedback for Microtask Designers in Crowdsourcing

One of the problems a requester faces when crowdsourcing a microtask is that, due to the underspecified or ambiguous task description, workers may misinterpret the microtask at hand. We call a set of such interpretations worker viewpoints. In this paper, we argue that assisting requesters to gather a worker's interpretation of the microtask can help in providing useful feedback to designers, who may restate the task description if necessary. In our method, we create a corpus of viewpoints annotated with the types of viewpoints that reflect the logical structure embedded in them. Our experimental results suggest that the logic-oriented annotation is effective in choosing useful viewpoints from a possibly huge set of collected viewpoints, in the sense that removing viewpoints of particular types did not affect the quality of revised task instructions. We also show that the logic-oriented annotation can perform comparably with an entropy-based method, without several workers performing the same task in parallel.
Climate Feedbacks in CCSM3 under Changing CO2 Forcing. Part I: Adapting the Linear Radiative Kernel Technique to Feedback Calculations for a Broad Range of Forcings

AbstractClimate feedbacks vary strongly among climate models and continue to represent a major source of uncertainty in estimates of the response of climate to anthropogenic forcings. One method to evaluate feedbacks in global climate models is the radiative kernel technique, which is well suited for model intercomparison studies because of its computational efficiency. However, the usefulness of this technique is predicated on the assumption of linearity between top-of-atmosphere (TOA) radiative fluxes and feedback variables, limiting its application to simulations of small climate perturbations, where nonlinearities can be neglected. This paper presents an extension of the utility of this linear technique to large forcings, using global climate model simulations forced with CO2 concentrations ranging from 2 to 8 times present-day values. Radiative kernels depend on the model’s radiative transfer algorithm and climate base state. For large warming, kernels based on the present-day climate significantly u.
Blood Levels of Brain-Derived Neurotrophic Factor Correlate with Several Psychopathological Symptoms in Anorexia Nervosa Patients

Background: Evidence of a role of brain-derived neurotrophic factor (BDNF) in the pathophysiology of eating disorders (ED) has been provided by association studies and by murine models. BDNF plasma levels have been found altered in ED and in psychiatric disorders that show comorbidity with ED. Aims: Since the role of BDNF levels in ED-related psychopathological symptoms has not been tested, we investigated the correlation of BDNF plasma levels with the Symptom Checklist 90 Revised (SCL-90R) questionnaire in a total of 78 ED patients. Methods: BDNF levels, measured by the enzyme-linked immunoassay system, and SCL-90R questionnaire, were assessed in a total of 78 ED patients. The relationship between BDNF levels and SCL-90R scales was calculated using a general linear model. Results: BDNF plasma levels correlated with the Global Severity Index and the Positive Symptom Distress Index global scales and five of the nine subscales in the anorexia nervosa patients. BDNF plasma levels were able to explain, in the case of the Psychoticism subscale, up to 17% of the variability (p = 0.006). Conclusion: Our data suggest that BDNF levels could be involved in the severity of the disease through the modulation of psychopathological traits that are associated with the ED phenotype.
ENTROPY ANALYSIS FOR ACHIEVING HIGH COMPRESSION RATIO 1

Deciding test data volume is a major challenge in present System on Chip (SoC) design. Compaction of test data volume results in high compression ratio. Entropy analysis plays a vital role in such cases. Entropy is measurement of the amount of information contained in the data set. Entropy calculations tell the need of how much test data vector that can be compressed. This paper is based on finding entropy calculations to achieve maximum test data compression. Further, entropy study for unspecified bits (don’t care bits) and complement method are also explored which result in high compression ratio. Entropy analysis for different symbol length partitioning is done. For the fixed-length symbol, one fill and zero fill algorithm is applied for unspecified bits, reducing the entropy. The proposed method is successfully tested on ISCAS89 which achieves maximum compression ratio