Miracle-dz/UniFAR-FTUs huggingface dataset:
Various filtering methods of motion vector fields

The Block matching algorithms suffers from limitation concerning the fidelity of the predicted image: blocking effect is introduced for the lack of coherence of the estimated motion to the actual motion. It is possible to partially overcome this drawback by smoothing the estimated vector field in a further stage. Smoothing can be accomplished, for example, by using various filtering methods. In this paper, it is main purpose to present the motion estimation and filtering techniques that give consideration to both estimation performance and computational efficiency.
From “Her” Problem to “Our” Problem: Using an Individual Lens Versus a Social-Structural Lens to Understand Gender Inequity in STEM

Increasing the representation of women in science, technology, engineering, and mathematics (STEM) is one of our nation's most pressing imperatives. As such, there has been increased lay and scholarly attention given to understanding the causes of women's underrepresentation in such fields. These explanations tend to fall into two main groupings: individual-level (i.e., her) explanations and social-structural (i.e., our) explanations. These two perspectives offer different lenses for illuminating the causes of gender inequity in STEM and point to different mechanisms by which to gain gender parity in STEM fields. In this article, we describe these two lenses and provide three examples of how each lens may differentially explain gender inequity in STEM. We argue that the social-structural lens provides a clearer picture of the causes of gender inequity in STEM, including how gaining gender equity in STEM may best be achieved. We then make a call to industrial/organizational psychologists to take a lead in addressing the societal-level causes of gender inequality in STEM.
FTIR study of primate color visual pigments

How do we distinguish colors? Humans possess three color pigments; red-, green-, and blue-sensitive proteins, which have maximum absorbance (λmax) at 560, 530, and 420 nm, respectively, and contribute to normal human trichromatic vision (RGB). Each color pigments consists of a different opsin protein bound to a common chromophore molecule, 11-cis-retinal, whereas different chromophore-protein interactions allow preferential absorption of different colors. However, detailed experimental structural data to explain the molecular basis of spectral tuning of color pigments are lacking, mainly because of the difficulty in sample preparation. We thus started structural studies of primate color visual pigments using low-temperature Fourier-transform infrared (FTIR) spectroscopy, which needs only 0.3 mg protein for a single measurement. Here we report the first structural data of monkey red- (MR) and green- (MG) sensitive pigments, in which the information about the protein, retinal chromophore, and internal water molecules is contained. Molecular mechanism of color discrimination between red and green pigments will be discussed based on the structural data by FTIR spectroscopy.
DANTE : a New Resource for Research at the Syntax-Semantics Interface

Since Levin’s seminal work (Levin, 1993) there has been a rising interest in computational linguistics research which aims to examine the relationship between the syntax and semantics of verbs. A substantial portion of the work comprises efforts to discover semantic classes from syntactic behaviour and also from selectional preferences. There is also some work on directly examining related phenomena, such as detecting subcategorisation frames and diathesis alternations. Work in this area is typically corpus based, although many manually constructed resources have also been used as start points and for evaluation. In this paper, we present an English lexical database (being finalised at the time of writing, and to be released late 2010) which we believe will be a major catalyst for work of this nature, both as a starting point for automatic methods and as a gold standard for evaluation
"Nijimi" rendering algorithm for creating quality black ink paintings

We present an interactive painting system for generating high quality and artistic calligraphy characters and black ink paintings. The system is based on our original algorithm that is unique in physically simulating the dynamic diffusion of liquid ink into absorbent painting paper. The algorithm creates remarkably realistic strokes efficiently. For rendering 20 strokes in different size, it takes only 30 seconds on a personal computer, fast enough for interactive performance. By using this system, the users can create realistic strokes with delicate "Nijimi"-rendering, "kasure"-rendering effects, which are essential to black ink painting and modern calligraphy.
I-LUTSim: An iterative look-up table based thermal simulator for 3-D ICs

This work presents an iterative look-up table based thermal simulator, I-LUTSim, to efficiently estimate the temperature profile of three-dimensional integrated circuits. I-LUTSim includes two stages. First, the pre-process stage constructs thermal impulse response tables. Then, the simulation stage iteratively calculates the temperature profile via the table lookup. With this two-stage scheme, the maximum absolute error of I-LUTSim is less than 0.41% compared with that of a commercial tool ANSYS. Moreover, I-LUTSim is at least an order of magnitude faster than a fast matrix solver SuperLU [1] for the full-chip temperature simulation.
Simultaneous assignment and structure determination of protein backbones by using NMR dipolar couplings.

