Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

LiDAR mapping is important yet challenging in selfdriving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping [1] converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cannot produce satisfactory results on large-scale datasets with thousands of frames. This is due to the lack of loop closures and exact cross-frame point correspondences, and the slow convergence of its global localization network. We propose DeepMapping2 by adding two novel techniques to address these issues: (1) organization of training batch based on map topology from loop closing, and (2) self-supervised local-to-global point consistency loss leveraging pairwise registration. Our experiments and ablation studies on public datasets such as KITTI, NCLT, and Nebula demonstrate the effectiveness of our method.
An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images

Diabetic retinopathy (DR) is a complication of diabetes, and one of the major causes of vision impairment in the global population. As the early-stage manifestation of DR is usually very mild and hard to detect, an accurate diagnosis via eye-screening is clinically important to prevent vision loss at later stages. In this work, we propose an ensemble method to automatically grade DR using ultra-wide optical coherence tomography angiography (UW-OCTA) images available from Diabetic Retinopathy Analysis Challenge (DRAC) 2022. First, we adopt the state-of-the-art classification networks, i.e., ResNet, DenseNet, EfficientNet, and VGG, and train them to grade UW-OCTA images with different splits of the available dataset. Ultimately, we obtain 25 models, of which, the top 16 models are selected and ensembled to generate the final predictions. During the training process, we also investigate the multi-task learning strategy, and add an auxiliary classification task, the Image Quality Assessment, to improve the model performance. Our final ensemble model achieved a quadratic weighted kappa (QWK) of 0.9346 and an Area Under Curve (AUC) of 0.9766 on the internal testing dataset, and the QWK of 0.839 and the AUC of 0.8978 on the DRAC challenge testing dataset.
Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset

Pedestrian safety is one primary concern in autonomous driving. The under-representation of vulnerable groups in today's pedestrian datasets points to an urgent need for a dataset of vulnerable road users. In order to help train comprehensive models and subsequently drive research to improve the accuracy of vulnerable pedestrian identification, we first introduce a new dataset for vulnerable pedestrian detection in this paper: the BG Vulnerable Pedestrian (BGVP) dataset. The dataset includes four classes, i.e., Children Without Disability, Elderly without Disability, With Disability, and Non-Vulnerable. This dataset consists of images collected from the public domain and manually-annotated bounding boxes. In addition, on the proposed dataset, we have trained and tested five classic or state-of-the-art object detection models, i.e., YOLOv4, YOLOv5, YOLOX, Faster R-CNN, and EfficientDet. Our results indicate that YOLOX and YOLOv4 perform the best on our dataset, YOLOv4 scoring 0.7999 and YOLOX scoring 0.7779 on the mAP 0.5 metric, while YOLOX outperforms YOLOv4 by 3.8 percent on the mAP 0.5:0.95 metric. Generally speaking, all five detectors do well predicting the With Disability class and perform poorly in the Elderly Without Disability class. YOLOX consistently outperforms all other detectors on the mAP (0.5:0.95) per class metric, obtaining 0.5644, 0.5242, 0.4781, and 0.6796 for Children Without Disability, Elderly Without Disability, Non-vulnerable, and With Disability, respectively. Our dataset and codes are available at https://github.com/devvansh1997/BGVP.
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification

The automated machine learning (AutoML) field has become increasingly relevant in recent years. These algorithms can develop models without the need for expert knowledge, facilitating the application of machine learning techniques in the industry. Neural Architecture Search (NAS) exploits deep learning techniques to autonomously produce neural network architectures whose results rival the state-of-the-art models hand-crafted by AI experts. However, this approach requires significant computational resources and hardware investments, making it less appealing for real-usage applications. This article presents the third version of Pareto-Optimal Progressive Neural Architecture Search (POPNASv3), a new sequential model-based optimization NAS algorithm targeting different hardware environments and multiple classification tasks. Our method is able to find competitive architectures within large search spaces, while keeping a flexible structure and data processing pipeline to adapt to different tasks. The algorithm employs Pareto optimality to reduce the number of architectures sampled during the search, drastically improving the time efficiency without loss in accuracy. The experiments performed on images and time series classification datasets provide evidence that POPNASv3 can explore a large set of assorted operators and converge to optimal architectures suited for the type of data provided under different scenarios.
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. 2) Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. 3) Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained 97%-99% performance of its fully-supervised version with only 10 annotated points per image.
On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI’s CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online.1
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?

