Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrophotometry of the infrared emission of Earth-like Planets

Le signal thermique d'une exoplanete tellurique est une cle pour caracteriser les proprietes physiques et chimiques de son atmosphere. La resolution spectrale, et donc la caracterisation spectrale que nous pouvons realiser des planetes extrasolaires, est malheureusement tres limite, en particulier pour les planetes similaires a la Terre. Dans cette these, j'ai etudie la possibilite de caracteriser des exoplanetes telluriques par l'analyse de la variabilite de son emission infrarouge. L'emission thermique apparente est en effet modulee par les saisons, la rotation de la planete, le mouvement et la variabilite des nuages, la phase orbitale, et aussi par la presence des satellites naturels.Premierement, nous avons etudie l' emission thermique de la Terre vue en tant que point-source distante, ainsi que la variabilite et la dependance du signal de la geometrie d'observation. J'ai modelise l'emission de la Terre a l'aide des donnees satellitaires et des donnees produites par des modeles de circulation general (GCMs) du Laboratoire de Meteorologie Dynamique de Paris (LMD), en comparant les deux types de donnees afin de valider les simulations.Pendant la deuxieme partie de mon travail, j'ai utilise des donnes du GCM pour modeliser des planetes telluriques qui different de la Terre par seulement un ou deux parametres tels que: la vitesse de rotation (y compris des planetes synchrones), une surface planetaire entierement recouverte par la glace ou de l'eau, l'obliquite de l'axe de rotation ou l'excentricite de l'orbite. Pour toutes ces planetes virtuelles, j'ai etudie le climat, deduit les proprietes physiques de la planete, et produit et analyse les signaux integres associes a differentes geometries d'observation.La derniere partie de la these est un travail preliminaire qui consiste en ne plus considerer l'emission bolometrique mais le signal de bandes spectrales etroites, grâce a une nouvelle generation de GCM. Parce que chaque bande explore un niveau specifique dans l'atmosphere, l'etude de la variation du spectre en comparant les variabilites photometriques entre les bandes, permet d'etudier la dynamique, la composition, la distribution et l'evolution de l'atmosphere de la planete, ce qui ouvre un champ encore inexplore pour la caracterisation des exoplanetes.
The structural invariants of Goursat distributions

This is the first of a pair of papers devoted to the local invariants of Goursat distributions. The study of these distributions naturally leads to a tower of spaces over an arbitrary surface, called the monster tower, and thence to connections with the topic of singularities of curves on surfaces. Here we study those invariants of Goursat distributions akin to those of curves on surfaces, which we call structural invariants. In the subsequent paper we will relate these structural invariants to the small-growth invariants.
Derived Bockstein regulators and anticyclotomic $p$-adic Birch and Swinnerton-Dyer conjectures

We introduce"derived Bockstein regulators"by using an idea of Nekov\'a\v{r}. We establish a general descent formalism involving derived Bockstein regulators. We give three applications of this formalism. Firstly, we show that a conjecture of Birch and Swinnerton-Dyer type for Heegner points formulated by Bertolini and Darmon in 1996 follows from Perrin-Riou's Heegner point main conjecture up to a $p$-adic unit. Secondly, we show that a $p$-adic Birch and Swinnerton-Dyer conjecture for the Bertolini-Darmon-Prasanna $p$-adic $L$-function recently formulated by Agboola and Castella follows from the Iwasawa-Greenberg main conjecture up to a $p$-adic unit. Finally, we extend conjectures and results on derivatives of Euler systems for a general motive given by Kataoka and the present author into a natural derived setting.
MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimation

Recently there has been a growing interest in category-level object pose and size estimation, and prevailing methods commonly rely on single view RGB-D images. However, one disadvantage of such methods is that they require accurate depth maps which cannot be produced by consumer-grade sensors. Furthermore, many practical real-world situations involve a moving camera that continuously observes its surroundings, and the temporal information of the input video streams is simply overlooked by single-view methods. We propose a novel solution that makes use of RGB video streams. Our framework consists of three modules: a scale-aware monocular dense SLAM solution, a lightweight object pose predictor, and an object-level pose graph optimizer. The SLAM module utilizes a video stream and additional scale-sensitive readings to estimate camera poses and metric depth. The object pose predictor then generates canonical object representations from RGB images. The object pose is estimated through geometric registration of these canonical object representations with estimated object depth points. All per-view estimates finally undergo optimization within a pose graph, culminating in the output of robust and accurate canonical object poses. Our experimental results demonstrate that when utilizing public dataset sequences with high-quality depth information, the proposed method exhibits comparable performance to state-of-the-art RGB-D methods. We also collect and evaluate on new datasets containing depth maps of varying quality to further quantitatively benchmark the proposed method alongside previous RGB-D based methods. We demonstrate a significant advantage in scenarios where depth input is absent or the quality of depth sensing is limited.
Where to Go Now? Finding Alternatives for Declining Packages in the npm Ecosystem

