Miracle-dz/UniFAR-FTUs huggingface dataset:
Relative Localization of Mobile Robots with Multiple Ultra-WideBand Ranging Measurements

Relative localization between autonomous robots without infrastructure is crucial to achieve their navigation, path planning, and formation in many applications, such as emergency response, where acquiring a prior knowledge of the environment is not possible. The traditional Ultra-WideBand (UWB)-based approach provides a good estimation of the distance between the robots, but obtaining the relative pose (including the displacement and orientation) remains challenging. We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB ranging nodes. We determine the pose between two robots by minimizing the residual error of the ranging measurements from all UWB nodes. To improve the localization accuracy, we propose to utilize the odometry constraints through a sliding window-based optimization. The optimized pose is then fused with the odometry in a particle filtering for pose tracking among a group of mobile robots. We have conducted extensive experiments to validate the effectiveness of the proposed approach.
Looking Twice for Partial Clues: Weakly-supervised Part-Mentored Attention Network for Vehicle Re-Identification

—Vehicle re-identiﬁcation (Re-ID) is to retrieve images of the same vehicle across different cameras. Two key challenges lie in the subtle inter-instance discrepancy caused by near-duplicate identities and the large intra-instance variance caused by different views. Since the holistic appearance suffers from viewpoint variation and distortion, part-level feature learning has been introduced to enhance vehicle description. However, existing approaches to localize and amplify signiﬁcant parts often fail to handle spatial misalignment as well as occlusion and require expensive annotations. In this paper, we propose a weakly-supervised Part-Mentored Attention Network (PMANet) consisting of a Part Attention Network (PANet) for vehicle part localization with self-attention and a Part-Mentored Network (PMNet) for mentoring the global and local feature aggregation. Firstly, PANet is introduced to predict a foreground mask and pinpoint K prominent vehicle parts only with a weak identity supervision. Secondly, we propose a PMNet to learn global and part-level features with multi-scale spatial and channel attention and aggregate them in K main-partial tasks via part transfer. Like humans who ﬁrst differentiate objects with general information and then observe salient parts for more detailed clues, PANet and PMNet construct a two-stage attention structure to perform a coarse-to-ﬁne search among identities. Finally, we address this Re-ID issue as a multi-task problem, including global feature learning, identity classiﬁcation, and part transfer. We adopt Homoscedastic Uncertainty to learn the optimal weighing of different losses. Comprehensive experiments are conducted on two benchmark datasets. Our approach outperforms recent state-of-the-art methods by averagely 2.63% in CMC@1 on VehicleID and 2.2% in mAP on VeRi776. Moreover, Experimental results on occluded test sets demonstrate that PMANet has a good generalization ability.
Dynamic Convolution for 3D Point Cloud Instance Segmentation

In this paper, we come up with a simple yet effective approach for instance segmentation on 3D point cloud with strong robustness. Previous top-performing methods for this task adopt a bottom-up strategy, which often involves various inefficient operations or complex pipelines, such as grouping over-segmented components, introducing heuristic post-processing steps, and designing complex loss functions. As a result, the inevitable variations of the instances sizes make it vulnerable and sensitive to the values of pre-defined hyper-parameters. To this end, we instead propose a novel pipeline that applies dynamic convolution to generate instance-aware parameters in response to the characteristics of the instances. The representation capability of the parameters is greatly improved by gathering homogeneous points that have identical semantic categories and close votes for the geometric centroids. Instances are then decoded via several simple convolution layers, where the parameters are generated depending on the input. In addition, to introduce a large context and maintain limited computational overheads, a light-weight transformer is built upon the bottleneck layer to capture the long-range dependencies. With the only post-processing step, non-maximum suppression (NMS), we demonstrate a simpler and more robust approach that achieves promising performance on various datasets: ScanNetV2, S3DIS, and PartNet. The consistent improvements on both voxel- and point-based architectures imply the effectiveness of the proposed method. Code is available at: https://git.io/DyCo3D.
Stability of superthermal strahl electrons in the solar wind

