Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Community Discovery from Dynamic Multi-relational CQA Networks

As a knowledge sharing platform, Community Question Answering (CQA) services have attracted much attention from both academic and industry. This paper studies the problem of mining evolutionary community structures in CQA, through analysis of time-varying, multi-relational data among users and contents. We propose a unified framework for this problem, which makes the following contributions: 1) We propose an AT-LDA model, which combines author-topic model with topological structure analysis, to discover densely connected communities and the community topics in a unified process; 2) Our framework captures community structures and their evolution with temporal smoothing given by historic community structures. Empirical evaluation on real-world dataset shows that interesting communities and their evolution patterns can be detected.
Robinson-Schensted correspondence and left cells

Although the Robinson-Schensted-Knuth correspondence is a classical subject, its study is still active because of new development in last two decades. In this field, fundamental results are sometimes proved by using machineries which may be bypassed. Hence we still feel a lack of good source for these elementary materials. This paper is intended to fill in this lack about the RSK correspondence and the left cells of the symmetric group. It is mostly survey-like, but shows the variety of fields the RSK correspondence is related to.
Fuzzy Logic Reasoning to Control Mobile Robot on Pre-defined Strip Path

mobile robot is a small four-wheeled mobile platform, which was controlled by a micro-controller. The robot could sense its surroundings with the aid of various electronic sensors while mechanical actuators were used to move it around. Robot behaviour was determined by the program, which was loaded to the microcontroller. In that way, it could be used as a general robotics experimental platform. The autonomous mobile robot was designed and built in order to perform various navigation algorithms. The design consisted of two main sections: Electronic analysis of the various robot sensors and Programming techniques used to interface the sensors with the robot's microcontroller. In this paper it's shown that the path-guiding robot with IR sensors and obstacle detection is using IR proximity sensors. The predefined path is having varied turns, the fuzzy reasoning take care of speed to keep mobile robot in the defined path. The results are proved experimentally and the surface viewer graph is obtained from the Mat Lab.
Abundance analysis of targets for the COROT/MONS asteroseismology missions. II. Abundance analysis of the COROT main targets

One of the goals of the ground-based support program for the  and / satellite missions is to charac- terize suitable target stars for the part of the missions dedicated to asteroseismology. We present the detailed abundance analysis of nine of the potential  main targets using the semi-automatic software . For two additional  targets we could not perform the analysis due to the high rotational velocity of these stars. For five stars with low rotational velocity we have also performed abundance analysis by a classical equivalent width method in order to test the reliability of the  software. The agreement between the different methods is good. We find that it is necessary to measure abundances extracted from each line relative to the abundances found from a spectrum of the Sun in order to remove systematic errors. We have constrained the global atmospheric parameters Teff ,l ogg ,a nd (Fe/H) to within 70−100 K, 0.1−0.2 dex, and 0.1 dex for five stars which are slow rotators (v sini 60 km s −1 ) it is not possible to constrain the atmospheric parameters.
Leveraging the Global Information Revolution for Economic Development: Singapore's Evolving Information Industry Strategy

Despite her small size and late industrialization, Singapore has managed to capture a significant share of the global information industry over the last three decades. This paper analyzed the structure and growth dynamics of Singapore's information industries using an analytical framework that integrates the four key components of an information economy: ICT goods industry, content industry, network infrastructure, and informatization. The paper identifies four generic stages in the development path of Singapore's information economy and highlights policy implications for other small, open economies.
XMMU J134736.6+173403: an eclipsing LMXB in quiescence or a peculiar AGN? (Research Note)

Aims. We report the discovery of a peculiar object observed serendipitously with XMM-Newton. We present its timing and spectral properties and investigate its optical counterpart. Methods. The light curve of the X-ray source, its spectrum, and the spectrum of the best optical counterpart are presented and analyzed. Results. The X-ray flux decreases by a factor of 6.5 within 1 h and stays in a low state for at least 10 h, thereby suggesting the presence of an eclipse. The spectrum is very soft, a power law with a slope of Γ ∼ 2.8, and does not change significantly before and after the flux drop. The source is spatially coincident within few arc-seconds with a Seyfert 2 galaxy belonging to a galaxy pair. Conclusions. Although the background AGN seems the best counterpart, neither the temporal nor the spectral properties of the X-ray source are compatible with it. We investigate the possibility of having a foreground low-mass X-ray binary in quiescence, where the companion is not detected in the optical wavelength.
Learning a Semi-Autonomous Vehicle Control Scheme for Intelligent Navigation in 3-D Environments

