Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-dimensional semiclassical static black holes: Finite-mass correction to the Hawking temperature and outflux

In the two-dimensional framework, the surface gravity of a (classical) black hole is independent of its mass $M$. As a consequence, the Hawking temperature and outflux are also independent of $M$ at the large-$M$ limit. (This contrasts with the four-dimensional framework, in which the surface gravity and temperature scale as 1/M.) However, when the semiclassical backreaction effects on the black-hole geometry are taken into account, the surface gravity is no longer $M$-independent, and the same applies to the Hawking temperature and outflux. This effect, which vanishes at the large-$M$ limit, increases with decreasing $M$. Here we analyze the semiclassical field equations for a two-dimensional static black hole, and calculate the leading-order backreaction effect ($\propto 1/M$) on the Hawking temperature and outflux. We then confirm our analytical result by numerically integrating the semiclassical field equations.
Dynamic decision calculus : formulating long-run strategies

This paper presents a general approach to assist managers in strategically setting prices and allocating resources over the product, brand, or adoption (diffusion) life cycle. While substantial theoretical work has been achieved in this area in the management science and operations research disciplines, approaches which can be impiemented as managerial tools are generally lacking. Our methodology, which has been applied as a PC-based decision support tool in the telecommunications industry, marries the optimal control literature with decision calculus approaches offering sufficient flexibility for applied contexts. We discuss the approach and describe its use to derive optimal price and advertising policies. Strategies not commonly suggested using traditional formulations are also presented.
Twelfth International Multi-Conference on Information Processing-2016 ( IMCIP-2016 ) Cloud Audit : A Data Integrity Verification Approach for Cloud Computing

Cloud computing is the fastest growing and promising field in the sector of service provision. It is a very trendy situation for security implementation in the Cloud. Thus, security restrictions are increasing on users and service providers as the growth of Cloud environment. The purpose of this paper is to suggest a better and efficient integrity verification technique for data (referred as “Cloud Audit”). The building blocks of our technique are a variant of the Paillier homomorphic cryptography system with homomorphic tag and combinatorial batch codes. A Paillier Homomorphic Cryptography (PHC) system used to obtain homomorphic encryption on data blocks. Homomorphic tags along with the Paillier cryptography system assigns a special verifiable value to each data block, which helps us in unleashing data operations on this block. Combinatorial batch codes are used to assign and store integral data into different distributed cloud sever. To demonstrate our approach, we have implemented an application based on Hadoop and MapReduce framework. We have tested this application based on various parameters. Effectiveness of the proposed method has been shown by the experimental results. Our method has shown large improvement over the other modern methods. © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the Twelfth International Multi-Conference on Information Processing-2016 (IMCIP-2016).
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity

Sample efficiency is a crucial problem in deep reinforcement learning. Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency by increasing the update-to-data (UTD) ratio to 20 gradient update steps on the critic per environment sample. However, this comes at the expense of a greatly increased computational cost. To reduce this computational burden, we introduce CrossQ: A lightweight algorithm for continuous control tasks that makes careful use of Batch Normalization and removes target networks to surpass the current state-of-the-art in sample efficiency while maintaining a low UTD ratio of 1. Notably, CrossQ does not rely on advanced bias-reduction schemes used in current methods. CrossQ's contributions are threefold: (1) it matches or surpasses current state-of-the-art methods in terms of sample efficiency, (2) it substantially reduces the computational cost compared to REDQ and DroQ, (3) it is easy to implement, requiring just a few lines of code on top of SAC.
The role of mesons in muon g − 2

The muon anomaly aμ=(gμ-2)/2 showing a persisting 3 to 4 σ deviation between the SM prediction and the experiment is one of the most promising signals for physics beyond the SM. As is well known, the hadronic uncertainties are limiting the accuracy of the Standard Model prediction. Therefore a big effort is going on to improve the evaluations of hadronic effects in order to keep up with the 4-fold improved precision expected from the new Fermilab measurement in the near future. A novel complementary type experiment planned at J-PARC in Japan, operating with ultra cold muons, is expected to be able to achieve the same accuracy but with completely different systematics. So exciting times in searching for New Physics are under way. I discuss the role of meson physics in calculations of the hadronic part of the muon g-2. The improvement is expected to substantiate the present deviation $\Delta a_\mu ^{{\rm{New}}\,{\rm{physics}}} = \Delta a_\mu ^{{\rm{Experiment}}} - \Delta a_\mu ^{{\rm{Standard Model}}}$ to a 6 to 10 standard deviation effect, provided hadronic uncertainties can be reduce by a factor two. This concerns the hadronic vacuum polarization as well as the hadronic light-by-light scattering contributions, both to a large extent determined by the low lying meson spectrum. Better meson production data and progress in modeling meson form factors could greatly help to improve the precision and reliability of the SM prediction of aμ and thereby provide more information on what is missing in the SM.
Early warning theory for regional geo-hazards anddesign of explicit statistical system

