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Observation of Generalized t-J Spin Dynamics with Tunable Dipolar Interactions

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arXiv 2024年

作者： Carroll, Annette N. Hirzler, Henrik Miller, Calder Wellnitz, David Muleady, Sean R. Lin, Junyu Zamarski, Krzysztof P. Wang, Reuben R.W. Bohn, John L. Rey, Ana Maria Ye, Jun JILA National Institute of Standards and Technology Department of Physics University of Colorado BoulderCO80309 United States Joint Center for Quantum Information and Computer Science Joint Quantum Institute National Institute of Standards and Technology University of Maryland College ParkMD20742 United States Institut für Experimentalphysik Zentrum für Quantenphysik Universität Innsbruck Innsbruck6020 Austria

Long-range and anisotropic dipolar interactions profoundly modify the dynamics of particles hopping in a periodic lattice potential. Here, we report the realization of a generalized t-J model with dipolar interactions using a system of ultracold fermionic molecules with spin encoded in the two lowest rotational states. We systematically explore the role of dipolar Ising and spin-exchange couplings and the effect of motion on spin dynamics. The model parameters can be controlled independently, with dipolar couplings tuned by electric fields and motion regulated by optical lattices. Using Ramsey spectroscopy, we observed interaction-driven contrast decay that depends strongly both on the strength of the anisotropy between Ising and spin-exchange couplings and on motion. These observations are supported by theory models established in different motional regimes that provide intuitive pictures of the underlying physics. This study paves the way for future exploration of kinetic spin dynamics and quantum magnetism with highly tunable molecular platforms in regimes challenging for existing numerical and analytical methods, and it could shed light on the complex behaviors observed in real materials. Copyright © 2024, The Authors. All rights reserved.

关键词： Spin dynamics

Digital Video Forensics: Review, Challenges, Parameters and Future Direction

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Digital Video Forensics: Review, Challenges, Parameters and ...

Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Gurvinder Singh Raj Gaurang Tiwari Ambuj Kumar Agarwal Himani Maheshwari Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Engineering Sharda School of Engineering and Technology Sharda University Greater Noida School of Computing Graphic Era Hill University Dehradun Uttarakhand

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Digital videos are recorded in everyday circumstances for a variety of reasons, including protection, preserving life-related experiences, and providing accurate proof. The growth of multimedia technology has led to share the digital videos on social media platforms. A variety of editing tools is used to alter the information presented in the digital videos with software. Consequently, it is more significant to secure the information in the digital videos. This work provides a review of digital video forensics. Firstly, digital video forensics has been introduced with its types firstly and after that, various kinds of digital video forgery like intra-frame forgery and inter-frame forgery are discussed. These forgery kinds have been further divided into different categories. The types of existing passive techniques have also been described for the detection of digital video forgeries in detail. Moreover, this paper delivers the new and innovative challenges within the digital video forensics. Besides, the evaluation parameters which are used in the existing techniques are discussed in detail. At last, future directions in the realm of digital video forensics are detailed in this paper.

关键词： Visualization Reviews Social networking (online) Forensics Streaming media Feature extraction Forgery Software Smart phones Videos

Multimodal Fusion of Echocardiography and Electronic Health Records for the Detection of Cardiac Amyloidosis

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arXiv 2024年

作者： Feng, Zishun Sivak, Joseph A. Krishnamurthy, Ashok K. Department of Computer Science University of North Carolina Chapel Hill United States Division of Cardiology University of North Carolina Chapel Hill United States Renaissance Computing Institute University of North Carolina Chapel Hill United States

Cardiac amyloidosis, a rare and highly morbid condition, presents significant challenges for detection through echocardiography. Recently, there has been a surge in proposing machine-learning algorithms to identify cardiac amyloidosis, with the majority being imaging-based deep-learning approaches that require extensive data. In this study, we introduce a novel transformer-based multimodal fusion algorithm that leverages information from both imaging and electronic health records. Specifically, our approach utilizes echocardiography videos from both the parasternal long-axis (PLAX) view and the apical 4-chamber (A4C) view along with patients’ demographic data, laboratory tests, and cardiac metrics to predict the probability of cardiac amyloidosis. We evaluated our method using 5-fold cross-validation on a dataset comprising 41 patients and achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.94. The experimental results demonstrate that our approach can achieve competitive results with a significantly smaller dataset compared to prior imaging-based methods that required data from thousands of patients. This underscores the potential of leveraging multimodal data to enhance diagnostic accuracy in the identification of complex cardiac conditions such as cardiac amyloidosis. Copyright © 2024, The Authors. All rights reserved.

关键词： Echocardiography

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Umlaut information

arXiv 2025年

作者： Girardi, Filippo Oufkir, Aadil Regula, Bartosz Tomamichel, Marco Berta, Mario Lami, Ludovico Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa56126 Italy QuSoft Science Park 123 Amsterdam1098 XG Netherlands Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands Institute for Quantum Information RWTH Aachen University Aachen Germany Saitama Wako351-0198 Japan Centre for Quantum Technologies National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Institute for Quantum Information RWTH Aachen University Germany

The sphere-packing bound quantifies the error exponent for noisy channel coding for rates above a critical value. Here, we study the zero-rate limit of the sphere-packing bound and show that it has an intriguing single-letter form, which we call the umlaut information of the channel, inspired by the lautum information introduced by Palomar and Verdú. Unlike the latter quantity, we show that the umlaut information is additive for parallel uses of channels. We show that it has a twofold operational interpretation: as the zero-rate error exponent of non-signalling–assisted coding on the one hand, and as the zero-rate error exponent of list decoding in the large list limit on the other. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel coding

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

SUPPLYDECK – A Blockchain-Based Pharmaceutical Supply Chain Management

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SUPPLYDECK – A Blockchain-Based Pharmaceutical Supply Chain...

