arXiv subjects

Peng Cheng

Publications and source records attributed to Peng Cheng.

At least 145 records · Page 8Linked to original sources

arXiv2024

Structural and magnetic characterization of CeTa$_7$O$_{19}$ and YbTa$_7$O$_{19}$ with two-dimensional pseudospin-1/2 triangular lattice

Triangular lattice antiferromagnets are prototypes for frustrated magnetism and may potentially realize novel quantum magnetic states such as a quantum spin liquid ground state. A recent work suggests NdTa$_7$O$_{19}$ with rare-earth triangular lattice is a quantum spin liquid candidate and highlights the large family of rare-earth heptatantalates as a framework for quantum magnetism investigation. In this paper, we report the structural and magnetic characterization of CeTa$_7$O$_{19}$ and YbTa$_7$O$_{19}$. Both compounds are isostructural to NdTa$_7$O$_{19}$ with no detectable structural disorder. For CeTa$_7$O$_{19}$, the crystal field energy levels and parameters are determined by inelastic neutron scattering measurements. Based on the crystal field result, the magnetic susceptibility data could be well fitted and explained, which reveals that CeTa$_7$O$_{19}$ is a highly anisotropic Ising triangular-lattice antiferromagnet ($g_z$/$g_{xy}$$\sim$3) with very weak exchange interaction (J$\sim$0.22~K). For YbTa$_7$O$_{19}$, millimeter sized single crystals could be grown. The anisotropic magnetization and electron spin resonance data show that YbTa$_7$O$_{19}$ has a contrasting in-plane magnetic anisotropy with $g_z$/$g_{xy}$$\sim$0.67 similar as that of YbMgGaO$_4$. The above results indicate that CeTa$_7$O$_{19}$ and YbTa$_7$O$_{19}$ with pseudospin-1/2 ground states might either be quantum spin liquid candidate materials or find applications in adiabatic demagnetization refrigeration due to the weak exchange interaction.

cond-mat.str-el↗

arXiv2024

Thermodynamics of Einstein-Gauss-Bonnet Black Holes and Ensemble-averaged Theory

In this paper, using the ensemble-averaged theory, we define the thermodynamic free energy of Einstein-Gauss-Bonnet (EGB) black holes in anti-de Sitter (AdS) spacetime. This approach derives the gravitational partition function by incorporating non-saddle geometries besides the classical solutions. Unlike the sharp transition points seen in free energy calculated via saddle-point approximation, the ensemble-averaged free energy plotted against temperature shows a smoother behavior, suggesting that black hole phase transitions may be viewed as a small $G_N$ (Newton's gravitational constant) limit of the ensemble theory. This is similar to the behavior of black hole solutions in Einstein's gravity theory in AdS spacetime. We have obtained an expression for the quantum-corrected free energy for EGB-AdS black holes, and in the six-dimensional case, we observe a well-defined local minimum after the transition temperature which was absent in the earlier analysis of the classical free energy landscape. Furthermore, we expand the ensemble-averaged free energy in powers of $G_N$ to identify non-classical contributions. Our findings indicate that the similarities in the thermodynamic behavior between five-dimensional EGB-AdS and Reissner-Nordström-AdS (RN-AdS) black holes, as well as between six-dimensional EGB-AdS and Schwarzschild-AdS black holes, extend beyond the classical regime.

gr-qc↗

arXiv2024

A Survey of Distributed Graph Algorithms on Massive Graphs

Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been devoted to analyzing these, with most analyzing them based on programming models, less research focuses on understanding their challenges in distributed environments. Applying graph tasks to distributed environments is not easy, often facing numerous challenges through our analysis, including parallelism, load balancing, communication overhead, and bandwidth. In this paper, we provide an extensive overview of the current state-of-the-art in this field by outlining the challenges and solutions of distributed graph algorithms. We first conduct a systematic analysis of the inherent challenges in distributed graph processing, followed by presenting an overview of existing general solutions. Subsequently, we survey the challenges highlighted in recent distributed graph processing papers and the strategies adopted to address them. Finally, we discuss the current research trends and identify potential future opportunities.

