Miracle-dz/UniFAR-FTUs huggingface dataset:
Free surface flow simulation in estuarine and coastal environments : numerical development and application on unstructured meshes

Ces dernieres decennies, une attention particuliere a ete portee sur la modelisation mathematique et la simulation numerique de la propagation de vagues en environnements cotiers. Une description physiquement correcte des phenomenes a grande echelle, qui apparaissent dans les regions d'eau peu profonde, doit prendre en compte de forts effets non-lineaires et dispersifs, ainsi que l'interaction avec des bathymetries complexes. Dans un premier temps, une etude du comportement en regime non lineaire de differents modeles de type Boussinesq est proposee, demontrant l'avantage d'utiliser des modeles fortement non-lineaires par rapport a des modeles faiblement non-lineaires et faiblement dispersifs (couramment utilises). Ensuite, une nouvelle approche flexible pour resoudre les equations fortement non-lineaires et faiblement dispersives de Green-Naghdi est presentee. Cette strategie permet d'ameliorer un code "shallow water" existant par le simple ajout d'un terme algebrique dans l'equation du moment et est particulierement adapte a l'utilisation de techniques hybrides pour le deferlement des vagues. De plus, la premiere discretisation des equations de Green-Naghdi sur maillage non structure est proposee via des schemas hybrides Volume Fini/Element Fini. Finalement, les modeles et methodes developpes dans la these sont appliques a l'etude du probleme physique de la formation du mascaret dans des estuaires convergents et alluviaux. Cela a amene a la premiere caracterisation d'estuaire naturel en terme d'apparition de mascaret.
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets

Large-scale photorealistic datasets of indoor scenes, with ground truth geometry, materials and lighting, are important for deep learning applications in scene reconstruction and augmented reality. The associated shape, material and lighting assets can be scanned or artist-created, both of which are expensive; the resulting data is usually proprietary. We aim to make the dataset creation process for indoor scenes widely accessible, allowing researchers to transform casually acquired scans to large-scale datasets with high-quality ground truth. We achieve this by estimating consistent furniture and scene layout, ascribing high quality materials to all surfaces and rendering images with spatially-varying lighting consisting of area lights and environment maps. We demonstrate an instantiation of our approach on the publicly available ScanNet dataset. Deep networks trained on our proposed dataset achieve competitive performance for shape, material and lighting estimation on real images and can be used for photorealistic augmented reality applications, such as object insertion and material editing. Importantly, the dataset and all the tools to create such datasets from scans will be released, enabling others in the community to easily build large-scale datasets of their own. All code, models, data, dataset creation tool will be publicly released on our project page.
Adding Data-driven Modelling to Causal Inference And Financial Economics

Factor investing involves choosing securities to construct portfolios with particular risk-return profiles. With the proliferation of benchmark-tracking exchange traded funds (ETFs), virtually any risk-return profile can be reconstructed though the challenge is to find the “right” ETFs, as the number of relevant ETFs is very large. This challenge is essentially a dimension reduction problem. The conventional approach used in machine learning, which is to constrain the complexity of the model at the cost of worse statistical loss, is not ideal in this investment context where complexity is the number of investment instruments in the tracking portfolio, and loss is portfolio tracking error. The standard statistical resampling procedure called cross-validation that optimize the complexityloss trade-off is not designed to respect model user’s economic loss or utility. This paper proposes an innovative modification to fill in the gap between statistical loss and economic loss, which is investment cost. Apart from solving a general problem, the new cross validation procedure is also useful for special dynamic problems like portfolio tracking, where portfolio drifts in every period due to changing market condition, and the investment cost associated with re-balancing the portfolio back to its desired state is changing over time. I illustrate this methodology by replicating hedge 1Department of Economics and Department of Statistical and Actuarial Sciences, University of Western Ontario, Canada, lars.stentoft@uwo.ca. 2Department of Economics, University of Western Ontario, Canada, swang684@uwo.ca
Keep Off the Grass : Grassland Scarcity and the Security Implications of Cross-Border Transhumance Between Niger and Nigeria

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Keep Off the Grass : Grassland Scarcity and the Security Implications of Cross-Border Transhumance Between Niger and Nigeria Camille Laville
THE CONDITIONING OF BLOCK KRONECKER l-IFICATIONS OF MATRIX POLYNOMIALS

