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Range-View(RV)-based 3D point cloud segmentation is widely adopted due to its compact data form. However, RV-based methods fall short in providing robust segmentation for the occluded points and suffer from distortion of projected RGB images due to the sparse nature of 3D point clouds. To alleviate these problems, we propose a new LiDAR and Camera Range-view-based 3D point cloud semantic segmentation method (LaCRange). Specifically, a distortion-compensating knowledge distillation (DCKD) strategy is designed to remedy the adverse effect of RV projection of RGB images. Moreover, a context-based feature fusion module is introduced for robust and preservative sensor fusion. Finally, in order to address the limited resolution of RV and its insufficiency of 3D topology, a new point refinement scheme is devised for proper aggregation of features in 2D and augmentation of point features in 3D. We evaluated the proposed method on large-scale autonomous driving datasets \ie SemanticKITTI and nuScenes. In addition to being real-time, the proposed method achieves state-of-the-art results on nuScenes benchmark

Video Frame Interpolation (VFI) has been extensively explored and demonstrated, yet its application to polarization remains largely unexplored. Due to the selective transmission of light by polarized filters, longer exposure times are typically required to ensure sufficient light intensity, which consequently lower the temporal sample rates. Furthermore, because polarization reflected by objects varies with shooting perspective, focusing solely on estimating pixel displacement is insufficient to accurately reconstruct the intermediate polarization. To tackle these challenges, this study proposes a multi-stage and multi-scale network called Swin-VFI based on the Swin-Transformer and introduces a tailored loss function to facilitate the network's understanding of polarization changes. To ensure the practicality of our proposed method, this study evaluates its interpolated frames in Shape from Polarization (SfP) and Human Shape Reconstruction tasks, comparing them with other state-of-the-art methods such as CAIN, FLAVR, and VFIT. Experimental results demonstrate our approach's superior reconstruction accuracy across all tasks., Comment: 18 pages, 10 figures, 7 tables, 73 citations

The on-chip measurement of polarization states plays an increasingly crucial role in modern sensing and imaging applications. While high-performance monolithic linearly polarized photodetectors have been extensively studied, integrated circularly polarized light (CPL) photodetectors are still hindered by inadequate discrimination capability. In this study, we employ achiral all-dielectric nanostructures to develop a broadband CPL photodetector with an impressive discrimination ratio of ~107 at the wavelength of 405 nm, significantly surpassing its counterparts by two orders of magnitude. Our device shows outstanding CPL discrimination capability across the visible band without requiring intensity calibration. Its function mechanism is based on the CPL-dependent near-field modes within achiral structures: under left or right CPL illumination, distinct near-field modes are excited, resulting in asymmetric irradiation of the two electrodes and generating a photovoltage with directions determined by the chirality of the incident light field. The proposed design strategy facilitates the realization of ultra-compact CPL detection across diverse materials, structures, and spectral ranges, presenting a novel avenue for achieving high-performance monolithic CPL detection., Comment: 20 pages, 4 figures

Polymer microgels exhibit intriguing macroscopic flow properties arising from their unique microscopic structure. Microgel colloids comprise a crosslinked polymer network with a radially decaying density profile, resulting in a dense core surrounded by a fuzzy corona. Notably, microgels synthesized from poly(N-isopropylacrylamide) (PNIPAM) are thermoresponsive, capable of adjusting their size and density profile based on temperature. Above the lower critical solution temperature ($T_\text{LCST}\sim 33$ $^\circ$C), the microgel's polymer network collapses, leading to the expulsion of water through a reversible process. Conversely, below $33$ $^\circ$C, the microgel's network swells, becoming highly compressible and allowing overpacking to effective volume fractions exceeding one. Under conditions of dense packing, microgels undergo deformation in distinct stages: corona compression and faceting, interpenetration, and finally, isotropic compression. Each stage exhibits a characteristic signature in the yield stress and elastic modulus of the dense microgel suspensions. Here, we introduce a model for the linear elastic shear modulus through the minimization of a quasi-equilibrium free energy, encompassing all relevant energetic contributions. We validate our model by comparing its predictions to experimental results from oscillatory shear rheology tests on microgel suspensions at different densities and temperatures. Our findings demonstrate that combining macroscopic rheological measurements with the model allows for temperature-dependent characterization of polymer interaction parameters., Comment: 23 pages, 4 figures; We have corrected the polymer concentrations; they should be given in wt % instead of g/ml