We present a kinetic stability analysis of the solar wind electron distribution function consisting of the Maxwellian core and the magnetic-field aligned strahl, a superthermal electron beam propagating away from the sun. We use an electron strahl distribution function obtained as a solution of a weakly collisional drift-kinetic equation, representative of a strahl affected by Coulomb collisions but unadulterated by possible broadening from turbulence. This distribution function is essentially non-Maxwellian and varies with the heliospheric distance. The stability analysis is performed with the Vlasov–Maxwell linear solver leopard. We find that depending on the heliospheric distance, the core-strahl electron distribution becomes unstable with respect to sunward-propagating kinetic-Alfvén, magnetosonic, and whistler modes, in a broad range of propagation angles. The wavenumbers of the unstable modes are close to the ion inertial scales, and the radial distances at which the instabilities first appear are on the order of 1 au. However, we have not detected any instabilities driven by resonant wave interactions with the superthermal strahl electrons. Instead, the observed instabilities are triggered by a relative drift between the electron and ion cores necessary to maintain zero electric current in the solar wind frame (ion frame). Contrary to strahl distributions modelled by shifted Maxwellians, the electron strahl obtained as a solution of the kinetic equation is stable. Our results are consistent with the previous studies based on a more restricted solution for the electron strahl.
Steep Cosmic-Ray Spectra with Revised Diffusive Shock Acceleration

Galactic cosmic rays (CRs) are accelerated at the forward shocks of supernova remnants (SNRs) via diffusive shock acceleration (DSA), an efficient acceleration mechanism that predicts power-law energy distributions of CRs. However, observations of nonthermal SNR emission imply CR energy distributions that are generally steeper than E −2, the standard DSA prediction. Recent results from kinetic hybrid simulations suggest that such steep spectra may arise from the drift of magnetic structures with respect to the thermal plasma downstream of the shock. Using a semi-analytic model of nonlinear DSA, we investigate the implications that these results have on the phenomenology of a wide range of SNRs. By accounting for the motion of magnetic structures in the downstream, we produce CR energy distributions that are substantially steeper than E −2 and consistent with observations. Our formalism reproduces both modestly steep spectra of Galactic SNRs (∝E −2.2) and the very steep spectra of young radio supernovae (∝E −3).
CodeMapping: Real-Time Dense Mapping for Sparse SLAM using Compact Scene Representations

We propose a novel dense mapping framework for sparse visual SLAM systems which leverages a compact scene representation. State-of-the-art sparse visual SLAM systems provide accurate and reliable estimates of the camera trajectory and locations of landmarks. While these sparse maps are useful for localization, they cannot be used for other tasks such as obstacle avoidance or scene understanding. In this letter we propose a dense mapping framework to complement sparse visual SLAM systems which takes as input the camera poses, keyframes and sparse points produced by the SLAM system and predicts a dense depth image for every keyframe. We build on CodeSLAM [1] and use a variational autoencoder (VAE) which is conditioned on intensity, sparse depth and reprojection error images from sparse SLAM to predict an uncertainty-aware dense depth map. The use of a VAE then enables us to refine the dense depth images through multi-view optimization which improves the consistency of overlapping frames. Our mapper runs in a separate thread in parallel to the SLAM system in a loosely coupled manner. This flexible design allows for integration with arbitrary metric sparse SLAM systems without delaying the main SLAM process. Our dense mapper can be used not only for local mapping but also globally consistent dense 3D reconstruction through TSDF fusion. We demonstrate our system running with ORB-SLAM3 and show accurate dense depth estimation which could enable applications such as robotics and augmented reality.
A Discriminative Semantic Ranker for Question Retrieval

Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typically implemented as multi-stage rankers: The first-stage ranker aims to recall potentially relevant questions from a large repository, and the latter stages attempt to re-rank the retrieved results. Most existing works on question retrieval mainly focused on the re-ranking stages, leaving the first-stage ranker to some traditional term-based methods. However, term-based methods often suffer from the vocabulary mismatch problem, especially on short texts, which may block the re-rankers from relevant questions at the very beginning. An alternative is to employ embedding-based methods for the first-stage ranker, which compress texts into dense vectors to enhance the semantic matching. However, these methods often lose the discriminative power as term-based methods, thus introduce noise during retrieval and hurt the recall performance. In this work, we aim to tackle the dilemma of the first-stage ranker, and propose a discriminative semantic ranker, namely DenseTrans, for high-recall retrieval. Specifically, DenseTrans is a densely connected Transformer, which learns semantic embeddings for texts based on Transformer layers. Meanwhile, DenseTrans promotes low-level features through dense connections to keep the discriminative power of the learned representations. DenseTrans is inspired by DenseNet in computer vision (CV), but poses a new way to use the dense connectivity which is totally different from its original design purpose. Experimental results over two question retrieval benchmark datasets show that our model can obtain significant gain on recall against strong term-based methods as well as state-of-the-art embedding-based methods.
Diagnosing the cosmic coincidence problem and its evolution with recent observations

