An independent, general method for checking consistency between diffraction data and partial radial distribution functions derived from them: the example of liquid water

There are various routes for deriving partial radial distribution functions of disordered systems from experimental diffraction (and/or EXAFS) data. Due to limitations and errors of experimental data, as well as to imperfections of the evaluation procedures, it is of primary importance to confirm that the end result (partial radial distribution functions) and the primary information (diffraction data) are consistent with each other. We introduce a simple approach, based on Reverse Monte Carlo modelling, that is capable of assessing this dilemma. As a demonstration, we use the most frequently cited set of "experimental" partial radial distribution functions on liquid water and investigate whether the 3 partials (O-O, O-H and H-H) are consistent with the total structure factor of pure liquid D_2O from neutron diffraction and that of H_2O from X-ray diffraction. We find that while neutron diffraction on heavy water is in full agreement with all the 3 partials, the addition of X-ray diffraction data clearly shows problems with the O-O partial radial distribution function. We suggest that the approach introduced here may also be used to establish whether partial radial distribution functions obtained from statistical theories of the liquid state are consistent with the measured structure factors.Comment: 6 pages, 3 figure

Determination of Parabolic Rate Constants from a Local Analysis of Mass-Gain Curves

A method is proposed to allow a more accurate evaluation of thermogravimetric data to identify diffusion or partial diffusion control of scaling kinetics. This method is based on the fitting of mass-gain data to a parabola over a short time interval. The translation of the time interval over the entire test time period provides an actual instantaneous parabolic rate constant independently of any transient stage or simultaneous reaction steps. The usefulness and limitations of this procedure are illustrated from oxidation tests performed on several metallic materials (pure nickel, single-crystal superalloys, and NbTi-Al alloy)

Effectiveness of Physical Therapy Intervention following Partial Medial Meniscectomy: A Case Report

Background and Purpose. Partial meniscectomy is a very commonly performed surgical procedure and many cases are uncomplicated and successfully treated with home-based exercise programs. However, some patients experience more significant problems following surgery and require rehabilitation services. Research is limited regarding physical therapy (PT) effectiveness in the post-operative management of such complicated cases. This case report aims to evaluate the effectiveness of PT treatment of a 34-year-old male with significant impairments and functional limitations following partial medial meniscectomy. Case Description. Examination and evaluation included ROM/flexibility and strength measurements as well as functional assessment. PT included therapeutic exercise which was aimed at quadriceps and hip musculature strengthening and also incorporated soft tissue and joint mobilization. A home exercise program (HEP), patient education and modalities for pain modulation were also utilized. Outcomes. Outcomes following approximately one month of PT treatment revealed decreased pain ratings and increases in range of motion, strength and functional abilities. However, at one month post-surgery the patient still demonstrated quadriceps fatigue with prolonged ambulation and had not yet reached his previous level of function. Discussion. After PT intervention this patient had decreased impairments and functional limitations. Further research involving large sample sizes needs to be gathered in order to determine PT effectiveness in complicated cases following partial meniscectomy

Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis

This tutorial paper introduces a common framework for the evaluation of widely used frequency-domain measures of coupling (coherence, partial coherence) and causality (directed coherence, partial directed coherence) from the parametric representation of linear multivariate (MV) processes. After providing a comprehensive time-domain definition of the various forms of connectivity observed in MV processes, we particularize them to MV autoregressive (MVAR) processes and derive the corresponding frequency-domain measures. Then, we discuss the theoretical interpretation of these MVAR-based connectivity measures, showing that each of them reflects a specific time-domain connectivity definition and how this results in the description of peculiar aspects of the information transfer in MV processes. Furthermore, issues related to the practical utilization of these measures on real-time series are pointed out, including MVAR model estimation and significance assessment. Finally, limitations and pitfalls arising from model mis-specification are discussed, indicating possible solutions and providing practical recommendations for a safe computation of the connectivity measures. An example of estimation of the presented measures from multiple EEG signals recorded during a combined visuomotor task is also reported, showing how evaluation of coupling and causality in the frequency domain may help describing specific neurophysiological mechanisms

Value of Traveler Information for Adaptive Routing in Stochastic Time-Dependent Networks

Real-time information plays an important role in travelers’ routing choices in an uncertain network by enabling online adaptation to revealed traffic conditions. The quality of the information affects its effectiveness. Usually there are some limitations in the information provided to the travelers, spatially, temporally or both. In this thesis, three variants of an optimal adaptive routing problem with partial online information problem are introduced: global information with time lag, global pre-trip information and radio information on a subset of links without time lag. A generic description of online information is provided. An algorithm is designed for the optimal routing problem in stochastic time-dependent networks with partial online information and specializations required for each of the three variants are given. A test example is conducted and computationally verifies the non-negative value of information. The work in this thesis is potentially of interest to traveler information systems evaluation and design

A Study of Dynamic Optimization Techniques: Lessons and Directions in Kernel Design

The Synthesis kernel [21,22,23,27,28] showed that dynamic code generation, software feedback, and fine-grain modular kernel organization are useful implementation techniques for improving the performance of operating system kernels. In addition, and perhaps more importantly, we discovered that there are strong interactions between the techniques. Hence, a careful and systematic combination of the techniques can be very powerful even though each one by itself may have serious limitations. By identifying these interactions we illustrate the problems of applying each technique in isolation to existing kernels. We also highlight the important common under-pinnings of the Synthesis experience and present our ideas on future operating system design and implementation. Finally, we outline a more uniform approach to dynamic optimizations called incremental partial evaluation

A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition

The goal of building a benchmark (suite of datasets) is to provide a unified protocol for fair evaluation and thus facilitate the evolution of a specific area. Nonetheless, we point out that existing protocols of action recognition could yield partial evaluations due to several limitations. To comprehensively probe the effectiveness of spatiotemporal representation learning, we introduce BEAR, a new BEnchmark on video Action Recognition. BEAR is a collection of 18 video datasets grouped into 5 categories (anomaly, gesture, daily, sports, and instructional), which covers a diverse set of real-world applications. With BEAR, we thoroughly evaluate 6 common spatiotemporal models pre-trained by both supervised and self-supervised learning. We also report transfer performance via standard finetuning, few-shot finetuning, and unsupervised domain adaptation. Our observation suggests that current state-of-the-art cannot solidly guarantee high performance on datasets close to real-world applications, and we hope BEAR can serve as a fair and challenging evaluation benchmark to gain insights on building next-generation spatiotemporal learners. Our dataset, code, and models are released at: https://github.com/AndongDeng/BEARComment: ICCV 202