179 research outputs found
Cross section design of holey optical fibers with coating based on stress analysis in tension
In this paper, the mechanical behavior of newly developed holey optical fibers with and without coating was investigated by numerical analysis. Based on experimental work, the tensile failure characteristics were observed. The stress characteristics of some typical holey fibers with different section design were studied though the finite element method under tensile load. The optimum design of air hole arrangements and sizes were proposed according to the numerical results. The influence of the coating thickness on the axial stress of holey optical fiber was also investigated. The numerical results and conclusions will be useful for the cross section optimum design of holey optical fiber for increase its strength
GW26-e1392 Impact of Tongguan Capsule on periprocedural myocardial injury undergoing percutaneous coronary intervention in coronary heart disease
Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
The hadron spectra and pion form factor in dynamical holographic QCD model with anomalous 5D mass of scalar field
The simplest version of the dynamical holographic QCD model is described by
adding the KKSS model action on a dilaton-graviton coupled background, in which
the AdS metric is deformed by the gluon condensation and further deformed
by the chiral condensation. In this framework, both the chiral symmetry
breaking and linear confinement can be realized, the light-flavor hadron
spectra and the pion form factor were investigated but it was difficult to
reconcile the light-flavor hadron spectra and pion form factor. By considering
the anomalous 5-dimension mass correction of the scalar field from QCD running
coupling, it is found that the light flavor hadron spectra and pion form factor
can be described well simultaneously, especially the ground state and lower
excitation states of scalar, pseudo scalar and axial vector meson spectra are
improved, but the vector meson spectra is not sensitive to the anomalous
5-dimension mass correction of the scalar field.Comment: 14 pages, 5 figure
The Standardization Breeding Systematic Construction for Seed Production of Qiancao NO. 2 Tall Fescue
Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
In the field of natural language processing, the prevalent approach involves
fine-tuning pretrained language models (PLMs) using local samples. Recent
research has exposed the susceptibility of PLMs to backdoor attacks, wherein
the adversaries can embed malicious prediction behaviors by manipulating a few
training samples. In this study, our objective is to develop a
backdoor-resistant tuning procedure that yields a backdoor-free model, no
matter whether the fine-tuning dataset contains poisoned samples. To this end,
we propose and integrate a honeypot module into the original PLM, specifically
designed to absorb backdoor information exclusively. Our design is motivated by
the observation that lower-layer representations in PLMs carry sufficient
backdoor features while carrying minimal information about the original tasks.
Consequently, we can impose penalties on the information acquired by the
honeypot module to inhibit backdoor creation during the fine-tuning process of
the stem network. Comprehensive experiments conducted on benchmark datasets
substantiate the effectiveness and robustness of our defensive strategy.
Notably, these results indicate a substantial reduction in the attack success
rate ranging from 10\% to 40\% when compared to prior state-of-the-art methods
AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control
Neural implicit fields are powerful for representing 3D scenes and generating
high-quality novel views, but it remains challenging to use such implicit
representations for creating a 3D human avatar with a specific identity and
artistic style that can be easily animated. Our proposed method, AvatarCraft,
addresses this challenge by using diffusion models to guide the learning of
geometry and texture for a neural avatar based on a single text prompt. We
carefully design the optimization framework of neural implicit fields,
including a coarse-to-fine multi-bounding box training strategy, shape
regularization, and diffusion-based constraints, to produce high-quality
geometry and texture. Additionally, we make the human avatar animatable by
deforming the neural implicit field with an explicit warping field that maps
the target human mesh to a template human mesh, both represented using
parametric human models. This simplifies animation and reshaping of the
generated avatar by controlling pose and shape parameters. Extensive
experiments on various text descriptions show that AvatarCraft is effective and
robust in creating human avatars and rendering novel views, poses, and shapes.
Our project page is: https://avatar-craft.github.io/.Comment: ICCV 2023 Camera Read
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Evaluating the general abilities of foundation models to tackle human-level
tasks is a vital aspect of their development and application in the pursuit of
Artificial General Intelligence (AGI). Traditional benchmarks, which rely on
artificial datasets, may not accurately represent human-level capabilities. In
this paper, we introduce AGIEval, a novel benchmark specifically designed to
assess foundation model in the context of human-centric standardized exams,
such as college entrance exams, law school admission tests, math competitions,
and lawyer qualification tests. We evaluate several state-of-the-art foundation
models, including GPT-4, ChatGPT, and Text-Davinci-003, using this benchmark.
Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math
competitions, attaining a 95% accuracy rate on the SAT Math test and a 92.5%
accuracy on the English test of the Chinese national college entrance exam.
This demonstrates the extraordinary performance of contemporary foundation
models. In contrast, we also find that GPT-4 is less proficient in tasks that
require complex reasoning or specific domain knowledge. Our comprehensive
analyses of model capabilities (understanding, knowledge, reasoning, and
calculation) reveal these models' strengths and limitations, providing valuable
insights into future directions for enhancing their general capabilities. By
concentrating on tasks pertinent to human cognition and decision-making, our
benchmark delivers a more meaningful and robust evaluation of foundation
models' performance in real-world scenarios. The data, code, and all model
outputs are released in https://github.com/microsoft/AGIEval.Comment: 19 page
Crack Initiation Mechanism and Life Prediction of Ti60 Titanium Alloy Considering Stress Ratios Effect in Very High Cycle Fatigue Regime
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