Miracle-dz/UniFAR-FTUs huggingface dataset:
SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition

Multilingual automatic speech recognition (ASR) systems have garnered attention for their potential to extend language coverage globally. While self-supervised learning (SSL) has demonstrated its effectiveness in multilingual ASR, it is worth noting that the various layers' representations of SSL potentially contain distinct information that has not been fully leveraged. In this study, we propose a novel method that leverages self-supervised hierarchical representations (SSHR) to fine-tune multilingual ASR. We first analyze the different layers of the SSL model for language-related and content-related information, uncovering layers that show a stronger correlation. Then, we extract a language-related frame from correlated middle layers and guide specific content extraction through self-attention mechanisms. Additionally, we steer the model toward acquiring more content-related information in the final layers using our proposed Cross-CTC. We evaluate SSHR on two multilingual datasets, Common Voice and ML-SUPERB, and the experimental results demonstrate that our method achieves state-of-the-art performance to the best of our knowledge.
Parallel Computation of Multi-Slice Clustering of Third-Order Tensors

Machine Learning approaches like clustering methods deal with massive datasets that present an increasing challenge. We devise parallel algorithms to compute the Multi-Slice Clustering (MSC) for 3rd-order tensors. The MSC method is based on spectral analysis of the tensor slices and works independently on each tensor mode. Such features fit well in the parallel paradigm via a distributed memory system. We show that our parallel scheme outperforms sequential computing and allows for the scalability of the MSC method.
Module-wise Training of Neural Networks via the Minimizing Movement Scheme

Greedy layer-wise or module-wise training of neural networks is compelling in constrained and on-device settings where memory is limited, as it circumvents a number of problems of end-to-end back-propagation. However, it suffers from a stagnation problem, whereby early layers overfit and deeper layers stop increasing the test accuracy after a certain depth. We propose to solve this issue by introducing a module-wise regularization inspired by the minimizing movement scheme for gradient flows in distribution space. We call the method TRGL for Transport Regularized Greedy Learning and study it theoretically, proving that it leads to greedy modules that are regular and that progressively solve the task. Experimentally, we show improved accuracy of module-wise training of various architectures such as ResNets, Transformers and VGG, when our regularization is added, superior to that of other module-wise training methods and often to end-to-end training, with as much as 60% less memory usage.
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval

Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts. In this paper, we propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity. Concretely, we first construct a set of various learnable prototypes for each modality to represent the entire semantics subspace. Then Dempster-Shafer Theory and Subjective Logic Theory are utilized to build an evidential theoretical framework by associating evidence with Dirichlet Distribution parameters. The PAU model induces accurate uncertainty and reliable predictions for cross-modal retrieval. Extensive experiments are performed on four major benchmark datasets of MSR-VTT, MSVD, DiDeMo, and MS-COCO, demonstrating the effectiveness of our method. The code is accessible at https://github.com/leolee99/PAU.
Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation

Renal biopsies are the gold standard for diagnosis of kidney diseases. Lesion scores made by renal pathologists are semi-quantitative and exhibit high inter-observer variability. Automating lesion classification within segmented anatomical structures can provide decision support in quantification analysis and reduce the inter-observer variability. Nevertheless, classifying lesions in regions-of-interest (ROIs) is clinically challenging due to (a) a large amount of densely packed anatomical objects (up to 1000), (b) class imbalance across different compartments (at least 3), (c) significant variation in object scales (i.e. sizes and shapes), and (d) the presence of multi-label lesions per anatomical structure. Existing models lack the capacity to address these complexities efficiently and generically. This paper presents \textbf{a generalized technical solution} for large-scale, multi-source datasets with diverse lesions. Our approach utilizes two sub-networks: dense instance segmentation and lesion classification. We introduce \textbf{DiffRegFormer}, an end-to-end dense instance segmentation model designed for multi-class, multi-scale objects within ROIs. Combining diffusion models, transformers, and RCNNs, DiffRegFormer efficiently recognizes over 500 objects across three anatomical classes (glomeruli, tubuli, arteries) within ROIs on a single NVIDIA GeForce RTX 3090 GPU. On a dataset of 303 ROIs (from 148 Jones' silver-stained renal WSIs), it outperforms state of art models, achieving AP of 52.1\% (detection) and 46.8\% (segmentation). Our lesion classification sub-network achieves 89.2\% precision and 64.6\% recall on 21889 object patches (from the 303 ROIs). Importantly, the model demonstrates direct domain transfer to PAS-stained WSIs without fine-tuning.
Randomly sparsified Richardson iteration is really fast