A strong l-ification of a matrix polynomial P (λ) = ∑ Aiλ i of degree d is a matrix polynomial L(λ) of degree l having the same finite and infinite elementary divisors, and the same number of left and right minimal indices as P (λ). Strong l-ifications can be used to transform the polynomial eigenvalue problem associated with P (λ) into an equivalent polynomial eigenvalue problem associated with a larger matrix polynomial L(λ) of lower degree. Of most interest in applications is l = 1, for which L(λ) receives the name of strong linearization. However, there exist some situations, e.g., the preservation of algebraic structures, in which it is more convenient to replace strong linearizations by other low degree matrix polynomials. In this work, we investigate the eigenvalue conditioning of l-ifications from a family of matrix polynomials recently identified and studied by Dopico, Pérez and Van Dooren, the so-called block Kronecker companion forms. We compare the conditioning of these l-ifications with that of the matrix polynomial P (λ), and show that they are about as well conditioned as the polynomial itself, provided we scale P (λ) so that max{‖Ai‖2} = 1, and the quantity min{‖A0‖2, ‖Ad‖2} is not too small. Moreover, under the scaling assumption max{‖Ai‖2} = 1, we show that any block Kronecker companion form, regardless of its degree or block structure, is about as well-conditioned as the well-known Frobenius companion forms. Our theory is illustrated by numerical examples.
DEPARTMENT OF ECONOMICS DISCUSSION PAPER SERIES ORDERED

This paper demonstrates the incentives for an oligopolist to obfuscate by deliberately increasing the cost with which consumers can locate its product and price. Consumers are allowed to choose the optimal order in which to search (cid:133)rms and (cid:133)rms are able to in(cid:135)uence this order through their choice of search costs and prices. Competition does not ensure market transparency - equilibrium search costs are positive and asymmetric across (cid:133)rms. Intuitively, an obfuscating (cid:133)rm can soften the competition for consumers with low time costs by inducing the remaining consumers to optimally (cid:133)rst search its rival.
When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs’ strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation meth-ods on P OP QA, our new open-domain QA dataset with 14k questions. We ﬁnd that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those ﬁndings, we devise a simple, yet effective, method for powerful and efﬁcient retrieval-augmented LMs, which re-trieves non-parametric memories only when necessary. Experimental results show that this signiﬁcantly improves models’ performance while reducing the inference costs. 1
An Iterative Closest Point Method for Unsupervised Word Translation

Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks of generative adversarial models: sensitivity to hyper-parameters, long training time and lack of interpretability. In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods. We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment. Our simple linear method is able to achieve better or equal performance to recent state-of-the-art deep adversarial approaches and typically does a little better than the supervised baseline. Our method is also efficient, easy to parallelize and interpretable.
The physical conditions within dense cold clouds in cooling flows

Cold condensations are inferred to occur throughout X-ray-emitting cooling flows. This paper investigates the physical conditions within these clouds. Photoionization by the diffuse continuum produced by the surrounding hot gas heats and ionizes the surface of the cloud to intermediate temperatures (typically 6000 K) and low ionization (the main species present are atomic or singly ionized). A thermal front occurs at a depth of roughly 6×10 15 cm (which corresponds to a hydrogen column density of ∼3×10 17 cm −2 ), where the conditions change over to those similar to the cold phase of the interstellar medium. The gas within is predominantly cold (well below 100 K), molecular, and X-ray-heated. Molecular hydrogen forms via H − in the dust-free conditions expected for gas that has rapidly cooled from X-ray-emitting temperatures
Conducting Credit Assignment by Aligning Local Representations

The use of back-propagation and its variants to train deep networks is often problematic for new users, with issues such as exploding gradients, vanishing gradients, and high sensitivity to weight initialization strategies often making networks difficult to train. In this paper, we present Local Representation Alignment (LRA), a training procedure that is much less sensitive to bad initializations, does not require modifications to the network architecture, and can be adapted to networks with highly nonlinear and discrete-valued activation functions. Furthermore, we show that one variation of LRA can start with a null initialization of network weights and still successfully train networks with a wide variety of nonlinearities, including tanh, ReLU-6, softplus, signum and others that are more biologically plausible. Experiments on MNIST and Fashion MNIST validate the performance of the algorithm and show that LRA can train networks robustly and effectively, succeeding even when back-propagation fails and outperforming other alternative learning algorithms, such as target propagation and feedback alignment.
Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings

Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. Here we focus on organizing biomolecular graphs in ways that expose meaningful relations and variations between them. We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings. Our embedding network first extracts rich graph features using the recently proposed geometric scattering transform. Then, it leverages a semi-supervised variational autoencoder to extract a low-dimensional embedding that retains the information in these features that enable prediction of molecular properties as well as characterize graphs. We show that GSAE organizes RNA graphs both by structure and energy, accurately reflecting b istable R NA s tructures. A lso, the model is generative and can sample new folding trajectories.
Detecting cognitive impairments by agreeing on interpretations of linguistic features Anonymous

Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or linguistic features have been two applicable approaches. However, acquiring additional clinical data could be expensive, and hand-carving features are hard. In this paper, we take a third approach, putting forward the scheme “diagnosis after reaching consensus”, where non-overlapping subsets (modalities) of linguistic features are compressed into low-dimension interpretation vectors by neural networks (“ePhysicians”). By encouraging interpretation vectors from multiple modalities to be indistinguishable, the “ePhysicians” extract important information for classification. We show that with the same subsets of features, our models outperform baseline neural network classifiers on clinical data. Using all of the 413 linguistic features, our best models have accuracies in detecting cognitive impairments comparable to the state-of-the-art models on several balanced datasets (.82 on DementiaBank in detecting Alzheimer’s Disease (AD) and . 66 in detecting Mild Cognitive Impairment (MCI)).
Spectral analysis of forced turbulence in a non-neutral plasma

The paper investigates the dynamics of magnetized non-neutral (electron) plasmas subjected to external electric field perturbations. A two-dimensional (2-D) particle-in-cell code is effectively exploited to model this system with a special attention to the role that non-axisymmetric, multipolar radio frequency (RF) drives applied to the cylindrical (circular) boundary play on the insurgence of azimuthal instabilities and the subsequent formation of coherent structures preventing the relaxation to a fully developed turbulent state, when the RF fields are chosen in the frequency range of the low-order fluid modes themselves. The isomorphism of such system with a 2-D inviscid incompressible fluid offers an insight into the details of forced 2-D fluid turbulence. The choice of different initial density (i.e. fluid vorticity) distributions allows for a selection of conditions where different levels of turbulence and intermittency are expected and a range of final states is achieved. Integral and spectral quantities of interest are computed along the flow using a multiresolution analysis based on a wavelet decomposition of both enstrophy and energy 2-D maps. The analysis of a variety of cases shows that the qualitative features of turbulent relaxation are similar in conditions of both free and forced evolution; at the same time, fine details of the flow beyond the self-similarity turbulence properties are highlighted in particular in the formation of structures and their timing, where the influence of the initial conditions and the effect of the external forcing can be distinguished.
Human bocavirus in Jordan: prevalence and clinical symptoms in hospitalised paediatric patients and molecular virus characterisation.

INTRODUCTION This study was aimed at investigating the prevalence of human bocavirus (HBoV) among Jordanian children hospitalised with lower respiratory tract infection (LRTI) as well as the clinical feature associated with HBoV infection, the seasonal distribution of HBoV and the DNA sequencing of HBoV positive samples. METHODS A total of 220 nasopharyngeal aspirates were collected from children below 13 years of age who were hospitalised with LRTI in order to detect the presence of HBoV using real-time polymerase chain reaction assay and direct HBoV sequencing. RESULTS HBoV was detected in 20 (9.1 percent) patients, whose median age was four (range 0.8-12) months. Children under the age of 12 months were more susceptible to HBoV infection (p-value is 0.016). The main clinical diagnoses of patients infected with HBoV were bronchopneumonia (35 percent) and bronchiolitis (30 percent). Coughing (100 percent), wheezing (82.7 percent) and fever (68.2 percent) were the most prominent symptoms in infected patients. HBoV infections were seasonal; increasing in cooler months, diminishing in the summer and peaking in March (45 percent). Direct DNA sequencing revealed that three out of 20 (15 percent) specimens were identical to Stockholm 1 and 2 isolates, and single base pair substitution (A to T) at codon 92 was found in 17 out of the 20 (85 percent) specimens that were positive for HBoV, resulting in a threonine-to-serine substitution. CONCLUSION More attention should be given to diagnosing HBoV in patients with LRTI using molecular techniques.
Dynamical systems on hypergraphs

We present a general framework that enables one to model high-order interaction among entangled dynamical systems, via hypergraphs. Several relevant processes can be ideally traced back to the proposed scheme. We shall here solely elaborate on the conditions that seed the spontaneous emergence of patterns, spatially heterogeneous solutions resulting from the many-body interaction between fundamental units. In particular we will focus, on two relevant settings. First, we will assume long-ranged mean field interactions between populations, and then turn to considering diffusive-like couplings. Two applications are presented, respectively to a generalised Volterra system and the Brusselator model.
Regularization of inverse problems in image processing

