arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 343 records · Page 19Linked to original sources

arXiv2024

Simulating unsteady fluid flows on a superconducting quantum processor

Recent advancements of intermediate-scale quantum processors have triggered tremendous interest in the exploration of practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a good candidate for showing quantum utility. Here, we report an experiment on the digital simulation of unsteady flows, which consists of quantum encoding, evolution, and detection of flow states, with a superconducting quantum processor. The quantum algorithm is based on the Hamiltonian simulation using the hydrodynamic formulation of the Schrödinger equation. With the median fidelities of 99.97% and 99.67% for parallel single- and two-qubit gates respectively, we simulate the dynamics of a two-dimensional (2D) compressible diverging flow and a 2D decaying vortex with ten qubits. The experimental results well capture the temporal evolution of averaged density and momentum profiles, and qualitatively reproduce spatial flow fields with moderate noises. This work demonstrates the potential of quantum computing in simulating more complex flows, such as turbulence, for practical applications.

quant-ph↗

arXiv2024

AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaboration

The potential of automatic task-solving through Large Language Model (LLM)-based multi-agent collaboration has recently garnered widespread attention from both the research community and industry. While utilizing natural language to coordinate multiple agents presents a promising avenue for democratizing agent technology for general users, designing coordination strategies remains challenging with existing coordination frameworks. This difficulty stems from the inherent ambiguity of natural language for specifying the collaboration process and the significant cognitive effort required to extract crucial information (e.g. agent relationship, task dependency, result correspondence) from a vast amount of text-form content during exploration. In this work, we present a visual exploration framework to facilitate the design of coordination strategies in multi-agent collaboration. We first establish a structured representation for LLM-based multi-agent coordination strategy to regularize the ambiguity of natural language. Based on this structure, we devise a three-stage generation method that leverages LLMs to convert a user's general goal into an executable initial coordination strategy. Users can further intervene at any stage of the generation process, utilizing LLMs and a set of interactions to explore alternative strategies. Whenever a satisfactory strategy is identified, users can commence the collaboration and examine the visually enhanced execution result. We develop AgentCoord, a prototype interactive system, and conduct a formal user study to demonstrate the feasibility and effectiveness of our approach.

cs.HC↗

arXiv2024

Online Multi-Contact Receding Horizon Planning via Value Function Approximation

Planning multi-contact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing trajectories in a prediction horizon (never executed) that foresees the future beyond the execution horizon. However, given the non-convex dynamics of multi-contact motions, this approach is computationally expensive. To enable online Receding Horizon Planning (RHP) of multi-contact motions, we find efficient approximations of the value function. Specifically, we propose a trajectory-based and a learning-based approach. In the former, namely RHP with Multiple Levels of Model Fidelity, we approximate the value function by computing the prediction horizon with a convex relaxed model. In the latter, namely Locally-Guided RHP, we learn an oracle to predict local objectives for locomotion tasks, and we use these local objectives to construct local value functions for guiding a short-horizon RHP. We evaluate both approaches in simulation by planning centroidal trajectories of a humanoid robot walking on moderate slopes, and on large slopes where the robot cannot maintain static balance. Our results show that locally-guided RHP achieves the best computation efficiency (95\%-98.6\% cycles converge online). This computation advantage enables us to demonstrate online receding horizon planning of our real-world humanoid robot Talos walking in dynamic environments that change on-the-fly.

cs.RO↗

arXiv2024

Geometric deformation and redshift structure caused by plane gravitational waves

The curved spacetime induced by gravitational waves can give rise to visual effects such as geometric distortions and redshift structures in the observed image. By establishing a mapping from the object's surface coordinates to the observer's screen coordinates, we study these effects in the context of plane gravitational waves. The simulation reveals that the image of an object doesn't merely seem compressed or stretched, but rather appears twisted and wobbled. Furthermore, the redshift structure on the object's surface appears to rotate as a whole. This outcome offers an intuitive depiction of the lensing effect in plane gravitational wave spacetimes.

