Miracle-dz/UniFAR-FTUs huggingface dataset:
CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds

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.
Mediated semi-quantum key distribution with improved efficiency

Mediated semi-quantum key distribution involves the use of two end-users who have very restricted, almost classical, capabilities, who wish to establish a shared secret key using the help of a fully-quantum server who may be adversarial. In this paper, we introduce a new mediated semi-quantum key distribution protocol, extending prior work, which has asymptotically perfect efficiency. Though this comes at the cost of decreased noise tolerance, our protocol is backwards compatible with prior work, so users may easily switch to the old (normally less efficient) protocol if the noise level is high enough to justify it. To prove security, we show an interesting reduction from the mediated semi-quantum scenario to a fully-quantum entanglement based protocol which may be useful when proving the security of other multi-user quantum key distribution protocols.
Beyond thermodynamic defect models: A kinetic simulation of arsenic activation in CdTe

To overcome the limitations of thermodynamic defect models, we introduce a multilevel kinetic approach to simulate the activation of $p$ doping in CdTe by group V elements using arsenic as a ``model'' dopant. On the lowest level, we calculate thermodynamic and kinetic parameters of point defects, complexes, and reactions from first principles. On the intermediate level, we use these parameters to calculate the kinetic rates of defect reactions. Finally, we simulate the time evolution of defects and free carriers. Our results show the importance of kinetic factors in defect chemistry models. We reveal the primary arsenic activation pathway to be a fast reaction in which the tellurium atoms get kicked out and replaced on the regular anion sites by interstitial arsenic species. We discover the important role of ($\mathrm{A}{\mathrm{s}}_{\mathrm{i}}\mathrm{A}{\mathrm{s}}_{\mathrm{Te}}$) and ($\mathrm{A}{\mathrm{s}}_{\mathrm{i}}\mathrm{A}{\mathrm{s}}_{\mathrm{i}}$) complexes that arise during activation anneal to form kinetically stabilized transient states that not only compensate the doping but also can produce deep recombination levels. We expect that our modeling approach and the gained insight into the atomic processes behind the doping formation will advance the defect chemistry modeling of electronic properties of materials.
Quantum signatures in nonlinear gravitational waves

The effective quantum field theory description of gravity, despite its non-renormalizability, allows for predictions beyond classical general relativity. As we enter the age of gravitational wave astronomy, an important and timely question is whether measurable quantum predictions that depart from classical gravity, analogous to quantum optics effects which cannot be explained by classical electrodynamics, can be found. In this work, we investigate quantum signatures in gravitational waves using tools from quantum optics. Squeezed-coherent gravitational waves, which can exhibit sub-Poissonian graviton statistics, can enhance or suppress the signal measured by an interferometer, a characteristic effect of quantum squeezing. Moreover, we show that Gaussian gravitational wave quantum states can be reconstructed from measurements over an ensemble of optical fields interacting with a single copy of the gravitational wave, thus opening the possibility of detecting quantum features of gravity beyond classical general relativity.
PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild

—Automatic nuclei segmentation and classiﬁ-cation plays a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks. In this paper, we aim to build a reliable and robust method capable of dealing with data from the ‘the clinical wild’. Speciﬁcally, we study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin (H&E) stained histopathology data, and evaluate our approach using the recent largest dataset: PanNuke. We address the detection and classiﬁcation of each nuclei as a novel semantic keypoint estimation problem to determine the center point of each nuclei. Next, the corresponding class-agnostic masks for nuclei center points are obtained using dynamic instance segmentation. By decoupling two simultaneous challenging tasks, our method can beneﬁt from class-aware detection and class-agnostic segmentation, thus leading to a signiﬁcant performance boost. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classiﬁcation across 19 different tissue types, delivering new benchmark results.
Beyond Point Clouds: A Knowledge-Aided High Resolution Imaging Radar Deep Detector for Autonomous Driving

