Searcharxiv⌕ Search

arXiv subjects

Jiale Chen

Publications and source records attributed to Jiale Chen.

72 records · Page 4Linked to original sources
arXiv2024

Tent Carleson measures and superposition operators on Hardy type tent spaces

In this paper, we completely characterize the positive Borel measures $μ$ on the unit ball $\mathbb{B}_n$ of $\mathbb{C}^n$ such that the Carleson embedding from holomorphic Hardy type tent spaces $\mathcal{HT}^p_{q,α}$ into the tent spaces $T^t_s(μ)$ is bounded for all $0 -n-1$. As an application, we determine the indices $0 -n-1$ such that the inclusion $\mathcal{HT}^p_{q,α}\subset\mathcal{HT}^t_{s,β}$ holds, which allows us to completely characterize the nonlinear superposition operators between Hardy type tent spaces.

math.FA↗
arXiv2024

Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning

We introduce a novel method for acquiring boundary representations (B-Reps) of 3D CAD models which involves a two-step process: it first applies a spatial partitioning, referred to as the ``split``, followed by a ``fit`` operation to derive a single primitive within each partition. Specifically, our partitioning aims to produce the classical Voronoi diagram of the set of ground-truth (GT) B-Rep primitives. In contrast to prior B-Rep constructions which were bottom-up, either via direct primitive fitting or point clustering, our Split-and-Fit approach is top-down and structure-aware, since a Voronoi partition explicitly reveals both the number of and the connections between the primitives. We design a neural network to predict the Voronoi diagram from an input point cloud or distance field via a binary classification. We show that our network, coined NVD-Net for neural Voronoi diagrams, can effectively learn Voronoi partitions for CAD models from training data and exhibits superior generalization capabilities. Extensive experiments and evaluation demonstrate that the resulting B-Reps, consisting of parametric surfaces, curves, and vertices, are more plausible than those obtained by existing alternatives, with significant improvements in reconstruction quality. Code will be released on https://github.com/yilinliu77/NVDNet.

cs.CV↗
arXiv2024

Volterra operators between Hardy spaces of vector-valued Dirichlet series

Let $2\leq p<\infty$ and $X$ be a complex infinite-dimensional Banach space. It is proved that if $X$ is $p$-uniformly PL-convex, then there is no nontrivial bounded Volterra operator from the weak Hardy space $\mathscr{H}^{\text{weak}}_p(X)$ to the Hardy space $\mathscr{H}^+_p(X)$ of vector-valued Dirichlet series. To obtain this, a Littlewood--Paley inequality for Dirichlet series is established.

math.FA↗
arXiv2024

Littlewood-type theorems for random Dirichlet series

In this paper, we completely give the solution of the problem of Littlewood-type randomization in the Hardy and Bergman spaces of Dirichlet series. The Littlewood-type theorem for Bergman spaces of Dirichlet series is very different from the corresponding version for Hardy spaces of Dirichlet series; but also exhibits various pathological phenomena compared with the setting of analytic Bergman spaces over the unit disk, due to the fact that Dirichlet series behave as power series of infinitely many variables. A description for the inclusion between some mixed norm spaces of Dirichlet series plays an essential role in our investigation. Finally, as another application of the inclusion, we completely characterize the superposition operators between Bergman spaces of Dirichlet series.

math.FA↗
arXiv2024

Tropical cryptography III: digital signatures

We use tropical algebras as platforms for a very efficient digital signature protocol. Security relies on computational hardness of factoring one-variable tropical polynomials; this problem is known to be NP-hard.

cs.CR↗
arXiv2023

Multifunctional imaging enabled by optical bound states in the continuum with broken symmetry

For photonic crystal slab (PCS) structures, bound states in the continuum (BICs) and circularly polarized states (dubbed C-points) are important topological polarization singularities in momentum-space and have attracted burgeoning attention due to their novel topological and optical properties. In our work, the evolution of polarization singularities from BICs to C-points is achieved by breaking the in-plane C2 symmetry of a PCS structure of a square lattice with C4v symmetry. Correspondingly, a BIC is split into two C-points with opposite chirality, incurring distinct optical transmission responses with the incidence of right or left circular polarization (RCP or LCP). Harnessing such chirality selectivity of the C-points, we propose a multifunctional imaging system by integrating the designed PCS into a conventional 4-f imaging system, to realize both the edge imaging and conventional bright-field imaging, determined by the circular polarization state of the light source. In addition to multifunctional imaging, our system also provides a vivid picture about the evolution of the PCS platforms' singularities.