gr-qc↗

arXiv2024

INvestigations of massive Filaments ANd sTar formation (INFANT). I. Core Identification and Core Mass Function

Filamentary structures are ubiquitously found in high-mass star-forming clouds. To investigate the relationship between filaments and star formation, we carry out the INFANT (INvestigations of massive Filaments ANd sTar formation) survey, a multi-scale, multi-wavelength survey of massive filamentary clouds with ALMA band 3/band 6 and VLA K band. In this first paper, we present the ALMA band 6 continuum observations toward a sample of 8 high-mass star forming filaments. We covered each target with approximately rectangular mosaic field of view with two 12-m array configurations, achieving an angular resolution of $\sim$0.6" (2700 AU at 4.5 kpc) and a continuum rms of $\sim$0.1 mJy/beam ($\sim$0.06 Msun in gas mass assuming 15 K). We identify cores using the getsf and astrodendro and find the former is more robust in terms of both identification and measuring flux densities. We identify in total 183 dense cores (15--36 cores in each cloud) and classify their star formation states via outflow and warm gas tracers. The protostellar cores are statistically more massive than the prestellar cores, possibly indicating further accretion onto cores after formation of protostars. For the high-mass end ($M_\text{core}$ $>$ 1.5 Msun) of the core mass function (CMF) we derive a power-law index of $-$1.15 $\pm$ 0.12 for the whole sample, and $-$1.70 $\pm$ 0.25 for the prestellar population. We also find a steepening trend in CMF with cloud evolution ($-$0.89 $\pm$ 0.15 for the young group v.s. $-$1.44 $\pm$ 0.25 for the evolved group) and discuss its implication for cluster formation.

astro-ph.GA↗

arXiv2024

The ALMA-QUARKS Survey: II. the ACA 1.3 mm continuum source catalog and the assembly of dense gas in massive star-forming clumps

Leveraging the high resolution, high sensitivity, and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array (ALMA), the QUARKS survey, standing for "Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures", is observing 139 massive star-forming clumps at ALMA Band 6 ($λ\sim$ 1.3 mm). This paper introduces the Atacama Compact Array (ACA) 7-m data. Combining multi-wavelength data, we provide the first edition of QUARKS atlas, offering insights into the multiscale and multiphase interstellar medium in high-mass star formation. The ACA 1.3 mm catalog includes 207 continuum sources that are called ACA sources. Their gas kinetic temperatures are estimated using three formaldehyde (H$_2$CO) transitions with a non-LTE radiation transfer model, and the mass and density are derived from a dust emission model. The ACA sources are massive (16-84 percentile values of 6-160 $M_{\odot}$), gravity-dominated ($M\propto R^{1.1}$) fragments within massive clumps, with supersonic turbulence ($\mathcal{M}>1$) and embedded star-forming protoclusters. We find a linear correlation between the masses of the fragments and the massive clumps, with a ratio of 6% between the two. When considering the fragments as representative of dense gas, the ratio indicates a dense gas fraction (DGF) of 6%, although with a wide scatter ranging from 1% to 10%. If we consider the QUARKS massive clumps to be what is observed at various scales, then the size-independent DGF indicates a self-similar fragmentation or collapsing mode in protocluster formation. With the ACA data over four orders of magnitude of luminosity-to-mass ratio ($L/M$), we find that the DGF increases significantly with $L/M$, which indicates clump evolutionary stage. We observed a limited fragmentation at the subclump scale, which can be explained by dynamic global collapse process.

astro-ph.GA↗

arXiv2024

Easy-to-configure zero-dimensional valley-chiral modes in a graphene point junction