— In this paper, we propose a scheme for semi-autonomous vehicle control in structured 3-D environments. We attempt to find an efficient learning algorithm for automated vehicle navigation in well-defined terrain, such as city roads and highways, by progressively constructing a geometric model of the environment. The model thus constructed is used by the vehicle, in conjugation with certain human inputs for learning an optimal navigation policy. We adopt an off-policy Q-learning algorithm to formulate the vehicle's learning behavior, and simultaneously generate a virtual map of the environment, which simplifies further learning. Our approach has been implemented on a virtual car and tested in a simulated 3-D environment, the results of which are analyzed in this paper. N this paper, we propose an algorithm that facilitates semi-autonomous driving of vehicles in structured terrain. By semi-autonomous driving, we refer to a land vehicle that is imparted with sufficient AI in order to chart its own course and navigate itself through the environment, with minimum possible human/external assistance. As mentioned in [14] autonomous vehicles have largely used techniques of passive computer vision and sensor-based approaches in order to construct the virtual environment. The learning behavior has often been implemented using back-propagation Neural Networks [15, 16] resulting in intractable computational complexities and a black-box nature. Further, the focus has been more on the camera calibration angles [5,7] in most of the 3-D environment modeling algorithms. Some other existing algorithms implement an eigen-vector based approach [10], but in general most of these methods have been proven to have computational complexities of very high order, and hence a practical implementation of these techniques is not very feasible as of now. In this paper however, we implement a learning algorithm based on a model of the environment that is being generated continuously by the agent, with the help of sensors and a CCD camera. The agent uses a Modified Eigen-Space algorithm (MESA) [12] to construct an accurate 3-D model of its surroundings, and makes use of an Image-Validation scheme [12] in order to check for consistency and continuity in the environment. A scheme of Temporal Difference learning [2], namely the Q-Learning algorithm [9] has been implemented over the thus obtained model, so that the agent learns to control itself by experimenting with the possible actions, within the generated model of the environment as its domain. The approach elaborated above has been tested on a computer-simulated agent …
The Application of Fuzzy Neural Networks in Formation Control for Multi-Robot System

The formation problem of multi-robot is a type problem in cooperation and it is one of challenging research directions of multi-robot system. Aiming at the formation control problem, in this paper we propose a kind of hybrid architecture firstly, which combines the reactive architecture based on Motor Schema with the hierarchical architecture. Secondly we design four kinds of basic behaviors based on the reactive architecture, and design each of behaviors using fuzzy neural networks. Lastly we use nerve cell fuse the outputs of each of behaviors qua the final outputs of system, which controls executive appliance to act. This method put forward enhances intelligence and the environmental applicability in this paper. The simulation results validate the feasibility of this method.
Software-Change Prediction: Estimated+Actual

The authors advocate that combining the estimated change sets computed from impact analysis techniques with the actual change sets that can be recovered from version histories will result in improved software-change prediction. An overview of both impact analysis (IA) and mining software repositories (MSR) is given. These are compared and a discussion of their expressiveness and effectiveness is presented. A framework is proposed to integrate these two approaches for software-change prediction
Automatic Identification of Concepts and Conceptual relations from Pa- tents Using Machine Learning Methods

This paper presents a machine learning approach to automatically extract concepts and the conceptual relations towards creation of Conceptual Graphs (CGs) from patent documents using shallow parser and NER. The main challenge in the creation of conceptual graphs from the natural language texts is the automatic identification of concepts and conceptual relations. The texts analyzed in this work are patent documents, focused mainly on the claim‟s section (Claim) of the documents. The task of automatically identifying the concept and conceptual relation becomes difficult due to the complexities in the writing style of these documents as they are technical as well as legal. The analysis we have done shows that the general in-depth parsers available in the open domain fail to parse the „claims section‟ sentences in patent documents. The failure of in-depth parsers led us, to develop a methodology to extract CGs using other resources. Thus in the present work we came up with a methodology which uses shallow parsing, NER and machine learning technique for extracting concepts and conceptual relationships from sentences in the claim/novelty section of patent documents. The results obtained from our experiments are encouraging and are discussed in detail in this paper. We have obtained a precision of 73.2 % and a recall of 68.3%.
Ramanujan identities and Euler products for a type of Dirichlet series

