Search

Many neurodevelopmental disorders can be understood as divergent patterns of neural interactions during brain development. Advances in neuroimaging have illuminated these patterns by modeling the brain as a network structure using diffution MRI tractography. However, characterizing and quantifying individual heterogeneity in neurodevelopmental disorders within these highly complex brain networks remains a significant challenge. In this paper, we present for the first time, a framework that integrates deep generative models with graph-based normative modeling to characterize brain network development in the neurotypical population, which can then be used to quantify the individual-level neurodivergence associated with disorders. Our deep generative model incorporates bio-inspired wiring constraints to effectively capture the developmental trajectories of neurotypical brain networks. Neurodivergence is quantified by comparing individuals to this neurotypical trajectory, enabling the creation of region-wise divergence maps that reveal latent developmental differences at each brain regions, along with overall neurodivergence scores based on predicted brain age gaps. We demonstrate the clinical utility of this framework by applying it to a large sample of children with autism spectrum disorders, showing that the individualized region-wise maps help parse the heterogeneity in autism, and the neurodivergence scores correlate with clinical assessments. Together, we provide powerful tools for quantifying neurodevelopmental divergence in brain networks, paying the way for developing imaging markers that will support disorder stratification, monitor progression, and evaluate therapeutic effectiveness.

Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales, which are not sufficient for proper investigation of model's capabilities. We present P-FOLIO, a human-annotated dataset consisting of diverse and complex reasoning chains for a set of realistic logical reasoning stories also written by humans. P-FOLIO is collected with an annotation protocol that facilitates humans to annotate well-structured natural language proofs for first-order logic reasoning problems in a step-by-step manner. The number of reasoning steps in P-FOLIO span from 0 to 20. We further use P-FOLIO to evaluate and improve large-language-model (LLM) reasoning capabilities. We evaluate LLM reasoning capabilities at a fine granularity via single-step inference rule classification, with more diverse inference rules of more diverse and higher levels of complexities than previous works. Given that a single model-generated reasoning chain could take a completely different path than the human-annotated one, we sample multiple reasoning chains from a model and use pass@k metrics for evaluating the quality of model-generated reasoning chains. We show that human-written reasoning chains significantly boost the logical reasoning capabilities of LLMs via many-shot prompting and fine-tuning. Furthermore, fine-tuning Llama3-7B on P-FOLIO improves the model performance by 10% or more on three other out-of-domain logical reasoning datasets. We also conduct detailed analysis to show where most powerful LLMs fall short in reasoning. We will release the dataset and code publicly.

Nowadays, federated recommendation technology is rapidly evolving to help multiple organisations share data and train models while meeting user privacy, data security and government regulatory requirements. However, federated recommendation increases customer system costs such as power, computational and communication resources. Besides, federated recommendation systems are also susceptible to model attacks and data poisoning by participating malicious clients. Therefore, most customers are unwilling to participate in federated recommendation without any incentive. To address these problems, we propose a blockchain-based federated recommendation system with incentive mechanism to promote more trustworthy, secure, and efficient federated recommendation service. First, we construct a federated recommendation system based on NeuMF and FedAvg. Then we introduce a reverse auction mechanism to select optimal clients that can maximize the social surplus. Finally, we employ blockchain for on-chain evidence storage of models to ensure the safety of the federated recommendation system. The experimental results show that our proposed incentive mechanism can attract clients with superior training data to engage in the federal recommendation at a lower cost, which can increase the economic benefit of federal recommendation by 54.9\% while improve the recommendation performance. Thus our work provides theoretical and technological support for the construction of a harmonious and healthy ecological environment for the application of federal recommendation., Comment: This paper has been accepted on 2024 Blockchain and Web3 Technology Innovation and Application Exchange Conference (BWTAC 2024)

