Mechanism of formation of half-doped stripes in underdoped cuprates

Using a variational Monte-Carlo approach with a recently proposed stripe wave function, we showed that the strong correlation included in a t-J-type model has essentially all the necessary ingredients to form these stripes with modulations of charge density, spin magnetization, and pair field. If a perturbative effect of electron-phonon coupling to renormalize the effective mass or the hopping rate of holes is considered with the model, we find the half-doped stripes, which has on the average one half of a hole in one period of charge modulation, to be most stable, energetic wise in the underdoped region, $1/12\leq\delta\leq1/8$. This is in good agreement with the observation in the neutron scattering experiments. We also find long range Coulomb interaction to be less effective in the formation of half-doped stripes.Comment: 4 pages, 4 figure

Prevention Strategies of Traumatic Brain Injury in Football Players

In this project, we study the effects of the impact angle during a helmet-to-helmet collision on stress distribution and deacceleration values of the brain tissue in efforts to reduce brain injuries for football players. The overall goal is to provide sufficient information to improve the design of helmet technology. In addition, our study will be able to inform the athletes to avoid a helmet-to-helmet collision, such that, they can be trained to shift their heads in the horizontal direction to create a “glancing blow” effect. It is suggested that by avoiding a helmet-to-helmet collision, both rotational acceleration and stress can be reduced leading to a lower probability of receiving concussions and subsequent brain injuries [1].Cockrell School of Engineerin

Multiway pruning for efficient iceberg cubing

Effective pruning is essential for efficient iceberg cube computation. Previous studies have focused on exclusive pruning: regions of a search space that do not satisfy some condition are excluded from computation. In this paper we propose inclusive and anti-pruning. With inclusive pruning, necessary conditions that solutions must satisfy are identified and regions that can not be reached by such conditions are pruned from computation. With anti-pruning, regions of solutions are identified and pruning is not applied. We propose the multiway pruning strategy combining exclusive, inclusive and anti-pruning with bounding aggregate functions in iceberg cube computation. Preliminary experiments demonstrate that the multiway-pruning strategy improves the efficiency of iceberg cubing algorithms with only exclusive pruning

Computing complex iceberg cubes by multiway aggregation and bounding

Iceberg cubing is a valuable technique in data warehouses. The efficiency of iceberg cube computation comes from efficient aggregation and effective pruning for constraints. In advanced applications, iceberg constraints are often non-monotone and complex, for example, "Average cost in the range [51, 52] and standard deviation of cost less than beta". The current cubing algorithms either are efficient in aggregation but weak in pruning for such constraints, or can prune for non-monotone constraints but are inefficient in aggregation. The best algorithm of the former, Star-cubing, computes aggregations of cuboids simultaneously but its pruning is specific to only monotone constraints such as "COUNT(*) greater than or equal to delta". In the latter case, the Divide and Approximate pruning technique can prune for non-monotone constraints but is limited to bottom-up single-group aggregation. We propose a solution that exhibits both efficiency in aggregation and generality and effectiveness in pruning for complex constraints. Our bounding techniques are as general as the Divide and Approximate pruning techniques for complex constraints and yet our multiway aggregation is as efficient as Star-cubing

Efficient computation of iceberg cubes by bounding aggregate functions

The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n-dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach

A novel phase-aligned analysis on motion patterns of table tennis strokes

© 2016, Routledge. All rights reserved. A wide range of human motion represent repetitive patterns particularly in racket sports. Quantitative analysis of the continuous variables during the different phases of the motion cycle helps to investigate more deeply the specific movement of the racket or player. Table tennis biomechanics research to date lacks the necessary detail of phase decomposition and phase-based quantitative analysis. Therefore, this study proposes a novel velocity-based piecewise alignment method to identify the different phases of a table tennis forehand stroke. A controlled experiment was conducted on a number of players of two differing ability levels (experts vs. novices) to implement this novel methodology. Detailed results are shown for the quantitative analysis on multiple strokes of the two groups of participants. Significant differences were found in both the displacement and velocity of the racket movement in the backswing, forward swing and follow-through phases. For example, it is clear that experts’ strokes show higher racket resultant velocity than novices during both the forward swing and follow-through phases by up to a factor of two. Furthermore, the phase-based approach to analysing racket motions leads to interrogation over a greater duration than the traditional time-based method which is generally only concerned with impact ±0.25s

Shoulder joint angle errors caused by marker offset

Crown Copyright © 2015 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. The International Society of Biomechanics (ISB) has recommended a standardization of the definition of the joint coordinate system (JCS) and use of a sequential rotation to describe human shoulder joint rotation. Markers attached to the surface of the body may move during the process of motion data capture, resulting in an offset from their initial location. This leads to a change of the JCS and therefore affects the calculated shoulder joint angles. In this research study, we presented a simple marker offset model to quantify the shoulder joint errors for both static poses and dynamic activities. Specific conditions of different offsets and elbow flexion angles were studied. Results showed that the errors should not be neglected when the shoulder elevation angle was near -90° and 90°, or elbow flexion was very small. Attention should be paid to these errors for such activities especially walking and throwing

Mathematical models for vulnerable plaques

A plaque is an accumulation and swelling in the artery walls and typically consists of cells, cell debris, lipids, calcium deposits and fibrous connective tissue. A person is likely to have many plaques inside his/her body even if they are healthy. However plaques may become "vulnerable", "high-risk" or "thrombosis-prone" if the person engages in a high-fat diet and does not exercise regularly. In this study group, we proposed two mathematical models to describe plaque growth and rupture. The first model is a mechanical one that approximately treats the plaque as an inflating elastic balloon. In this model, the pressure inside the core increases and then decreases suggesting that plaque stabilization and prevention of rupture is possible. The second model is a biochemical one that focuses on the role of MMPs in degrading the fibrous plaque cap. The cap stress, MMP concentration, plaque volume and cap thickness are coupled together in a system of phenomenological equations. The equations always predict an eventual rupture since the volume, stresses and MMP concentrations generally grow without bound. The main weakness of the model is that many of the important parameters that control the behavior of the plaque are unknown. The two simple models suggested by this group could serve as a springboard for more realistic theoretical studies. But most importantly, we hope they will motivate more experimental work to quantify some of the important mechanical and biochemical properties of vulnerable plaques

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches