检索结果-内蒙古大学图书馆

Reasonable Bounds for Combinatorial Lines of Length Three

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Bhangale, Amey Khot, Subhash Liu, Yang P. Minzer, Dor Department of Computer Science and Engineering University of California Riverside United States Department of Computer Science Courant Institute of Mathematical Sciences New York University United States School of Mathematics Institute for Advanced Study PrincetonNJ United States Department of Mathematics Massachusetts Institute of Technology United States

We prove that any subset A ⊆ [3]n with 3−n|A| > (log log log log n)−c contains a combinatorial line of length 3, i.e., x, y, z ∈ A, not all equal, with xi = yi = zi or (xi, yi, zi) = (0, 1, 2) for all i = 1, 2, . . ., n. This improves on the previous best bound of 3−n|A| > Ω((log∗ n)−1/2) of [D.H.J. Polymath, Ann. of Math. 2012]. Copyright © 2024, The Authors. All rights reserved.

关键词：

On Approximability of Satisfiable k-CSPs: VI

学校读者我要写书评

暂无评论

arXiv 2024年

We prove local and global inverse theorems for general 3-wise correlations over pairwise-connected distributions. Let µ be a distribution over Σ × Γ × Φ such that the supports of µxy, µxz, and µyz are all connected, and let f: Σn → C, g: Γn → C, h: Φn → C be 1-bounded functions satisfying (Equation presented) In this setting, our local inverse theorem asserts that there is (Equation presented) such that with probability at least δ, a random restriction of f down to δn coordinates δ-correlates to a product function. To get a global inverse theorem, we prove a restriction inverse theorem for general product functions, stating that if a random restriction of f down to δn coordinates is δcorrelated with a product function with probability at least δ, then f is 2−poly(log(1/δ))-correlated with a function of the form L · P, where L is a function of degree poly(1/δ), kLk2 6 1, and P is a product function. We show applications to property testing and to additive combinatorics. In particular, we show the following result via a density increment argument. Let Σ be a finite set and S ⊆ Σ × Σ × Σ such that: (1) (x, x, x) ∈ S for all x ∈ S, and (2) the supports of Sxy, Sxz, and Syz are all connected. Then, any set A ⊆ Σn with |Σ|−n|A| > Ω((log log log n)−c) contains x, y, z ∈ A, not all equal, such that (xi, yi, zi) ∈ S for all i. This gives the first reasonable bounds for the restricted 3-AP problem over finite fields. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

Learning to Generalize for Cross-domain QA

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Niu, Yingjie Yang, Linyi Dong, Ruihai Zhang, Yue School of Computer Science University College Dublin Ireland SFI Centre for Research Training in Machine Learning Institute of Advanced Technology Westlake Institute for Advanced Study China School of Engineering Westlake University China

There have been growing concerns regarding the out-of-domain generalization ability of natural language processing (NLP) models, particularly in question-answering (QA) tasks. Current synthesized data augmentation methods for QA are hampered by increased training costs. To address this issue, we propose a novel approach that combines prompting methods and linear probing then fine-tuning strategy, which does not entail additional cost. Our method has been theoretically and empirically shown to be effective in enhancing the generalization ability of both generative and discriminative models. Our approach outperforms state-of-the-art baselines, with an average increase in F1 score of 4.5%-7.9%. Furthermore, our method can be easily integrated into any pre-trained models and offers a promising solution to the under-explored cross-domain QA task. We release our source code at Github. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

Super-Robust Nonadiabatic Holonomic Quantum Computation in coherence-protected Superconducting Circuits

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Yuan-Sheng Su, Zhaofeng Chen, Xiaosong Yung, Man-Hong School of Systems Science Beijing Normal University Beijing100875 China School of Electronics and Information Engineering Suzhou Vocational University Suzhou215104 China School of Computer Science and Technology University of Science and Technology of China Hefei230094 China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Hefei230026 China Institute for Advanced Study in Physics School of Physics Zhejiang University Hangzhou310058 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The schmeme of nonadiabatic holonomic quantum computation (NHQC) offers an error-resistant method for implementing quantum gates, capable of mitigating certain errors. However, the conventional NHQC schemes often entail longer operations concerning standard gate operations, making them more vulnerable to the effects of quantum decoherence. In this research, we propose an implementation of the Super-Robust NHQC scheme within the Decoherence-Free Subspace (DFS). SR-NHQC has demonstrated robustness against Global Control Errors (GCEs). By utilizing capacitance-coupled transmon qubits within a DFS, our approach enables universal gate operations on a scalable two-dimensional square lattice of superconducting qubits. Numerical simulations demonstrate the practicality of SR-NHQC in DFS, showcasing its superiority in mitigating GCEs and decoherence effects compared to conventional NHQC schemes. Our work presents a promising strategy for advancing the reliability of quantum computation in real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

Smart Home Security System Using Face Recognition Based on IoT- CNN

学校读者我要写书评

暂无评论

Smart Home Security System Using Face Recognition Based on I...

Information technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners.

关键词：

Realizing Linear Synaptic Plasticity in Electric Double Layer-Gated Transistors for Improved Predictive Accuracy and Efficiency in Neuromorphic Computing

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Manimaran, Nithil Harris Sutton, Cori Streamer, Jake Merkel, Cory Xu, Ke Microsystems Engineering School of Physics and Astronomy Rochester Institute of Technology RochesterNY14623 United States Multidisciplinary Study Rochester Institute of Technology RochesterNY14623 United States Department of Electrical and Computer Engineering Rochester Institute of Technology RochesterNY14623 United States School of Chemistry and Material Science Rochester Institute of Technology RochesterNY14623 United States

Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses with their ionic response and offer low power operations, making them suitable for neuromorphic applications. A critical consideration for artificial neural networks (ANNs) is achieving linear and symmetric plasticity (i.e., weight updates) during training, as this directly affects accuracy and efficiency. This study uses finite element modeling to explore EDLTs as artificial synapses in ANNs and investigates the underlying mechanisms behind the nonlinear weight updates observed experimentally in previous studies. By solving modified Poisson-Nernst-Planck (mPNP) equations, we examined ion dynamics within an EDL capacitor and their effects on plasticity, revealing that the rates of EDL formation and dissipation are concentration-dependent. Fixed-magnitude pulse inputs result in decreased formation and increased dissipation rates, leading to nonlinear weight updates. For a pulse magnitude of 1 V, both 1 ms 500 Hz and 5 ms 100 Hz pulse inputs saturated at less than half of the steady state EDL concentration, limiting the number of accessible states and operating range of devices. To address this, we developed a predictive linear ionic weight update solver (LIWUS) in Python to predict voltage pulse inputs that achieve linear plasticity. We then evaluated an ANN with linear and nonlinear weight updates on the MNIST classification task. The ANN with LIWUS-provided linear weight updates required 19% fewer (i.e. 5) epochs in both training and validation than the network with nonlinear weight updates to reach optimal performance. It achieved a 97.6% recognition accuracy, 1.5 – 4.2% higher than with nonlinear updates, and a low standard deviation of 0.02%. The network model is amenable to future spiking neural network applications, and the

关键词：

Suppression of magneto-optical transport in tilted Weyl semimetals by orbital magnetic moment

学校读者我要写书评

暂无评论

Physical Review B 2022年第16期105卷 165307-165307页

作者： Yang Gao Zhi-Qiang Zhang Hua Jiang Kai-He Ding College of Computer Science and Engineering Anhui University of Science and Technology Huainan 232001 China School of Physical Science and Technology Soochow University Suzhou 215006 China Institute for Advanced Study Soochow University Suzhou 215006 China Department of Physics and Electronic Science Changsha University of Science and Technology Changsha 410076 China

By incorporating the orbital magnetic moment, we investigate the magneto-optical transport of Weyl semimetals within a semiclassical approximation. In the linear or nonlinear response regime, an analytic expression for the magnetoconductivity is obtained, where a new term due to the orbital magnetic moment is added, and leads to a partial cancellation for the total magnetoconductivities. This suppressed feature is further manifested by analyzing the linear and quadratic contribution in the magnetic field to magnetoconductivities. This result may be used to explain the deviation between the recent theory and experiment.

关键词： Photoconductivity Weyl semimetal Boltzmann theory

Impact-aware Maneuver Decision with Enhanced Perception for Autonomous Vehicle

学校读者我要写书评

暂无评论

Impact-aware Maneuver Decision with Enhanced Perception for ...

International Conference on Data engineering

作者： Shuncheng Liu Yuyang Xia Xu Chen Jiandong Xie Han Su Kai Zheng School of Computer Science and Engineering University of Electronic Science and Technology of China China Huawei Cloud Database Innovation Lab China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Autonomous driving is an emerging technology that has developed rapidly over the last decade. There have been numerous interdisciplinary challenges imposed on the current transportation system by autonomous vehicles. In this paper, we conduct an algorithmic study on the autonomous vehicle decision-making process, which is a fundamental problem in the vehicle automation field and the root cause of most traffic congestion. We propose a perception-and-decision framework, called HEAD, which consists of an enHanced pErception module and a mAneuver Decision module. HEAD aims to enable the autonomous vehicle to perform safe, efficient, and comfortable maneuvers with minimal impact on other vehicles. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to predict the one-step future states for multiple surrounding vehicles in parallel, which deals with sensor limitations such as limited detection range and poor detection accuracy under occlusions. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a policy for the autonomous vehicle to perform maneuvers in continuous action space w.r.t. a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. Extensive experiments offer evidence that HEAD can advance the state of the art in terms of both macroscopic and microscopic effectiveness.

关键词：

Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve

学校读者我要写书评

暂无评论

Proceedings of the ACM on Management of Data 2025年第1期3卷 1-26页

作者： Xu Chen Shuncheng Liu Tong Yuan Tao Ye Kai Zeng Han Su Kai Zheng University of Electronic Science and Technology of China Chengdu China & GaussDB(DWS) Team Huawei Technologies Co. Ltd. Hangzhou China Huawei Technologies Co. Ltd. Hangzhou China Huawei Technologies Co. Ltd. Hangzhou China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of China Quzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China & Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

In the realm of big data and cloud analytics, efficiently managing and retrieving high-dimensional data presents a critical challenge. Traditional indexes often struggle with the storage overhead inherent in large datasets. There is a growing interest in the adoption of Small Materialize Aggregation (SMA) among cloud database vendors due to its ability to maintain lightweight block-level metadata, facilitating efficient block skipping. However, SMA performance relies heavily on data layout. This is especially critical in scenarios with wide tables containing hundreds of dimensions, where the curse of dimensionality exacerbates the issue. In this paper, we propose AdaCurve, a novel approach aimed at enhancing block skipping in high-dimensional datasets through adaptive optimization of data layout. Unlike conventional static and non-adaptive space-filling curves (SFCs), AdaCurve leverages machine learning to develop an adaptive curve---a dynamically adjusting optimal projection function tailored to high-dimensional workloads and data characteristics. We introduce an attention-based network to handle high-dimensional data and a learnable objective for training adaptive curves in an end-to-end manner. Extensive experiments conducted on the Spark with real-world datasets demonstrate the effectiveness of AdaCurve. We have shown that AdaCurve effectively scales to datasets with dimensions of up to 1,000 columns, achieving a 2.8× improvement in block skipping compared to SFCs.

关键词： data layout high dimension machine learning