918 research outputs found
Comparing the Overhead of Topological and Concatenated Quantum Error Correction
This work compares the overhead of quantum error correction with concatenated
and topological quantum error-correcting codes. To perform a numerical
analysis, we use the Quantum Resource Estimator Toolbox (QuRE) that we recently
developed. We use QuRE to estimate the number of qubits, quantum gates, and
amount of time needed to factor a 1024-bit number on several candidate quantum
technologies that differ in their clock speed and reliability. We make several
interesting observations. First, topological quantum error correction requires
fewer resources when physical gate error rates are high, white concatenated
codes have smaller overhead for physical gate error rates below approximately
10E-7. Consequently, we show that different error-correcting codes should be
chosen for two of the studied physical quantum technologies - ion traps and
superconducting qubits. Second, we observe that the composition of the
elementary gate types occurring in a typical logical circuit, a fault-tolerant
circuit protected by the surface code, and a fault-tolerant circuit protected
by a concatenated code all differ. This also suggests that choosing the most
appropriate error correction technique depends on the ability of the future
technology to perform specific gates efficiently
Alcohol Use, Abuse, and Dependency in Shanghai
The use of alcohol for social and ceremonial occasions was recorded in Chinese history as early as 1760 B.C. during the Yin Dynasty (Ci-Hai Encyclopedia, 1979:936). The cultural tradition of ancient China placed alcoholic beverages at the center of social occasions, which presumably was the origin of the adage: Without wine, there is no li (or etiquette). Thus, the use of alcoholic beverages has always been accompanied by the concept of propriety and the discharging of one\u27s role obligations m social functions, rather than that of personal indulgence
Herbal Therapies for Type 2 Diabetes Mellitus:Chemistry, Biology, and Potential Application ofSelected Plants and Compounds
Diabetes mellitus has been recognized since antiquity. It currently affects as many as 285 million people worldwide and results in heavy personal and national economic burdens. Considerable progress has been made in orthodox antidiabetic drugs. However, new remedies are still in great demand because of the limited efficacy and undesirable side effects of current orthodox drugs. Nature is an extraordinary source of antidiabetic medicines. To date, more than 1200 flowering plants have been claimed to have antidiabetic properties. Among them, one-third have been scientifically studied and documented in around 460 publications. In this review, we select and discuss blood glucose-lowering medicinal herbs that have the ability to modulate one or more of the pathways that regulate insulin resistance, -cell function, GLP-1 homeostasis, and glucose (re)absorption. Emphasis is placed on phytochemistry, anti-diabetic bioactivities, and likely mechanism(s). Recent progress in the understanding of the biological actions, mechanisms, and therapeutic potential of compounds and extracts of plant origin in type 2 diabetes is summarized. This review provides a source of up-to-date information for further basic and clinical research into herbal therapy for type 2 diabetes. Emerging views on therapeutic strategies for type 2 diabetes are also discussed
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The p110 delta structure: mechanisms for selectivity and potency of new PI(3)K inhibitors.
Deregulation of the phosphoinositide-3-OH kinase (PI(3)K) pathway has been implicated in numerous pathologies including cancer, diabetes, thrombosis, rheumatoid arthritis and asthma. Recently, small-molecule and ATP-competitive PI(3)K inhibitors with a wide range of selectivities have entered clinical development. In order to understand the mechanisms underlying the isoform selectivity of these inhibitors, we developed a new expression strategy that enabled us to determine to our knowledge the first crystal structure of the catalytic subunit of the class IA PI(3)K p110 delta. Structures of this enzyme in complex with a broad panel of isoform- and pan-selective class I PI(3)K inhibitors reveal that selectivity toward p110 delta can be achieved by exploiting its conformational flexibility and the sequence diversity of active site residues that do not contact ATP. We have used these observations to rationalize and synthesize highly selective inhibitors for p110 delta with greatly improved potencies
Nonlinear and conventional biosignal analyses applied to tilt table test for evaluating autonomic nervous system and autoregulation
Copyright © Tseng et al.; Licensee Bentham Open.
This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.Tilt table test (TTT) is a standard examination for patients with suspected autonomic nervous system (ANS) dysfunction or uncertain causes of syncope. Currently, the analytical method based on blood pressure (BP) or heart rate (HR) changes during the TTT is linear but normal physiological modulations of BP and HR are thought to be predominately nonlinear. Therefore, this study consists of two parts: the first part is analyzing the HR during TTT which is compared to three methods to distinguish normal controls and subjects with ANS dysfunction. The first method is power spectrum density (PSD), while the second method is detrended fluctuation analysis (DFA), and the third method is multiscale entropy (MSE) to calculate the complexity of system. The second part of the study is to analyze BP and cerebral blood flow velocity (CBFV) changes during TTT. Two measures were used to compare the results, namely correlation coefficient analysis (nMxa) and MSE. The first part of this study has concluded that the ratio of the low frequency power to total power of PSD, and MSE methods are better than DFA to distinguish the difference between normal controls and patients groups. While in the second part, the nMxa of the three stages moving average window is better than the nMxa with all three stages together. Furthermore the analysis of BP data using MSE is better than CBFV data.The Stroke Center and Department of Neurology, National Taiwan University, National Science Council in Taiwan, and the Center for Dynamical Biomarkers
and Translational Medicine, National Central University, which is sponsored by National Science Council and Min-Sheng General Hospital Taoyuan
A powerful and efficient multivariate approach for voxel-level connectome-wide association studies
We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.
Structural Learning of Attack Vectors for Generating Mutated XSS Attacks
Web applications suffer from cross-site scripting (XSS) attacks that
resulting from incomplete or incorrect input sanitization. Learning the
structure of attack vectors could enrich the variety of manifestations in
generated XSS attacks. In this study, we focus on generating more threatening
XSS attacks for the state-of-the-art detection approaches that can find
potential XSS vulnerabilities in Web applications, and propose a mechanism for
structural learning of attack vectors with the aim of generating mutated XSS
attacks in a fully automatic way. Mutated XSS attack generation depends on the
analysis of attack vectors and the structural learning mechanism. For the
kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the
structure of the attack vector model to capture the implicit manner of the
attack vector, and this manner is benefited from the syntax meanings that are
labeled by the proposed tokenizing mechanism. Bayes theorem is used to
determine the number of hidden states in the model for generalizing the
structure model. The paper has the contributions as following: (1)
automatically learn the structure of attack vectors from practical data
analysis to modeling a structure model of attack vectors, (2) mimic the manners
and the elements of attack vectors to extend the ability of testing tool for
identifying XSS vulnerabilities, (3) be helpful to verify the flaws of
blacklist sanitization procedures of Web applications. We evaluated the
proposed mechanism by Burp Intruder with a dataset collected from public XSS
archives. The results show that mutated XSS attack generation can identify
potential vulnerabilities.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330
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