Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate Stabilization Techniques for RBF-FD Meshless Discretizations with Neumann Boundary Conditions

A major obstacle to the application of the standard Radial Basis Functiongenerated Finite Difference (RBF-FD) meshless method is constituted by its inability to accurately and consistently solve boundary value problems involving Neumann boundary conditions (BCs). This is also due to ill-conditioning issues affecting the interpolation matrix when boundary derivatives are imposed in strong form. In this paper these ill-conditioning issues and subsequent instabilities affecting the application of the RBF-FD method in presence of Neumann BCs are analyzed both theoretically and numerically. The theoretical motivations for the onset of such issues are derived by highlighting the dependence of the determinant of the local interpolation matrix upon the boundary normals. Qualitative investigations are also carried out numerically by studying a reference stencil and looking for correlations between its geometry and the properties of the associated interpolation matrix. Based on the previous analyses, two approaches are derived to overcome the initial problem. The corresponding stabilization properties are finally assessed by succesfully applying such approaches to the stabilization of the HelmholtzHodge decomposition.
Efficient matter-wave lensing of ultracold atomic mixtures

Mixtures of ultracold quantum gases are at the heart of high-precision quantum tests of the weak equivalence principle, where extremely low expansion rates have to be reached with matter-wave lensing techniques. We propose to simplify this challenging atom-source preparation by employing magic laser wavelengths for the optical lensing potentials, which guarantee that all atomic species follow identical trajectories and experience common expansion dynamics. In this way, the relative shape of the mixture is conserved during the entire evolution while cutting in half the number of required lensing pulses compared to standard approaches.
Controlling Andreev Bound States with the Magnetic Vector Potential

Tunneling spectroscopy measurements are often used to probe the energy spectrum of Andreev bound states (ABSs) in semiconductor-superconductor hybrids. Recently, this spectroscopy technique has been incorporated into planar Josephson junctions (JJs) formed in two-dimensional electron gases, a potential platform to engineer phase-controlled topological superconductivity. Here, we perform ABS spectroscopy at the two ends of planar JJs and study the effects of the magnetic vector potential on the ABS spectrum. We show that the local superconducting phase difference arising from the vector potential is equal in magnitude and opposite in sign at the two ends, in agreement with a model that assumes localized ABSs near the tunnel barriers. Complemented with microscopic simulations, our experiments demonstrate that the local phase difference can be used to estimate the relative position of localized ABSs separated by a few hundred nanometers.
Price's law on Minkowski space in the presence of an inverse square potential

We consider the pointwise decay of solutions to wave-type equations in two model singular settings. Our main result is a form of Price's law for solutions of the massless Dirac-Coulomb system in (3+1)-dimensions. Using identical techniques, we prove a similar theorem for the wave equation on Minkowski space with an inverse square potential. One novel feature of these singular models is that solutions exhibit two different leading decay rates at timelike infinity in two regimes, distinguished by whether the spatial momentum along a curve which approaches timelike infinity is zero or non-zero. An important feature of our analysis is that it yields a precise description of solutions at the interface of these two regions which comprise the whole of timelike infinity.
Bootstrapped Masked Autoencoders for Vision BERT Pretraining

We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1) momentum encoder that provides online feature as extra BERT prediction targets; 2) target-aware decoder that tries to reduce the pressure on the encoder to memorize target-specific information in BERT pretraining. The first design is motivated by the observation that using a pretrained MAE to extract the features as the BERT prediction target for masked tokens can achieve better pretraining performance. Therefore, we add a momentum encoder in parallel with the original MAE encoder, which bootstraps the pretraining performance by using its own representation as the BERT prediction target. In the second design, we introduce target-specific information (e.g., pixel values of unmasked patches) from the encoder directly to the decoder to reduce the pressure on the encoder of memorizing the target-specific information. Thus, the encoder focuses on semantic modeling, which is the goal of BERT pretraining, and does not need to waste its capacity in memorizing the information of unmasked tokens related to the prediction target. Through extensive experiments, our BootMAE achieves $84.2\%$ Top-1 accuracy on ImageNet-1K with ViT-B backbone, outperforming MAE by $+0.8\%$ under the same pre-training epochs. BootMAE also gets $+1.0$ mIoU improvements on semantic segmentation on ADE20K and $+1.3$ box AP, $+1.4$ mask AP improvement on object detection and segmentation on COCO dataset. Code is released at https://github.com/LightDXY/BootMAE.
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification

Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases. The state-of-the-art approaches apply a two-stage learning framework to alleviate the class-imbalance issue, where the first stage focuses on training of a general feature extractor and the second stage focuses on fine-tuning the classifier head for class rebalancing. However, existing two-stage approaches do not consider the fine-grained property between different diseases, often causing the first stage less effective for medical image classification than for natural image classification tasks. In this study, we propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Extensive experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches, suggesting that metric learning can be used as an effective plug-in component in the two-stage framework for fine-grained class-imbalanced image classification tasks.
Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations

The randomly pivoted partial Cholesky algorithm (RPCholesky) computes a factorized rank-k approximation of an N x N positive-semidefinite (psd) matrix. RPCholesky requires only (k + 1) N entry evaluations and O(k^2 N) additional arithmetic operations, and it can be implemented with just a few lines of code. The method is particularly useful for approximating a kernel matrix. This paper offers a thorough new investigation of the empirical and theoretical behavior of this fundamental algorithm. For matrix approximation problems that arise in scientific machine learning, experiments show that RPCholesky matches or beats the performance of alternative algorithms. Moreover, RPCholesky provably returns low-rank approximations that are nearly optimal. The simplicity, effectiveness, and robustness of RPCholesky strongly support its use in scientific computing and machine learning applications.
A Single Self-Supervised Model for Many Speech Modalities Enables Zero-Shot Modality Transfer

While audio-visual speech models can yield superior performance and robustness compared to audio-only models, their development and adoption are hindered by the lack of labeled and unlabeled audio-visual data and the cost to deploy one model per modality. In this paper, we present u-HuBERT, a self-supervised pre-training framework that can leverage both multimodal and unimodal speech with a uniﬁed masked cluster prediction objective. By utilizing modality dropout during pre-training, we demonstrate that a single ﬁne-tuned model can achieve performance on par or better than the state-of-the-art modality-speciﬁc models. Moreover, our model ﬁne-tuned only on audio can perform well with audio-visual and visual speech input, achieving zero-shot modality generalization for speech recognition and speaker veriﬁcation. In particular, our single model yields 1.2%/1.4%/27.2% speech recognition word error rate on LRS3 with audio-visual/audio/visual input.
Fraisse structures with SDAP+, Part II: Simply characterized big Ramsey structures

This is Part II of a two-part series regarding Ramsey properties of Fräıssé structures satisfying a property called SDAP+, which strengthens the Disjoint Amalgamation Property. In Part I, we prove that every Fräıssé structure in a finite relational language with relation symbols of any finite arity satisfying this property is indivisible. In Part II, we prove that every Fräıssé structure in a finite relational language with relation symbols of arity at most two having this property has finite big Ramsey degrees which have a simple characterization. It follows that any such Fräıssé structure admits a big Ramsey structure. Part II utilizes the notion of coding trees of 1-types developed in Part I and a theorem from Part I which functions as a pigeonhole principle for induction arguments in this paper. Our approach yields a direct characterization of the degrees without appeal to the standard method of “envelopes”. This work offers a streamlined and unifying approach to Ramsey theory on some seemingly disparate classes of Fräıssé structures.
Imaging Through the Atmosphere Using Turbulence Mitigation Transformer

Restoring images distorted by atmospheric turbulence is a ubiquitous problem in long-range imaging applications. While existing deep-learning-based methods have demonstrated promising results in specific testing conditions, they suffer from three limitations: (1) lack of generalization capability from synthetic training data to real turbulence data; (2) failure to scale, hence causing memory and speed challenges when extending the idea to a large number of frames; (3) lack of a fast and accurate simulator to generate data for training neural networks. In this paper, we introduce the turbulence mitigation transformer (TMT) that explicitly addresses these issues. TMT brings three contributions: Firstly, TMT explicitly uses turbulence physics by decoupling the turbulence degradation and introducing a multi-scale loss for removing distortion, thus improving effectiveness. Secondly, TMT presents a new attention module along the temporal axis to extract extra features efficiently, thus improving memory and speed. Thirdly, TMT introduces a new simulator based on the Fourier sampler, temporal correlation, and flexible kernel size, thus improving our capability to synthesize better training data. TMT outperforms state-of-the-art video restoration models, especially in generalizing from synthetic to real turbulence data.
The Dynamical Viability of an Extended Jupiter Ring System