Since 2003,national early warning for regional geo-hazards has been continuously done based on weather condition in China.With four years research and application,there have been some obvious results in disaster prevention and reduction.Based on inland and overseas experience,early warning methods for regional geo-hazards are divided into three types: implicit statistics,explicit statistics,and dynamic method.In the method of implicit statistics,the effect of geo-environment factors is implied in rainfall criterion,that is,there is only rainfall parameter in early warning model of different areas.However,in the method of explicit statistics,geo-environment and induced factors are coupled to set up early warning model.The dynamic method is one that the dynamic process of slope during rainfall is analyzed to find early warning criterion,which is an analytical method virtually.With the system engineering theory,design method of explicit statistics early warning system is set up entirely,which includes some contents as following: requirement analysis,problem expression,aim designing,principle of design,map factor design,system model,system compute,early warning grade,early warning product building,product discussion,product issuance,early warning check,early warning discussion,contingency operation,cost-benefit analysis,and system upgrade,etc.The design of this method sets up a public idea platform for some area early warning system,and can act as the theory basis of a regional geo-hazard early warning system,including national,provincial and county levels based on weather factors in different scale of area.
A COMPARATIVE TEST OF THE REALISTIC JOB PREVIEW AND THE EXPECTANCY LOWERING PROCEDURE

Morse, Brendan J., M.S. June 2007. Industrial/Organizational Psychology A COMPARATIVE TEST OF THE REALISTIC JOB PREVIEW AND THE EXPECTANCY LOWERING PROCEDURE (96 pp.) Director of Thesis: Paula M. Popovich Realistic Job Previews (RJPs) have been studied by researchers and utilized by practitioners to attenuate early turnover by providing both positive and negative information pertaining to a job and organization to job applicants. Historically, RJPs have suffered from several criticisms including modest effect sizes (Phillips, 1998), cost of development (Buckley et al. 1998), and adverse self-selection effects (Bretz & Judge, 1998). Recently, the expectancy lowering procedure (ELP) has been developed and tested to address these limitations of the RJP. This study compared the RJP and the ELP in terms of applicant self-selection and expectations. Results indicated consistent findings with past RJP literature in terms of expectation calibration. However, no support was found for the self-selection effect. Furthermore, the ELP demonstrated results consistent with Buckley et al. (2002) as a useful, generalized realistic recruitment procedure that was also resistant to adverse self-selection. Approved:____________________________________________________________ Paula M. Popovich Associate Professor of Psychology Acknowledgements First and foremost, I would like to thank my committee chair and advisor, Paula Popovich, for her time, encouragement, and invaluable guidance. I would also like to thank my committee members, G. Daniel Lassiter and Jeffrey Vancouver, for their time, insight, and constructive criticisms of this project. I would also like to acknowledge my mother and father, Deborah and Gary Morse, for their support and encouragement throughout the tenure of this project. Thank you all, I could not have done it on my own.
Proper motion separation of Be star candidates in the Magellanic Clouds and the Milky Way

We present a proper motion investigation of a sample of Be star candidates towards the Magellanic Clouds, which has resulted in the identification of separate populations, in the Galactic foreground and in the Magellanic background. Be stars are broadly speaking B-type stars that have shown emission lines in their spectra. In this work, we studied a sample of 2446 and 1019 Be star candidates towards the LMC and SMC respectively, taken from the literature and proposed as possible Be stars due to their variability behaviour in the OGLE-II I band. JHKs magnitudes from the IRSF catalog and proper motions from the SPM4 catalog, were obtained for 1188 and 619 LMC and SMC Be stars candidates, respectively. Color-color and vector-point diagrams were used to identify different populations among the Be star candidates. In the LMC sample, two populations with distinctive infrared colours and kinematics were found, the bluer sample is consistent with being in the LMC and the redder one with belonging to the Milky Way disk. This settles the nature of the redder sample which had been described in previous publications as a possible unknown subclass of stars among the Be candidates in the LMC. In the SMC sample, a similar but less evident result was obtained, since this apparent unknown subclass was not seen in this galaxy. We confirm that in the selection of Be stars by their variability, although generally successful, there is a higher risk of contamination by Milky Way objects towards redder B$-$V and V$-$I colors.
Program Synthesis for Empowering End Users and Stress-Testing Compilers

