Miracle-dz/UniFAR-FTUs huggingface dataset:
Extrinsic calibration for highly accurate trajectories reconstruction

In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long distances, total stations provide accurate and precise measurements, that are unaffected by the usual factors that deteriorate the accuracy of Global Navigation Satellite System (GNSS). While a single robotic total station can track the position of a target in three Degrees Of Freedom (DOF), three robotic total stations and three targets are necessary to yield the full six DOF pose reference. Since it is crucial to express the position of targets in a common coordinate frame, we present a novel extrinsic calibration method of multiple robotic total stations with field deployment in mind. The proposed method does not require the manual collection of ground control points during the system setup, nor does it require tedious synchronous measurement on each robotic total station. Based on extensive experimental work, we compare our approach to the classical extrinsic calibration methods used in geomatics for surveying and demonstrate that our approach brings substantial time savings during the deployment. Tested on more than 30 km of trajectories, our new method increases the precision of the extrinsic calibration by 25 % compared to the best state-of-the-art method, which is the one taking manually static ground control points.
Infinite homotopy stable class for 4-manifolds with boundary

We show that for every odd prime $q$, there exists an infinite family $\{M_i\}_{i=1}^{\infty}$ of topological 4-manifolds that are all stably homeomorphic to one another, all the manifolds $M_i$ have isometric rank one equivariant intersection pairings and boundary $L(2q, 1) # (S^1 \times S^2)$, but they are pairwise not homotopy equivalent via any homotopy equivalence that restricts to a homotopy equivalence of the boundary.
Physical modelling of dust polarization from magnetically enhanced radiative torque alignment in protostellar cores with polaris

Magnetic fields (B) are an important factor controlling the star-formation process. The leading method to observe B orientation is to use polarized thermal emission from aligned dust grains. In dense environments such as protostellar cores, however, dust grains may be inefficiently aligned owing to strong gas randomizations, making the use of dust polarization to trace Buncertain. The study of Hoang and Lazarian in 2016 demonstrated that grain alignment by radiative torques is enhanced if dust grains contain embedded iron inclusions. Here we extend the polaris code to study the effect of iron inclusions on grain alignment and thermal dust polarization towards a protostellar core, assuming uniform B. We found that paramagnetic grains produce a low polarization degree of $p \sim 1{{\ \rm per\ cent}}$ in the envelope and a negligible $p \ll 1{{\ \rm per\ cent}}$ in the central region owing to the loss of grain alignment. In contrast, grains with a high level of iron inclusions have perfect alignment and produce a high $p \sim 40{{\ \rm per\ cent}}$ in the envelope and a low $p \le 10{{\ \rm per\ cent}}$ in the central region. Grains with a moderate level of iron inclusions induce the polarization flipping from P ‖ B at millimetre to P ⊥ B at submillimetre wavelengths owing to the change in the internal alignment caused by slow internal relaxation. The weak alignment of very large grains with $a \ge 10\, {\mu \rm {m}}$ reduces dichroic extinction efficiency at submillimetre wavelengths. We found a positive correlation between p and the level of iron inclusions, which introduces a new option to constrain the abundance of solid iron locked in dust through dust polarimetry.
Observation of gapless nodal-line states in NdSbTe

Lanthanide (Ln) based systems in the ZrSiS-type nodal-line semimetals have been subjects of research investigations as grounds for studying the interplay of topology with possible magnetic ordering and electronic correlations that may originate from the presence of Ln 4f electrons. In this study, we carried out a thorough study of a LnSbTe system - NdSbTe - by using angle-resolved photoemission spectroscopy along with first-principles calculations and thermodynamic measurements. We experimentally detect the presence of multiple gapless nodal-line states, which is well supported by first-principles calculations. A dispersive and an almost non-dispersive nodal-line exist along the bulk X-R direction. Another nodal-line is present well below the Fermi level across the G- M direction, which is formed by bands with high Fermi velocity that seem to be sensitive to light polarization. Our study provides an insight into the electronic structure of a new LnSbTe material system that will aid towards understanding the connection of Ln elements with topological electronic structure in these systems.
Optimizing for the Rupert Property

