gebhart/patrepeval huggingface dataset:
High Performance Dense Linear System Solver with Soft Error Resilience (2011)

As the scale of modern high end computing systems continues to grow rapidly, system failure has become an issue that requires a better solution than the commonly used scheme of checkpoint and restart (C/R). While hard errors have been studied extensively over the years, soft errors are still under-studied especially for modern HPC systems, and in some scientific applications C/R is not applicable for soft error at all due to error propagation and lack of error awareness. In this work, we propose an algorithm based fault tolerance (ABFT) for high performance dense linear system solver with soft error resilience. By adapting a mathematical model that treats soft error during LU factorization as rank-one perturbation, the solution of Ax=b can be recovered with the Sherman-Morrison formula. Our contribution includes extending error model from Gaussian elimination and pair wise pivoting to LU with partial pivoting, and we provide a practical numerical bound for error detection and a scalable check pointing algorithm to protect the left factor that is needed for recovering x from soft error. Experimental results on cluster systems with ScaLAPACK show that the fault tolerance functionality adds little overhead to the linear system solving and scales well on such systems.
Hardware Development of Hybrid-Sensor Cameras and Gantry for an Adaptive SPECT System (2020)

Stationary single photon emission computed tomography (SPECT) systems offer numerous advantages over rotating-head systems, but have one notable drawback in that the array of relatively smaller cameras contains more edges and gaps than clinical two headed systems. Scintillation events occurring at the edges of traditional photomultiplier-tube (PMT)-based SPECT cameras lose spatial resolution due to loss of light-sampling. Simulations using customized non-sequential raytracing scripts to model and analyze mean detector response functions (MDRFs) showed significant improvement in spatial resolution for hybrid-sensor cameras employing both silicon photomultipliers (SiPMs) and PMTs. The results inform the hardware design of AdaptiSPECT-C: a stationary clinical whole-brain SPECT imager with adaptive apertures for selective dynamic or high spatial resolution imaging. Its modular hybrid cameras use SiPMs to augment the PMTs and improve spatial resolution for position estimation tasks. SiPMs, having a small pitch and efficient fill factor, are employed as a border around the edges of each detector area. PMTs, being low cost and reliable, are packed in the center. The front end electronics are split into two main boards: one to drive and provide signal conditioning for the PMTs, and the other performing a similar function for the SiPMs. Ultimately, 81 total signal channels leave each camera as negative voltage pulses. AdaptiSPECT-C will have two equatorial rings of 10 cameras each and a quasi-vertex ring of 4 cameras, totaling 24. Modularity is the guiding design principle for the mechanical components of the cameras and ensures ease of assembly and field service in the completed system.
Bromine Radical (Br• and Br2•–) Reactivity with Dissolved Organic Matter and Brominated Organic Byproduct Formation (2022)

Dissolved organic matter (DOM) is a major scavenger of bromine radicals (e.g., Br<sup>•</sup> and Br<sub>2</sub><sup>•-</sup>) in sunlit surface waters and during oxidative processes used in water treatment. However, the literature lacks quantitative measurements of reaction rate constants between bromine radicals and DOM and lacks information on the extent to which these reactions form brominated organic byproducts. Based on transient kinetic analysis with different fractions and sources of DOM, we determined reaction rate constants for DOM with Br<sup>•</sup> ranging from <5.0 × 10<sup>7</sup> to (4.2 ± 1.3) × 10<sup>8</sup> M<sub>C</sub><sup>-1</sup> s<sup>-1</sup>, which are comparable with those of HO<sup>•</sup> but higher than those with Br<sub>2</sub><sup>•-</sup> (<i>k</i> = (9.0 ± 2.0) × 10<sup>4</sup> to (12.4 ± 2.1) × 10<sup>5</sup> M<sub>C</sub><sup>-1</sup> s<sup>-1</sup>). Br<sup>•</sup> and Br<sub>2</sub><sup>•-</sup> attack the aromatic and antioxidant moieties of DOM via the electron transfer mechanism, resulting in Br<sup>-</sup> release with minimal substitution of bromine into DOM. For example, the total organic bromine was less than 0.25 μM (as Br) at environmentally relevant bromine radicals' exposures of ∼10<sup>-9</sup> M·s. The results give robust evidence that the scavenging of bromine radicals by DOM is a crucial step to prevent inorganic bromine radical chemistry from producing free bromine (HOBr/OBr<sup>-</sup>) and subsequent brominated byproducts.
Graph identification techniques applied to network management problems (1993)

Communication networks have increased dramatically in size and complexity in the last few years. Furthermore, they tend to be more and more dynamic. Meanwhile, our dependence on networks has also drastically increased. The field of network management has thus drawn a wide interest in the industry and research. This thesis proposes new approaches to two network management problems: network topology identification and alarm correlation. In both cases, graph identification techniques are used.
In dynamic networks, a precise and timely knowledge of the topology is critical for network management and control (e.g., routing). If every network node could instantly provide reliable information about itself and its neighbors, it would be relatively easy to assemble this information and derive the overall network topology. However, both the assumptions of correctness and instantaneous delivery of the information do not hold in practice. We thus address the problem of identifying the topology of a network (i.e., a graph) from noisy data collected at some node. Our model describes and relates the network topology and the collected data using a phrase-structured grammar. We reduce our problem to a combinatorial optimization problem and propose a suboptimal pseudo polynomial-time algorithm which yields reasonable solutions as shown by a range of examples.
In today's networks, a large number of alarms exist to signal any abnormal network behavior and a network fault typically results in a number of correlated observed alarms. Thus, a major problem in fault management is of correlating these different alarms and identifying their source. This problem has been so far handled from a model-based expert system point of view. We propose a new approach where each fault is modeled as a stochastic grammar (represented by a probabilistic graph). Alarm sequences then correspond to sentences generated by the grammar. The structure of each grammar is learned automatically from the history of data associated with given faults. By classifying new alarm sequences as members of particular grammars, we then are able to identify the occurrence of possibly simultaneous faults. This method was tested on data from a real network.
On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps (2019)

The study of universal approximation of arbitrary functions $f: \mathcal{X} \to \mathcal{Y}$ by neural networks has a rich and thorough history dating back to Kolmogorov (1957). In the case of learning finite dimensional maps, many authors have shown various forms of the universality of both fixed depth and fixed width neural networks. However, in many cases, these classical results fail to extend to the recent use of approximations of neural networks with infinitely many units for functional data analysis, dynamical systems identification, and other applications where either $\mathcal{X}$ or $\mathcal{Y}$ become infinite dimensional. Two questions naturally arise: which infinite dimensional analogues of neural networks are sufficient to approximate any map $f: \mathcal{X} \to \mathcal{Y}$, and when do the finite approximations to these analogues used in practice approximate $f$ uniformly over its infinite dimensional domain $\mathcal{X}$? In this paper, we answer the open question of universal approximation of nonlinear operators when $\mathcal{X}$ and $\mathcal{Y}$ are both infinite dimensional. We show that for a large class of different infinite analogues of neural networks, any continuous map can be approximated arbitrarily closely with some mild topological conditions on $\mathcal{X}$. Additionally, we provide the first lower-bound on the minimal number of input and output units required by a finite approximation to an infinite neural network to guarantee that it can uniformly approximate any nonlinear operator using samples from its inputs and outputs.
A new notion of commutativity for the algorithmic Lovász Local Lemma (2020)

The Lovász Local Lemma (LLL) is a powerful tool in probabilistic combinatorics which can be used to establish the existence of objects that satisfy certain properties. The breakthrough paper of Moser and Tardos and follow-up works revealed that the LLL has intimate connections with a class of stochastic local search algorithms for finding such desirable objects. In particular, it can be seen as a sufficient condition for this type of algorithms to converge fast. Besides conditions for existence of and fast convergence to desirable objects, one may naturally ask further questions regarding properties of these algorithms. For instance, "are they parallelizable?", "how many solutions can they output?", "what is the expected "weight" of a solution?", etc. These questions and more have been answered for a class of LLL-inspired algorithms called commutative. In this paper we introduce a new, very natural and more general notion of commutativity (essentially matrix commutativity) which allows us to show a number of new refined properties of LLL-inspired local search algorithms with significantly simpler proofs.
An unusual formaldehyde oxidizing system inRhodococcus erythropolisgrown on compounds containing methyl groups (1984)

