Miracle-dz/UniFAR-FTUs huggingface dataset:
SE-LIO: Semantics-enhanced Solid-State-LiDAR-Inertial Odometry for Tree-rich Environments

In this letter, we propose a semantics-enhanced solid-state-LiDAR-inertial odometry (SE-LIO) in tree-rich environments. Multiple LiDAR frames are first merged and compensated with the inertial navigation system (INS) to increase the point-cloud coverage, thus improving the accuracy of semantic segmentation. The unstructured point clouds, such as tree leaves and dynamic objects, are then removed with the semantic information. Furthermore, the pole-like point clouds, primarily tree trunks, are modeled as cylinders to improve positioning accuracy. An adaptive piecewise cylinder-fitting method is proposed to accommodate environments with a high prevalence of curved tree trunks. Finally, the iterated error-state Kalman filter (IESKF) is employed for state estimation. Point-to-cylinder and point-to-plane constraints are tightly coupled with the prior constraints provided by the INS to obtain the maximum a posteriori estimation. Targeted experiments are conducted in complex campus and park environments to evaluate the performance of SE-LIO. The proposed methods, including removing the unstructured point clouds and the adaptive cylinder fitting, yield improved accuracy. Specifically, the positioning accuracy of the proposed SE-LIO is improved by 43.1% compared to the plane-based LIO.
Macroscopically Self-Aligned and Chiralized Carbon Nanotubes: From Filtration to Innovation

Because of their natural one-dimensional (1D) structure combined with intricate chiral variations, carbon nanotubes (CNTs) exhibit various exceptional physical properties, such as ultrahigh electrical and thermal conductivity, exceptional mechanical strength, and chirality-dependent metallicity. These properties make CNTs highly promising for diverse applications, including field-effect transistors, sensors, photodetectors, and thermoelectric devices. While CNTs excel individually at the nanoscale, their 1D and chiral nature can be lost on a macroscopic scale when they are randomly assembled. Therefore, the alignment and organization of CNTs in macroscopic structures is crucial for harnessing their full potential. In this review, we explore recent advancements in understanding CNT alignment mechanisms, improving CNT aligning methods, and demonstrating macroscopically 1D properties of ordered CNT assemblies. We also focus on a newly discovered class of CNT architectures, combining CNT alignment and twisting mechanisms to create artificial radial and chiral CNT films at wafer scales. Finally, we summarize recent developments related to aligned and chiral CNT films in optoelectronics, highlighting their unique roles in solar cells, thermal emitters, and optical modulators.
Asymptotic analysis for shear waves in a fluid‐filled borehole

We focus on the theoretical analysis of the resonance phenomena and the geometric attenuation behaviour of critical refracted shear waves propagating along a fluid‐filled borehole. Using integration by parts, we asymptotically expand the vertical branch‐cut integral of shear waves in an infinite series related to each order of the derivative of the response function of the formation. It is proved that the vertical branch‐cut integral of shear waves at large offsets consists mainly of the contribution of the second asymptotic series, which is related to the first derivative of the response function of the formation at the shear branch point. Using the asymptotic expansion, we develop a simplified amplitude expression for shear waves, and the resonant frequency formula. The validity of the resonance frequencies obtained by the resonant frequency formula is verified numerically by comparison with the corresponding frequencies of the numerical integral results. We also give a rational explanation for the phenomenon of two peaks appearing within each resonant peak zone: i.e. that these are the contributions of the constructive interference of the shear waves and the mode poles.
Learning-based Ecological Adaptive Cruise Control of Autonomous Electric Vehicles: A Comparison of ADP, DQN and DDPG Approaches

This paper presents model-based and model-free learning methods for economic and ecological adaptive cruise control (Eco-ACC) of connected and autonomous electric vehicles. For model-based optimal control of Eco-ACC, we considered longitudinal vehicle dynamics and a quasi-steady-state powertrain model including the physical limits of a commercial electric vehicle. We used adaptive dynamic programming (ADP), in which the value function was trained using data obtained from IPG CarMaker simulations. For real-time implementation, forward multi-step look-ahead prediction and optimization were executed in a receding horizon scheme to maximize the energy efficiency of the electric machine while avoiding rear-end collisions and satisfying the powertrain, speed, and distance-gap constraints. For model-free optimal control of Eco-ACC, we applied two reinforcement learning methods, Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), in which deep neural networks were trained in IPG CarMaker simulations. For performance demonstrations, the HWFET, US06, and WLTP Class 3b driving cycles were used to simulate the front vehicle, and the energy consumptions of the host vehicle and front vehicle were compared. In high-fidelity IPG CarMaker simulations, the proposed learning-based Eco-ACC methods demonstrated approximately 3-5% and 10-14% efficiency improvements in highway and city-highway driving scenarios, respectively, compared with the front vehicle. A video of the CarMaker simulation is available at https://youtu.be/DIXzJxMVig8.
Regret Optimality of GP-UCB

