Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrequent Basic Block-based Program Phase Classification and Power Behavior Characterization

Power and energy optimizations include both reducing total energy consumption and improving time-dependent power behavior. Fine-grained program power behavior is useful in evaluating power optimizations and observing power optimization opportunities. In this paper, we present a physical measurement-based infrastructure for program time-dependent power behavior characterization and optimization evaluation. This infrastructure does basic block profiling, a SimPoint-like phase classification, and precise physical measurement for selected representative program execution slices. Our phase classification method uses infrequently executed basic blocks to demarcate intervals and uses a user-specified interval size to control the length of the resulting intervals. This partitioning method incurs low instrumentation overhead for dynamic identification of simpoints during program execution. This physical measurement infrastructure enables precise power measurement for any program execution. This infrastructure is built on our Camino compiler, which supports static instrumentation on various levels. This infrastructure can be used to characterize detailed program power behavior, as well as evaluate compiler and architecture level power and energy optimizations. We validate the feasibility of this phase classification method in power behavior characterization through the physical measurement of 10 SPEC CPU2000 integer benchmarks on a Pentium 4 machine. Experimental results show that our method can also find representative slices for power behavior estimation, and the physical measurement with negligible instrumentation overhead enables us to estimate detailed time-dependent program power behavior.
A Controlled Sensing Approach to Graph Classification

The problem of classifying graphs with respect to connectivity via partial observations of nodes is posed as a composite hypothesis testing problem with controlled sensing. An observation at a node is a subset of edges incident to the node on the complete graph drawn according to a probability model, which is a function of a fixed underlying graph. Connectivity is measured through average node degree and is classified with respect to a threshold. A simple approximation of the controlled sensing test is derived and simulated on Erdös-Rènyi graphs to characterize the error probabilities as a function of the expected stopping times. The test is also experimentally validated on a real-world example of the social structure of Long-Tailed Manakins. It is shown that the proposed test achieves favorable tradeoffs between the classification error and the number of measurements. Furthermore, the test outperforms existing approaches, especially at low target error rates. In addition, the proposed test achieves the optimal error exponent.
A prefetch taxonomy

The growing difference between processor and main memory cycle time demands the use of aggressive prefetch algorithms to reduce the effective memory access latency. However, prefetching can significantly increase memory traffic and unsuccessful prefetches may pollute the cache. Metrics such as coverage and accuracy result from a simplistic classification of individual prefetches as "good" or "bad. " They do not capture the full effect of each prefetch and, hence, do not accurately reflect the quality of the prefetch algorithm. Gross statistics such as changes in the number of misses, total traffic, and IPC are not attributable to individual prefetches. Such gross metrics are therefore useful only for ranking existing prefetch algorithms; they do not evaluate the effect of individual prefetches so that an algorithm might be tuned. We introduce a new, accurate, and complete taxonomy, called the Prefetch Traffic and Miss Taxonomy (PTMT), for classifying each prefetch by precisely accounting for the difference in traffic and misses it generates, either directly or indirectly. We illustrate the use of PTMT by evaluating two data prefetch algorithms.
On the Use of Neuropyhsiological Tools in IS Research: Developing a Research Agenda for NeuroIS

This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social sciences in capturing presumably objective data directly from the human body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major strengths and weaknesses. It then discusses several promising application areas and research questions where IS researchers can benefit from the use of neurophysiological data. The proposed research topics are presented within three thematic areas: (1) development and use of systems, (2) IS strategy and business outcomes, and (3) group work and decision support. The article concludes with recommendations on how to use neurophysiological tools in IS research along with a set of practical suggestions for developing a research agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.
Game Over Interdependent Networks: Rationality Makes the System Inherent Deficient

Many real-world systems are composed of interdependent net works that rely on one another. Such networks are typically designed and operated by different entities, who aim at maximizing their own interest. In this paper, we study the game over interdependent networks, inve st gating how the rational behaviors of entities impact the whole system. We first introduce a mathematical mo del to quantify the interacting payoffs among varying entities. Then we study the Nash equilibrium and com pare it with the optimal social welfare. We reveal that the cooperation between different entities can be reac hed to maximize the social welfare only when the average degree of each network is constant. Otherwise, ther may be a huge gap between the Nash equilibrium and optimal social welfare. Therefore, the rationality of d ifferent entities that operates these networks makes the system inherently deficient and even extremely vulnerable i n some cases. Furthermore, we uncover some factors (such as weakening coupled strength of interdependent netw orks, designing suitable topology dependency of the system) that help reduce the gap and improve the system vu lnerability.
Functional roles of the amino terminal domain in determining biophysical properties of Cx50 gap junction channels