The potentials of automotive radar for autonomous driving have not been fully exploited. We present a multi-input multi-output (MIMO) radar transmit and receive signal processing chain, a knowledge-aided approach exploiting the radar domain knowledge and signal structure, to generate high resolution radar range-azimuth spectra for object detection and classification using deep neural networks. To achieve waveform orthogonality among a large number of transmit antennas cascaded by four automotive radar transceivers, we propose a staggered time division multiplexing (TDM) scheme and velocity unfolding algorithm using both Chinese remainder theorem and overlapped array. Field experiments with multi-modal sensors were conducted at The University of Alabama. High resolution radar spectra were obtained and labeled using the camera and LiDAR recordings. Initial experiments show promising performance of object detection using an image-oriented deep neural network with an average precision of 96.1% at an intersection of union (IoU) of typically 0.5 on 2,000 radar frames.
An Asymptotic Analysis of Random Partition Based Minibatch Momentum Methods for Linear Regression Models

Abstract Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the random partition based minibatch gradient descent methods with momentum (MGDM) are widely used to solve large-scale optimization problems with massive datasets. Despite the great popularity of the MGDM methods in practice, their theoretical properties are still underexplored. To this end, we investigate the theoretical properties of MGDM methods based on the linear regression models. We first study the numerical convergence properties of the MGDM algorithm and derive the conditions for faster numerical convergence rate. In addition, we explore the relationship between the statistical properties of the resulting MGDM estimator and the tuning parameters. Based on these theoretical findings, we give the conditions for the resulting estimator to achieve the optimal statistical efficiency. Finally, extensive numerical experiments are conducted to verify our theoretical results. Supplementary materials for this article are available online.
A Red-noise Eigenbasis for the Reconstruction of Blobby Images

We demonstrate the use of an eigenbasis that is derived from principal component analysis (PCA) applied on an ensemble of random-noise images that have a “red” power spectrum; i.e., a spectrum that decreases smoothly from large to small spatial scales. The pattern of the resulting eigenbasis allows for the reconstruction of images with a broad range of image morphologies. In particular, we show that this general eigenbasis can be used to efficiently reconstruct images that resemble possible astronomical sources for interferometric observations, even though the images in the original ensemble used to generate the PCA basis are significantly different from the astronomical images. We further show that the efficiency and fidelity of the image reconstructions depends only weakly on the particular parameters of the red-noise power spectrum used to generate the ensemble of images.
The Security Risk of Lacking Compiler Protection in WebAssembly

WebAssembly is increasingly used as the compilation target for cross-platform applications. In this paper, we investigate whether one can rely on the security measures enforced by existing C compilers when compiling C programs to WebAssembly. We compiled 4,469 C programs with known buffer overflow vulnerabilities to x86 code and to WebAssembly, and observed the outcome of the execution of the generated code to differ for 1,088 programs. Through manual inspection, we identified that the root cause for these is the lack of security measures such as stack canaries in the generated WebAssembly: while x86 code crashes upon a stack-based buffer overflow, the corresponding WebAssembly continues to be executed. We conclude that compiling an existing C program to WebAssembly without additional precautions may hamper its security, and we encourage more research in this direction.
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning

Neuromorphic data carries information in spatio-temporal patterns encoded by spikes. Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli. Most existing approaches model the input-output behavior of an SNN in a deterministic fashion by assigning each input to a specific desired output spiking sequence. In contrast, in order to fully leverage the time-encoding capacity of spikes, this work proposes to train SNNs so as to match <italic>distributions</italic> of spiking signals rather than individual spiking signals. To this end, the paper introduces a novel hybrid architecture comprising a conditional generator, implemented via an SNN, and a discriminator, implemented by a conventional artificial neural network (ANN). The role of the ANN is to provide feedback during training to the SNN within an adversarial iterative learning strategy that follows the principle of generative adversarial network (GANs). In order to better capture multi-modal spatio-temporal distribution, the proposed approach – termed SpikeGAN – is further extended to support Bayesian learning of the generator's weight. Finally, settings with time-varying statistics are addressed by proposing an online meta-learning variant of SpikeGAN. Experiments bring insights into the merits of the proposed approach as compared to existing solutions based on (static) belief networks and maximum likelihood (or empirical risk minimization). In our experiments, handwritten digit images generated by SpikeGAN are observed to train an ANN classifier with <inline-formula><tex-math notation="LaTeX">$20\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>20</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="rosenfeld-ieq1-3191738.gif"/></alternatives></inline-formula> higher accuracy than a comparable belief network. Our experiments also demonstrate the use of SpikeGAN to generate neuromorphic data sets from handwritten digits. It is shown that these data can be used to train an SNN classifier that achieves an accuracy level approaching the baseline accuracy of an SNN classifier trained on rate-encoded real data.
Smooth local rigidity for hyperbolic toral automorphisms