cs.DC↗

arXiv2024

SPFresh: Incremental In-Place Update for Billion-Scale Vector Search

Approximate Nearest Neighbor Search (ANNS) is now widely used in various applications, ranging from information retrieval, question answering, and recommendation, to search for similar high-dimensional vectors. As the amount of vector data grows continuously, it becomes important to support updates to vector index, the enabling technique that allows for efficient and accurate ANNS on vectors. Because of the curse of high dimensionality, it is often costly to identify the right neighbors of a single new vector, a necessary process for index update. To amortize update costs, existing systems maintain a secondary index to accumulate updates, which are merged by the main index by global rebuilding the entire index periodically. However, this approach has high fluctuations of search latency and accuracy, not even to mention that it requires substantial resources and is extremely time-consuming for rebuilds. We introduce SPFresh, a system that supports in-place vector updates. At the heart of SPFresh is LIRE, a lightweight incremental rebalancing protocol to split vector partitions and reassign vectors in the nearby partitions to adapt to data distribution shift. LIRE achieves low-overhead vector updates by only reassigning vectors at the boundary between partitions, where in a high-quality vector index the amount of such vectors are deemed small. With LIRE, SPFresh provides superior query latency and accuracy to solutions based on global rebuild, with only 1% of DRAM and less than 10% cores needed at the peak compared to the state-of-the-art, in a billion scale vector index with 1% of daily vector update rate.

cs.IR↗

arXiv2024

SurrogatePrompt: Bypassing the Safety Filter of Text-to-Image Models via Substitution

Advanced text-to-image models such as DALL$\cdot$E 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential proliferation of unsafe content. This includes adult, violent, or deceptive imagery of political figures. Despite claims of rigorous safety mechanisms implemented in these models to restrict the generation of not-safe-for-work (NSFW) content, we successfully devise and exhibit the first prompt attacks on Midjourney, resulting in the production of abundant photorealistic NSFW images. We reveal the fundamental principles of such prompt attacks and suggest strategically substituting high-risk sections within a suspect prompt to evade closed-source safety measures. Our novel framework, SurrogatePrompt, systematically generates attack prompts, utilizing large language models, image-to-text, and image-to-image modules to automate attack prompt creation at scale. Evaluation results disclose an 88% success rate in bypassing Midjourney's proprietary safety filter with our attack prompts, leading to the generation of counterfeit images depicting political figures in violent scenarios. Both subjective and objective assessments validate that the images generated from our attack prompts present considerable safety hazards.

cs.CV↗

arXiv2024

FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection

Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioritization techniques. However, the fact that DNNs can deliver high-confidence predictions for incorrectly predicted examples, known as the over-confidence problem, causes these methods to fail to reveal high-confidence errors. To address this limitation, in this work, we propose FAST, a method that boosts existing prioritization methods through guided FeAture SelecTion. FAST is based on the insight that certain features may introduce noise that affects the model's output confidence, thereby contributing to high-confidence errors. It quantifies the importance of each feature for the model's correct predictions, and then dynamically prunes the information from the noisy features during inference to derive a new probability vector for the uncertainty estimation. With the help of FAST, the high-confidence errors and correctly classified examples become more distinguishable, resulting in higher APFD (Average Percentage of Fault Detection) values for test prioritization, and higher generalization ability for model enhancement. We conduct extensive experiments to evaluate FAST across a diverse set of model structures on multiple benchmark datasets to validate the effectiveness, efficiency, and scalability of FAST compared to the state-of-the-art prioritization techniques.

