arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 271 records · Page 15Linked to original sources

arXiv2025

ATOMS: ALMA Three-millimeter Observations of massive Star-forming regions -XX. Probability distribution function of integrated intensity for dense molecular gas tracers

We report the observations of J=1-0 of HCN, HCO+, H13CO+, and H13CN, HC3N (J=11-10) emission towards 135 massive star-forming clumps, as part of the ATOMS (ALMA Three-millimeter Observations of Massive Star-forming regions) Survey. We present the integrated intensity probability distribution function for these molecular tracers, modeled as a combination of a log-normal distribution and a power-law tail. The molecular line luminosities for the power-law tail segment, Lmol(p), have been calculated. We have investigated the correlation between the bolometric luminosity, Lbol, and the power-law part of the molecular line luminosity, Lmol(p). Our findings suggest that the scaling relationships between Lbol and Lmol(p) for HCN and HCO+ are sublinear, indicating that these molecules might not be the most effective tracers for the dense gas. In contrast, H13CN and HC3N exhibit a nearly linear relationship between Lbol and Lmol(p), indicating that they can well trace gravitationally bound dense gas. The ratios of Lbol-to-Lmol(p), serving as indicators of star formation efficiency within massive star-forming clumps, exhibit a weak anti-correlation with the power-law index in the I-PDF. In addition, the star formation efficiency is also weakly anti-correlated with the exponent U of the corresponding equivalent density distribution. Our results implie that clumps with substantial gas accumulation may still display low star formation efficiencies.

astro-ph.GA↗

arXiv2025

Higher order analysis of the gravitational wave velocity memory effect between two free-falling gyroscopes in the plane wave spacetime

In the plane wave spacetime, when gravitational waves pass by, an angular deviation exists between two free-falling gyroscopes, which naturally corresponds to the velocity memory effect. In the shackwave spacetime background, the angular deviation between two free-falling gyroscopes is calculated, which is also found to correspond to the velocity memory effect. In the plane wave spacetime, with linear polarization taken into account, no contribution is made by the first-order terms of the initial separation distance $L$ (or the initial separation velocity $v_0$), $\bm{P}$ (or $\bm{M}$) of two free-falling gyroscopes to the velocity memory effect, while contributions are initiated from the second-order terms. Under certain circumstances, the second-order contribution of the initial separation distance $L$ is of the same order of magnitude as the first-order contribution of the initial separation velocity $v_0$. When both + polarization and $\times$ polarization are taken into account, in the context of the merger of supermassive black holes and with the initial separation velocity approaching the speed of light, the order of magnitude of the angle is $10^{-16}$ rads.

gr-qc↗

arXiv2025

Experimental realization of the ground state for the antiferromagnetic Ising model on a triangular lattice

The antiferromagnetic Ising model on a triangular lattice (AFIT) exemplifies the most classical frustration system, arising from its triangular geometry that prevents all interactions from being simultaneously satisfied. Understanding geometric frustration in AFIT is crucial for advancing our knowledge of materials science and complex phases of matter. Here, we present a simple platform to study AFIT by arranging cylindrical magnets in vertical cavities of a triangular lattice, where magnets can slide along the cavity axis and stabilize either at the bottom or at the top of the cavity, analogous to the bistability of the Ising spin. The strong interactions of the magnets and the unique growing process allow the frustrated behavior and its ground state configurations to be directly observed. Notably, we observe a curved stripe phase, which is exotic to the Ising model. An effective thermalization process is developed to minimize the interaction energy, facilitating the evolution of various magnetic states, thereby visually realizing the ground state antiferromagnetic Ising model. Theoretical simulations and machine learning are performed concurrently to reveal the ground state and its evolution under effective thermal fluctuations, which are remarkably consistent with experimental results. Our system provides a unique platform to study frustrated systems and pave the way for future explorations in complex geometries.

