Miracle-dz/UniFAR-FTUs huggingface dataset:
Hashing Based Fast Palmprint Identification for Large-Scale Databases

In this paper, we present two hashing based techniques for fast palmprint identification. We first propose three properties required by a hash function and then introduce our first fast identification method based on orientation pattern (OP) hashing. We give the definition of OP and demonstrate that it meets all the requirements of the hash function, and thus, would be appropriate for hashing based fast palmprint identification. We then introduce the second fast identification method based on principal orientation pattern (POP) hashing. Because the POPs are constructed using more stable orientation features, POP hashing can find the target template more quickly thus causing earlier termination of the identification process. We evaluate our methods on the Hong Kong PolyU large-scale database (9667 palms) and the CASIA palmprint database (600 palms) plus a synthetic database (100 000 palms). Experimental results show that, on the Hong Kong PolyU large-scale database, the speedups of OP hashing and POP hashing over brute-force search are 16.93 and 19.91, respectively, and the identification accuracy is slightly higher. While on the CASIA database plus the synthetic database, the speedups of OP hashing and POP hashing are 8.03 and 15.67, respectively, and the identification accuracy almost remains the same. Results also show that, in terms of accuracy, our methods are comparable to several state-of-the-art palmprint identification approaches, while in terms of speed, our methods are much faster.
Molecular Dynamics Simulation of Amorphous SiO2 Fracture

This video demonstrates output of a large scale molecular dynamics computation of 4.7 million atoms, simulating glass fracture over the course of over a nanosecond. Post-processed data consists of 500 frames at 3 GB per frame. We generate approximate charge density volume data to highlight the overall structure. Rendering is performed using Nanovol, a volume raycasting tool for computational chemistry.
Physical Imagery: Kinematic versus Dynamic Models

Physical imagery occurs when people imagine one object causing a change to a second object. To make inferences through physical imagery, people must represent information that coordinates the interactions among the imagined objects. The current research contrasts two proposals for how this coordinating information is realized in physical imagery. In the traditional kinematic formulation, imagery transformations are coordinated by geometric information in analog spatial representations. In the dynamic formulation, transformations may also be regulated by analog representations of force and resistance. Four experiments support the dynamic formulation. They show, for example, that without making changes to the spatial properties of a problem, dynamic perceptual information (e.g., torque) and beliefs about physical properties (e. g., viscosity) affect the inferences that people draw through imagery. The studies suggest that physical imagery is not so much an analog of visual perception as it is an analog of physical action. A simple model that represents force as a rate helps explain why inferences can emerge through imagined actions even though people may not know the answer explicitly. It also explains how and when perception, beliefs, and learning can influence physical imagery.
Long-Short Range Context Neural Networks for Language Modeling

The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.
CAFASP3: The third critical assessment of fully automated structure prediction methods

We present the results of the fully automated CAFASP3 experiment, which was carried out in parallel with CASP5, using the same set of prediction targets. CAFASP participation is restricted to fully automatic structure prediction servers. The servers' performance is evaluated by using previously announced, objective, reproducible and fully automated evaluation methods. More than 60 servers participated in CAFASP3, covering all categories of structure prediction. As in the previous CAFASP2 experiment, it was possible to identify a group of 5–10 top performing independent servers. This group of top performing independent servers produced relatively accurate models for all the 32 “Homology Modeling” targets, and for up to 43% of the 30 “Fold Recognition” targets. One of the most important results of CAFASP3 was the realization of the value of all the independent servers as a group, as evidenced by the superior performance of “meta‐predictors” (defined here as predictors that make use of the output of other CAFASP servers). The performance of the best automated meta‐predictors was roughly 30% higher than that of the best independent server. More significantly, the performance of the best automated meta‐predictors was comparable with that of the best 5–10 human CASP predictors. This result shows that significant progress has been achieved in automatic structure prediction and has important implications to the prospects of automated structure modeling in the context of structural genomics. Proteins 2003;53:503–516. © 2003 Wiley‐Liss, Inc.
GWAF: an R package for genome-wide association analyses with family data