physics.optics↗
arXiv2023

Parametric study of helicon wave current drive in CFETR

This paper evaluates the feasibility of helicon current drive (HCD) in a hybrid scenario for the China Fusion Engineering Test Reactor (CFETR). Utilizing the GENRAY/CQL3D package, a large number of simulations (over 5 000) were conducted, with parametric scans in the antenna's poloidal position, launched parallel refractive index, and wave frequency. The analysis reveals that helicon has excellent accessibility under reactor-level conditions, and smaller n|| and higher wave frequency result in enhanced wave absorption. The simulations demonstrate an optimal launched parallel refractive index of approximately 1.6 for the CFETR hybrid scenario. The best launch position is found to be within a poloidal angle range of 25 degrees to 65 degrees. Additionally, it is preferable to have a narrow parallel refractive index spectrum for wave absorption when operating below the threshold value of Δn|| (~0.6), beyond which the effect of Δn|| on wave absorption is negligible. This study provides valuable insights into the potential application of HCD in CFETR.

physics.plasm-ph↗
arXiv2023

Fair Grading Algorithms for Randomized Exams

This paper studies grading algorithms for randomized exams. In a randomized exam, each student is asked a small number of random questions from a large question bank. The predominant grading rule is simple averaging, i.e., calculating grades by averaging scores on the questions each student is asked, which is fair ex-ante, over the randomized questions, but not fair ex-post, on the realized questions. The fair grading problem is to estimate the average grade of each student on the full question bank. The maximum-likelihood estimator for the Bradley-Terry-Luce model on the bipartite student-question graph is shown to be consistent with high probability when the number of questions asked to each student is at least the cubed-logarithm of the number of students. In an empirical study on exam data and in simulations, our algorithm based on the maximum-likelihood estimator significantly outperforms simple averaging in prediction accuracy and ex-post fairness even with a small class and exam size.

stat.ML↗
arXiv2021

Equal Affection or Random Selection: the Quality of Subjective Feedback from a Group Perspective

In the setting where a group of agents is asked a single subjective multi-choice question (e.g. which one do you prefer? cat or dog?), we are interested in evaluating the quality of the collected feedback. However, the collected statistics are not sufficient to reflect how informative the feedback is since fully informative feedback (equal affection of the choices) and fully uninformative feedback (random selection) have the same uniform statistics. Here we distinguish the above two scenarios by additionally asking for respondents' predictions about others' choices. We assume that informative respondents' predictions strongly depend on their own choices while uninformative respondents' do not. With this assumption, we propose a new definition for uninformative feedback and correspondingly design a family of evaluation metrics, called f-variety, for group-level feedback which can 1) distinguish informative feedback and uninformative feedback (separation) even if their statistics are both uniform and 2) decrease as the ratio of uninformative respondents increases (monotonicity). We validate our approach both theoretically and numerically. Moreover, we conduct two real-world case studies about 1) comparisons about athletes and 2) comparisons about stand-up comedians to show the superiority of our approach.

cs.GT↗
arXiv2020

VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge

This paper introduces VESR-Net, a method for video enhancement and super-resolution (VESR). We design a separate non-local module to explore the relations among video frames and fuse video frames efficiently, and a channel attention residual block to capture the relations among feature maps for video frame reconstruction in VESR-Net. We conduct experiments to analyze the effectiveness of these designs in VESR-Net, which demonstrates the advantages of VESR-Net over previous state-of-the-art VESR methods. It is worth to mention that among more than thousands of participants for Youku video enhancement and super-resolution (Youku-VESR) challenge, our proposed VESR-Net beat other competitive methods and ranked the first place.

cs.CV↗
arXiv2019

A Study of Multilingual Neural Machine Translation

Multilingual neural machine translation (NMT) has recently been investigated from different aspects (e.g., pivot translation, zero-shot translation, fine-tuning, or training from scratch) and in different settings (e.g., rich resource and low resource, one-to-many, and many-to-one translation). This paper concentrates on a deep understanding of multilingual NMT and conducts a comprehensive study on a multilingual dataset with more than 20 languages. Our results show that (1) low-resource language pairs benefit much from multilingual training, while rich-resource language pairs may get hurt under limited model capacity and training with similar languages benefits more than dissimilar languages; (2) fine-tuning performs better than training from scratch in the one-to-many setting while training from scratch performs better in the many-to-one setting; (3) the bottom layers of the encoder and top layers of the decoder capture more language-specific information, and just fine-tuning these parts can achieve good accuracy for low-resource language pairs; (4) direct translation is better than pivot translation when the source language is similar to the target language (e.g., in the same language branch), even when the size of direct training data is much smaller; (5) given a fixed training data budget, it is better to introduce more languages into multilingual training for zero-shot translation.