Software ecosystems (e.g., npm, PyPI) are the backbone of modern software developments. Developers add new packages to ecosystems every day to solve new problems or provide alternative solutions, causing obsolete packages to decline in their importance to the community. Packages in decline are reused less over time and may become less frequently maintained. Thus, developers usually migrate their dependencies to better alternatives. Replacing packages in decline with better alternatives requires time and effort by developers to identify packages that need to be replaced, find the alternatives, asset migration benefits, and finally, perform the migration. This paper proposes an approach that automatically identifies packages that need to be replaced and finds their alternatives supported with real-world examples of open source projects performing the suggested migrations. At its core, our approach relies on the dependency migration patterns performed in the ecosystem to suggest migrations to other developers. We evaluated our approach on the npm ecosystem and found that 96% of the suggested alternatives are accurate. Furthermore, by surveying expert JavaScript developers, 67% of them indicate that they will use our suggested alternative packages in their future projects.
Exploring Quantum Cosmology within the Framework of Teleparallel $f(T)$-gravity

We investigate quantum cosmology in teleparallel $f(T)$-gravity. We delve extensively into the minisuperspace description within the context of teleparallelism. The $f(T)$-theory constitutes a second-order theory of gravity, whose cosmological counterpart is delineated by a degenerate point-like Lagrangian. To formulate the Hamiltonian function encompassing all constraints and degrees of freedom inherent to $f(T)$ cosmology, we employ the Dirac-Bergmann algorithm. Subsequently, we determine the wave function of the universe and introduce a ``probabilistic'' interpretation. We perform comparisons to some classical solutions to see to what extent the quantum approach can cure classical singularities.
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding

We introduce EgoSchema, a very long-form video question-answering dataset, and benchmark to evaluate long video understanding capabilities of modern vision and language systems. Derived from Ego4D, EgoSchema consists of over 5000 human curated multiple choice question answer pairs, spanning over 250 hours of real video data, covering a very broad range of natural human activity and behavior. For each question, EgoSchema requires the correct answer to be selected between five given options based on a three-minute-long video clip. While some prior works have proposed video datasets with long clip lengths, we posit that merely the length of the video clip does not truly capture the temporal difficulty of the video task that is being considered. To remedy this, we introduce temporal certificate sets, a general notion for capturing the intrinsic temporal understanding length associated with a broad range of video understanding tasks&datasets. Based on this metric, we find EgoSchema to have intrinsic temporal lengths over 5.7x longer than the second closest dataset and 10x to 100x longer than any other video understanding dataset. Further, our evaluation of several current state-of-the-art video and language models shows them to be severely lacking in long-term video understanding capabilities. Even models with several billions of parameters achieve QA accuracy less than 33% (random is 20%) on the EgoSchema multi-choice question answering task, while humans achieve about 76% accuracy. We posit that \name{}{}, with its long intrinsic temporal structures and diverse complexity, would serve as a valuable evaluation probe for developing effective long-term video understanding systems in the future. Data and Zero-shot model evaluation code are open-sourced for both public and commercial use under the Ego4D license at http://egoschema.github. io
Representation learning by hierarchical ELM auto-encoder with double random hidden layers

: Recent research developments of extreme learning machine (ELM) with multilayer network architecture lead to a promising high performance with extremely fast training speed for representation learning. In this work, the authors are dedicated to develop an efficient and expressive representation learning method with hierarchical ELM, and proposing a novel architectural unit named as double random hidden layers ELM auto-encoder (DELM-AE). The novel DELM-AE consists of one input layer, two random hidden mapping layers for encoding feature, and one output layer for decoding feature. When stacking DELM-AE in the hierarchical structure, they can construct an H-DELM model, where the input of the current AE is the feature representation learned by the previous one, but the output is identical to the original input information and is not the input. Hence, the H-DELM can reproduce the original input data as much as possible to learn more expressive and compact feature. They validate their method on various widely public datasets, and the results demonstrate that H-DELM can bring significant performance improvements in terms of classification accuracy and robustness compared with existing relevant multilayer ELM and other deep learning algorithms at a slight computational cost.
Visualizing the localized electrons of a kagome flat band

