Miracle-dz/UniFAR-FTUs huggingface dataset:
An extended data set of river discharges for validation of general circulation models

Using existing measurements of streamflow data, we present a new data set of river discharges to the ocean, for use in validating climate models. This data set includes annual mean discharges for 981 rivers, representing approximately 824,500 m3/s of global river discharge. This is about 65% of contemporary estimates of continental precipitation minus continental evaporation (P-E). Using our new data set, we present a self-consistent water budget over continents. We find that rivers with annual mean discharges between about 250 m3/s and about 20,000 m3/s closely follow a power law size distribution. We estimate the total flow from rivers smaller than 250 m3/s by assuming they obey the same power law size distribution. Under this assumption, the estimated total flow from these rivers is about 33% of P-E. Including an existing estimate of soil moisture entering the ocean underground, our estimated total flow into the ocean is within about 2% of contemporary estimates of continental P-E. Our data set is available in digital format.
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach

We propose the Kuramoto Graph Neural Network (KuramotoGNN), a novel class of continuous-depth graph neural networks (GNNs) that employs the Kuramoto model to mitigate the over-smoothing phenomenon, in which node features in GNNs become indistinguishable as the number of layers increases. The Kuramoto model captures the synchronization behavior of non-linear coupled oscillators. Under the view of coupled oscillators, we first show the connection between Kuramoto model and basic GNN and then over-smoothing phenomenon in GNNs can be interpreted as phase synchronization in Kuramoto model. The KuramotoGNN replaces this phase synchronization with frequency synchronization to prevent the node features from converging into each other while allowing the system to reach a stable synchronized state. We experimentally verify the advantages of the KuramotoGNN over the baseline GNNs and existing methods in reducing over-smoothing on various graph deep learning benchmark tasks.
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars. Stationary solutions and modeling potential

Slowly pulsating B (SPB) stars display multi-periodic variability in the gravito-inertial mode regime with indications of non-linear resonances between modes. Several have undergone asteroseismic modeling in the past few years to infer their internal properties, but only in a linear setting. These stars rotate fast, so that rotation is typically included in the modeling by means of the traditional approximation of rotation (TAR). We aim to extend the set of tools available for asteroseismology, by describing time-independent (stationary) resonant non-linear coupling among three gravito-inertial modes within the TAR. Such coupling offers the opportunity to use mode amplitude ratios in the asteroseismic modeling process, instead of only relying on frequencies of linear eigenmodes, as has been done so far. Following observational detections, we derive expressions for the resonant stationary non-linear coupling between three gravito-inertial modes in rotating stars. We assess selection rules and stability domains for stationary solutions. We also predict non-linear frequencies and amplitude ratio observables that can be compared with their observed counterparts. The non-linear frequency shifts of stationary couplings are negligible compared to typical frequency errors derived from observations. The theoretically predicted amplitude ratios of combination frequencies match with some of their observational counterparts in the SPB targets. Other, unexplained observed ratios could be linked to other saturation mechanisms, to interactions between different modes, or to different opacity gradients in the driving zone. For the purpose of asteroseismic modeling, our non-linear mode coupling formalism can explain some of the stationary amplitude ratios of observed resonant mode couplings in single SPB stars monitored during 4 years by the Kepler space telescope.
Hypsometric relation in pre-harvest inventory in stands of Acacia mearnsii De Wild.

The purpose of this work was to evaluate mathematical models to describe the hypsometric relation of Acacia mearnsii De Wild (black wattle). The study was conducted in seven year old 9 x14 m sample plots (at the end of rotation) located in the municipalities of Cristal and Piratini, both situated in the region where the species is most widely cultivated in the state of Rio Grande do Sul, Brazil. In each plot, all the individuals had their total height and diameter at breast height measured. Ten mathematical models were tested (of logarithmic and arithmetic nature). The selection of the best model was based on the adjusted determination coefficient (R), standard error of estimate (Sxy%), Schlaegel index (IA), and graphic analysis of the residues. The model which presented the best result was the following : Ln h = 3.1851 – 3.8129 x 1/dbh – 5.3335 x 1/dbh which showed a IA of 0.53 and Syx of 0.98% and can be used to predict the height in pre-harvest inventories. Additional keywords: Acacia mearnsii; heights; modeling.
Lost Your Style? Navigating with Semantic-Level Approach for Text-to-Outfit Retrieval