The valley degree of freedom in 2D materials can be manipulated for low-dissipation quantum electronics called valleytronics. At the boundary between two regions of bilayer graphene with different atomic or electrostatic configuration, valley-polarized current has been realized. However, the demanding fabrication and operation requirements limit device reproducibility and scalability toward more advanced valleytronics circuits. We demonstrate a new device architecture of a point junction where a valley-chiral 0D PN junction is easily configured, switchable, and capable of carrying valley current with an estimated polarization of ~80%. This work provides a new building block in manipulating valley quantum numbers and scalable valleytronics.

cond-mat.mes-hall↗

arXiv2024

Electron Collimation in Twisted Bilayer Graphene via Gate-defined Moiré Barriers

Electron collimation via a graphene pn-junction allows electrostatic control of ballistic electron trajectories akin to that of an optical circuit. Similar manipulation of novel correlated electronic phases in twisted-bilayer graphene (tBLG) can provide additional probes to the underlying physics and device components towards advanced quantum electronics. In this work, we demonstrate collimation of the electron flow via gate-defined moiré barriers in a tBLG device, utilizing the band-insulator gap of the moiré superlattice. A single junction can be tuned to host a chosen combination of conventional pseudo barrier and moiré tunnel barriers, from which we demonstrate improved collimation efficiency. By measuring transport through two consecutive moiré collimators separated by 1 um, we demonstrate evidence of electron collimation in tBLG in the presence of realistic twist-angle inhomogeneity.

cond-mat.mes-hall↗

arXiv2024

Aligning Logits Generatively for Principled Black-Box Knowledge Distillation

Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server. B2KD faces challenges such as limited Internet exchange and edge-cloud disparity of data distributions. In this paper, we formalize a two-step workflow consisting of deprivatization and distillation, and theoretically provide a new optimization direction from logits to cell boundary different from direct logits alignment. With its guidance, we propose a new method Mapping-Emulation KD (MEKD) that distills a black-box cumbersome model into a lightweight one. Our method does not differentiate between treating soft or hard responses, and consists of: 1) deprivatization: emulating the inverse mapping of the teacher function with a generator, and 2) distillation: aligning low-dimensional logits of the teacher and student models by reducing the distance of high-dimensional image points. For different teacher-student pairs, our method yields inspiring distillation performance on various benchmarks, and outperforms the previous state-of-the-art approaches.

cs.LG↗

arXiv2024

Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning

Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks. However, when used as agents for dealing with complex problems in the real world, their performance is far inferior to large commercial models such as ChatGPT and GPT-4. As intelligent agents, LLMs need to have the capabilities of task planning, long-term memory, and the ability to leverage external tools to achieve satisfactory performance. Various methods have been proposed to enhance the agent capabilities of LLMs. On the one hand, methods involve constructing agent-specific data and fine-tuning the models. On the other hand, some methods focus on designing prompts that effectively activate the reasoning abilities of the LLMs. We explore both strategies on the 7B and 13B models. We propose a comprehensive method for constructing agent-specific data using GPT-4. Through supervised fine-tuning with constructed data, we find that for these models with a relatively small number of parameters, supervised fine-tuning can significantly reduce hallucination outputs and formatting errors in agent tasks. Furthermore, techniques such as multi-path reasoning and task decomposition can effectively decrease problem complexity and enhance the performance of LLMs as agents. We evaluate our method on five agent tasks of AgentBench and achieve satisfactory results.

