Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-myopic Beam Scheduling for Multiple Smart Target Tracking in Phased Array Radar Network

A smart target, also referred to as a reactive target, can take maneuvering motions to hinder radar tracking. We address beam scheduling for tracking multiple smart targets in phased array radar networks. We aim to mitigate the performance degradation in previous myopic tracking methods and enhance the system performance, which is measured by a discounted cost objective related to the tracking error covariance (TEC) of the targets. The scheduling problem is formulated as a restless multi-armed bandit problem (RMABP) with state variables, following the Markov decision process. In particular, the problem consists of parallel bandit processes. Each bandit process is associated with a target and evolves with different transition rules for different actions, i.e., either the target is tracked or not. We propose a non-myopic, scalable policy based on Whittle indices for selecting the targets to be tracked at each time. The proposed policy has a linear computational complexity in the number of targets and the truncated time horizon in the index computation, and is hence applicable to large networks with a realistic number of targets. We present numerical evidence that the model satisfies sufficient conditions for indexability (existence of the Whittle index) based upon partial conservation laws, and, through extensive simulations, we validate the effectiveness of the proposed policy in different scenarios.
Integral Representations of Three Novel Multiple Zeta Functions for Barnes Type: A Probabilistic Approach

Integral representation is one of the powerful tools for studying analytic continuation of the zeta functions. It is known that Hurwitz zeta function generalizes the famous Riemann zeta function which plays an important role in analytic number theory. They both have several multiple versions in the literature. In this paper, we introduce three novel multiple zeta functions for Barnes type and study their integral representations through hyperbolic probability distributions given by Pitman and Yor (2003, Canad. J. Math., 55, 292-330). The analytically continued properties of the three multiple zeta functions are also investigated. Surprisingly, two of them, unlike the previous results, can extend analytically to entire functions in the whole complex plane.
Dixmier trace and the DOS of perturbed magnetic operators

The main goal of this work is to provide a description of the {trace per unit volume} in terms of the {Dixmier trace} (regularized by the resolvent of the harmonic oscillator) for a large class of two-dimensional \emph{magnetic operators} perturbed by (homogeneous) {potentials}. One of the payoffs of this result is the possibility of reinterpreting the {density of states} (DOS) of these perturbed magnetic systems via the {Dixmier trace}, and taking advantage of the fact that this quantity can be conveniently calculated on the basis of the Laguerrre functions that diagonalize the harmonic oscillator.
Design and Control of an Energy Accumulative Hopping Robot

Jumping and hopping locomotion are efficient means of traversing unstructured rugged terrain with the former being the focus of roboticists. This focus has led to significant performance and understanding in jumping robots but with limited practical applications as they require significant time between jumps to store energy, thus relegating jumping to a secondary role in locomotion. Hopping locomotion, however, can preserve and transfer energy to subsequent hops without long energy storage periods. Therefore, hopping has the potential to be far more energy efficient and agile than jumping. However, to date, only a single untethered hopping robot exists with limited payload and hopping heights (<1 meter). This is due to the added design and control complexity inherent in the requirements to input energy during dynamic locomotion and control the orientation of the system throughout the hopping cycle, resulting in low energy input and control torques; a redevelopment from basic principles is necessary to advance the capabilities of hopping robots. Here we report hopping robot design principles for efficient and robust systems with high energy input and control torques that are validated through analytical, simulation, and experimental results. The resulting robot (MultiMo-MHR) can hop nearly 4 meters (>6 times the current state-of-the-art); and is only limited by the impact mechanics and not energy input. The results also directly contradict a recent work that concluded hopping with aerodynamic energy input would be less efficient than flight for hops greater than 0.4 meters.
Unconventional correlated metallic behavior due to interorbital Coulomb interaction

We study the non-degenerate one dimensional two-orbital Hubbard model with interorbital Coulomb interaction. By means of the density-matrix renormalization group technique, we calculate the local single-particle density of states and the optical conductivity at zero temperature. We find that a finite interorbital Coulomb repulsion $V$ generates a new class of states within the Mott-Hubbard band which has a large weight of holon-doublon pairs, which we hence call the holon-doublon band (HDB). When $V$ is sufficiently large, the HDB specifies the gapless low-energy excitations, and the system becomes an unconventional correlated metal. Optical conductivity results resolve different metallic behaviors for zero and finite interaction $V$. Compared to the case without interorbital interaction, the conductivity is strongly reduced in the correlated holon-doublon metal for finite $V$. In addition, the absorption spectrum is dominated by the HDB, which is clearly distinguishable from the Mott-Hubbard band.
Set-valued propagation of chaos for controlled path-dependent McKean-Vlasov SPDEs

