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Energy-efficient flocking with nonlinear navigational feedback
No full textModeling collective motion in multi-agent systems has gained significant attention. Of particular interest are sufficient conditions for flocking dynamics. We present a generalization of the multi-agent model of Olfati--Saber with nonlinear navigational feedback forces. Unlike the original model, ours is not generally dissipative and lacks an obvious Lyapunov function. We address this by proposing a method to prove the existence of an attractor without relying on LaSalle\u27s principle. Other contributions are as follows. We prove that, under mild conditions, agents\u27 velocities approach the center of mass velocity exponentially, with the distance between the center of mass and the virtual leader being bounded. In the dissipative case, we show existence of a broad class of nonlinear control forces for which the attractor does not contain periodic trajectories, which cannot be ruled out by LaSalle\u27s principle. Finally, we conduct a computational investigation of the problem of reducing propulsion energy consumption by selecting appropriate navigational feedback forces
DecTest: A Decentralised Testing Architecture for Improving Data Accuracy of Blockchain Oracle
No full textBlockchain technology ensures secure and trustworthy data flow between multiple participants on the chain, but interoperability of on-chain and off-chain data has always been a difficult problem that needs to be solved. To solve the problem that blockchain systems cannot access off-chain data, oracle is introduced. However, existing research mainly focuses on the consistency and integrity of data, but ignores the problem that oracle nodes may be externally attacked or provide false data for selfish motives, resulting in the unresolved problem of data accuracy. In this paper, we introduce a new Decentralized Testing architecture (DecTest) that aims to improve data accuracy. A blockchain oracle random secret testing mechanism is first proposed to enhance the monitoring and verification of nodes by introducing a dynamic anonymized question-verification committee. Based on this, a comprehensive evaluation incentive mechanism is designed to incentivize honest work performance by evaluating nodes based on their reputation scores. The simulation results show that we successfully reduced the discrete entropy value of the acquired data and the real value of the data by 61.4%
Twisted Magnetic Van der Waals Bilayers: An Ideal Platform for Altermagnetism
No full textWe introduce a universal methodology for generating and manipulating altermagnetism in two-dimensional (2D) magnetic van der Waals (MvdW) materials through twisting. We find that a key in-plane 2-fold rotational operation can be achieved in a twisted bilayer of any 2D MvdW material, which takes one of all five 2D Bravais lattices, thereby inducing altermagnetism. By choosing the constituent MvdW monolayer with specific symmetry, our approach can tailor altermagnetism of any type, such as -wave, -wave, and -wave. Furthermore, the properties of our twisted altermagnetic materials can be easily engineered. Taking a transition-metal oxyhalide VOBr as an example, we find that by tuning the twist angle and Fermi level a giant spin Hall angle can be obtained, much larger than the experimentally reported. This approach establishes a general, robust, and adjustable platform to explore altermagnetism, and provides a new efficient way to generate and manipulate the spin current
Phase transitions, critical behavior and microstructure of the FRW universe in the framework of higher order GUP
No full textIn this paper, we explore the the phase transition, critical behavior and microstructure of the FRW in the framework of a new higher order generalized uncertainty principle. Our initial step involves deriving the equation of state by defining the work density from GUP-corrected Friedmann equations as the thermodynamic pressure . Based on the modified equation of state, we conduct an analysis of the phase transition in the FRW universe. Subsequently, we obtain the critical exponents and coexistence curves for the small and large phases of the FRW universe around the critical point. Finally, employing Ruppeiner geometry, we derive the thermodynamic curvature scalar , investigating its sign-changing curve and spinodal curve. The results reveal distinctive thermodynamic properties for FRW universes with positive and negative GUP parameters . In the case of , the phase transition, critical behavior and microstructure of FRW universe are consistent with those of Van der Waals fluids. Conversely, for , the results resemble those obtained through effective scalar field theory. These findings underscore the capacity of quantum gravity to induce phase transitions in the universe, warranting further in-depth exploration.12 pages, 8 figure
A Recursive Lower Bound on the Energy Improvement of the Quantum Approximate Optimization Algorithm
No full textThe Quantum Approximate Optimization Algorithm (QAOA) uses a quantum computer to implement a variational method with layers of alternating unitary operators, optimized by a classical computer to minimize a cost function. While rigorous performance guarantees exist for the QAOA at small depths , the behavior at large depths remains less clear, though simulations suggest exponentially fast convergence for certain problems. In this work, we gain insights into the deep QAOA using an analytic expansion of the cost function around transition states. Transition states are constructed recursively: from a local minima of the QAOA with layers we obtain transition states of the QAOA with layers, which are stationary points characterized by a unique direction of negative curvature. We construct an analytic estimate of the negative curvature and the corresponding direction in parameter space at each transition state. Expansion of the QAOA cost function along the negative direction to the quartic order gives a lower bound of the QAOA cost function improvement. We provide physical intuition behind the analytic expressions for the local curvature and quartic expansion coefficient. Our numerical study confirms the accuracy of our approximations, and reveals that the obtained bound and the true value of the QAOA cost function gain have a characteristic exponential decrease with the number of layers , with the bound decreasing more rapidly. Our study establishes an analytical method for recursively studying the QAOA applicable in the regime of high circuit depth.18 pages, 13 figure
Language Models Need Inductive Biases to Count Inductively