Traditional multi-task learning architectures train a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer. A new type of multi-task learning within NLP homogenizes multi-task architectures as a shared encoder and language model decoder, which does surprisingly well across a range of diverse tasks. Does this new architecture suffer from task-conflicts that require specialized training algorithms? We study how certain factors in the shift towards text-to-text models affects multi-task conflict and negative transfer, finding that both directional conflict and transfer are surprisingly constant across architectures.
Gershgorin Disc-based Voltage Stability Regions for DER Siting and Control in Distribution Grids

As a consequence of the transition to distributed and renewable energy systems, some distribution system operators are increasingly concerned about power quality, including steady-state voltage volatility. In this paper, we study the control of real and reactive power injections by inverter-based distributed energy resources to regulate voltage magnitudes and phase angles measured by sensors positioned across 3-phase unbalanced distribution grids. Our proposed controllers are agnostic to the location and type of communications supporting energy resource operations. To design the controllers, we apply the Gershgorin Disc Theorem and determine analytic stability regions in terms of renewable energy operating parameters and grid impedances. The stability regions yield direct relationships between renewable energy system siting and the convergence of voltage phasors to references. Beyond defining stability regions, we compute ranges of stable operating parameters for renewable energy systems that promote operational flexibility by including customer economics. By means of a case study on the IEEE 123-node test circuit, we observe our approach to coordinating renewable energy systems achieves non-oscillatory voltage regulation and reduces the duration of voltage violations by 26%.
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs

Optimizing the expected values of probabilistic processes is a central problem in computer science and its applications, arising in fields ranging from artificial intelligence to operations research to statistical computing. Unfortunately, automatic differentiation techniques developed for deterministic programs do not in general compute the correct gradients needed for widely used solutions based on gradient-based optimization. In this paper, we present ADEV, an extension to forward-mode AD that correctly differentiates the expectations of probabilistic processes represented as programs that make random choices. Our algorithm is a source-to-source program transformation on an expressive, higher-order language for probabilistic computation, with both discrete and continuous probability distributions. The result of our transformation is a new probabilistic program, whose expected return value is the derivative of the original program’s expectation. This output program can be run to generate unbiased Monte Carlo estimates of the desired gradient, that can be used within the inner loop of stochastic gradient descent. We prove ADEV correct using logical relations over the denotations of the source and target probabilistic programs. Because it modularly extends forward-mode AD, our algorithm lends itself to a concise implementation strategy, which we exploit to develop a prototype in just a few dozen lines of Haskell (https://github.com/probcomp/adev).
Classes of weakly associative operations on trellises

In this paper, we introduce the notion of pseudo-triangular norm (pseudo-t-norm, for short) as a classes of weakly associative operations on trellises and as a generalization of triangular norm (t-norm, for short) on bounded trellises and we investigate their various properties and present some constructions of pseudo-t-norms on bounded trellises. Also, we study the T-distributivity on bounded trellises. Moreover, we show the relationship among pseudo-t-norms and isomorphisms on bounded trellises, which are more complicated in absence of the property of (transitivity) associativity of the trellis meet and join operations.
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits

Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by applications to cognitive radio systems. Most research for this problem focuses exclusively on the settings that players have full access to all arms and receive no reward when pulling the same arm. Hence all players solve the same bandit problem with the goal of maximizing their cumulative reward. However, these settings neglect several important factors in many real-world applications, where players have limited access to a dynamic local subset of arms (i.e., an arm could sometimes be ``walking'' and not accessible to the player). To this end, this paper proposes a multi-player multi-armed walking bandits model, aiming to address aforementioned modeling issues. The goal now is to maximize the reward, however, players can only pull arms from the local subset and only collect a full reward if no other players pull the same arm. We adopt Upper Confidence Bound (UCB) to deal with the exploration-exploitation tradeoff and employ distributed optimization techniques to properly handle collisions. By carefully integrating these two techniques, we propose a decentralized algorithm with near-optimal guarantee on the regret, and can be easily implemented to obtain competitive empirical performance.
Continuity of robustness measures in quantum resource theories

Robustness measures are increasingly prominent resource quantifiers that have been introduced for quantum resource theories such as entanglement and coherence. Despite the generality of these measures, their usefulness is hindered by the fact that some of their mathematical properties remain unclear, especially when the set of resource-free states is non-convex. In this paper, we investigate continuity properties of different robustness functions. We show that their continuity depends on the shape of the set of free states. In particular, we demonstrate that in many cases, star-convexity is sufficient for Lipschitz-continuity of the robustness, and we provide specific examples of sets leading to non-continuous measures. Finally, we illustrate the applicability of our results by defining a robustness of teleportability and of quantum discord.
A Silicon Nitride Microring Based High-Speed, Tuning-Efficient, Electro-Refractive Modulator

The use of the Silicon-on-Insulator (SOI) plat-form has been prominent for realizing CMOS-compatible, high-performance photonic integrated circuits (PICs). But in recent years, the silicon-nitride-on-silicon-dioxide (SiN-on-SiO2) plat-form has garnered increasing interest as an alternative to the SOI platform for realizing high-performance PICs. This is because of its several beneficial properties over the SOI platform, such as low optical losses, high thermo-optic stability, broader wavelength transparency range, and high tolerance to fabrication-process variations. However, SiN-on-SiO2 based active devices such as modulators are scarce and lack in desired performance, due to the absence of free-carrier based activity in the SiN material and the complexity of integrating other active materials with SiN-on-SiO2 platform. This shortcoming hinders the SiN-on-SiO2 platform for realizing active PICs. To address this shortcoming, we demonstrate a SiN -on-SiO2 microring resonator (MRR) based active modulator in this article. Our designed MRR modulator employs an Indium-Tin-Oxide (ITO)-SiN-ITO thin-film stack, in which the ITO thin films act as the upper and lower claddings of the SiN MRR. The ITO-SiN-ITO thin-film stack leverages the free-carrier assisted, high-amplitude refractive index change in the ITO films to effect a large electro-refractive optical modulation in the device. Based on the electrostatic, transient, and finite difference time domain (FDTD) simulations, conducted using photonics foundry-validated tools, we show that our modulator achieves 280 pm/V resonance modulation efficiency, 67.8 GHz 3-dB modulation bandwidth, ~ 19 nm free-spectral range (FSR), ~0.23 dB insertion loss, and 10.31 dB extinction ratio for optical on-off-keying (OOK) modulation at 30 Gb/s.
An ODE approach to multiple choice polynomial programming