Communication through gap junction channels is essential for synchronized and coordinated cellular activities. The gap junction channel pore size, its switch control for opening/closing, and the modulations by chemicals can be different depending on the connexin subtypes that compose the channel. Recent structural and functional studies provide compelling evidence that the amino terminal (NT) domains of several connexins line the pore of gap junction channels and play an important role in single channel conductance (γj) and transjunctional voltage-dependent gating (Vj-gating). This article reviews recent studies conducted on a series of mutations/chimeras in the NT domain of connexin50 (Cx50). Functional examination of the gap junction channels formed by these mutants/chimeras shows the net charge number at the NT domain to be an important factor in γj and in Vj-gating. Furthermore, with an increase in the net negative charge at the NT domain, we observed an increase in the γj as well as changes in the parameters of the Boltzmann fit of the normalized steady-state conductance and Vj relationship. Our data are consistent with a structural model where the NT domain of Cx50 lines the gap junction pore and plays an important role in sensing Vj and in the subsequent conformational changes leading to gating, as well as in limiting the rate of ion permeation.
A research program to assess user perceptions of group work support

Computer support for group work is a technological innovation receiving considerable attention from developmental researchers. This paper reports the preliminary results from two surveys which assessed user perceived needs for various types of group work support. The instruments, distributed to managers and professionals in a variety of organizations, described group support scenarios and associated functions/tools and asked for an assessment of their usefulness to one of the respondent's organizational work groups. Support for between meetings group work was perceived to be more useful than support for either face to face or electronic meetings. Common single user tools were generally perceived to be more useful than multi-user group tools. Individual differences and implications are addressed.
Dynamic Probabilistic Volumetric Models

This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4-d) data. Existing methods typically store separate 3-d models at each time step and do not address such limitations. A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-d dynamic surfaces. This representation is shown to achieve compression of 4-d data and provide efficient spatio-temporal processing. The advances of the proposed framework is demonstrated on standard datasets using free-viewpoint video and 3-d tracking applications.
Robustness in ASR: An Experimental Study of the Interrelationship between Discriminant Feature-Space Transformation, Speaker Normalization and Environment Compensation

This thesis addresses the general problem of maintaining robust automatic speech recognition (ASR) performance under diverse speaker populations, channel conditions, and acoustic environments. To this end, the thesis analyzes the interactions between environment compensation techniques, frequency warping based speaker normalization, and discriminant feature-space transformation (DFT). These interactions were quantified by performing experiments on the connected digit utterances comprising the Aurora 2 database, using continuous density hidden Markov models (HMM) representing individual digits. Firstly, given that the performance of speaker normalization techniques degrades in the presence of noise, it is shown that reducing the effects of noise through environmental compensation, prior to speaker normalization, leads to substantial improvements in ASR performance. The speaker normalization techniques considered here were vocal tract length normalization (VTLN) and the augmented state-space acoustic decoder (MATE). Secondly, given that discriminant feature-space transformation (DFT) are known to increase class separation, it is shown that performing speaker normalization using VTLN in a discriminant feature-space leads to improvements in the performance of this technique. Classes, in our experiments, corresponded to HMM states. Thirdly, an effort was made to achieve higher class discrimination by normalizing the speech data used to estimate the discriminant feature-space transform. Normalization, in our experiments, corresponded to reducing the variability within each class through the use of environment compensation and speaker normalization. Significant ASR performance improvements were obtained when normalization was performed using environment compensation, while our results were inconclusive for the case where normalization consisted of speaker normalization. Finally, aimed at increasing its noise robustness, a simple modification of MATE is presented. This modification consisted of using, during recognition, knowledge of the distribution of warping factors selected by MATE during training.
Radio and infrared structure of the colliding-wind Wolf-Rayet system WR147

