Miracle-dz/UniFAR-FTUs huggingface dataset:
Canonizing Graphs of Bounded Rank-Width in Parallel via Weisfeiler--Leman

In this paper, we show that computing canonical labelings of graphs of bounded rank-width is in $\textsf{TC}^{2}$. Our approach builds on the framework of K\"obler&Verbitsky (CSR 2008), who established the analogous result for graphs of bounded treewidth. Here, we use the framework of Grohe&Neuen (ACM Trans. Comput. Log., 2023) to enumerate separators via split-pairs and flip functions. Nonetheless, these separators may not in general split the graph into connected components of sufficiently small size. This presents an obstacle for controlling the depth of our circuit. In order to overcome this obstacle, we leverage the fact that any graph of rank-width $k$ admits a rank decomposition of width $\leq 2k$ and height $O(\log n)$ (Courcelle&Kant\'e, WG 2007), which allows us to utilize an idea from Wagner (CSR 2011) of tracking the depth of the recursion in our computation. Furthermore, after splitting the graph into connected components, it is necessary to decide isomorphism of said components in $\textsf{TC}^{1}$. To this end, we extend the work of Grohe&Neuen (ibid.) to show that the $(6k+3)$-dimensional Weisfeiler--Leman (WL) algorithm can identify graphs of rank-width $k$ using only $O(\log n)$ rounds. As a consequence, we obtain that graphs of bounded rank-width are identified by $\textsf{FO} + \textsf{C}$ formulas with $6k+4$ variables and quantifier depth $O(\log n)$. Prior to this paper, isomorphism testing for graphs of bounded rank-width was not known to be in $\textsf{NC}$.
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation

This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment. We observe that due to its high complexity, the training objective of panoptic segmentation will inevitably lead to much higher false positive penalization. Such unbalanced loss makes the training process of the end-to-end mask-transformer based architectures difficult, especially for efficient models. In this paper, we present ReMaX that adds relaxation to mask predictions and class predictions during training for panoptic segmentation. We demonstrate that via these simple relaxation techniques during training, our model can be consistently improved by a clear margin \textbf{without} any extra computational cost on inference. By combining our method with efficient backbones like MobileNetV3-Small, our method achieves new state-of-the-art results for efficient panoptic segmentation on COCO, ADE20K and Cityscapes. Code and pre-trained checkpoints will be available at \url{https://github.com/google-research/deeplab2}.
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models

Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal structure is often unknown, and estimating it from data remains a challenging task. In many situations, however, the end goal is to localize the changes (shifts) in the causal mechanisms between related datasets instead of learning the full causal structure of the individual datasets. Some applications include root cause analysis, analyzing gene regulatory network structure changes between healthy and cancerous individuals, or explaining distribution shifts. This paper focuses on identifying the causal mechanism shifts in two or more related datasets over the same set of variables -- without estimating the entire DAG structure of each SCM. Prior work under this setting assumed linear models with Gaussian noises; instead, in this work we assume that each SCM belongs to the more general class of nonlinear additive noise models (ANMs). A key technical contribution of this work is to show that the Jacobian of the score function for the mixture distribution allows for the identification of shifts under general non-parametric functional mechanisms. Once the shifted variables are identified, we leverage recent work to estimate the structural differences, if any, for the shifted variables. Experiments on synthetic and real-world data are provided to showcase the applicability of this approach. Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/iSCAN.
A self‐weakening mechanism for tropical cyclones

A mechanism leading to the self‐weakening of tropical cyclones is proposed using the Weather and Research and Forecasting model. A comparison between an experiment with variable Coriolis parameter f and one on an f‐plane shows that, after the initial intensification, the former is characterized by a smaller intensity. As opposed to the tropical cyclone on the f‐plane, the one with variable f weakens significantly after reaching maturity. Analyses of the 3D circulation show that the main reason for the weakening is dry intrusion in the mid‐upper troposphere from the west. Once the dry intrusion reaches the inner vortex, strong downdraughts reduce the high equivalent potential temperature in the boundary‐layer inflow. The subsequent updraughts in the eyewall, characterized by lower equivalent potential temperature, are considerably reduced and, consequently, the secondary circulation weakens.
Secure and Efficient Flexibility Service Procurement: A Game-Theoretic Approach

Procuring flexibility services from energy consumers has been a potential solution to accommodating renewable generations in future power system. However, efficiently and securely coordinating the behaviors of diverse market participants within a privacy-preserving environment remains a challenge. This paper addresses this issue by introducing a game-theoretic market framework for real-time energy balancing. The competition among energy consumers is modeled as a Generalized Nash Game (GNG), which enables the analysis of their strategic decision-making. To mitigate the market power exerted by active energy consumers, we employ a supply function-based bidding method in this market design. We incorporate physical constraints to ensure the secure operation of the distribution network. Previous approaches to steering consumers towards the Generalized Nash Equilibrium (GNE) of this game often necessitate the sharing of private information, either in full or in part, which may not be practically feasible. To overcome this limitation, we propose a preconditioned forward-backward algorithm, with analytical convergence guarantees, that only requires participants to share limited, non-private sensitive information with others. Finally, numerical simulations on the enhanced IEEE 33-bus test case validate the effectiveness of our proposed market mechanism and algorithm.
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude

Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to-end design, development and evaluation of FarSight, an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. FarSight accepts videos from elevated platforms and drones as input and outputs a candidate list of identities from a gallery. The system is designed to address several challenges, including (i) low-quality imagery, (ii) large yaw and pitch angles, (iii) robust feature extraction to accommodate large intra-person variabilities and large inter-person similarities, and (iv) the large domain gap between training and test sets. FarSight combines the physics of imaging and deep learning models to enhance image restoration and biometric feature encoding. We test FarSight's effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) dataset. Notably, FarSight demonstrated a substantial performance increase on the BRIAR dataset, with gains of +11.82% Rank-20 identification and +11.3% TAR@1% FAR.
Time Interfaces in Bianisotropic Media

Wave phenomena in bianisotropic media have been broadly scrutinized in classical electrodynamics, as these media offer additional degrees of freedom to engineer electromagnetic waves. However, all investigations concerning such systems have so far been limited to stationary (time-invariant) media. Temporally varying the magnetoelectric coupling manifesting bianisotropy engenders a unique prospect to manipulate wave-matter interactions in new ways. In this paper, we theoretically contemplate electromagnetic effects in weakly dispersive bianisotropic media of all classes when the corresponding magnetoelectric coupling parameter suddenly jumps in time, creating a time interface in spatially uniform bianisotropic media. We investigate scattering effects at such time interfaces, revealing novel polarization- and direction-dependent phenomena. We anticipate that our work paves the road for further exploration of time-varying bianisotropic metamaterials (metasurfaces) and bianisotropic photonic time crystals, thus opening up interesting possibilities to control wave polarization and amplitude in reciprocal and nonreciprocal manners.
Navigation of micro-robot swarms for targeted delivery using reinforcement learning

Micro robotics is quickly emerging to be a promising technological solution to many medical treatments with focus on targeted drug delivery. They are effective when working in swarms whose individual control is mostly infeasible owing to their minute size. Controlling a number of robots with a single controller is thus important and artificial intelligence can help us perform this task successfully. In this work, we use the Reinforcement Learning (RL) algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization (RPO) to navigate a swarm of 4, 9 and 16 microswimmers under hydrodynamic effects, controlled by their orientation, towards a circular absorbing target. We look at both PPO and RPO performances with limited state information scenarios and also test their robustness for random target location and size. We use curriculum learning to improve upon the performance and demonstrate the same in learning to navigate a swarm of 25 swimmers and steering the swarm to exemplify the manoeuvring capabilities of the RL model.
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks

Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms for solving those tasks? Several recent studies, on tasks ranging from group arithmetic to in-context linear regression, have suggested that the answer is yes. Using modular addition as a prototypical problem, we show that algorithm discovery in neural networks is sometimes more complex. Small changes to model hyperparameters and initializations can induce the discovery of qualitatively different algorithms from a fixed training set, and even parallel implementations of multiple such algorithms. Some networks trained to perform modular addition implement a familiar Clock algorithm; others implement a previously undescribed, less intuitive, but comprehensible procedure which we term the Pizza algorithm, or a variety of even more complex procedures. Our results show that even simple learning problems can admit a surprising diversity of solutions, motivating the development of new tools for characterizing the behavior of neural networks across their algorithmic phase space.
Input-to-State Stabilization of Delayed Differential Systems With Exogenous Disturbances: The Event-Triggered Case

This paper is concerned with the input-to-state stabilization problem for a class of delayed differential systems. Both time-delay in state and bounded exogenous disturbances are taken into account in the model. An event-triggered strategy, which depends simultaneously on the latest sampled state and a non-negative threshold, is proposed to reduce the transmission frequency of the feedback control signals with guaranteed performance requirements. The notion of input-to-state practical stability is introduced to evaluate the dynamical performance of the controlled systems with considering the effects from both exogenous disturbances and event-triggered scheme. The estimations of the upper bounds for the system state and the measurement error are employed to analyze and further exclude the Zeno behavior for the proposed event-triggered scheme. The controller gain and the event-trigger parameters are co-designed in terms of the feasibility of certain matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of theoretical results.
Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multi-channel prediction module, we transform entity and multi-event extraction into a single task of predicting token-event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F 1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework.
Space Solar Physics in China:2014—2016

The activities of Chinese space solar physics in 2014—2016 were mainly undertaken within the framework of Strategic Priority Program on Space Science,sponsored by CAS,which include:to accomplish the last version for the mid and long-term(2016—2030) plan of Chinese space solar physics;to subsidy a few of pre-study projects of space solar physics;to implement two intensive study projects,ASO-S and SPORT.This paper summarizes these activities briefly.
Efficient uniform approximation using Random Vector Functional Link networks