We introduce Wonderful Team, a multi-agent Vision Large Language Model (VLLM) framework designed to solve robotics problems in a zero-shot regime. In our context, zero-shot means that for a novel environment, we provide a VLLM with an image of the robot's surroundings and a task description, and the VLLM outputs the sequence of actions necessary for the robot to complete the task. Unlike prior work that requires fine-tuning parts of the pipeline -- such as adjusting an LLM on robot-specific data or training separate vision encoders -- our approach demonstrates that with careful engineering, a single off-the-shelf VLLM can autonomously handle all aspects of a robotics task, from high-level planning to low-level location extraction and action execution. Crucially, compared to using GPT-4o alone, Wonderful Team is self-corrective and capable of iteratively fixing its own mistakes, enabling it to solve challenging long-horizon tasks. We validate our framework through extensive experiments, both in simulated environments using VIMABench and in real-world settings. Our system showcases the ability to handle diverse tasks such as manipulation, goal-reaching, and visual reasoning -- all in a zero-shot manner. These results underscore a key point: vision-language models have progressed rapidly in the past year and should be strongly considered as a backbone for many robotics problems moving forward., Comment: aka Wonderful Team

Sensors play a crucial role in advanced apparatuses and it is persistently pursued to improve their sensitivities. Recently, the singularity of a non-Hermitian system, known as the exceptional point (EP), has drawn much attention for this goal. Response of the eigenfrequency shift to a perturbation $\epsilon$ follows the $\epsilon^{1/n}$-dependence at an $n$th-order EP, leading to significantly enhanced sensitivity via a high-order EP. However, due to the requirement of increasingly complicated systems, great difficulties will occur along the path of increasing the EP order to enhance the sensitivity. Here we report that by utilizing the spectral anomaly of the coherent perfect absorption (CPA), the sensitivity at a third-order EP can be further enhanced owing to the cooperative effects of both CPA and EP. We realize this synthetically enhanced sensor using a pseudo-Hermitian cavity magnonic system composed of two yttrium iron garnet spheres and a microwave cavity. The detectable minimum change of the magnetic field reaches $4.2\times10^{-21}$T. It opens a new avenue to design novel sensors using hybrid non-Hermitian quantum systems., Comment: 18 pages,5 figures

Learning from demonstrations (LfD) has successfully trained robots to exhibit remarkable generalization capabilities. However, many powerful imitation techniques do not prioritize the feasibility of the robot behaviors they generate. In this work, we explore the feasibility of plans produced by LfD. As in prior work, we employ a temporal diffusion model with fixed start and goal states to facilitate imitation through in-painting. Unlike previous studies, we apply cold diffusion to ensure the optimization process is directed through the agent's replay buffer of previously visited states. This routing approach increases the likelihood that the final trajectories will predominantly occupy the feasible region of the robot's state space. We test this method in simulated robotic environments with obstacles and observe a significant improvement in the agent's ability to avoid these obstacles during planning.

Recently, large language models (LLMs), especially those that are pretrained on code, have demonstrated strong capabilities in generating programs from natural language inputs in a few-shot or even zero-shot manner. Despite promising results, there is a notable lack of a comprehensive evaluation of these models language-to-code generation capabilities. Existing studies often focus on specific tasks, model architectures, or learning paradigms, leading to a fragmented understanding of the overall landscape. In this work, we present L2CEval, a systematic evaluation of the language-to-code generation capabilities of LLMs on 7 tasks across the domain spectrum of semantic parsing, math reasoning and Python programming, analyzing the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs. This enables us to identify and analyze the typical failure modes across various tasks and models. L2CEval offers a comprehensive understanding of the capabilities and limitations of LLMs in language-to-code generation. We also release the evaluation framework and all model outputs, hoping to lay the groundwork for further future research in this domain., Comment: Project Website: https://l2c-eval.github.io/

We present single soliton states and soliton-antisoliton states with an adjoint Higgs field in 4D flat space. The action of a single soliton state diverges, while the action of soliton-antisoliton states converges. This means such solitons can exist in soliton-antisoliton states, although they cannot exist individually. The interaction in a soliton-antisoliton state takes a logarithmic dependence on separation. Such soliton-antisoliton states exhibit stability under a scaling transformation.