The wealth of genomic data that has recently become available with completion of the sequencing of both the human and a variety of other genomes has created a need for rapid and efficient determination of three-dimensional (3D) structures of the corresponding proteins. To date, most effort in this so-called structural genomics has focused on Xray crystallography, but NMR spectroscopy also shows considerable potential. 3] Conventionally, NMR spectroscopic structure determination consists of a time-consuming, mainly manually performed resonance-assignment phase followed by a phase in which the 3D structure is determined. Herein we describe an automated approach where the assignment and the determination of backbone structures of medium accuracy is performed simultaneously using chemical shifts and residual dipolar couplings (RDCs). The new software, called Itas (ITerative Assignment and Structure), is applicable to proteins of up to around 125 amino acid residues and offers the opportunity to greatly accelerate NMR structure determination. Rapid fold determination starts from a list of unassigned chemical shifts and RDCs that are grouped according to their common H,N chemical shifts (see Supporting Information). In the first step, assignment of backbone resonances is automatically performed by Itas using only chemical shift information and with algorithms developed from previous methods. No information about the tertiary structure is available at this point. The degree of assignment that is obtained depends on the size and complexity of the protein and on the type and quality of NMR spectra available (Table 1). RDCs and chemical shifts, which were assigned, are used subsequently for a molecular-fragment search. Molecular fragments are selected from a nonredundant database of protein crystal structures of a resolution of 2 1 or better. The selection is performed on the basis of multiple sequence alignment and the fit between measured RDCs and chemical shifts and those back-calculated from the structure of each data base fragment. Then a compact structure is assembled from these fragments using the structure-calculation program RosettaNmr. 1000 low-resolution structural models are generated and are ranked according to agreement with experimental RDCs and energetically favorable nonlocal interactions which include hydrophobic burial, pairing of bstrands, and overall compactness. The 20 lowest-energy structures are used by Itas in a second iteration step to
Discriminating proteins using a novel ensemble algorithm

Decisions of multiple hypotheses are combined in ensemble learning to produce more accurate and less risky results. In this article, we present a novel ensemble machine learning approach for the development of robust thermo stable protein discrimination. But unlike widely used ensemble approaches in which bootstrapped training data are used, we keep the original data unchanged. Instead, we build an ensemble of base classifiers that each of them uses a division of features (called feature group) to predict the class label of each sample. Then, we try to learn the base classifiers' outputs (behavior) for different samples. By testing the proposed method on a well-known dataset, we show that our ensemble method is comparable in precision, recall and f-measure to the state of the art classifiers.
RELEVANCE FEEDBACK IN CBIR USING THE RLS CLASSIFIER

The Relevance Feedback has been used to improve the performance of CBIR algorithms. This paper presents a relevance feedback method based on the regularized least squares classifier, and a technique to select feedback information in order to increase the learning rate. Experimental results are presented in the paper to illustrate the performance of the proposed relevance feedback method.
Combining Symbolic Execution and Model Checking to Reduce Dynamic Program Analysis Overhead

This paper addresses the problem of reducing the runtime monitoring overhead for programs where ¯ne-grained monitoring of events is required. To this end we complement model checking techniques with symbolic reasoning methods and show that, under certain circumstances, code fragments do not a®ect the validity of underlying properties. We consider safety properties given as regular expressions on events generated by the program. Further, we show how our framework can be extended to consider programs with cycles. We sample our presentation with the aid of the Java PathFinder model checker [13]. Keywords: model checking, Java PathFinder, symbolic reasoning, instrumentation, monitoring, invariant strengthening.
Severity of illness scoring systems in the intensive care unit

Objective:Adult intensive care unit prognostic models have been used for predicting patient outcome for three decades. The goal of this review is to describe the different versions of the main adult intensive care unit prognostic models and discuss their potential roles. Data Source:PubMed search and review of the relevant medical literature. Summary:The main prognostic models for assessing the overall severity of illness in critically ill adults are Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Mortality Probability Model. Simplified Acute Physiology Score and Mortality Probability Model have been updated to their third versions and Acute Physiology and Chronic Health Evaluation to its fourth version. The development of prognostic models is usually followed by internal and external validation and performance assessment. Performance is assessed by area under the receiver operating characteristic curve for discrimination and Hosmer-Lemeshow statistic for calibration. The areas under the receiver operating characteristic curve of Simplified Acute Physiology Score 3, Acute Physiology and Chronic Health Evaluation IV, and Mortality Probability Model0 III were 0.85, 0.88, and 0.82, respectively, and all these three fourth-generation models had good calibration. The models have been extensively used for case-mix adjustment in clinical research and epidemiology, but their role in benchmarking, performance improvement, resource use, and clinical decision support has been less well studied. Conclusions:The fourth-generation Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score 3, Acute Physiology and Chronic Health Evaluation IV, and Mortality Probability Model0 III adult prognostic models, perform well in predicting mortality. Future studies are needed to determine their roles for benchmarking, performance improvement, resource use, and clinical decision support.
Image recognition system

