High-dimensional functional data have become increasingly prevalent in modern applications such as high-frequency financial data and neuroimaging data analysis. We investigate a class of high-dimensional linear regression models, where each predictor is a random element in an infinite-dimensional function space, and the number of functional predictors p can potentially be ultra-high. Assuming that each of the unknown coefficient functions belongs to some reproducing kernel Hilbert space (RKHS), we regularize the fitting of the model by imposing a group elastic-net type of penalty on the RKHS norms of the coefficient functions. We show that our loss function is Gateaux sub-differentiable, and our functional elastic-net estimator exists uniquely in the product RKHS. Under suitable sparsity assumptions and a functional version of the irrepresentable condition, we derive a non-asymptotic tail bound for variable selection consistency of our method. Allowing the number of true functional predictors $q$ to diverge with the sample size, we also show a post-selection refined estimator can achieve the oracle minimax optimal prediction rate. The proposed methods are illustrated through simulation studies and a real-data application from the Human Connectome Project.

Controlling nonlinear systems, especially when data are being used to offset uncertainties in the model, is hard. A natural approach when dealing with the challenges of nonlinear control is to reduce the system to a linear one via change of coordinates and feedback, an approach commonly known as feedback linearization. Here we consider the feedback linearization problem of an unknown system when the solution must be found using experimental data. We propose a new method that learns the change of coordinates and the linearizing controller from a library (a dictionary) of candidate functions with a simple algebraic procedure - the computation of the null space of a data-dependent matrix. Remarkably, we show that the solution is valid over the entire state space of interest and not just on the dataset used to determine the solution.

The additional layer degree of freedom in trilayer moiré superlattices of transition metal dichalcogenides enables the emergence of novel excitonic species, such as quadrupolar excitons, which exhibit unique excitonic interactions and hold promise for realizing intriguing excitonic phases and their quantum phase transitions. Concurrently, the presence of strong electronic correlations in moiré superlattices, as exemplified by the observations of Mott insulators and generalized Wigner crystals, offers a direct route to manipulate these new excitonic states and resulting collective excitonic phases. Here, we demonstrate that strong exciton-exciton and electron-exciton interactions, both stemming from robust electron correlations, can be harnessed to controllably drive transitions between quadrupolar and dipolar excitons. This is achieved by tuning either the exciton density or electrostatic doping in a trilayer semiconducting moiré superlattice. Our findings not only advance the fundamental understanding of quadrupolar excitons but also usher in new avenues for exploring and engineering many-body quantum phenomena through novel correlated excitons in semiconducting moiré systems.

Certain duality of relative entropy can fail for chiral conformal net with nontrivial representations. In this paper we quantify such statement by defining a quantity which measures the failure of such duality, and identify this quantity with relative entropy and global index associated with multi-interval subfactors for a large class of conformal nets. In particular we show that the duality holds for a large class of conformal nets if and only if they are holomorphic. The same argument also applies to CFT in two dimensions. In particular we show that the duality holds for a large class of CFT in two dimensions if and only if they are modular invariant. We also obtain various limiting properties of relative entropies which naturally follow from our formula.

A precise measurement of the cross section for the process e+e- --> K+K-(gamma) from threshold to an energy of 5 GeV is obtained with the initial-state radiation (ISR) method using 232 fb^{-1} of data collected with the BaBar detector at e+e- center-of-mass energies near 10.6 GeV. The measurement uses the effective ISR luminosity determined from the e+e- --> mu+mu-(gamma)gamma_ISR process with the same data set. The corresponding lowest-order contribution to the hadronic vacuum polarization term in the muon magnetic anomaly is found to be a_mu^{KK, LO}=(22.93 +- 0.18_{stat} +- 0.22_{syst}) * 10^{-10}. The charged kaon form factor is extracted and compared to previous results. Its magnitude at large energy significantly exceeds the asymptotic QCD prediction, while the measured slope is consistent with the prediction.

Efficient preparation of many-body ground states is key to harnessing the power of quantum computers in studying quantum many-body systems. In this work, we propose a simple method to design exact linear-depth parameterized quantum circuits which prepare a family of ground states across topological quantum phase transitions in 2D. We achieve this by constructing ground states represented by isometric tensor networks (isoTNS), which form a subclass of tensor network states that are efficiently preparable. By continuously tuning a parameter in the wavefunction, the many-body ground state undergoes quantum phase transitions, exhibiting distinct 2D quantum phases. We illustrate this by constructing an isoTNS path with bond dimension $D = 2$ interpolating between distinct symmetry-enriched topological (SET) phases. At the transition point, the wavefunction is related to a gapless point in the classical six-vertex model. Furthermore, the critical wavefunction supports a power-law correlation along one spatial direction while remaining long-range ordered in the other spatial direction. We provide an explicit parametrized local quantum circuit for the path and show that the 2D isoTNS can also be efficiently simulated by a holographic quantum algorithm requiring only an 1D array of qubits.