cond-mat.mes-hall↗

arXiv2025

Demonstrating quantum error mitigation on logical qubits

A long-standing challenge in quantum computing is developing technologies to overcome the inevitable noise in qubits. To enable meaningful applications in the early stages of fault-tolerant quantum computing, devising methods to suppress post-correction logical failures is becoming increasingly crucial. In this work, we propose and experimentally demonstrate the application of zero-noise extrapolation, a practical quantum error mitigation technique, to error correction circuits on state-of-the-art superconducting processors. By amplifying the noise on physical qubits, the circuits yield outcomes that exhibit a predictable dependence on noise strength, following a polynomial function determined by the code distance. This property enables the effective application of polynomial extrapolation to mitigate logical errors. Our experiments demonstrate a universal reduction in logical errors across various quantum circuits, including fault-tolerant circuits of repetition and surface codes. We observe a favorable performance in multi-round error correction circuits, indicating that this method remains effective when the circuit depth increases. These results advance the frontier of quantum error suppression technologies, opening a practical way to achieve reliable quantum computing in the early fault-tolerant era.

quant-ph↗

arXiv2025

Critical Current Density and AC Magnetic Susceptibility of High-quality FeTe$_{0.5}$Se$_{0.5}$ Superconducting Tapes

Iron telluride-selenium superconducting materials, known for their non-toxicity, ease of preparation, simple structure, and high upper critical fields, have attracted much research interest in practical application. In this work, we conducted electrical transport measurements, magneto-optical imaging, and AC magnetic susceptibility measurements on FeTe$_{0.5}$Se$_{0.5}$ superconducting long tapes fabricated via reel-to-reel pulsed laser deposition. Our transport measurements revealed a high critical current density that remains relatively stable even with increasing external magnetic fields, reaching over $1\times 10^5$ A/cm$^2$ at 8 K and 9 T. The calculated pinning force density indicates that normal point pinning is the primary mechanism in these tapes. The magneto-optical images demonstrated that the tapes show homogeneous superconductivity and uniform distribution of critical current density. The AC magnetic susceptibility measurements also confirmed their strong flux pinning nature of withstanding high magnetic field. Based on these characteristics, FeTe$_{0.5}$Se$_{0.5}$ superconducting tapes show promising prospects for applications under high magnetic fields.

cond-mat.supr-con↗

arXiv2025

Observation of topological prethermal strong zero modes

Symmetry-protected topological phases cannot be described by any local order parameter and are beyond the conventional symmetry-breaking paradigm for understanding quantum matter. They are characterized by topological boundary states robust against perturbations that respect the protecting symmetry. In a clean system without disorder, these edge modes typically only occur for the ground states of systems with a bulk energy gap and would not survive at finite temperatures due to mobile thermal excitations. Here, we report the observation of a distinct type of topological edge modes, which are protected by emergent symmetries and persist even up to infinite temperature, with an array of 100 programmable superconducting qubits. In particular, through digital quantum simulation of the dynamics of a one-dimensional disorder-free "cluster" Hamiltonian, we observe robust long-lived topological edge modes over up to 30 cycles at a wide range of temperatures. By monitoring the propagation of thermal excitations, we show that despite the free mobility of these excitations, their interactions with the edge modes are substantially suppressed in the dimerized regime due to an emergent U(1)$\times$U(1) symmetry, resulting in an unusually prolonged lifetime of the topological edge modes even at infinite temperature. In addition, we exploit these topological edge modes as logical qubits and prepare a logical Bell state, which exhibits persistent coherence in the dimerized and off-resonant regime, despite the system being disorder-free and far from its ground state. Our results establish a viable digital simulation approach to experimentally exploring a variety of finite-temperature topological phases and demonstrate a potential route to construct long-lived robust boundary qubits that survive to infinite temperature in disorder-free systems.

quant-ph↗

arXiv2025

Exploring nontrivial topology at quantum criticality in a superconducting processor

The discovery of nontrivial topology in quantum critical states has introduced a new paradigm for classifying quantum phase transitions and challenges the conventional belief that topological phases are typically associated with a bulk energy gap. However, realizing and characterizing such topologically nontrivial quantum critical states with large particle numbers remains an outstanding experimental challenge in statistical and condensed matter physics. Programmable quantum processors can directly prepare and manipulate exotic quantum many-body states, offering a powerful path for exploring the physics behind these states. Here, we present an experimental exploration of the critical cluster Ising model by preparing its low-lying critical states on a superconducting processor with up to $100$ qubits. We develop an efficient method to probe the boundary $g$-function based on prepared low-energy states, which allows us to uniquely identify the nontrivial topology of the critical systems under study. Furthermore, by adapting the entanglement Hamiltonian tomography technique, we recognize two-fold topological degeneracy in the entanglement spectrum under periodic boundary condition, experimentally verifying the universal bulk-boundary correspondence in topological critical systems. Our results demonstrate the low-lying critical states as useful quantum resources for investigating the interplay between topology and quantum criticality.