Advancements in computing (ICAC), International Conference on

作者： A.A.B. Amarasinghe G G.M.H. Gamagedara S Pitigala K.K H.D.A. Subawickrama E Kavinga Yapa Abeywardena Kanishka Yapa Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application.

关键词： Drugs Supply chain management Supply chains Smart contracts Manuals Machine learning Companies

REVEALING THE 3D COSMIC WEB THROUGH GRAVITATIONALLY CONSTRAINED NEURAL FIELDS

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arXiv 2025年

作者： Zhao, Brandon Levis, Aviad Connor, Liam Srinivasan, Pratul P. Bouman, Katherine L. Department of Computing and Mathematical Sciences California Institute of Technology United States Department of Computer Science University of Toronto Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto Canada Center for Astrophysics Harvard & Smithsonian United States Google DeepMind United Kingdom Departments of Astronomy and Electrical Engineering California Institute of Technology United States

Weak gravitational lensing is the slight distortion of galaxy shapes caused primarily by the gravitational effects of dark matter in the universe. In our work, we seek to invert the weak lensing signal from 2D telescope images to reconstruct a 3D map of the universe’s dark matter field. While inversion typically yields a 2D projection of the dark matter field, accurate 3D maps of the dark matter distribution are essential for localizing structures of interest and testing theories of our universe. However, 3D inversion poses significant challenges. First, unlike standard 3D reconstruction that relies on multiple viewpoints, in this case, images are only observed from a single viewpoint. This challenge can be partially addressed by observing how galaxy emitters throughout the volume are lensed. However, this leads to the second challenge: the shapes and exact locations of unlensed galaxies are unknown, and can only be estimated with a very large degree of uncertainty. This introduces an overwhelming amount of noise which nearly drowns out the lensing signal completely. Previous approaches tackle this by imposing strong assumptions about the structures in the volume. We instead propose a methodology using a gravitationally-constrained neural field to flexibly model the continuous matter distribution. We take an analysis-by-synthesis approach, optimizing the weights of the neural network through a fully differentiable physical forward model to reproduce the lensing signal present in image measurements. We showcase our method on simulations, including realistic simulated measurements of dark matter distributions that mimic data from upcoming telescope surveys. Our results show that our method can not only outperform previous methods, but importantly is also able to recover potentially surprising dark matter structures. © 2025, CC BY.

关键词： Dark Matter

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

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Intelligent Automation & Soft computing 2024年第3期39卷 417-436页

作者： Muhammad S.Alam Farhan B.Mohamed Ali Selamat Faruk Ahmed AKM B.Hossain Department of Emergent Computing School of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Malaysia-Japan International Institute of Technology(MJIIT) Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia Media and Game Innovation Centre of Excellence(MaGICX) Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Engineering Technology University of MemphisMemphisTN38152USA Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Information System and Cyber Security College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance *** scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and *** research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image *** annotated image dataset trains the proposed system and predicts the camera pose in *** system mainly improved the localization performance of indoor cameras by more accurately predicting the camera *** also recognizes the scene changes during the sequence and evaluates the effects of these *** system achieved high accuracy and real-time *** scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact ***,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

关键词： Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)

Security in the World of Internet of Things (IoT): Overcoming Challenges

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Security in the World of Internet of Things (IoT): Overcomin...

Sustainable computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Venkat Tulasi Krishna Gannavaram Sravya Desu Venkata Rohith Jujjuru Sri Vatsa Venkata Tejaswi Renikunta Arun Kumar Gajula Ganta Raghotham Reddy School of Electrical Computer and Energy Engineering Arizona State University Tempe USA School of Computing and Augmented Intelligence Arizona State University Tempe USA Department of Electronics and Communication Engineering Kakatiya Institute of Technology and Science Warangal India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Internet of Things (IoT) is turning out to be more prominent in our everyday existence, with smart home appliances and industrial control systems serving as examples. But the proliferation of IoT devices and internet connectivity increased the potential of security breaches. Botnet attacks, injection attacks, Domain Name Systems (DNS) spoofing attacks, firmware assaults, zero-day attacks, brute-force attacks, and cryptographic attacks may be used against IoT devices. These assaults may result in the loss of data, illegal access, and even physical damage to infrastructure and people. There are various potential courses of action. Machine Learning subpart of Artificial Intelligence have the capacity for finding and prevent dangers in real time. With blockchain technology, an IoT network can have a secure and unbreakable a record of every exchange and data transfers. Hardware-based security techniques, such as secure boot and trusted execution environments, may protect IoT devices against attacks that seek to compromise the underlying hardware or firmware. Standardizing security procedures may ensure that all devices can work together safely and achieve a given degree of security. The use of privacy-enhancing technology, IoT data may be kept confidential and sensitive information disguised.

关键词： Data privacy Protocols Technology Smart homes Turning Real-time systems Internet of Things Security Domain Name System Microprogramming