cs.SE↗

arXiv2024

NeoMem: Hardware/Software Co-Design for CXL-Native Memory Tiering

The Compute Express Link (CXL) interconnect makes it feasible to integrate diverse types of memory into servers via its byte-addressable SerDes links. Considering the various access latency, harnessing the full potential of CXL-based heterogeneous memory systems requires efficient memory tiering. However, prior work can hardly make a fundamental progress owing to low-resolution and high-overhead memory access profiling techniques. To address this critical challenge, we propose a novel memory tiering solution called NeoMem, which features a hardware/software co-design. NeoMem offloads memory profiling functions to CXL device-side controllers, integrating a dedicated hardware unit called NeoProf. NeoProf readily monitors memory accesses and provides the OS with crucial page hotness statistics and other useful system state information. On the OS kernel side, we design a revamped memory-tiering strategy, enabling accurate and timely hot page promotion based on NeoProf statistics. We implement NeoMem on a real FPGA-based CXL memory platform and Linux kernel v6.3. Comprehensive evaluations demonstrate that NeoMem achieves 32% to 67% geomean speedup over several existing memory tiering solutions.

cs.AR↗

arXiv2024

FePd2Te2: An Anisotropic Two-Dimensional Ferromagnet with One-Dimensional Fe Chains

Two-dimensional (2D) magnets have attracted significant attentions in recent years due to their importance in the research on both fundamental physics and spintronic applications. Here, we report the discovery of a new ternary compound FePd2Te2. It features a layered quasi-2D crystal structure with one-dimensional Fe zigzag chains extending along the b-axis in the cleavage plane. Single crystals of FePd2Te2 with centimeter-size could be grown. Density functional theory calculations, mechanical exfoliation and atomic force microscopy on these crystals reveal that they are 2D materialsthat can be thinned down to 5 nm. Magnetic characterization shows that FePd2Te2 is an easy-plane ferromagnet with Tc 183 K and strong in-plane uniaxial magnetic anisotropy. Magnetoresistance and anomalous Hall effect demonstrate that ferromagnetism could maintain in FePd2Te2 flakes with large coercivity. A crystal twinning effect is observed by scanning tunneling microscopy which makes the Fe chains right-angle bent in the cleavage plane and creates an intriguing spin texture. Our results show that FePd2Te2 is a correlated anisotropic 2D magnets that may attract multidisciplinary research interests.

cond-mat.mtrl-sci↗

arXiv2024

Xinyu: An Efficient LLM-based System for Commentary Generation

Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skilled commentators. Large language models (LLMs) have simplified the process of natural language generation, but their direct application in commentary creation still faces challenges due to unique task requirements. These requirements can be categorized into two levels: 1) fundamental requirements, which include creating well-structured and logically consistent narratives, and 2) advanced requirements, which involve generating quality arguments and providing convincing evidence. In this paper, we introduce Xinyu, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries. To meet the fundamental requirements, we deconstruct the generation process into sequential steps, proposing targeted strategies and supervised fine-tuning (SFT) for each step. To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology. To evaluate the generated commentaries more fairly, corresponding to the two-level requirements, we introduce a comprehensive evaluation metric that considers five distinct perspectives in commentary generation. Our experiments confirm the effectiveness of our proposed system. We also observe a significant increase in the efficiency of commentators in real-world scenarios, with the average time spent on creating a commentary dropping from 4 hours to 20 minutes. Importantly, such an increase in efficiency does not compromise the quality of the commentaries.

cs.CL↗

arXiv2024

ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features

Extensive research has revealed that adversarial examples (AE) pose a significant threat to voice-controllable smart devices. Recent studies have proposed black-box adversarial attacks that require only the final transcription from an automatic speech recognition (ASR) system. However, these attacks typically involve many queries to the ASR, resulting in substantial costs. Moreover, AE-based adversarial audio samples are susceptible to ASR updates. In this paper, we identify the root cause of these limitations, namely the inability to construct AE attack samples directly around the decision boundary of deep learning (DL) models. Building on this observation, we propose ALIF, the first black-box adversarial linguistic feature-based attack pipeline. We leverage the reciprocal process of text-to-speech (TTS) and ASR models to generate perturbations in the linguistic embedding space where the decision boundary resides. Based on the ALIF pipeline, we present the ALIF-OTL and ALIF-OTA schemes for launching attacks in both the digital domain and the physical playback environment on four commercial ASRs and voice assistants. Extensive evaluations demonstrate that ALIF-OTL and -OTA significantly improve query efficiency by 97.7% and 73.3%, respectively, while achieving competitive performance compared to existing methods. Notably, ALIF-OTL can generate an attack sample with only one query. Furthermore, our test-of-time experiment validates the robustness of our approach against ASR updates.

