Miracle-dz/UniFAR-FTUs huggingface dataset:
Etude des propriétés thermo-optiques des milieux polyphasiques : application aux jets de propulseurs

Cette etude porte sur la determination des champs de mesures de temperatures, par voie optique passive, dans les jets gazeux contenant des particules. Les methodes optiques ont ete retenues pour ne pas perturber l'ecoulement de la flamme. Des modeles d'emission moleculaire developpes a partir de donnees spectroscopiques, ont permis l'etude systematique du spectre d'emission des flammes dans l'i. R. Ainsi que celui des particules dans l'u. V. Ce modele a permis de caracteriser et de modeliser les effets thermometriques des molecules h#2o et co#2 afin de developper des methodes de mesures originales. Ces methodes ont servi ensuite a l'elaboration de systemes de mesures experimentaux permettant l'analyse des flammes non symetriques. Les methodes de mesures sont conjointement developpees avec les algorithmes d'inversion qui permettent le calcul du champ de temperature et de concentration en gaz ou en particules au sein de la flamme. Les systemes de mesures utilises ont ete developpes pour repondre aux contraintes de la methodologie. Ils acquierent le rayonnement de plusieurs longueurs d'ondes, sur deux axes de visee et balayent le plan de coupe de la flamme. Tous les systemes developpes ont ete testes dans des conditions d'environnement acoustique tres severes. Les resultats experimentaux ont confirme le choix des methodes de mesures, ainsi que celui du modele theorique. La validation a ete effectuee sur un bruleur de petite dimension sur les deux gaz h#2o et co#2 avec deux methodes et deux appareillages differents. Les mesures ont ete ensuite appliquees a des tuyeres asymetriques et de grandes dimensions (1m de diametre, superstatoreacteur et jets de fusees a propergol solide).
Reinforced FDM

The anisotropy of mechanical strength on a 3D printed model can be controlled in a multi-axis 3D printing system as materials can be accumulated along dynamically varied directions. In this paper, we present a new computational framework to generate specially designed layers and toolpaths of multi-axis 3D printing for strengthening a model by aligning filaments along the directions with large stresses. The major challenge comes from how to effectively decompose a solid into a sequence of strength-aware and collision-free working surfaces. We formulate it as a problem to compute an optimized governing field together with a selected orientation of fabrication setup. Iso-surfaces of the governing field are extracted as working surface layers for filament alignment. Supporting structures in curved layers are constructed by extrapolating the governing field to enable the fabrication of overhangs. Compared with planar-layer based Fused Deposition Modeling (FDM) technology, models fabricated by our method can withstand up to 6.35× loads in experimental tests.
Surface-only ferrofluids

We devise a novel surface-only approach for simulating the three dimensional free-surface flow of incompressible, inviscid, and linearly magnetizable ferrofluids. A Lagrangian velocity field is stored on a triangle mesh capturing the fluid's surface. The two key problems associated with the dynamic simulation of the fluid's interesting geometry are the magnetization process transitioning the fluid from a non-magnetic into a magnetic material, and the evaluation of magnetic forces. In this regard, our key observation is that for linearly incompressible ferrofluids, their magnetization and application of magnetic forces only require knowledge about the position of the fluids' boundary. Consequently, our approach employs a boundary element method solving the magnetization problem and evaluating the so-called magnetic pressure required for the force evaluation. The magnetic pressure is added to the Dirichlet boundary condition of a surface-only liquids solver carrying out the dynamical simulation. By only considering the fluid's surface in contrast to its whole volume, we end up with an efficient approach enabling more complex and realistic ferrofluids to be explored in the digital domain without compromising efficiency. Our approach allows for the use of physical parameters leading to accurate simulations as demonstrated in qualitative and quantitative evaluations.
Frequency-domain smoke guiding

We propose a simple and efficient method for guiding an Eulerian smoke simulation to match the behavior of a specified velocity field, such as a low-resolution animation of the same scene, while preserving the rich, turbulent details arising in the simulated fluid. Our method works by simply combining the high-frequency component of the simulated fluid velocity with the low-frequency component of the input guiding field. We show how to eliminate the grid-aligned artifacts that appear in naive guiding approaches, and provide a frequency-domain analysis that motivates the use of ideal low-pass and high-pass filters to prevent artificial dissipation of small-scale details. We demonstrate our method on many scenes including those with static and moving obstacles, and show that it produces high-quality results with very little computational overhead.
RBF liquids