A polyhedron is Rupert if it is possible to cut a hole in it and thread an identical polyhedron through the hole. It is known that all 5 Platonic solids, 10 of the 13 Archimedean solids, 9 of the 13 Catalan solids, and 82 of the 92 Johnson solids are Rupert. Here, a nonlinear optimization method is devised that is able to validate the previously known results in seconds. It is also used to show that 2 additional Catalan solids -- the triakis tetrahedron and the pentagonal icositetrahedron -- and 5 additional Johnson solids are Rupert.
Displacement smoothness of entropic optimal transport

The function that maps a family of probability measures to the solution of the dual entropic optimal transport problem is known as the Schr ̈odinger map. We prove that when the cost function is Ck+1 with k ∈ N∗ then this map is Lipschitz continuous from the L2-Wasserstein space to the space of Ck functions. Our result holds on compact domains and covers the multi-marginal case. We also include regularity results under negative Sobolev metrics weaker than Wasserstein under stronger smoothness assumptions on the cost. As applications, we prove displacement smoothness of the entropic optimal transport cost and the well-posedness of certain Wasserstein gradient flows involving this functional, including the Sinkhorn divergence and a multi-species system.
Electron transport properties in high-purity Ge down to cryogenic temperatures

Electron transport in Ge at various temperatures down to 20 mK has been investigated using particle Monte Carlo simulation taking into account ionized impurity and inelastic phonon scattering. The simulations account for the essential features of electron transport at cryogenic temperature: Ohmic regime, anisotropy of the drift velocity relative to the direction of the electric field, as well as a negative differential mobility phenomenon along the ⟨111⟩ field orientation. Experimental data for the electron velocities are reproduced with a satisfactory accuracy. Examples of electron position in the real space during the simulations are given and evidence separated clouds of electrons propagating along different directions depending on the valley they belong to.
Seventy Years of Radar and Communications: The road from separation to integration

Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age. Although R&C had been historically progressing separately, in recent decades, they have been converging toward integration, forming integrated sensing and communication (ISAC) systems, giving rise to new highly desirable capabilities in next-generation wireless networks and future radars. To better understand the essence of ISAC, this article provides a systematic overview of the historical development of R&C from a signal processing (SP) perspective. We first interpret the duality between R&C as signals and systems, followed by an introduction of their fundamental principles. We then elaborate on the two main trends in their technological evolution, namely, the increase of frequencies and bandwidths and the expansion of antenna arrays. We then show how the intertwined narratives of R&C evolved into ISAC and discuss the resultant SP framework. Finally, we overview future research directions in this field.
Improved Algorithms for Neural Active Learning

We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work. Then, the proposed algorithm leverages the powerful representation of NNs for both exploitation and exploration, has the query decision-maker tailored for $k$-class classification problems with the performance guarantee, utilizes the full feedback, and updates parameters in a more practical and efficient manner. These careful designs lead to an instance-dependent regret upper bound, roughly improving by a multiplicative factor $O(\log T)$ and removing the curse of input dimensionality. Furthermore, we show that the algorithm can achieve the same performance as the Bayes-optimal classifier in the long run under the hard-margin setting in classification problems. In the end, we use extensive experiments to evaluate the proposed algorithm and SOTA baselines, to show the improved empirical performance.
Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments

Low-overhead visual place recognition (VPR) is a highly active research topic. Mobile robotics applications often operate under low-end hardware, and even more hardware capable systems can still benefit from freeing up onboard system resources for other navigation tasks. This work addresses lightweight VPR by proposing a novel system based on the combination of binary-weighted classifier networks with a one-dimensional convolutional network, dubbed merger. Recent work in fusing multiple VPR techniques has mainly focused on increasing VPR performance, with computational efficiency not being highly prioritized. In contrast, we design our technique prioritizing low inference times, taking inspiration from the machine learning literature where the efficient combination of classifiers is a heavily researched topic. Our experiments show that the merger achieves inference times as low as 1 millisecond, being significantly faster than other well-established lightweight VPR techniques, while achieving comparable or superior VPR performance on several visual changes such as seasonal variations and viewpoint lateral shifts.
Economic-Driven Adaptive Traffic Signal Control

