Miracle-dz/UniFAR-FTUs huggingface dataset:
PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features

Event cameras are motion-activated sensors that capture pixel-level illumination changes instead of the intensity image with a fixed frame rate. Compared with the standard cameras, it can provide reliable visual perception during high-speed motions and in high dynamic range scenarios. However, event cameras output only a little information or even noise when the relative motion between the camera and the scene is limited, such as in a still state. While standard cameras can provide rich perception information in most scenarios, especially in good lighting conditions. These two cameras are exactly complementary. In this paper, we proposed a robust, high-accurate, and real-time optimization-based monocular event-based visual-inertial odometry (VIO) method with event-corner features, line-based event features, and point-based image features. The proposed method offers to leverage the point-based features in the nature scene and line-based features in the human-made scene to provide more additional structure or constraints information through well-design feature management. Experiments in the public benchmark datasets show that our method can achieve superior performance compared with the state-of-the-art image-based or event-based VIO. Finally, we used our method to demonstrate an onboard closed-loop autonomous quadrotor flight and large-scale outdoor experiments. Videos of the evaluations are presented on our project website: https://b23.tv/OE3QM6j
Rigid comparison geometry for Riemannian bands and open incomplete manifolds

Comparison theorems are foundational to our understanding of the geometric features implied by various curvature constraints. This paper considers manifolds with a positive lower bound on either scalar, 2-Ricci, or Ricci curvature, and contains a variety of theorems which provide sharp relationships between this bound and notions of {\em{width}}. Some inequalities leverage geometric quantities such as boundary mean curvature, while others involve topological conditions in the form of linking requirements or homological constraints. In several of these results open and incomplete manifolds are studied, one of which partially addresses a conjecture of Gromov in this setting. The majority of results are accompanied by rigidity statements which isolate various model geometries -- both complete and incomplete -- including a new characterization of round lens spaces, and other models that have not appeared elsewhere. As a byproduct, we additionally give new and quantitative proofs of several classical comparison statements such as Bonnet-Myers' and Frankel's Theorem, as well as a version of Llarull's Theorem and a notable fact concerning asymptotically flat manifolds. The results that we present vary significantly in character, however a common theme is present in that the lead role in each proof is played by \emph{spacetime harmonic functions}, which are solutions to a certain elliptic equation originally designed to study mass in mathematical general relativity.
Equivalence of state equations from different methods in high-dimensional regression

State equations (SEs) were firstly introduced in the approximate message passing (AMP) to describe the mean square error (MSE) in compressed sensing. Since then a set of state equations have appeared in studies of logistic regression, robust estimator and other high-dimensional statistics problems. Recently, a convex Gaussian min-max theorem (CGMT) approach was proposed to study high-dimensional statistic problems accompanying with another set of different state equations. This paper provides a uniform viewpoint on these methods and shows the equivalence of their reduction forms, which causes that the resulting SEs are essentially equivalent and can be converted into the same expression through parameter transformations. Combining these results, we show that these different state equations are derived from several equivalent reduction forms. We believe that this equivalence will shed light on discovering a deeper structure in high-dimensional statistics.
THE BLACK HOLE–BULGE RELATIONSHIP IN LUMINOUS BROAD-LINE ACTIVE GALACTIC NUCLEI AND HOST GALAXIES

We have measured the stellar velocity dispersions (σ*) and estimated the central black hole (BH) masses for over 900 broad-line active galactic nuclei (AGNs) observed with the Sloan Digital Sky Survey. The sample includes objects which have redshifts up to z = 0.452, high-quality spectra, and host galaxy spectra dominated by an early-type (bulge) component. The AGN and host galaxy spectral components were decomposed using an eigenspectrum technique. The BH masses (MBH) were estimated from the AGN broad-line widths, and the velocity dispersions were measured from the stellar absorption spectra of the host galaxies. The range of black hole masses covered by the sample is approximately 106 < MBH < 109 M☉. The host galaxy luminosity–velocity dispersion relationship follows the well-known Faber–Jackson relation for early-type galaxies, with a power-law slope 4.33 ± 0.21. The estimated BH masses are correlated with both the host luminosities (LH) and the stellar velocity dispersions (σ*), similar to the relationships found for low-redshift, bulge-dominated galaxies. The intrinsic scatters in the correlations are large (∼0.4 dex), but the very large sample size allows tight constraints to be placed on the mean relationships: MBH ∝ L0.73±0.05H and MBH ∝ σ3. 34±0.24*. The amplitude of the MBH–σ* relation depends on the estimated Eddington ratio, such that objects with larger Eddington ratios have smaller black hole masses than expected at a given velocity dispersion. While this dependence is probably caused at least in part by sample selection effects, it can account for the intrinsic scatter in the MBH–σ* relation, and may tie together the accretion rate with physical properties of the host bulge component. We find no significant evolution in the MBH–σ* relation with redshift, up to z ≈ 0.4, after controlling for possible dependences on other variables. Interested readers can contact the authors to obtain the eigenspectrum decomposition coefficients of our objects.
FORESEE: Prediction with Expansion-Compression Unscented Transform for Online Policy Optimization