We introduce a novel liquid simulation approach that combines a spatially adaptive pressure projection solver with the Particle-in-Cell (PIC) method. The solver relies on a generalized version of the Finite Difference (FD) method to approximate the pressure field and its gradients in tree-based grid discretizations, possibly non-graded. In our approach, FD stencils are computed by using meshfree interpolations provided by a variant of Radial Basis Function (RBF), known as RBF-Finite-Difference (RBF-FD). This meshfree version of the FD produces differentiation weights on scattered nodes with high-order accuracy. Our method adapts a quadtree/octree dynamically in a narrow-band around the liquid interface, providing an adaptive particle sampling for the PIC advection step. Furthermore, RBF affords an accurate scheme for velocity transfer between the grid and particles, keeping the system's stability and avoiding numerical dissipation. We also present a data structure that connects the spatial subdivision of a quadtree/octree with the topology of its corresponding dual-graph. Our data structure makes the setup of stencils straightforward, allowing its updating without the need to rebuild it from scratch at each time-step. We show the effectiveness and accuracy of our solver by simulating incompressible inviscid fluids and comparing results with regular PIC-based solvers available in the literature.
Scene mover

We propose a novel approach for automatically generating a move plan for scene arrangement.1 Given a scene like an apartment with many furniture objects, to transform its layout into another layout, one would need to determine a collision-free move plan. It could be challenging to design this plan manually because the furniture objects may block the way of each other if not moved properly; and there is a large complex search space of move action sequences that grow exponentially with the number of objects. To tackle this challenge, we propose a learning-based approach to generate a move plan automatically. At the core of our approach is a Monte Carlo tree that encodes possible states of the layout, based on which a search is performed to move a furniture object appropriately in the current layout. We trained a policy neural network embedded with a LSTM module for estimating the best actions to take in the expansion step and simulation step of the Monte Carlo tree search process. Leveraging the power of deep reinforcement learning, the network learned how to make such estimations through millions of trials of moving objects. We demonstrated our approach for moving objects under different scenarios and constraints. We also evaluated our approach on synthetic and real-world layouts, comparing its performance with that of humans and other baseline approaches.
Identification of Clinically and Pathophysiologically Relevant Rheumatoid Factor Epitopes by Engineered IgG Targets

Rheumatoid factors (RFs), which are anti‐IgG autoantibodies strongly associated with rheumatoid arthritis (RA), are also found in other diseases and in healthy individuals. RFs bind to various epitopes in the constant (Fc‐) domain of IgG. Therefore, disease‐specific reactivity patterns may exist. This study was undertaken in order to develop a new approach to dissecting RF epitope binding patterns across different diseases.
A new conceptual framework for enhancing legal information retrieval at the Brazilian Superior Court of Justice

Effective retrieval of jurisprudence (case-law) is imperative to achieve consistency and predictability for any legal system. In this work, we propose and proceed to an empirical evaluation of a framework for jurisprudence retrieval of the Brazilian Superior Court of Justice in order to ease the task of retrieval of other decisions with the same legal opinion. The experimental results shown that our approach based on text similarity performs better than the legacy system of the Court based on Boolean queries. The building of complex Boolean queries is very specialized and we aim to offer a tool able to use free text as queries without any operator. With the legacy system as baseline, we compare the TF-IDF traditional retrieval model, the BM25 probabilistic model and the Word2Vec model. Our results indicate that the Word2Vec Skip-Gram model, trained on a specialized legal corpus and BM25 yield similar performance and surpasses the legacy system. Combining BM25 model with embedding models improved the performance up to 19%.
Effect of Increased Lactate Dehydrogenase A Activity and Aerobic Glycolysis on the Proinflammatory Profile of Autoimmune CD8+ T Cells in Rheumatoid Arthritis

CD8+ T cells contribute to rheumatoid arthritis (RA) by releasing proinflammatory and cytolytic mediators, even in a challenging hypoxic and nutrient‐poor microenvironment such as the synovial membrane. This study was undertaken to explore the mechanisms through which CD8+ T cells meet their metabolic demands in the blood and synovial membrane of patients with RA.
Bidirectional Dependency-Guided Attention for Relation Extraction

