arXiv subjects

Zihan Zhou

Publications and source records attributed to Zihan Zhou.

At least 127 records · Page 7Linked to original sources

arXiv2024

Model-Free, Regret-Optimal Best Policy Identification in Online CMDPs

This paper considers the best policy identification (BPI) problem in online Constrained Markov Decision Processes (CMDPs). We are interested in algorithms that are model-free, have low regret, and identify an approximately optimal policy with a high probability. Existing model-free algorithms for online CMDPs with sublinear regret and constraint violation do not provide any convergence guarantee to an optimal policy and provide only average performance guarantees when a policy is uniformly sampled at random from all previously used policies. In this paper, we develop a new algorithm, named Pruning-Refinement-Identification (PRI), based on a fundamental structural property of CMDPs proved before, which we call limited stochasticity. The property says for a CMDP with $N$ constraints, there exists an optimal policy with at most $N$ stochastic decisions. The proposed algorithm first identifies at which step and in which state a stochastic decision has to be taken and then fine-tunes the distributions of these stochastic decisions. PRI achieves trio objectives: (i) PRI is a model-free algorithm; and (ii) it outputs an approximately optimal policy with a high probability at the end of learning; and (iii) PRI guarantees $\tilde{\mathcal{O}}(H\sqrt{K})$ regret and constraint violation, which significantly improves the best existing regret bound $\tilde{\mathcal{O}}(H^4 \sqrt{SA}K^{\frac{4}{5}})$ under a model-free algorithm, where $H$ is the length of each episode, $S$ is the number of states, $A$ is the number of actions, and the total number of episodes during learning is $2K+\tilde{\cal O}(K^{0.25}).$ We further present a matching lower via an example that shows under any online learning algorithm, there exists a well-separated CMDP instance such that either the regret or violation has to be $Ω(H\sqrt{K}),$ which matches the upper bound by a polylogarithmic factor.

cs.LG↗

arXiv2024

Revisiting the matching of black hole tidal responses: a systematic study of relativistic and logarithmic corrections

The worldline effective field theory (EFT) gives a gauge-invariant definition of black hole conservative tidal responses (Love numbers), dissipation numbers, and their spin-0 and spin-1 analogs. In the first part of this paper we show how the EFT allows us to circumvent the source/response ambiguity without having to use the analytic continuation prescription. The source/response ambiguity appears if Post-Newtonian (PN)corrections to external sources overlap with the response. However, these PN corrections can be clearly identified and isolated using the EFT. We illustrate that by computing static one-point functions of various external fields perturbing the four-dimensional Schwarzschild geometry. Upon resumming all relevant Feynman diagrams, we find that the PN terms that may mimic the response actually vanish for static black holes. Thus, the extraction of Love numbers from matching the EFT and general relativity (GR) calculations is completely unambiguous, and it implies that the Love numbers vanish identically for all types of perturbations. We also study in detail another type of fine tuning in the EFT, the absence of Love numbers' running. We show that logarithmic corrections to Love numbers do stem from individual loop diagrams in generic gauges, but cancel after all diagrams are summed over. In the particular cases of spin-0 and spin-2 fields the logarithms are completely absent if one uses the Kaluza-Klein metric decomposition. In the second part of the paper we compute frequency-dependent dissipative response contributions to the one-point functions using the Schwinger-Keldysh formalism. We extract black hole dissipation numbers by comparing the one-point functions in the EFT and GR. Our results are in perfect agreement with those obtained from a manifestly gauge-invariant matching of absorption cross-sections.

hep-th↗

arXiv2024

Dynamical Tidal Response of Kerr Black Holes from Scattering Amplitudes

We match scattering amplitudes in point particle effective field theory (EFT) and general relativity to extract low frequency dynamical tidal responses of rotating (Kerr) black holes to all orders in spin. In the conservative sector, we study local worldline couplings that correspond to the time-derivative expansion of the black hole tidal response function. These are dynamical (frequency-dependent) generalizations of the static Love numbers. We identify and extract couplings of three types of subleading local worldline operators: the curvature time derivative terms, the spin - curvature time derivative couplings, and quadrupole - octupole mixing operators that arise due to the violation of spherical symmetry. The first two subleading couplings are non-zero and exhibit a classical renormalization group running; we explicitly present their scheme-independent beta functions. The conservative mixing terms, however, vanish as a consequence of vanishing static Love numbers. In the non-conservative sector, we match the dissipation numbers at next-to-leading and next-to-next-to leading orders in frequency. In passing, we identify terms in the general relativity absorption probabilities that originate from tails and short-scale logarithmic corrections to the lowest order dissipation contributions.