Planetary rings are often speculated as being a relatively common attribute of giant planets, partly based on their prevalence within the solar system. However, their formation and sustainability remain a topic of open discussion, and the most massive planet within our planetary system harbors a very modest ring system. Here, we present the results of an N-body simulation that explores dynamical constraints on the presence of substantial ring material for Jupiter. Our simulations extend from within the rigid satellite Roche limit to 10% of the Jupiter Hill radius, and include outcomes from 106 and 107 yr integrations. The results show possible regions of a sustained dense ring material presence around Jupiter that may comprise the foundation for moon formation. The results largely demonstrate the truncation of stable orbits imposed by the Galilean satellites, and dynamical desiccation of dense ring material within the range ∼3–29 Jupiter radii. We discuss the implications of these results for exoplanets, and the complex relationship between the simultaneous presence of rings and massive moon systems.
Formation of Quiescent Prominence Magnetic Fields by Supergranulations

To understand the formation of quiescent solar prominences, the origin of their magnetic field structures, i.e., magnetic flux ropes (MFRs), must be revealed. We use three-dimensional magnetofriction simulations in a spherical subdomain to investigate the role of typical supergranular motions in the long-term formation of a prominence magnetic field. Time-dependent horizontal supergranular motions with and without the effect of Coriolis force are simulated on the solar surface via Voronoi tessellation. The vortical motions by the Coriolis effect at boundaries of supergranules inject magnetic helicity into the corona. The helicity is transferred and accumulated along the polarity inversion line (PIL) as a strongly sheared magnetic field via helicity condensation. The diverging motions of supergranules converge opposite magnetic polarities at the PIL and drive the magnetic reconnection between footpoints of the sheared magnetic arcades to form an MFR. The magnetic network, negative-helicity MFR in the northern hemisphere, and fragmented-to-continuous formation process of magnetic dip regions are in agreement with observations. Although diverging supergranulations, differential rotation, and meridional flows are included, the simulation without the Coriolis effect cannot produce an MFR or sheared arcades to host a prominence. Therefore, Coriolis force is a key factor for helicity injection and the formation of magnetic structures of quiescent solar prominences.
Compact and Variable Radio Emission from an Active Galaxy with Supersoft X-Ray Emission

RX J1301.9+2747 is a unique active galaxy with a supersoft X-ray spectrum that lacks significant emission at energies above 2 keV. In addition, it is one of few galaxies displaying quasiperiodic X-ray eruptions that recur on a timescale of 13–20 ks. We present multiepoch radio observations of RX J1301.9+2747 using GMRT, Very Large Array (VLA), and Very Long Baseline Array (VLBA). The VLBA imaging at 1.6 GHz reveals a compact radio emission unresolved at a scale of <0.7 pc, with a brightness temperature of T b > 5 × 107 K. The radio emission is variable by more than a factor of 2.5 over a few days, based on the data taken from VLA monitoring campaigns. The short-term radio variability suggests that the radio emitting region has a size as small as 8 × 10−4 pc, resulting in an even higher brightness temperature of T b ∼ 1012 K. A similar limit on the source size can be obtained if the observed flux variability is not intrinsic and caused by the interstellar scintillation effect. The overall radio spectrum is steep with a time-averaged spectral index α = −0.78 ± 0.03 between 0.89 and 14 GHz. These observational properties rule out a thermal or star formation origin of the radio emission, and appear to be consistent with the scenario of episodic jet ejections driven by a magnetohydrodynamic process. Simultaneous radio and X-ray monitoring observations down to a cadence of hours are required to test whether the compact and variable radio emission is correlated with the quasiperiodic X-ray eruptions.
How do tuna schools associate to dFADs? A study using echo-sounder buoys to identify global patterns

Based on the data gathered by echo-sounder buoys attached to drifting Fish Aggregating Devices (dFADs) across tropical oceans, the current study applies a Machine Learning protocol to examine the temporal trends of tuna schools’ association to drifting objects. Using a binary output, metrics typically used in the literature were adapted to account for the fact that the entire tuna aggregation under the dFAD was considered. The median time it took tuna to colonize the dFADs for the first time varied between 25 and 43 days, depending on the ocean, and the longest soak and colonization times were registered in the Pacific Ocean. The tuna schools’ Continuous Residence Times were generally shorter than Continuous Absence Times (median values between 5 and 7 days, and 9 and 11 days, respectively), in line with the results found by previous studies. Using a regression output, two novel metrics, namely aggregation time and disaggregation time, were estimated to obtain further insight into the symmetry of the aggregation process. Across all oceans, the time it took for the tuna aggregation to depart from the dFADs was not significantly longer than the time it took for the aggregation to form. The value of these results in the context of the “ecological trap” hypothesis is discussed, and further analyses to enrich and make use of this data source are proposed.
Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021

