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MM-PIHM, v. 0.10.10
[article]
2018
Zenodo
Preserved Fulltext
and biogeochemistry modules (Flux-PIHM-BGC), and Flux-PIHM data assimilation system using the ensemble Kalman filter (Flux-PIHM-EnKF system; Shi et al. 2014). ...
Snowmelt process 2.6 Evapotranspiration 3 Flux-PIHM: PIHM with a surface heat flux module Flux-PIHM (Shi et al. 2013 ) is a land surface hydrologic model, which couples PIHM with the land surface schemes ...
doi:10.5281/zenodo.4533259
fatcat:6gy6ryll3zd3jf62u5k7bnqoou
Distributed Optimization for Massive Connectivity
[article]
2020
arXiv
pre-print
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Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses significant communication challenges. To overcome this challenge, the serving base station is required to detect the active devices and estimate the corresponding channel state information during each coherence block. The corresponding joint activity detection and channel estimation problem can be formulated as a group sparse estimation problem, also known under the name "Group Lasso". This letter
arXiv:2006.05637v1
fatcat:xvjt76zjqvhkhcrxhedxwfv2gu
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... s a fast and efficient distributed algorithm to solve such Group Lasso problems, which alternates between solving small-scaled problems in parallel and dealing with a linear equation for consensus. Numerical results demonstrate the speedup of this algorithm compared with the state-of-the-art methods in terms of convergence speed and computation time.
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
[article]
2022
arXiv
pre-print
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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
arXiv:2210.01452v1
fatcat:4h7kvbemsjeufb5jdxw7tvlxly
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... ing 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.
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples
[article]
2018
arXiv
pre-print
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We study the problem of grounding distributional representations of texts on the visual domain, namely visual-semantic embeddings (VSE for short). Begin with an insightful adversarial attack on VSE embeddings, we show the limitation of current frameworks and image-text datasets (e.g., MS-COCO) both quantitatively and qualitatively. The large gap between the number of possible constitutions of real-world semantics and the size of parallel data, to a large extent, restricts the model to establish
arXiv:1806.10348v1
fatcat:pak6zo2i2vdzppenlrmejut74u
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... the link between textual semantics and visual concepts. We alleviate this problem by augmenting the MS-COCO image captioning datasets with textual contrastive adversarial samples. These samples are synthesized using linguistic rules and the WordNet knowledge base. The construction procedure is both syntax- and semantics-aware. The samples enforce the model to ground learned embeddings to concrete concepts within the image. This simple but powerful technique brings a noticeable improvement over the baselines on a diverse set of downstream tasks, in addition to defending known-type adversarial attacks. We release the codes at https://github.com/ExplorerFreda/VSE-C.
Learning Harmonic Molecular Representations on Riemannian Manifold
[article]
2023
arXiv
pre-print
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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
arXiv:2303.15520v1
fatcat:kbds4s4ch5co7kocchvfucnh4u
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... nfunctions 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.
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
[article]
2024
arXiv
pre-print
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., Ns}, (41e) x ∈ X ,
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids Qi Li Student Member, IEEE, Ye Shi * , Member, IEEE, Yuning Jiang, Member, ...
IEEE, Yuanming Shi, Senior Member, IEEE, Haoyu Wang, Senior Member, IEEE, and H. ...
arXiv:2403.16402v1
fatcat:etxmczkht5adtpl6eh4a4znwda
Parallel MPC for Linear Systems with State and Input Constraints
[article]
2022
arXiv
pre-print
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This paper proposes a parallelizable algorithm for linear-quadratic model predictive control (MPC) problems with state and input constraints. The algorithm itself is based on a parallel MPC scheme that has originally been designed for systems with input constraints. In this context, one contribution of this paper is the construction of time-varying yet separable constraint margins ensuring recursive feasibility and asymptotic stability of sub-optimal parallel MPC in a general setting, which
arXiv:2207.00037v1
fatcat:pt6s23hcwvfutogkxxtkq2esti
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... includes state constraints. Moreover, it is shown how to tradeoff online run-time guarantees versus the conservatism that is introduced by the tightened state constraints. The corresponding performance of the proposed method as well as the cost of the recursive feasibility guarantees is analyzed in the context of controlling a large-scale mechatronic system. This is illustrated by numerical experiments for a large-scale control system with more than 100 states and 60 control inputs leading to run-times in the millisecond range.
FoveaBox: Beyond Anchor-based Object Detector
[article]
2019
arXiv
pre-print
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We present FoveaBox, an accurate, flexible and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize the 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
arXiv:1904.03797v1
fatcat:tp5r2nyzone4bjbnbnr4z6p64i
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... ed 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 for each input image. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance of 42.1 AP on the standard COCO detection benchmark. Specially for the objects with arbitrary aspect ratios, FoveaBox brings in significant improvement compared to the anchor-based detectors. More surprisingly, when it is challenged by the stretched testing images, FoveaBox shows great robustness and generalization ability to the changed distribution of bounding box shapes. The code will be made publicly available.