hep-th↗

arXiv2024

On the $A_α$-index of graphs with given order and dissociation number

Given a graph $G,$ a subset of vertices is called a maximum dissociation set of $G$ if it induces a subgraph with vertex degree at most 1, and the subset has maximum cardinality. The cardinality of a maximum dissociation set is called the dissociation number of $G$. The adjacency matrix and the degree diagonal matrix of $G$ are denoted by $A(G)$ and $D(G),$ respectively. In 2017, Nikiforov proposed the $A_α$-matrix: $A_α(G)=αD(G)+(1-α)A(G),$ where $α\in[0,1].$ The largest eigenvalue of this novel matrix is called the $A_α$-index of $G.$ In this paper, we firstly determine the connected graph (resp. bipartite graph, tree) having the largest $A_α$-index over all connected graphs (resp. bipartite graphs, trees) with fixed order and dissociation number. Secondly, we describe the structure of all the $n$-vertex graphs having the minimum $A_α$-index with dissociation number $τ$, where $τ\geqslant\lceil\frac{2}{3}n\rceil.$ Finally, we identify all the connected $n$-vertex graphs with dissociation number $τ\in\{2,\lceil\frac{2}{3}n\rceil,n-1,n-2\}$ having the minimum $A_α$-index.

math.CO↗

arXiv2024

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization

Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns. Despite its importance, the use of Large Language Models (LLMs) for scientific data visualization remains rather unexplored. In this study, we introduce MatPlotAgent, an efficient model-agnostic LLM agent framework designed to automate scientific data visualization tasks. Leveraging the capabilities of both code LLMs and multi-modal LLMs, MatPlotAgent consists of three core modules: query understanding, code generation with iterative debugging, and a visual feedback mechanism for error correction. To address the lack of benchmarks in this field, we present MatPlotBench, a high-quality benchmark consisting of 100 human-verified test cases. Additionally, we introduce a scoring approach that utilizes GPT-4V for automatic evaluation. Experimental results demonstrate that MatPlotAgent can improve the performance of various LLMs, including both commercial and open-source models. Furthermore, the proposed evaluation method shows a strong correlation with human-annotated scores.

cs.CL↗

arXiv2024

On Diffusion Process in SE(3)-invariant Space

Sampling viable 3D structures (e.g., molecules and point clouds) with SE(3)-invariance using diffusion-based models proved promising in a variety of real-world applications, wherein SE(3)-invariant properties can be naturally characterized by the inter-point distance manifold. However, due to the non-trivial geometry, we still lack a comprehensive understanding of the diffusion mechanism within such SE(3)-invariant space. This study addresses this gap by mathematically delineating the diffusion mechanism under SE(3)-invariance, via zooming into the interaction behavior between coordinates and the inter-point distance manifold through the lens of differential geometry. Upon this analysis, we propose accurate and projection-free diffusion SDE and ODE accordingly. Such formulations enable enhancing the performance and the speed of generation pathways; meanwhile offering valuable insights into other systems incorporating SE(3)-invariance.

cs.LG↗

arXiv2024

RePLan: Robotic Replanning with Perception and Language Models

Advancements in large language models (LLMs) have demonstrated their potential in facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs have also been able to generate reward functions for low-level robot actions, effectively bridging the interface between high-level planning and low-level robot control. However, the challenge remains that even with syntactically correct plans, robots can still fail to achieve their intended goals due to imperfect plans or unexpected environmental issues. To overcome this, Vision Language Models (VLMs) have shown remarkable success in tasks such as visual question answering. Leveraging the capabilities of VLMs, we present a novel framework called Robotic Replanning with Perception and Language Models (RePLan) that enables online replanning capabilities for long-horizon tasks. This framework utilizes the physical grounding provided by a VLM's understanding of the world's state to adapt robot actions when the initial plan fails to achieve the desired goal. We developed a Reasoning and Control (RC) benchmark with eight long-horizon tasks to test our approach. We find that RePLan enables a robot to successfully adapt to unforeseen obstacles while accomplishing open-ended, long-horizon goals, where baseline models cannot, and can be readily applied to real robots. Find more information at https://replan-lm.github.io/replan.github.io/

cs.RO↗

arXiv2024

On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space

Diffusion-based generative models in SE(3)-invariant space have demonstrated promising performance in molecular conformation generation, but typically require solving stochastic differential equations (SDEs) with thousands of update steps. Till now, it remains unclear how to effectively accelerate this procedure explicitly in SE(3)-invariant space, which greatly hinders its wide application in the real world. In this paper, we systematically study the diffusion mechanism in SE(3)-invariant space via the lens of approximate errors induced by existing methods. Thereby, we develop more precise approximate in SE(3) in the context of projected differential equations. Theoretical analysis is further provided as well as empirical proof relating hyper-parameters with such errors. Altogether, we propose a novel acceleration scheme for generating molecular conformations in SE(3)-invariant space. Experimentally, our scheme can generate high-quality conformations with 50x--100x speedup compared to existing methods.

