108 research outputs found
FoveaBox: Beyond Anchor-based Object Detector
We present FoveaBox, an accurate, flexible, and completely anchor-free
framework for object detection. While almost all state-of-the-art object
detectors utilize predefined anchors to enumerate possible locations, scales
and aspect ratios for the search of the objects, their performance and
generalization ability are also limited to the design of anchors. Instead,
FoveaBox directly learns the object existing possibility and the bounding box
coordinates without anchor reference. This is achieved by: (a) predicting
category-sensitive semantic maps for the object existing possibility, and (b)
producing category-agnostic bounding box for each position that potentially
contains an object. The scales of target boxes are naturally associated with
feature pyramid representations. In FoveaBox, an instance is assigned to
adjacent feature levels to make the model more accurate.We demonstrate its
effectiveness on standard benchmarks and report extensive experimental
analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single
model performance on the standard COCO and Pascal VOC object detection
benchmark. More importantly, FoveaBox avoids all computation and
hyper-parameters related to anchor boxes, which are often sensitive to the
final detection performance. We believe the simple and effective approach will
serve as a solid baseline and help ease future research for object detection.
The code has been made publicly available at
https://github.com/taokong/FoveaBox .Comment: IEEE Transactions on Image Processing, code at:
https://github.com/taokong/FoveaBo
Is China financialised? The significance of two historic transformations of Chinese finance
This article tackles the question of whether financialisation is present in the Chinese economy by analysing two key transformations of the country’s financial system. The first was a state-led reform process through which the Chinese financial system introduced market practices, similarly to the rest of the economy. The second was a market-led process, reflected in the emergence and rise of shadow banking, which originates from within financial markets with the aim of bypassing loan restrictions. The article shows that despite the two transformations and the enormous growth of finance during the past four decades, the underlying character of the Chinese financial system exhibits remarkable continuity. Namely, it remains bank based – albeit partially liberalised – with a predominant role for bank credit and a strong presence for the state. The relational and government-controlled structures of Chinese finance have not been replaced by arm’s length and private mechanisms. On these grounds, it is premature to consider the Chinese economy to be financialised
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
With the recent advances in mobile energy storage technologies, electric
vehicles (EVs) have become a crucial part of smart grids. When EVs participate
in the demand response program, the charging cost can be significantly reduced
by taking full advantage of the real-time pricing signals. However, many
stochastic factors exist in the dynamic environment, bringing significant
challenges to design an optimal charging/discharging control strategy. This
paper develops an optimal EV charging/discharging control strategy for
different EV users under dynamic environments to maximize EV users' benefits.
We first formulate this problem as a Markov decision process (MDP). Then we
consider EV users with different behaviors as agents in different environments.
Furthermore, a horizontal federated reinforcement learning (HFRL)-based method
is proposed to fit various users' behaviors and dynamic environments. This
approach can learn an optimal charging/discharging control strategy without
sharing users' profiles. Simulation results illustrate that the proposed
real-time EV charging/discharging control strategy can perform well among
various stochastic factors
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Distributed learning has become a promising computational parallelism
paradigm that enables a wide scope of intelligent applications from the
Internet of Things (IoT) to autonomous driving and the healthcare industry.
This paper studies distributed learning in wireless data center networks, which
contain a central edge server and multiple edge workers to collaboratively
train a shared global model and benefit from parallel computing. However, the
distributed nature causes the vulnerability of the learning process to faults
and adversarial attacks from Byzantine edge workers, as well as the severe
communication and computation overhead induced by the periodical information
exchange process. To achieve fast and reliable model aggregation in the
presence of Byzantine attacks, we develop a signed stochastic gradient descent
(SignSGD)-based Hierarchical Vote framework via over-the-air computation
(AirComp), where one voting process is performed locally at the wireless edge
by taking advantage of Bernoulli coding while the other is operated
over-the-air at the central edge server by utilizing the waveform superposition
property of the multiple-access channels. We comprehensively analyze the
proposed framework on the impacts including Byzantine attacks and the wireless
environment (channel fading and receiver noise), followed by characterizing the
convergence behavior under non-convex settings. Simulation results validate our
theoretical achievements and demonstrate the robustness of our proposed
framework in the presence of Byzantine attacks and receiver noise.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
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China versus the US in the Pandemic Crisis: Governance and Politics Confronting Systemic Challenges
Because of its peculiar nature as a systemic challenge, the control of the COVID-19 crisis requires strong and rapid actions. It appears that China has employed a “tough model” whereas the erratic measures of the US have formed a “loose model”. This paper seeks to characterize and assess the two models from the perspective of the governance structures and the corrective capabilities of the two different political establishments. The exposition draws on the framework of “exit, voice, and loyalty” developed by Albert Hirschman, and rejects the hegemonic ideology of the “liberal democracy versus authoritarianism” dichotomy
Learning Harmonic Molecular Representations on Riemannian Manifold
Molecular representation learning plays a crucial role in AI-assisted drug
discovery research. Encoding 3D molecular structures through Euclidean neural
networks has become the prevailing method in the geometric deep learning
community. However, the equivariance constraints and message passing in
Euclidean space may limit the network expressive power. In this work, we
propose a Harmonic Molecular Representation learning (HMR) framework, which
represents a molecule using the Laplace-Beltrami eigenfunctions of its
molecular surface. HMR offers a multi-resolution representation of molecular
geometric and chemical features on 2D Riemannian manifold. We also introduce a
harmonic message passing method to realize efficient spectral message passing
over the surface manifold for better molecular encoding. Our proposed method
shows comparable predictive power to current models in small molecule property
prediction, and outperforms the state-of-the-art deep learning models for
ligand-binding protein pocket classification and the rigid protein docking
challenge, demonstrating its versatility in molecular representation learning.Comment: 25 pages including Appendi
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Federated learning (FL), as an emerging edge artificial intelligence
paradigm, enables many edge devices to collaboratively train a global model
without sharing their private data. To enhance the training efficiency of FL,
various algorithms have been proposed, ranging from first-order to second-order
methods. However, these algorithms cannot be applied in scenarios where the
gradient information is not available, e.g., federated black-box attack and
federated hyperparameter tuning. To address this issue, in this paper we
propose a derivative-free federated zeroth-order optimization (FedZO) algorithm
featured by performing multiple local updates based on stochastic gradient
estimators in each communication round and enabling partial device
participation. Under non-convex settings, we derive the convergence performance
of the FedZO algorithm on non-independent and identically distributed data and
characterize the impact of the numbers of local iterates and participating edge
devices on the convergence. To enable communication-efficient FedZO over
wireless networks, we further propose an over-the-air computation (AirComp)
assisted FedZO algorithm. With an appropriate transceiver design, we show that
the convergence of AirComp-assisted FedZO can still be preserved under certain
signal-to-noise ratio conditions. Simulation results demonstrate the
effectiveness of the FedZO algorithm and validate the theoretical observations.Comment: This work was accepted to Transaction on Signal Processin
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