Propagating state distributions through a generic, uncertain nonlinear dynamical model is known to be intractable and usually begets numerical or analytical approximations. We introduce a method for state prediction, called the Expansion-Compression Unscented Transform, and use it to solve a class of online policy optimization problems. Our proposed algorithm propagates a finite number of sigma points through a state-dependent distribution, which dictates an increase in the number of sigma points at each time step to represent the resulting distribution; this is what we call the expansion operation. To keep the algorithm scalable, we augment the expansion operation with a compression operation based on moment matching, thereby keeping the number of sigma points constant across predictions over multiple time steps. Its performance is empirically shown to be comparable to Monte Carlo but at a much lower computational cost. Under state and control input constraints, the state prediction is subsequently used in tandem with a proposed variant of constrained gradient-descent for online update of policy parameters in a receding horizon fashion. The framework is implemented as a differentiable computational graph for policy training. We showcase our framework for a quadrotor stabilization task as part of a benchmark comparison in safe-control-gym and for optimizing the parameters of a Control Barrier Function based controller in a leader-follower problem.
A generalized Suzuki–Berinde contraction that characterizes Banach spaces

Abstract We introduce a large class of contractive mappings, called Suzuki–Berinde type contraction. We show that any Suzuki–Berinde type contraction has a fixed point and characterizes the completeness of the underlying normed space. A fixed point theorem for multivalued mappings is also obtained. These results unify, generalize and complement various known comparable results in the literature.
Cooperative Resource Management in Quantum Key Distribution (QKD) Networks for Semantic Communication

The increasing focus on privacy and security in 6G networks, which are intelligence-native, necessitates the use of quantum key distribution-secured semantic information communication (QKD-SIC) to protect confidential data. In QKD-SIC systems, edge devices connected via quantum channels can efficiently encrypt semantic information from the semantic source, and securely transmit the encrypted semantic information to the semantic destination. In this article, we consider an efficient resource (i.e., quantum key distribution (QKD) and KM wavelengths) sharing problem to support QKD-SIC systems under the uncertainty of semantic information generated by edge devices. In such a system, QKD service providers offer QKD services with different subscription options to the edge devices. The QKD services are envisioned to follow cloud computing that has the subscription in the reservation and on-demand options, i.e., for long and short (immediate) terms, respectively. As such, to reduce the cost for the edge device users, we propose a QKD resource management framework for the edge devices communicating semantic information. The framework is based on a two-stage stochastic optimization model to achieve optimal QKD deployment. Moreover, to reduce the deployment cost of QKD service providers, QKD resources in the proposed framework can be utilized based on efficient QKD-SIC resource management, including semantic information transmission among edge devices, secret-key provisioning, and cooperation formation among QKD service providers. In detail, the formulated two-stage stochastic optimization model can achieve the optimal QKD-SIC resource deployment while meeting the secret-key requirements for semantic information transmission of edge devices. Moreover, to share the cost of the QKD resource pool among cooperative QKD service providers forming a coalition in a fair and interpretable manner, the proposed framework leverages the concept of Shapley value from cooperative game theory as a solution. Experimental results demonstrate that the proposed framework can reduce the deployment cost by about 40% compared with existing noncooperative baselines.
New finite type multi-indexed orthogonal polynomials obtained from state-adding darboux transformations

The Hamiltonians of finite type discrete quantum mechanics with real shifts are real symmetric matrices of order N + 1. We discuss the Darboux transformations with higher degree (>N) polynomial solutions as seed solutions. They are state-adding and the resulting Hamiltonians after M-steps are of order N + M + 1. Based on twelve orthogonal polynomials ((q-)Racah, (dual, q-)Hahn, Krawtchouk and five types of q-Krawtchouk), new finite type multi-indexed orthogonal polynomials are obtained, which satisfy second order difference equations, and all the eigenvectors of the deformed Hamiltonian are described by them. We also present explicit forms of the Krein-Adler type multi-indexed orthogonal polynomials and their difference equations, which are obtained from the state-deleting Darboux transformations with lower degree (≤N) polynomial solutions as seed solutions.
Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?