Image recognition has been an active and challenging area of research. In recent years image recognition has received veritable attention from the research communities but it still remained very challenging in real applications. It plays a very important role in today’s world. Though we might have seen various applications and websites of text recognition, label detection and face detection they are all separate applications this review paper tells us how to combine them together so that user don’t need to download numerous software but just a simple image recognition system that uses Google cloud vision API. In image recognition, we have combined 3 things together that is text, label, and face. It should be noted that the combination of these three things together is nowhere found and might be difficult to evolve. This review paper will focus on scattering and separating the image by defining each element of the image by recognizing the text, detecting its label and detecting the face that can make the human work’s easy. By focusing on the history and evolution of the text, label and face recognition system we combine them together so that it can be used for security purposes, organization, computer software and it can also be used in the government field. This review paper will represent the limitations and superiorities of different techniques which are used for image recognition system.
The Security Analysis for Ad Hoc Routing Protocols Based on Improved Strand Space

Strand space is often used to analyze the secure protocols. However, the protocol consistency conditions defined by strand space do not satisfy the characteristics of ad hoc routing protocol. Based on the characteristics of ad hoc mobile network, the thesis re-defines the consistency conditions for the normal operation of the protocol and at the same time adds intermediator credibility condition, thus adapts the strand space method to the security analysis for ad hoc routing protocols. The SRP protocol is taken as an example for the analysis of its security, and accordingly valuable results have been obtained
Efficient Cobb-Douglas Production Function

In this paper, we present a function for the share of factors of output, which is in complete agreement with primary production theories in microeconomics. We follow some assumptions for production function, and also payment to each factor equals their marginal products, and we create a new production function which is called the efficient production function. We then try not only to remove the significant defects in Cobb-Douglas production function but also by accept the presented theory. We conclude that the need for most previous approaches have caused problems and even the developed functions have not been able to solve the problem. The presented model in complete agreement is also supported by the empirical findings for the United States (U.S.) economy.
A secured communication link design using narrow line of sight technique

In recent wireless networking optical communication is an emerging field. Another novel approach of optical communication using Narrow Line of Sight technique is discussed. Array of trans receivers are placed on the ceiling to communicate with the mobile nodes (users / trans receivers) instead of a single transmitter. An intelligent position tracking technique based on the informations available from prepositioned sensors and using them in simple geometric equations is described. Intelligent cell concept is used to facilitate the tracking system. The whole wide area is divided into several sub areas and some of the sub areas are designated as boundary sub areas which hold the data regarding the movement of the mobile nodes. The Diffie Hellman simple authenticated key agreement is used for secure communication.
Efficient runtime quantitative verification using caching, lookahead, and nearly-optimal reconfiguration

Self-adaptive systems used in safety-critical and business-critical applications must continue to comply with strict non-functional requirements while evolving in order to adapt to changing workloads, environments, and goals. Runtime quantitative verification (RQV) has been proposed as an effective means of enhancing self-adaptive systems with this capability. However, RQV frequently fails to provide the fast response times and low computation overheads required by real-world self-adaptive systems. In this paper, we investigate how three techniques, namely caching, lookahead and nearly-optimal reconfiguration, and combinations thereof, can help address this limitation. Extensive experiments in a case study involving the RQV-driven self-adaptation of an unmanned underwater vehicle indicate that these techniques can lead to significant reductions in RQV response times and computation overheads.
P ERSPECTIVE M AKING AND P ERSPECTIVE T AKING IN C OMMUNITIES OF K NOWING

Communication in knowledge intensive firms involves perspective making and perspective taking within and between their communities of knowing. In this paper we look to science as an example of knowledge work in a community of knowing, and draw implications for the design of electronic communication systems and policies to support perspective making and perspective taking. The role of narrative in perspective making and of reflexivity in perspective taking are highlighted. Two models of communication, the conduit model and the language games model, are proposed for guiding the design of electronic communication media, and their relative strengths and weaknesses are considered.
An Evaluation of Universal Grammar and the Phonological Mind1