In the framework of a phenomenological cosmological model with the assumption of ρX ∝ ρma (ρX and ρm are the energy densities of dark energy and matter, respectively.), we intend to diagnose the cosmic coincidence problem by using the recent samples of Type Ia supernovae (SNe Ia), baryon acoustic oscillation (BAO) and cosmic microwave background (CMB). ξ is a key parameter to characterize the severity of the coincidence problem, wherein ξ = 3 and 0 correspond to the ΛCDM scenario and the self-similar solution without the coincidence problem, respectively. The case of ξ = Constant has been investigated in the previous studies, while we further consider the case of ξ(z) = ξ0 + ξz ∗ z 1+z to explore the possible evolution. A joint analysis of the Pantheon SNe Ia sample with the recent BAO and CMB data figures out that ξ = 3.28 ± 0.15 in the case of ξ = Constant at 68% confidence level (CL), in addition, ξ0 = 2.78+0.28 −1.01 and ξz = 0.93 +1.56 −0.91 in the case of ξ(z) at 68% CL. It implies that the temporal evolution of the scaling parameter ξ is accepted by the joint sample at 68% CL; however, the joint sample also cannot distinguish whether the scaling parameter ξ is variable or not at 95% CL. Moreover, the ΛCDM scenario is accepted by the joint sample at 95% CL in both cases, and the coincidence problem still exists. In addition, we apply the Bayesian evidence to compare the models with the analysis of the joint sample, it turns out that the ΛCDM scenario is most supported by the joint sample; furthermore, the joint sample prefers the scenario with a constant ξ to the one with a variable ξ(z).
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative Model

The high-dimensional or sparse reward task of a reinforcement learning (RL) environment requires a superior potential controller such as hierarchical reinforcement learning (HRL) rather than an atomic RL because it absorbs the complexity of commands to achieve the purpose of the task in its hierarchical structure. One of the HRL issues is how to train each level policy with the optimal data collection from its experience. That is to say, how to synchronize adjacent level policies optimally. Our research finds that a HRL model through the off-policy correction technique of HRL, which trains a higher-level policy with the goal of reflecting a lower-level policy which is newly trained using the off-policy method, takes the critical role of synchronizing both level policies at all times while they are being trained. We propose a novel HRL model supporting the optimal level synchronization using the off-policy correction technique with a deep generative model. This uses the advantage of the inverse operation of a flow-based deep generative model (FDGM) to achieve the goal corresponding to the current state of the lower-level policy. The proposed model also considers the freedom of the goal dimension between HRL policies which makes it the generalized inverse model of the model-free RL in HRL with the optimal synchronization method. The comparative experiment results show the performance of our proposed model.
Quantum characteristic classes, moment correspondences and the Hamiltonian groups of coadjoint orbits

For any coadjoint orbit G/L, we determine all useful terms of the associated SavelyevSeidel morphism defined on H−∗(ΩG). Immediate consequences are: (1) the dimension of the kernel of the natural map π∗(G)⊗Q → π∗(Ham(G/L))⊗Q is at most the semi-simple rank of L, and (2) the Bott-Samelson cycles in ΩG which correspond to Peterson elements are solutions to the min-max problem for Hofer’s max-length functional on ΩHam(G/L). The proof is based on Bae-Chow-Leung’s recent computation of Ma’u-Wehrheim-Woodward morphism for the moment correspondence associated to G/T where T is a maximal torus, the computation of Abbondandolo-Schwarz isomorphism for G, and two theoretical results including the coincidence of the above Savelyev-Seidel and Ma’u-Wehrheim-Woodward morphisms, and a Leraytype spectral sequence relating Savelyev-Seidel morphisms for G/L and G/T . These ingredients also allow us to obtain an alternative proof of Peterson-Woodward’s comparison formula which relates the quantum cohomology of G/T to that of G/L.
Characterizing Online Engagement with Disinformation and Conspiracies in the 2020 U.S. Presidential Election

Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world. Persistent manipulation of social media has resulted in increased concerns regarding the 2020 U.S. Presidential Election, due to its potential to influence individual opinions and social dynamics. In this work, we focus on the identification of distorted facts, in the form of unreliable and conspiratorial narratives in election-related tweets, to characterize discourse manipulation prior to the election. We apply a detection model to separate factual from unreliable (or conspiratorial) claims analyzing a dataset of 242 million election-related tweets. The identified claims are used to investigate targeted topics of disinformation, and conspiracy groups, most notably the far-right QAnon conspiracy group. Further, we characterize account engagements with unreliable and conspiracy tweets, and with the QAnon conspiracy group, by political leaning and tweet types. Finally, using a regression discontinuity design, we investigate whether Twitter's actions to curb QAnon activity on the platform were effective, and how QAnon accounts adapt to Twitter's restrictions.
Wolf 503 b: Characterization of a Sub-Neptune Orbiting a Metal-poor K Dwarf