Fashion stylists have historically bridged the gap between consumers' desires and perfect outfits, which involve intricate combinations of colors, patterns, and materials. Although recent advancements in fashion recommendation systems have made strides in outfit compatibility prediction and complementary item retrieval, these systems rely heavily on pre-selected customer choices. Therefore, we introduce a groundbreaking approach to fashion recommendations: text-to-outfit retrieval task that generates a complete outfit set based solely on textual descriptions given by users. Our model is devised at three semantic levels-item, style, and outfit-where each level progressively aggregates data to form a coherent outfit recommendation based on textual input. Here, we leverage strategies similar to those in the contrastive language-image pretraining model to address the intricate-style matrix within the outfit sets. Using the Maryland Polyvore and Polyvore Outfit datasets, our approach significantly outperformed state-of-the-art models in text-video retrieval tasks, solidifying its effectiveness in the fashion recommendation domain. This research not only pioneers a new facet of fashion recommendation systems, but also introduces a method that captures the essence of individual style preferences through textual descriptions.
Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models

A restaurant dinner may become a memorable experience due to an unexpected aspect enjoyed by the customer, such as an origami-making station in the waiting area. If aspects that are atypical for a restaurant experience were known in advance, they could be leveraged to make recommendations that have the potential to engender serendipitous experiences, further increasing user satisfaction. Although relatively rare, whenever encountered, atypical aspects often end up being mentioned in reviews due to their memorable quality. Correspondingly, in this paper we introduce the task of detecting atypical aspects in customer reviews. To facilitate the development of extraction models, we manually annotate benchmark datasets of reviews in three domains - restaurants, hotels, and hair salons, which we use to evaluate a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5.
The Effect of Bedding Laminations on Crack Propagation in the Marcellus Shale

In this study I conducted deformation experiments on three Marcellus Shale samples, to investigate the effect of laminations on crack propagation. Cylindrical samples were used in the mechanical test. Two of the samples were taken parallel to the bedding planes, and one was taken perpendicular to the bedding planes. Prior to the deformation tests, initial porosity and permeability of the undeformed samples were measured. At the confinements and strain rates used in the study, all three samples failed by brittle fractures. Porosity and permeability measurements were then conducted on the deformed samples. By mechanically fracturing the samples, permeability and crack networks have been enhanced due to the internal features of the shale. I also performed microstructural analysis of undeformed and deformed samples. Comparison of the mechanical data and microstructure of bedding parallel to bedding perpendicular samples supports my hypothesis that the bedding orientation relative to stress orientation affects crack propagation. I also observed that crack growth is not only affected by the thin laminations, but also the strength contrast between silt-sized particles and clay minerals in these Marcellus Shale samples. Experimental Results
Tensor Golub Kahan based on Einstein product

The Singular Value Decomposition (SVD) of matrices is a widely used tool in scientific computing. In many applications of machine learning, data analysis, signal and image processing, the large datasets are structured into tensors, for which generalizations of SVD have already been introduced, for various types of tensor-tensor products. In this article, we present innovative methods for approximating this generalization of SVD to tensors in the framework of the Einstein tensor product. These singular elements are called singular values and singular tensors, respectively. The proposed method uses the tensor Lanczos bidiagonalization applied to the Einstein product. In most applications, as in the matrix case, the extremal singular values are of special interest. To enhance the approximation of the largest or the smallest singular triplets (singular values and left and right singular tensors), a restarted method based on Ritz augmentation is proposed. Numerical results are proposed to illustrate the effectiveness of the presented method. In addition, applications to video compression and facial recognition are presented.
CarbonFish - A Bistable Underactuated Compliant Fish Robot capable of High Frequency Undulation

The Hair Clip Mechanism HCM represents an innovative in plane prestressed bistable mechanism, as delineated in our preceding studies, devised to augment the functional prowess of soft robotics. When juxtaposed with conventional soft and compliant robotic systems, HCMs exhibit pronounced rigidity, augmented mobility, reproducible repeatability, and an effective design and fabrication paradigm. In this research, we investigate the feasibility of utilizing carbon fiber reinforced plastic CFRP as the foundational material for an HCM based fish robot, herein referred to as CarbonFish. Our objective centers on realizing high frequency undulatory motion, thereby laying the groundwork for accelerated aquatic locomotion in subsequent models. We proffer an exhaustive design and fabrication schema underpinned by mathematical principles. Preliminary evaluations of our single actuated CarbonFish have evidenced an undulation frequency approaching 10 Hz, suggesting its potential to outperform other biologically inspired aquatic entities as well as real fish.
ITEm: Unsupervised Image-Text Embedding Learning for eCommerce