We develop a limit theory for controlled path-dependent mean field stochastic partial differential equations (SPDEs) within the semigroup approach of Da Prato and Zabczyk. More precisely, we prove existence results for mean field limits and particle approximations, and we establish set-valued propagation of chaos in the sense that we show convergence of sets of empirical distributions to sets of mean field limits in the Hausdorff metric topology. Furthermore, we discuss consequences of our results to stochastic optimal control. As another application, we deduce a propagation of chaos result for Peng's $G$-Brownian motion with drift interaction.
TERM Model: Tensor Ring Mixture Model for Density Estimation

Efficient probability density estimation is a core challenge in statistical machine learning. Tensor-based probabilistic graph methods address interpretability and stability concerns encountered in neural network approaches. However, a substantial number of potential tensor permutations can lead to a tensor network with the same structure but varying expressive capabilities. In this paper, we take tensor ring decomposition for density estimator, which significantly reduces the number of permutation candidates while enhancing expressive capability compared with existing used decompositions. Additionally, a mixture model that incorporates multiple permutation candidates with adaptive weights is further designed, resulting in increased expressive flexibility and comprehensiveness. Different from the prevailing directions of tensor network structure/permutation search, our approach provides a new viewpoint inspired by ensemble learning. This approach acknowledges that suboptimal permutations can offer distinctive information besides that of optimal permutations. Experiments show the superiority of the proposed approach in estimating probability density for moderately dimensional datasets and sampling to capture intricate details.
Toolbox for nonreciprocal dispersive models in circuit QED

We provide a systematic method for constructing effective dispersive Lindblad master equations to describe weakly-anharmonic superconducting circuits coupled by a generic dissipationless nonreciprocal linear system, with effective coupling parameters and decay rates written in terms of the immittance parameters characterizing the coupler. This article extends the foundational work of Solgun et al. (2019) for linear reciprocal couplers described by an impedance response. Here, we expand the existing toolbox to incorporate nonreciprocal elements, account for direct stray coupling between immittance ports, circumvent potential singularities, and include dissipative interactions arising from interaction with a common bath. We illustrate the use of our results with a circuit of weakly-anharmonic Josephson junctions coupled to a multiport nonreciprocal environment and a dissipative port. The results obtained here can be used for the design of complex superconducting quantum processors with non-trivial routing of quantum information, as well as analog quantum simulators of condensed matter systems.
\emph{Lifted} RDT based capacity analysis of the 1-hidden layer treelike \emph{sign} perceptrons neural networks

We consider the memorization capabilities of multilayered \emph{sign} perceptrons neural networks (SPNNs). A recent rigorous upper-bounding capacity characterization, obtained in \cite{Stojnictcmspnncaprdt23} utilizing the Random Duality Theory (RDT), demonstrated that adding neurons in a network configuration may indeed be very beneficial. Moreover, for particular \emph{treelike committee machines} (TCM) architectures with $d\leq 5$ neurons in the hidden layer, \cite{Stojnictcmspnncaprdt23} made a very first mathematically rigorous progress in over 30 years by lowering the previously best known capacity bounds of \cite{MitchDurb89}. Here, we first establish that the RDT bounds from \cite{Stojnictcmspnncaprdt23} scale as $\sim \sqrt{d}$ and can not on their own \emph{universally} (over the entire range of $d$) beat the best known $\sim \log(d)$ scaling of the bounds from \cite{MitchDurb89}. After recognizing that the progress from \cite{Stojnictcmspnncaprdt23} is therefore promising, but yet without a complete concretization, we then proceed by considering the recently developed fully lifted RDT (fl RDT) as an alternative. While the fl RDT is indeed a powerful juggernaut, it typically relies on heavy numerical evaluations. To avoid such heavy numerics, we here focus on a simplified, \emph{partially lifted}, variant and show that it allows for very neat, closed form, analytical capacity characterizations. Moreover, we obtain the concrete capacity bounds that \emph{universally} improve for \emph{any} $d$ over the best known ones of \cite{MitchDurb89}.
Direct visualization of molecular stacking in quasi-2D hexagonal ice

The structure and properties of water or ice are of great interest to researchers due to their importance in the biological, cryopreservation and environmental fields. Hexagonal ice (Ih) is a common ice phase in nature and has been extensively studied; however, microstructural investigations at the atomic or molecular scale are still lacking. In this paper, the fine structure of quasi-2-dimensional ice Ih films was directly examined using cryogenic transmission electron microscopy. Two types of thin Ih films were observed: perfect single crystals growing along the [0001] direction and crystals with stacking faults, including both basal (BSF) and prismatic (PSF) ones, along the orientation of [11-20]; these results were further confirmed by theoretical calculations. Importantly, for the first time, the stacking faults in ice Ih were directly visualized and resolved. In light of the extension behavior of the chair conformation composed of the water molecules, we elucidated the formation mechanism of BSF in Ih, namely, the Ic phase. This study not only determined the structural characteristics of ice structure at the molecular scale but also provided important concepts for researchers to more fully understand the growth kinetics of ice crystals at the atomic scale.
CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation

Reliable object grasping is a crucial capability for autonomous robots. However, many existing grasping approaches focus on general clutter removal without explicitly modeling objects and thus only relying on the visible local geometry. We introduce CenterGrasp, a novel framework that combines object awareness and holistic grasping. CenterGrasp learns a general object prior by encoding shapes and valid grasps in a continuous latent space. It consists of an RGB-D image encoder that leverages recent advances to detect objects and infer their pose and latent code, and a decoder to predict shape and grasps for each object in the scene. We perform extensive experiments on simulated as well as real-world cluttered scenes and demonstrate strong scene reconstruction and 6-DoF grasp-pose estimation performance. Compared to the state of the art, CenterGrasp achieves an improvement of 38.5 mm in shape reconstruction and 33 percentage points on average in grasp success. We make the code and trained models publicly available at http://centergrasp.cs. uni-freiburg.de.
Tensile Strain Induced Anomalous Enhancement in the Lattice Thermal Transport of Monolayer ZnO: A First Principles Study

Density functional theory based calculations have been performed for solving the phonon Boltzmann transport equation to investigate the thermal transport properties of monolayer (ML) ZnO under in-plane isotropic biaxial tensile strain. The in-plane lattice thermal conductivity ($\kappa_{\text{L}}$) of ML-ZnO increases dramatically in response to the biaxial tensile strain ranging from 0% to 10%, conflicting with the general belief. The strain-induced stiffening of the ZA phonon mode and the resulting concomitant increase in group velocity and decrease in phonon population is found to play a significant role behind the unusual enhancement of $\kappa_{\text{L}}$. The mode resolved analysis shows the tensile strain driven competitive behavior between different phonon properties, mainly the group velocity and phonon lifetimes, being responsible for the observed unusual enhancement in $\kappa_{\text{L}}$. Additionally, the phonon scattering calculations show the importance of inclusion of 4-phonon scattering in the thermal transport calculations suggesting the significance of higher-order anharmonicity in ML-ZnO. A strikingly high 4-phonon scattering strength in ML-ZnO primarily results from the strong anharmonicity, quadratic ZA mode dispersion, large frequency gap in phonon dispersion, and reflection symmetry induced selection rule. The incorporation of 4-phonon scattering significantly alters the transport characteristics of all the phonon modes, in general and ZA phonons, in particular. At large strains, a linear dispersion of the ZA mode and closure of the frequency gap is observed, which results in a significant reduction of 4-phonon scattering strength in ML-ZnO.
VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space

Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized into two main groups: parametric and non-parametric approaches. Parametric techniques leverage a low-dimensional statistical body model for realistic results, whereas recent non-parametric methods achieve higher precision by directly regressing the 3D coordinates of the human body. Despite their strengths, both approaches face limitations: the parameters of statistical body models pose challenges as regression targets, and predicting 3D coordinates introduces computational complexities and issues related to smoothness. In this work, we take a novel approach to address the HPSE problem. We introduce a unique method involving a low-dimensional discrete latent representation of the human mesh, framing HPSE as a classification task. Instead of predicting body model parameters or 3D vertex coordinates, our focus is on forecasting the proposed discrete latent representation, which can be decoded into a registered human mesh. This innovative paradigm offers two key advantages: firstly, predicting a low-dimensional discrete representation confines our predictions to the space of anthropomorphic poses and shapes; secondly, by framing the problem as a classification task, we can harness the discriminative power inherent in neural networks. Our proposed model, VQ-HPS, a transformer-based architecture, forecasts the discrete latent representation of the mesh, trained through minimizing a cross-entropy loss. Our results demonstrate that VQ-HPS outperforms the current state-of-the-art non-parametric approaches while yielding results as realistic as those produced by parametric methods. This highlights the significant potential of the classification approach for HPSE.
On Time-Inconsistency in Mean Field Games

We investigate an infinite-horizon time-inconsistent mean-field game (MFG) in a discrete time setting. We first present a classic equilibrium for the MFG and its associated existence result. This classic equilibrium aligns with the conventional equilibrium concept studied in MFG literature when the context is time-consistent. Then we demonstrate that while this equilibrium produces an approximate optimal strategy when applied to the related $N$-agent games, it does so solely in a precommitment sense. Therefore, it cannot function as a genuinely approximate equilibrium strategy from the perspective of a sophisticated agent within the $N$-agent game. To address this limitation, we propose a new consistent equilibrium concept in both the MFG and the $N$-agent game. We show that a consistent equilibrium in the MFG can indeed function as an approximate consistent equilibrium in the $N$-agent game. Additionally, we analyze the convergence of consistent equilibria for $N$-agent games toward a consistent MFG equilibrium as $N$ tends to infinity.
Estimation of Concept Explanations Should be Uncertainty Aware

