Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic data-driven parameterization of unresolved mesoscale eddies

In this work, a stochastic representation based on a physical transport principle is proposed to account for mesoscale eddy effects on the large-scale oceanic circulation. This stochastic framework arises from a decomposition of the Lagrangian velocity into a smooth-in-time component and a highly oscillating noise term. One important characteristic of this random model is that it conserves the total energy of the resolved flow for any realization. Such an energy-preserving representation is successfully implemented in a well established multi-layered quasi-geostrophic dynamical core. The empirical spatial correlation of the unresolved noise is calibrated from the eddy-resolving simulation data. In particular, a stationary correction drift can be introduced in the noise through Girsanov transformation. This non intuitive term appears to be important in reproducing on a coarse mesh the eastwards jet of the wind-driven double-gyre circulation. In addition, a projection method has been proposed to constrain the noise living along the iso-surfaces of the vertical stratification. The resulting noise enables us to improve the intrinsic low-frequency variability of the large-scale current.
First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006

We introduce the first AI-based framework for deep, super-resolution, wide-field radio interferometric imaging and demonstrate it on observations of the ESO 137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image reconstruction builds on a recent “plug-and-play” scheme whereby a denoising operator is injected as an image regularizer in an optimization algorithm, which alternates until convergence between denoising steps and gradient-descent data fidelity steps. We investigate handcrafted and learned variants of high-resolution, high dynamic range denoisers. We propose a parallel algorithm implementation relying on automated decompositions of the image into facets and the measurement operator into sparse low-dimensional blocks, enabling scalability to large data and image dimensions. We validate our framework for image formation at a wide field of view containing ESO 137-006 from 19 GB of MeerKAT data at 1053 and 1399 MHz. The recovered maps exhibit significantly more resolution and dynamic range than CLEAN, revealing collimated synchrotron threads close to the galactic core.
Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles

In modern autonomy stacks, prediction modules are paramount to planning motions in the presence of other mobile agents. However, failures in prediction modules can mislead the downstream planner into making unsafe decisions. Indeed, the high uncertainty inherent to the task of trajectory forecasting ensures that such mispredictions occur frequently. Motivated by the need to improve safety of autonomous vehicles without compromising on their performance, we develop a probabilistic run-time monitor that detects when a"harmful"prediction failure occurs, i.e., a task-relevant failure detector. We achieve this by propagating trajectory prediction errors to the planning cost to reason about their impact on the AV. Furthermore, our detector comes equipped with performance measures on the false-positive and the false-negative rate and allows for data-free calibration. In our experiments we compared our detector with various others and found that our detector has the highest area under the receiver operator characteristic curve.
Image Denoising Using Convolutional Autoencoder

With the inexorable digitalisation of the modern world, every subset in the field of technology goes through major advancements constantly. One such subset is digital images which are ever so popular. Images can not always be as visually pleasing or clear as you would want them to be and are often distorted or obscured with noise. A number of techniques to enhance images have come up as the years passed, all with their own respective pros and cons. In this paper, we look at one such particular technique which accomplishes this task with the help of a neural network model commonly known as an autoencoder. We construct different architectures for the model and compare results in order to decide the one best suited for the task. The characteristics and working of the model are discussed briefly knowing which can help set a path for future research.
OCTAve: 2D En Face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning With Locality Augmentation

Objective: While the microvasculature annotation within Optical Coherence Tomography Angiography (OCTA) can be leveraged using deep-learning techniques, expensive annotation processes are required to create sufficient training data. One way to avoid the expensive annotation is to use a type of weak annotation in which only the center of the vessel is annotated. However, retaining the final segmentation quality with roughly annotated data remains a challenge. Methods: Our proposed methods, called OCTAve, provide a new way of using weak-annotation for microvasculature segmentation. Since the centerline labels are similar to scribble annotations, we attempted to solve this problem by using the scribble-based weakly-supervised learning method. Even though the initial results look promising, we found that the method could be significantly improved by adding our novel self-supervised deep supervision method based on Kullback-Liebler divergence. Results: The study on large public datasets with different annotation styles (i.e., ROSE, OCTA-500) demonstrates that our proposed method gives better quantitative and qualitative results than the baseline methods and a naive approach, with a p-value less than 0.001 on dice's coefficient and a lot fewer artifacts. Conclusion: The segmentation results are both qualitatively and quantitatively superior to baseline weakly-supervised methods when using scribble-based weakly-supervised learning augmented with self-supervised deep supervision, with an average drop in segmentation performance of less than 10%. Significance: This work gives a new perspective on how weakly-supervised learning can be used to reduce the cost of annotating microvasculature, which can make the annotating process easier and reduce the amount of work for domain experts.
How much of UCCA can be predicted from AMR?

