298 research outputs found
Well-posedness of the IBVP for 2-D Euler Equations with Damping
In this paper we focus on the initial-boundary value problem of the 2-D
isentropic Euler equations with damping. We prove the global-in-time existence
of classical solution to the initial-boundary value problem by the method of
energy estimates.Comment: 26 pages,no figure
Byzantine-Resilient Federated Learning with Heterogeneous Data Distribution
For mitigating Byzantine behaviors in federated learning (FL), most
state-of-the-art approaches, such as Bulyan, tend to leverage the similarity of
updates from the benign clients. However, in many practical FL scenarios, data
is non-IID across clients, thus the updates received from even the benign
clients are quite dissimilar. Hence, using similarity based methods result in
wasted opportunities to train a model from interesting non-IID data, and also
slower model convergence. We propose DiverseFL to overcome this challenge in
heterogeneous data distribution settings. Rather than comparing each client's
update with other client updates to detect Byzantine clients, DiverseFL
compares each client's update with a guiding update of that client. Any client
whose update diverges from its associated guiding update is then tagged as a
Byzantine node. The FL server in DiverseFL computes the guiding update in every
round for each client over a small sample of the client's local data that is
received only once before start of the training. However, sharing even a small
sample of client's data with the FL server can compromise client's data privacy
needs. To tackle this challenge, DiverseFL creates a Trusted Execution
Environment (TEE)-based enclave to receive each client's sample and to compute
its guiding updates. TEE provides a hardware assisted verification and
attestation to each client that its data is not leaked outside of TEE. Through
experiments involving neural networks, benchmark datasets and popular Byzantine
attacks, we demonstrate that DiverseFL not only performs Byzantine mitigation
quite effectively, it also almost matches the performance of OracleSGD, where
the server only aggregates the updates from the benign clients
Attribute Prototype Network for Zero-Shot Learning
From the beginning of zero-shot learning research, visual attributes have
been shown to play an important role. In order to better transfer
attribute-based knowledge from known to unknown classes, we argue that an image
representation with integrated attribute localization ability would be
beneficial for zero-shot learning. To this end, we propose a novel zero-shot
representation learning framework that jointly learns discriminative global and
local features using only class-level attributes. While a visual-semantic
embedding layer learns global features, local features are learned through an
attribute prototype network that simultaneously regresses and decorrelates
attributes from intermediate features. We show that our locality augmented
image representations achieve a new state-of-the-art on three zero-shot
learning benchmarks. As an additional benefit, our model points to the visual
evidence of the attributes in an image, e.g. for the CUB dataset, confirming
the improved attribute localization ability of our image representation.Comment: NeurIPS 2020. The code is publicly available at
https://wenjiaxu.github.io/APN-ZSL
LALM: Long-Term Action Anticipation with Language Models
Understanding human activity is a crucial yet intricate task in egocentric
vision, a field that focuses on capturing visual perspectives from the camera
wearer's viewpoint. While traditional methods heavily rely on representation
learning trained on extensive video data, there exists a significant
limitation: obtaining effective video representations proves challenging due to
the inherent complexity and variability in human activities.Furthermore,
exclusive dependence on video-based learning may constrain a model's capability
to generalize across long-tail classes and out-of-distribution scenarios.
In this study, we introduce a novel approach for long-term action
anticipation using language models (LALM), adept at addressing the complex
challenges of long-term activity understanding without the need for extensive
training. Our method incorporates an action recognition model to track previous
action sequences and a vision-language model to articulate relevant
environmental details. By leveraging the context provided by these past events,
we devise a prompting strategy for action anticipation using large language
models (LLMs). Moreover, we implement Maximal Marginal Relevance for example
selection to facilitate in-context learning of the LLMs. Our experimental
results demonstrate that LALM surpasses the state-of-the-art methods in the
task of long-term action anticipation on the Ego4D benchmark. We further
validate LALM on two additional benchmarks, affirming its capacity for
generalization across intricate activities with different sets of taxonomies.
These are achieved without specific fine-tuning
Dynamic Recrystallization Behavior of TA15 Titanium Alloy under Isothermal Compression during Hot Deformation
In order to improve the understanding of the dynamic recrystallization (DRX) behaviors of TA15 titanium alloy (Ti-6Al-2Zr-1Mo-1V), a series of experiments were conducted on a TMTS thermal simulator at temperatures of 1173 K, 1203 K, 1223 K, and 1273 K with the strain rates of 0.005 s−1, 0.05 s−1, 0.5 s−1, and 1 s−1. By the regression analysis for conventional hyperbolic sine equation, the activation energy of DRX in α+β two-phase region is QS=588.7 Kg/mol and in β region is QD=225.8 Kg/mol, and a dimensionless parameter controlling the stored energy was determined as Z/A=ε˙exp(588.7×103)/RT/6.69×1026 in α+β two-phase region and as Z/A=ε˙exp(225.8×103)/RT/5.13×1011 in β region. The DRX behaviors of TA15 titanium alloy were proposed on the strength of the experiment results. Finally, the theoretical prediction results of DRX volume fraction were shown to be in agreement with experimental observations
Temporal variation of bacterial community and nutrients in Tibetan glacier snowpack
The Tibetan Plateau harbors the largest number of glaciers outside the polar regions, which are the source of several major rivers in Asia. These glaciers are also major sources of nutrients for downstream ecosystems, while there is a little amount of data available on the nutrient transformation processes on the glacier surface. Here, we monitored the carbon and nitrogen concentration changes in a snowpit following a snowfall in the Dunde Glacier of the Tibetan Plateau. The association of carbon and nitrogen changes with bacterial community dynamics was investigated in the surface and subsurface snow (depth at 0–15 and 15–30 cm, respectively) during a 9 d period. Our results revealed rapid temporal changes in nitrogen (including nitrate and ammonium) and bacterial communities in both surface and subsurface snow. Nitrate and ammonium concentrations increased from 0.44 to 1.15 mg L−1 and 0.18 to 0.24 mg L−1 in the surface snow and decreased from 3.81 to 1.04 and 0.53 to 0.25 mg L−1 in the subsurface snow over time. Therefore, we suggest that the surface snow is not nitrogen-limited, while the subsurface snow is associated with nitrogen consumption processes and is nitrogen-limited. The nitrate concentration co-varied with bacterial diversity, community structure, and the predicted nitrogen fixation and nitrogen assimilation/denitrification-related genes (narG), suggesting nitrogen could mediate bacterial community changes. The nitrogen limitation and enriched denitrification-related genes in subsurface snow suggested stronger environmental and biotic filtering than those in surface snow, which may explain the lower bacterial diversity, more pronounced community temporal changes, and stronger biotic interactions. Collectively, these findings advance our understanding of bacterial community variations and bacterial interactions after snow deposition and provide a possible biological explanation for nitrogen dynamics in snow
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