A Random Vector Functional Link (RVFL) network is a depth-2 neural network with random inner weights and biases. As only the outer weights of such an architecture need to be learned, the learning process boils down to a linear optimization task, allowing one to sidestep the pitfalls of nonconvex optimization problems. In this paper, we prove that an RVFL with ReLU activation functions can approximate Lipschitz continuous functions provided its hidden layer is exponentially wide in the input dimension. Although it has been established before that such approximation can be achieved in L2 sense, we prove it for L∞ approximation error and Gaussian inner weights. To the best of our knowledge, our result is the first of this kind. We give a non-asymptotic lower bound for the number of hidden layer nodes, depending on, among other things, the Lipschitz constant of the target function, the desired accuracy, and the input dimension. Our method of proof is rooted in probability theory and harmonic analysis.
Nonuniqueness results for constant sixth order $Q$-curvature metrics on spheres with higher dimensional singularities

We prove nonuniqueness results for constant sixth order $Q$-metrics on complete locally conformally flat $n$-dimensional Riemannian manifolds with $n\geqslant 7$. More precisely, assuming a positive Green function exists for the sixth order GJMS operator, our objective is two-fold. First, we use a classical bifurcation technique to prove that there exists infinitely many constant $Q$-curvature metrics on $\mathbb{S}^1\times\mathbb{S}^{n-1}$. As a by-product, we find the sixth order Yamabe invariant on this product manifold can be arbitrarily close to that of the round dimensional sphere, generalizing a result of Schoen about the classical Yamabe invariant. Second, when the underlying manifold is noncompact, we apply a bifurcation technique on Riemannian covering to construct infinitely many complete metrics with constant sixth order $Q$-curvature conformal to $\mathbb{S}^{n_1} \times \mathbb{R}^{n_2}$ or $\mathbb{S}^{n_1} \times \mathbb{H}^{n_2}$, where $n_1+n_2\geqslant 7$. Consequently, we obtain infinitely many solutions to the singular constant GJMS equation on round spheres $\mathbb{S}^n\setminus \mathbb{S}^k$ blowing up along a minimal equatorial subsphere with $0 \leqslant k<\frac{n-6}{2}$; this dimension restriction is sharp in the topological sense.
High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a simulation study

Objective. Studying motor units is essential for understanding motor control, the detection of neuromuscular disorders and the control of human–machine interfaces. Individual motor unit firings are currently identified in vivo by decomposing electromyographic (EMG) signals. Due to our body’s properties and anatomy, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields do not interact with human tissues. This physical property and the emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored. Approach. In this work, we perform in silico trials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy. Main results. It is shown that non-invasive high-density MMG data is superior over comparable high-density surface EMG data for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 76%. Notably, MMG exhibits a less pronounced bias to detect superficial motor units. Significance. The presented simulations provide insights into methods to study the neuromuscular system non-invasively and in vivo that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.
Phase transitions induced by standard and predetermined measurements in transmon arrays

The confluence of unitary dynamics and non-unitary measurements gives rise to intriguing and relevant phenomena, generally referred to as measurement-induced phase transitions. These transitions have been observed in quantum systems composed of trapped ions and superconducting quantum devices. However, their experimental realization demands substantial resources, primarily owing to the classical tracking of measurement outcomes, known as post-selection of trajectories. In this work, we first describe the statistical properties of an interacting transmon array which is repeatedly measured, and predict the behavior of relevant quantities in the area-law phase using a combination of the replica method and non-Hermitian perturbation theory. We show numerically that a transmon array, modeled by an attractive Bose-Hubbard model, in which local measurements of the number of bosons are probabilistically interleaved, exhibits a phase transition in the entanglement entropy properties of the ensemble of trajectories in the steady state. Furthermore, by using deterministic feedback operations after the local number measurements, the distribution of the number of bosons measured at a single site carries information on the phase in the entanglement of individual trajectories. Interestingly, we can extract information about the phase and the phase transition from simple observables without considering an absorbing state in the feedback pattern. This implies that the feedback measurement approach might be a viable experimental option to use simple observables to study some aspects of the entanglement phase transition in individual trajectories.
LIMIP: Lifelong Learning to Solve Mixed Integer Programs

Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bound algorithm. Recently, Learning to imitate fast approximations of the expert strong branching heuristic has gained attention due to its success in reducing the running time for solving MIPs. However, existing learning-to-branch methods assume that the entire training data is available in a single session of training. This assumption is often not true, and if the training data is supplied in continual fashion over time, existing techniques suffer from catastrophic forgetting. In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs. To mitigate catastrophic forgetting, we propose LIMIP, which is powered by the idea of modeling an MIP instance in the form of a bipartite graph, which we map to an embedding space using a bipartite Graph Attention Network. This rich embedding space avoids catastrophic forgetting through the application of knowledge distillation and elastic weight consolidation, wherein we learn the parameters key towards retaining efficacy and are therefore protected from significant drift. We evaluate LIMIP on a series of NP-hard problems and establish that in comparison to existing baselines, LIMIP is up to 50% better when confronted with lifelong learning