202 research outputs found
Rigidity of 3D spherical caps via -bubbles
By using Gromov's -bubble technique, we show that the -dimensional
spherical caps are rigid under perturbations that do not reduce the metric, the
scalar curvature, and the mean curvature along its boundary. Several
generalizations of this result will be discussed.Comment: 20 pages, 1 figure, All comments are welcom
Rigidity and non-rigidity of \H^n/\Z^{n-2} with scalar curvature bounded from below
We show that the hyperbolic manifold \H^n/\Z^{n-2} is not rigid under all
compactly supported deformations that preserve the scalar curvature lower bound
, and that it is rigid under deformations that are further constrained
by certain topological conditions. In addition, we prove two related splitting
results.Comment: 29 pages, 3 figures, all comments are welcome
The Anneal Temperature Effect on the BTO and NZFO Flims and Their Capacitance - Inductance Integrated Device
In this paper, a novel capacitor-inductor integrated structure was proposed. The dielectric material BaTiO3 (BTO) and ferromagnetic material Ni0.5Zn0.5Fe2O4 (NZFO) was prepared by sol-gel method. Phase composition and morphology of the thin films were characterized by XRD, SEM and AFM. The effect of annealing temperature on film crystallinity, surface morphology, dielectric properties and ferromagnetism were investigated. When the annealing temperature was 700 °C, the BTO film and the NZFO film got the better dielectric properties and ferromagnetic properties. Then the BTO thin film was spin-coated on the substrate, and the NZFO thin film was in-situ sintered on the BTO thin film. The composite film possessed both ferromagnetism and dielectric properties. Finally, an inductive coil was fabricated on the BTO/NZFO composite film to produce a capacitance and inductance integrated device
Personalized Federated Learning with Hidden Information on Personalized Prior
Federated learning (FL for simplification) is a distributed machine learning
technique that utilizes global servers and collaborative clients to achieve
privacy-preserving global model training without direct data sharing. However,
heterogeneous data problem, as one of FL's main problems, makes it difficult
for the global model to perform effectively on each client's local data. Thus,
personalized federated learning (PFL for simplification) aims to improve the
performance of the model on local data as much as possible. Bayesian learning,
where the parameters of the model are seen as random variables with a prior
assumption, is a feasible solution to the heterogeneous data problem due to the
tendency that the more local data the model use, the more it focuses on the
local data, otherwise focuses on the prior. When Bayesian learning is applied
to PFL, the global model provides global knowledge as a prior to the local
training process. In this paper, we employ Bayesian learning to model PFL by
assuming a prior in the scaled exponential family, and therefore propose
pFedBreD, a framework to solve the problem we model using Bregman divergence
regularization. Empirically, our experiments show that, under the prior
assumption of the spherical Gaussian and the first order strategy of mean
selection, our proposal significantly outcompetes other PFL algorithms on
multiple public benchmarks.Comment: 19 pages, 6 figures, 3 table
Minimizing the Maximum Flow Time in the Online Food Delivery Problem
We study a common delivery problem encountered in nowadays online food-ordering platforms: Customers order dishes online, and the restaurant delivers the food after receiving the order. Specifically, we study a problem where k vehicles of capacity c are serving a set of requests ordering food from one restaurant. After a request arrives, it can be served by a vehicle moving from the restaurant to its delivery location. We are interested in serving all requests while minimizing the maximum flow-time, i.e., the maximum time length a customer waits to receive his/her food after submitting the order.
We show that the problem is hard in both offline and online settings even when k = 1 and c = ?: There is a hardness of approximation of ?(n) for the offline problem, and a lower bound of ?(n) on the competitive ratio of any online algorithm, where n is number of points in the metric.
We circumvent the strong negative results in two directions. Our main result is an O(1)-competitive online algorithm for the uncapacitated (i.e, c = ?) food delivery problem on tree metrics; we also have negative result showing that the condition c = ? is needed. Then we explore the speed-augmentation model where our online algorithm is allowed to use vehicles with faster speed. We show that a moderate speeding factor leads to a constant competitive ratio, and we prove a tight trade-off between the speeding factor and the competitive ratio
The Model Inversion Eavesdropping Attack in Semantic Communication Systems
In recent years, semantic communication has been a popular research topic for
its superiority in communication efficiency. As semantic communication relies
on deep learning to extract meaning from raw messages, it is vulnerable to
attacks targeting deep learning models. In this paper, we introduce the model
inversion eavesdropping attack (MIEA) to reveal the risk of privacy leaks in
the semantic communication system. In MIEA, the attacker first eavesdrops the
signal being transmitted by the semantic communication system and then performs
model inversion attack to reconstruct the raw message, where both the white-box
and black-box settings are considered. Evaluation results show that MIEA can
successfully reconstruct the raw message with good quality under different
channel conditions. We then propose a defense method based on random
permutation and substitution to defend against MIEA in order to achieve secure
semantic communication. Our experimental results demonstrate the effectiveness
of the proposed defense method in preventing MIEA.Comment: Accepted by 2023 IEEE Global Communications Conference (GLOBECOM
DBS: Dynamic Batch Size For Distributed Deep Neural Network Training
Synchronous strategies with data parallelism, such as the Synchronous
StochasticGradient Descent (S-SGD) and the model averaging methods, are widely
utilizedin distributed training of Deep Neural Networks (DNNs), largely owing
to itseasy implementation yet promising performance. Particularly, each worker
ofthe cluster hosts a copy of the DNN and an evenly divided share of the
datasetwith the fixed mini-batch size, to keep the training of DNNs
convergence. In thestrategies, the workers with different computational
capability, need to wait foreach other because of the synchronization and
delays in network transmission,which will inevitably result in the
high-performance workers wasting computation.Consequently, the utilization of
the cluster is relatively low. To alleviate thisissue, we propose the Dynamic
Batch Size (DBS) strategy for the distributedtraining of DNNs. Specifically,
the performance of each worker is evaluatedfirst based on the fact in the
previous epoch, and then the batch size and datasetpartition are dynamically
adjusted in consideration of the current performanceof the worker, thereby
improving the utilization of the cluster. To verify theeffectiveness of the
proposed strategy, extensive experiments have been conducted,and the
experimental results indicate that the proposed strategy can fully utilizethe
performance of the cluster, reduce the training time, and have good
robustnesswith disturbance by irrelevant tasks. Furthermore, rigorous
theoretical analysis hasalso been provided to prove the convergence of the
proposed strategy.Comment: The latest version of this article has been accepted by IEEE TETC
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