Destructive interference between electron wavefunctions on the two-dimensional (2D) kagome lattice induces an electronic flat band, which could host a variety of interesting many-body quantum states. Key to realize these proposals is to demonstrate the real space localization of kagome flat band electrons. In particular, the extent to which the often more complex lattice structure and orbital composition of realistic materials counteract the localizing effect of destructive interference, described by the 2D kagome lattice model, is hitherto unknown. We used scanning tunneling microscopy (STM) to visualize the non-trivial Wannier states of a kagome flat band at the surface of CoSn, a kagome metal. We find that the local density of states associated with the flat bands of CoSn is localized at the center of the kagome lattice, consistent with theoretical expectations for their corresponding Wannier states. Our results show that these states exhibit an extremely small localization length of two to three angstroms concomitant with a strongly renormalized quasiparticle velocity, which is comparable to that of moir\'e superlattices. Hence, interaction effects in the flat bands of CoSn could be much more significant than previously thought. Our findings provide fundamental insight into the electronic properties of kagome metals and are a key step for future research on emergent many-body states in transition metal based kagome materials.
Localizations for quiver Hecke algebras III

Let $R$ be a quiver Hecke algebra, and let $\mathcal{C}_{w,v}$ be the category of finite-dimensional graded $R$-module categorifying a $q$-deformation of the doubly-invariant algebra $^{N'(w)} \mathbb{C}[N] ^{N(v)} $. In this paper, we prove that the localization $\tilde{\mathcal{C}}_{w,v}$ of the category $\mathcal{C}_{w,v}$ can be obtained as the localization by right braiders arising from determinantial modules. As its application, we show several interesting properties of the localized category $\tilde{\mathcal{C}}_{w,v} $ including the right rigidity.
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity

Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusing on node classification and link prediction tasks. However, the existing studies mostly only consider local connectivity while ignoring long-range connectivity and the roles of nodes. In this paper, we propose Unified Graph Transformer Networks (UGT) that effectively integrate local and global structural information into fixed-length vector representations. First, UGT learns local structure by identifying the local substructures and aggregating features of the $k$-hop neighborhoods of each node. Second, we construct virtual edges, bridging distant nodes with structural similarity to capture the long-range dependencies. Third, UGT learns unified representations through self-attention, encoding structural distance and $p$-step transition probability between node pairs. Furthermore, we propose a self-supervised learning task that effectively learns transition probability to fuse local and global structural features, which could then be transferred to other downstream tasks. Experimental results on real-world benchmark datasets over various downstream tasks showed that UGT significantly outperformed baselines that consist of state-of-the-art models. In addition, UGT reaches the expressive power of the third-order Weisfeiler-Lehman isomorphism test (3d-WL) in distinguishing non-isomorphic graph pairs. The source code is available at https://github.com/NSLab-CUK/Unified-Graph-Transformer.
Can mass drug administration of moxidectin accelerate onchocerciasis elimination in Africa?

Epidemiological and modelling studies suggest that elimination of Onchocerca volvulus transmission (EoT) throughout Africa may not be achievable with annual mass drug administration (MDA) of ivermectin alone, particularly in areas of high endemicity and vector density. Single-dose Phase II and III clinical trials demonstrated moxidectin's superiority over ivermectin for prolonged clearance of O. volvulus microfilariae. We used the stochastic, individual-based EPIONCHO-IBM model to compare the probabilities of reaching EoT between ivermectin and moxidectin MDA for a range of endemicity levels (30 to 70% baseline microfilarial prevalence), treatment frequencies (annual and biannual) and therapeutic coverage/adherence values (65 and 80% of total population, with, respectively, 5 and 1% of systematic non-adherence). EPIONCHO-IBM's projections indicate that biannual (six-monthly) moxidectin MDA can reduce by half the number of years necessary to achieve EoT in mesoendemic areas and might be the only strategy that can achieve EoT in hyperendemic areas. Data needed to improve modelling projections include (i) the effect of repeated annual and biannual moxidectin treatment; (ii) inter- and intra-individual variation in response to successive treatments with moxidectin or ivermectin; (iii) the effect of moxidectin and ivermectin treatment on L3 development into adult worms; and (iv) patterns of adherence to moxidectin and ivermectin MDA. This article is part of the theme issue ‘Challenges in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs’.
Multi-Classification Data Stream Algorithm Based on One-Vs-Rest Strategy

Abstract: In the data stream learning scenario, the whole picture of the data can't be observed, and the data may change dynamically, thus increasing the complexity and imbalance of the data. Aiming at the characteristics of data class changes (appearance, disappearance, and reappearance) of multi-class data stream, a Matthews Adaptive XGBoost algorithm based on One-Vs-Rest strategy is proposed. For the three cases of class change, the One-Vs-Rest strategy is used to build the model, and a binary classification model is created for each class. Aiming at the problem that the model gradually forgets the knowledge of the class after the class disappears, a management mechanism based on class frequency is proposed. In view of the problem that the restarted model affects the overall performance of the ensemble after the class is reproduced, the sliding window is used to initialize it. Aiming at the problem of ensemble classifiers, a mechanism to adapt the number of base classifiers is proposed. Experiments on real and synthetic datasets show that the improved algorithm improves Kappa and PMAUC indicators by 1.03% and 0.82%, respectively. CCS CONCEPTS • Computing methodologies • Machine learning • Machine learning algorithms
Unseen Codec Spoof Speech Detection Based on Channel-Robust Feature

