Miracle-dz/UniFAR-FTUs huggingface dataset:
InsClustering: Instantly Clustering LiDAR Range Measures for Autonomous Vehicle

LiDARs are usually more accurate than cameras in distance measuring. Hence, there is strong interest to apply LiDARs in autonomous driving. Different existing approaches process the rich 3D point clouds for object detection, tracking and recognition. These methods generally require two initial steps: (1) filter points on the ground plane and (2) cluster non-ground points into objects. This paper proposes a field-tested fast 3D point cloud segmentation method for these two steps. Our specially designed algorithms allow instantly process raw LiDAR data packets, which significantly reduce the processing delay. In our tests on Velodyne UltraPuck, a 32 layers spinning LiDAR, the processing delay of clustering all the 360° LiDAR measures is less than 1ms. Meanwhile, a coarse-to-fine scheme is applied to ensure the clustering quality. Our field experiments in public roads have shown that the proposed method significantly improves the speed of 3D point cloud clustering whilst maintains good accuracy.
Semantic Flow-guided Motion Removal Method for Robust Mapping

Moving objects in scenes are still a severe challenge for the SLAM system. Many efforts have tried to remove the motion regions in the images by detecting moving objects. In this way, the keypoints belonging to motion regions will be ignored in the later calculations. In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions. Different from previous works, we don't predict moving objects or motion regions directly from image sequences. We computed rigid optical flow, synthesized by the depth and pose, and compared it against the estimated optical flow to obtain initial motion regions. Then, we utilized K-means to finetune the motion region masks with instance segmentation masks. The ORB-SLAM2 integrated with the proposed motion removal method achieved the best performance in both indoor and outdoor dynamic environments.
Keys from the Sky: A First Exploration of Physical-Layer Security Using Satellite Links

In this paper, we investigate physical-layer security (PLS) methods for proximity-based group-key establishment and proof of location. Fields of application include secure car-to-car communication, privacy-preserving and secure distance evidence for healthcare, or location-based feature activation. Existing technologies do not solve the problem satisfactorily due to communication restrictions, e.g., ultra-wideband (UWB) based time of flight measurements, or trusted hardware, e.g., using global navigation satellite system (GNSS) positioning data.We introduce PLS as a possible solution candidate. It is information-theoretically secure, thereby also post-quantum resistant, and has the potential to run on resource-constrained devices with low latency. We use wireless channel properties of satellite-to-earth links, demonstrate the first feasibility study using off-the-shelf hardware testbeds, and present first evaluation results and future directions for research.
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation

We propose a new approach for the problem of relative depth estimation from a single image. Instead of directly regressing over depth scores, we formulate the problem as estimation of a probability distribution over depth and aim to learn the parameters of the distributions which maximize the likelihood of the given data. To train our model, we propose a new ranking loss, Distributional Loss, which tries to increase the probability of farther pixel’s depth being greater than the closer pixel’s depth. Our proposed approach allows our model to output confidence in its estimation in the form of standard deviation of the distribution. We achieve state of the art results against a number of baselines while providing confidence in our estimations. Our analysis show that estimated confidence is actually a good indicator of accuracy. We investigate the usage of confidence information in a downstream task of metric depth estimation, to increase its performance.
Stationary states of the one-dimensional facilitated asymmetric exclusion process

We describe the translation invariant stationary states (TIS) of the one-dimensional facilitated asymmetric exclusion process in continuous time, in which a particle at site $i\in\mathbb{Z}$ jumps to site $i+1$ (respectively $i-1$) with rate $p$ (resp. $1-p$), provided that site $i-1$ (resp. $i+1$) is occupied and site $i+1$ (resp. $i-1$) is empty. All TIS states with density $\rho\le1/2$ are supported on trapped configurations in which no two adjacent sites are occupied; we prove that if in this case the initial state is Bernoulli then the final state is independent of $p$. This independence also holds for the system on a finite ring. For $\rho>1/2$ there is only one TIS. It is the infinite volume limit of the probability distribution that gives uniform weight to all configurations in which no two holes are adjacent, and is isomorphic to the Gibbs measure for hard core particles with nearest neighbor exclusion.
Binarization Methods for Motor-Imagery Brain–Computer Interface Classification