Kondo insulators are a paradigmatic strongly correlated electron system, arising from the hybridization between itinerary conduction electrons and localized magnetic moments, which opens a gap in the band of conduction electrons. Traditionally, the known Kondo insulators are found in materials with f-electrons. Recent developments in two-dimensional (2D) moire systems provide a new approach to generate flat bands with strong electron correlation, which host localized moments at half filling. In this work, we demonstrate the realization of a Kondo insulator phase in a moire superlattice of monolayer WS2 / bilayer WSe2 which hosts a set of moire flat bands in the WSe2 layer interfacing the WS2 layer and dispersive bands in the other WSe2 layer. When both WSe2 layers are partially doped but with a total density of two holes per moire unit cell, an insulating state appears when the density of the moire band is below one hole per moire unit cell. The insulating state disappears above a certain threshold magnetic field and the system becomes metallic, which is a telltale signature of the Kondo insulator. The physics can be well explained by a periodic Anderson lattice model that includes both the on-site Coulomb repulsion in the moire flat band and the hybridization between moire flat and non-moire dispersive bands. Our results suggest that multilayer moire structures of transition metal dichalcogenides provide a tunable platform to simulate the Kondo insulator, which holds promise to tackle many critical open questions in the Kondo insulators.

A precise measurement of the cross section of the process $e^+e^-\to\pi^+\pi^-(\gamma)$ from threshold to an energy of 3GeV is obtained with the initial-state radiation (ISR) method using 232fb$^{-1}$ of data collected with the BaBar detector at $e^+e^-$ center-of-mass energies near 10.6GeV. The ISR luminosity is determined from a study of the leptonic process $e^+e^-\to\mu^+\mu^-(\gamma)\gamma_{\rm ISR}$, which is found to agree with the next-to-leading-order QED prediction to within 1.1%. The cross section for the process $e^+e^-\to\pi^+\pi^-(\gamma)$ is obtained with a systematic uncertainty of 0.5% in the dominant $\rho$ resonance region. The leading-order hadronic contribution to the muon magnetic anomaly calculated using the measured $\pi\pi$ cross section from threshold to 1.8GeV is $(514.1 \pm 2.2({\rm stat}) \pm 3.1({\rm syst}))\times 10^{-10}$.

We search for the flavor-changing neutral-current decays B->K(*)nu nubar, and the invisible decays J/psi->nu nubar and psi(2S)->nu nubar via B->K(*)J/psi and B->K(*)psi(2S) respectively, using a data sample of 471 x10^6 BB pairs collected by the BaBar experiment. We fully reconstruct the hadronic decay of one of the B mesons in the Upsilon(4S)->BB decay, and search for the B->K(*)nu nubar decay in the rest of the event. We observe no significant excess of signal decays over background and report branching fraction upper limits of BR(B+->K+nu nubar)<3.7 x10^-5, BR(B0->K0nu nubar)< 8.1 x10^-5, BR(B+->K*+nu nubar)<11.6 x10^-5, BR(B0->K*0nu nubar)<9.3 x10^-5, and combined upper limits of BR(B->Knu nubar)<3.2 x10^-5 and BR(B->K*nu nubar)<7.9 x10^-5, all at the 90% confidence level. For the invisible quarkonium decays, we report branching fraction upper limits of BR(J/psi->nu nubar)<3.9 x10^-3 and BR(psi(2S)->nu nubar)<15.5 x10^-3 at the 90% confidence level. Using the improved kinematic resolution achieved from hadronic reconstruction, we also provide partial branching fraction limits for the B->K(*)nu nubar decays over the full kinematic spectrum.

We report on an improved measurement of the Cabibbo-Kobayashi-Maskawa {\it CP}-violating phase $\gamma$ through a Dalitz plot analysis of neutral $D$ meson decays to $K_S^0 \pi^+ \pi^-$ and $K_S^0 K^+ K^-$ in the processes $B^\mp \to D K^\mp$, $B^\mp \to D^* K^\mp$ with $D^* \to D\pi^0,D\gamma$, and $B^\mp \to D K^{*\mp}$ with $K^{*\mp} \to K_S^0 \pi^\mp$. Using a sample of 383 million $B\bar{B}$ pairs collected by the BABAR detector, we measure $\gamma=(76 \pm 22 \pm 5 \pm 5)^\circ$ (mod $180^\circ$), where the first error is statistical, the second is the experimental systematic uncertainty and the third reflects the uncertainty on the description of the Dalitz plot distributions. The corresponding two standard deviation region is 29^\circ &lt; \gamma &lt; 122^\circ. This result has a significance of direct {\it CP} violation ($\gamma \ne 0$) of 3.0 standard deviations.