SUMMARY GWAF, Genome-Wide Association analyses with Family, is an R package designed for GWAF. It implements association tests between a batch of genotyped or imputed single nucleotide polymorphisms (SNPs) and a binary or continuous trait with user specified genetic model, and generates informative results from the analyses. In addition, GWAF provides functions to visualize results. We evaluated GWAF using a simulated continuous trait and a binary trait dichotomized from the simulated continuous trait with real genotype data from the Framingham Heart Study's SNP Health Association Resource project.
Analysis of nonstationarity in low flow in the Loess Plateau of China

An approach for nonstationary low‐flow frequency analysis is developed and demonstrated on a dataset from the rivers on the Loess Plateau of China. Nonstationary low‐flow frequency analysis has drawn significant attention in recent years by establishing relationships between low‐flow series and explanatory variables series, but few studies have tested whether the time‐varying moments of low flow can be fully described by the time‐varying moments of the explanatory variables. In this research, the low‐flow distributions are analytically derived from the 2 basic explanatory variables—the recession duration and the recession coefficient—with the assumption that the recession duration and recession coefficient variables follow exponential and gamma distributions, respectively; the derived low‐flow distributions are applied to test whether the time‐varying moments of explanatory variables can explain the nonstationarities found in the low‐flow variable. The effects of ecosystem construction measures, that is, check dam, terrace, forest, and grassland, on the recession duration and recession coefficient are further discussed. Daily flow series from 11 hydrological stations from the Loess Plateau are used and processed with a moving average technique. Low‐flow data are extracted following the pit under threshold approach. Six of the 11 low‐flow series show significant nonstationarities at the 5% significance level, and the trend curves of the moments of low flow are in close agreement with the curves estimated from the derived distribution with time‐dependent moments of the recession duration and time‐constant moments of the recession coefficient. It is indicated that the nonstationarity in the low‐flow distribution results from the nonstationarity in the recession duration in all 6 cases, and the increase in the recession duration is resulted from large‐scale ecosystem constructions rather than climate change. The large‐scale ecosystem constructions are found to have more influence on the decrease in streamflow than on the increase in watershed storage, thus resulting in the reduction of low flow. A high return period for the initial fixed design value decreases dramatically with an increasing recession duration.
Touch&Type: a novel pointing device for notebook computers

The widespread use of the mouse as an input device for notebook computers indicates that many users are reluctant to use alternative built-in pointing devices. We present a novel input method called Touch&Type™™ which is meant to overcome some of the drawbacks encountered with conventional built-it pointing devices. Touch&Type™™ combines a conventional keyboard with an extended touch pad whereby the touch pad's sensitive area is formed by the surface of the keys themselves and thus can be made as large as the whole key area. A comparative study of pointing operation is presented with a Touch&Type™™ prototype in comparison with the mouse and the conventional touch pad. While the mouse outperformed its two counterparts, Touch&Type™™ was found to be superior to the conventional touch pad (after a short learning period) with a confidence level of 73%. The study investigated pointing operation only, not taking homing time into account.
Improving graph-walk-based similarity with reranking: Case studies for personal information management

Relational or semistructured data is naturally represented by a graph, where nodes denote entities and directed typed edges represent the relations between them. Such graphs are heterogeneous, describing different types of objects and links. We represent personal information as a graph that includes messages, terms, persons, dates, and other object types, and relations like sent-to and has-term. Given the graph, we apply finite random graph walks to induce a measure of entity similarity, which can be viewed as a tool for performing search in the graph. Experiments conducted using personal email collections derived from the Enron corpus and other corpora show how the different tasks of alias finding, threading, and person name disambiguation can be all addressed as search queries in this framework, where the graph-walk-based similarity metric is preferable to alternative approaches, and further improvements are achieved with learning. While researchers have suggested to tune edge weight parameters to optimize the graph walk performance per task, we apply reranking to improve the graph walk results, using features that describe high-level information such as the paths traversed in the walk. High performance, together with practical runtimes, suggest that the described framework is a useful search system in the PIM domain, as well as in other semistructured domains.
Higher risk of death and stroke in patients with persistent vs. paroxysmal atrial fibrillation: results from the ROCKET-AF Trial

Aim Anticoagulation prophylaxis for stroke is recommended for at-risk patients with either persistent or paroxysmal atrial fibrillation (AF). We compared outcomes in patients with persistent vs. paroxysmal AF receiving oral anticoagulation. Methods and results Patients randomized in the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET-AF) trial (n = 14 264) were grouped by baseline AF category: paroxysmal or persistent. Multivariable adjustment was performed to compare thrombo-embolic events, bleeding, and death between groups, in high-risk subgroups, and across treatment assignment (rivaroxaban or warfarin). Of 14 062 patients, 11 548 (82%) had persistent AF and 2514 (18%) had paroxysmal AF. Patients with persistent AF were marginally older (73 vs. 72, P = 0.03), less likely female (39 vs. 45%, P < 0.0001), and more likely to have previously used vitamin K antagonists (64 vs. 56%, P < 0.0001) compared with patients with paroxysmal AF. In patients randomized to warfarin, time in therapeutic range was similar (58 vs. 57%, P = 0.94). Patients with persistent AF had higher adjusted rates of stroke or systemic embolism (2.18 vs. 1.73 events per 100-patient-years, P = 0.048) and all-cause mortality (4.78 vs. 3.52, P = 0.006). Rates of major bleeding were similar (3.55 vs. 3.31, P = 0.77). Rates of stroke or systemic embolism in both types of AF did not differ by treatment assignment (rivaroxaban vs. warfarin, Pinteraction = 0.6). Conclusion In patients with AF at moderate-to-high risk of stroke receiving anticoagulation, those with persistent AF have a higher risk of thrombo-embolic events and worse survival compared with paroxysmal AF.
The pecularities of shear crack pre-rupture evolution and distribution of seismicity before strong earthquakes

Abstract. Several methods are presently suggested for investigating pre-earthquake evolution of the regions of high tectonic activity based on analysis of the seismicity spatial distribution. Some precursor signatures are detected before strong earthquakes: decrease in fractal dimension of the continuum of earthquake epicenters, cluster formation, concentration of seismic events near one of the nodal planes of the future earthquake, and others. In the present paper, it is shown that such peculiarities are typical of the evolution of the shear crack network under external stresses in elastic bodies with inhomogeneous distribution of strength. The results of computer modeling of crack network evolution are presented. It is shown that variations of the fractal dimension of the earthquake epicenters’ continuum and other precursor signatures contain information about the evolution of the destruction process towards the main rupture.
Exercise Mediates the Association Between Positive Affect and 5-Year Mortality in Patients With Ischemic Heart Disease

Background—Positive affect has been associated with better prognosis in patients with ischemic heart disease, but the underlying mechanisms remain unclear. We examined whether positive affect predicted time to first cardiac-related hospitalization and all-cause mortality, and whether exercise mediated this relationship in patients with established ischemic heart disease. Methods and Results—The sample comprised 607 patients with ischemic heart disease from Holbæk Hospital, Denmark. In 2005, patients completed the Global Mood Scale (GMS) to assess positive affect and a purpose-designed question on exercise. Data on mortality and hospitalization were collected from Danish national registers for the period 2006–2010. Adjusted Cox and logistic regression were used to analyze the mediation model. Because no significant association between positive affect and cardiac-related hospitalization was found, we constructed no mediation model for hospitalization. Importantly, patients with high positive affect had a significantly reduced risk of all-cause mortality (hazard ratio, 0.58; 95% confidence interval, 0.37–0.92; unadjusted analysis) and were more likely to exercise (odds ratio, 1.99; 95% confidence interval, 1.44–2.76; unadjusted analysis; odds ratio, 1.48; 95% confidence interval, 1.03–2.13; adjusted analysis). When controlling for positive affect and other relevant variables, patients engaged in exercise were less likely to die during follow-up (hazard ratio, 0.50; 95% confidence interval, 0.31–0.80; P=0.004). Importantly, exercise acted as a mediator in the relationship between positive affect and mortality. Conclusions—Patients with higher levels of positive affect were more likely to exercise and had a lower risk of dying during 5-year follow-up, with exercise mediating the relationship between positive affect and mortality. Interventions aimed at increasing both positive affect and exercise may have better results with respect to patients’ prognosis and psychological well-being than interventions focusing on 1 of these factors alone.
Analysis of Oxidative Modification of Proteins

Protein oxidation has been implicated in a variety of degenerative diseases as well as in the aging process. This unit describes techniques for the quantification of various protein oxidation products, including protein carbonyls, loss of protein thiol groups, dityrosine and nitrotyrosine, and isoaspartate formation. Such oxidatively modified products may also be used as biomarkers for the assessment of oxidative stress during aging and/or disease.
Scale equalization higher-order neural networks

This paper presents a novel approach, called scale equalization (SE), to implement higher-order neural networks. SE is particularly useful in eliminating the scale divergence problem commonly encountered in higher order networks. Generally, the larger the scale divergence is, the more the number of training steps required to complete the training process. Effectiveness of SE is illustrated with an exemplar higher-order network built on the Sigma-Pi network (SESPN) applied to function approximation. SESPN requires the same computation time as SPN per epoch, but it takes much less number of epochs to compete the training process. Empirical results are provided to verify that SESPN outperforms other higher-order neural networks in terms of computation efficiency.
Small Instantons, Little Strings and Free Fermions

We present new evidence for the conjecture that BPS correlation functions in the N=2 supersymmetric gauge theories are described by an auxiliary two dimensional conformal field theory. We study deformations of the N=2 supersymmetric gauge theory by all gauge-invariant chiral operators. We calculate the partition function of the N=2 theory on R^4 with appropriately twisted boundary conditions. For the U(1) theory with instantons (either noncommutative, or D-instantons, depending on the construction) the partition function has a representation in terms of the theory of free fermions on a sphere, and coincides with the tau-function of the Toda lattice hierarchy. Using this result we prove to all orders in string loop expansion that the effective prepotential (for U(1) with all chiral couplings included) is given by the free energy of the topological string on CP^1. Gravitational descendants play an important role in the gauge fields/string correspondence. The dual string is identified with the little string bound to the fivebrane wrapped on the two-sphere. We also discuss the theory with fundamental matter hypermultiplets.
Achievable Rate Estimation of IEEE 802.11ad Visual Big-Data Uplink Access in Cloud-Enabled Surveillance Applications

This paper addresses the computation procedures for estimating the impact of interference in 60 GHz IEEE 802.11ad uplink access in order to construct visual big-data database from randomly deployed surveillance camera sensing devices. The acquired large-scale massive visual information from surveillance camera devices will be used for organizing big-data database, i.e., this estimation is essential for constructing centralized cloud-enabled surveillance database. This performance estimation study captures interference impacts on the target cloud access points from multiple interference components generated by the 60 GHz wireless transmissions from nearby surveillance camera devices to their associated cloud access points. With this uplink interference scenario, the interference impacts on the main wireless transmission from a target surveillance camera device to its associated target cloud access point with a number of settings are measured and estimated under the consideration of 60 GHz radiation characteristics and antenna radiation pattern models.
The effects of obstacle size on periodic travelling waves in oscillatory reaction–diffusion equations

Many natural populations undergo multi-year cycles, and field studies have shown that these can be organized into periodic travelling waves (PTWs). Mathematical studies have shown that large-scale landscape obstacles represent a natural mechanism for wave generation. Here, we investigate how the amplitude and wavelength of the selected waves depend on the obstacle size. We firstly consider a large circular obstacle in an infinite domain for a reaction–diffusion system of ‘λ–ω’ type. We use perturbation theory to derive a leading order approximation to the wave generated by the obstacle. This shows the dependence of the wave properties on both parameter values and obstacle size. We find that the limiting values of the amplitude and wavelength are approached algebraically with distance from the obstacle edge, rather than exponentially in the case of a flat boundary. We use our results to predict the properties of waves generated by a large circular obstacle for an oscillatory predator–prey system, via a reduction of the predator–prey model to normal form close to Hopf bifurcation. Our predictions compare well with numerical simulations. We also discuss the implications of these results for wave stability and briefly investigate the effects of obstacles with elliptical geometries.
People tracking with human motion predictions from social forces

For many tasks in populated environments, robots need to keep track of current and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity or acceleration. But even over a short period, human behavior is more complex and influenced by factors such as the intended goal, other people, objects in the environment, and social rules. This motivates the use of more sophisticated motion models for people tracking especially since humans frequently undergo lengthy occlusion events. In this paper, we consider computational models developed in the cognitive and social science communities that describe individual and collective pedestrian dynamics for tasks such as crowd behavior analysis. In particular, we integrate a model based on a social force concept into a multi-hypothesis target tracker. We show how the refined motion predictions translate into more informed probability distributions over hypotheses and finally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range finder, the social force model leads to more accurate tracking with up to two times fewer data association errors.