cs.CL↗
arXiv2019

Progressive Image Inpainting with Full-Resolution Residual Network

Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes. However, they fail to generate plausible textures inside damaged area because there lacks surrounding information. A progressive inpainting approach would be advantageous for eliminating central blurriness, i.e., restoring well and then updating masks. In this paper, we propose full-resolution residual network (FRRN) to fill irregular holes, which is proved to be effective for progressive image inpainting. We show that well-designed residual architecture facilitates feature integration and texture prediction. Additionally, to guarantee completion quality during progressive inpainting, we adopt N Blocks, One Dilation strategy, which assigns several residual blocks for one dilation step. Correspondingly, a step loss function is applied to improve the performance of intermediate restorations. The experimental results demonstrate that the proposed FRRN framework for image inpainting is much better than previous methods both quantitatively and qualitatively. Our codes are released at: \url{https://github.com/ZongyuGuo/Inpainting_FRRN}.

eess.IV↗
arXiv2019

Multilingual Neural Machine Translation with Language Clustering

Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the training process, reducing online maintenance costs, and enhancing low-resource and zero-shot translation. Given there are thousands of languages in the world and some of them are very different, it is extremely burdensome to handle them all in a single model or use a separate model for each language pair. Therefore, given a fixed resource budget, e.g., the number of models, how to determine which languages should be supported by one model is critical to multilingual NMT, which, unfortunately, has been ignored by previous work. In this work, we develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space. In particular, we obtain the embedding vectors of all the languages by training a universal neural machine translation model. Our experiments on 23 languages show that the first clustering method is simple and easy to understand but leading to suboptimal translation accuracy, while the second method sufficiently captures the relationship among languages well and improves the translation accuracy for almost all the languages over baseline methods

cs.CL↗
arXiv2019

Language Graph Distillation for Low-Resource Machine Translation

Neural machine translation on low-resource language is challenging due to the lack of bilingual sentence pairs. Previous works usually solve the low-resource translation problem with knowledge transfer in a multilingual setting. In this paper, we propose the concept of Language Graph and further design a novel graph distillation algorithm that boosts the accuracy of low-resource translations in the graph with forward and backward knowledge distillation. Preliminary experiments on the TED talks multilingual dataset demonstrate the effectiveness of our proposed method. Specifically, we improve the low-resource translation pair by more than 3.13 points in terms of BLEU score.

cs.CL↗
arXiv2019

Development of CFETR scenarios with self-consistent core-pedestal coupled simulations

This paper develops two non-inductive steady state scenarios for larger size configuration of China Fusion Engineering Test Reactor (CFETR) with integrated modeling simulations. A self-consistent core-pedestal coupled workflow for CFETR is developed under integrated modeling framework OMFIT, which allows more accurate evaluation of CFETR performance. The workflow integrates equilibrium code EFIT, transport codes ONETWO and TGYRO, and pedestal code EPED. A fully non-inductive baseline phase I scenario is developed with the workflow, which satisfies the minimum goal of Fusion Nuclear Science Facility. Compared with previous work, which proves the larger size and higher toroidal field CFETR configuration than has the advantages of reducing heating and current drive requirements, lowering divertor and wall power loads, allowing higher bootstrap current fraction and better confinement. A fully non-inductive high-performance phase II scenario is developed, which explores the alpha-particle dominated self-heating regime. Phase II scenario achieves the target of fusion power Pfus>1GW and fusion gain Qfus>20, and it largely reduces auxiliary heating and current drive power. Moreover, the large neutron production of phase II increases the energy generation power and tritium breeding rate.

physics.plasm-ph↗
arXiv2019

Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments

Deep Deterministic Policy Gradient (DDPG) has been proved to be a successful reinforcement learning (RL) algorithm for continuous control tasks. However, DDPG still suffers from data insufficiency and training inefficiency, especially in computationally complex environments. In this paper, we propose Asynchronous Episodic DDPG (AE-DDPG), as an expansion of DDPG, which can achieve more effective learning with less training time required. First, we design a modified scheme for data collection in an asynchronous fashion. Generally, for asynchronous RL algorithms, sample efficiency or/and training stability diminish as the degree of parallelism increases. We consider this problem from the perspectives of both data generation and data utilization. In detail, we re-design experience replay by introducing the idea of episodic control so that the agent can latch on good trajectories rapidly. In addition, we also inject a new type of noise in action space to enrich the exploration behaviors. Experiments demonstrate that our AE-DDPG achieves higher rewards and requires less time consuming than most popular RL algorithms in Learning to Run task which has a computationally complex environment. Not limited to the control tasks in computationally complex environments, AE-DDPG also achieves higher rewards and 2- to 4-fold improvement in sample efficiency on average compared to other variants of DDPG in MuJoCo environments. Furthermore, we verify the effectiveness of each proposed technique component through abundant ablation study.

cs.LG↗
arXiv2018

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.

cs.LG↗