has an Euler product and gave an explicit formulation for the Euler product. In this paper we develop the theory of binary quadratic forms in order to determine the Euler product for ∑∞ n=1 a(n)n −s, and other similarly defined Dirichlet series, in a completely elementary and natural manner. Let N, Z, R and C be the sets of natural numbers, integers, real numbers and complex numbers respectively. A nonsquare integer d with d ≡ 0, 1 (mod 4) is called a discriminant. The conductor of the discriminant d is the largest positive integer f = f(d) such that d/f ≡ 0, 1 (mod 4). As usual we set w(d) = 1, 2, 4, 6 according as d > 0, d 0 and c > 0. Two forms (a, b, c) and (a′, b′, c′) are equivalent ((a, b, c) ∼ (a′, b′, c′)) if there exist integers α, β, γ and δ with αδ − βγ = 1 such that the substitution x = αX+βY, y = γX+δY transforms (a, b, c) to (a′, b′, c′). It is known that (a, b, c) ∼ (c,−b, a) and for k ∈ Z that (a, b, c) ∼ (a, 2ak + b, ak + bk + c). We denote the equivalence class of (a, b, c) by [a, b, c]. The equivalence classes of primitive, integral, binary quadratic forms
AN EMPIRICAL STUDY OF WEAKCONVERGENCE FOR AUTOMATIC LEARNINGFelisa

The algorithms described in this paper use estimates of the sensitivities in a gradient-based stochastic approximation procedure for adaptive control. Learning rates are kept constant for adaptability reasons. For these procedures, the control processes do not converge almost surely, but in distribution. For a exible machine where the control parameter is a probability vector, a new sensitivity estimator is described, generalizing the Phantom RPA to multi-valued decisions. Several updating schemes are built based on the weak convergence theory to ensure asymptotic optimality. Results show that weak convergence yields a dramatic improvement in the rate of convergence, in addition to the capability of tracking.
Virtual machine live migration for pervasive services in cloud-assisted vehicular networks

The physical resources of vehicles and roadside infrastructures are stringently constrained in vehicular networks. The application of mobile cloud computing technology will significantly improve the utilization of intensive physical resources. In the newly emerged paradigm of cloud-assisted vehicular networks, vehicle mobility poses a significant challenge to the continuity of cloud services. This paper proposes efficient Virtual Machine (VM) live migration mechanisms to deal with the problem. In particular, a selective dirty page transfer strategy is designed to enhance the efficiency of data transfer in VM live migration. Besides, an optimal resource reservation scheme is proposed to ensure sufficient physical resources at a target cloud site such that migration dropping is significantly reduced. Simulations are carried out to demonstrate the efficiency of the two proposed mechanisms.
Sequential change‐point detection based on direct density‐ratio estimation

Change‐point detection is the problem of discovering time points at which properties of time‐series data change. This covers a broad range of real‐world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel nonparametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid nonparametric density estimation, which is known to be a difficult problem. We provide a change‐point detection algorithm based on direct density‐ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real‐world datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011
Expected EPS and EPS Growth as Determinants of Value By

This paper develops a parsimonious model relating a firm's price per share to, (i), next year expected earnings per share (eps), (ii), short term growth (FY-2 vs. FY.1) in eps, (iii), long-term (asymptotic) growth in eps, and, (iv), cost-of equity capital. The model assumes that the present value of dividends per share (dps) determines price, but it does not restrict how the dps-sequence is expected to evolve. All of these aspects of the model contrast sharply with the standard textbook approach, which equates the growth rates of expected dps and eps and fixes the growth rate and the payout rate. Though the constant growth model arises as a peculiar special case, the analysis in this paper rests on more general principles, including dividend policy irrelevancy. In addition to the valuation formula, a key result shows how one expresses cost-of-capital as a function of next-year expected eps and the two measures of growth in expected eps. This expression generalizes the textbook equation in which cost-of-capital equals dps-yield plus the growth in expected eps.
Approved for Public Release; Distribution Unlimited Nociceptin/orphanin Fq Peptide (nop) Receptors Table of Contents Comparison of Effectiveness of Systemically Administered Ro 64-6198 in Different