Hybrid organic-inorganic perovskites are famous for the diversity of their chemical compositions, phases and phase transitions, and associated physical properties. We use a combination of experimental and computational techniques to reveal strong coupling between structure, magnetism, and spin splitting in a representative of the largest family of hybrid organic-inorganic perovskites: the formates. With the help of first-principles simulations, we find spin splitting in both conduction and valence bands of [NH$_2$NH$_3$]Co(HCOO)$_3$, induced by spin-orbit interactions, which can reach up to 14~meV. Our magnetic measurements reveal that this material exhibits canted antiferromagnetism below 15.5 K. The direction of the associated antiferromagnetic order parameter is strongly coupled with the spin splitting already in the centrosymmetric phase, allowing for the creation and annihilation of spin splitting through the application of a magnetic field. Furthermore, the structural phase transition into experimentally observed polar Pna2$_1$ phase completely changes the aforementioned spin splitting and its coupling to magnetic degrees of freedom. This reveals that in [NH$_2$NH$_3$]Co(HCOO)$_3$, the structure and magnetism are strongly coupled to spin splitting in a way that allows for its manipulation through both magnetic and electric fields. As an example, for a given point inside the Brillouin zone of centrosymmetric Pnma phase of [NH$_2$NH$_3$]Co(HCOO)$_3$, spin splitting can be turned on/off by aligning the antiferromagnetic vector along certain crystallographic directions or through inducing a polar phase by the application of an electric field. We believe that our findings offer an important step toward fundamental understanding and practical applications of materials with coupled properties.

Bilateral trade is one of the most natural and important forms of economic interaction: A seller has a single, indivisible item for sale, and a buyer is potentially interested. The two parties typically have different, privately known valuations for the item, and ideally, they would like to trade if the buyer values the item more than the seller. The celebrated impossibility result by Myerson and Satterthwaite shows that any mechanism for this setting must violate at least one important desideratum. In this paper, we investigate a richer paradigm of bilateral trade, with many self-interested buyers and sellers on both sides of a single trade who cannot be excluded from the trade. We show that this allows for more positive results. In fact, we establish a dichotomy in the possibility of trading efficiently. If in expectation, the buyers value the item more, we can achieve efficiency in the limit. If this is not the case, then efficiency cannot be achieved in general. En route, we characterize trading mechanisms that encourage truth-telling, which may be of independent interest. We also evaluate our trading mechanisms experimentally, and the experiments align with our theoretical results., Comment: 14 pages, 2 figures, 1 table, aaai 2024

Coalitions naturally exist in many real-world systems involving multiple decision makers such as ridesharing, security, and online ad auctions, but the coalition structure among the agents is often unknown. We propose and study an important yet previously overseen problem -- Coalition Structure Learning (CSL), where we aim to carefully design a series of games for the agents and infer the underlying coalition structure by observing their interactions in those games. We establish a lower bound on the sample complexity -- defined as the number of games needed to learn the structure -- of any algorithms for CSL and propose the Iterative Grouping (IG) algorithm for designing normal-form games to achieve the lower bound. We show that IG can be extended to other succinct games such as congestion games and graphical games. Moreover, we solve CSL in a more restrictive and practical setting: auctions. We show a variant of IG to solve CSL in the auction setting even if we cannot design the bidder valuations. Finally, we conduct experiments to evaluate IG in the auction setting and the results align with our theoretical analysis., Comment: 13 pages, 4 figures, 3 tables, aaai 2024

This study explores the robustness of label noise classifiers, aiming to enhance model resilience against noisy data in complex real-world scenarios. Label noise in supervised learning, characterized by erroneous or imprecise labels, significantly impairs model performance. This research focuses on the increasingly pertinent issue of label noise's impact on practical applications. Addressing the prevalent challenge of inaccurate training data labels, we integrate adversarial machine learning (AML) and importance reweighting techniques. Our approach involves employing convolutional neural networks (CNN) as the foundational model, with an emphasis on parameter adjustment for individual training samples. This strategy is designed to heighten the model's focus on samples critically influencing performance., Comment: 21 pages, 11 figures

