Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Rate Estimation for H.264/AVC CAVLC Encoder Based on Parallel Processing

The entropy engine of context adaptive variable-length coding (CAVLC) has been employed in the H.264/AVC baseline for its high coding efficiency. However, the related complexity is also high owing to the utilization of inter-symbol dependency in CAVLC. In this paper, an efficient and accurate bit-rate estimation scheme is proposed to avoid the actual CAVLC encoding in RDO for mode decision and to meet the requirement of low-complexity video coding implementations. The proposed approach firstly parses the characteristics of syntax elements. Then guided by the principle of CAVLC, a fast and precise algorithm is designed for accurate estimation of the bit-rate in rate-distortion mode decision. The structure of the proposed algorithm is well suited to parallel processing for hardware implementation such as chip design regarding H.264/AVC, which is beneficial to further reduce the encoding time for video coding applications. When the proposed method is incorporated into reference software of H.264/AVC in a sequential order, a reduction about 62% in the CAVLC encoding time in RDO can be observed with negligible degradation in the rate-distortion performance.
Neural Network Image

We examine the problem of deconvolving blurred text. This is a task in which there is strong prior knowledge (e.g., font characteristics) that is hard to express computationally. These priors are implicit, however, in mock data for which the true image is known. When trained on such mock data, a neural network is able to learn a solution to the image deconvolution problem which takes advantage of this implicit prior knowledge. Prior knowledge of image positivity can be hard{wired into the functional architecture of the network, but we leave it to the network to learn most of the parameters of the task from the data. We do not need to tell the network about the point spread function, the intrinsic correlation function, or the noise process. Neural networks have been compared with the optimal linear lter, and with the Bayesian algorithm MemSys, on a variety of problems. The networks, once trained, were faster image recon-structors than MemSys, and had similar performance. 1 Traditional image reconstruction methods Optimal linear filters In many imaging problems, the data measurements fd m g are linearly related to the underlying image f: The vector denotes the inevitable noise which corrupts real data. In the case of a camera which produces a blurred picture, the vector f denotes the true image, d denotes the blurred and noisy picture, and the linear operator R is a convolution deened by the point spread function of the camera. In this special case, the true image and the data vector reside in the same space; but it is important to maintain a distinction between them. We will use the subscript m = 1 : : :N to run over data measurements, and the subscripts i; j = 1 : : :k to run over image pixels. One might speculate that since the blur was created by a linear operation, then perhaps it might be deblurred by another linear operation. We derive the optimal linear lter in two ways. Bayesian derivation We assume that the linear operator R is known, and that the noise is Gaussian and independent, with a known standard deviation .
Bmc Medical Informatics and Decision Making Users and Non-users of Web-based Health Advice Service among Finnish University Students – Chronic Conditions and Self-reported Health Status (a Cross-sectional Study)

Background: The Internet is increasingly used by citizens as source of health information. Young, highly educated adults use the Internet frequently to search for health-related information. Our study explores whether reported chronic conditions or self-reported health status differed among Finnish university students using the Finnish Student Health Services web-based health advice service compared with those not using the service.
Enhancement of the Face Recognition Using Gabor Filter

In the industry the major challenge is to match the infrared face images to the optical face images. The problem arises of the variation between two images (modality gap). Gabor filters have proven themselves to be a powerful tool for facial feature extraction. An abundance of recognition techniques presented in the literature exploits these filters to achieve robust face recognition. However, while exhibiting desirable properties, such as orientation selectivity or spatial locality, Gabor filters have also some shortcomings which crucially affect the characteristics and size of the Gabor representation of a given face pattern. This is because of the infrared image captured by inferred imaging device and optical image captured by optical imaging device. To reduce the modality gap between infrared and optical images one method is use. Common feature discriminant analysis this method improves infrared-optical face recognition performance. This method provides extract the common features from heterogeneous face images, (infrared face image and optical face images). Second matching method is applied to resulting features to obtain final decision. Keywords— Gabor filter, Gabor wavelet, PCA, Principle Gabor Filter, Eigen images.
Policies to Foster Human Capital

This paper considers the sources of skill formation in a modern economy and emphasizes the importance of both cognitive and noncognitive skills in producing economic and social success and the importance of both formal academic institutions and families and firms as sources of learning. Skill formation is a dynamic process with strong synergistic components. Skill begets skill. Early investment promotes later investment. Noncognitive skills and motivation are important determinants of success and these can be improved more successfully and at later ages than basic cognitive skills. Methods currently used to evaluate educational interventions ignore these noncogntive skills and therefore substantially understate the benefits of early intervention programs and mentoring and teenage motivation programs. At current levels of investment, American society underinvests in the very young and overinvests in mature adults with low skills.
Online Packing of Equilateral Triangles