Social media's growing popularity raises concerns around children's online safety. Interactions between minors and adults with predatory intentions is a particularly grave concern. Research into online sexual grooming has often relied on domain experts to manually annotate conversations, limiting both scale and scope. In this work, we test how well-automated methods can detect conversational behaviors and replace an expert human annotator. Informed by psychological theories of online grooming, we label $6772$ chat messages sent by child-sex offenders with one of eleven predatory behaviors. We train bag-of-words and natural language inference models to classify each behavior, and show that the best performing models classify behaviors in a manner that is consistent, but not on-par, with human annotation.
The Online Knapsack Problem with Departures

The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of different sizes and values into a capacity-limited knapsack. In this paper, we study a general version that includes item departures, while also considering multiple knapsacks and multi-dimensional item sizes. We design a threshold-based online algorithm and prove that the algorithm can achieve order-optimal competitive ratios. Beyond worst-case performance guarantees, we also aim to achieve near-optimal average performance under typical instances. Towards this goal, we propose a data-driven online algorithm that learns within a policy-class that guarantees a worst-case performance bound. In trace-driven experiments, we show that our data-driven algorithm outperforms other benchmark algorithms in an application of online knapsack to job scheduling for cloud computing.
Deontic Meta-Rules

The use of meta-rules in logic, i.e., rules whose content includes other rules, has recently gained attention in the setting of nonmonotonic reasoning: a first logical formalisation and efficient algorithms to compute the (meta)-extensions of such theories were proposed in [65]. This work extends such a logical framework by considering the deontic aspect. The resulting logic will not just be able to model policies but also tackle well-known aspects that occur in numerous legal systems. The use of Defeasible Logic (DL) to model meta-rules in the application area we just alluded to has been investigated. Within this line of research, the study mentioned above was not focusing on the general computational properties of meta-rules. This study fills this gap with two major contributions. First, we introduce and formalise two variants of Defeasible Deontic Logic with Meta-Rules to represent (1) defeasible meta-theories with deontic modalities, and (2) two different types of conflicts among rules: Simple Conflict Defeasible Deontic Logic, and Cautious Conflict Defeasible Deontic Logic. Second, we advance efficient algorithms to compute the extensions for both variants.
Deep Convolutional Neural Network and Transfer Learning for Locomotion Intent Prediction

Powered prosthetic legs must anticipate the user's intent when switching between different locomotion modes (e.g., level walking, stair ascent/descent, ramp ascent/descent). Numerous data-driven classification techniques have demonstrated promising results for predicting user intent, but the performance of these intent prediction models on novel subjects remains undesirable. In other domains (e.g., image classification), transfer learning has improved classification accuracy by using previously learned features from a large dataset (i.e., pre-trained models) and then transferring this learned model to a new task where a smaller dataset is available. In this paper, we develop a deep convolutional neural network with intra-subject (subject-dependent) and inter-subject (subject-independent) validations based on a human locomotion dataset. We then apply transfer learning for the subject-independent model using a small portion (10%) of the data from the left-out subject. We compare the performance of these three models. Our results indicate that the transfer learning (TL) model outperforms the subject-independent (IND) model and is comparable to the subject-dependent (DEP) model (DEP Error: 0.74 $\pm$ 0.002%, IND Error: 11.59 $\pm$ 0.076%, TL Error: 3.57 $\pm$ 0.02% with 10% data). Moreover, as expected, transfer learning accuracy increases with the availability of more data from the left-out subject. We also evaluate the performance of the intent prediction system in various sensor configurations that may be available in a prosthetic leg application. Our results suggest that a thigh IMU on the the prosthesis is sufficient to predict locomotion intent in practice.
Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers

Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change as the robot interacts with the world. To this end, we propose a novel graph neural network framework for multi-object manipulation to predict how inter-object relations change given robot actions. Our model operates on partial-view point clouds and can reason about multiple objects dynamically interacting during the manipulation, By learning a dynamics model in a learned latent graph embedding space, our model enables multi-step planning to reach target goal relations. We show our model trained purely in simulation transfers well to the real world. Our planner enables the robot to rearrange a variable number of objects with a range of shapes and sizes using both push and pick-and-place skills.
Multiparametric analysis of conic linear optimization based on the lift-and-project procedure