Building hybrid quantum systems is a crucial step for realizing multifunctional quantum technologies, quantum information processing, and hybrid quantum networks. A functional hybrid quantum system requires strong coupling among its components. However, couplings between distinct physical systems are typically very weak. Experimental realization of strong coupling in a hybrid system remains a long-standing challenge, especially when it has multiple components and the components are of different nature. Here we demonstrate the realization of triple strong coupling in a novel polaromechanical hybrid system, where polaritons, formed by strongly coupled ferromagnetic magnons and microwave photons, are further strongly coupled to phonons. The corresponding polaromechanical normal-mode splitting is observed. A high polaromechanical cooperativity of $9.4\times10^3$ is achieved by significantly reducing the polariton decay rate via exploiting coherent perfect absorption. The quantum cooperativity much greater than unity is achievable if placing the system at low bath temperatures, which would enable various quantum applications. Our results pave the way towards coherent quantum control of photons, magnons and phonons, and are a crucial step for building functional hybrid quantum systems based on magnons.

We show how to entangle two cavity-magnon polaritons (CMPs) formed by two strongly coupled microwave cavity and magnon modes. This is realized by introducing vibration phonons, via magnetostriction, into the system that are dispersively coupled to the magnon mode. Stationary entanglement between two CMPs can be achieved when they are respectively resonant with the two sidebands of the drive field scattered by the phonons, and when the proportions of the cavity and magnon modes in the two polaritons are appropriately chosen. The entangled CMPs can improve the detection sensitivity in the dark matter search experiments using CMPs, and can also lead to the emission of frequency-entangled microwave photons.

The dipole approximation is usually employed to describe light-matter interactions under ordinary conditions. With the development of artificial atomic systems, `giant atom' physics is possible, where the scale of atoms is comparable to or even greater than the wavelength of the light they interact with, and the dipole approximation is no longer valid. It reveals interesting physics impossible in small atoms and may offer useful applications. Here, we experimentally demonstrate the giant spin ensemble (GSE), where a ferromagnetic spin ensemble interacts twice with the meandering waveguide, and the coupling strength between them can be continuously tuned from finite (coupled) to zero (decoupled) by varying the frequency. In the nested configuration, we investigate the collective behavior of two GSEs and find extraordinary phenomena that cannot be observed in conventional systems. Our experiment offers a new platform for `giant atom' physics., Comment: 16 pages, 8 figures

Bistable mechanical vibration is observed in a cavity magnomechanical system, which consists of a microwave cavity mode, a magnon mode, and a mechanical vibration mode of a ferrimagnetic yttrium-iron-garnet (YIG) sphere. The bistability manifests itself in both the mechanical frequency and linewidth under a strong microwave drive field, which simultaneously activates three different kinds of nonlinearities, namely, magnetostriction, magnon self-Kerr, and magnon-phonon cross-Kerr nonlinearities. The magnon-phonon cross-Kerr nonlinearity is first predicted and measured in magnomechanics. The system enters a regime where Kerr-type nonlinearities strongly modify the conventional cavity magnomechanics that possesses only a radiation-pressure-like magnomechanical coupling. Three different kinds of nonlinearities are identified and distinguished in the experiment. Our work demonstrates a new mechanism for achieving mechanical bistability by combining magnetostriction and Kerr-type nonlinearities, and indicates that such Kerr-modified cavity magnomechanics provides a unique platform for studying many distinct nonlinearities in a single experiment., Comment: with Suppl. Mat. To appear in PRL