Product embedding serves as a cornerstone for a wide range of applications in eCommerce. The product embedding learned from multiple modalities shows significant improvement over that from a single modality, since different modalities provide complementary information. However, some modalities are more informatively dominant than others. How to teach a model to learn embedding from different modalities without neglecting information from the less dominant modality is challenging. We present an image-text embedding model (ITEm), an unsupervised learning method that is designed to better attend to image and text modalities. We extend BERT by (1) learning an embedding from text and image without knowing the regions of interest; (2) training a global representation to predict masked words and to construct masked image patches without their individual representations. We evaluate the pre-trained ITEm on two tasks: the search for extremely similar products and the prediction of product categories, showing substantial gains compared to strong baseline models.
Signal Processing Meets SGD: From Momentum to Filter

In deep learning, stochastic gradient descent (SGD) and its momentum-based variants are widely used in optimization algorithms, they usually face the problem of slow convergence. Meanwhile, existing adaptive learning rate optimizers accelerate convergence but often at the expense of generalization ability. We demonstrate that the adaptive learning rate property impairs generalization. To address this contradiction, we propose a novel optimization method that aims to accelerate the convergence rate of SGD without loss of generalization. This approach is based on the idea of reducing the variance of the historical gradient, enhancing the first-order moment estimation of the SGD by applying Wiener filtering theory, and introducing a time-varying adaptive weight. Experimental results show that SGDF achieves a trade-off between convergence and generalization compared to state-of-the-art optimizers.
LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition

Place recognition is one of the most crucial modules for autonomous vehicles to identify places that were previously visited in GPS-invalid environments. Sensor fusion is considered an effective method to overcome the weaknesses of individual sensors. In recent years, multimodal place recognition fusing information from multiple sensors has gathered increasing attention. However, most existing multimodal place recognition methods only use limited field-of-view camera images, which leads to an imbalance between features from different modalities and limits the effectiveness of sensor fusion. In this letter, we present a novel neural network named LCPR for robust multimodal place recognition, which fuses LiDAR point clouds with multi-view RGB images to generate discriminative and yaw-rotation invariant representations of the environment. A multi-scale attention-based fusion module is proposed to fully exploit the panoramic views from different modalities of the environment and their correlations. We evaluate our method on the nuScenes dataset, and the experimental results show that our method can effectively utilize multi-view camera and LiDAR data to improve the place recognition performance while maintaining strong robustness to viewpoint changes.
Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval

Lexical and semantic matches are commonly used as relevance measurements for information retrieval. Together they estimate the semantic equivalence between the query and the candidates. However, semantic equivalence is not the only relevance signal that needs to be considered when retrieving evidences for multi-hop questions. In this work, we demonstrate that textual entailment relation is another important relevance dimension that should be considered. To retrieve evidences that are either semantically equivalent to or entailed by the question simultaneously, we divide the task of evidence retrieval for multi-hop question answering (QA) into two sub-tasks, i.e., semantic textual similarity and inference similarity retrieval. We propose two ensemble models, EAR and EARnest, which tackle each of the sub-tasks separately and then jointly re-rank sentences with the consideration of the diverse relevance signals. Experimental results on HotpotQA verify that our models not only significantly outperform all the single retrieval models it is based on, but is also more effective than two intuitive ensemble baseline models.
Multiple integral formulas for weighted zeta moments: the case of the sixth moment

We prove exact formulas for weighted $2k$th moments of the Riemann zeta function for all integer $k\geq 1$ in terms of the analytic continuation of an auto-correlation function. This latter enjoys several functional equations. One of them, following from a fundamental lemma of Bettin and Conrey (2013), yields to new formulas for the moments: our second main result is the case $k=3$, but there is no obstruction to obtain higher moments. This generalizes results by Titchmarsh (1928) for $k=1$ and $k=2$. A basic and powerful tool is a special Fourier transform unveiled by Ramanujan (1915). In a nutshell, the new idea is to consider the associated structures to $\Gamma^k\zeta^k$, which enjoy remarkable properties that are not satisfied by the more classically studied structure $\Gamma\zeta^k$.
Structures of Coastal Resilience: Designs for Climate Change