Successful motor-imagery brain–computer interface (MI-BCI) algorithms either extract a large number of handcrafted features and train a classifier, or combine feature extraction and classification within deep convolutional neural networks (CNNs). Both approaches typically result in a set of real-valued weights, that pose challenges when targeting real-time execution on tightly resource-constrained devices. We propose methods for each of these approaches that allow transforming real-valued weights to binary numbers for efficient inference. Our first method, based on sparse bipolar random projection, projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. By tuning the dimension of the binary embedding, we achieve almost the same accuracy in 4-class MI (≤1.27% lower) compared to models with float16 weights, yet delivering a more compact model with simpler operations to execute. Second, we propose to use memory-augmented neural networks (MANNs) for MI-BCI such that the augmented memory is binarized. Our method replaces the fully connected layer of CNNs with a binary augmented memory using bipolar random projection, or learned projection. Our experimental results on EEGNet, an already compact CNN for MI-BCI, show that it can be compressed by $1.28\times $ at iso-accuracy using the random projection. On the other hand, using the learned projection provides 3.89% higher accuracy but increases the memory size by $28.10\times $ .
Cramér‐von Mises tests for change points

We study two nonparametric tests of the hypothesis that a sequence of independent observations is identically distributed against the alternative that at a single change point the distribution changes. The tests are based on the Cramér–von Mises two‐sample test computed at every possible change point. One test uses the largest such test statistic over all possible change points; the other averages over all possible change points. Large sample theory for the average statistic is shown to provide useful p‐values much more quickly than bootstrapping, particularly in long sequences. Power is analyzed for contiguous alternatives. The average statistic is shown to have limiting power larger than its level for such alternative sequences. Evidence is presented that this is not true for the maximal statistic. Asymptotic methods and bootstrapping are used for constructing the test distribution. Performance of the tests is checked with a Monte Carlo power study for various alternative distributions.
$\textit{ab initio}$ electrostatic potentials for transmission electron microscopy

The simulation of transmission electron microscopy (TEM) images or diffraction patterns is often required to interpret their contrast and extract specimen features. This is especially true for high-resolution phase-contrast imaging of materials, but model-based reconstructions in TEM are widely used across the physical and biological sciences. Since electron scattering is dominated by the nuclear cores, the scattering potential is typically described by the widely applied independent atom model. This approximation is fast and accurate, especially for annular dark-field contrast, but it completely neglects valence bonding and its effect on the transmitting electrons. However, an emerging trend in electron microscopy is to use new instrumentation and methods to extract the maximum amount of information from each electron. This is evident in the increasing popularity of techniques such as 4D-STEM combined with ptychography in materials science, and cryogenic microcrystal electron diffraction in structural biology, where subtle differences in the scattering potential may be both measurable and contain additional insights. Thus, there is increasing interest in electron scattering simulations based on electrostatic potentials obtained from first principles, mainly via density functional theory. In this Review, we discuss the motivation and basis for these developments, survey the pioneering work that has been published thus far, and give our outlook for the future. We argue that a physically better justified $\textit{ab initio}$ description of the scattering potential is both useful and viable for an increasing number of systems, and we expect such simulations to steadily gain in popularity and importance.
Exploiting Interfaces of Secure Encrypted Virtual Machines

Cloud computing is a convenient model for processing data remotely. However, users must trust their cloud provider with the confidentiality and integrity of the stored and processed data. To increase the protection of virtual machines, AMD introduced SEV, a hardware feature which aims to protect code and data in a virtual machine. This allows to store and process sensitive data in cloud environments without the need to trust the cloud provider or the underlying software. However, the virtual machine still depends on the hypervisor for performing certain activities, such as the emulation of special CPU instructions, or the emulation of devices. Yet, most code that runs in virtual machines was not written with an attacker model which considers the hypervisor as malicious. In this work, we introduce a new class of attacks in which a malicious hypervisor manipulates external interfaces of an SEV or SEV-ES virtual machine to make it act against its own interests. We start by showing how we can make use of virtual devices to extract encryption keys and secret data of a virtual machine. We then show how we can reduce the entropy of probabilistic kernel defenses in the virtual machine by carefully manipulating the results of the CPUID and RDTSC instructions. We continue by showing an approach for secret data exfiltration and code injection based on the forgery of MMIO regions over the VM’s address space. Finally, we show another attack which forces decryption of the VM’s stack and uses Return Oriented Programming to execute arbitrary code inside the VM. While our approach is also applicable to traditional virtualization environments, its severity significantly increases with the attacker model of SEV-ES, which aims to protect a virtual machine from a benign but vulnerable hypervisor.
Fusing electrical and elasticity imaging