We study the application of the lift-and-project procedure to the multi-parametric analysis of conic linear optimization (CLO) problems. We ﬁrst introduce the concept of a pair of primal and dual conic representable sets, and deﬁne the set-valued mappings between them. We then explore a novel duality of mpCLOs, that allows us to generalize and treat the previous results for parametric analysis in a uniﬁed framework. In particular, we discuss the behavior of the optimal partition of a conic representable set. This leads to invariant region decomposition of a conic representable set, which is more general than the results in the literature. Finally, we study the properties of the optimal objective values as a function of parametric vectors. All the results are corroborated by examples with correlations
On Rozanov's Theorem and strenghtened asymptotic uniform distribution

For sums Sn = ∑n k=1 Xk, n ≥ 1 of independent random variables Xk taking values in Z we prove, as a consequence of a more general result, that if (i) For some function 1 ≤ φ(t) ↑ ∞ as t → ∞, and some constant C, we have for all n and ν ∈ Z,
Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers

In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal out-put regulation of continuous-time linear multi-agent systems based on adaptive dynamic programming (ADP). The proposed method is different from those in the existing literature of ADP and cooperative output regulation in the sense that the knowledge of the exosystem dynamics is not required in the design of the exostate observers for those agents with no direct access to the exosystem. Moreover, an optimal control policy is obtained without the prior knowledge of the modeling information of any agent while achieving the cooperative output regulation. Instead, we use the state/input information along the trajectories of the underlying dynamical systems and the estimated exostates to learn the optimal control policy. Simulation results show the efficacy of the proposed algorithm, where both estimation errors of exosystem matrix and exostates, and the tracking errors converge to zero in an optimal sense, which solves the cooperative optimal output regulation problem.
Spatial Signal Design for Positioning via End-to-End Learning

This letter considers the problem of end-to-end (E2E) learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user equipment (UE) position with multiple base stations (BSs) and demonstrate that E2E learning can match model-based design, both for angle-of-departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.
Momentum transfer in the dark sector and lensing convergence in upcoming galaxy surveys

We investigated a cosmological model that allows a momentum transfer between dark matter and dark energy. The interaction in the dark sector mainly affects the behaviour of perturbations on small scales while the background evolution matches the wCDM solution. As a result of the momentum transfer, these kinds of models help alleviating the σ 8 discrepancy in the standard model, but do not resolve the so-called H 0 tension. We confirm that this is indeed the case by computing cosmological constraints. While our analysis tends to favour σ 8 values lower than in ΛCDM, we do not find evidence for a non-vanishing momentum transfer in the dark sector. Since upcoming galaxy surveys will deliver information on scales and red-shift relevant for testing models allowing momentum transfer in the dark sector, we also carried out forecasts using different survey configurations. We assessed the relevance of neglecting lensing convergence κ when modelling the angular power spectrum of number counts fluctuations C ℓ ij(z,z'). We found that not including κ in analyses leads to biased constraints (≈ 1-5σ) of cosmological parameters even when including information from other experiments. Incorrectly modelling C ℓ ij(z,z') might lead to spurious detection of neutrino masses and exacerbate discrepancies in H 0 and σ 8.
XMM-Newton and Chandra cross-calibration using HIFLUGCS galaxy clusters - Systematic temperature differences and cosmological impact

Cosmological constraints from clusters rely on accurate gravitational mass estimates, which strongly depend on cluster gas temperature measurements. Therefore, systematic calibration differences may result in biased, instrument-dependent cosmological constraints. This is of special interest in the light of the tension between the Planck results of the primary temperature anisotropies of the CMB and Sunyaev-Zel'dovich plus X-ray cluster counts analyses. We quantify in detail the systematics and uncertainties of the cross-calibration of the effective area between five X-ray instruments, EPIC-MOS1/MOS2/PN onboard XMM-Newton and ACIS-I/S onboard Chandra, and the influence on temperature measurements. Furthermore, we assess the impact of the cross calibration uncertainties on cosmology. Using the HIFLUGCS sample, consisting of the 64 X-ray brightest galaxy clusters, we constrain the ICM temperatures through spectral fitting in the same, mostly isothermal, regions and compare them. Our work is an extension to a previous one using X-ray clusters by the IACHEC. Performing spectral fitting in the full energy band we find that best-fit temperatures determined with XMM-Newton/EPIC are significantly lower than Chandra/ACIS temperatures. We demonstrate that effects like multitemperature structure and different relative sensitivities of the instruments at certain energy bands cannot explain the observed differences. We conclude that using XMM-Newton/EPIC, instead of Chandra/ACIS to derive full energy band temperature profiles for cluster mass determination results in an 8% shift towards lower OmegaM values and <1% shift towards higher sigma8 values in a cosmological analysis of a complete sample of galaxy clusters. Such a shift is insufficient to significantly alleviate the tension between Planck CMB anisotropies and SZ plus XMM-Newton cosmological constraints.