quant-ph↗

arXiv2024

Gravitational Waves from Post-Collision of Fuzzy Dark Matter Solitons

According to the Schrödinger-Poisson (SP) equations, fuzzy dark matter (FDM) can form a stable equilibrium configuration, the so-called FDM soliton. The SP system can also determine the evolution of FDM solitons, such as head-on collision. In this paper, we first propose a new adimensional unit of length, time and mass. And then, we simulate the adimensional SP system with $\mathtt{PyUltraLight}$ to study the GWs from post-collision of FDM solitons when the linearized theory is valid and the GW back reaction on the evolution of FDM solitons is ignored. Finally, we find that the GWs from post-collisions have a frequency of (few ten-years)$^{-1}$ or (few years)$^{-1}$ when FDM mass is $m=10^{-18}\rm{eV}/c^2$ or $m=10^{-17}\rm{eV}/c^2$. Therefore, future detection of such GWs will constrain the property of FDM particle and solitons.

astro-ph.CO↗

arXiv2024

Algorithmic Strategies for Sustainable Reuse of Neural Network Accelerators with Permanent Faults

Hardware failures are a growing challenge for machine learning accelerators, many of which are based on systolic arrays. When a permanent hardware failure occurs in a systolic array, existing solutions include localizing and isolating the faulty processing element (PE), using a redundant PE for re-execution, or in some extreme cases decommissioning the entire accelerator for further investigation. In this paper, we propose novel algorithmic approaches that mitigate permanent hardware faults in neural network (NN) accelerators by uniquely integrating the behavior of the faulty component instead of bypassing it. In doing so, we aim for a more sustainable use of the accelerator where faulty hardware is neither bypassed nor discarded, instead being given a second life. We first introduce a CUDA-accelerated systolic array simulator in PyTorch, which enabled us to quantify the impact of permanent faults appearing on links connecting two PEs or in weight registers, where one bit is stuck at 0 or 1 in the float32, float16, or bfloat16 representation. We then propose several algorithmic mitigation techniques for a subset of stuck-at faults, such as Invertible Scaling or Shifting of activations and weights, or fine tuning with the faulty behavior. Notably, the proposed techniques do not require any hardware modification, instead relying on existing components of widely used systolic array based accelerators, such as normalization, activation, and storage units. Extensive experimental evaluations using fully connected and convolutional NNs trained on MNIST, CIFAR-10 and ImageNet show that the proposed fault-tolerant approach matches or gets very close to the original fault-free accuracy.

cs.LG↗

arXiv2024

SAM Decoding: Speculative Decoding via Suffix Automaton

Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration. Retrieval-based SD methods, one kind of model-free method, have yielded promising speedup, but they often rely on incomplete retrieval resources, inefficient retrieval methods, and are constrained to certain domains. This paper presents a novel retrieval-based speculative decoding method that adapts suffix automaton (SAM) for efficient and accurate draft generation by utilizing common text corpus and dynamic text sequence. Unlike existing $n$-gram matching methods, SAM-Decoding finds the exact longest suffix match, achieving an average time complexity of O(1) per generation step of SAM update and suffix retrieval. It can also integrate with existing methods, adaptively selecting a draft generation strategy based on match length to generalize to broader domains. Extensive experiments on Spec-Bench show that our method is $18\%+$ faster than other retrieval-based SD methods. Additionally, when combined with advanced EAGLE-2, it provides an additional speedup of $3.28\%$ -- $11.13\%$ across various-sized LLM backbones. Our code is available at our \href{https://github.com/hyx1999/SAM-Decoding}{repository}.