During utilization of compounds containing methyl groups, the non-methylotrophic bacteria Rhodococcus erythropolis oxidized the methyl groups entirely to carbon dioxide. This oxidation was linked to the presence of an NAD-dependent formaldehyde dehydrogenase activity which was lost on dialysis. The activity could be restored by the addition of boiled extract but not by adding the known cofactors glutathione or tetrahydrofolate. A further dehydrogenase activity with formaldehyde as substrate was found in ethanolgrown cells. This activity could be differentiated from that in methyl group metabolizing cells.
Topological defects in dispersed words and worlds around liquid crystals, or liquid crystal drops (1998)

Abstract The structure of dispersed liquid crystal droplets elasticity, surface tension, and surface anchoring. For sufficiently large droplets with radius R K / W a, where K is the bulk elastic constant and W a is the anchoring coefficient, the surface terms prevail. As a result, the equilibrium states of large droplets contain topologically stable defects. Application of topological theorems to and hedgehogs is reviewed. is controlled by a balance of the bulk defect structures, e.g. monopoles, boojums
A New Model for Evaluation of Biocompatibility: Combined Determination of Neoepitopes in Blood and on Artificial Surfaces Demonstrates Reduced Complement Activation by Immobilization of Heparin (1995)

An in vitro technique was developed for assessment of the biocompatibility of materials for use in clinical applications. Artificial materials were exposed to blood, and the resulting complement activation was quantified both in the plasma and on the material surface by enzyme immunoassays based on monoclonal antibodies specific for neoepitopes exposed in complement activation products. Several materials were evaluated, and the effect of surface modifications, including end-point immobilized heparin, was studied. The results revealed widely varying complement activation properties of the different materials and confirmed that heparin markedly improves biocompatibility. The present method is superior to analysis limited to either the fluid phase or solid phase since certain materials adsorb activation products (exemplified by Tecoflex) whereas others do not although activation was evident from fluid-phase assay (silicone). Furthermore, direct determination of activation-specific neoepitopes on the surface represents an improvement compared with previously described methods for detection of adsorbed components.
Supplementary material for: High crystalline biaxially aligned CNT web for long cycling Li-O2 batteries (2021)

The movie shows the real-time 'growth to spinning process' of a biaxially aligned CNT film. To fabricate the designed CNT film through floating catalyst chemical vapor deposition method, the roller was systematically controlled so that rotation and pendulum motion can be performed at the same.
Autophagy-Related Proteins GABARAP and LC3B Label Structures of Similar Size but Different Shape in Super-Resolution Imaging (2019)

Subcellular structures containing autophagy-related proteins of the Atg8 protein family have been investigated with conventional wide-field fluorescence and single molecule localisation microscopy. Fusion proteins of GABARAP and LC3B, respectively, with EYFP were overexpressed in HEK293 cells. While size distributions of structures labelled by the two proteins were found to be similar, shape distributions appeared quite disparate, with EYFP-GABARAP favouring circular structures and elliptical structures being dominant for EYFP-LC3B. The latter also featured a nearly doubled fraction of U-shape structures. The experimental results point towards highly differential localisation of the two proteins, which appear to label structures representing distinct stages or even specific channels of vesicular trafficking pathways. Our data also demonstrate that the application of super-resolution techniques expands the possibilities of fluorescence-based methods in autophagy studies and in some cases can rectify conclusions obtained from conventional fluorescence microscopy with diffraction-limited resolution.
Trends in Surgical Management for Type II Odontoid Fracture: 20 Years of Experience at a Regional Spinal Cord Injury Center (2008)

This study reviewed surgical treatment methods in patients with type II odontoid fracture who were admitted to a level 1 spinal cord injury center during a 20-year period. Of the 186 patients who met inclusion criteria, 75 were treated operatively. The rate of surgical intervention increased during the study period. Length of hospital stay was not related to treatment. Estimated blood loss was significantly higher for posterior techniques. The Brooks technique and anterior odontoid screw fixation declined in frequency, whereas the Magerl technique has been used at a consistent rate since its introduction. The C1 lateral mass and C2 pedicle/isthmus screw technique has increased significantly since its description. Pneumonia, vocal cord, and swallowing problems occurred more often with anterior approaches.
Ranking Cost: One-Stage Circuit Routing by Directly Optimizing Global Objective Function (2021)

