Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-Aware Virtual Machine Placement in Cloud Computing Systems

Cloud service providers operate multiple geographically distributed data centers. These data centers c onsume huge amounts of energy, which translate into high operating costs. Interestingly, the geographical distribution of the data centers provides many opportunities for cost savings. For example, the electricity prices and outside temperatures may be widely different at the data centers. This diversity suggests that intelligently placing load across the data centers maylead to large cost savings. However, aggressively directing loa d to the cheapest data center may render its cooling infrastruct ure unable to adjust in time to prevent server overheating. In this paper, we study the impact of load placement policies on cooling and maximum data center temperatures. Based on this study, we propose load placement policies that conside r, not only all electricity-related costs, but also transientcooling effects. Our evaluation studies the ability of different co ling strategies to handle load spikes, demonstrates the behavio r f our policies as compared to their simpler counterparts, and explores the effect of many parameter settings. Among other interesting results, we demonstrate that (1) our policies c an provide large cost savings, (2) load migration enables savi ngs in many scenarios, and (3) all electricity-related costs mu st be considered at the same time for higher and consistent cost savings.
Intergenerational Earnings Mobility among the Children of Canadian Immigrants

The intergenerational earnings mobility of Canadians born to immigrants is examined using the 2001 Census. A detailed portrait of the Canadian population is offered as are estimates of the degree of generational mobility among the children of immigrants from seventy countries. The degree of intergenerational persistence is about the same for immigrants as for the entire population, and there is more generational mobility among immigrants in Canada than in the United States. We also use quantile regressions to distinguish between the role of social capital from other constraints limiting mobility and find that these are present.
Mostly-Copying Collection: A Viable Alternative to Conservative Mark-Sweep

Many high-level language compilers generate C code and then invoke a C compiler to do code generation, register allocation, stack management, and low-level optimization. To date, most of these compilers link the resulting code against a conservative mark-sweep garbage collector in order to reclaim unused memory. We introduce a new collector, MCC, based on mostly-copying collection, and characterize the conditions that favor such a collector over a mark-sweep collector. In particular we demonstrate that mostly-copying collection outperforms conservative mark-sweep under the same conditions that accurate copying collection outperforms accurate mark-sweep: Specifically, MCC meets or exceeds the performance of a mature mark-sweep collector when allocation rates are high, and physical memory is large relative to the live data.
Athletes' Knowledge and Perceptions of Sport Related Concussions and the Decision to Seek Treatment

The purpose of the study was to examine specific factors (namely knowledge, perceptions, and demographic characteristics including gender differences) that may influence an athlete's decision to report a sport related concussion (SRC). Participants were Division I and [I[ soccer and lacrosse athletes (n: 618) who participated in an online assessment intended to evaluate knowledge of concussive symptoms and previous reporting behavior. Descriptive statistics were calculated to determine rates of reported (57 .lyo, 1761305) and unreported (42.3%, 1291305) concussions, as well as reasons why athletes failed to report SRCs. Athletes identified not wanting to be withheld from competition or practice (43.2%) and not thinking the injury was serious enough (40.6%) as the primary reasons for failing to report a prior SRC. Average scores on the concussion knowledge quiz were consistently high for athletes both with (90.2%) and without (87.4%) previous concussion history. Logistic regression analysis examined the main effects of the independent variables (knowledge, perceptions, and the demographic characteristics of gender, age, sport played, and division level of competition) on reporting behavior; specifically, which variables increased the prediction that an athlete would fail to report a SRC (p < .05). Significant variables that contributed to the model and the corresponding odds ratios (OR) that predicted failure to report SRC included: perceiving an expectation to play through or minimize injury in sport ('culture of risk') (OR : 2.12), an increase in age by one year (OR : 1.39), playing lacrosse (OR = 2.40), and competing at the Division III level (OR: 2.91). Chi-square tests revealed no significant overall association between the genders on failure to report a SRC, but posthoc analysis revealed that significantly more Division tI[ male athletes failed to report concussions than females (X2 (l): 8.04, p < . 01). Results from this study helps identiff iii additional factors besides knowledge of SRC symptoms that may influence an athlete's decision to report SRC, specifically the previously uninvestigated influence of culture of risk and the potential influence of athletic identity. Results also expand the literature that currently debates whether male or female athletes sustain more SRC's (Dick, 2009). This information may help sports medicine professionals and sport psychology consultants target areas of intervention and education that can improve SRC management.
LINGUISTIC HEURISTICS IN WORD ALIGNMENT