Electrical and elasticity imaging are promising modalities for a suite of different applications, including medical tomography, non-destructive testing and structural health monitoring. These emerging modalities are capable of providing remote, non-invasive and low-cost opportunities. Unfortunately, both modalities are severely ill-posed nonlinear inverse problems, susceptive to noise and modelling errors. Nevertheless, the ability to incorporate complimentary datasets obtained simultaneously offers mutually beneficial information. By fusing electrical and elastic modalities as a joint problem, we are afforded the possibility to stabilize the inversion process via the utilization of auxiliary information from both modalities as well as joint structural operators. In this study, we will discuss a possible approach to combine electrical and elasticity imaging in a joint reconstruction problem giving rise to novel multi-modality applications for use in both medical and structural engineering. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.
Analytical Parameter Estimation of the SIR Epidemic Model. Applications to the COVID-19 Pandemic

The SIR (Susceptible-Infected-Removed) model is a simple mathematical model of epidemic outbreaks, yet for decades it evaded the efforts of the mathematical community to derive an explicit solution. The present paper reports novel analytical results and numerical algorithms suitable for parametric estimation of the SIR model. Notably, a series solution of the incidence variable of the model is derived. It is proven that the explicit solution of the model requires the introduction of a new transcendental special function, describing the incidence, which is a solution of a non-elementary integral equation. The paper introduces iterative algorithms approximating the incidence variable, which allows for estimation of the model parameters from the numbers of observed cases. The approach is applied to the case study of the ongoing coronavirus disease 2019 (COVID-19) pandemic in five European countries: Belgium, Bulgaria, Germany, Italy and the Netherlands. Incidence and case fatality data obtained from the European Centre for Disease Prevention and Control (ECDC) are analysed and the model parameters are estimated and compared for the period Jan-Dec 2020.
On Distributionally Robust Multistage Convex Optimization: New Algorithms and Complexity Analysis.

This paper presents a novel algorithmic study and complexity analysis of distributionally robust multistage convex optimization (DR-MCO). We propose a new class of algorithms for solving DR-MCO, namely a sequential dual dynamic programming (Seq-DDP) algorithm and its nonsequential version (NDDP). The new algorithms generalize and strengthen existing DDP-type algorithms by introducing the technique of regularization that enables the algorithms to handle 1) fast growth of Lipschitz constants, which is a common phenomenon in multistage optimization, and 2) problems without relatively complete recourse. We then provide a thorough complexity analysis of the new algorithms, proving both upper complexity bounds and a matching lower bound. To the best of our knowledge, this is the first complexity analysis of DDP-type algorithms for DR-MCO problems, quantifying the dependence of the oracle complexity of DDP-type algorithms on the number of stages, the dimension of the decision space, and various regularity characteristics of DR-MCO. Numerical examples are also given to show the effectiveness of the proposed regularization technique on reducing computation time or the number of oracle evaluations and on solving problems without relatively complete recourse.
On stability of rotational 2D binary Bose–Einstein condensates

We consider a two-dimensional nonlinear Schrodinger equation proposed in Physics to model rotational binary Bose-Einstein condensates. The nonlinearity is a logarithmic modification of the usual cubic nonlinearity. The presence of both the external confining potential and rotating frame makes it difficult to apply standard techniques to directly construct ground states, as we explain in an appendix. The goal of the present paper is to analyze the orbital stability of the set of energy minimizers under mass constraint, according to the relative strength of the confining potential compared to the angular frequency. The main novelty concerns the critical case where these two effects compensate exactly, and orbital stability is established by using techniques related to magnetic Schrodinger operators.
Small Private Online Judge: A New Tool for Empirical Education Research

This paper puts forward the concept of Small Private Online Judge (SPOJ). Compared with Massive Open Online Judge (MOOJ), SPOJ has advantages in structured data acquisition of students' virtual behavior for its specific function and tight coupling with the classroom. SPOJ-based empirical education research can be conducted within "Acquisition-Analysis-Application" (3A) Framework. The case study of a SPOJ program clarifies the standard pattern of SPOJ-based 3A research and highlights the emergence of education-intelligence concept. The challenges of SPOJ-based empirical education research and implications of SPOJ are also discussed.
Understanding Twitter Engagement with a Click-Through Rate-based Method

This paper presents the POLINKS solution to the RecSys Challenge 2020 that ranked 6th in the final leaderboard. We analyze the performance of our solution that utilizes the click-through rate value to address the challenge task, we compare it with a gradient boosting model, and we report the quality indicators utilized for computing the final leaderboard.
Equitable Allocation of Healthcare Resources with Fair Cox Models

This material is based upon work supported by the National Science Foundation under Grant No.’s IIS1927486; IIS1850023. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work was performed under the following financial assistance award: 60NANB18D227 from U.S. Department of Commerce, National Institute of Standards and Technology
Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability

Any prediction from a model is made by a combination of learning history and test stimuli. This provides significant insights for improving model interpretability: {\it because of which part(s) of which training example(s), the model attends to which part(s) of a test example}. Unfortunately, existing methods to interpret a model's predictions are only able to capture a single aspect of either test stimuli or learning history, and evidences from both are never combined or integrated. In this paper, we propose an efficient and differentiable approach to make it feasible to interpret a model's prediction by jointly examining training history and test stimuli. Test stimuli is first identified by gradient-based methods, signifying {\it the part of a test example that the model attends to}. The gradient-based saliency scores are then propagated to training examples using influence functions to identify {\it which part(s) of which training example(s)} make the model attends to the test stimuli. The system is differentiable and time efficient: the adoption of saliency scores from gradient-based methods allows us to efficiently trace a model's prediction through test stimuli, and then back to training examples through influence functions. We demonstrate that the proposed methodology offers clear explanations about neural model decisions, along with being useful for performing error analysis, crafting adversarial examples and fixing erroneously classified examples.
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems

A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs while most existing works only focused on one-unknown linear MWPs. Herein, we propose a simple but efficient method called Universal Expression Tree (UET) to make the first attempt to represent the equations of various MWPs uniformly. Then a semantically-aligned universal tree-structured solver (SAU-Solver) based on an encoder-decoder framework is proposed to resolve multiple types of MWPs in a unified model, benefiting from our UET representation. Our SAU-Solver generates a universal expression tree explicitly by deciding which symbol to generate according to the generated symbols' semantic meanings like human solving MWPs. Besides, our SAU-Solver also includes a novel subtree-level semanticallyaligned regularization to further enforce the semantic constraints and rationality of the generated expression tree by aligning with the contextual information. Finally, to validate the universality of our solver and extend the research boundary of MWPs, we introduce a new challenging Hybrid Math Word Problems dataset (HMWP), consisting of three types of MWPs. Experimental results on several MWPs datasets show that our model can solve universal types of MWPs and outperforms several state-of-the-art models.
Language Networks: a Practical Approach

This manuscript provides a short and practical introduction to the topic of language networks. This text aims at assisting researchers with no practical experience in text and/or network analysis. We provide a practical tutorial on how to model and characterize texts using network-based features. In this tutorial, we also include examples of pre-processing and network representations. A brief description of the main tasks allying network science and text analysis is also provided. A further development of this text shall include a practical description of network classification via machine learning methods.
GreedyFool: An Imperceptible Black-box Adversarial Example Attack against Neural Networks

Deep neural networks (DNNs) are inherently vulnerable to well-designed input samples called adversarial examples. The adversary can easily fool DNNs by adding slight perturbations to the input. In this paper, we propose a novel black-box adversarial example attack named GreedyFool, which synthesizes adversarial examples based on the differential evolution and the greedy approximation. The differential evolution is utilized to evaluate the effects of perturbed pixels on the confidence of the DNNs-based classifier. The greedy approximation is an approximate optimization algorithm to automatically get adversarial perturbations. Existing works synthesize the adversarial examples by leveraging simple metrics to penalize the perturbations, which lack sufficient consideration of the human visual system (HVS), resulting in noticeable artifacts. In order to sufficient imperceptibility, we launch a lot of investigations into the HVS and design an integrated metric considering just noticeable distortion (JND), Weber-Fechner law, texture masking and channel modulation, which is proven to be a better metric to measure the perceptual distance between the benign examples and the adversarial ones. The experimental results demonstrate that the GreedyFool has several remarkable properties including black-box, 100% success rate, flexibility, automation and can synthesize the more imperceptible adversarial examples than the state-of-the-art pixel-wise methods.
Adaptive Testing for Specification Coverage

Ensuring correctness of cyber-physical systems (CPS) is an extremely challenging task that is in practice often addressed with simulation based testing. Formal specification languages, such as Signal Temporal Logic (STL), are used to mathematically express CPS requirements and thus render the simulation activity more systematic and principled. We propose a novel method for adaptive generation of tests with specification coverage for STL. To achieve this goal, we devise cooperative reachability games that we combine with numerical optimization to create tests that explore the system in a way that exercise various parts of the specification. To the best of our knowledge our approach is the first adaptive testing approach that can be applied directly to MATLAB\texttrademark\; Simulink/Stateflow models. We implemented our approach in a prototype tool and evaluated it on several illustrating examples and a case study from the avionics domain, demonstrating the effectiveness of adaptive testing to (1) incrementally build a test case that reaches a test objective, (2) generate a test suite that increases the specification coverage, and (3) infer what part of the specification is actually implemented.