physics.comp-ph↗

arXiv2023

High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting

Purpose: This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time. Methods: We developed 3D ViSTa-MRF, which combined Visualization of Short Transverse relaxation time component (ViSTa) technique with MR Fingerprinting (MRF), to achieve high-fidelity whole-brain MWF and T1/T2/PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling spiral-projection acquisition and joint spatial-temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF mapping was achieved without a need for multi-compartment fitting that could introduce bias and/or noise from additional assumptions or priors. Results: The in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi-parametric mapping with high SNR and good quality. The in-vivo results of 1mm- and 0.66mm-iso datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30x slower with lower SNR. Furthermore, we applied the proposed method to enable 5-minute whole-brain 1mm-iso assessment of MWF and T1/T2/PD mappings for infant brain development and for post-mortem brain samples. Conclusions: In this work, we have developed a 3D ViSTa-MRF technique that enables the acquisition of whole-brain MWF, quantitative T1, T2, and PD maps at 1mm and 0.66mm isotropic resolution in 5 and 15 minutes, respectively. This advancement allows for quantitative investigations of myelination changes in the brain.

physics.med-ph↗

arXiv2023

Sequence adaptive field-imperfection estimation (SAFE): retrospective estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced MRF quantification

$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and robustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and $B_0$ calibration scans can mitigate this but add scan time and cannot be applied retrospectively to previously collected data. Here, we proposed a calibration-free sequence-adaptive deep-learning framework, to estimate and correct for $B_1^+$ and $B_0$ effects of any MRF sequence. We demonstrate its capability on arbitrary MRF sequences at 3T, where no training data were previously obtained. Such approach can be applied to any previously-acquired and future MRF-scans. The flexibility in directly applying this framework to other quantitative sequences is also highlighted.

eess.IV↗

arXiv2023

Molecular Conformation Generation via Shifting Scores

Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule. Generating molecular conformation via diffusion requires learning to reverse a noising process. Diffusion on inter-atomic distances instead of conformation preserves SE(3)-equivalence and shows superior performance compared to alternative techniques, whereas related generative modelings are predominantly based upon heuristical assumptions. In response to this, we propose a novel molecular conformation generation approach driven by the observation that the disintegration of a molecule can be viewed as casting increasing force fields to its composing atoms, such that the distribution of the change of inter-atomic distance shifts from Gaussian to Maxwell-Boltzmann distribution. The corresponding generative modeling ensures a feasible inter-atomic distance geometry and exhibits time reversibility. Experimental results on molecular datasets demonstrate the advantages of the proposed shifting distribution compared to the state-of-the-art.

physics.comp-ph↗

arXiv2023

NeRF-Enhanced Outpainting for Faithful Field-of-View Extrapolation

In various applications, such as robotic navigation and remote visual assistance, expanding the field of view (FOV) of the camera proves beneficial for enhancing environmental perception. Unlike image outpainting techniques aimed solely at generating aesthetically pleasing visuals, these applications demand an extended view that faithfully represents the scene. To achieve this, we formulate a new problem of faithful FOV extrapolation that utilizes a set of pre-captured images as prior knowledge of the scene. To address this problem, we present a simple yet effective solution called NeRF-Enhanced Outpainting (NEO) that uses extended-FOV images generated through NeRF to train a scene-specific image outpainting model. To assess the performance of NEO, we conduct comprehensive evaluations on three photorealistic datasets and one real-world dataset. Extensive experiments on the benchmark datasets showcase the robustness and potential of our method in addressing this challenge. We believe our work lays a strong foundation for future exploration within the research community.

cs.CV↗

arXiv2023

PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs

In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practice where the input drawings created by human designers are imperfect. To overcome this difficulty, we leverage the attention mechanism in a Transformer-based sequence generation model to learn flexible mappings between the input and output. Further, we design shape programs which are suitable for generating the objects of interest to boost the reconstruction accuracy and facilitate CAD modeling applications. Experiments on a new benchmark dataset show that our method significantly outperforms existing ones when the inputs are noisy or incomplete.

