537 research outputs found
Mechanical rolling formation of interpenetrated lithium metal/lithium tin alloy foil for ultrahigh-rate battery anode
To achieve good rate capability of lithium metal anodes for high-energy-density batteries, one fundamental challenge is the slow lithium diffusion at the interface. Here we report an interpenetrated, three-dimensional lithium metal/lithium tin alloy nanocomposite foil realized by a simple calendering and folding process of lithium and tin foils, and spontaneous alloying reactions. The strong affinity between the metallic lithium and lithium tin alloy as mixed electronic and ionic conducting networks, and their abundant interfaces enable ultrafast charger diffusion across the entire electrode. We demonstrate that a lithium/lithium tin alloy foil electrode sustains stable lithium stripping/plating under 30mAcm(-2) and 5mAhcm(-2) with a very low overpotential of 20mV for 200 cycles in a commercial carbonate electrolyte. Cycled under 6C (6.6mAcm(-2)), a 1.0mAhcm(-2) LiNi0.6Co0.2Mn0.2O2 electrode maintains a substantial 74% of its capacity by pairing with such anode
Implication of Climate Change Induced Variation in Wind Extremes on Wind Farm in Mountainous Area of Central China—A Case Study of Hengshan
AbstractWind load is critical to the safety of wind turbines. Wind turbines are designed according to the referrence wind speed of 50-year recurrence interval. The climate change induced variation in extremes of wind could impact safety of wind turbines. Meteorological data from Hengshan weather station in central China is investigated. The wind data of 1973–1992 and 1992–2011 are utilized to estimate the extreme wind of 50-year recurrence interval using method of independent storm and generalized pareto distribution model. It is uncovered that although extreme wind of 50-year recurrence interval escalate a little during the two time spans, it will not affect the safety of wind turbines over there notably
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
In this paper, we systematically study the potential of pre-training with
Large Language Model(LLM)-based document expansion for dense passage retrieval.
Concretely, we leverage the capabilities of LLMs for document expansion, i.e.
query generation, and effectively transfer expanded knowledge to retrievers
using pre-training strategies tailored for passage retrieval. These strategies
include contrastive learning and bottlenecked query generation. Furthermore, we
incorporate a curriculum learning strategy to reduce the reliance on LLM
inferences. Experimental results demonstrate that pre-training with LLM-based
document expansion significantly boosts the retrieval performance on
large-scale web-search tasks. Our work shows strong zero-shot and out-of-domain
retrieval abilities, making it more widely applicable for retrieval when
initializing with no human-labeled data.Comment: 10 pages, 3 tables, 4 figures, under revie
Diffraction-Free Bloch Surface Waves
In this letter, we demonstrate a novel diffraction-free Bloch surface wave
(DF-BSW) sustained on all-dielectric multilayers that does not diffract after
being passed through three obstacles or across a single mode fiber. It can
propagate in a straight line for distances longer than 110 {\mu}m at a
wavelength of 633 nm and could be applied as an in-plane optical virtual probe,
both in air and in an aqueous environment. The ability to be used in water, its
long diffraction-free distance, and its tolerance to multiple obstacles make
this DF-BSW ideal for certain applications in areas such as the biological
sciences, where many measurements are made on glass surfaces or for which an
aqueous environment is required, and for high-speed interconnections between
chips, where low loss is necessary. Specifically, the DF-BSW on the dielectric
multilayer can be used to develop novel flow cytometry that is based on the
surface wave, but not the free space beam, to detect the surface-bound targets
Cluster Contrast for Unsupervised Person Re-Identification
State-of-the-art unsupervised re-ID methods train the neural networks using a
memory-based non-parametric softmax loss. Instance feature vectors stored in
memory are assigned pseudo-labels by clustering and updated at instance level.
However, the varying cluster sizes leads to inconsistency in the updating
progress of each cluster. To solve this problem, we present Cluster Contrast
which stores feature vectors and computes contrast loss at the cluster level.
Our approach employs a unique cluster representation to describe each cluster,
resulting in a cluster-level memory dictionary. In this way, the consistency of
clustering can be effectively maintained throughout the pipline and the GPU
memory consumption can be significantly reduced. Thus, our method can solve the
problem of cluster inconsistency and be applicable to larger data sets. In
addition, we adopt different clustering algorithms to demonstrate the
robustness and generalization of our framework. The application of Cluster
Contrast to a standard unsupervised re-ID pipeline achieves considerable
improvements of 9.9%, 8.3%, 12.1% compared to state-of-the-art purely
unsupervised re-ID methods and 5.5%, 4.8%, 4.4% mAP compared to the
state-of-the-art unsupervised domain adaptation re-ID methods on the Market,
Duke, and MSMT17 datasets. Code is available at
https://github.com/alibaba/cluster-contrast
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