Inverse problems are to recover the data that has been processed or corrupted. Since they are ill-posed they require a regularization. In image processing, the total variation as a regularization tool has the advantage of preserving the discontinuities while creating smooth regions. These results are established in this thesis in a continuous setting for general energies. In addition, we propose and examine a variant of the total variation. We establish a dual formulation that allows us to prove that this variant coincides with the total variation for sets of finite perimeter. Nowadays, non-local methods exploiting the self-similarities of images is particularly successful. We adapt this approach to the problem of spectrum completion, which has applications for general inverse problems. The final part is devoted to the algorithmic aspects inherent to the optimization of the convex energies we considered. We study the convergence and the complexity of the recently developed Primal-Dual algorithms.
ANNALES DE L’INSTITUT FOURIER

— We prove that coronizations with respect to arbitrary d -regular sets (not necessarily graphs) imply big pieces squared of these (approximating) sets. This is known (and due to David and Semmes in the case of suﬃciently large co-dimension, and to Azzam and Schul in general) in the (classical) setting of Euclidean spaces with Hausdorﬀ measure of integer dimension, where the approxi- mating sets are Lipschitz graphs. Our result is a far reaching generalization of these results and we prove that coronizations imply big pieces squared is a generic prop- erty. In particular, our result applies, when suitably interpreted, in metric spaces having a ﬁxed positive (perhaps non-integer) dimension, equipped with a Borel regular measure and with arbitrary approximating sets. As a novel application we highlight how to utilize this general setting in the context of parabolic uniform rectiﬁability. positive ﬁxée (éventuellement non entière), munisavec une mesure de Borel régulière et avec des espaces approximants arbitraires. Comme application nouvelle, nous illus-trons comment on peut utiliser ces outils généraux dans le cadre de la rectiﬁabilité parabolique uniforme.
The diffusion phenomenon for damped wave equations with space-time dependent coefficients

We introduce a method to study the long-time behavior of solutions to damped wave equations, where the coefficients of the equations are space-time dependent. We show that solutions exhibit the diffusion phenomenon, connecting their asymptotic behaviors with the asymptotic behaviors of solutions to corresponding parabolic equations. Sharp decay estimates for solutions to damped wave equations are given, and decay estimates for derivatives of solutions are also discussed.
Phytochemical Analysis and Evaluation of Antidiabetic Effects in Alloxan-Induced Diabetic Rats Treated with Aqueous Leaf Extract of Acanthospermum hispidum

The current study is aimed at investigating the antidiabetic activity of the leaves of Acanthospermum hispidum, a medicinal plant traditionally used to treat diabetes mellitus in NorthWestern Nigeria. Material from the plant was extracted using water (guided by the traditional mode of extraction) and the phytochemicals contained in the extract were analyzed qualitatively and quantitatively. Acute toxicity study of the extract was conducted, and diabetic rats induced using alloxan (80 mg/kg administered intraperitoneally once daily for 3 days) were treated with the extract for 28 days at 3 incremental doses (70, 210 and 700 mg/kg). At the end of the experiment, fasting blood glucose and glycogen content of the liver and skeletal muscle were determined. The study findings indicate that the extract was relatively safe with LD50 above 5000 mg/kg. Treatment with Acanthospermum hispidum extract resulted in a significant (P<0.05) reduction in FBG as well as a significant improvement in oral glucose tolerance at all doses. A significant elevation in the hepatic content of glycogen was also observed at the highest dose (700 mg/kg). Results from this study have demonstrated the potent antidiabetic activity of the aqueous extract of the leaves of Acanthospermum hispidum, thus justifying the traditional claim.
Spectra and flow of light nuclei in relativistic heavy ion collisions at energies available at the BNL Relativistic Heavy Ion Collider and at the CERN Large Hadron Collider

Within the framework of the coalescence model based on the phase-space distributions of protons and neutrons generated from the {{\tt iEBE-VISHNU}} hybrid model with {{\tt AMPT}} initial conditions, we study the spectra and elliptic flow of deuterons and helium-3 in relativistic heavy ion collisions at the Relativistic Heavy Ion Collider (RHIC) and the Larger Hadron Collider (LHC). Results from our model calculations for Au + Au collisions at $\sqrt{s_{NN}}=200$ GeV at RHIC and Pb+Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV at the LHC are compared with available experimental data. Good agreements are generally seen between theoretical results and experimental data, except that the calculated yield of helium-3 in Pb + Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV underestimates the data by about a factor of two. Possible reasons for these discrepancies are discussed. We also make predictions on the spectra and elliptic flow of deuterons and helium-3 in Pb + Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV that are being studied at LHC.