cs.CL↗

arXiv2024

Pullback attractors for nonclassical diffusion equations with a delay operator

In this paper, we consider the asymptotic behavior of weak solutions for nonclassical non-autonomous diffusion equations with a delay operator in time-dependent spaces when the nonlinear function $g$ satisfies subcritical exponent growth conditions, the delay operator $φ(t, u_t)$ contains some hereditary characteristics and the external force $k \in L_{l o c}^{2}\left(\mathbb{R} ; L^{2}(Ω)\right)$. First, we prove the well-posedness of solutions by using the Faedo-Galerkin approximation method. Then after a series of elaborate energy estimates and calculations, we establish the existence and regularity of pullback attractors in time-dependent spaces $C_{\mathcal{H}_{t}(Ω)}$ and $C_{\mathcal{H}^{1}_{t}(Ω)}$, respectively.

math.AP↗

arXiv2024

FastFace: Fast-converging Scheduler for Large-scale Face Recognition Training with One GPU

Computing power has evolved into a foundational and indispensable resource in the area of deep learning, particularly in tasks such as Face Recognition (FR) model training on large-scale datasets, where multiple GPUs are often a necessity. Recognizing this challenge, some FR methods have started exploring ways to compress the fully-connected layer in FR models. Unlike other approaches, our observations reveal that without prompt scheduling of the learning rate (LR) during FR model training, the loss curve tends to exhibit numerous stationary subsequences. To address this issue, we introduce a novel LR scheduler leveraging Exponential Moving Average (EMA) and Haar Convolutional Kernel (HCK) to eliminate stationary subsequences, resulting in a significant reduction in converging time. However, the proposed scheduler incurs a considerable computational overhead due to its time complexity. To overcome this limitation, we propose FastFace, a fast-converging scheduler with negligible time complexity, i.e. O(1) per iteration, during training. In practice, FastFace is able to accelerate FR model training to a quarter of its original time without sacrificing more than 1% accuracy, making large-scale FR training feasible even with just one single GPU in terms of both time and space complexity. Extensive experiments validate the efficiency and effectiveness of FastFace. The code is publicly available at: https://github.com/amoonfana/FastFace

cs.CV↗

arXiv2024

Pioplat: A Scalable, Low-Cost Framework for Latency Reduction in Ethereum Blockchain

As decentralized applications on permissionless blockchains are prevalent, more and more latency-sensitive usage scenarios emerged, where the lower the latency of sending and receiving messages, the better the chance of earning revenue. To reduce latency, we present Pioplat, a feasible, customizable, and low-cost latency reduction framework consisting of multiple relay nodes on different continents and at least one instrumented variant of a full node. The node selection strategy of Pioplat and the low-latency communication protocol offer an elastic way to reduce latency effectively. We demonstrate Pioplat's feasibility with an implementation running on five continents and show that Pioplat can significantly reduce the latency of receiving blocks/transactions and sending transactions, thus fulfilling the requirements of most latency-sensitive use cases. Furthermore, we provide the complete implementation of Pioplat to promote further research and allow people to apply the framework to more blockchain systems.

cs.DC↗

arXiv2024

LLaVA-Zip: Adaptive Visual Token Compression with Intrinsic Image Information

Multi-modal large language models (MLLMs) utilizing instruction-following data, such as LLaVA, have achieved great progress in the industry. A major limitation in these models is that visual tokens consume a substantial portion of the maximum token limit in large language models (LLMs), leading to increased computational demands and decreased performance when prompts include multiple images or videos. Industry solutions often mitigate this issue by increasing computational power, but this approach is less feasible in academic environments with limited resources. In this study, we propose Dynamic Feature Map Reduction (DFMR) based on LLaVA-1.5 to address the challenge of visual token overload. DFMR dynamically compresses the visual tokens, freeing up token capacity. Our experimental results demonstrate that integrating DFMR into LLaVA-1.5 significantly improves the performance of LLaVA in varied visual token lengths, offering a promising solution for extending LLaVA to handle multi-image and video scenarios in resource-constrained academic environments and it can also be applied in industry settings for data augmentation to help mitigate the scarcity of open-domain image-text pair datasets in the continued pretraining stage.

cs.CV↗

arXiv2024

Origin of Increased Curie Temperature in Lithium-Substituted Ferroelectric Niobate Perovskite: Enhancement of the Soft Polar Mode