This paper defines a strong convertible nonconvex(SCN) function for solving the unconstrained optimization problems with the nonconvex or nonsmooth(nondifferentiable) function. First, many examples of SCN function are given, where the SCN functions are nonconvex or nonsmooth. Second, the operational properties of the SCN functions are proved, including addition, multiplication, compound operations and so on. Third, the SCN forms of some special functions common in machine learning and engineering applications are presented respectively where these SCN function optimization problems can be transformed into minmax problems with a convex and concave objective function. Fourth,a minmax optimization problem of SCN function and its penalty function are defined. The optimization condition,exactness and stability of the minmax optimization problem are proved. Finally, an algorithm of penalty function to solve the minmax optimization problem and its convergence are given. This paper provides an efficient technique for solving unconstrained nonconvex or nonsmooth(nondifferentiable) optimization problems to avoid using subdifferentiation or smoothing techniques.

The paper introduces several new concepts for solving nonconvex or nonsmooth optimization problems, including convertible nonconvex function, exact convertible nonconvex function and differentiable convertible nonconvex function. It is proved herein many nonconvex functions or nonsmooth (or discontinuous) functions are actually convertible nonconvex functions and convertible nonconvex function operations such as addition, subtraction, multiplication or division result in convertible nonconvex functions. The sufficient condition for judging a global optimal solution to unconstrained optimization problems with differentiable convertible nonconvex functions is proved, which is equivalent to Karush-Kuhn-Tucker(KKT) condition. Two Lagrange functions of differentiable convertible nonconvex function are defined with their dual problems defined accordingly. The strong duality theorem is proved, showing that the optimal objective value of the global optimal solution is equal to the optimal objective value of the dual problem, which is equivalent to KKT condition. An augmented Lagrangian penalty function algorithm is proposed and its convergence is proved. So the paper provides a new idea for solving unconstrained nonconvex or non-smooth optimization problems and avoids subdifferentiation or smoothing techniques by using some gradient search algorithms, such as gradient descent algorithm, Newton algorithm and so on.

The theory of urban traffic flow has been developed and new types of meta-automata have emerged and simulate realistic traffic conditions relatively well. Among these models, the brake light model can simulate the three-phase traffic flow theory very well. However, the existing brake light model also has certain shortcomings, in that the model will change the speed of congestion propagation upward when the model is covariant, which is not realistic, and the model also lacks simulation parameters for driver behavior. In this paper, we propose a new model based on the brake light model, which can achieve a certain degree of simulation of driver behavior by adjusting the parameters, and also achieve a stabilization of the propagation speed of the congestion wave when the parameters are changed.

We show how to measure a steady-state magnon population in a magnetostatic mode of a ferromagnet or ferrimagnet, such as yttrium iron garnet. We adopt an optomechanical approach and utilize the magnetoelasticity of the ferromagnet. The magnetostrictive force dispersively couples magnons to the deformation displacement of the ferromagnet, which is proportional to the magnon population. By further coupling the mechanical displacement to an optical cavity that is resonantly driven by a weak laser, the magnetostrictively induced displacement can be sensed by measuring the phase quadrature of the optical field. The phase shows an excellent linear dependence on the magnon population for a not very large population, and can thus be used as a `magnometer' to measure the magnon population. We further study the effect of thermal noises, and find a high signal-to-noise ratio even at room temperature. At cryogenic temperatures, the resolution of magnon excitation numbers is essentially limited by the vacuum fluctuations of the phase, which can be significantly improved by using a squeezed light.

Multistability is an extraordinary nonlinear property of dynamical systems and can be explored to implement memory and switches. Here we experimentally realize the tristability in a three-mode cavity magnonic system with Kerr nonlinearity. The three stable states in the tristable region correspond to the stable solutions of the frequency shift of the cavity magnon polariton under specific driving conditions. We find that the system staying in which stable state depends on the history experienced by the system, and this state can be harnessed to store the history information. In our experiment, the memory time can reach as long as 5.11 s. Moreover, we demonstrate the ternary logic gate with good on-off characteristics using this multistable hybrid system. Our new findings pave a way towards cavity magnonics-based information storage and processing.