In this paper, we consider two of the currently popular semantic frameworks: Abstract Meaning Representation (AMR) - a more abstract framework, and Universal Conceptual Cognitive Annotation (UCCA) - an anchored framework. We use a corpus-based approach to build two graph rewriting systems, a deterministic and a non-deterministic one, from the former to the latter framework. We present their evaluation and a number of ambiguities that we discovered while building our rules. Finally, we provide a discussion and some future work directions in relation to comparing semantic frameworks of different flavors.
Keypoint-less Camera Calibration for Sports Field Registration in Soccer

Sports ﬁeld registration in broadcast videos is typically interpreted as the task of homography estimation, which provides a mapping between a planar ﬁeld and the corresponding visible area of the image. In contrast to previous approaches, we consider the task as a camera calibration problem. First, we introduce a differentiable objective function that is able to learn the camera pose and focal length from segment correspondences (e.g., lines, point clouds), based on pixel-level annotations for segments of a known calibration object. The calibration module iteratively minimizes the segment reprojection error induced by the estimated camera parameters. Second, we propose a novel approach for 3D sports ﬁeld registration from broadcast soccer images. The calibration module does not require any training data and compared to the typical solution, which subsequently reﬁnes an initial estimation,
RayPC: Interactive Ray Tracing Meets Parallel Coordinates

Large-scale numerical simulations of planetary interiors require dedicated visualization algorithms that are able to efficiently extract a large amount of information in an interactive and user-friendly way. Here we present a software framework for the visualization of mantle convection data. This framework combines real-time volume rendering, pathline visualization, and parallel coordinates to explore the fluid dynamics in an interactive way and to identify correlations between various output variables.
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network

Deep learning‐based structural damage detection methods overcome the limitation of inferior adaptability caused by extensively varying real‐world situations (e.g., lighting and shadow changes). However, most deep learning‐based methods detect structural damage at the image level and grid‐cell level. To provide pixel‐level detection of multiple damages, a Fully Convolutional Network (FCN)‐based multiple damages detection method for concrete structure is proposed. To realize this method, a database of 2,750 images (with 504 × 376 pixels) including crack, spalling, efflorescence, and hole images in concrete structure is built, and the four damages included in those images are labeled manually. Then, the architecture of the FCN is modified, trained, validated, and tested using this database. A strategy of model‐based transfer learning is used to initialize the parameters of the FCN during the training process. The results show 98.61% pixel accuracy (PA), 91.59% mean pixel accuracy (MPA), 84.53% mean intersection over union (MIoU), and 97.34% frequency weighted intersection over union (FWIoU). Subsequently, the robustness and adaptability of the trained FCN model is tested and the damage is extracted, where damage areas are provided according to a calibrated relation between the ratio (the pixel area and true area of the detected object) and the distance from the smartphone to the concrete surface using a laser range finder. A comparative study is conducted to examine the performance of the proposed FCN‐based approach using a SegNet‐based method. The results show that the proposed method substantiates quite better performance and can indeed detect multiple concrete damages at the pixel level in realistic situations.
Improving Pseudo Labels With Intra-Class Similarity for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a different but related fully-unlabeled target domain. To address the problem of domain shift, more and more UDA methods adopt pseudo labels of the target samples to improve the generalization ability on the target domain. However, inaccurate pseudo labels of the target samples may yield suboptimal performance with error accumulation during the optimization process. Moreover, once the pseudo labels are generated, how to remedy the generated pseudo labels is far from explored. In this paper, we propose a novel approach to improve the accuracy of the pseudo labels in the target domain. It first generates coarse pseudo labels by a conventional UDA method. Then, it iteratively exploits the intra-class similarity of the target samples for improving the generated coarse pseudo labels, and aligns the source and target domains with the improved pseudo labels. The accuracy improvement of the pseudo labels is made by first deleting dissimilar samples, and then using spanning trees to eliminate the samples with the wrong pseudo labels in the intra-class samples. We have applied the proposed approach to several conventional UDA methods as an additional term. Experimental results demonstrate that the proposed method can boost the accuracy of the pseudo labels and further lead to more discriminative and domain invariant features than the conventional baselines.
Quasi-periodic Gaussian processes for stellar activity: From physical to kernel parameters

In recent years, Gaussian Process (GP) regression has become widely used to analyse stellar and exoplanet time-series data sets. For spotted stars, the most popular GP covariance function is the quasi-periodic (QP) kernel, whose the hyperparameters of the GP have a plausible interpretation in terms of physical properties of the star and spots. In this paper, we test the reliability of this interpretation by modelling data simulated using a spot model using a QP GP, and the recently proposed quasi-periodic plus cosine (QPC) GP, comparing the posterior distributions of the GP hyperparameters to the input parameters of the spot model. We find excellent agreement between the input stellar rotation period and the QP and QPC GP period, and very good agreement between the spot decay timescale and the length scale of the squared exponential term. We also compare the hyperparameters derived from light and radial velocity (RV) curves for a given star, finding that the period and evolution timescales are in good agreement. However, the harmonic complexity of the GP, while displaying no clear correlation with the spot properties in our simulations, is systematically higher for the RV than for the light curve data. Finally, for the QP kernel, we investigate the impact of noise and time-sampling on the hyperparameters in the case of RVs. Our results indicate that good coverage of rotation period and spot evolution time-scales is more important than the total number of points, and noise characteristics govern the harmonic complexity.
TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring

—Continuous monitoring of trained ML models to determine when their predictions should and should not be trusted is essential for their safe deployment. Such a framework ought to be high-performing, explainable, post-hoc and actionable. We propose TRUST-LAPSE, a “mistrust” scoring framework for continuous model monitoring. We assess the trustworthiness of each input sample’s model prediction using a sequence of latent-space embeddings. Speciﬁcally, (a) our latent-space mistrust score estimates mistrust using distance metrics (Mahalanobis distance) and similarity metrics (cosine similarity) in the latent-space and (b) our sequential mistrust score determines deviations in correlations over the sequence of past input representations in a non-parametric, sliding-window based algorithm for actionable continuous monitoring. We evaluate TRUST-LAPSE via two downstream tasks: (1) distributionally shifted input detection and (2) data drift detection, across diverse domains– audio & vision using public datasets and further benchmark our approach on challenging, real-world electroencephalograms (EEG) datasets for seizure detection. Our latent-space mistrust scores achieve state-of-the-art results with AUROCs of 84.1 (vision), 73.9 (audio), 77.1 (clinical EEGs), outperforming baselines by over 10 points. We expose critical failures in popular baselines that remain insensitive to input semantic content, rendering them unﬁt for real-world model monitoring. We show that our sequential mistrust scores achieve high drift detection rates: over 90% of the streams show < 20% error for all domains. Through extensive qualitative and quantitative evaluations, we show that our mistrust scores are more robust and provide explainability for easy adoption into practice.
Salient Object Detection for Point Clouds

This paper researches the unexplored task-point cloud salient object detection (SOD). Differing from SOD for images, we find the attention shift of point clouds may provoke saliency conflict, i.e., an object paradoxically belongs to salient and non-salient categories. To eschew this issue, we present a novel view-dependent perspective of salient objects, reasonably reflecting the most eye-catching objects in point cloud scenarios. Following this formulation, we introduce PCSOD, the first dataset proposed for point cloud SOD consisting of 2,872 in-/out-door 3D views. The samples in our dataset are labeled with hierarchical annotations, e.g., super-/sub-class, bounding box, and segmentation map, which endows the brilliant generalizability and broad applicability of our dataset verifying various conjectures. To evidence the feasibility of our solution, we further contribute a baseline model and benchmark five representative models for a comprehensive comparison. The proposed model can effectively analyze irregular and unordered points for detecting salient objects. Thanks to incorporating the task-tailored designs, our method shows visible superiority over other baselines, producing more satisfactory results. Extensive experiments and discussions reveal the promising potential of this research field, paving the way for further study.
A primal finite element scheme of the $\mathbf{H}(\mathbf{d})\cap \mathbf{H}(\boldsymbol{\delta})$ elliptic problem

Let Ω ⊂ R be a domain with Lipschitz boundary. In this paper, we study the H(d) ∩ H(δ) elliptic problem: given f ∈ LΛ(Ω), find ω ∈ HΛ(Ω) ∩ H∗ 0Λk(Ω), such that (1.1) 〈dω, dμ〉L2Λk+1 + 〈δkω, δkμ〉L2Λk−1 + 〈ω, μ〉L2Λk = 〈 f , μ〉L2Λk , ∀μ ∈ HΛ(Ω) ∩ H∗ 0Λk(Ω). Here, following [1], we denote by Λ(Ξ) the space of differential k-forms on an n-dimensional domain Ξ, and LΛ(Ξ) consists of differential k-forms with coefficients in L(Ξ) component by component, and 〈·, ·〉L2Λk(Ξ) is the inner product of the Hilbert space LΛ(Ξ). In this paper, we will occasionally drop Ω for differential forms on Ω. The exterior differential operator d : Λ(Ξ) → Λ (Ξ) is unbounded from LΛ(Ξ) to Λ(Ξ). Denote, HΛ(Ξ) = H(d,Ξ) := {
Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud

In the field of 3D perception using 3D LiDAR sensors, ground segmentation is an essential task for various purposes, such as traversable area detection and object recognition. Under these circumstances, several ground segmentation methods have been proposed. However, some limitations are still encountered. First, some ground segmentation methods require fine-tuning of parameters depending on the surroundings, which is excessively laborious and time-consuming. Moreover, even if the parameters are well adjusted, a partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions. Finally, ground segmentation methods typically fail to estimate an appropriate ground plane when the ground is above another structure, such as a retaining wall. To address these problems, we propose a robust ground segmentation method called Patchwork++, an extension of Patchwork. Patchwork++ exploits adaptive ground likelihood estimation (A-GLE) to calculate appropriate parameters adaptively based on the previous ground segmentation results. Moreover, temporal ground revert (TGR) alleviates a partial under-segmentation problem by using the temporary ground property. Also, region-wise vertical plane fitting (R-VPF) is introduced to segment the ground plane properly even if the ground is elevated with different layers. Finally, we present reflected noise removal (RNR) to eliminate virtual noise points efficiently based on the 3D LiDAR reflection model. We demonstrate the qualitative and quantitative evaluations using a SemanticKITTI dataset. Our code is available at https://aithub.com/url-kaist/patchwork-plusplus
Crosscap states in the XXX spin-1/2 spin chain

We consider integrable boundary states in the XXX spin-1/2 spin chain. We begin by briefly reviewing the algebraic Bethe Ansatz as well as integrable boundary states in spin chains. Then a recently discovered class of integrable states known as crosscap states is described and expanded. In these states each spin is entangled with its antipodal spin. We present a novel proof of the integrability of both a crosscap state that is known in the literature and one that has not previously been studied. We then use the machinery of the algebraic Bethe Ansatz to derive the overlaps between the crosscap states and off-shell Bethe states.
Supporting peace negotiations in the Yemen war through machine learning

Abstract Today’s conflicts are becoming increasingly complex, fluid, and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace-making, or the identification of key conflict issues and their interdependence. International peace efforts appear ill-equipped to successfully address these challenges. While technology is already being experimented with and used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study contributes to emerging research on the use of state-of-the-art machine learning technologies and techniques in conflict mediation processes. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning can effectively support mediating teams by providing them with tools for knowledge management, extraction and conflict analysis. Apart from illustrating the potential of machine learning tools in conflict mediation, the article also emphasizes the importance of interdisciplinary and participatory, cocreation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation.
Consensus with Oral/Written Messages: Link Faults Revisited

This paper1 shows that deterministic consensus in synchronous distributed systems with link faults is possible, despite the impossibility result of (Gray, 1978). Instead of using randomization, we overcome this impossibility result by moderately restricting the inconsistency that link faults may cause system-wide. Relying upon a novel perceptionbased hybrid fault model that provides different classes of faults for both nodes and links, we prove that the m + 1round Byzantine agreement algorithms OMH (Lincoln & Rushby, 1993) and its authenticated variants OMHA, ZA (Gong, Lincoln & Rushby, 1995) require n > 2f ` + f r ` + f ra ` + 2(fa + fs) + fo + fm + m n > 2f ` + f r ` + 2(fa + fs) + fo + fm + m n > f ` + f r ` + fa + fs + fo + fm + 1 nodes for transparently masking at most f s ` broadcast and f ` receive link faults (including at most f ra ` arbitrary ones) per node(! ) in each round, in addition to at most fa, fs, fo, fm arbitrary, symmetric, omission, and manifest node faults, provided that m ≥ fa + fo + 1. If signatures are broken, OMHA degrades to OMH, whereas ZA can be made tolerant to fb broken signatures by increasing fa accordingly. An analysis of the assumption coverage in systems where links fail independently with probability p reveals that adding nodes for tolerating link faults yields a vanishing probability of violating the fault model as long as np < 1. A number of theoretical results, including tight lower bounds for the number of nodes in presence of link faults and a precise characterization of what makes a node fault Byzantine, establish a sound theoretical foundation for our framework as well.