Since 2012, the number of people with access to computing devices has exploded. This trend is due to these devices’ falling prices, and their increased functionalities and programmability. Two challenges arise from this trend: (1) How to assist the increasing number of device owners, most of whom are non-programmers, yet hope to take full advantage of their devices, and (2) how to make critical software, which is relied upon by other software running on these devices, more reliable? Our vision is that program synthesis is the solution for the above arising challenges. This dissertation describes various program synthesis techniques to synthesize programs from natural languages, input/output examples, and existing programs to tackle these challenges. We present SmartSynth, a natural language interface that synthesizes smartphone programs from natural language descriptions. SmartSynth enables users to automate their repetitive tasks by leveraging various phone sensors. It is the first system that combines the advances in both natural language processing (NLP) and program synthesis: it uses NLP techniques to parse a given command, and applies program synthesis techniques to resolve parsing ambiguities. We have adapted and extended SmartSynth’s algorithms to integrate it into TouchDevelop, a popular touch-based programming environments for end users. We develop FlashExtract, a system that extracts data from semi-structured document (such as text files, webpages, and spreadsheets) using examples. Natural language does not work well in this domain because the tasks are complicated and users usually do not know how to perform them. In FlashExtract, users only need to highlight some sample regions to be extracted, the system will learn a program to select similar regions
Situated Design Computing: Principles

This paper introduces the principles of situated design computing. Situated design computing expands the concept of computing from being the encoding of objective knowledge that is fixed by the programmers of the system to include first-person knowledge acquired while the programs are being used. This first-person knowledge forms the basis of the system’s experience which is then made available as the system is used further. The four principles of situatedness are presented along with the basic concept of the mechanism by which such systems operate: constructive memory.
Transient and Spatial Evolution of Clogging of Porous Material by Filtrating Particles

In hypersonic vehicles, regenerative cooling is used to handle the thermal load encountered in the combustion chamber. In this method, the fuel itself flows through a cooling channel and is circulated around the combustion chamber. The channel is made of porous material that enables fuel to also partially flow directly from the channel to the chamber by transpiration, which cools the wall by internal convection. The exposure of the fuel to high temperatures results in fuel pyrolysis producing carbon particles (coke). These particles are transported by the fuel and deposit inside the pores of the material resulting in a decrease of permeability of the medium. This process is very complex since it affects the heat transfers which generate the fuel pyrolysis and the formation of the particles. A test bench is developed in the current work to study the transient effect of the key parameters (i.e., filtrating mass flow rate, a load of particles in the flow) on the particles’ transport and on their spatial dist.
An Adaptive Sensorimotor Network Inspired by the Anatomy and Physiology

IN THIS CHAPTER WE REVIEW THE ANATOMY AND PHYSIOLOGY OF THE CEREBELLUM, STRESSING NEW KNOWLEDGE ABOUT INFORMATION PROCESSING IN CEREBELLAR CIRCUITS, NOVEL BIOPHYSICAL PROPERTIES OF PURKINJE NEURONS AND CELLULAR MECHANISMS FOR ADJUSTING SYNAPTIC WEIGHTS. WE THEN EXPLORE THE IMPACT OF THESE IDEAS ON DESIGNS FOR ADAPTIVE SENSORIMOTOR NETWORKS. A NETWORK IS PROPOSED THAT IS COMPRISED OF AN ARRAY OF ADJUSTABLE PATTERN GENERATORS. EACH PATTERN GENERATOR IN THE ARRAY PRODUCES AN ELEMENT OF A COMPOSITE MOTOR PROGRAM. MOTOR PROGRAMS CAN BE STORED, RETRIEVED AND EXECUTED USING ADJUSTABLE PATTERN GENERATOR MODULES.
Fast and Efficient RP-HPLC Method for Simultaneous Determination of Water-Soluble Vitamins in Some Nutraceutical Supplements

The purpose of this study is the development of analytical method for simultaneous determination of water-soluble vitamins (B1, B2, B3, B6, B9, B12 and vitamin C) in dietary supplement syrup, multivitamins and mineral dietary supplement syrup with iron, dietary supplement capsules and balanced supplemental nutrition for children by using gradient RP-HPLC methods with PDA detector. The separation of water-soluble vitamins was performed on waters Spherisorb ODS2 (250 mm×4.6mm, 5μm) column. The wavelength was 272 nm at room temperature and the detection limits ranged from 2.4 to 8.3 ng/µl. The average recovery was 98%-102 % and correlation coefficient (r) was 0.9992 to 1.
Relevance Feedback-based Query Expansion Model using Ranks Combining and Word2Vec Approach

ABSTRACT Query expansion is a well-known method for improving the performance of information retrieval systems. Pseudo-relevance feedback (PRF)-based query expansion is a type of query expansion approach that assumes the top-ranked retrieved documents are relevant. The addition of all the terms of PRF documents is not important or appropriate for expanding the original user query. Hence, the selection of proper expansion term is very important for improving retrieval system performance. Various individual query expansion term selection methods have been widely investigated for improving system performance. Every individual expansion term selection method has its own weaknesses and strengths. In order to minimize the weaknesses and utilizing the strengths of the individual method, we used multiple terms selection methods together. First, this paper explored the possibility of improving overall system performance by using individual query expansion terms selection methods. Further, ranks-aggregating method named Borda count is used for combining multiple query expansion terms selection methods. Finally, Word2vec approach is used to select semantically similar terms with query after applying Borda count rank combining approach. Our experimental results on both data-sets TREC and FIRE demonstrated that our proposed approaches achieved significant improvement over each individual terms selection method and other's related state-of-the-art method.
Radial Acceleration and Tully-Fisher Relations in Conformal Gravity