New, high-resolution infrared and radio images of the X-ray luminous Wolf-Rayet system WR147 (AS 431) are presented. The 5-GHz radio image resolves both components of this double source. The emission from one component, that to the South and associated with the wind of the WN8 star is 170 253 mas, indicating that the stellar wind is not spherically symmetric. The second, non-thermal component 0 00 :6 north of the WN8 star, is extended East-West to 267 mas. The infrared image reveals the presence of a companion to the WN8 star, close to the non-thermal radio source but slightly (60 mas) more distant from the WN8 star. The companion is K 3 mag. fainter than the WN8 star and has a luminosity of a B0.5V star, just suuciently luminous to possess a stellar wind capable of colliding with that of the WN8 star. The presence of the non-thermal emission between the two stars and much closer to that with the weaker wind is direct evidence for a colliding-wind origin for the emission. A signiicant portion of the X-ray emission can also be accounted for by the release of energy in the wind collision. Comparison of the non-thermal ux with those of the three systems incorporating WC type stars (WR125, WR140 and WR146) shows a correlation with velocity of the WR wind.
Age-Invariant Recognition for Classifying Human Faces by Fusion of Shape and Texture Features

Face recognition plays an important role in identifying a person from a set of test imagesused for personal identification, but an individual poses a serious problem in recognizing the facial images whenever age varies. So to overcome this problem, face recognition system is developed which is invariable of age. In support to that a paper was published by covering age contemplation. In the proposed system the problem was attended by fusion of phase congruency and local binary patterns. The advantages of the proposed system are, it improves the accuracy of recognizing the facial expressions in different pose and appearances, it works accurately without contemplation of age, and it can also work in Broad training set. Finally by implementing this we can recognize an individual without depending on age with resistance to pose variations and proper illuminations and gives a better computational efficiency.
7 Putting genetics into optogenetics: knocking out proteins with light

The disruption of gene function has been a central method for understanding biological processes for over a century. However, there are three major weaknesses to classical genetic analysis: pleiotropic mutations, strain lethals, and cell lethals. First, many genetic knockouts affect multiple tissues, which can complicate the interpretation of phenotypes for any particular cell. These pleiotropies may arise from nonautonomous effects from other tissues or broad nonspecific defects, such as general ill health. Second, some genes are mutated to lethality; that is, the mutants die during development, making it difficult to study the role of a gene at later time points. Third, some genes are required for the viability of a cell; for example, genes involved in basic cellular functions, like transcription and translation. A cell with such a mutation will die even as a mosaic in an otherwise wild-type animal. The most common methods to circumvent these drawbacks of traditional genetic analysis are mRNA destabilization by RNA interference, or inducible DNA mutations; for example, by using Cre/ Lox recombination. These methods allow the study of gene function later in development in specific tissues. However, the temporal resolution of these methods is limited by the half-life of the protein of interest. Ideally, an experimenter would be able to instantaneously remove a protein from any cell of choice. Here we review methods for the induction of protein degradation, and speculate about the potential use of light to stimulate protein degradation.
PowerMarker: an integrated analysis environment for genetic marker analysis

SUMMARY PowerMarker delivers a data-driven, integrated analysis environment (IAE) for genetic data. The IAE integrates data management, analysis and visualization in a user-friendly graphical user interface. It accelerates the analysis lifecycle and enables users to maintain data integrity throughout the process. An ever-growing list of more than 50 different statistical analyses for genetic markers has been implemented in PowerMarker. AVAILABILITY www.powermarker. net
Modified Decoding for Low Density Parity Check Codes over Power Line Channels

Power line communication for home networking is a new convenient method. However, power line communication makes degradation because of the impulsive interference generated by electrical appliances. Therefore, it is the first problem we need to be solved. In this paper, we apply LDPC (low density parity check) codes to AWAN (additive white class a noise) channels which is usually used as the model of impulsive noise environment of power line channels. We propose a modification to Log-BP (log domain belief propagation) decoding which is suitable for AWAN channels. At last, we show the BER (bit error rate) performance of the modified Log-BP decoding over AWAN channels by Monte-Carlo simulation.
A MPI PARALLEL PRECONDITIONED SPECTRAL ELEMENT METHOD FOR THE HELMHOLTZ EQUATION

Spectral element method is well known as high-order method, and has potential better parallel feature as compared with low order methods. In this paper, a parallel preconditioned conjugate gradient iterative method is proposed to solving the spectral element approximation of the Helmholtz equation. The parallel algorithm is shown to have good performance as compared to non parallel cases, especially when the stiffness matrix is not memorized. A series of numerical experiments in one dimensional case is carried out to demonstrate the efficiency of the proposed method.
As Simple as It Gets - A Sentence Simplifier for Different Learning Levels and Contexts