We investigate the online triangle packing problem in which a sequence of equilateral triangles with dierent sizes appear in an online, sequential manner. The goal is to place these triangles into a minimum number of squares of unit size. We provide upper and lower bounds for the competitive ratio of online algorithms. In particular, we introduce an algorithm which achieves a competitive ratio of at most 2.474. On the other hand, we show that no online algorithm can have a competitive ratio better than 1.509.
Identifying Semantic Relations in Context: Near-misses and Overlaps

This paper addresses the problem of semantic relation identification for a set of relations difficult to differentiate: near-misses and overlaps. Based on empirical observations on a fairly large dataset of such examples we provide an analysis and a taxonomy of such cases. Using this taxonomy we create various contingency sets of relations. These semantic categories are automatically identified by training and testing three state-of-the-art semantic classifiers employing various feature sets. The results show that in order to identify such near-misses and overlaps accurately, a semantic relation identification system needs to go beyond the ontological information of the two nouns and rely heavily on contextual and pragmatic knowledge.
Recognizing Unnecessary Inference

Intelligent reasoners sometimes draw conclusions that lack new or relevant information. Similarly, automated reasoning systems can produce formulas that are not necessary for the problem at hand. We concentrate on the problem of unnecessary inference in the context of resolution based systems. In such systems several strategies have been developed that allow for the deletion of clauses without sacrificing completeness. Unfortunately these strategies fail to recognize other frequently generated unnecessary formulas. We will present a generalized subsumption theorem that can be used to recognize such formulas and to develop new deletion methods which retain completeness.
Framework for the cost benefit analysis of the NSW Department of Community Services Brighter Futures Program. CHERE Working Paper 2009/4

Brighter Futures is the NSW DOCS early intervention program targeting children aged 0 to 8. The program is designed to ? support pregnant women and families with young children aged 0-8 years who require long-term support (up to two years) by a range of services. The program gives particular priority to those families with children under three years. (Department of Community Services, 2007). To be able to access the Brighter Futures program, parents must also have a vulnerability that is likely to worsen if not addressed with the services and support available to the parents and children which include home visits, quality children? s services and a parenting program (Department of Community Services, 2007). A Consortium led by the Social Policy Research Centre, and including the Centre for Health Economics Research and Evaluation (CHERE) at UTS and Pamela Meadows from the National Institute of Economic and Social Research, has been commissioned to undertake the evaluation of all aspects of the program. An important component of the evaluation process is economic evaluation. This paper is set out as follows; Section 2 provides a brief introduction to economic appraisal in relation to the Brighter Futures program and sets out the links between the envisaged cost-effectiveness analysis (CEA) and cost benefit analysis (CBA). It also outlines the broad challenges faced when undertaking economic appraisals in the field of program such as Brighter Futures. Section 3 sets out a methodological framework for undertaking the CBA of the Brighter Futures program, including methods for addressing key evaluation challenges and Section 4 outlines the main tasks for the CBA work.
Consumers as Resellers: Exploring the Entrepreneurial Mind of North American Consumers Reselling Online

The use of the internet has dramatically changed not only the way consumers buy products, but also the way consumers resell products by allowing them to reach an international customer base that rivals that of the largest retailers. With the developments and improvements in the ability to resell, consumers are learning and cultivating their own strategies for online resale. This changing marketplace puts consumers head-to-head with retailers, and many of them are embracing the challenge and letting their entrepreneurial skills shine through. In this study, a geographically diverse group of 22 online consumer resellers throughout North America participated in in-depth interviews to help shed light on how North American consumers resell online. The paper discusses resale types, motivations, and strategies and develops the groundwork for further inquiry into the latest phenomenon of the online resale marketplace.
On the Geometry of Maximum Entropy Problems

We show that a simple geometric result suffices to derive the form of the optimal solution in a large class of finite- and infinite-dimensional maximum entropy problems concerning probability distributions, spectral densities, and covariance matrices. These include Burg's spectral estimation method and Dempster's covariance completion, as well as various recent generalizations of the above. We then apply this orthogonality principle to the new problem of completing a block-circulant covariance matrix when an a priori estimate is available.
MODELING UTTERANCE-DRIVEN VISUAL ATTENTION DURING SITUATED COMPREHENSION