With the emerging connected-vehicle technologies and smart roads, the need for intelligent adaptive traffic signal controls is more than ever before. This paper proposes a novel Economic-driven Adaptive Traffic Signal Control (eATSC) model with a hyper control variable - interest rate defined in economics for traffic signal control at signalized intersections. The eATSC uses a continuous compounding function that captures both the total number of vehicles and the accumulated waiting time of each vehicle to compute penalties for different directions. The computed penalties grow with waiting time and is used for signal control decisions. Each intersection is assigned two intelligent agents adjusting interest rate and signal length for different directions according to the traffic patterns, respectively. The problem is formulated as a Markov Decision Process (MDP) problem to reduce congestions, and a two-agent Double Dueling Deep Q Network (DDDQN) is utilized to solve the problem. Under the optimal policy, the agents can select the optimal interest rates and signal time to minimize the likelihood of traffic congestion. To evaluate the superiority of our method, a VISSIM simulation model with classic four-leg signalized intersections is developed. The results indicate that the proposed model is adequately able to maintain healthy traffic flow at the intersection.
Alignment-guided Temporal Attention for Video Action Recognition

Temporal modeling is crucial for various video learning tasks. Most recent approaches employ either factorized (2D+1D) or joint (3D) spatial-temporal operations to extract temporal contexts from the input frames. While the former is more efficient in computation, the latter often obtains better performance. In this paper, we attribute this to a dilemma between the sufficiency and the efficiency of interactions among various positions in different frames. These interactions affect the extraction of task-relevant information shared among frames. To resolve this issue, we prove that frame-by-frame alignments have the potential to increase the mutual information between frame representations, thereby including more task-relevant information to boost effectiveness. Then we propose Alignment-guided Temporal Attention (ATA) to extend 1-dimensional temporal attention with parameter-free patch-level alignments between neighboring frames. It can act as a general plug-in for image backbones to conduct the action recognition task without any model-specific design. Extensive experiments on multiple benchmarks demonstrate the superiority and generality of our module.
Variability in Protoplanetary Nebulae. IX. Evidence for Evolution in a Decade

We have carried out a new photometric V,R C study of 12 protoplanetary nebulae, objects in the short-lived transition between the asymptotic giant branch and planetary nebula phases of stellar evolution. These had been the subjects of an earlier study, using data from 1994−2007, that found that all 12 varied periodically, with pulsation periods in the range of ∼38 to ∼150 days. They are all carbon-rich, with F−G spectral types. We combined our new (2008–2018) data with publicly available All-Sky Automated Survey for Supernovae data and determined new periods for their variability. The older and newer period values were compared to investigate evidence of period change, for which there is theoretical support that it might be detectable in a decade or two in some cases. Such a detection is challenging since the light curves are complicated, with multiple periods, changing amplitudes, and evidence of shocks. Nevertheless, we found one, and possibly two, such cases, which are associated with the higher-temperature stars in the sample (7250 and 8000 K). These results are most consistent with the evolution of stars at the lower end of the mass range of carbon stars, ∼1.5–2 M ☉. Several of the stars show longer-term trends of increasing (six cases) or decreasing (one case) brightness, which we think is most likely due to changes in the circumstellar dust opacity. There is one case of a possible ∼1.8 yr period in addition to the shorter pulsation. This is interpreted as possible evidence of an orbiting companion.
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge that multimodal remote sensing images cannot be directly compared due to their modal heterogeneity, we take advantage of two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. First, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations, and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on six datasets with different modal combinations demonstrate the effectiveness of the proposed method.
Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States

Consider an online convex optimization problem where the loss functions are self-concordant barriers, smooth relative to a convex function $h$, and possibly non-Lipschitz. We analyze the regret of online mirror descent with $h$. Then, based on the result, we prove the following in a unified manner. Denote by $T$ the time horizon and $d$ the parameter dimension. 1. For online portfolio selection, the regret of $\widetilde{\text{EG}}$, a variant of exponentiated gradient due to Helmbold et al., is $\tilde{O} ( T^{2/3} d^{1/3} )$ when $T>4 d / \log d$. This improves on the original $\tilde{O} ( T^{3/4} d^{1/2} )$ regret bound for $\widetilde{\text{EG}}$. 2. For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is $\tilde{O}(\sqrt{T d})$. The regret bound is the same as that of Soft-Bayes due to Orseau et al. up to logarithmic terms. 3. For online learning quantum states with the logarithmic loss, the regret of online mirror descent with the log-determinant function is also $\tilde{O} ( \sqrt{T d} )$. Its per-iteration time is shorter than all existing algorithms we know.
Throwing Objects into A Moving Basket While Avoiding Obstacles