Gaussian Process Upper Confidence Bound (GP-UCB) is one of the most popular methods for optimizing black-box functions with noisy observations, due to its simple structure and superior performance. Its empirical successes lead to a natural, yet unresolved question: Is GP-UCB regret optimal? In this paper, we offer the first generally affirmative answer to this important open question in the Bayesian optimization literature. We establish new upper bounds on both the simple and cumulative regret of GP-UCB when the objective function to optimize admits certain smoothness property. These upper bounds match the known minimax lower bounds (up to logarithmic factors independent of the feasible region's dimensionality) for optimizing functions with the same smoothness. Intriguingly, our findings indicate that, with the same level of exploration, GP-UCB can simultaneously achieve optimality in both simple and cumulative regret. The crux of our analysis hinges on a refined uniform error bound for online estimation of functions in reproducing kernel Hilbert spaces. This error bound, which we derive from empirical process theory, is of independent interest, and its potential applications may reach beyond the scope of this study.
Bagged Regularized k-Distances for Anomaly Detection

We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples. Though distance-based methods are top-performing for unsupervised anomaly detection, they suffer heavily from the sensitivity to the choice of the number of the nearest neighbors. In this paper, we propose a new distance-based algorithm called bagged regularized $k$-distances for anomaly detection (BRDAD) converting the unsupervised anomaly detection problem into a convex optimization problem. Our BRDAD algorithm selects the weights by minimizing the surrogate risk, i.e., the finite sample bound of the empirical risk of the bagged weighted $k$-distances for density estimation (BWDDE). This approach enables us to successfully address the sensitivity challenge of the hyperparameter choice in distance-based algorithms. Moreover, when dealing with large-scale datasets, the efficiency issues can be addressed by the incorporated bagging technique in our BRDAD algorithm. On the theoretical side, we establish fast convergence rates of the AUC regret of our algorithm and demonstrate that the bagging technique significantly reduces the computational complexity. On the practical side, we conduct numerical experiments on anomaly detection benchmarks to illustrate the insensitivity of parameter selection of our algorithm compared with other state-of-the-art distance-based methods. Moreover, promising improvements are brought by applying the bagging technique in our algorithm on real-world datasets.
OpEnCam: Lensless Optical Encryption Camera

Lensless cameras multiplex the incoming light before it is recorded by the sensor. This ability to multiplex the incoming light has led to the development of ultra-thin, high-speed, and single-shot 3D imagers. Recently, there have been various attempts at demonstrating another useful aspect of lensless cameras - their ability to preserve the privacy of a scene by capturing encrypted measurements. However, existing lensless camera designs suffer numerous inherent privacy vulnerabilities. To demonstrate this, we develop the first comprehensive attack model for encryption cameras, and propose OpEnCam -- a novel lensless OPtical ENcryption CAmera design that overcomes these vulnerabilities. OpEnCam encrypts the incoming light before capturing it using the modulating ability of optical masks. Recovery of the original scene from an OpEnCam measurement is possible only if one has access to the camera's encryption key, defined by the unique optical elements of each camera. Our OpEnCam design introduces two major improvements over existing lensless camera designs - (a) the use of two co-axially located optical masks, one stuck to the sensor and the other a few millimeters above the sensor and (b) the design of mask patterns, which are derived heuristically from signal processing ideas. We show, through experiments, that OpEnCam is robust against a range of attack types while still maintaining the imaging capabilities of existing lensless cameras. We validate the efficacy of OpEnCam using simulated and real data. Finally, we built and tested a prototype in the lab for proof-of-concept.
Searching Dense Representations with Inverted Indexes

Nearly all implementations of top-$k$ retrieval with dense vector representations today take advantage of hierarchical navigable small-world network (HNSW) indexes. However, the generation of vector representations and efficiently searching large collections of vectors are distinct challenges that can be decoupled. In this work, we explore the contrarian approach of performing top-$k$ retrieval on dense vector representations using inverted indexes. We present experiments on the MS MARCO passage ranking dataset, evaluating three dimensions of interest: output quality, speed, and index size. Results show that searching dense representations using inverted indexes is possible. Our approach exhibits reasonable effectiveness with compact indexes, but is impractically slow. Thus, while workable, our solution does not provide a compelling tradeoff and is perhaps best characterized today as a"technical curiosity".
Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation

Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl’s causality, and determines their explainability and interpretability. Despite their popularity, there are almost no resources in the literature on how to compute Shannon’s entropy and the Kullback–Leibler (KL) divergence for BNs under their most common distributional assumptions. In this paper, we provide computationally efficient algorithms for both by leveraging BNs’ graphical structure, and we illustrate them with a complete set of numerical examples. In the process, we show it is possible to reduce the computational complexity of KL from cubic to quadratic for Gaussian BNs.
Light Field Imaging in the Restrictive Object Space based on Flexible Angular Plane

In some applications, the object space of light field imaging system is restrictive, such as industrial and medical endoscopes. If the traditional light field imaging system is used in the restrictive object space (ROS) directly but without any specific considerations, the ROS will lead to severe microlens image distortions and then affects light field decoding, calibration and 3D reconstruction. The light field imaging in restrictive object space (ROS-LF) is complicated but significant. In this paper, we first deduce that the reason of the microlens image deviation is the position variation of the angular plane, then we propose the flexible angular plane for ROS-LF, while in the traditional light field the angular plane always coincides with the main lens plane. Subsequently, we propose the microlens image non-distortion principle for ROS-LF and introduce the ROS-LF imaging principle. We demonstrate that the difference is an aperture constant term between the ROS-LF and traditional light field imaging models. At last, we design a ROS-LF simulated system and calibrate it to verify principles proposed in this paper.
Occurrence of larvae and young mackerel Rastrelliger kanagurta (Cuvier ) off Vizhinjam near Trivandrum

This note deals with the appearance of spawners, juveniles, young ones and post larvae of the mackerel, Rasttelliger canagurta off Vizhinjam (near Trivandmun) from late March early August in 1955 and early March to end of August in 1956.
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression

We study the fundamental problems of Gaussian mean estimation and linear regression with Gaussian covariates in the presence of Huber contamination. Our main contribution is the design of the first sample near-optimal and almost linear-time algorithms with optimal error guarantees for both of these problems. Specifically, for Gaussian robust mean estimation on $\mathbb{R}^d$ with contamination parameter $\epsilon \in (0, \epsilon_0)$ for a small absolute constant $\epsilon_0$, we give an algorithm with sample complexity $n = \tilde{O}(d/\epsilon^2)$ and almost linear runtime that approximates the target mean within $\ell_2$-error $O(\epsilon)$. This improves on prior work that achieved this error guarantee with polynomially suboptimal sample and time complexity. For robust linear regression, we give the first algorithm with sample complexity $n = \tilde{O}(d/\epsilon^2)$ and almost linear runtime that approximates the target regressor within $\ell_2$-error $O(\epsilon)$. This is the first polynomial sample and time algorithm achieving the optimal error guarantee, answering an open question in the literature. At the technical level, we develop a methodology that yields almost-linear time algorithms for multi-directional filtering that may be of broader interest.
AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix

Adaptive optimizers, such as Adam, have achieved remarkable success in deep learning. A key component of these optimizers is the so-called preconditioning matrix, providing enhanced gradient information and regulating the step size of each gradient direction. In this paper, we propose a novel approach to designing the preconditioning matrix by utilizing the gradient difference between two successive steps as the diagonal elements. These diagonal elements are closely related to the Hessian and can be perceived as an approximation of the inner product between the Hessian row vectors and difference of the adjacent parameter vectors. Additionally, we introduce an auto-switching function that enables the preconditioning matrix to switch dynamically between Stochastic Gradient Descent (SGD) and the adaptive optimizer. Based on these two techniques, we develop a new optimizer named AGD that enhances the generalization performance. We evaluate AGD on public datasets of Natural Language Processing (NLP), Computer Vision (CV), and Recommendation Systems (RecSys). Our experimental results demonstrate that AGD outperforms the state-of-the-art (SOTA) optimizers, achieving highly competitive or significantly better predictive performance. Furthermore, we analyze how AGD is able to switch automatically between SGD and the adaptive optimizer and its actual effects on various scenarios. The code is available at https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers.
Inclusion and estimates for the jumps of minimizers in variational denoising

We study in this note the stability and inclusion of the jump set of minimizers of convex denoising functionals, such as the celebrated"Rudin-Osher-Fatemi"functional, for scalar or vectorial signals. We show that under mild regularity assumptions on the data fidelity term and the regularizer, the jump set of the minimizer is essentially a subset of the original jump set. Moreover, we give an estimate on the magnitude of jumps in terms of the data. This extends old results, in particular of the first author (with V. Caselles and M. Novaga) and of T. Valkonen, to much more general cases. We also consider the case where the original datum has unbounded variation, and define a notion of its jump set which, again, must contain the jump set of the solution.
A multi-channel cycleGAN for CBCT to CT synthesis