In 2016 McGaugh, Lelli and Schombert established a universal Radial Acceleration Relation for centripetal accelerations in spiral galaxies. Their work showed a strong correlation between observed centripetal accelerations and those predicted by luminous Newtonian matter alone. Through the use of the fitting function that they introduced, mass discrepancies in spiral galaxies can be constrained in a uniform manner that is completely determined by the baryons in the galaxies. Here we present a new empirical plot of the observed centripetal accelerations and the luminous Newtonian expectations, which more than doubles the number of observed data points considered by McGaugh et al. while retaining the Radial Acceleration Relation. If this relation is not to be due to dark matter, it would then have to be due to an alternate gravitational theory that departs from Newtonian gravity in some way. In this paper we show how the candidate alternate conformal gravity theory can provide a natural description of the Radial Acceleration Relation, without any need for dark matter or its free halo parameters. We discuss how the empirical Tully-Fisher relation follows as a consequence of conformal gravity.
Technology use by pre-service teachers during teaching practice: Are new teachers embracing technology right away in their first teaching experience?

Equipping pre-service teachers with technology skills and knowledge is now regarded as a vital element of any teacher-training program so as to capacitate the new teachers to meet the educational demands of the twenty-first century. Therefore, it is important to establish whether these teachers do actually apply these skills when they first get to the field and if not, what could be the hindering factors. This study investigated technology use by pre-service teachers during teaching practice. The unified theory of acceptance and use of technology (UTAUT) was used as an evaluative framework for the study. Observations, interviews, and document analysis were used to collect data. The results indicated that the majority of the participants did not use technology in the delivery of their lessons, even though they reported high competency levels in the use of technology resources. The underlying reasons for this lack of technology use were identified. The results point to a need to develop a systematic and comprehensive strategy toward technology implementation involving all stakeholders to ensure a smooth transition for pre-service teachers as they move from training to practice and to have technology use reinforced at all levels.
Class Imbalance Learning Methods for Support Vector Machines

Support vector machines (SVMs) is a very popular machine learning technique, which has been successfully applied to many real-world classification problems from various domains. Despite of all its theoretical and practical advantages, SVMs could produce suboptimal results with imbalanced datasets. This chapter briefly reviews the learning algorithm of SVMs. It discusses why SVMs are sensitive to the imbalance in datasets. The chapter also reviews the methods found in the literature to handle the class imbalance problem for SVMs. These methods have been developed as both data preprocessing methods (called external methods) and algorithmic modifications to the SVM algorithm (called internal methods). Fuzzy SVMs for Class Imbalance Learning (FSVM-CIL) settings have resulted in better classification results on the datasets than the existing CIL methods applied for standard SVMs, namely random oversampling, random undersampling, synthetic minority oversampling technique (SMOTE), different error costs (DEC), and zSVM methods.
Automatic improvement of machine translation systems

Achieving high translation quality remains the most daunting challenge Machine Translation (MT) systems currently face. Researchers have explored a variety of methods for including translator feedback in the MT loop. However, most MT systems have failed to incorporate post-editing efforts beyond the addition of corrected translations to the parallel training data for Example-Based and Statistical systems or to a translation memory database. This thesis describes a novel approach that utilizes post-editing information to automatically improve the underlying rules and lexical entries of a Transfer-Based MT system. This process can be divided into two main steps. First, an online translation correction tool allows for easy error diagnosis and implicit error categorization. Then, an Automatic Rule Refiner performs error remediation by tracing errors back to the problematic rules and lexical entries and executing repairs that are mostly lexical and morpho-syntactic in nature (such as word-order, missing constituents or incorrect agreement in transfer rules). This approach directly improves the intelligibility of corrected MT output and, more significantly, it generalizes over unseen data, providing improved MT output for similar sentences that have not been corrected. Experimental results on an English-Spanish MT system show that automatic rule refinements triggered by bilingual speaker corrections successfully translate unseen data that was incorrectly translated by the original, unrefined grammar. Improvements on translation quality over a baseline, as measured by standard automatic evaluation metrics, are statistically significant on a paired two-tailed t-test (p = 0.0051). One practical application of this research is extending and refining relatively small translation grammars for resource-poor languages, such as Mapudungun and Quechua, into a major language, such as English or Spanish. Initial experimental results on a SpanishMapudungun MT system show that rule refinement operations generalize well to a different language pair and are able to correct errors in the grammar and the lexicon.