Recently, a class of algorithms combining classical fixed point iterations with repeated random sparsification of approximate solution vectors has been successfully applied to eigenproblems with matrices as large as $10^{108} \times 10^{108}$. So far, a complete mathematical explanation for their success has proven elusive. Additionally, the methods have not been extended to linear system solves. In this paper we propose a new scheme based on repeated random sparsification that is capable of solving linear systems in extremely high dimensions. We provide a complete mathematical analysis of this new algorithm. Our analysis establishes a faster-than-Monte Carlo convergence rate and justifies use of the scheme even when the solution vector itself is too large to store.
Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints

The inherently diverse and uncertain nature of trajectories presents a formidable challenge in accurately modeling them. Motion prediction systems must effectively learn spatial and temporal information from the past to forecast the future trajectories of the agent. Many existing methods learn temporal motion via separate components within stacked models to capture temporal features. Furthermore, prediction methods often operate under the assumption that observed trajectory waypoint sequences are complete, disregarding scenarios where missing values may occur, which can influence their performance. Moreover, these models may be biased toward particular waypoint sequences when making predictions. We propose a novel approach called Temporal Waypoint Dropping (TWD) that explicitly incorporates temporal dependencies during the training of a trajectory prediction model. By stochastically dropping waypoints from past observed trajectories, the model is forced to learn the underlying temporal representation from the remaining waypoints, resulting in an improved model. Incorporating stochastic temporal waypoint dropping into the model learning process significantly enhances its performance in scenarios with missing values. Experimental results demonstrate our approach's substantial improvement in trajectory prediction capabilities. Our approach can complement existing trajectory prediction methods to improve their prediction accuracy. We evaluate our proposed approach on three datasets: NBA Sports VU, ETH-UCY, and TrajNet++.
CM-values of $p$-adic $\Theta$-functions

We prove a $p$-adic version of the work by Gross and Zagier on the differences between singular moduli by proving a set of conjectures by Giampietro and Darmon, who investigated the factorisation of a rational invariant associated to a pair of CM-points on a genus zero Shimura curve, obtained as the ratio of the CM-values of $p$-adic $\Theta$-functions. As did Gross and Zagier, we give two proofs; an algebraic proof using CM-theory, and more interestingly, also an analytic proof using $p$-adic infinitesimal deformations of Hilbert Eisenstein series. Since there are no explicit formulae for its cuspidal $p$-adic deformations, we instead compute the Frobenius traces of the appropriate Galois deformation, and show their modularity via an $R = T$ theorem. This approach aims to bridge the gap between classical CM-theory and the more recent $p$-adic advances in the theory of real multiplication.
On the Influence of Bottom Topography on the Agulhas Eddy

Abstract A series of numerical experiments with a two-layer primitive equation model is presented to study the dynamics of Agulhas eddies. The main goal of the paper is to examine the influence of an underwater meridional ridge (modeled after the Walvis Ridge) on an Agulhas eddy hitting it. First, the propagation of an eddy of the specified vertical structure over a flat bottom is considered, varying the initial eddy horizontal scale from 40 to 120 km. Unlike small nonlinear eddies, large nonlinear eddies (on the scale of Agulhas eddies) do not rapidly evolve into a compensated state (no motion in the lower layer). Second, the influence of a ridge on eddies of differing vertical structures having a specified intensity in the upper layer and a prescribed horizontal scale is analyzed. Significantly baroclinic eddies can cross the Walvis Ridge, but barotropic or near-barotropic ones cannot. The evolution of eddies crossing the ridge is compared with that of initially identical eddies moving over a flat botto.
SWAHILI MODERNITIES : Culture, Politics, and Identity on the East Coast of Africa

This book considers the themes of modernity, identity and politics on the East African coast and islands. This area is undergoing rapid change as globalisation makes its impact. Tourism, increased monetisation, emigration and immigration and various multinational agencies are all significant factors. The volume also focuses on how the Swahili language, literature and culture have been affected by modernity and the way in which Swahili women continue to live under the strong social constraints that this community place them under.
Crustal structure of Iceland and Greenland from receiver function studies

[1] We used data from both permanent and temporary seismic networks on Iceland and Greenland to investigate the crustal thickness by partly reinterpreting earlier data (P receiver functions) and adding S receiver functions to better constrain the results. We obtained good conversions from the Moho and also crustal multiples in both Iceland and Greenland. The central ice covered part of Greenland has an average crustal thickness of 40 km, typical for a craton. At the edges of Greenland the crustal thickness decreases to 30–40 km. On the east coast of Greenland, where the track of the Iceland plume is thought to have affected the lithosphere, the crustal thickness is only 24–26 km. In contrast to previous studies, we find that the crustal thickness in the east and the northwest coastal regions of Iceland is more than 40 km, similar to beneath the active volcanic region. In the southwest region of Iceland and along the mid-ocean ridge, the crustal thickness is only 25 km or less. Also in contrast to earlier receiver function interpretations, which deduced a broad crust-mantle transition zone for Iceland, we find indications for a normal, sharper Moho beneath a number of sites.
Continual Action Assessment via Task-Consistent Score-Discriminative Feature Distribution Modeling