cs.CR↗

arXiv2024

Meili: Enabling SmartNIC as a Service in the Cloud

SmartNICs are touted as an attractive substrate for network application offloading, offering benefits in programmability, host resource saving, and energy efficiency. The current usage restricts offloading to local hosts and confines SmartNIC ownership to individual application teams, resulting in poor resource efficiency and scalability. This paper presents Meili, a novel system that realizes SmartNIC as a service to address these issues. Meili organizes heterogeneous SmartNIC resources as a pool and offers a unified one-NIC abstraction to application developers. This allows developers to focus solely on the application logic while dynamically optimizing their performance needs. Our evaluation on NVIDIA BlueField series and AMD Pensando SmartNICs demonstrates that Meili achieves scalable single-flow throughput with a maximum 8 μs latency overhead and enhances resource efficiency by 3.07$\times$ compared to standalone deployments and 1.44$\times$ compared to state-of-the-art microservice deployments.

cs.NI↗

arXiv2024

MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search

Traffic allocation is a process of redistributing natural traffic to products by adjusting their positions in the post-search phase, aimed at effectively fostering merchant growth, precisely meeting customer demands, and ensuring the maximization of interests across various parties within e-commerce platforms. Existing methods based on learning to rank neglect the long-term value of traffic allocation, whereas approaches of reinforcement learning suffer from balancing multiple objectives and the difficulties of cold starts within realworld data environments. To address the aforementioned issues, this paper propose a multi-objective deep reinforcement learning framework consisting of multi-objective Q-learning (MOQ), a decision fusion algorithm (DFM) based on the cross-entropy method(CEM), and a progressive data augmentation system(PDA). Specifically. MOQ constructs ensemble RL models, each dedicated to an objective, such as click-through rate, conversion rate, etc. These models individually determine the position of items as actions, aiming to estimate the long-term value of multiple objectives from an individual perspective. Then we employ DFM to dynamically adjust weights among objectives to maximize long-term value, addressing temporal dynamics in objective preferences in e-commerce scenarios. Initially, PDA trained MOQ with simulated data from offline logs. As experiments progressed, it strategically integrated real user interaction data, ultimately replacing the simulated dataset to alleviate distributional shifts and the cold start problem. Experimental results on real-world online e-commerce systems demonstrate the significant improvements of MODRL-TA, and we have successfully deployed MODRL-TA on an e-commerce search platform.

cs.LG↗

arXiv2024

$μ$Drive: User-Controlled Autonomous Driving

Autonomous Vehicles (AVs) rely on sophisticated Autonomous Driving Systems (ADSs) to provide passengers a satisfying and safe journey. The individual preferences of riders plays a crucial role in shaping the perception of safety and comfort while they are in the car. Existing ADSs, however, lack mechanisms to systematically capture and integrate rider preferences into their planning modules. To bridge this gap, we propose $μ$Drive, an event-based Domain-Specific Language (DSL) designed for specifying autonomous vehicle behaviour. $μ$Drive enables users to express their preferences through rules triggered by contextual events, such as encountering obstacles or navigating complex traffic situations. These rules dynamically adjust the parameter settings of the ADS planning module, facilitating seamless integration of rider preferences into the driving plan. In our evaluation, we demonstrate the feasibility and efficacy of $μ$Drive by integrating it with the Apollo ADS framework. Our findings show that users can effectively influence Apollo's planning through $μ$Drive, assisting ADS in achieving improved compliance with traffic regulations. The response time for $μ$Drive commands remains consistently at the second or millisecond level. This suggests that $μ$Drive may help pave the way to more personalizsed and user-centric AV experiences.

cs.SE↗

arXiv2024

Contact interaction study of proton parton distributions

Using a symmetry-preserving formulation of a vector$\,\times\,$vector contact interaction (SCI) and treating the proton as a quark + interacting-diquark bound state, whose structure is obtained by solving a Poincaré-covariant Faddeev equation, we provide a comprehensive, coherent set of predictions for unpolarised and polarised proton parton distribution functions (DFs): valence, glue, and four-flavour separated sea. The results enable many themes to be addressed, including: the asymmetry of antimatter in the proton; the neutron:proton structure function ratio; helicity retention in hard scattering processes; the charm quark momentum fraction; the sign and size of the polarised gluon DF; and the origin of the proton spin. In all cases where sound analyses of data are available, SCI predictions are semiquantitatively in agreement with the results. Those mismatches which exist are typically attributable to the momentum-independence of the underlying interaction. Judiciously interpreted, the SCI delivers a sound and insightful explanation of proton structure as expressed in DFs.

hep-ph↗

arXiv2024

Technique Report of CVPR 2024 PBDL Challenges

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches.

cs.CV↗

arXiv2024

Magnetic properties of van der Waals layered single crystals DyOBr and SmOCl

Two-dimensional van der Waals single crystals DyOBr and SmOCl have been grown by flux method and their anisotropic magnetic properties are reported. DyOBr orders antiferromagnetically at T$_{N}$=9.5 K with magnetic moments lying along $a$-axis, similar as DyOCl. Its magnetic susceptibility shows an anomaly at T$^{*}$=30 K possibly due to the crystal field effect. Furthermore a 1/3 magnetization plateau is clearly observed under H$\parallel$a and H$\parallel$[110], which might be a field-induced spin-flop phase or some exotic quantum magnetic state. On the other hand, isostructural SmOCl undergoes an antiferromagnetic transition at T$_{N}$=7.1 K and exhibits a contrasting Ising-like perpendicular $c$-axis magnetic anisotropy, which could be well explained by our crystal field calculations. Both DyOBr and SmOCl are insulators with band gap of $\sim$5 eV, our results suggest they are promising in building van der Waals heterostructures and applications in multifunctional devices.

cond-mat.mtrl-sci↗

arXiv2024

Stability of Weighted Majority Voting under Estimated Weights

Weighted Majority Voting (WMV) is a well-known optimal decision rule for collective decision making, given the probability of sources to provide accurate information (trustworthiness). However, in reality, the trustworthiness is not a known quantity to the decision maker - they have to rely on an estimate called trust. A (machine learning) algorithm that computes trust is called unbiased when it has the property that it does not systematically overestimate or underestimate the trustworthiness. To formally analyse the uncertainty to the decision process, we introduce and analyse two important properties of such unbiased trust values: stability of correctness and stability of optimality. Stability of correctness means that the decision accuracy that the decision maker believes they achieved is equal to the actual accuracy. We prove stability of correctness holds. Stability of optimality means that the decisions made based on trust, are equally good as they would have been if they were based on trustworthiness. Stability of optimality does not hold. We analyse the difference between the two, and bounds thereon. We also present an overview of how sensitive decision correctness is to changes in trust and trustworthiness.

cs.AI↗

arXiv2024

Cross-domain-aware Worker Selection with Training for Crowdsourced Annotation

Annotation through crowdsourcing draws incremental attention, which relies on an effective selection scheme given a pool of workers. Existing methods propose to select workers based on their performance on tasks with ground truth, while two important points are missed. 1) The historical performances of workers in other tasks. In real-world scenarios, workers need to solve a new task whose correlation with previous tasks is not well-known before the training, which is called cross-domain. 2) The dynamic worker performance as workers will learn from the ground truth. In this paper, we consider both factors in designing an allocation scheme named cross-domain-aware worker selection with training approach. Our approach proposes two estimation modules to both statistically analyze the cross-domain correlation and simulate the learning gain of workers dynamically. A framework with a theoretical analysis of the worker elimination process is given. To validate the effectiveness of our methods, we collect two novel real-world datasets and generate synthetic datasets. The experiment results show that our method outperforms the baselines on both real-world and synthetic datasets.

cs.LG↗