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

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