Action Quality Assessment (AQA) is a task that tries to answer how well an action is carried out. While remarkable progress has been achieved, existing works on AQA assume that all the training data are visible for training in one time, but do not enable continual learning on assessing new technical actions. In this work, we address such a Continual Learning problem in AQA (Continual-AQA), which urges a unified model to learn AQA tasks sequentially without forgetting. Our idea for modeling Continual-AQA is to sequentially learn a task-consistent score-discriminative feature distribution, in which the latent features express a strong correlation with the score labels regardless of the task or action types. From this perspective, we aim to mitigate the forgetting in Continual-AQA from two aspects. Firstly, to fuse the features of new and previous data into a score-discriminative distribution, a novel Feature-Score Correlation-Aware Rehearsal is proposed to store and reuse data from previous tasks with limited memory size. Secondly, an Action General-Specific Graph is developed to learn and decouple the action-general and action-specific knowledge so that the task-consistent score-discriminative features can be better extracted across various tasks. Extensive experiments are conducted to evaluate the contributions of proposed components. The comparisons with the existing continual learning methods additionally verify the effectiveness and versatility of our approach.
Intriguing properties of generative classifiers

What is the best paradigm to recognize objects -- discriminative inference (fast but potentially prone to shortcut learning) or using a generative model (slow but potentially more robust)? We build on recent advances in generative modeling that turn text-to-image models into classifiers. This allows us to study their behavior and to compare them against discriminative models and human psychophysical data. We report four intriguing emergent properties of generative classifiers: they show a record-breaking human-like shape bias (99% for Imagen), near human-level out-of-distribution accuracy, state-of-the-art alignment with human classification errors, and they understand certain perceptual illusions. Our results indicate that while the current dominant paradigm for modeling human object recognition is discriminative inference, zero-shot generative models approximate human object recognition data surprisingly well.
Stratospheric warming effects on thermospheric O(1S) dayglow dynamics

[1] This study examines the effect of a sudden stratospheric warming (SSW) on the thermosphere, and in particular the response of thermospheric O(1S) dayglow as a proxy for SSW-induced variations in the atomic oxygen volume mixing ratio. Thermospheric O(1S) volume emission rates and temperatures observed by the WIND Imaging Interferometer on UARS in February 1993 at latitudes from 50°N to 70°N and from 90 km to 280 km height have shown a depletion above 140 km in the daytime O(1S) volume emission rates, which commenced around the onset of the SSW and lasted over a period of 3–4 days before returning to and exceeding the pre-SSW values during the SSW recovery phase. Below 140 km height the effect was manifested by a fourfold enhancement in the O(1S) volume emission rate at ∼100 km, which correlated with the cold temperature anomaly of the SSW at and below the stratopause.
Demographic Parity: Mitigating Biases in Real-World Data

Computer-based decision systems are widely used to automate decisions in many aspects of everyday life, which include sensitive areas like hiring, loaning and even criminal sentencing. A decision pipeline heavily relies on large volumes of historical real-world data for training its models. However, historical training data often contains gender, racial or other biases which are propagated to the trained models influencing computer-based decisions. In this work, we propose a robust methodology that guarantees the removal of unwanted biases while maximally preserving classification utility. Our approach can always achieve this in a model-independent way by deriving from real-world data the asymptotic dataset that uniquely encodes demographic parity and realism. As a proof-of-principle, we deduce from public census records such an asymptotic dataset from which synthetic samples can be generated to train well-established classifiers. Benchmarking the generalization capability of these classifiers trained on our synthetic data, we confirm the absence of any explicit or implicit bias in the computer-aided decision.
Probabilistic Characterization of Spatially Correlated Response Spectra for Earthquakes in Japan

Seismic hazard and risk assessments of spatially distributed infrastructural systems require seismic demand models that capture random but correlated simultaneous seismic effects at multiple sites. This study characterizes spatially correlated ground-motion parameters probabilistically using comprehensive databases of the K-NET and KiK-net strong-motion networks in Japan by developing a ground-motion prediction equation and then investigating the correlation structure of regression residuals from the prediction equation. Analysis results indicate that (1) interevent residuals of ground-motion parameters at different vibration periods are more strongly correlated than intraevent and total residuals with zero separation distance; and (2) intraevent spatial correlation coefficients can be described as a simple exponential decay function that is independent of the way the event-based intraevent standard deviation is calculated, of the earthquake type, and of the vibration period. The developed overall correlation model of spatially correlated ground-motion parameters may be used for seismic hazard and risk assessments in a subduction environment.
Unveiling Document Structures with YOLOv5 Layout Detection

The current digital environment is characterized by the widespread presence of data, particularly unstructured data, which poses many issues in sectors including finance, healthcare, and education. Conventional techniques for data extraction encounter difficulties in dealing with the inherent variety and complexity of unstructured data, hence requiring the adoption of more efficient methodologies. This research investigates the utilization of YOLOv5, a cutting-edge computer vision model, for the purpose of rapidly identifying document layouts and extracting unstructured data. The present study establishes a conceptual framework for delineating the notion of"objects"as they pertain to documents, incorporating various elements such as paragraphs, tables, photos, and other constituent parts. The main objective is to create an autonomous system that can effectively recognize document layouts and extract unstructured data, hence improving the effectiveness of data extraction. In the conducted examination, the YOLOv5 model exhibits notable effectiveness in the task of document layout identification, attaining a high accuracy rate along with a precision value of 0.91, a recall value of 0.971, an F1-score of 0.939, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.975. The remarkable performance of this system optimizes the process of extracting textual and tabular data from document images. Its prospective applications are not limited to document analysis but can encompass unstructured data from diverse sources, such as audio data. This study lays the foundation for future investigations into the wider applicability of YOLOv5 in managing various types of unstructured data, offering potential for novel applications across multiple domains.
Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN

Recently, there has been a high demand for accelerating and improving the detection of automatic cadastral mapping. As this problem is in its starting point, there are many methods of computer vision and deep learning that have not been considered yet. In this paper, we focus on deep learning and provide three geometric post-processing methods that improve the quality of the work. Our framework includes two parts, each of which consists of a few phases. Our solution to this problem uses instance segmentation. In the first part, we use Mask R-CNN with the backbone of pre-trained ResNet-50 on the ImageNet dataset. In the second phase, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call it pocket-based simplification algorithm. For evaluating the quality of our solution, we use popular formulas in this field which are recall, precision and F-score. The highest recall we gain is 95 percent which also maintains high Precision of 72 percent. This resulted in an F-score of 82 percent. Implementing instance segmentation using Mask R-CNN with some geometric post-processes to its output gives us promising results for this field. Also, results show that pocket-based simplification algorithms work better for simplifying lines than Douglas-Puecker algorithm.
Non-convex functionals penalizing simultaneous oscillations along two independent directions: structure of the defect measure

We continue the analysis of a family of energies penalizing oscillations in oblique directions: they apply to functions $u(x_1,x_2)$ with $x_l\in\mathbb{R}^{n_l}$ and vanish when $u(x)$ is of the form $u_1(x_1)$ or $u_2(x_2)$. We mainly study the rectifiability properties of the defect measure $\nabla_1\nabla_2u$ of functions with finite energy. The energies depend on a parameter $\theta\in(0,1]$ and the set of functions with finite energy grows with $\theta$. For $\theta<1$ we prove that the defect measure is $(n_1-1,n_2-1)$-tensor rectifiable in $\Omega_1\times\Omega_2$. We first get the result for $n_1=n_2=1$ and deduce the general case through slicing using White's rectifiability criterion. When $\theta=1$ the situation is less clear as measures of arbitrary dimensions from zero to $n_1+n_2-1$ are possible. We show however, in the case $n_1=n_2=1$ and for Lipschitz continuous functions, that the defect measures are $1\,$-rectifiable. This case bears strong analogies with the study of entropic solutions of the eikonal equation.
A Survey on Deep Learning Techniques for Action Anticipation

The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numerous methods have been introduced for action anticipation in recent years, with deep learning-based approaches being particularly popular. In this work, we review the recent advances of action anticipation algorithms with a particular focus on daily-living scenarios. Additionally, we classify these methods according to their primary contributions and summarize them in tabular form, allowing readers to grasp the details at a glance. Furthermore, we delve into the common evaluation metrics and datasets used for action anticipation and provide future directions with systematical discussions.
The Prediction of Permafrost Thicknesses

WITH A KNOWLEDGE OF THE MEAN ANNUAL GROUND TEMPERATURES, THE GENERAL LITHOLOGY AT A GIVEN LOCATION AND A REGIONAL DISTRIBUTION OF THE GEOTHERMAL FLUX FROM THE EARTH'S INFERIOR, IT IS POSSIBLE TO MAKE REASONABLE ESTIMATES OF THE PRESENCE OR ABSENCE OF PERMAFROST AND ITS THICKNESS. THE PREDICTION IS COMPLICATED BY CHANGES OF THE LOCAL SURFACE TEMPERATURE IN THE PAST DUE TO A VARIETY OF FACTORS RANGING FROM CHANGES OF CLIMATE TO FOREST FIRES.