Using radial-velocity measurements from four instruments, we report the mass and density of a 2.043 ±0.069 R ⊕ sub-Neptune orbiting the quiet K-dwarf Wolf 503 (HIP 67285). In addition, we present improved orbital and transit parameters by analyzing previously unused short-cadence K2 campaign 17 photometry and conduct a joint radial-velocity-transit fit to constrain the eccentricity at 0.41 ± 0.05. The addition of a transit observation by Spitzer also allows us to refine the orbital ephemeris in anticipation of further follow-up. Our mass determination, 6.26 −0.70+0.69 M ⊕ , in combination with the updated radius measurements, gives Wolf 503 b a bulk density of ρ=2.92−0.44+0.50 g cm−3. Using interior composition models, we find this density is consistent with an Earth-like core with either a substantial H2O mass fraction (45 −16+19 %) or a modest H/He envelope (0.5% ± 0.3%). The low H/He mass fraction, along with the old age of Wolf 503 (11 ± 2 Gyr), makes this sub-Neptune an opportune subject for testing theories of XUV-driven mass loss while the brightness of its host (J = 8.3 mag) makes it an attractive target for transmission spectroscopy.
Coherent mode-combined ultra-narrow-linewidth single-mode micro-disk laser

State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-Intense Laser Science, Shanghai Institute of Optics and Fine Mechanics (SIOM), Chinese Academy of Sciences (CAS), Shanghai 201800, China. Department of Electrical and Computer Engineering, University of Victoria, Victoria, British Columbia V8P 5C2, Canada. XXL—The Extreme Optoelectromechanics Laboratory, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China. Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, China. The Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China. State Key Laboratory of Precision Spectroscopy, East China Normal University, Shanghai 200062, China. Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China. Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China. Collaborative Innovation Center of Light Manipulations and Applications, Shandong Normal University, Jinan 250358, China. Shanghai Research Center for Quantum Sciences, Shanghai 201315, China. These authors contributed equally to the work. †Electronic address: taolu@ece.uvic. ca ‡Electronic address: wfang08@zju.edu. cn *Electronic address: ya.cheng@siom.ac. cn
GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies

Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar continues to function robustly in compromised circumstances in which cameras become impaired, guaranteeing a steady inflow of information. Yet, camera images provide a more intuitive and readily applicable impression of the world. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. After reducing the memory requirements of both high-dimensional measurements through a decoupled stochastic self-supervised compression technique, the proposed algorithm exploits similarities and establishes correspondences between both domains at different feature levels during training. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. These discrete tokens are finally transformed back into an instructive view of the respective surrounding, allowing to visually perceive potential dangers for important tasks downstream.
Stock Movement Prediction with Financial News using Contextualized Embedding from BERT

News events can greatly influence equity markets. In this paper, we are interested in predicting the short-term movement of stock prices after financial news events using only the headlines of the news. To achieve this goal, we introduce a new text mining method called Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN). Compared with previous approaches which use static vector representations of the news (static embedding), our model uses contextualized vector representations of the headlines (contextualized embeddings) generated from Bidirectional Encoder Representations from Transformers (BERT). Our model obtains the state-of-the-art result on this stock movement prediction task. It shows significant improvement compared with other baseline models, in both accuracy and trading simulations. Through various trading simulations based on millions of headlines from Bloomberg News, we demonstrate the ability of this model in real scenarios.
Simple and practical DIQKD security analysis via BB84-type uncertainty relations and Pauli correlation constraints

According to the entropy accumulation theorem, proving the unconditional security of a device-independent quantum key distribution protocol reduces to deriving tradeoff functions, i.e., bounds on the single-round von Neumann entropy of the raw key as a function of Bell linear functionals, conditioned on an eavesdropper's quantum side information. In this work, we describe how the conditional entropy can be bounded in the 2-input/2-output setting, where the analysis can be reduced to qubit systems, by combining entropy bounds for variants of the well-known BB84 protocol with quantum constraints on qubit operators on the bipartite system shared by Alice and Bob. The approach gives analytic bounds on the entropy, or semi-analytic ones in reasonable computation time, which are typically close to optimal. We illustrate the approach on a variant of the device-independent CHSH QKD protocol where both bases are used to generate the key as well as on a more refined analysis of the original single-basis variant with respect to losses. We obtain in particular a detection efficiency threshold slightly below 80.26%, within reach of current experimental capabilities.