Image synthesis is used to generate synthetic CTs (sCTs) from on-treatment cone-beam CTs (CBCTs) with a view to improving image quality and enabling accurate dose computation to facilitate a CBCT-based adaptive radiotherapy workflow. As this area of research gains momentum, developments in sCT generation methods are difficult to compare due to the lack of large public datasets and sizeable variation in training procedures. To compare and assess the latest advancements in sCT generation, the SynthRAD2023 challenge provides a public dataset and evaluation framework for both MR and CBCT to sCT synthesis. Our contribution focuses on the second task, CBCT-to-sCT synthesis. By leveraging a multi-channel input to emphasize specific image features, our approach effectively addresses some of the challenges inherent in CBCT imaging, whilst restoring the contrast necessary for accurate visualisation of patients' anatomy. Additionally, we introduce an auxiliary fusion network to further enhance the fidelity of generated sCT images.
Large Language Models for Travel Behavior Prediction

Travel behavior prediction is a fundamental task in transportation demand management. The conventional methods for travel behavior prediction rely on numerical data to construct mathematical models and calibrate model parameters to represent human preferences. Recent advancement in large language models (LLMs) has shown great reasoning abilities to solve complex problems. In this study, we propose to use LLMs to predict travel behavior with prompt engineering without data-based parameter learning. Specifically, we carefully design our prompts that include 1) task description, 2) travel characteristics, 3) individual attributes, and 4) guides of thinking with domain knowledge, and ask the LLMs to predict an individual's travel behavior and explain the results. We select the travel mode choice task as a case study. Results show that, though no training samples are provided, LLM-based predictions have competitive accuracy and F1-score as canonical supervised learning methods such as multinomial logit, random forest, and neural networks. LLMs can also output reasons that support their prediction. However, though in most of the cases, the output explanations are reasonable, we still observe cases that violate logic or with hallucinations.
Switchable band topology and geometric current in sliding bilayer elemental ferroelectric

We demonstrate that sliding motion between two layers of the newly discovered ferroelectric and topologically trivial bismuth (Bi) monolayer [Nature 617, 67 (2023)] can induce a sequence of topological phase transitions, alternating between trivial and nontrivial states. Interestingly, a lateral shift, even when preserving spatial symmetry, can still switch the quantum spin Hall state on and off. The substantial band-gap modulation and band inversion due to interlayer sliding arise primarily from the intralayer in-plane charge transfer processes involving Bi atoms at the outermost atomic layers, rather than the interlayer charge redistribution. We map out the topological phase diagram and the geometric Berry curvature-dipole induced nonlinear anomalous Hall response resulting from sliding, highlighting the potential for robust mechanical control over the edge current and the Hall current. Bilayer configurations that are $\mathbb{Z}_2$ nontrivial can produce drastically different transverse currents orthogonal to the external electric field. This occurs because both the direction and magnitude of the Berry curvature dipole at the Fermi level depend sensitively on the sliding displacement. Our results suggest that bilayer bismuth could serve as a platform to realize power-efficient ``Berry slidetronics"for topology memory applications.
Geochemistry of Picritic and Associated Basalt Flows of the Western Emeishan Flood Basalt Province, China

Picritic lava flows near Lijiang in the late Permian Emeishan flood basalt province are associated with augite-phyric basalt, aphyric basalt, and basaltic pyroclastic units. The dominant phenocryst in the picritic flows is Mg-rich olivine (up to 91.6% forsterite component) with high CaO contents (to 0.42 wt %) and glass inclusions, indicating that the olivine crystallized from a melt. Associated chromite has a high Cr-number (73–75). The estimated MgO content of the primitive picritic liquids is about 22 wt %, and initial melt temperature may have been as high as 1630– 1690 � C. The basaltic lavas appear to be related to the picritic ones principally by olivine and clinopyroxene fractionation. Agecorrected Nd–Sr–Pb isotope ratios of the picritic and basaltic lavas are indistinguishable and cover a relatively small range [e.g. eNd(t) ¼� 1. 3t oþ4. 0]. The higher eNd(t) lavas are isotopically similar to those of several modern oceanic hotspots, and have oceanisland-like patterns of alteration-resistant incompatible elements. Heavy rare earth element characteristics indicate an important role for garnet during melting and that the lavas were formed by fairly small degrees of partial melting. Rough correlations of isotope ratios with ratios of alteration-resistant highly incompatible elements (e.g. Nb/La) suggest modest amounts of contamination involving continental material or a relatively low-eNd component in the source. Overall, our results are consistent with other evidence suggesting some type of plume-head origin for the Emeishan province.