We study the regularity of a conjugacy $H$ between a hyperbolic toral automorphism $A$ and its smooth perturbation $f$ We show that if $H$ is weakly differentiable then it is $C^{1+H\"older}$ and, if $A$ is also weakly irreducible, then $H$ is $C^\infty$. As a part of the proof, we establish results of independent interest on H\"older continuity of a measurable conjugacy between linear cocycles over a hyperbolic system. As a corollary, we improve regularity of the conjugacy to $C^\infty$ in prior local rigidity results.
Stable discontinuous stationary solutions to reaction-diffusion-ODE systems

Abstract A general system of n ordinary differential equations coupled with one reaction-diffusion equation, considered in a bounded N-dimensional domain, with no-flux boundary condition is studied in a context of pattern formation. Such initial boundary value problems may have different types of stationary solutions. In our parallel work [Instability of all regular stationary solutions to reaction-diffusion-ODE systems (2021)], regular (i.e. sufficiently smooth) stationary solutions are shown to exist, however, all of them are unstable. The goal of this work is to construct discontinuous stationary solutions to general reaction-diffusion-ODE systems and to find sufficient conditions for their stability.
DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method

We introduce the so called DeepParticle method to learn and generate invariant measures of stochastic dynamical systems with physical parameters based on data computed from an interacting particle method (IPM). We utilize the expressiveness of deep neural networks (DNNs) to represent the transform of samples from a given input (source) distribution to an arbitrary target distribution, neither assuming distribution functions in closed form nor a finite state space for the samples. In training, we update the network weights to minimize a discrete Wasserstein distance between the input and target samples. To reduce computational cost, we propose an iterative divide-and-conquer (a mini-batch interior point) algorithm, to find the optimal transition matrix in the Wasserstein distance. We present numerical results to demonstrate the performance of our method for accelerating IPM computation of invariant measures of stochastic dynamical systems arising in computing reaction-diffusion front speeds through chaotic flows. The physical parameter is a large Pecl\'et number reflecting the advection dominated regime of our interest.
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research.
Positivity and renormalization of parton densities

There have been recent debates about whether MS parton densities exactly obey positivity bounds (including the Soﬀer bound), and whether the bounds should be applied as a constraint on global ﬁts to parton densities and on nonperturbative calculations. A recent paper (JHEP 11 (2020) 129) appears to provide a proof of positivity in contradiction with earlier work by other authors. We examine their derivation and ﬁnd that its primary failure is in the apparently uncontroversial statement that bare pdfs are always positive. We show that under the conditions used in the derivation, that statement fails. This is associated with the use of dimensional regularization for both UV divergences (space-time dimension n < 4) and for collinear divergences, with n > 4. Collinear divergences appear in massless partonic quantities convoluted with bare pdfs, in the approach used by these and other authors, which we call “track B”. Divergent UV contributions are regulated and are positive when n < 4, but can and often do become negative after analytic continuation to n > 4. We explore ramiﬁcations of this idea, and provide some elementary calculations in a model QFT that show how this situation can generically arise in reality. We examine the connection with the origin of the track B method. Our examination pinpoints considerable diﬃculties with track B that render it either wrong or highly problematic, and explain that a diﬀerent approach, which appears in some literature and that we call track A does not suﬀer from this set of problems. The issue of positivity highlights that track-B methods can lead to wrong results of phenomenological importance. From our analysis we identify the restricted situations in which positivity tends to be violated.
Molecular dynamics simulations of CO2 permeation through ionic liquids confined in γ-alumina nanopores