Non-negative matrix factorization (NMF) and its variants have been widely employed in clustering and classification tasks (Long, & Jian , 2021). However, noises can seriously affect the results of our experiments. Our research is dedicated to investigating the noise robustness of non-negative matrix factorization (NMF) in the face of different types of noise. Specifically, we adopt three different NMF algorithms, namely L1 NMF, L2 NMF, and L21 NMF, and use the ORL and YaleB data sets to simulate a series of experiments with salt-and-pepper noise and Block-occlusion noise separately. In the experiment, we use a variety of evaluation indicators, including root mean square error (RMSE), accuracy (ACC), and normalized mutual information (NMI), to evaluate the performance of different NMF algorithms in noisy environments. Through these indicators, we quantify the resistance of NMF algorithms to noise and gain insights into their feasibility in practical applications., Comment: 22 pages, 6 figures

The assignment of papers to reviewers is a crucial part of the peer review processes of large publication venues, where organizers (e.g., conference program chairs) rely on algorithms to perform automated paper assignment. As such, a major challenge for the organizers of these processes is to specify paper assignment algorithms that find appropriate assignments with respect to various desiderata. Although the main objective when choosing a good paper assignment is to maximize the expertise of each reviewer for their assigned papers, several other considerations make introducing randomization into the paper assignment desirable: robustness to malicious behavior, the ability to evaluate alternative paper assignments, reviewer diversity, and reviewer anonymity. However, it is unclear in what way one should randomize the paper assignment in order to best satisfy all of these considerations simultaneously. In this work, we present a practical, one-size-fits-all method for randomized paper assignment intended to perform well across different motivations for randomness. We show theoretically and experimentally that our method outperforms currently-deployed methods for randomized paper assignment on several intuitive randomness metrics, demonstrating that the randomized assignments produced by our method are general-purpose., Comment: 24 pages, 8 figures, 3 tables, neurips 2023 spotlight

In the Bidder Selection Problem (BSP) there is a large pool of $n$ potential advertisers competing for ad slots on the user's web page. Due to strict computational restrictions, the advertising platform can run a proper auction only for a fraction $k

Hybrid organic inorganic formate perovskites, AB(HCOO)$_3$, is a large family of compounds which exhibit variety of phase transitions and diverse properties. Some examples include (anti)ferroelectricity, ferroelasticity, (anti)ferromagnetism, and multiferroism. While many properties of these materials have already been characterized, we are not aware of any study that focuses on comprehensive property assessment of a large number of formate perovskites. Comparison of the materials property within the family is challenging due to systematic errors attributed to different techniques or the lack of data. For example, complete piezoelectric, dielectric and elastic tensors are not available. In this work, we utilize first-principles density functional theory based simulations to overcome these challenges and to report structural, mechanical, dielectric, piezoelectric, and ferroelectric properties for 29 formate perovskites. We find that these materials exhibit elastic stiffness in the range 0.5 to 127.0 GPa , highly anisotropic linear compressibility, including zero and even negative values; dielectric constants in the range 0.1 to 102.1; highly anisotropic piezoelectric response with the longitudinal values in the range 1.18 to 21.12 pC/N, and spontaneous polarizations in the range 0.2 to 7.8 $\mu$C/cm$^2$. Furthermore, we propose and computationally characterize a few formate perovskites, which have not been reported yet.

LiDAR-based 3D object detection and panoptic segmentation are two crucial tasks in the perception systems of autonomous vehicles and robots. In this paper, we propose All-in-One Perception Network (AOP-Net), a LiDAR-based multi-task framework that combines 3D object detection and panoptic segmentation. In this method, a dual-task 3D backbone is developed to extract both panoptic- and detection-level features from the input LiDAR point cloud. Also, a new 2D backbone that intertwines Multi-Layer Perceptron (MLP) and convolution layers is designed to further improve the detection task performance. Finally, a novel module is proposed to guide the detection head by recovering useful features discarded during down-sampling operations in the 3D backbone. This module leverages estimated instance segmentation masks to recover detailed information from each candidate object. The AOP-Net achieves state-of-the-art performance for published works on the nuScenes benchmark for both 3D object detection and panoptic segmentation tasks. Also, experiments show that our method easily adapts to and significantly improves the performance of any BEV-based 3D object detection method., Comment: Under review