Model explanations are very valuable for interpreting and debugging prediction models. We study a specific kind of global explanations called Concept Explanations, where the goal is to interpret a model using human-understandable concepts. Recent advances in multi-modal learning rekindled interest in concept explanations and led to several label-efficient proposals for estimation. However, existing estimation methods are unstable to the choice of concepts or dataset that is used for computing explanations. We observe that instability in explanations is due to high variance in point estimation of importance scores. We propose an uncertainty aware Bayesian estimation method, which readily improved reliability of the concept explanations. We demonstrate with theoretical analysis and empirical evaluation that explanations computed by our method are more reliable while also being label-efficient and faithful.
Motion of a rotating black hole in a homogeneous scalar field

In the present paper, we consider a rotating black hole moving in a homogeneous massless scalar field. We assume that the field is weak and neglect its backreaction, so that the metric at far distance from the black hole is practically flat. In this domain one can introduce two reference frames, $K$ and $\tilde{K}$. The frame $\tilde{K}$ is associated with the homogeneous scalar field, in which its constant gradient has only time component. The other frame, $K$, is the frame in which the black hole is at rest. To describe the Kerr metric of the black hole we use its Kerr-Schild form $g_{\mu\nu}=\eta_{\mu\nu}+\Phi l_{\mu}l_{\mu}$, where $\eta_{\mu\nu}$ is the (asymptotic) flat metric in $K$ frame. We find an explicit solution of the scalar field equation which is regular at the horizon and properly reproduce the asymptotic form of the scalar field at the infinity. Using this solution we calculate the fluxes of the energy, momentum and the angular momentum of the scalar field into the black hole. This allows us to derive the equation of motion of the rotating black hole. We discuss main general properties of solutions of these equations and obtain explicit solutions for special type of the motion of the black hole.
Automated Behavioral Analysis Using Instance Segmentation

Animal behavior analysis plays a crucial role in various fields, such as life science and biomedical research. However, the scarcity of available data and the high cost associated with obtaining a large number of labeled datasets pose significant challenges. In this research, we propose a novel approach that leverages instance segmentation-based transfer learning to address these issues. By capitalizing on fine-tuning the classification head of the instance segmentation network, we enable the tracking of multiple animals and facilitate behavior analysis in laboratory-recorded videos. To demonstrate the effectiveness of our method, we conducted a series of experiments, revealing that our approach achieves exceptional performance levels, comparable to human capabilities, across a diverse range of animal behavior analysis tasks. Moreover, we emphasize the practicality of our solution, as it requires only a small number of labeled images for training. To facilitate the adoption and further development of our method, we have developed an open-source implementation named Annolid (An annotation and instance segmentation-based multiple animal tracking and behavior analysis package). The codebase is publicly available on GitHub at https://github.com/cplab/annolid. This resource serves as a valuable asset for researchers and practitioners interested in advancing animal behavior analysis through state-of-the-art techniques.
A new understanding of nuclei spectra properties with propagation model

The AMS-02 experiment has observed new properties of primary cosmic rays (CRs) categorized into two groups: He-C-O-Fe and Ne-Mg-Si-S, which are independent of CR propagation. In this study, we investigate the unexpected properties of these nuclei using a spatial propagation model. All nuclei spectra are accurately reproduced and separated into primary and secondary contributions. Our findings include: 1. Primary CR spectra are identical. 2. Our calculations align with AMS-02 results for primary-dominated nuclei within a 10\% difference, but show significant discrepancies for the secondary-dominated nuclei. 3. The primary element abundance is presented for the first time. We anticipate that the DAMPE and future HERD experiments will provide observations of nuclei spectra above TeV energy.
Entanglement and factorization in axion-de Sitter universes

We study Renyi entropies and late-time bulk correlators between asymptotically de Sitter space universes connected through an Euclidean axion wormhole in arbitrary dimensions. We first establish the notion of entropy with respect to these observers within the background-independent approach to the algebra of operators. We then provide an explicit derivation of holographic Renyi entropies between the universes considering the dS/CFT correspondence. In the quantum mechanical description, the results can be recasted in terms of reduced density matrix where one of the asymptotically dS universes is traced out. Remarkably, our work shows that the throat of the Euclidean wormhole is associated with the entanglement between the universes. Later, we study correlators for heavy particles in the presence of an observer. We find that the Euclidean wormhole saddle allows for the late-time correlators with respect to observers located in the asymptotically dS universes to achieve a constant value at late times, while for the disconnected saddles do not contribute. The result is compatible with each of the asymptotically de Sitter universes being described by a finite-dimensional quantum dual theory. Lastly, we provide with an effective theory description of the dimensional reduction of these geometries in terms of dilaton-gravity theory with conformally coupled matter.