Searcharxiv⌕ Search

arXiv subjects

Bin Chen

Publications and source records attributed to Bin Chen.

At least 379 records · Page 21Linked to original sources
arXiv2023

Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression

In this paper, we propose a progressive learning paradigm for transformer-based variable-rate image compression. Our approach covers a wide range of compression rates with the assistance of the Layer-adaptive Prompt Module (LPM). Inspired by visual prompt tuning, we use LPM to extract prompts for input images and hidden features at the encoder side and decoder side, respectively, which are fed as additional information into the Swin Transformer layer of a pre-trained transformer-based image compression model to affect the allocation of attention region and the bits, which in turn changes the target compression ratio of the model. To ensure the network is more lightweight, we involves the integration of prompt networks with less convolutional layers. Exhaustive experiments show that compared to methods based on multiple models, which are optimized separately for different target rates, the proposed method arrives at the same performance with 80% savings in parameter storage and 90% savings in datasets. Meanwhile, our model outperforms all current variable bitrate image methods in terms of rate-distortion performance and approaches the state-of-the-art fixed bitrate image compression methods trained from scratch.

cs.CV↗
arXiv2023

Perceptual Image Compression with Cooperative Cross-Modal Side Information

The explosion of data has resulted in more and more associated text being transmitted along with images. Inspired by from distributed source coding, many works utilize image side information to enhance image compression. However, existing methods generally do not consider using text as side information to enhance perceptual compression of images, even though the benefits of multimodal synergy have been widely demonstrated in research. This begs the following question: How can we effectively transfer text-level semantic dependencies to help image compression, which is only available to the decoder? In this work, we propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features. This is done by predicting a semantic mask to guide the learned text-adaptive affine transformation at the pixel level. Furthermore, we design a text-conditional generative adversarial networks to improve the perceptual quality of reconstructed images. Extensive experiments involving four datasets and ten image quality assessment metrics demonstrate that the proposed approach achieves superior results in terms of rate-perception trade-off and semantic distortion.

cs.CV↗
arXiv2023

Contractibility of the solution sets for set optimization problems

This paper aims at investigating the contractibility of the solution sets for set optimization problems by utilizing strictly quasi cone-convexlikeness, which is an assumption weaker than strictly cone-convexity, strictly cone-quasiconvexity and strictly naturally quasi cone-convexity. We establish the contractibility of l-minimal, l-weak minimal, u-minimal and u-weak minimal solution sets for set optimization problems by using the star-shape sets and the nonlinear scalarizing functions for sets. Moreover, we also discuss the arcwise connectedness and the contractibility of p-minimal and p-weak minimal solution sets for set optimization problems by using the scalarization technique. Finally, our main results are applied to the contractibility of the solution sets for vector optimization problems.

math.OC↗
arXiv2023

Imaging a Semi-Analytical Jet model Generated by 3D GRMHD Simulation

Employing 3D GRMHD simulation, we study the images of a geometrically thin jet, whose emissions concentrate on its surface, for accretion system surrounding a central spinning BH. By introducing a strong magnetic field, we observe three phases of BH accretion evolution: (a) initially, both the accretion rate and the magnetic flux on the horizon gradually increase; (b) at an intermediate stage, the magnetic flux approximately reaches saturation, and a jet forms via the Blandford-Znajek (BZ) mechanism; (c) ultimately, the entire system achieves a dynamic equilibrium, and a magnetically arrested disk (MAD) forms. We carefully study the jet images during the saturation and MAD regimes at various frequencies and from different observational angles. We reveal the presence of U-shaped brighter lines near the jet surface boundaries, which can be attributed to the photons whose trajectories skim over the jet surface. The existence of these brighter lines is a unique feature of a geometrically thin jet. Moreover, we notice that the jet images are relatively insensitive to the observed frequencies of interest. Additionally, we observe that the time-averaged images for the highly oscillating MAD regime show only slight differences from those of the saturation regime.

astro-ph.HE↗
arXiv2023

Reducing Ambiguities in Line-based Density Plots by Image-space Colorization

Line-based density plots are used to reduce visual clutter in line charts with a multitude of individual lines. However, these traditional density plots are often perceived ambiguously, which obstructs the user's identification of underlying trends in complex datasets. Thus, we propose a novel image space coloring method for line-based density plots that enhances their interpretability. Our method employs color not only to visually communicate data density but also to highlight similar regions in the plot, allowing users to identify and distinguish trends easily. We achieve this by performing hierarchical clustering based on the lines passing through each region and mapping the identified clusters to the hue circle using circular MDS. Additionally, we propose a heuristic approach to assign each line to the most probable cluster, enabling users to analyze density and individual lines. We motivate our method by conducting a small-scale user study, demonstrating the effectiveness of our method using synthetic and real-world datasets, and providing an interactive online tool for generating colored line-based density plots.

cs.GR↗
arXiv2023

Polarized images of charged particles in vortical motions around a magnetized Kerr black hole

In this work, we study the images of a Kerr black hole (BH) immersed in uniform magnetic fields, illuminated by the synchrotron radiation of charged particles in the jet. We particularly focus on the spontaneously vortical motions (SVMs) of charged particles in the jet region and investigate the polarized images of electromagnetic radiations from the trajectories along SVMs. We notice that there is a critical value $ω_c$ for charged particle released at a given initial position and subjected an outward force, and once $|qB_0/m|=|ω_B|>|ω_c|$ charged particles can move along SVMs in the jet region. We obtain the polarized images of the electromagnetic radiations from the trajectories along SVMs. Our simplified model suggests that the SVM radiations can act as the light source to illuminate the BH and form a photon ring structure.

gr-qc↗
arXiv2023

Co-regulation of multi-cellular swirling by individual cellular chirality and boundaries

To understand the relationship between the chirality of individual cells and that of tissues and organisms, we have developed a chiral polarized particle model to investigate the movement of cell populations on substrates. Our model analysis indicates that cells with the same chirality can form distinct chiral patterns on ring-shaped or rectangular substrates. Our model analysis also reveals the importance of coordination between boundary features and individual cellular chirality in regulating the movement of cell populations. This work provides valuable insights into comprehending the intricate connection between the chirality of single cells and that of tissues and organisms.

physics.bio-ph↗
arXiv2023

A new connectivity bound for a tournament to be highly linked

A digraph $D$ is $k$-linked if for any pair of two disjoint sets $\{x_{1},x_{2},\ldots,x_{k}\}$ and $\{y_{1},y_{2},\ldots,y_{k}\}$ of vertices in $D$, there exist vertex disjoint dipaths $P_{1},P_{2},\ldots,P_{k}$ such that $P_{i}$ is a dipath from $x_{i}$ to $y_{i}$ for each $i\in[k]$. Pokrovskiy (JCTB, 2015) confirmed a conjecture of Kühn et al. (Proc. Lond. Math. Soc., 2014) by verifying that every $452k$-connected tournament is $k$-linked. Meng et al. (Eur. J. Comb., 2021) improved this upper bound by showing that any $(40k-31)$-connected tournament is $k$-linked. In this paper, we show a better upper bound by proving that every $\lceil 12.5k-6\rceil$-connected tournament with minimum out-degree at least $21k-14$ is $k$-linked. Furthermore, we improve a key lemma that was first introduced by Pokrovskiy (JCTB, 2015) and later enhanced by Meng et al. (Eur. J. Comb., 2021).

math.CO↗
arXiv2023

Bending short DNAs as transversely isotropic rings in series

Despite the significance of the high flexibility exhibited by short DNAs, there remains an incomplete understanding of their anomalous persistence length. In this study, we propose a novel approach wherein each fundamental characteristic of gene sequences within short DNAs is modeled as a transversely isotropic ring. Our comprehensive model analysis not only successfully replicates the observed high flexibility of short DNAs but also sheds light on the impact of sequence dependence, aligning with experimental findings. Furthermore, our analysis suggests that the bending behavior of short DNAs can be effectively described by the Timoshenko beam theory, accounting for shear considerations.

physics.bio-ph↗
arXiv2023

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.

cs.CL↗
arXiv2023

USDC: Unified Static and Dynamic Compression for Visual Transformer

Visual Transformers have achieved great success in almost all vision tasks, such as classification, detection, and so on. However, the model complexity and the inference speed of the visual transformers hinder their deployments in industrial products. Various model compression techniques focus on directly compressing the visual transformers into a smaller one while maintaining the model performance, however, the performance drops dramatically when the compression ratio is large. Furthermore, several dynamic network techniques have also been applied to dynamically compress the visual transformers to obtain input-adaptive efficient sub-structures during the inference stage, which can achieve a better trade-off between the compression ratio and the model performance. The upper bound of memory of dynamic models is not reduced in the practical deployment since the whole original visual transformer model and the additional control gating modules should be loaded onto devices together for inference. To alleviate two disadvantages of two categories of methods, we propose to unify the static compression and dynamic compression techniques jointly to obtain an input-adaptive compressed model, which can further better balance the total compression ratios and the model performances. Moreover, in practical deployment, the batch sizes of the training and inference stage are usually different, which will cause the model inference performance to be worse than the model training performance, which is not touched by all previous dynamic network papers. We propose a sub-group gates augmentation technique to solve this performance drop problem. Extensive experiments demonstrate the superiority of our method on various baseline visual transformers such as DeiT, T2T-ViT, and so on.

cs.CV↗
arXiv2023

ASP: Automatic Selection of Proxy dataset for efficient AutoML

Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden as the amount of training data is proportional to the training time. In addition, a well-behaved model requires repeated trials of different structure designs and hyper-parameters, which may take a large amount of time even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms and neural architecture search (NAS) algorithms. In this paper, we propose an Automatic Selection of Proxy dataset framework (ASP) aimed to dynamically find the informative proxy subsets of training data at each epoch, reducing the training data size as well as saving the AutoML processing time. We verify the effectiveness and generalization of ASP on CIFAR10, CIFAR100, ImageNet16-120, and ImageNet-1k, across various public model benchmarks. The experiment results show that ASP can obtain better results than other data selection methods at all selection ratios. ASP can also enable much more efficient AutoML processing with a speedup of 2x-20x while obtaining better architectures and better hyper-parameters compared to utilizing the entire dataset.