For speech anti-spoofing, the ability of countermeasures (CMs) to cope with unseen attacks has been under scrutiny. Since the previous LA attack was mainly for ASV, which required that the spoofed speech be clean enough to be parsed properly by the ASV and that the unseen scenario be limited to the types of synthesis algorithms. With the development of DeepFake, spoofed speech is more often used to spread fake information so that the unseen codecs channel effects needs to be considered. Based on this, we propose a channel-robust spoof detection method based on the wav2vec2.0 and a channel augmentation adversarial (AUG-ADV) strategy. Our method was experimented on the FMFCC-A dataset and achieves the best results with several evaluation metrics.
Entropy-based Time-series Financial Distress Model Based on Attribute Selection and MetaCost Methods for Imbalance Class

Financial distress prediction is an important and challenging issue in the financial field. Now, many methods have been proposed to forecast company bankruptcy and financial crisis, and many studies show that artificial intelligence is better than traditional statistical methods in prediction capacity. To overcome the imbalance class, this study employs the MetaCost algorithm to add cost-sensitive classification in the training of base classifiers, then establishes a financial crisis prediction model. In a time series and non-stationary problems, this study proposes a novel time-series financial distress model based on artificial intelligence (including attribute selection and classifiers) to predict the financial distress of a company. All in all, the proposed model has several advantages: (1) utilize the MetaCost algorithm to handle the imbalance class; (2) the proposed model is a seasonal time-series model; (3) employ attribute selection to find the core attributes and reduce data dimension; (4) the research results can be provided to investors and decision makers as reference. At last, the results show that the proposed method is better than the listed classifiers and the MetaCost algorithm is superior to the general classifier method, and the MetaCost method raises a little sensitivity, it lifts to identify the companies’ financial health when the companies are actually healthy; and type II errors are reduced by 21.6%, it denotes that the proposed method can raise the correct classification of financial distress.
IEEE-754 Precision-p base-β Arithmetic Implemented in Binary

We show how an IEEE-754 conformant precision-p base-β arithmetic can be implemented based on some binary floating-point and/or integer arithmetic. This includes the four basic operations and square root subject to the five IEEE-754 rounding modes, namely the nearest roundings with roundTiesToEven and roundTiesToAway, the directed roundings downwards and upwards, as well as rounding towards zero. Exceptional values like ∞ of NaN are covered according to the IEEE-754 arithmetic standard. The results of the precision-p base-β operations are computed using some underlying precision-q binary arithmetic. We distinguish two cases. When using a precision-q binary integer arithmetic, the base-β precision p is limited for all operations by β2p ≤ 2q, whereas using a precision-q binary floating-point arithmetic imposes stronger limits on the base-β precision, namely β2p ≤ 2q for addition and multiplication, β2p ≤ 2q-1 for division and β2p ≤ 2q-3 for the square root. Those limitations cannot be improved. The algorithms are implemented in a Matlab/Octave flbeta-toolbox with the choice of using uint64 or binary64 as underlying arithmetic. The former allows larger precisions, the latter is advantageous for the square root, whereas computing times are similar. The flbeta-toolbox offers precision-p base-β scalar, vector and matrix operations including sparse matrices as well as corresponding interval operations. The base β can be chosen in the range β ∊ [2,64]. The flbeta-toolbox will be part of Version 13 of INTLAB [18], the Matlab/Octave toolbox for reliable computing.
Using the GP2X framework for center-of-mass energy precision studies at $e^+e^-$ Higgs factories

Two channels for measuring the absolute center-of-mass energy, $\sqrt{s}$, and collision beam energies, $E_-$ and $E_+$, are investigated for future $e^+e^-$ Higgs factories. These two channels of DiMuons and Bhabhas are simulated in event generators and a new fast Monte Carlo, GP2X, has been developed to boost the events into the lab frame, and thus include luminosity spectrum effects from beamstrahlung and beam energy spread. GP2X also includes tracker and ECAL detector resolution effects. The performance of GP2X with WHIZARD, as GP2WHIZ, is found to be statistically consistent with iLCSoft while GP2X with KKMC, as GP2KKMC, is consistent within 3\%. We use the design concept for the ILC at $\sqrt{s}=250$GeV and ILD. Taking advantage of ILD's high-precision tracker we find precision near the 1-10 MeV level for all of $\sqrt{s}$ , $E_{\pm}$ for a 100~$\text{fb}^{-1}$ dataset. This is done using a new six parameter Beta distribution convolved with Gaussian fit. A Fourier transform deconvolution method with Savitzky-Golay filtering is used to improve the fitting of detector level data. Feasibility of these measurements will depend on future studies and detector calibration.