2,651 research outputs found
Filling Radius, Quantitative -theory and Positive Scalar Curvature
We prove a quantitative upper bound on the filling radius of complete, spin
manifolds with uniformly positive scalar curvature using the quantitative
operator -theory and index theory.Comment: minor revisio
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
Federated learning (FL) enables distributed devices to jointly train a shared
model while keeping the training data local. Different from the horizontal FL
(HFL) setting where each client has partial data samples, vertical FL (VFL),
which allows each client to collect partial features, has attracted intensive
research efforts recently. In this paper, we identified two challenges that
state-of-the-art VFL frameworks are facing: (1) some works directly average the
learned feature embeddings and therefore might lose the unique properties of
each local feature set; (2) server needs to communicate gradients with the
clients for each training step, incurring high communication cost that leads to
rapid consumption of privacy budgets. In this paper, we aim to address the
above challenges and propose an efficient VFL with multiple linear heads (VIM)
framework, where each head corresponds to local clients by taking the separate
contribution of each client into account. In addition, we propose an
Alternating Direction Method of Multipliers (ADMM)-based method to solve our
optimization problem, which reduces the communication cost by allowing multiple
local updates in each step, and thus leads to better performance under
differential privacy. We consider various settings including VFL with model
splitting and without model splitting. For both settings, we carefully analyze
the differential privacy mechanism for our framework. Moreover, we show that a
byproduct of our framework is that the weights of learned linear heads reflect
the importance of local clients. We conduct extensive evaluations and show that
on four real-world datasets, VIM achieves significantly higher performance and
faster convergence compared with state-of-the-arts. We also explicitly evaluate
the importance of local clients and show that VIM enables functionalities such
as client-level explanation and client denoising
An All Deep System for Badminton Game Analysis
The CoachAI Badminton 2023 Track1 initiative aim to automatically detect
events within badminton match videos. Detecting small objects, especially the
shuttlecock, is of quite importance and demands high precision within the
challenge. Such detection is crucial for tasks like hit count, hitting time,
and hitting location. However, even after revising the well-regarded
shuttlecock detecting model, TrackNet, our object detection models still fall
short of the desired accuracy. To address this issue, we've implemented various
deep learning methods to tackle the problems arising from noisy detectied data,
leveraging diverse data types to improve precision. In this report, we detail
the detection model modifications we've made and our approach to the 11 tasks.
Notably, our system garnered a score of 0.78 out of 1.0 in the challenge.Comment: Golden Award for IJCAI CoachAI Challenge 2023: Team NTNUEE AIoTLa
Reliability-Constrained Economic Dispatch with Analytical Formulation of Operational Risk Evaluation
Operational reliability and the decision-making process of economic dispatch (ED) are closely related and important for power system operation. Consideration of reliability indices and reliability constraints together in the operation problem is very challenging due to the problem size and tight reliability constraints. In this paper, a comprehensive reliability-constrained economic dispatch model with analytical formulation of operational risk evaluation (RCED-AF) is proposed to tackle the operational risk problem of power systems. An operational reliability evaluation model considering the ED decision is designed to accurately assess the system behavior. A computation scheme is also developed to achieve efficient update of risk indices for each ED decision by approximating the reliability evaluation procedure with an analytical polynomial function. The RCED-AF model can be constructed with decision-dependent reliability constraints expressed by the sparse polynomial chaos expansion. Case studies demonstrate that the proposed RCED-AF model is effective and accurate in the optimization of the reliability and the cost for day-ahead economic dispatch
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