We propose an ODE approach to solving multiple choice polynomial programming (MCPP) after assuming that the optimum point can be approximated by the expected value of so-called thermal equilibrium as usually did in simulated annealing. The explicit form of the feasible region and the affine property of the objective function are both fully exploited in transforming the MCPP problem into the ODE system. We also show theoretically that a local optimum of the former can be obtained from an equilibrium point of the latter. Numerical experiments on two typical combinatorial problems, MAX-$k$-CUT and the calculation of star discrepancy, demonstrate the validity of our ODE approach, and the resulting approximate solutions are of comparable quality to those obtained by the state-of-the-art heuristic algorithms but with much less cost. This paper also serves as the first attempt to use a continuous algorithm for approximating the star discrepancy.
An activity-based spatial-temporal community electricity vulnerability assessment framework

The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk management
Quantifying the influence of bars on action-based dynamical modelling of disc galaxies

Action-based dynamical modelling, using stars as dynamical tracers, is an excellent diagnostic to estimate the underlying axisymmetric matter distribution of the Milky Way. However, the Milky Way’s bar causes non-axisymmetric resonance features in the stellar disc. Using RoadMapping (an action-based dynamical modelling framework to estimate the gravitational potential and the stellar distribution function), we systematically quantify the robustness of action-based modelling in the presence of a bar. We construct a set of test-particle simulations of barred galaxies (with varying bar properties), and apply RoadMapping to different survey volumes (with varying azimuthal position, size) drawn from these barred models. For realistic bar parameters, the global potential parameters are still recovered to within ∼1 − 17 percent. However, with increasing bar strength, the best-fit values of the parameters progressively deviate from their true values. This happens due to a combination of radial heating, radial migration, and resonance overlap phenomena in our bar models. Furthermore, the azimuthal location and the size of the survey volumes play important roles in the successful recovery of the parameters. Survey volumes along the bar major axis produce larger (relative) errors in the best-fit parameter values. In addition, the potential parameters are better recovered for survey volumes with larger spatial coverage. As the Sun is located just ∼28 − 33○ behind the bar’s major axis, an estimate for the bar-induced systematic bias – as provided by this study – is therefore crucial for future modelling attempts of the Milky Way.
Chaos for endomorphisms of completely metrizable groups and linear operators on Fr\'echet spaces

Using the techniques in topological dynamics, we give a uniform treatment of Li-Yorke chaos, mean Li-Yorke chaos and distributional chaos for continuous endomorphisms of completely metrizable groups, and characterize three kinds of chaos (resp. extreme chaos) in term of the existence of corresponding semi-irregular points (resp. irregular points). We demonstrate some inner automorphisms of Polish groups to illustrate the results. We also apply the results to the chaos theory of continuous linear operators on Fr\'echet spaces, which improves some results in the literature.
Interrogating solar flare loop models with IRIS observations 1: Overview of the models, and mass flows

Solar flares are transient yet dramatic events in the atmosphere of the Sun, during which a vast amount of magnetic energy is liberated. This energy is subsequently transported through the solar atmosphere or into the heliosphere, and together with coronal mass ejections flares comprise a fundamental component of space weather. Thus, understanding the physical processes at play in flares is vital. That understanding often requires the use of forward modelling in order to predict the hydrodynamic and radiative response of the solar atmosphere. Those predictions must then be critiqued by observations to show us where our models are missing ingredients. While flares are of course 3D phenomenon, simulating the flaring atmosphere including an accurate chromosphere with the required spatial scales in 3D is largely beyond current computational capabilities, and certainly performing parameter studies of energy transport mechanisms is not yet tractable in 3D. Therefore, field-aligned 1D loop models that can resolve the relevant scales have a crucial role to play in advancing our knowledge of flares. In recent years, driven in part by the spectacular observations from the Interface Region Imaging Spectrograph (IRIS), flare loop models have revealed many interesting features of flares. For this review I highlight some important results that illustrate the utility of attacking the problem of solar flares with a combination of high quality observations, and state-of-the-art flare loop models, demonstrating: 1) how models help to interpret flare observations from IRIS, 2) how those observations show us where we are missing physics from our models, and 3) how the ever increasing quality of solar observations drives model improvements. Here in Paper one of this two part review I provide an overview of modern flare loop models, and of electron-beam driven mass flows during solar flares.