A Systematic Exploration of Joint-training for Singing Voice Synthesis
[article]
2023
arXiv
pre-print
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There has been a growing interest in using end-to-end acoustic models for singing voice synthesis (SVS). Typically, these models require an additional vocoder to transform the generated acoustic features into the final waveform. However, since the acoustic model and the vocoder are not jointly optimized, a gap can exist between the two models, leading to suboptimal performance. Although a similar problem has been addressed in the TTS systems by joint-training or by replacing acoustic features
arXiv:2308.02867v1
fatcat:swxixigl4fa25fkam7t4cwwl2a
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... th a latent representation, adopting corresponding approaches to SVS is not an easy task. How to improve the joint-training of SVS systems has not been well explored. In this paper, we conduct a systematic investigation of how to better perform a joint-training of an acoustic model and a vocoder for SVS. We carry out extensive experiments and demonstrate that our joint-training strategy outperforms baselines, achieving more stable performance across different datasets while also increasing the interpretability of the entire framework.
Over-the-Air Federated Learning via Second-Order Optimization
[article]
2022
arXiv
pre-print
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Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in
arXiv:2203.15488v1
fatcat:6s2mq7lckjgxhpkpmncrprcpte
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... ssive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches.
Consistent Optimization for Single-Shot Object Detection
[article]
2019
arXiv
pre-print
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We present consistent optimization for single stage object detection. Previous works of single stage object detectors usually rely on the regular, dense sampled anchors to generate hypothesis for the optimization of the model. Through an examination of the behavior of the detector, we observe that the misalignment between the optimization target and inference configurations has hindered the performance improvement. We propose to bride this gap by consistent optimization, which is an extension
arXiv:1901.06563v2
fatcat:ztznizqzczhrholve5bpyn4q4m
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... the traditional single stage detector's optimization strategy. Consistent optimization focuses on matching the training hypotheses and the inference quality by utilizing of the refined anchors during training. To evaluate its effectiveness, we conduct various design choices based on the state-of-the-art RetinaNet detector. We demonstrate it is the consistent optimization, not the architecture design, that yields the performance boosts. Consistent optimization is nearly cost-free, and achieves stable performance gains independent of the model capacities or input scales. Specifically, utilizing consistent optimization improves RetinaNet from 39.1 AP to 40.1 AP on COCO dataset without any bells or whistles, which surpasses the accuracy of all existing state-of-the-art one-stage detectors when adopting ResNet-101 as backbone. The code will be made available.
Multi-Wavelength Spot-Array Beams Based on Tunable Dammann Grating Metasurface
2023
Photonics
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The structured light projection (SLP) method occupies a crucial position in three-dimensional (3D) imaging technology. Different working wavelengths of structured light can be employed depending on the situation. However, there are few structured lights that can be modulated based on wavelength at present. Therefore, we have comprehensively investigated and designed a Dammann grating (DG) based on metasurface, which can be controlled through multi-beam interference (MBI) to achieve a change of
doi:10.3390/photonics10020141
fatcat:jmlaappiizdtpltj234thxtvbq
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... he working wavelength. In this work, we can convert the straight waveguide to the helical waveguide by fine-tuning the related parameters of the incident lights and generate 5 × 5 diffraction spot arrays in the wavelength range of 480–510 nm and 950–1020 nm, respectively. Furthermore, the metasurfaces exhibit good performance. For example, their spread angles can be up to 44° × 44° and they can reach a conversion efficiency of over ≥50% while maintaining a contrast ratio of roughly 40%. Compared with traditional structured light, it can be used in different working wavelengths and has a broader application range in 3D sensing systems.
PHONEix: Acoustic Feature Processing Strategy for Enhanced Singing Pronunciation with Phoneme Distribution Predictor
[article]
2023
arXiv
pre-print
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Singing voice synthesis (SVS), as a specific task for generating the vocal singing voice from a music score, has drawn much attention in recent years. SVS faces the challenge that the singing has various pronunciation flexibility conditioned on the same music score. Most of the previous works of SVS can not well handle the misalignment between the music score and actual singing. In this paper, we propose an acoustic feature processing strategy, named PHONEix, with a phoneme distribution
arXiv:2303.08607v1
fatcat:l2lv6fiynvd3tiyajzrelhcn2u
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... r, to alleviate the gap between the music score and the singing voice, which can be easily adopted in different SVS systems. Extensive experiments in various settings demonstrate the effectiveness of our PHONEix in both objective and subjective evaluations.
China versus Estados Unidos na crise da pandemia: governança e política confrontam desafios sistêmicos
2020
Princípios
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. ** Yuning Shi é doutoranda em economia na SOAS. Os autores agradecem a Thanos Moraitis, Xinwen Zhang, Di Fang, Longcan Zou, Jiaxing Li e Huangnan Shen, por sua ajuda na pesquisa desse trabalho. ...
doi:10.4322/principios.2675-6609.2020.160.001
fatcat:vgjp33xdcjc6hf3mq7qvr24awu
Large benthic fluxes of dissolved iron in China coastal seas revealed by 224Ra/228Th disequilibria
2019
Geochimica et Cosmochimica Acta
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Similar problems have been identified in a core incubation experiment for Hg, a redox sensitive metal like Fe (Shi et al., 2018) . ...
Solid circles represent Fe flux estimates collected from a salt marsh in the United States (Shi et al., in press); (b) water exchange rate across the sedimentwater interface; (c) Fe concentration in the ...
doi:10.1016/j.gca.2019.06.026
fatcat:4sroztnqbndm3dc36veash45pm
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