Structures of Coastal Resilience (SCR) is a multi-university collaborative developing adaptive design strategies at four coastal bays along the east coast, complementing the United States Army Corps of Engineers’ North Atlantic Coast Comprehensive Study (NACCS), released in January 2015. The Corps was tasked by Congress in January 2013 to conduct this $19.5 million two year comprehensive study of the areas affected by Hurricane Sandy, evaluating flood risks and identifying areas in need of additional analysis. SCR seeks to inform the work of the Comprehensive Study through a focused look at social, environmental, and infrastructural vulnerabilities at four communities within the USACE study zone.
Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition

One of the major challenges for developing automatic speech recognition (ASR) for low-resource languages is the limited access to labeled data with domain-specific variations. In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. With the proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We then exploited the developed corpus to design a conformer-based ASR system. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. To investigate the efficacy, we designed and developed a human-annotated domain-agnostic test set composed of news, telephony, and conversational data among others. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets. The experimental resources will be publicly available.https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR
Solving High Dimensional Partial Differential Equations Using Tensor Neural Network and A Posteriori Error Estimators

In this paper, we first propose a new type of tensor neural network and the corresponding machine learning method to solve high-dimensional boundary value problems with Dirichlet or Neumann type of boundary conditions and eigenvalue problems of the second order elliptic operator. The most important advantage of the proposed network is that when calculating the loss function, the high dimensional integration can be computed with high accuracy using fixed quadrature points within tolerable computational complexity. Based on the theory of a posteriori error estimation, a machine learning method which use a posteriori error estimator as the loss function is designed to select optimal network parameters adaptively. The theoretical analysis and numerical examples are provided to validate the proposed methods.
Improved Recursive Algorithms for V-BLAST to Save Computations and Memories

—For vertical Bell Laboratories layered space-time architecture (V-BLAST), the original fast recursive algorithm was proposed, and then Improvements I-IV were introduced to further reduce the computational complexity. The existing recursive algorithm with speed advantage and that with memory saving incorporate Improvements I-IV and only Improvements III-IV into the original algorithm, respectively, and as far as we know, they require the least computations and memories, respectively, among the existing recursive algorithms. In this paper, we propose Improvements V and VI to replace Improvements I and II, respectively. Instead of the lemma for inversion of partitioned matrix applied in Improvement I, Improvement V uses another lemma to speed up the matrix inversion step by the factor of 1 . 67 . Then the formulas adopted in our Improvement V are applied to deduce Improvement VI for interference cancellation, which saves memories without sacriﬁcing speed compared to Improvement II. In the existing algorithm with speed advantage, the proposed algorithm I with speed advantage replaces Improvement I with Improvement V, while the proposed algorithm II with both speed advantage and memory saving replaces Improvements I and II with Improvements V and VI, respectively. Both proposed algorithms speed up the existing algorithm with speed advantage by the factor of 1 . 3 , while the proposed algorithm II achieves the speedup of 1 . 86 and saves about half memories, compared to the existing algorithm with memory saving.
Expanding the Conceptual Description of Verbs in WordNet with Semantic and Syntactic Information

This paper describes an ongoing effort towards expanding the semantic and conceptual description of verbs in WordNet by combining information from two other resources, FrameNet and VerbNet, as well as enriching the verbs’ description with syntactic patterns extracted from the three resources. The conceptual description of verb synsets is provided by assigning a FrameNet frame which provides the relevant set of frame elements denoting the predicate’s participants and props. This information is supplemented by assigning a VerbNet class and the set of semantic roles associated with it. The information extracted from FrameNet and VerbNet and assigned to a synset is aligned (semi-automatically with subsequent manual corrections) at the following levels: (i) FrameNet frame: VerbNet class; (ii) FrameNet frame elements: VerbNet semantic roles; (iii) FrameNet semantic types and restrictions: VerbNet selectional restrictions. We then link the syntactic patterns associated with the units in FrameNet, VerbNet and WordNet, by unifying their representation and by matching the corresponding patterns at the level of syntactic groups. The alignment of the semantic components and their syntactic realisations is essential for the better exploitation of the abundance of information across resources, including shedding light on cross-resource similarities, discrepancies and inconsistencies. The syntactic patterns can facilitate the extraction of examples illustrating the use of verb synset literals in corpora and their semantic characterisation through the association of the syntactic groups with the components of semantic description (frame elements or semantic roles) and can be employed in various tasks requiring semantic and syntactic description. The resource is publicly available to the community. The components of the conceptual description are visualised showing the links to the original resources each component is drawn from.