This paper presents a text simplification method that transforms complex sentences into simplified forms. Our method uses NLP-techniques to simplify the text based on the target audience context, improving its overall understandability. We evaluate our approach in two aspects: grammatical structure and understandability. In both aspects, our approach achieved good results, showing its applicability to the learning process.
Secondary school leader perceptions about the inclusion of queer materials in the school course curricula

ABSTRACT The US school system is dominated by heteronormativity in the formal curriculum that gives preference to binary conceptions of sexuality. School leaders play a role in supporting or disrupting school policies facilitating or inhibiting curricular inclusion of queer content in course materials and an instructional pedagogy that promotes an equitable and inclusive educational process. Using a qualitative design and phenomenographic data analysis method, the purpose of the study was to use a queer/curriculum theory lens to explore secondary level school leaders’ perceptions about their experiences with the inclusion of queer related content in the course curricula in their schools. The sample included seven secondary level educational leaders from schools in a northeastern state in the USA. Using a qualitative design and phenomenographic data analysis methods of in-depth interviews, five categories of description of leader perceptions were identified: (1) lack of inclusion of queer content in formal state curricula, (2) recognized need for inclusion of queer content in the formal curricula, (3) informal inclusion of queer content in the course curriculum, (4) teacher reluctance to include queer content in the curriculum, and (5) providing teachers with resources and professional development opportunities related to integrating queer content into their course curriculum.
Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems

The use of Relevance-Based Language Models for top-N recommendation has become a promising line of research. Previous works have used collection-based smoothing methods for this task. However, a recent analysis on RM1 (an estimation of Relevance-Based Language Models) in document retrieval showed that this type of smoothing methods demote the IDF effect in pseudo-relevance feedback. In this paper, we claim that the IDF effect from retrieval is closely related to the concept of novelty in recommendation. We perform an axiomatic analysis of the IDF effect on RM2 concluding that this kind of smoothing methods also demotes the IDF effect in recommendation. By axiomatic analysis, we find that a collection-agnostic method, Additive smoothing, does not demote this property. Our experiments confirm that this alternative improves the accuracy, novelty and diversity figures of the recommendations.
Improving Thai word segmentation with Named Entity Recognition

Segmenting words in Thai language is a very difficult task since there is no distinguished clue such as blank, period and other punctuations as in English. Several previous researches employed dictionary as the main resource for consideration. However there still exist two problems including ambiguous words and unknown words. These unknown words can be categorized into two groups, -i.e., newly defined words and named entities. This paper presents an approach for improving the performance of Thai word segmentation by merging Named Entity Recognition (NER) to the Thai word segmentation. The Conditional Random Fields (CRFs) algorithm is applied for training and recognizing Thai named entities. The prefixes and suffixes of Thai named entities are selected as main features for learning the models. The performance evaluations are experimented by using the Thai standard word segmentation corpus, namely BEST20091, which consists of 5 million words. Various word-level grams (i.e., three, five and seven) are also employed to construct the Thai NER models. The experimental results show that the 7-gram NER model provides the best performance. Merging the proposed NER model to the Thai word segmentation called TLex (Thai Lexeme Analyzer) can improve the performance measured by F1-measure from 92.39% to 93.96%.
Democratizing 3D dynamic gestures recognition

Developing vision-based 3D gestures recognition systems requires strong expertise and knowledge in computer vision and machine learning techniques. As human-computer interaction researchers do not generally have a thorough knowledge of these techniques, we developed Gesta. Gesta is a tool that enables non-experts in vision computing and artificial intelligence techniques to rapidly develop a 3D gestures recognition system prototype and to support the gesture design process. This tool works with up to two Microsoft Kinects, and integrates the depth cameras calibration algorithm and the hidden Markov models classifier. The users can manage these complex functions through a simple graphical user interface, even if they do not have any expertise in computer vision and machine learning domains. A usability test with 12 researchers with experience in human-computer interaction has been conducted in order to evaluate the overall usability of this tool. Results demonstrate that the testers appreciated the Gesta tool which scored 88.9 points out of 100 in the Brooke's system usability scale.