The IBM statistical machine translation (SMT) models [Brown et al.1993] have been extremely influential in computational linguistics in the past decade. The (arguably) most striking characteristic of the IBM-style SMT models is their total lack of inherent linguistic knowledge. The IBM models demonstrated how much one can do with pure statistical techniques. This has inspired a whole new generation of NLP research and systems. Word alignments were first introduced as an intermediate result of statistical machine translation systems [Brown et al.1993]. They indicate which words correspond in a bilingual sentence pair. Since their introduction, many researchers have become interested in word alignments as a knowledge source. For example, alignments can be used to learn translation lexicons [Melamed1996], transfer rules [Carbonell et al.2002, Menezes and Richardson2001], and classifiers to find safe sentence segmentation points [Berger et al.1996]. While the IBM models work entirely within a statistical framework, other groups have opted to use some statistics along with linguistic intuitions to align words in sentences. Examples include Melamed’s word-to-word model [Melamed1997] and work in synchronous parsing [Wu1997, Alshawi et al.2000, Lopez et al.2002]. We present a word-alignment algorithm that uses simple statistics and linguistic heuristics to guide a search for high quality alignments. The search is constrained to a set of alignments that are cohesive with respect to the English parse tree. That is, disjoint phrases in the English parse tree are mapped to disjoint intervals in the French sentence. We show that this method can produce alignments that are competitive with model-based statistical techniques.
Fast Tetrahedron-Tetrahedron Overlap Algorithm

Abstract We present an algorithm to test two tetrahedra for overlap. The algorithm is based on a dimension reduction technique that allows the application of the Separating Axis Theorem, thus avoiding part of the computation needed to perform the Separating Axis Test. Source code is available on the website listed at the end of this paper.
Pointing the way: active collaborative filtering

Collaborative filtering is based on the premise that people looking for information should be able to make use of what others have already found and evaluated. Current collaborative filtering systems provide tools for readers to filter documents based on which ones were read and liked by previous readers. This paper describes a different type of collaborative filtering system in which people who find interesting documents actively send "pointers" to those documents to their colleagues. A "pointer" contains a hypertext link to the source document as well as contextual information intended to help the recipient determine the potential interest and relevance of the document prior to accessing it. A preliminary version of our system has already proven easy t o use, with people using it to "bookmark" documents, send pointers to their colleagues and create "digests" that combine pointers with original text. Based on our experience we discuss the benefits of this form of filtering as well as its limitations.
Development of quadruped walking robot with spherical shell-mechanical design for rotational locomotion

We have proposed and developed a new quadruped walking robot with a spherical shell, called “QRoSS”. QRoSS is a transformable robot that can store its legs in the spherical shell. The shell not only absorbs external forces from all directions, but also improves mobile performance because of its round shape. In rescue operations at a disaster site, carrying robots into a site is dangerous for operators because doing so may result in a second accident. If QRoSS is used, instead of carrying robots in, they are thrown in, making the operation safe and easy. We developed QRoSS-I and conducted basic experiments to verify performance which includes landing, rising and walking through a series of movements. This paper reports details of a mechanical design to perform a rotational locomotion using spherical shell and development of the prototype model of QRoSS-II. We discuss the rising motion and the rotational locomotion from the results of Basic experiments.
Multilevel Coding for Nonlinear ISI Channels

This paper proposes a new nonlinear equalization approach for the transmission of high-density constellations over nonlinear ISI channels. Multilevel coding is performed at the transmitter, and multistage equalization and decoding at the receiver. Two equalizers are introduced. One uses nonlinear intersymbol interference (ISI) cancellation and has linear complexity in the channel memory L , and the number of decoding stages K. The other employs the reduced-state Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm, which shows better performance and complexity on the order of Kmiddot2L. Capacity analysis and code simulation results show that these two schemes can achieve significant improvement over the conventional approaches that view nonlinear ISI as additive noise.
Parameters estimation of LFM signal based on fusion of signals with the same length and known frequency-difference

To attain fast and accurate parameters estimation of LFM signal under low signal and noise ratio conditions, a novel algorithm is presented. In this algorithm, LFM signal is first converted into multi-section signals with the same length and known frequency-difference by an easy sampling method, and the relation between the parameters of LFM signal and frequencies as well as frequency-differences of these multi-section signals is deduced. Then these multi-section signals are fused into one fusion signal by some techniques such as parameter matrix frequency-domain analysis,phase-difference compensating factor matrix and search frequency array. At last, high precision parameters estimation of LFM signal are gotten by frequencies estimation of the fusion signal through an algorithm based on fusing multi-section signals with the same length and known frequency- difference. The results show that the presented algorithm is with good noise immunity, good universality, high precision of parameter estimation, as well as nicer performance under low signal and noise ratio conditions.
An Overview of Different Image Fusion Methods for Medical Applications

— Image fusion is an important research topic in many related areas such as computer vision, remote sensing, robotics, and medical imaging etc. Image fusion is the process of combining relevant information from several images into single image. The final output image can provide more information than any of the single image. Now-a-days, almost all areas of medical diagnosis are impacted by the digital image processing. For medical diagnosis, MRI and CT images are of main concern, both images give special sophisticated characteristics of the organ to be imaged. Computed Tomography (CT) provides the best information on denser tissue like bones. Magnetic Resonance Image (MRI) provides better information on soft tissues. With more available multimodality medical images in clinical applications, the idea of combining images from different modalities become very important and medical image fusion has emerged as a new promising research field that help physicians in the diagnosis process. More research has been done for fusion of MRI and CT images using traditional wavelet transform and few attempts using curvelet transform. This paper provides an overview of different image fusion methods for medical applications in brief and to improve the fusion results proposed method is described shortly.
A new closed-form expression for capacitance per unit length of VLSI interconnects

A set of closed-form expressions is derived for the capacitance per unit length of the most common two-dimensional VLSI interconnection structures. The results have been obtained by using an approximate electromagnetic analysis of two coupled interconnect conductors over a ground plane (silicon substrate). We assume that the electric field meets the boundary conditions of a single isolated infinite line; therefore the boundary conditions for the conductors in the structure are approximately satisfied. It is shown by the numerical results that the derived formulas in this paper can provide an easy-to-use and fast-to-compute solver that results in accurate and reliable capacitance per unit length values.
Extraction of Signals Buried in Noise: Non-Ergodic Processes

In this paper, we propose extraction of signals buried in non-ergodic processes. It is shown that the proposed method extracts signals defined in a non-ergodic framework without averaging or smoothing in the direct time or frequency domain. Extraction is achieved independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Performances of the pro-posed extraction method and comparative results with other methods are demonstrated via experimental Doppler velocimetry measurements.
ADHD Discrimination Based on Social Network

Attention Deficit Hyperactivity Disorder (ADHD) is one of the common diseases of brain and has brought the growth of teenagers and even the adult indelible damage. It is very different to classify the ADHD symptoms and normal by the existing research. In this paper, the contributions are as two aspects: one is that the attributes of brain network of the resting state fMRI data have been calculated to discriminate three categories ADHD from the controls. And the average accuracies of various categories are 42.49% and 63.46% on the ADHD-200 datasets of NYU and KKI respectively, which is better than the average best imaging-based diagnostic performance of 35.19% and 61.90% achieved in the ADHD-200 global competition. The other one is that we put forward a new method named G-algorithm to construct the whole brain network, which based on certain rules. The same or even better classification results have been achieved by this method, which also verifies its feasibility and effectiveness.
Effects of the Sex of Group Leaders on Women Participants in Assertion Training.

The purpose of th is study was to evaluate the effectiveness of assertion train ing as well as the re la tiv e effectiveness of women's assertion groups conducted by male-female or female-female leader dyads. That is , th is research investigated the effects of the presence or absence of a single male in the position of co-leader on women partic ipating in assertion tra in in g . Co-leaders were used to minimize possible idiosyncratic styles o f leadership which might affect treatment outcome. To fu rther minimize possible idiosyncratic co-leader interactions affecting outcome, co-leader conditions were each repeated once with d iffe re n t group leaders, and one female served as a co-leader constant in each co-leader dyad. Hence, four assertion groups were conducted by four d iffe re n t leaders, two male and two female, plus an additional female who co-led each of the four groups. Subjects in th is study were 32 females who responded to assertion train ing advertisements and who met screening c r ite r ia . Group leaders were three female and two male c lin ic a l psychology interns; three were doctoral candidates and two, a male and a female, were recent doctoral degree recip ients. Each assertion tra in ing group met on a weekly basis fo r 5 weeks and followed a theme oriented format. S e lfreport measures employed to evaluate tra in ing and the re la tive effects of the male-female or female-female condition included: (a) assertion
STEM-Based Project Based Learning Model to Increase Science Process and Creative Thinking Skills of 5th Grade