The dependency relation between words in the sentence is critical for the relation extraction. Existing methods often utilize the dependencies accompanied with various pruning strategies, thus suﬀer from the loss of detailed semantic information. In order to exploit dependency structure more eﬀectively, we propose a novel bidirectional dependency-guided attention model. The main idea is to use a top-down attention as well as a bottom-up attention to fully capture the dependencies from diﬀerent granularity. Speciﬁcally, the bottom-up attention aims to model the local semantics from the subtree of each node, while the top-down attention is to model the global semantics from the ancestor nodes. Moreover, we employ a label embedding component to attend the contextual features, which are extracted by the dependency-guided attention. Overall, the proposed model is fully attention-based which make it easy for parallel computing. Experiment results on TACRED dataset and SemEval 2010 Task 8 dataset show that our model outperforms existing dependency based models as well as the powerful pretraining model. Moreover, the proposed model achieves the state-of-the-art performance on TACRED dataset.
Learning Code Changes by Exploiting Bidirectional Converting Deviation

Software systems evolve with constant code changes when requirements change or bugs are found. Assessing the quality of code change is a vital part of software development. However, most existing software mining methods inspect software data from a static view and learn global code semantics from a snapshot of code, which cannot capture the semantic information of small changes and are under-representation for rich historical code changes. How to build a model to emphasize the code change remains a great challenge. In this paper, we propose a novel deep neural network called CCL, which models a forward converting process from the code before change to the code after change and a backward converting process inversely, and the change representations of bidirectional converting processes can be learned. By exploiting the deviation of the converting processes, the code change can be evaluated by the network. Experimental results on open source projects indicate that CCL signiﬁcantly outperforms the compared methods in code change learning.
Time-Aware Transformer-based Network for Clinical Notes Series Prediction

A patient’s clinical notes correspond to a sequence of free-form text documents generated by healthcare professionals over time. Rich and unique information in clinical notes is useful for clinical decision making. In this work, we propose a time-aware transformer-based hierarchical architecture, which we call F lexible T ime-aware L STM Trans former ( FTL-Trans ), for classifying a patient’s health state based on her series of clinical notes. FTL-Trans addresses the problem that current transformer-based architectures cannot handle, which is the multi-level structure inherent in clinical note series where a note contains a sequence of chucks and a chuck contains further a sequence of words. At the bottom layer, FTL-Trans encodes equal-length subsequences of a patient’s clinical notes (“chunks”) into content embeddings using a pre-trained ClinicalBERT model. Unlike ClinicalBERT, however, FTL-Trans merges each content embedding and sequential information into a new position-enhanced chunk representation in the second layer by an augmented multi-level position embedding. Next, the time-aware layer tackles the irregularity in the spacing of notes in the note series by learning a ﬂexible time decay function and utilizing the time decay function to incorporate both the position-enhanced chunk embedding and time information into a patient representation. This patient representation is then fed into the top layer for classiﬁcation. Together, this hierarchical design of FTL-Trans successfully captures the multi-level sequential structure of
Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions

We propose a holistic framework based on state-of-the-art methods in Machine Learning and Optimization to prescribe inﬂuenza vaccine composition that are speciﬁc to a region, or a country based on historical data concerning the rates of circulation of predominant viruses. First, we develop a tensor completion formulation to predict rates of circulation of viruses for the next season based on historical data. Then, taking into account the uncertainty in the predicted rates of circulation of predominant viruses, we propose a novel robust prescriptive framework for selecting suitable strains for each subtypes of the ﬂu virus: Inﬂuenza A (H1N1 and H3N2) and B viruses for production. Finally, we train optimal regression trees to predict eﬃcacy of the prescribed vaccine in terms of both morbidity and mortality rates using a set of weighted distances between the vaccine-strain and the actual circulating viruses during a ﬂu season for each subtypes of the ﬂu virus. Through numerical experiments, we show that our proposed vaccine compositions could potentially lower morbidity by 11-14% and mortality by 8-11% over vaccine compositions proposed by World Health Organization (WHO) for Northern hemisphere, and lower morbidity by 8-10% and mortality by 6-9% over vaccine compositions proposed by U.S Food and Drug Administration (FDA) for USA, and ﬁnally, lower morbidity by 10-12% and mortality by 9-11% over vaccine compositions proposed by European Medicines Agency (EMA) for Europe.
Spatial Information Retrieval in Digital Ecosystems: A Comprehensive Survey