The capabilities of a robot will be increased significantly by exploiting throwing behavior. In particular, throwing will enable robots to rapidly place the object into the target basket, located outside its feasible kinematic space, without traveling to the desired location. In previous approaches, the robot often learned a parameterized throwing kernel through analytical approaches, imitation learning, or hand-coding. There are many situations in which such approaches do not work/generalize well due to various object shapes, heterogeneous mass distribution, and also obstacles that might be presented in the environment. It is obvious that a method is needed to modulate the throwing kernel through its meta-parameters. In this paper, we tackle object throwing problem through a deep reinforcement learning approach that enables robots to precisely throw objects into a moving basket while there is an obstacle obstructing the path. To the best of our knowledge, we are the first group that addresses throwing objects with obstacle avoidance. Such a throwing skill not only increases the physical reachability of a robotic arm but also improves the execution time. In particular, the robot detects the pose of the target object, basket, and obstacle at each time step, predicts the proper grasp configuration for the target object, and then infers appropriate parameters to throw the object into the basket. Due to safety constraints, we develop a simulation environment in Gazebo to train the robot and then use the learned policy in real-robot directly. To assess the performers of the proposed approach, we perform extensive sets of experiments in both simulation and real-robot in three scenarios. Experimental results showed that the robot could precisely throw a target object into the basket outside its kinematic range and generalize well to new locations and objects without colliding with obstacles. The video of our experiments can be found at https://youtu.be/VmIFF__c_84.
Decompiling x86 Deep Neural Network Executables

Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitives. This approach allows DL computations to be undertaken at low cost across a variety of computing platforms, including CPUs, GPUs, and various hardware accelerators. We present BTD (Bin to DNN), a decompiler for deep neural network (DNN) executables. BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. BTD delivers a practical framework to process DNN executables compiled by different DL compilers and with full optimizations enabled on x86 platforms. It employs learning-based techniques to infer DNN operators, dynamic analysis to reveal network architectures, and symbolic execution to facilitate inferring dimensions and parameters of DNN operators. Our evaluation reveals that BTD enables accurate recovery of full specifications of complex DNNs with millions of parameters (e.g., ResNet). The recovered DNN specifications can be re-compiled into a new DNN executable exhibiting identical behavior to the input executable. We show that BTD can boost two representative attacks, adversarial example generation and knowledge stealing, against DNN executables. We also demonstrate cross-architecture legacy code reuse using BTD, and envision BTD being used for other critical downstream tasks like DNN security hardening and patching.
Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction

The continuous expansion of the urban construction scale has recently contributed to the demand for the dynamics of traffic intersections that are managed, making adaptive modellings become a hot topic. Existing deep learning methods are powerful to fit complex heterogeneous graphs. However, they still have drawbacks, which can be roughly classified into two categories, 1) spatiotemporal async-modelling approaches separately consider temporal and spatial dependencies, resulting in weak generalization and large instability while aggregating; 2) spatiotemporal sync-modelling is hard to capture long-term temporal dependencies because of the local receptive field. In order to overcome above challenges, a \textbf{C}ombined \textbf{D}ynamic \textbf{V}irtual spatiotemporal \textbf{G}raph \textbf{M}apping \textbf{(CDVGM)} is proposed in this work. The contributions are the following: 1) a dynamic virtual graph Laplacian ($DVGL$) is designed, which considers both the spatial signal passing and the temporal features simultaneously; 2) the Long-term Temporal Strengthen model ($LT^2S$) for improving the stability of time series forecasting; Extensive experiments demonstrate that CDVGM has excellent performances of fast convergence speed and low resource consumption and achieves the current SOTA effect in terms of both accuracy and generalization. The code is available at \hyperlink{https://github.com/Dandelionym/CDVGM. }{https://github.com/Dandelionym/CDVGM. }