Abstract CO2 permeation through imidazolium-based ionic liquids (ILs, [BMIM][Ac], [EMIM][Ac], [OMIM][Ac], [BMIM][BF4], and [BMIM][PF6]) confined in 1.0, 2.0, and 3.5 nm γ-alumina pores was investigated using molecular dynamics simulation. It was found that the nanopore confinement effect influenced the structure of confined ILs greatly, resulting in a layered structure and anisotropic orientation of ILs. In the center of 2.0-nm pore, the long alkyl chain of [BMIM]+ tended to be parallel to the wall, providing a straight diffusion path benefiting the CO2 permeation. The CO2 diffusion coefficients in confined [EMIM][Ac], [BMIM][Ac], and [OMIM][Ac] were 2.3–4.1, 2.4–6.4, and 14.4–21.7 × 10−10 m2 s−1, respectively. This order was opposite to that in the bulk ILs, because the longer alkyl chain led to a more ordered structure, facilitating CO2 diffusion. In addition, the CO2 solubilities were 445–722 mol m−3 MPa−1 for the five ILs confined in 1.0 nm pore, which were larger than those in 2.0 and 3.5 nm pores (196–335 mol·m−3 MPa−1), due to the larger free volume. Both parallel orientation of alkyl chain and large free volume could increase the CO2 permeability in confined ILs.
Strain hardening by sediment transport

The critical ﬂuid-shear stress for the onset of sediment transport, θ c , varies with the history of applied shear. This effect has been primarily attributed to compaction; the role of shear jamming is less explored. We examine the response of a granular bed to ﬂuid-shear stress cycles of varying magnitude and direction, and determine isotropic and anisotropic contributions. Creep and bed-load transport result in direction-dependent strain hardening for θ/θ c < 4. Dilation-induced weakening, and memory loss, occur for larger stresses that ﬂuidize the bed. Our ﬁndings provide a granular explanation for the formation and breakup of hard-packed riverbed “armor. ”
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging

Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and climate science. In these domains, both the model parameters to be inferred and the measurement noise may exhibit a complex spatio-temporal structure. Existing work either neglects the temporal structure or leads to computationally demanding inference schemes. Overcoming these limitations, we devise a novel flexible hierarchical Bayesian framework within which the spatio-temporal dynamics of model parameters and noise are modeled to have Kronecker product covariance structure. Inference in our framework is based on majorization-minimization optimization and has guaranteed convergence properties. Our highly efficient algorithms exploit the intrinsic Riemannian geometry of temporal autocovariance matrices. For stationary dynamics described by Toeplitz matrices, the theory of circulant embeddings is employed. We prove convex bounding properties and derive update rules of the resulting algorithms. On both synthetic and real neural data from M/EEG, we demonstrate that our methods lead to improved performance.
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge

We consider the problem of online linear regression in the stochastic setting. We derive high probability regret bounds for online ridge regression and the forward algorithm. This enables us to compare online regression algorithms more accurately and eliminate assumptions of bounded observations and predictions. Our study advocates for the use of the forward algorithm in lieu of ridge due to its enhanced bounds and robustness to the regularization parameter. Moreover, we explain how to integrate it in algorithms involving linear function approximation to remove a boundedness assumption without deteriorating theoretical bounds. We showcase this modification in linear bandit settings where it yields improved regret bounds. Last, we provide numerical experiments to illustrate our results and endorse our intuitions.
Auditory-visual scenes for hearing research

While experimentation with synthetic stimuli in abstracted listening situations has a long standing and successful history in hearing research, an increased interest exists on closing the remaining gap towards real-life listening by replicating situations with high ecological validity in the lab. This is important for understanding the underlying auditory mechanisms and their relevance in real-life situations as well as for developing and evaluating increasingly sophisticated algorithms for hearing assistance. A range of ‘classical’ stimuli and paradigms have evolved to de-facto standards in psychoacoustics, which are simplistic and can be easily reproduced across laboratories. While they ideally allow for across laboratory comparisons and reproducible research, they, however, lack the acoustic stimulus complexity and the availability of visual information as observed in everyday life communication and listening situations. This contribution aims to provide and establish an extendable set of complex auditory-visual scenes for hearing research that allow for ecologically valid testing in realistic scenes while also supporting reproducibility and comparability of scientific results. Three virtual environments are provided (underground station, pub, living room), consisting of a detailed visual model, an acoustic geometry model with acoustic surface properties as well as a set of acoustic measurements in the respective real-world environments. The current data set enables i) audio–visual research in a reproducible set of environments, ii) comparison of room acoustic simulation methods with “ground truth” acoustic measurements, iii) a condensation point for future extensions and contributions for developments towards standardized test cases for ecologically valid hearing research in complex scenes.