Real-world Vehicle-to-Everything (V2X) cooperative perception systems often operate under heterogeneous sensor configurations due to cost constraints and deployment variability across vehicles and infrastructure. This heterogeneity poses significant challenges for feature fusion and perception reliability. To address these issues, we propose HeCoFuse, a unified framework designed for cooperative perception across mixed sensor setups where nodes may carry Cameras (C), LiDARs (L), or both. By introducing a hierarchical fusion mechanism that adaptively weights features through a combination of channel-wise and spatial attention, HeCoFuse can tackle critical challenges such as cross-modality feature misalignment and imbalanced representation quality. In addition, an adaptive spatial resolution adjustment module is employed to balance computational cost and fusion effectiveness. To enhance robustness across different configurations, we further implement a cooperative learning strategy that dynamically adjusts fusion type based on available modalities. Experiments on the real-world TUMTraf-V2X dataset demonstrate that HeCoFuse achieves 43.22% 3D mAP under the full sensor configuration (LC+LC), outperforming the CoopDet3D baseline by 1.17%, and reaches an even higher 43.38% 3D mAP in the L+LC scenario, while maintaining 3D mAP in the range of 21.74% to 43.38% across nine heterogeneous sensor configurations. These results, validated by our first-place finish in the CVPR 2025 DriveX challenge, establish HeCoFuse as the current state-of-the-art on TUM-Traf V2X dataset while demonstrating robust performance across diverse sensor deployments.

We search for the neutrinoless, lepton-flavor-violating tau decays tau -> l V^0, where l is an electron or muon and V^0 is a vector meson reconstructed as phi -> K^+K^-, rho -> pi^+pi^-, K^* -> K^+pi^-, or K^*bar -> K^-pi^+. The analysis has been performed using 451 fb^{-1} of data collected at an e^+e^- center-of-mass energy near 10.58 GeV with the BaBar detector at the PEP-II storage rings. The number of events found in the data is compatible with the background expectation, and upper limits on the branching fractions are set in the range (2.6-19) x 10^{-8} at the 90% confidence level.

In his 2018 paper, Herlihy introduced an atomic protocol for multi-party asset swaps across different blockchains. His model represents an asset swap by a directed graph whose nodes are the participating parties and edges represent asset transfers, and rational behavior of the participants is captured by a preference relation between a protocol's outcomes. Asset transfers between parties are achieved using smart contracts. These smart contracts are quite involved and they require storage and processing of a large number of paths in the swap digraph, limiting practical significance of his protocol. His paper also describes a different protocol that uses only standard hash time-lock contracts (HTLC's), but this simpler protocol applies only to some special types of digraphs. He left open the question whether there is a simple and efficient protocol for cross-chain asset swaps in arbitrary digraphs. Motivated by this open problem, we conducted a comprehensive study of \emph{HTLC-based protocols}, in which all asset transfers are implemented with HTLCs. Our main contribution is a full characterization of swap digraphs that have such protocols.

A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity. To address these problems, we present a Dynamic lightweight High-Resolution Network (Dite-HRNet), which can efficiently extract multi-scale contextual information and model long-range spatial dependency for human pose estimation. Specifically, we propose two methods, dynamic split convolution and adaptive context modeling, and embed them into two novel lightweight blocks, which are named dynamic multi-scale context block and dynamic global context block. These two blocks, as the basic component units of our Dite-HRNet, are specially designed for the high-resolution networks to make full use of the parallel multi-resolution architecture. Experimental results show that the proposed network achieves superior performance on both COCO and MPII human pose estimation datasets, surpassing the state-of-the-art lightweight networks. Code is available at: this https URL.

The event camera's low power consumption and ability to capture microsecond brightness changes make it attractive for various computer vision tasks. Existing event representation methods typically convert events into frames, voxel grids, or spikes for deep neural networks (DNNs). However, these approaches often sacrifice temporal granularity or require specialized devices for processing. This work introduces a novel token-based event representation, where each event is considered a fundamental processing unit termed an event-token. This approach preserves the sequence's intricate spatiotemporal attributes at the event level. Moreover, we propose a Three-way Attention mechanism in the Event Transformer Block (ETB) to collaboratively construct temporal and spatial correlations between events. We compare our proposed token-based event representation extensively with other prevalent methods for object classification and optical flow estimation. The experimental results showcase its competitive performance while demanding minimal computational resources on standard devices. Our code is publicly accessible at \url{this https URL}.

This paper presents a system identification framework -- inspired by multi-task learning -- to estimate the dynamics of a given number of linear time-invariant (LTI) systems jointly by leveraging structural similarities across the systems. In particular, we consider LTI systems that model networked systems with similar connectivity, or LTI systems with small differences in their matrices. The system identification task involves the minimization of the least-squares (LS) fit for individual systems, augmented with a regularization function that enforces structural similarities. The proposed method is particularly suitable for cases when the recorded trajectories for one or more LTI systems are not sufficiently rich, leading to ill-conditioning of LS methods. We analyze the performance of the proposed method when the matrices of the LTI systems feature a common sparsity pattern (i.e., similar connectivity), and provide simulations based on real data for the estimation of the brain dynamics. We show that the proposed method requires a significantly smaller number of fMRI scans to achieve similar error levels of the LS.

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes may play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably versus dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results indicate how the presence of neuron-astrocyte interaction in the brain may benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics.