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In this paper, we present rIC3, an efficient bit-level hardware model checker primarily based on the IC3 algorithm. It boasts a highly efficient implementation and integrates several recently proposed optimizations, such as the specifically optimized SAT solver, dynamically adjustment of generalization strategies, and the use of predicates with internal signals, among others. As a first-time participant in the Hardware Model Checking Competition, rIC3 was independently evaluated as the best-performing tool, not only in the bit-level track but also in the word-level bit-vector track through bit-blasting. Our experiments further demonstrate significant advancements in both efficiency and scalability. rIC3 can also serve as a backend for verifying industrial RTL designs using SymbiYosys. Additionally, the source code of rIC3 is highly modular, with the IC3 algorithm module being particularly concise, making it an academic platform that is easy to modify and extend.
A muon collider (MC) would require a high-power proton driver to generate intense muon beams at the start of the accelerator chain. Like other high-power facilities, the driver would accumulate intense proton bunches via charge-exchange injection from a linac into a ring. However, unlike other facilities, the MC would require an extreme longitudinal compression of the bunch. We aim to experimentally study the proposed compression scheme at the Spallation Neutron Source (SNS), which boasts a world-leading proton bunch intensity. In this paper, we describe the operating parameters of the SNS compared to the MC design, the capabilities of the current RF system, and the available beam diagnostics in the SNS accumulator ring. We also present initial simulations and experiments to judge the feasibility of the proposed research.
Cable transmission enables motors of robotic arm to operate lightweight and low-inertia joints remotely in various environments, but it also creates issues with motion coupling and cable routing that can reduce arm's control precision and performance. In this paper, we present a novel motion decoupling mechanism with low-friction to align the cables and efficiently transmit the motor's power. By arranging these mechanisms at the joints, we fabricate a fully decoupled and lightweight cable-driven robotic arm called D3-Arm with all the electrical components be placed at the base. Its 776 mm length moving part boasts six degrees of freedom (DOF) and only 1.6 kg weights. To address the issue of cable slack, a cable-pretension mechanism is integrated to enhance the stability of long-distance cable transmission. Through a series of comprehensive tests, D3-Arm demonstrated 1.29 mm average positioning error and 2.0 kg payload capacity, proving the practicality of the proposed decoupling mechanisms in cable-driven robotic arm.
Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7\% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data.
With its significant security potential, the quantum internet is poised to revolutionize technologies like cryptography and communications. Although it boasts enhanced security over traditional networks, the quantum internet still encounters unique security challenges essential for safeguarding its Confidentiality, Integrity, and Availability (CIA). This study explores these challenges by analyzing the vulnerabilities and the corresponding mitigation strategies across different layers of the quantum internet, including physical, link, network, and application layers. We assess the severity of potential attacks, evaluate the expected effectiveness of mitigation strategies, and identify vulnerabilities within diverse network configurations, integrating both classical and quantum approaches. Our research highlights the dynamic nature of these security issues and emphasizes the necessity for adaptive security measures. The findings underline the need for ongoing research into the security dimension of the quantum internet to ensure its robustness, encourage its adoption, and maximize its impact on society.
To address the magnetization dynamics in ferromagnetic materials described by the Landau-Lifshitz-Gilbert equation under large damping parameters, a third-order accurate numerical scheme is developed by building upon a second-order method \cite{CaiChenWangXie2022} and leveraging its efficiency. This method boasts two key advantages: first, it only involves solving linear systems with constant coefficients, enabling the use of fast solvers and thus significantly enhancing numerical efficiency over existing first or second-order approaches. Second, it achieves third-order temporal accuracy and fourth-order spatial accuracy, while being unconditionally stable for large damping parameters. Numerical tests in 1D and 3D scenarios confirm both its third-order accuracy and efficiency gains. When large damping parameters are present, the method demonstrates unconditional stability and reproduces physically plausible structures. For domain wall dynamics simulations, it captures the linear relationship between wall velocity and both the damping parameter and external magnetic field, outperforming lower-order methods in this regard.
The Guitar nebula surrounding PSR B2224+65 boasts a pulsar X-ray filament likely aligned with the local magnetic field. We present new RoboPol stellar polarization data distributed along the line-of-sight to the pulsar. The polarizing effect of intervening magnetized dust allows us to extract a model for the dust-weighted magnetic field. We detect a magnetic field angle consistent with the filament if the pulsar is located in the more distant zone of its parallax-estimated distance range.
We introduce a novel random integration algorithm that boasts both high convergence order and polynomial tractability for functions characterized by sparse frequencies or rapidly decaying Fourier coefficients. Specifically, for integration in periodic isotropic Sobolev space and the isotropic Sobolev space with compact support, our approach attains a nearly optimal root mean square error (RMSE) bound. In contrast to previous nearly optimal algorithms, our method exhibits polynomial tractability, ensuring that the number of samples does not scale exponentially with increasing dimensions. Our integration algorithm also enjoys nearly optimal bound for weighted Korobov space. Furthermore, the algorithm can be applied without the need for prior knowledge of weights, distinguishing it from the component-by-component algorithm. For integration in the Wiener algebra, the sample complexity of our algorithm is independent of the decay rate of Fourier coefficients. The effectiveness of the integration is confirmed through numerical experiments.
This paper presents a novel method of smoke removal from the laparoscopic images. Due to the heterogeneous nature of surgical smoke, a two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The utilization of the lightness channel plays a pivotal role in providing vital information pertaining to smoke density. The reconstruction of smoke-free image is guided by a hybrid embedding, which combines the estimated smoke mask with the initial image. Experimental results demonstrate that the proposed method boasts a Peak Signal to Noise Ratio that is $2.79\%$ higher than the state-of-the-art methods, while also exhibits a remarkable $38.2\%$ reduction in run-time. Overall, the proposed method offers comparable or even superior performance in terms of both smoke removal quality and computational efficiency when compared to existing state-of-the-art methods. This work will be publicly available on http://homepage.hit.edu.cn/wpgao
Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning models, such as logistic regression, as the meta-model. This paper introduces a novel approach that integrates computational geometry techniques, specifically solving the maximum weighted rectangle problem, to develop a new meta-model for binary classification. Our method is evaluated on multiple open datasets, with statistical analysis showing its stability and demonstrating improvements in accuracy compared to current state-of-the-art stacking methods with out-of-fold predictions. This new stacking method also boasts two significant advantages: enhanced interpretability and the elimination of hyperparameter tuning for the meta-model, thus increasing its practicality. These merits make our method highly applicable not only in stacking ensemble learning but also in various real-world applications, such as hospital health evaluation scoring and bank credit scoring systems, offering a fresh evaluation perspective.
We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations. We introduce a new approach Camal, which boasts the following features: (1) ML-Aided: Camal is the first attempt to apply active learning to tune LSM-tree based key-value stores. The learning process is coupled with traditional cost models to improve the training process; (2) Decoupled Active Learning: backed by rigorous analysis, Camal adopts active learning paradigm based on a decoupled tuning of each parameter, which further accelerates the learning process; (3) Easy Extrapolation: Camal adopts an effective mechanism to incrementally update the model with the growth of the data size; (4) Dynamic Mode: Camal is able to tune LSM-tree online under dynamically changing workloads; (5) Significant System Improvement: By integrating Camal into a full system RocksDB, the system performance improves by 28% on average and up to 8x compared to a state-of-the-art RocksDB design.
Watermarking has recently emerged as an effective strategy for detecting the outputs of large language models (LLMs). Most existing schemes require white-box access to the model's next-token probability distribution, which is typically not accessible to downstream users of an LLM API. In this work, we propose a principled watermarking scheme that requires only the ability to sample sequences from the LLM (i.e. black-box access), boasts a distortion-free property, and can be chained or nested using multiple secret keys. We provide performance guarantees, demonstrate how it can be leveraged when white-box access is available, and show when it can outperform existing white-box schemes via comprehensive experiments.
The primary objective of non-intrusive load monitoring (NILM) techniques is to monitor and track power consumption within residential buildings. This is achieved by approximating the consumption of each individual appliance from the aggregate energy measurements. Event-based NILM solutions are generally more accurate than other methods. Our paper introduces a novel event detection algorithm called Tukey's Fences-based event detector (TFED). This algorithm uses the fast Fourier transform in conjunction with the Tukey fences rule to detect variations in the aggregated current signal. The primary benefit of TFED is its superior ability to accurately pinpoint the start times of events, as demonstrated through simulations. Our findings reveal that the proposed algorithm boasts an impressive 99% accuracy rate, surpassing the accuracy of other recent works in the literature such as the Cepstrum and $χ^2$ GOF statistic-based analyses, which only achieved 98% accuracy.
The ground-based technique for imaging atmospheric Cherenkov telescopes became a rapidly developing and powerful branch of science. Thanks to this technique, over 250 very high-energy gamma-ray sources of galactic and extragalactic origin have been discovered. Many fundamental questions of astrophysics, astro-particle physics, the physics of cosmic rays and cosmology are the focus of this technique. In the past 33 years since the discovery of the first gamma-ray source, the Crab Nebula, the discipline has made remarkable progress. Today, the technology boasts highly sensitive telescopes capable of detecting a point source 100 times fainter than the standard candle, the Crab Nebula, in 25 hours of observation. Further developments in this technology led to the Cherenkov Telescope Array (CTA), the next-generation large instrument. The sensitivity of CTA will be several times higher than that of the current best instruments. This article presents a brief history of ground-based very high energy gamma-ray astrophysics.
Future robots will navigate perilous, remote environments with resilience and autonomy. Researchers have proposed building robots with compliant bodies to enhance robustness, but this approach often sacrifices the autonomous capabilities expected of rigid robots. Inspired by tensegrity architecture, we introduce a tensegrity robot -- a hybrid robot made from rigid struts and elastic tendons -- that demonstrates the advantages of compliance and the autonomy necessary for task performance. This robot boasts impact resistance and autonomy in a field environment and additional advances in the state of the art, including surviving harsh impacts from drops (at least 5.7 m), accurately reconstructing its shape and orientation using on-board sensors, achieving high locomotion speeds (18 bar lengths per minute), and climbing the steepest incline of any tensegrity robot (28 degrees). We characterize the robot's locomotion on unstructured terrain, showcase its autonomous capabilities in navigation tasks, and demonstrate its robustness by rolling it off a cliff.
Modern autonomous vehicle simulators feature an ever-growing library of assets, including vehicles, buildings, roads, pedestrians, and more. While this level of customization proves beneficial when creating virtual urban environments, this process becomes cumbersome when intending to train within a digital twin or a duplicate of a real scene. Gaussian splatting emerged as a powerful technique in scene reconstruction and novel view synthesis, boasting high fidelity and rendering speeds. In this paper, we introduce GSAVS, an autonomous vehicle simulator that supports the creation and development of autonomous vehicle models. Every asset within the simulator is a 3D Gaussian splat, including the vehicles and the environment. However, the simulator runs within a classical 3D engine, rendering 3D Gaussian splats in real-time. This allows the simulator to utilize the photorealism that 3D Gaussian splatting boasts while providing the customization and ease of use of a classical 3D engine.
The description of thermodynamic phase transitions in terms of percolation transitions of suitably defined clusters has a long tradition and boasts a number of important successes, the most prominent ones being in ferromagnetic lattice models. Spin glasses and other frustrated systems are not among them as the clusters of aligned spins usually considered in this context start to percolate in the disordered phase and hence fail to indicate the onset of ordering. In this mini-review we provide an overview of the state of the art in this field, including recent advances, and outline the main open questions in the area.
Amidst the COVID-19 pandemic, with many organizations, schools, colleges, and universities transitioning to virtual platforms, students encountered difficulties in acquiring PCs such as desktops or laptops. The starting prices, around 15,000 INR, often failed to offer adequate system specifications, posing a challenge for consumers. Additionally, those reliant on laptops for work found the conventional approach cumbersome. Enter the "Portable Smart Computer," a leap into the future of computing. This innovative device boasts speed and performance comparable to traditional desktops but in a compact, energy-efficient, and cost-effective package. It delivers a seamless desktop experience, whether one is editing documents, browsing multiple tabs, managing spreadsheets, or creating presentations. Moreover, it supports programming languages like Python, C, C++, as well as compilers such as Keil and Xilinx, catering to the needs of programmers.
In recent years, large language models (LLMs) have demonstrated notable success across various tasks, but the trustworthiness of LLMs is still an open problem. One specific threat is the potential to generate toxic or harmful responses. Attackers can craft adversarial prompts that induce harmful responses from LLMs. In this work, we pioneer a theoretical foundation in LLMs security by identifying bias vulnerabilities within the safety fine-tuning and design a black-box jailbreak method named DRA (Disguise and Reconstruction Attack), which conceals harmful instructions through disguise and prompts the model to reconstruct the original harmful instruction within its completion. We evaluate DRA across various open-source and closed-source models, showcasing state-of-the-art jailbreak success rates and attack efficiency. Notably, DRA boasts a 91.1% attack success rate on OpenAI GPT-4 chatbot.
Whether stemming from malicious intent or natural occurrences, faults and errors can significantly undermine the reliability of any architecture. In response to this challenge, fault detection assumes a pivotal role in ensuring the secure deployment of cryptosystems. Even when a cryptosystem boasts mathematical security, its practical implementation may remain susceptible to exploitation through side-channel attacks. In this paper, we propose a lightweight fault detection architecture tailored for modular exponentiation, a building block of numerous cryptographic applications spanning from classical cryptography to post quantum cryptography. Based on our simulation and implementation results on ARM Cortex-A72 processor, and AMD/Xilinx Zynq Ultrascale+, and Artix-7 FPGAs, our approach achieves an error detection rate close to 100%, all while introducing a modest computational overhead of approximately 7% and area overhead of less than 1% compared to the unprotected architecture. To the best of our knowledge, such an approach benchmarked on ARM processor and FPGA has not been proposed and assessed to date.