cs.CL↗

arXiv2024

Non-Abelian braiding of Fibonacci anyons with a superconducting processor

Non-Abelian topological orders offer an intriguing path towards fault-tolerant quantum computation, where information can be encoded and manipulated in a topologically protected manner immune to arbitrary local noises and perturbations. However, realizing non-Abelian topologically ordered states is notoriously challenging in both condensed matter and programmable quantum systems, and it was not until recently that signatures of non-Abelian statistics were observed through digital quantum simulation approaches. Despite these exciting progresses, none of them has demonstrated the appropriate type of topological orders and associated non-Abelian anyons whose braidings alone support universal quantum computation. Here, we report the realization of non-Abelian topologically ordered states of the Fibonacci string-net model and demonstrate braidings of Fibonacci anyons featuring universal computational power, with a superconducting quantum processor. We exploit efficient quantum circuits to prepare the desired states and verify their nontrivial topological nature by measuring the topological entanglement entropy. In addition, we create two pairs of Fibonacci anyons and demonstrate their fusion rule and non-Abelian braiding statistics by applying unitary gates on the underlying physical qubits. Our results establish a versatile digital approach to exploring exotic non-Abelian topological states and their associated braiding statistics with current noisy intermediate-scale quantum processors.

quant-ph↗

arXiv2024

Measuring Spectral Form Factor in Many-Body Chaotic and Localized Phases of Quantum Processors

The spectral form factor (SFF) captures universal spectral fluctuations as signatures of quantum chaos, and has been instrumental in advancing multiple frontiers of physics including the studies of black holes and quantum many-body systems. However, the measurement of SFF in many-body systems is challenging due to the difficulty in resolving level spacings that become exponentially small with increasing system size. Here we experimentally measure the SFF to probe the presence or absence of chaos in quantum many-body systems using a superconducting quantum processor with a randomized measurement protocol. For a Floquet chaotic system, we observe signatures of spectral rigidity of random matrix theory in SFF given by the ramp-plateau behavior. For a Hamiltonian system, we utilize SFF to distinguish the quantum many-body chaotic phase and the prethermal many-body localization. We observe the dip-ramp-plateau behavior of random matrix theory in the chaotic phase, and contrast the scaling of the plateau time in system size between the many-body chaotic and localized phases. Furthermore, we probe the eigenstate statistics by measuring a generalization of the SFF, known as the partial SFF, and observe distinct behaviors in the purities of the reduced density matrix in the two phases. This work unveils a new way of extracting the universal signatures of many-body quantum chaos in quantum devices by probing the correlations in eigenenergies and eigenstates.

quant-ph↗

arXiv2024

Structure-preserving, weighted implicit-explicit schemes for multi-phase incompressible Navier-Stokes/Darcy coupled nonlocal Allen-Cahn model

A multitude of substances exist as mixtures comprising multiple chemical components in the natural world. These substances undergo morphological changes under external influences. the phase field model coupled with fluid flow, the dynamic movement and evolution of the phase interface intricately interact with the fluid motion. This article focuses on the N-component models that couple the conservative Allen-Cahn equation with two types of incompressible fluid flow systems: the Navier-Stokes equation and the Darcy equation. By utilizing the scalar auxiliary variable method and the projection method, we innovatively construct two types of structure-preserving weighted implicit-explicit schemes for the coupled models, resulting in fully decoupled linear systems and second-order accuracy in time. The schemes are proved to be mass-conservative. In addition, with the application of $G$-norm inspired by the idea of $G$-stability, we rigorously establish its unconditional energy stability. Finally, the performance of the proposed scheme is verified by some numerical simulations.

math.NA↗

arXiv2024

Dark Dragon Breaks Magnetic Chain: Dynamical Substructures of IRDC G28.34 Form in Supported Environments