Ranking and Balance are arguably the two most important algorithms in the online matching literature. They achieve the same optimal competitive ratio of $1-1/e$ for the integral version and fractional version of online bipartite matching by Karp, Vazirani, and Vazirani (STOC 1990) respectively. The two algorithms have been generalized to weighted online bipartite matching problems, including vertex-weighted online bipartite matching and AdWords, by utilizing a perturbation function. The canonical choice of the perturbation function is $f(x)=1-e^{x-1}$ as it leads to the optimal competitive ratio of $1-1/e$ in both settings. We advance the understanding of the weighted generalizations of Ranking and Balance in this paper, with a focus on studying the effect of different perturbation functions. First, we prove that the canonical perturbation function is the \emph{unique} optimal perturbation function for vertex-weighted online bipartite matching. In stark contrast, all perturbation functions achieve the optimal competitive ratio of $1-1/e$ in the unweighted setting. Second, we prove that the generalization of Ranking to AdWords with unknown budgets using the canonical perturbation function is at most $0.624$ competitive, refuting a conjecture of Vazirani (2021). More generally, as an application of the first result, we prove that no perturbation function leads to the prominent competitive ratio of $1-1/e$ by establishing an upper bound of $1-1/e-0.0003$. Finally, we propose the online budget-additive welfare maximization problem that is intermediate between AdWords and AdWords with unknown budgets, and we design an optimal $1-1/e$ competitive algorithm by generalizing Balance., Comment: Conference version to appear at the European Symposium on Algorithms (ESA 2023). 16 pages, 2 figures, 8 pages appendix

3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation information to further guide the detection process. In this paper, we propose a novel multi-task framework that jointly performs 3D object detection and panoptic segmentation. In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone. This enables the detection backbone to leverage multi-view information to address the shortcomings of each projection view. Furthermore, foreground semantic information is incorporated to ease the detection task by highlighting the locations of each object class in the feature maps. Finally, a new center density heatmap generated based on the instance-level information further guides the detection backbone by suggesting possible box center locations for objects. Our method works with any BEV-based 3D object detection method, and as shown by extensive experiments on the nuScenes dataset, it provides significant performance gains. Notably, the proposed method based on a single-stage CenterPoint 3D object detection network achieved state-of-the-art performance on nuScenes 3D Detection Benchmark with 67.3 NDS., Comment: Accepted to CVPR 2022

A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes. Existing approaches usually rely on proposals or clustering to segment foreground instances. As a result, they struggle to achieve real-time performance. In this paper, we propose a novel real-time end-to-end panoptic segmentation network for LiDAR point clouds, called CPSeg. In particular, CPSeg comprises a shared encoder, a dual-decoder, and a cluster-free instance segmentation head, which is able to dynamically pillarize foreground points according to the learned embedding. Then, it acquires instance labels by finding connected pillars with a pairwise embedding comparison. Thus, the conventional proposal-based or clustering-based instance segmentation is transformed into a binary segmentation problem on the pairwise embedding comparison matrix. To help the network regress instance embedding, a fast and deterministic depth completion algorithm is proposed to calculate the surface normal of each point cloud in real-time. The proposed method is benchmarked on two large-scale autonomous driving datasets: SemanticKITTI and nuScenes. Notably, extensive experimental results show that CPSeg achieves state-of-the-art results among real-time approaches on both datasets., Comment: Accepted at ICRA 2023

Panoptic segmentation aims to address semantic and instance segmentation simultaneously in a unified framework. However, an efficient solution of panoptic segmentation in applications like autonomous driving is still an open research problem. In this work, we propose a novel LiDAR-based panoptic system, called SMAC-Seg. We present a learnable sparse multi-directional attention clustering to segment multi-scale foreground instances. SMAC-Seg is a real-time clustering-based approach, which removes the complex proposal network to segment instances. Most existing clustering-based methods use the difference of the predicted and ground truth center offset as the only loss to supervise the instance centroid regression. However, this loss function only considers the centroid of the current object, but its relative position with respect to the neighbouring objects is not considered when learning to cluster. Thus, we propose to use a novel centroid-aware repel loss as an additional term to effectively supervise the network to differentiate each object cluster with its neighbours. Our experimental results show that SMAC-Seg achieves state-of-the-art performance among all real-time deployable networks on both large-scale public SemanticKITTI and nuScenes panoptic segmentation datasets.