This paper argues against the hypothesis of a “phonological mind” advanced by Berent. It establishes that there is no evidence that phonology is innate and that, in fact, the simplest hypothesis seems to be that phonology is learned like other human abilities. Moreover, the paper fleshes out the original claim of Philip Lieberman that Universal Grammar predicts that not everyone should be able to learn every language, i.e., the opposite of what UG is normally thought to predict. The paper also underscores the problem that the absence of recursion in Pirahã represents for Universal Grammar proposals.
COMBO-17 measurements of the effect of environment on the type-dependent galaxy luminosity function

We have developed a method to calculate overdensities in multicolour surveys, facilitating a direct comparison of the local density contrast measured using galaxy samples that have different redshift error distributions, i.e. for red and blue, or bright and faint galaxies, respectively. We calculate overdensities in small redshift slices ($\Delta z =0.02$, which at $z=0.3$ corresponds roughly to $\Delta r_{\rm comoving}=53~h^{-1}$ Mpc) for 9176 galaxies with $R\leq23.65$, $M_{B}({\rm Vega})-5\log h\leq-18$, and $z\leq 0.7$, in three COMBO-17 fields (measuring $31'\times31'$ each). The mean redshift errors of this sample are approximately $\sigma_z/(1+z)\simeq 0.015$. In the Chandra Deep Field South we identify a region that is underdense by almost a factor 2 compared to the other two fields in the same redshift range ($0.25\la z \la 0.4$). This can be used for an investigation of the variation of the colour-dependent luminosity function with environment: We calculate the luminosity function in this redshift range for red sequence and blue cloud galaxies (as defined by Bell et al. 2004) in each of the fields separately. While the luminosity function of the blue galaxies remains unaffected by different density contrasts, the luminosity function of the red galaxies clearly has a more positive faint-end slope in the Chandra Deep Field South as compared to the other two COMBO-17 fields. The underdensity there is thus mainly due to a deficiency of faint red galaxies. This result is in qualitative agreement with the trends seen at $z=0.1$, e.g. in the 2dFGRS (Croton et al. 2005), or in the SDSS (Zandivarez et al. 2006).
LDAC

The trend of increasing the number of cores to achieve higher performance has challenged efficient management of on-chip data. Moreover, many emerging applications process massive amounts of data with varying degrees of locality. Therefore, exploiting locality to improve on-chip traffic and resource utilization is of fundamental importance. Conventional multicore cache management schemes either manage the private cache (L1) or the Last-Level Cache (LLC), while ignoring the other. We propose a holistic locality-aware cache hierarchy management protocol for large-scale multicores. The proposed scheme improves on-chip data access latency and energy consumption by intelligently bypassing cache line replication in the L1 caches, and/or intelligently replicating cache lines in the LLC. The approach relies on low overhead yet highly accurate in-hardware runtime classification of data locality at both L1 cache and the LLC. The decision to bypass L1 and/or replicate in LLC is then based on the measured reuse at the fine granularity of cache lines. The locality tracking mechanism is decoupled from the sharer tracking structures that cause scalability concerns in traditional cache coherence protocols. Moreover, the complexity of the protocol is low since no additional coherence states are created. However, the proposed classifier incurs a 5.6KB per-core storage overhead. On a set of parallel benchmarks, the locality-aware protocol reduces average energy consumption by 26% and completion time by 16%, when compared to the state-of-the-art Reactive-NUCA multicore cache management scheme.
Is Exposure to Violence a Persistent Risk Factor for Offending across the Life Course? Examining the Contemporaneous, Acute, Enduring, and Long-term Consequences of Exposure to Violence on Property Crime, Violent Offending, and Substance Use

Objectives: To examine the contemporaneous (cross-sectional), acute (1 year), enduring (5–7 years), and long-term (12–13 years) effects of exposure to violence on offending behaviors. Methods: We analyze four waves of data from the National Longitudinal Study of Adolescent to Adult Health (N = 7,706). Exposure to violence captures direct (interpersonal victimization and violent threats) and indirect (witnessing violence) experiences with violence. Outcome measures include property crime, violent offending, and substance use. A series of logistic regression models examine the acute, enduring, and long-term effects of exposure to violence on the offending outcomes at each study wave, controlling for exposure to violence, lagged dependent variables, and baseline covariates at all previous waves. Results: The effects of exposure to violence on violent offending persist over time, with effects attenuating over time. However, exposure to violence only has contemporaneous and acute effects on property crime and drug use. Conclusions: Long-term effects of exposure to violence on violent offending are not an artifact of confounding with more recent experiences with violence. Both distal and proximate effects of exposure to violence should be addressed in order to adequately disrupt the overlap between exposure to violence and violent offending.