The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of the Army position, policy or decision unless so designated by other documentation. Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. The studies proposed in this project will test the hypotheses that in the non-human primate (1) the functions and behavioral effects of the NOP receptor are independent of classical opioid receptors, (2) activation of the NOP receptor produces strong antinociception without abuse liability, and (3) NOP receptor agonists possess a promising therapeutic profile as analgesics compared to mu opioids following repeated administration in primates. Several key findings have been obtained and some have been published. First, intrathecal administration of N/OFQ only produced antinociception in primates. The functional profiles of spinal NOP receptors are different between primates and rodents. Second, intrathecal administration of N/OFQ and other NOP receptor agonists produced antinociception without eliciting itch/scratching responses, indicating that NOP receptor agonists represent a therapeutic target as spinal analgesics. Third, NOP receptor agonists produced antinociceptive effects comparable to clinically used mu opioids such as morphine and alfentanil in three different primate pain models, indicating that the analgesic effectiveness of NOP receptor agonists may be similar to that of mu opioid analgesics in humans. Finally, unlike mu opioids, NOP receptor agonists did not produce reinforcing effects, respiratory depressant, sedation, or itch/pruritic side effects, indicating that NOP receptor agonists may be a new generation of novel analgesics without abuse liability. INTRODUCTION Proposed studies intend to investigate the potential antinociceptive effects of NOP receptor agonists in monkeys. Both intrathecal and systemic administration are common routes for delivery of analgesics in the clinic. Future studies characterizing and comparing the behavioral effects of intrathecal and systemic administration of OFQ/N and Ro 64-6198 in monkeys would provide a great deal of information for potential pain …
Determining the Specificity of Terms using Compositional and Contextual Information

This paper introduces new specificity determining methods for terms using compositional and contextual information. Specificity of terms is the quantity of domain specific information that is contained in the terms. The methods are modeled as information theory like measures. As the methods don't use domain specific information, they can be applied to other domains without extra processes. Experiments showed very promising result with the precision of 82.0% when the methods were applied to the terms in MeSH thesaurus.
A Composite Benchmark for Online Transaction Processing and Operational Reporting

Up-to-date data is of immense importance for operational reporting. Global enterprises require such a high throughput during daily operations that reporting systems had to be separated from the transactional system to avoid inhibiting performance. These architectures, however, do not provide the required reporting flexibility as the data set is a pre-defined subset of the actual data and updated only at certain time intervals, e.g. nightly. The composite benchmark for online transaction processing (OLTP) and operational reporting, henceforth CBTR, provides means to evaluate the performance of enterprise systems for a mixed workload of OLTP and operational reporting queries. Such a system offers up-to-date information and the flexibility of the entire data set for reporting. CBTR provokes the conflicts that were the reason for separating the two workloads on different systems. In this paper we introduce the concepts of CBTR, which is based on the original data set and real workloads of an existing, globally operating enterprise.
A populist paradise? Examining populists’ Twitter adoption and use

ABSTRACT While populists are claimed to be masters of Twitter, we know surprisingly little about how they use the medium (if at all). This study examines (1) populists’ Twitter adoption and (2) also investigates whether they differ from other political actors in their posting, connecting, and engagement behaviour on Twitter. We use a series of regression analyses to examine populists’ Twitter adoption and use in the Netherlands (2010–2016). We find that populists are less likely to adopt Twitter and suggest this is due to the party structure of populist parties: they are typically highly centralized and are wary of internal dissent. However, when populists do have an account, they do not post less (or more) tweets. They are less likely to have reciprocal interactions though and are more selective in who they befriend. However, these friends are more likely to get retweeted, suggesting that populists actively contribute to creating echo chambers.
Mutiny on the Bay: Investigating the Presentation of the Scott Olsen Police Assault on the Websites of San Francisco Bay Area Local Television Stations

In the fall of 2011, the Occupy Wall Street (OWS) protests emerged, becoming a global movement. In the United States, the Occupy Oakland demonstrations witnessed instances of violence, most notably in the injury of Scott Olsen, an Occupy Oakland supporter and former U.S. Marine who was struck by a police projectile. This article investigates the presentation of the Olsen injury on the websites of five major local television stations in the San Francisco Bay area, as a way to illustrate the negative coverage of dissident social movement activists, even when they are former military veterans, a group treated respectfully in the media. In this case, Olsen’s presence created a conflict in the application of the themes and devices composing the “protest paradigm. ” The findings of this study suggest the existence of a “patriotism paradigm,” a news treatment that neutralizes the credibility of individuals or groups seeking a claim to the positive associations of patriotism and military service of the post-9/11 United States, and can allow news treatments such as the “protest paradigm” to exist without being contradicted.
Sequential learning of differential trend

This paper describes a simple learning method to sequentially select recent time series values as features to model the differential trend of a time series. This method is used to solve the First International Competition on Time Series Forecasting (ICTSF) [1]. The objective of ICTSF is to predict eight time series with different time frequency and different forecasting horizon. Experimental result shows viability of the method in multi-step forecasting.