The functionality of ferroelectrics is often constrained by their Curie temperature, above which depolarization occurs. Lithium (Li) is the only experimentally known substitute that can increase the Curie temperature in ferroelectric niobate-based perovskites, yet the mechanism remains unresolved. Here, the unique phenomenon in Li-substituted KNbO3 is investigated using first-principles density functional theory. Theoretical calculations show that Li substitution at the A-site of perovskite introduces compressive chemical pressure, reducing Nb-O hybridization and associated ferroelectric instability. However, the large off-center displacement of the Li cation compensates for this reduction and further enhances the soft polar mode, thereby raising the Curie temperature. In addition, the stability of the tetragonal phase over the orthorhombic phase is predicted upon Li substitution, which reasonably explains the experimental observation of a decreased orthorhombic-to-tetragonal phase transition temperature. Finally, a metastable anti-phase polar state in which the Li cation displaces oppositely to the Nb cation is revealed, which could also contribute to the variation of phase transition temperatures. These findings provide critical insights into the atomic-scale mechanisms governing Curie temperature enhancement in ferroelectrics and pave the way for designing advanced ferroelectric materials with improved thermal stability and functional performance.

cond-mat.mtrl-sci↗

arXiv2024

Bulk and surface Dirac states accompanied by two superconducting domes in FeSe-based superconductors

Recent investigations of FeSe-based superconductors have revealed the presence of two superconducting domes, and suggest possible distinct pairing mechanisms. Two superconducting domes are commonly found in unconventional superconductors and exhibit unique normal states and electronic structures. In this study, we conducted electromagnetic transport measurements to establish a complete phase diagram, successfully observing the two superconducting domes in FeSe$_{1-x}$S$_x$ (0 $\le x \le$ 0.25) and FeSe$_{1-x}$Te$_x$ (0 $\le x \le$ 1) superconductors. The normal state resistivity on SC1 shows the strange metal state, with a power exponent approximately equal to 1 ($ρ(T)\propto T^n$ with $n\sim 1$), whereas the exponent on SC2 is less than 1. A bulk Dirac state observed on SC1, completely synchronized with the strange metal behavior, indicating a close relationship between them. While a topological surface Dirac state is witnessed on SC2, and undergoes a sign change near the pure nematic quantum critical point. The evolution of the Dirac states indicates that the appearance of the two superconducting domes may originate from the Fermi surface reconstruction. Our findings highlight distinct Dirac states and normal state resistivity across the two superconducting domes, providing convincing evidence for the existence of the two different pairing mechanisms in FeSe-based superconductors.

cond-mat.supr-con↗

arXiv2024

DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity

In recent years, the underground economy is proliferating in the mobile system. These underground economy apps (UEware) make profits from providing non-compliant services, especially in sensitive areas such as gambling, pornography, and loans. Unlike traditional malware, most of them (over 80%) do not have malicious payloads. Due to their unique characteristics, existing detection approaches cannot effectively and efficiently mitigate this emerging threat. To address this problem, we propose a novel approach to effectively and efficiently detect UEware by considering their UI transition graphs (UTGs). Based on the proposed approach, we design and implement a system named DeUEDroid to perform the detection. To evaluate DeUEDroid, we collect 25,717 apps and build the first large-scale ground-truth dataset (1,700 apps) of UEware. The evaluation result based on the ground-truth dataset shows that DeUEDroid can cover new UI features and statically construct precise UTG. It achieves 98.22% detection F1-score and 98.97% classification accuracy, significantly outperforming traditional approaches. The evaluation involving 24,017 apps demonstrates the effectiveness and efficiency of UEware detection in real-world scenarios. Furthermore, the result reveals that UEware are prevalent, with 54% of apps in the wild and 11% of apps in app stores being UEware. Our work sheds light on future work in analyzing and detecting UEware.

cs.CR↗

arXiv2024

Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks

In this paper, we consider a wireless resource allocation problem in a cyber-physical system (CPS) where the control channel, carrying resource allocation commands, is subjected to denial-of-service (DoS) attacks. We propose a novel concept of collaborative distributed and centralized (CDC) resource allocation to effectively mitigate the impact of these attacks. To optimize the CDC resource allocation policy, we develop a new CDC-deep reinforcement learning (DRL) algorithm, whereas existing DRL frameworks only formulate either centralized or distributed decision-making problems. Simulation results demonstrate that the CDC-DRL algorithm significantly outperforms state-of-the-art DRL benchmarks, showcasing its ability to address resource allocation problems in large-scale CPSs under control channel attacks.

cs.IT↗