The background that drives this research is not yet honed the skills of the process of science and creative thinking. The aims of this research is to know the effect of STEM based learning based learning model on science process skill and creative thinking. The study was conducted on the water cycle material and its use. The research method is Quasi Eksperimental Design. The sampling technique is saturated sampling. The data collection tools in the form of observation instruments of science process skill, and instrument of creative thinking. The data were analyzed using independent t-test to test the difference. In testing the increase and effect of using N-gain. The result of the research shows that there is difference of influence of creative thinking skill, this is seen from the average of N-gain pretest and posttest in the students. The findings of the research are the significant increase of creative thinking skills in the experimental class 1. Improving creative thinking skill which in the high category is 23% and the middle category is 77%. In the experiment class 2, the high category is calculated as 4% and 96% are categorized into the medium category. The conclusion of the research is the learning model of learning based project based learning STEM can improve the skills of science process and creative thinking of the students. Learning based project based learning STEM is expected to be a reference in the application of learning.
Schistosome Vaccine Adjuvants in Preclinical and Clinical Research

There is currently no vaccine available for human use for any parasitic infections, including the helminth disease, schistosomiasis. Despite many researchers working towards this goal, one of the focuses has been on identifying new antigenic targets. The bar to achieve protective efficacy in humans was set at a consistent induction of 40% protection or better by the World Health Organisation (WHO), and although this is a modest goal, it is yet to be reached with the six most promising schistosomiasis vaccine candidates (Sm28GST, IrV5, Sm14, paramyosin, TPI, and Sm23). Adjuvant selection has a large impact on the effectiveness of the vaccine, and the use of adjuvants to aid in the stimulation of the immune system is a critical step and a major variable affecting vaccine development. In addition to a comprehensive understanding of the immune system, level of protection and the desired immune response required, there is also a need for a standardised and effective adjuvant formulation. This review summarises the status of adjuvants that have been or are being employed in schistosomiasis vaccine development focusing on immunisation outcomes at preclinical and clinical stages.
Foundations for Uniform Interpolation and Forgetting in Expressive Description Logics

We study uniform interpolation and forgetting in the description logic ALC. Our main results are model-theoretic characterizations of uniform interpolants and their existence in terms of bisimulations, tight complexity bounds for deciding the existence of uniform interpolants, an approach to computing interpolants when they exist, and tight bounds on their size. We use a mix of model-theoretic and automata-theoretic methods that, as a by-product, also provides characterizations of and decision procedures for conservative extensions.
Uncovering shared structures in multiclass classification

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.
Influences of Influential Users: An Empirical Study of Music Social Network

Influential user can play a crucial role in online social networks. This paper documents an empirical study aiming at exploring the effects of influential users in the context of music social network. To achieve this goal, music diffusion graph is developed to model how information propagates over network. We also propose a heuristic method to measure users' influences. Using the real data from Last. fm, our empirical test demonstrates key effects of influential users and reveals limitations of existing influence identification/characterization schemes.
Ctrl+ALT-RIGHT: reinterpreting our knowledge of white supremacy groups through the lens of street gangs

ABSTRACT For the last 30 years, gang scholars have explicitly disregarded white youth who are active in groups with overt or implicit ties to neo-Nazi/skinhead or white power ideologies from street gang research. This article argues that this decision to keep these two groups separate stems from a misinterpretation of the realities of these groups and this decision has had a long-standing impact on how researchers, law enforcement and policymakers understand and interact with youth active in these groups. The coining of the term ‘Alt-Right,’ and the re-emergence of white power youth in the international dialogue, underscores how the lack of systematic research severely limits our knowledge about youth involved in gangs with white power leanings. Based on these concerns, this article challenges the current understanding of both skinheads and their troublesome youth groups/gangs. By orienting the limited research on skinhead gangs within key street gang domains, this article draws attention to disconnects in the literature that have influenced how researchers approach the study of skinhead youth. This study’s conclusions support the purposeful inclusion of skinhead youth in future street gang research.
Applications for investigating therapy progress of autistic children

The paper regards supporting behavioral therapy of autistic children with mobile applications, specifically applied for measuring the child's progress. A family of five applications is presented, that was developed as an investigation tool within the project aimed at automation of therapy progress monitoring. The applications were already tested with children with autism spectrum disorder. Hereby we analyse children' experience with the games, as a positive attitude towards the application is the key factor enabling practical application of the solutions in therapy. The study might be interesting for both researchers and practitioners applying e-technologies in autistics therapy.