With the growth of social media platform influence, the effect of their misuse becomes more and more impactful. The importance of automatic detection of threatening and abusive language can not be overestimated. However, most of the existing studies and state-of-the-art methods focus on English as the target language, with limited work on low- and medium-resource languages. In this paper, we present two shared tasks of abusive and threatening language detection for the Urdu language which has more than 170 million speakers worldwide. Both are posed as binary classification tasks where participating systems are required to classify tweets in Urdu into two classes, namely: (i) Abusive and Non-Abusive for the first task, and (ii) Threatening and Non-Threatening for the second. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. The abusive dataset contains 2400 annotated tweets in the train part and 1100 annotated tweets in the test part. The threatening dataset contains 6000 annotated tweets in the train part and 3950 annotated tweets in the test part. We also provide logistic regression and BERT-based baseline classifiers for both tasks. In this shared task, 21 teams from six countries registered for participation (India, Pakistan, China, Malaysia, United Arab Emirates, and Taiwan), 10 teams submitted their runs for Subtask A, which is Abusive Language Detection and 9 teams submitted their runs for Subtask B, which is Threatening Language detection, and seven teams submitted their technical reports. The best performing system achieved an F1-score value of 0.880 for Subtask A and 0.545 for Subtask B. For both subtasks, m-Bert based transformer model showed the best performance.
How emerging digital technologies affect operations management through co-creation. Empirical evidence from the maritime industry

Abstract Globalization, variation in customer expectations, and the developments connected to Information & Communication Technology are usually considered as the main drivers of changes in Operations Management (OM) strategies by firms in both manufacturing and service industries. Emerging digital technologies, such as Internet of Things or Cloud Computing, represents a novel paradigm that is rapidly affecting on several aspects of everyday-life of private and business users. This paper contributes to extant literature about the relationship between emerging digital technologies and OM, by stressing the value co-creation issue. Specifically, we investigate how emerging digital technologies affect OM through value co-creation in the maritime industry. Our study reports the case of FairWind project, an open technology marine service and graphic user interface enabling sailors to personalize their experience on board through 3rd party boat applications. The case of Fair Wind points out how emerging digital technologies contribute to OM within both organizations and, more in general, maritime industry, as well as the critical role of user’s co-creation in defining and managing on-board operations.
DEXTER: An end-to-end system to extract table contents from electronic medical health documents

In this paper, we propose DEXTER, an end to end system to extract information from tables present in medical health documents, such as electronic health records (EHR) and explanation of benefits (EOB). DEXTER consists of four sub-system stages: i) table detection ii) table type classification iii) cell detection; and iv) cell content extraction. We propose a two-stage transfer learning-based approach using CDeC-Net architecture along with Non-Maximal suppression for table detection. We design a conventional computer vision-based approach for table type classification and cell detection using parameterized kernels based on image size for detecting rows and columns. Finally, we extract the text from the detected cells using pre-existing OCR engine Tessaract. To evaluate our system, we manually annotated a sample of the real-world medical dataset (referred to as Meddata) consisting of wide variations of documents (in terms of appearance) covering different table structures, such as bordered, partially bordered, borderless, or coloured tables. We experimentally show that DEXTER outperforms the commercially available Amazon Textract and Microsoft Azure Form Recognizer systems on the annotated real-world medical dataset
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to comprehend and retrieve information from documents is a challenging research problem. Previous work ignores the visual property of documents and treats them as plain text, resulting in incomplete modality. In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems. LIE contains 62k annotations of three extraction tasks from 4,061 pages in product and official documents, becoming the largest VRD-based information extraction dataset to the best of our knowledge. We also develop benchmark methods that extend the token-based language model to consider layout features like humans. Empirical results show that layout is critical for VRD-based extraction, and system demonstration also verifies that the extracted knowledge can help locate the answers that users care about.