cs.LG↗
arXiv2023

Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison

The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and generation abilities of commercial ones. In recent times, a number of models have emerged, claiming performance near that of GPT-3.5 or GPT-4 through various instruction-tuning methods. As practitioners of Text-to-SQL parsing, we are grateful for their valuable contributions to open-source research. However, it is important to approach these claims with a sense of scrutiny and ascertain the actual effectiveness of these models. Therefore, we pit six popular large language models against each other, systematically evaluating their Text-to-SQL parsing capability on nine benchmark datasets with five different prompting strategies, covering both zero-shot and few-shot scenarios. Regrettably, the open-sourced models fell significantly short of the performance achieved by closed-source models like GPT-3.5, highlighting the need for further work to bridge the performance gap between these models.

cs.CL↗
arXiv2023

Analytical Model of Nonlinear Fiber Propagation for General Dual-Polarization Four-Dimensional Modulation Format

Coherent dual-polarization (DP) optical transmission systems encode information on the four available degrees of freedom of an optical field: the two polarization states, each with two quadrature components. Such systems naturally operate based on a four-dimensional (4D) signal space. Having a general analytical model to accurately estimate nonlinear interference (NLI) is key to analyze such transmission systems as well as to study how different DP-4D formats are affected by NLI. However, the available models in the literature are not completely general. They either do not apply to the entire DP-4D formats or do not consider all the NLI contributions. In this paper, we develop a model that applies to all DP-4D modulation formats with independent symbols. Our model takes self-channel interference, cross-channel interference and multiple-channel interference effects into account. As an application of our model, we further study the effects of signal-noise interactions in long-haul transmission via the proposed model. When compared to previous results in the literature, our model is more accurate at predicting the contribution of NLI for both low and high dispersion fibers in single- and multi-channel transmission systems. For the NLI, we report an average gap from split step Fourier simulation results below 0.15 dB. The simulation results further show that by considering signal-noise interactions, the proposed model in long-haul transmission can reduce the transmission reach prediction error by 4%.

eess.SP↗
arXiv2023

SIAD: Self-supervised Image Anomaly Detection System

Recent trends in AIGC effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios. Benefit from the self-supervised learning, SsaA is effective to establish a visual inspection application for the whole life-cycle of manufacturing. In the early stage, with only the anomaly-free data, the unsupervised algorithms are adopted to process the pretext task and generate coarse labels for the following data. Then supervised algorithms are trained for the downstream task. With user-friendly web-based interfaces, SsaA is very convenient to integrate and deploy both of the unsupervised and supervised algorithms. So far, the SsaA system has been adopted for some real-life industrial applications.

cs.CV↗
arXiv2023

Reflected entropy and Markov gap in Lifshitz theories

We study the reflected entropy in $(1+1)$--dimensional Lifshitz field theory whose groundstate is described by a quantum mechanical model. Starting from tripartite Lifshitz groundstates, both critical and gapped, we derive explicit formulas for the Rényi reflected entropies reduced to two adjacent or disjoint intervals, directly in the continuum. We show that the reflected entropy in Lifshitz theory does not satisfy monotonicity, in contrast to what is observed for free relativistic fields. We analytically compute the full reflected entanglement spectrum for two disjoint intervals, finding a discrete set of eigenvalues which is that of a thermal density matrix. Furthermore, we investigate the Markov gap, defined as the difference between reflected entropy and mutual information, and find it to be universal and nonvanishing, signaling irreducible tripartite entanglement in Lifshitz groundstates. We also obtain analytical results for the reflected entropies and the Markov gap in $2 + 1$ dimensions. Finally, as a byproduct of our results on reflected entropy, we provide exact formulas for two other entanglement-related quantities, namely the computable cross-norm negativity and the operator entanglement entropy.

hep-th↗
arXiv2023

Detection of long-lasting aurora-like radio emission above a sunspot

Auroral radio emissions in planetary magnetospheres typically feature highly polarized, intense radio bursts, usually attributed to electron cyclotron maser (ECM) emission from energetic electrons in the planetary polar region that features a converging magnetic field. Similar bursts have been observed in magnetically active low-mass stars and brown dwarfs, often prompting analogous interpretations. Here we report observations of long-lasting solar radio bursts with high brightness temperature, wide bandwidth, and high circular polarization fraction akin to these auroral/exo-auroral radio emissions, albeit two to three orders of magnitude weaker than those on certain low-mass stars. Spatially, spectrally, and temporally resolved analysis suggests that the source is located above a sunspot where a strong, converging magnetic field is present. The source morphology and frequency dispersion are consistent with ECM emission due to precipitating energetic electrons produced by recurring flares nearby. Our findings offer new insights into the origin of such intense solar radio bursts and may provide an alternative explanation for auroral-like radio emissions on other flare stars with large starspots.

astro-ph.SR↗