We have comprehensively studied the multi-scale physical properties of the infrared dark cloud (IRDC) G28.34 (the Dragon cloud) with dust polarization and molecular line data from Planck, FCRAO-14m, JCMT, and ALMA. We find that the averaged magnetic fields of clumps tend to be either parallel with or perpendicular to the cloud-scale magnetic fields, while the cores in clump MM4 tend to have magnetic fields aligned with the clump fields. Implementing the relative orientation analysis (for magnetic fields, column density gradients, and local gravity), Velocity Gradient Technique (VGT), and modified Davis-Chandrasekhar-Fermi (DCF) analysis, we find that: G28.34 is located in a trans-to-sub-Alfvénic environment ($\mathcal{M}_{A}=0.74$ within $r=15$ pc); the magnetic field is effectively resisting gravitational collapse in large-scale diffuse gas, but is distorted by gravity within the cloud and affected by star formation activities in high-density regions; and the normalized mass-to-flux ratio tends to increase with increasing density and decreasing radius. Considering the thermal, turbulent, and magnetic supports, we find that the environmental gas of G28.34 is in a super-virial (supported) state, the infrared dark clumps may be in a near-equilibrium state, and core MM4-core4 is in a sub-virial (gravity-dominant) state. In summary, we suggest that magnetic fields dominate gravity and turbulence in the cloud environment at large scales, resulting in relatively slow cloud formation and evolution processes. Within the cloud, gravity could overwhelm magnetic fields and turbulence, allowing local dynamical star formation to happen.

astro-ph.GA↗

arXiv2024

Small, Versatile and Mighty: A Range-View Perception Framework

Despite its compactness and information integrity, the range view representation of LiDAR data rarely occurs as the first choice for 3D perception tasks. In this work, we further push the envelop of the range-view representation with a novel multi-task framework, achieving unprecedented 3D detection performances. Our proposed Small, Versatile, and Mighty (SVM) network utilizes a pure convolutional architecture to fully unleash the efficiency and multi-tasking potentials of the range view representation. To boost detection performances, we first propose a range-view specific Perspective Centric Label Assignment (PCLA) strategy, and a novel View Adaptive Regression (VAR) module to further refine hard-to-predict box properties. In addition, our framework seamlessly integrates semantic segmentation and panoptic segmentation tasks for the LiDAR point cloud, without extra modules. Among range-view-based methods, our model achieves new state-of-the-art detection performances on the Waymo Open Dataset. Especially, over 10 mAP improvement over convolutional counterparts can be obtained on the vehicle class. Our presented results for other tasks further reveal the multi-task capabilities of the proposed small but mighty framework.

cs.CV↗

arXiv2024

Rethinking Diversity in Deep Neural Network Testing

Motivated by the success of traditional software testing, numerous diversity measures have been proposed for testing deep neural networks (DNNs). In this study, we propose a shift in perspective, advocating for the consideration of DNN testing as directed testing problems rather than diversity-based testing tasks. We note that the objective of testing DNNs is specific and well-defined: identifying inputs that lead to misclassifications. Consequently, a more precise testing approach is to prioritize inputs with a higher potential to induce misclassifications, as opposed to emphasizing inputs that enhance "diversity." We derive six directed metrics for DNN testing. Furthermore, we conduct a careful analysis of the appropriate scope for each metric, as applying metrics beyond their intended scope could significantly diminish their effectiveness. Our evaluation demonstrates that (1) diversity metrics are particularly weak indicators for identifying buggy inputs resulting from small input perturbations, and (2) our directed metrics consistently outperform diversity metrics in revealing erroneous behaviors of DNNs across all scenarios.

cs.SE↗

arXiv2024

Existence and upper semicontinuity of pullback attractors for Kirchhoff wave equations in time-dependent spaces

In this paper, we shall investigate the existence and upper semicontinuity of pullback attractors for non-autonomous Kirchhoff wave equations with a strong damping in the time-dependent space $X_t$. After deriving the existence and uniqueness of solutions by the Faedo-Galerkin approximation method, we establish the existence of pullback attractors. Later on, we prove the upper semicontinuity of pullback attractors between the Kirchhoff-type wave equations with $δ\geq 0$ and the conventional wave equations with $δ=0$ by a series of complex energy estimates.

math.AP↗

arXiv2024

Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset

Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs. Through extensive experimentation, we unveil a notable performance gap between current LMMs and human performance on MATH-V, underscoring the imperative for further advancements in LMMs. Moreover, our detailed categorization allows for a thorough error analysis of LMMs, offering valuable insights to guide future research and development. The project is available at https://mathvision-cuhk.github.io

cs.CV↗