837 research outputs found
« Petit commerce aléatoire de chiens tibétains » Nouvelle de He Liwei
No full textLiwei He, Naour Françoise. « Petit commerce aléatoire de chiens tibétains » Nouvelle de He Liwei. In: Perspectives chinoises, n°42, 1997. pp. 61-66
Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations
No full textLow rank approximation of matrices is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns, and provides concise representations for the data. Research on low rank approximation usually focuses on real matrices. However, in many applications data are binary (categorical) rather than continuous. This leads to the problem of low rank approximation of binary matrices. Here we are given a d x n binary matrix A and a small integer k < d. The goal is to find two binary matrices U and V of sizes d x k and k x n respectively, so that the Frobenius norm of A - U V is minimized. There are two models of this problem, depending on the definition of the dot product of binary vectors: The GF(2) model and the Boolean semiring model. Unlike low rank approximation of a real matrix which can be efficiently solved by Singular Value Decomposition, we show that approximation of a binary matrix is NP-hard, even for k=1.
In this paper, our main concern is the problem of Column Subset Selection (CSS), in which the low rank matrix U must be formed by k columns of the data matrix, and we are interested in the approximation ratio achievable by CSS for binary matrices. For the GF(2) model, we show that CSS has approximation ratio bounded by k/2+1+k/(2(2^k-1)) and this is asymptotically tight. For the Boolean model, it turns out that CSS is no longer sufficient to obtain a bound. We then develop a Generalized CSS (GCSS) procedure in which the columns of U are generated from Boolean formulas operating bitwise on selected columns of the data matrix. We show that the approximation ratio achieved by GCSS is bounded by 2^(k-1)+1, and argue that an exponential dependency on k is seems inherent
Polynomial policies in supply chain networks
No full textThesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 63-64).This thesis aims to solve the periodic-reviewed inventory control problem in supply chain networks with uncertain demand so as to minimize the overall cost of the system over a fixed planning time horizon. In such problems, one seeks to optimally determine ordering quantities at different stages in time. We investigate the class of polynomial policies, where the control policy is directly parametrized polynomially in the observed uncertainties of previous stages. We use sum-of-square relaxations to reformulate the problem into a single semidefinite optimization problem for a specific polynomial degree. We consider both robust and stochastic approaches in order to address the uncertainties in demand. In extensive numerical studies, we find that polynomial policies exhibit better performance over basestock policies across a variety of networks and demand distributions under the mean and standard deviation criteria. However, when the uncertainty set turns out to be larger than planned, basestock policies start outperforming polynomial policies. Comparing the policies obtained under the robust and stochastic frameworks, we find that they are comparable in the average performance criterion, but the robust approach leads to better tail behavior and lower standard deviation in general.by Liwei He.S.M
sj-docx-1-ijl-10.1177_15347346231201696 - Supplemental material for Acellular Dermal Matrix for Treatment of Diabetic Foot Ulcer: An Overview of Systematic Reviews
No full textSupplemental material, sj-docx-1-ijl-10.1177_15347346231201696 for Acellular Dermal Matrix for Treatment of Diabetic Foot Ulcer: An Overview of Systematic Reviews by Li Lingyan, Zhao Han, Li Jialu, He Bingyang, Ma Yuanyuan, Qin Peiwei, Ma Peifen and Xu Liwei in The International Journal of Lower Extremity Wounds</p
Complete mitochondrial genome of<i>Tetraophasis szechenyii</i>Madarász, 1885 (Aves: Galliformes: Phasianidae), and its genetic variation as inferred from the mitochondrial DNA Control Region
No full textFigure 2. Median-joining network of all the control region haplotypes found in Tetraophasis szechenyii. Notes: Missing haplotypes in the network are represented by black dots; circle sizes are proportional to the number of individuals sharing the same haplotypes (n); each mutation step is shown as a short line connecting neighbouring haplotypes; numbers of mutations between haplotypes are indicated near branches if greater than 1.Published as part of Meng, Yang, He, Liwei, Wu, Ailin, Fan, Zhenxin, Ran, Jianghong, Yue, Bisong & Li, Jing, 2010, Complete mitochondrial genome of Tetraophasis szechenyii Madarász, 1885 (Aves: Galliformes: Phasianidae), and its genetic variation as inferred from the mitochondrial DNA Control Region, pp. 2955-2964 in Journal of Natural History 44 (47-48) on page 2960, DOI: 10.1080/00222933.2010.502596, http://zenodo.org/record/521226
Leaf stoichiometry is synergistically-driven by climate, site, soil characteristics and phylogeny in karst areas, Southwest China
No full textLeaf stoichiometry and its biogeography play vital roles in nutrient cycling of plant communities. To understand the potential drivers of leaf stoichiometry in karst ecosystems, we measured leaf morphological traits (dry mass content (DM), specific leaf area (SLA)), and biochemical traits (C, N, P, K and Ca stoichiometry) of 53 species of different functional groups, as well as soil properties, across seven karst sites in Southwest China, and explored the relationships between these leaf traits and environmental factors. The results showed that: (1) in karst areas of Southwest China, there were higher leaf C and Ca concentrations as well as higher N/P and K/P ratios compared to other ecosystems, plants were more limited by P rather than by N; (2) mean annual temperature positively influenced leaf N, P, and Ca, while mean annual precipitation exerted more influence on leaf K; (3) a strong phylogeny signal was detected in leaf N (p < 0.05), and significant influence of species composition on the variance of leaf N, K, and Ca was observed (p < 0.05); (4) the influence of soil properties on leaf P and Ca, and the influence of leaf features (SLA and DM) on leaf K were also observed based on a variance partitioning analysis. Abiotic factors such as soil, site, and climate were more important than biotic factors (leaf features and phylogeny) in determining leaf N, P, and Ca. In general, the driving factors exhibited a synergistic effect on leaf stoichiometry across different sites, offering a key mechanism that needs to be integrated into the modeling of biogeochemical nutrients cycling in karst ecosystems.link_to_subscribed_fulltex