Evidence from behavioral studies demonstrates that spoken language guides attention in a related visual scene and that attended scene information can influence the comprehension process. Here we model sentence comprehen- sion within visual contexts. A recurrent neural network is trained to associate the linguistic input with the visual scene and to produce the interpretation of the described event which is part of the visual scene. A feedback mechanism is inves- tigated, which enables explicit utterance-mediated attention shifts to the relevant part of the scene. We compare four models - a simple recurrent network (SRN) and three models with specific types of additional feedback - in order to explore the role of the attention mechanism in the comprehension process. The results show that all networks learn not only successfully to produce the interpretation at the sentence end, but also demonstrate predictive behavior reflected by the ability to anticipate upcoming constituents. The SRN performs expectedly very well, but demonstrates that adding an explicit attentional mechanism does not lead to loss of performance, and even results in a slight improvement in one of the models.
How to Motivate Whole Blood Donors to Become Plasma Donors

This study tested the efficacy of interventions to recruit new plasma donors among whole blood donors. A sample of 924 donors was randomized to one of three conditions: control; information only by nurse; and information plus self-positive image message by nurse (SPI). Participants in the control condition only received a leaflet describing the plasma donation procedure. In the two experimental conditions the leaflet was explained face-to-face by a nurse. The dependent variables were the proportion of new plasma donors and the number of donations at six months. Overall, 141 (15.3%) new plasma donors were recruited at six months. There were higher proportions of new plasma donors in the two experimental conditions compared to the control condition (P < .001); the two experimental conditions did not differ. Also, compared to the control condition, those in the experimental conditions (all Ps < .001) gave plasma more often (information only by nurse: d = .26; SPI: d = .32); the SPI intervention significantly outperformed (P < .05) the information only by nurse condition. The results suggest that references to feelings of SPI such as feeling good and being proud and that giving plasma is a rewarding personal experience favor a higher frequency of plasma donation.
Transboundary Fisheries Management Under Implementation Uncertainty

This paper examines how non-binding co-operative agreements on marine fisheries management can be sustained when management plans in participating countries are implemented with error. The effects of implementation uncertainty on voluntary co-operation are compared to those of recruitment uncertainty. A self-enforcing co-operative solution can only be sustained when uncertainty is not too pronounced. Even when a co-operative agreement can be achieved, frequent phases of reversion to non-co-operative harvest levels are needed to support the agreement. The implications of recruitment uncertainty for implicit co-operation are less detrimental than those of implementation uncertainty.
Mitochondrial Ca2+ uptake by the voltage-dependent anion channel 2 regulates cardiac rhythmicity

Tightly regulated Ca2+ homeostasis is a prerequisite for proper cardiac function. To dissect the regulatory network of cardiac Ca2+ handling, we performed a chemical suppressor screen on zebrafish tremblor embryos, which suffer from Ca2+ extrusion defects. Efsevin was identified based on its potent activity to restore coordinated contractions in tremblor. We show that efsevin binds to VDAC2, potentiates mitochondrial Ca2+ uptake and accelerates the transfer of Ca2+ from intracellular stores into mitochondria. In cardiomyocytes, efsevin restricts the temporal and spatial boundaries of Ca2+ sparks and thereby inhibits Ca2+ overload-induced erratic Ca2+ waves and irregular contractions. We further show that overexpression of VDAC2 recapitulates the suppressive effect of efsevin on tremblor embryos whereas VDAC2 deficiency attenuates efsevin's rescue effect and that VDAC2 functions synergistically with MCU to suppress cardiac fibrillation in tremblor. Together, these findings demonstrate a critical modulatory role for VDAC2-dependent mitochondrial Ca2+ uptake in the regulation of cardiac rhythmicity. DOI: http://dx.doi. org/10.7554/eLife.04801. 001
A survey and comparison of some popular IPTV applications

Recently, Peer-to-Peer (P2P) IPTV applications represents a challenging trend of the Internet. IPTV refers to the delivery of live video streaming and Video on Demand (VoD). In this paper, the basic concepts and algorithms of some popular existing IPTV applications are presented based on the experience of a real system deployed by PPLive, CoolStreaming, Sopcast and UUSee. The performance and technological Challenges are proposed. Finally, the advantages and disadvantages of the different IPTV applications are conclusion.
Multi level exceptions mining in OLAP data cubes

People nowadays are relying more and more on OLAP data to find business solutions. A typical OLAP data cube usually contains four to eight dimensions, with two to six hierarchical levels and tens to hundreds of categories for each dimension. It is often too large and has too many levels for users to browse it effectively. In this paper we propose a new definition of exception. This integrated system prototype will guide users to efficiently explore exceptions in data cubes. It automatically computes the degree of exceptions for cube cells at different aggregation levels. Different statistical methods such as log-linear model, adapted linear model and Z-tests are used to compute the degree of exceptions. We present algorithms and address the issue of improving the performance on large data sets.
STATEMATE applied to statistical software testing