No full textCounting is a fundamental example of generalization, whether viewed through the mathematical lens of Peano\u27s axioms defining the natural numbers or the cognitive science literature for children learning to count. The argument holds for both cases that learning to count means learning to count infinitely. While few papers have tried to distill transformer reasoning to the simplest case of counting, investigating length generalization does occur throughout the literature. In the train short, test long paradigm of NLP, length refers to the training sentence length. In formal language recognition, length refers to the input sequence length, or the maximum stack size induced by a pushdown automata. In general problem solving, length refers to the number of hops in a deductive reasoning chain or the recursion depth. For all cases, counting is central to task success. And crucially, generalizing counting inductively is central to success on OOD instances. This work provides extensive empirical results on training language models to count. We experiment with architectures ranging from RNNs, Transformers, State-Space Models and RWKV. We present carefully-designed task formats, auxiliary tasks and positional embeddings to avoid limitations in generalization with OOD-position and OOD-vocabulary. We find that while traditional RNNs trivially achieve inductive counting, Transformers have to rely on positional embeddings to count out-of-domain. As counting is the basis for many arguments concerning the expressivity of Transformers, our finding calls for the community to reexamine the application scope of primitive functions defined in formal characterizations. Finally, modern RNNs also largely underperform traditional RNNs in generalizing counting inductively. We discuss how design choices that enable parallelized training of modern RNNs cause them to lose merits of a recurrent nature
A case study of gas impacted by black-hole jets with the JWST: outflows, bow shocks, and high excitation of the gas in the galaxy IC5063
No full textWe present James Webb Space Telescope MIRI data of the inner 3x2kpc^2 of the galaxy IC5063, in which the jets of a supermassive black hole interact with the gaseous disk they are crossing. Jet-driven outflows were known to be initiated along or near the jet path and to modify the stability of molecular clouds, possibly altering their star formation properties. The MIRI data, of unprecedented resolution and sensitivity in the infrared, now reveal that there are more than ten discrete regions with outflows, nearly doubling the number of such known regions. Outflows exist near the radio lobes, at the nucleus, in a biconical structure perpendicular to the jet, and in a bubble moving against the disk. In some of them, velocities above escape velocity are observed. Stratification is also observed, with higher ionization or excitation gas attaining higher velocities. More outflows and bow shocks, found further away from the nucleus than the radio lobes, in regions without significant radio emission, reveal the existence of past or weak radio jets that interacted with the interstellar medium. The coincidence of the bow shocks with the optical extended emission line region (EELR) suggests that the jets also contributed to the gas ionization. Maps of the H2 gas excitation temperature, T_ex, indicate that the molecular gas is most excited in regions with radio emission. There, T_ex is >100 K higher than in the EELR interior. We argue that a combination of jet-related shocks and cosmic rays is likely responsible for this excess molecular gas excitation.The Astrophysical Journal, in press (accepted 21 October 2024
Surface criticality in the mixed-field Ising model with sign-inverted next-nearest-neighbor interaction
No full textRydberg atoms in an optical tweezer array have been used as a quantum simulator of the spin- antiferromagnetic Ising model with longitudinal and transverse fields. We suggest how to implement the next-nearest-neighbor (NNN) interaction whose sign is opposite to that of the nearest neighbor one in the Rydberg atom systems. We show that this can be achieved by weakly coupling one Rydberg state with another Rydberg state. We further study the surface criticality associated with the first-order quantum phase transition between the antiferromagnetic and paramagnetic phases, which emerges due to the sign-inverted NNN interaction. From the microscopic model, we derive a Ginzburg-Landau (GL) equation, which describes static and dynamic properties of the antiferromagnetic order parameter near the transition. Using both analytical GL theory and numerical method based on a mean-field theory, we calculate the order parameter in the proximity of a boundary of the system in order to show that the healing length of the order parameter logarithmically diverges, signaling the surface criticality.8 pages, 6 figure
3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement
No full textIn this paper, we present our approach to the 3D CBCT Challenge 2024, a part of ICASSP SP Grand Challenges 2024. Improvement in Cone Beam Computed Tomography (CBCT) reconstruction has been achieved by integrating Swin Image Restoration (SwinIR) based sinogram and image enhancement modules. The proposed methodology uses Nesterov Accelerated Gradient Descent (NAG) to solve the least squares (NAG-LS) problem in CT image reconstruction. The integration of sinogram and image enhancement modules aims to enhance image clarity and preserve fine details, offering a promising solution for both low dose and clinical dose CBCT reconstruction. The averaged mean squared error (MSE) over the validation dataset has decreased significantly, in the case of low dose by one-fifth and clinical dose by one-tenth. Our solution is one of the top 5 approaches in this challenge
MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images
No full textWe present MV2Cyl, a novel method for reconstructing 3D from 2D multi-view images, not merely as a field or raw geometry but as a sketch-extrude CAD model. Extracting extrusion cylinders from raw 3D geometry has been extensively researched in computer vision, while the processing of 3D data through neural networks has remained a bottleneck. Since 3D scans are generally accompanied by multi-view images, leveraging 2D convolutional neural networks allows these images to be exploited as a rich source for extracting extrusion cylinder information. However, we observe that extracting only the surface information of the extrudes and utilizing it results in suboptimal outcomes due to the challenges in the occlusion and surface segmentation. By synergizing with the extracted base curve information, we achieve the optimal reconstruction result with the best accuracy in 2D sketch and extrude parameter estimation. Our experiments, comparing our method with previous work that takes a raw 3D point cloud as input, demonstrate the effectiveness of our approach by taking advantage of multi-view images. Our project page can be found at http://mv2cyl.github.io .NeurIPS 2024. Project page: http://mv2cyl.github.i