Correction to: Leaf stoichiometry is synergistically-driven by climate, site, soil characteristics and phylogeny in karst areas, Southwest China
No full textIn the original version of this article the figure legends of Figs. 3–7 were incorrect. The correct legends are given below. The original article has been updated.</p
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Self-healing Materials for Sensor, Actuator and Energy Storage Applications
No full textFlexible and stretchable electronics can bridge interactions between human, machine, and artificial intelligence for practical applications but mechanical damages such as cutting, wearing, and tearing often cause their failures. This work has developed hydrogel-based self-healing materials by emulating the self-curing capability of living creatures such as jellyfish, and then, applications of self-healing materials in flexible and stretchable sensor, actuator, and energy storage devices are demonstrated. Hydrogels have been widely investigated with unique properties of high ion conductivity, high stretchability, and self-healing capability. However, hydrogels suffer from long-term stability issues due to the dehydration process in ambient environment. Here, a hydrogel-based ionic skin is developed to achieve a ultra-long operation time in the ambient for over 16 months with high stretchability (an fracture stretch of 2216%) and conductivity (23.5 mS/cm). After exposures to extreme environments, such as heating at 200℃, freezing -20℃, and vacuum drying (hundreds of mTorr, 16 hours), the hydrogel can self-replenish its water content from the ambient to regain its conductivity and stretchability. It can also heal multiple times of cut-through damages by recouping its conductivity (>90%) within one minute at room temperature and its stretchability can be restored up to ~100% after 24h at RH 40%. Utilizing the ionic skin as the building block, piezoelecret sensors have been constructed to record physiological signals.In a second demonstration example, a self-healable, flexible, and multimodal sweat sensor is developed based on laser-induced graphene electrodes for healthcare applications. The prototype device is capable of simultaneously detecting pH, uric acid, and tyrosine, which are crucial indicators for skin conditions and metabolic disorders. Experimental results show the sensor can maintain its sensitivity even when subjected to large strains, such as 180-degree folding with a bending radius of 1 mm. The conductivity and sensitivity of the sensor can be recovered via the self-healing process after it is cut into two pieces with a razor blade. The third example is an Lego-like reconfigurable soft haptic sensor/actuator system made of hydrogel and dielectric elastomer to realize module-to-module connections both electrically and mechanically. These individual modules can be re-assembled in different forms with little changes in their electrical conductivities (>90% recovery) and output actuating displacements (>80%) after sustaining a high tension force of over 4N. In a proof-of-concept demo, a soft haptic array is reconfigured to different sizes and shapes without affecting its stimulation outputs. Among many possible applications in AR/VR and human-machine interfaces, two common tasks are demonstrated in this work interchangeably by using these modules: (1) as a virtual keyboard with separated units; (2) as a smart safety warning patch for holding a car driving wheel when assembled together.The final demos are self-healable energy storage devices by using the ionic skins as quasi-solid-state electrolytes. A transfer-printing process is developed to allow high-resolution pattering on the hydrogel. Prototype micro-supercapacitors have been fabricated. Results show that they can restore energy storage performances after 10 cycles of the cut-through and subsequent self-healing process. The hydrogel exhibits a high electrochemical stability window of 2.8V as a non-toxic and quasi-solid-state electrolyte for Li-ion batteries. A prototype battery shows outstanding cyclic stability and stable operations in the ambient without the protection of rigid hermetic sealing package. The prototype strechable battery is functional while being punctured by a needle and it regains most of its capacity after the cut-through damages by a razor blade and a subsequent self-healing process
sj-docx-1-wso-10.1177_17474930231205221 – Supplemental material for The frequency of imaging markers adjusted for time since symptom onset in intracerebral hemorrhage: A novel predictor for hematoma expansion
No full textSupplemental material, sj-docx-1-wso-10.1177_17474930231205221 for The frequency of imaging markers adjusted for time since symptom onset in intracerebral hemorrhage: A novel predictor for hematoma expansion by Lei Song, Jun Cheng, Cun Zhang, Hang Zhou, Wenmin Guo, Yu Ye, Rujia Wang, Hui Xiong, Ji Zhang, Ren Ke, Dongfang Tang, Yufei Fu, Zhibing He, Liwei Zou, Longsheng Wang, Lianghong Kuang, Xiaoming Qiu, Tingting Guo and Yongqiang Yu in International Journal of Stroke</p
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