cs.CV↗

arXiv2023

A Review of Gaps between Web 4.0 and Web 3.0 Intelligent Network Infrastructure

World Wide Web is speeding up its pace into an intelligent and decentralized ecosystem, as seen in the campaign of Web 3.0 and forthcoming Web 4.0. Marked by the Europe Commission's latest mention of Web 4.0, a race towards strategic Web 4.0 success has started. Web 4.0 is committed to bringing the next technological transition with an open, secure, trustworthy fairness and digital ecosystem for individuals and businesses in private and public sectors. Despite overlapping scopes and objectives of Web 3.0 and Web 4.0 from academic and industrial perspectives, there are distinct and definitive features and gaps for the next generation of WWW. In this review, a brief introduction to WWW development unravels the entangled but consistent requirement of a more vivid web experience, enhancing human-centric experience in both societal and technical aspects. Moreover, the review brings a decentralized intelligence prospect of view on native AI entities for Web 4.0, envisioning sustainable, autonomous and decentralized AI services for the entire Web 4.0 environment, powering a self-sustainable Decentralized Physical and Software Infrastructure for Computing Force Network, Semantic Network, Virtual/Mixed Reality, and Privacy-preserving content presumption. The review aims to reveal that Web 4.0 offers native intelligence with focused thinking on utilizing decentralized physical infrastructure, in addition to sole requirements on decentralization, bridging the gap between Web 4.0 and Web 3.0 advances with the latest future-shaping blockchain-enabled computing and network routing protocols.

cs.NI↗

arXiv2023

Scene Separation & Data Selection: Temporal Segmentation Algorithm for Real-Time Video Stream Analysis

We present 2SDS (Scene Separation and Data Selection algorithm), a temporal segmentation algorithm used in real-time video stream interpretation. It complements CNN-based models to make use of temporal information in videos. 2SDS can detect the change between scenes in a video stream by com-paring the image difference between two frames. It separates a video into segments (scenes), and by combining itself with a CNN model, 2SDS can select the optimal result for each scene. In this paper, we will be discussing some basic methods and concepts behind 2SDS, as well as presenting some preliminary experiment results regarding 2SDS. During these experiments, 2SDS has achieved an overall accuracy of over 90%.

cs.CV↗

arXiv2023

Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward

We propose Structured Exploration with Achievements (SEA), a multi-stage reinforcement learning algorithm designed for achievement-based environments, a particular type of environment with an internal achievement set. SEA first uses offline data to learn a representation of the known achievements with a determinant loss function, then recovers the dependency graph of the learned achievements with a heuristic algorithm, and finally interacts with the environment online to learn policies that master known achievements and explore new ones with a controller built with the recovered dependency graph. We empirically demonstrate that SEA can recover the achievement structure accurately and improve exploration in hard domains such as Crafter that are procedurally generated with high-dimensional observations like images.

cs.LG↗

arXiv2023

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

cs.HC↗

arXiv2022

Highly non-Gaussian tails and primordial black holes from single-field inflation

For primordial perturbations, deviations from Gaussian statistics on the tail of the probability distribution can be associated with non-perturbative effects of inflation. In this paper, we present some particular examples in which the tail of the distribution becomes highly non-Gaussian although the statistics remains almost Gaussian in the perturbative regime. We begin with an extension of the ultra-slow-roll inflation that incorporates a transition process, where the inflaton climbs up a tiny potential step at the end of the non-attractor stage before it converges to the slow-roll attractor. Through this example, we identify the key role of the off-attractor behaviour for the upward-step transition, and then extend the analysis to another type of the transition with two slow-roll stages connected by a tiny step. We perform both the perturbative and non-perturbative analyses of primordial fluctuations generated around the step in detail, and show that the tiny but nontrivial transition may affect large perturbations in the tail of the distribution, while the perturbative non-Gaussianity remains small. Our result indicates that the non-Gaussian tails can have rich phenomenology which has been overlooked in conventional analyses. We also study the implications of this non-Gaussian tail for the formation of primordial black holes, and find that their mass fraction can be parametrically amplified by several orders of magnitudes in comparison with the case of the Gaussian distribution. Additionally, we also discuss a mechanism of primordial black holes formation for this upward step inflation model by trapping the inflaton in the bottom of the step.

astro-ph.CO↗