This paper is concerned with the use of statistical testing as a verification technique for complex software. Statistical testing involves exercising a program with random inputs, the test profile and the number of generated inputs being determined according to criteria based on program structure or software functionality. In case of complex programs, the probabilistic generation must be based on a black box analysis, the adopted criteria being defined from behavior models deduced from the specification. The proposed approach refers to a hierarchical specification produced in the STATEMATE environment. Its feasiblity is exemplified on a safety-critical module from the nuclear field, and the efficiency in revealing actual faults is investigated through experiments involving two versions of the module.
ColourNet: a system of interactive and interacting digital artworks

ColourNet is a digital art system composed of a set interactive and interacting artworks. Although the artworks are able to work independently, they can also operate together to provide enhanced possibilities for human interaction and creative participation. We describe the ColourNet digital art system and demonstrate how people can interact with a smartphone artwork and a screen-based work that each interact with one another.
Random Matrix Approach to Glassy Physics: Low Temperatures and Beyond

A random matrix approach to glassy physics is introduced. It leads to a class of models which exhibit both, glassy low-temperature phases, and double-- and single-well configurations in their potential energy. The distribution of parameters characterizing the local potential energy configurations can be computed, and differ from those assumed in the standard tunneling model and its variants. Still, low-temperature anomalies characteristic of amorphous systems are reproduced, and we are able to distinguish properties which can be expected to be universal from those which cannot.
Particulate Air Pollution, Ambulatory Heart Rate Variability, and Cardiac Arrhythmia in Retirement Community Residents with Coronary Artery Disease

Background: Decreased heart rate variability (HRV) has been associated with future cardiac morbidity and mortality and is often used as a marker of altered cardiac autonomic balance in studies of health effects of airborne particulate matter. Fewer studies have evaluated associations between air pollutants and cardiac arrhythmia. Objectives: We examined relationships between cardiac arrhythmias, HRV, and exposures to airborne particulate matter. Methods: We measured HRV and arrhythmia with ambulatory electrocardiograms in a cohort panel study for up to 235 hr per participant among 50 nonsmokers with coronary artery disease who were ≥ 71 years of age and living in four retirement communities in the Los Angeles, California, Air Basin. Exposures included hourly outdoor gases, hourly traffic-related and secondary organic aerosol markers, and daily size-fractionated particle mass. We used repeated measures analyses, adjusting for actigraph-derived physical activity and heart rate, temperature, day of week, season, and community location. Results: Ventricular tachycardia was significantly increased in association with increases in markers of traffic-related particles, secondary organic carbon, and ozone. Few consistent associations were observed for supraventricular tachycardia. Particulates were significantly associated with decreased ambulatory HRV only in the 20 participants using ACE (angiotensin I–converting enzyme) inhibitors. Conclusions: Although these data support the hypothesis that particulate exposures may increase the risk of ventricular tachycardia for elderly people with coronary artery disease, HRV was not associated with exposure in most of our participants. These results are consistent with previous findings in this cohort for systemic inflammation, blood pressure, and ST segment depression. Citation: Bartell SM, Longhurst J, Tjoa T, Sioutas C, Delfino RJ. 2013. Particulate air pollution, ambulatory heart rate variability, and cardiac arrhythmia in retirement community residents with coronary artery disease. Environ Health Perspect 121:1135–1141; http://dx.doi. org/10.1289/ehp.1205914
Excellence in clinical neurosurgery: practice and judgment make perfect.

Having discussed the importance of excellence in research in neurosurgery in my previous lecture, I will now make a strong case for the pursuit of excellence in clinical neurosurgery. Here, the adage ‘‘practice makes perfect’’ will be used time and again to underscore the important roles of repetition of manual and intellectual tasks to achieve excellence in our subspecialty. To this adage, however, I have also added the concept of ‘‘judgment,’’ which comes into play whenever we look after our patients with neurosurgical disease. It takes judgment to know when to operate, but perhaps more important, when not to operate. Even the most skillful technical neurosurgeons run the risk of unexpected patient outcomes if good judgment is not applied to the patients on whom they operate. It is the combination of practice and judgment that perhaps sets us apart as neurosurgeons from other professionals in which a lapse in judgment in their disciplines would not have the far-reaching ramifications that it does for us and our patients. Some of the other disciplines I will be reviewing here in terms of practice making perfect include performance piano and professional tennis and golf as examples.