Spatial information retrieval is a common task of digital ecosystems due to the popularity of collecting and storing spatial information and phenomena in the world of the Internet of Things (IoT). Spatial relationships play an important role in this context by specifying how two or more spatial objects are related or connected. Examples of spatial relationships include topological relationships (e.g., intersect, overlap, contains), metric relationships (e.g., nearest neighbors), and direction relationships (e.g., cardinal directions like north and south). Many works in the literature have proposed definitions and implementations of spatial queries based on specific types of spatial relationships. Hence, a holistic view of these works is important to understand their applicability and relations. This paper advances in the literature by providing a comprehensive survey of the implementations and types of spatial queries that can be used by digital ecosystems. We present a novel characterization based on spatial relationships to define topological-based, metric-based, and direction-based spatial queries. For each type of spatial query, we present its intuitive and formal definitions together with possible strategies of implementation. Further, we identify hybrid spatial queries as combinations of two or more spatial relationships, and spatial joins as generalization cases. In addition, we present some equivalences between some types of queries. As a result, we point out future research topics in spatial information retrieval.
Learning Dynamic Context Graph Embedding

Graph embeddings represent nodes as low-dimensional vectors to preserve the proximity between nodes and communities of graphs for network analysis. The temporal edges (e.g., relationships, contacts, and emails) in dynamic graphs are important for graph evolution analysis, but few existing methods in graph embeddings can capture the dynamic information from temporal edges. In this study, we propose a dynamic graph embedding method to analyze the evolution patterns of dynamic graphs eﬀectively. Our method uses diﬀuse context sampling to preserve the proximity between nodes, and applies dynamic context graph embeddings to train discrete-time graph embeddings in the same vector space without alignments to preserve the temporal continuity of stable nodes. We compare our method with several state-of-the-art methods for link prediction, and the experiments demonstrate that our method generally performs better at the task. Our method is further veriﬁed using a real-world dynamic graph by visualizing the evolution of its community structure at diﬀerent timesteps.
Preparing a Clinical Support Model for Silent Mode in General Internal Medicine

The general internal medicine (GIM) ward oversees the recovery of ill patients, excluding those who require intensive attention. Clinicians provide full recoveries, or when appropriate, end-of-life care. We hope to eliminate unexpected deaths in the GIM ward, promptly transfer patients who require escalated care to the intensive care unit, and proactively ∗ Equal Contribution
CCA-Flow: Deep Multi-view Subspace Learning with Inverse Autoregressive Flow

Multi-view subspace learning aims to learn a shared representation from multiple sources or views of an entity. The learned representation enables reconstruction of common patterns of multi-view data, which helps dimensional reduction, exploratory data analysis, missing view completion, and various downstream tasks. However, existing methods often use simple structured approximations of the posterior of shared latent variables for the sake of computational eﬃciency. Such oversimpliﬁed models have a huge impact on the inference quality and can hurt the representation power. To this end, we propose a new method for multi-view subspace learning that achieves eﬃcient Bayesian inference with strong representation power. Our method, coined CCA-Flow, bases on variational Canonical Correlation Analysis and models the inference network as an Inverse Autoregressive Flow (IAF). With the ﬂow-based variational inference imposed on the latent variables, the posterior approximations can be arbitrarily complex and ﬂexible, and the model can still be eﬃciently trained with stochastic gradient descent. Experiments on three benchmark multi-view datasets show that our model gives improved representations of shared latent variables and has superior performance against previous works.
Efficient Distributed In-Memory Processing of RDF Datasets

Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. Today, we count more than 10,000 datasets made available online following Semantic Web standards. A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. Thus, we introduce a distributed approach of quality assessment of large RDF datasets. It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. As a result, it has become difficult to efficiently process these large RDF datasets. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. All the proposed approaches during this thesis are integrated into the larger SANSA framework.