Circuit routing has been a historically challenging problem in designing electronic systems such as very large-scale integration (VLSI) and printed circuit boards (PCBs). The main challenge is that connecting a large number of electronic components under specific design rules and constraints involves a very large search space, which is proved to be NP-complete. Early solutions are typically designed with hard-coded heuristics, which suffer from problems of non-optimum solutions and lack of flexibility for new design needs. Although a few learning-based methods have been proposed recently, their methods are cumbersome and hard to extend to large-scale applications. In this work, we propose a new algorithm for circuit routing, named as Ranking Cost (RC), which innovatively combines search-based methods (i.e., A* algorithm) and learning-based methods (i.e., Evolution Strategies) to form an efficient and trainable router under a proper parameterization. Different from two-stage routing methods ( i.e., first global routing and then detailed routing), our method involves a one-stage procedure that directly optimizes the global objective function, thus it can be easy to adapt to new routing rules and constraints. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object. We also train a ranking parameter, which can produce the ranking order and further improve the performance of our method. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration. In the experiments, we compare our method with the sequential A* algorithm and a canonical reinforcement learning approach, and results show that our method outperforms these baselines with higher connectivity rates and better scalability. Our ablation study shows that our trained cost maps can capture the global information and guide the routing result to approach global optimum.
Augmenting Transfer Learning with Semantic Reasoning (2019)

Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with semantic representations. We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings. We further present a general framework that integrates the above measurements and embeddings with existing transfer learning algorithms for higher performance. It has demonstrated to be robust in two real-world applications: bus delay forecasting and air quality forecasting.
An Empirical Study of Cross-Lingual Transfer Learning Techniques for Small-Footprint Keyword Spotting (2017)

This paper presents our work on building a small-footprint keyword spotting system for a resource-limited language, which requires low CPU, memory and latency. Our keyword spotting system consists of deep neural network (DNN) and hidden Markov model (HMM), which is a hybrid DNN-HMM decoder. We investigate different transfer learning techniques to leverage knowledge and data from a resource-abundant source language to improve the keyword DNN training for a target language which has limited in-domain data. The approaches employed in this paper include training a DNN using source language data to initialize the target language DNN training, mixing data from source and target languages together in a multi-task DNN training setup, using logits computed from a DNN trained on the source language data to regularize the keyword DNN training in the target language, as well as combinations of these techniques. Given different amounts of target language training data, our experimental results show that these transfer learning techniques successfully improve keyword spotting performance for the target language, measured by the area under the curve (AUC) of DNN-HMM decoding detection error tradeoff (DET) curves using a large in-house far-field test set.
Corrections to Design, Synthesis, and Biological Evaluation of 6α- and 6β-N-Heterocyclic Substituted Naltrexamine Derivatives as μ Opioid Receptor Selective Antagonists (2009)

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrections to Design, Synthesis, and Biological Evaluation of 6α- and 6β-N-Heterocyclic Substituted Naltrexamine Derivatives as μ Opioid Receptor Selective AntagonistsGuo Li, Lindsey C. Aschenbach, Jianyang Chen, Michael P. Cassidy, David L. Stevens, Bichoy H. Gabra, Dana E. Selley, William L. Dewey, Richard B. Westkaemper, and Yan Zhang*Cite this: J. Med. Chem. 2009, 52, 24, 8057Publication Date (Web):November 10, 2009Publication History Published online10 November 2009Published inissue 24 December 2009https://pubs.acs.org/doi/10.1021/jm9016224https://doi.org/10.1021/jm9016224correctionACS PublicationsCopyright © 2009 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views893Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (563 KB) Get e-AlertscloseSUBJECTS:Antagonists,Ligands,Molecular design,Reaction products,Receptors Get e-Alerts
Update on an S-Band All-Digital Mobile Phased Array Radar (2021)

This paper provides an update on an S-band, polarimetric phased array radar designed to operate in the 2.7 - 3.1 GHz frequency band, which is being designed and built at the University of Oklahoma's Advanced Radar Research Center (ARRC). This is radar build 1 of 2 for our group in the S-band. Providing optimum radar flexibility, this phased array radar, known as Horus, is digital at every element and polarization.
Dangers of Opioid Prescribing for Young Adults After Dental Procedures (2019)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Internal Medicine HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Internal Medicine journal