Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential and correlational tractography as tract‐based biomarkers in mild traumatic brain injury: A longitudinal MRI study

We evaluated the fiber bundles in mild traumatic brain injury (mTBI) patients using differential and correlational tractography in a longitudinal analysis. Diffusion MRI data were acquired in 34 mTBI patients at 7 days (acute stage) and 3 months or longer (chronic stage) after mTBI. Trail Making Test A (TMT‐A) and Digital Symbol Substitution Test changes were used to evaluate the cognitive performance. Longitudinal correlational tractography showed decreased anisotropy in the corpus callosum during the chronic mTBI stage. The changes in anisotropy in the corpus callosum were significantly correlated with the changes in TMT‐A (false discovery rate [FDR] = 0.000094). Individual longitudinal differential tractography found that anisotropy decreased in the corpus callosum in 30 mTBI patients. Group cross‐sectional differential tractography found that anisotropy increased (FDR = 0.02) in white matter in the acute mTBI patients, while no changes occurred in the chronic mTBI patients. Our study confirms the feasibility of using correlational and differential tractography as tract‐based monitoring biomarkers to evaluate the disease progress of mTBI, and indicates that normalized quantitative anisotropy could be used as a biomarker to monitor the injury and/or repairs of white matter in individual mTBI patients.
Static and Dynamic Segregation Resistance of Self-Consolidating Rubberized Concrete

An experimental investigation was conducted to evaluate the effects of partial replacement of fine aggregate with waste crumb rubber (CR) on the fresh properties and stability of self-consolidating concrete. In total, eight mixtures with a water-to-binder ratio (0.4), a binder content 500 kg/m 3 , and varied percentages of CR (0-40% replacements by fine aggregate volume) were investigated. Slump flow and V-funnel tests were used to investigate the flowability of tested mixtures while the passing ability was evaluated using L-box test. Stability of mixtures was studied using four different techniques to evaluate the static and dynamic segregation resistance of coarse aggregate and CR aggregate. The results indicated that the addition of CR could adversely affect the flowability and passing ability of SCRC mixtures. Increasing the CR content also showed a clear instability for both coarse aggregate and suspension of CR particles themselves.
The distant, dusty universe : finding star-forming galaxies with the Herschel Space Observatory

The formation and growth of galaxies through star formation is an unsolved problem in astrophysics. Emission from galaxies in the far-infrared is known to be strongly correlated with obscured star formation and makes up a significant fraction of the cosmic infrared background. The Herschel Space Observatory was designed to cover the wavelengths where this background peaks and gave an unprecedented view of the Universe at this part of the electromagnetic spectrum. This thesis uses observations conducted for the Herschel Multi-tiered Extragalactic Survey (HerMES) by the Herschel Space Observatory's SPIRE instrument to catalogue sources of far infrared emission at 250μm, 350μm and 500μm which will allow models of galaxy evolution to be constrained. Source detection and extraction is performed with both blind and prior-driven algorithms on two newly constructed maps and, for the first time, on the largest of the HerMES' maps, HeLMS. Modifications are made to the algorithms to compensate for cirrus contamination. The accuracy and completeness of both algorithms are assessed and compared. In addition, emission from a galaxy across the three SPIRE bands is known to be strongly correlated. The extent of the covariance between the SPIRE images is explored using Principal Component Analysis, finding that up to 95% of the variance in the SPIRE maps can be explained by one linear combination of the three SPIRE bands. This new projection of data could allow previously unfeasible multi-dimensional probability of deflection analyses to be performed through a reduction of the data dimensionality. Looking forward, the Herschel Extragalactic Legacy Project (HELP) will be using Herschel observations from both HerMES and the H-ATLAS collaboration to create homogenous, multi-wavelength data products. In the final chapter, a comparison between datasets to test the homogeneity was undertaken, leading to a preliminary recalculation of the HerMES angular correlation function, a result that has been in contention between collaborations. Catalogues created as part of this thesis are available at hedam.lam. fr/HerMES/.
The interaction between stratospheric monthly mean regional winds and sporadic-E

In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency (foEs) of the sporadic-E layer that is occasionally seen on the E region of the ionosphere and the quasi-biennial oscillation (QBO) that flows in the east–west direction in the equatorial stratosphere. Multiple regression model as a statistical tool was used to determine the relationship between variables. In this model, the stationarity of the variables (foEs and QBO) was firstly analyzed for each station (Cocos Island, Gibilmanna, Niue Island, and Tahiti). Then, a co-integration test was made to determine the existence of a long-term relationship between QBO and foEs. After verifying the presence of a long-term relationship between the variables, the magnitude of the relationship between variables was further determined using the multiple regression model. As a result, it is concluded that the variations in foEs were explainable with QBO measured at 10 hPa altitude at the rate of 69%, 94%, 79%, and 58% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively. It is observed that the variations in foEs were explainable with QBO measured at 70 hPa altitude at the rate of 66%, 69%, 53%, and 47% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively.
Sex Difference in Spinal Muscular Atrophy Patients – are Males More Vulnerable?

Background: Sex is a significant risk factor in many neurodegenerative disorders. A better understanding of the molecular mechanisms behind sex differences could help develop more targeted therapies that would lead to better outcomes. Untreated spinal muscular atrophy (SMA) is the leading genetic motor disorder causing infant mortality. SMA has a broad spectrum of severity ranging from prenatal death to infant mortality to normal lifespan with some disability. Scattered evidence points to a sex-specific vulnerability in SMA. However, the role of sex as a risk factor in SMA pathology and treatment has received limited attention. Objective: Systematically investigate sex differences in the incidence, symptom severity, motor function of patients with different types of SMA, and in the development of SMA1 patients. Methods: Aggregated data of SMA patients were obtained from the TREAT-NMD Global SMA Registry and the Cure SMA membership database by data enquiries. Data were analyzed and compared with publicly available standard data and data from published literature. Results: The analysis of the aggregated results from the TREAT-NMD dataset revealed that the male/female ratio was correlated to the incidence and prevalence of SMA from different countries; and for SMA patients, more of their male family members were affected by SMA. However, there was no significant difference of sex ratio in the Cure SMA membership dataset. As quantified by the clinician severity scores, symptoms were more severe in males than females in SMA types 2 and 3b. Motor function scores measured higher in females than males in SMA types 1, 3a and 3b. The head circumference was more strongly affected in male SMA type 1 patients. Conclusions: The data in certain registry datasets suggest that males may be more vulnerable to SMA than females. The variability observed indicates that more investigation is necessary to fully understand the role of sex differences in SMA epidemiology, and to guide development of more targeted treatments.
Topics in High-Dimensional Statistics and the Analysis of Large Hyperspectral Images.

Advancement in imaging technology has made hyperspectral images gathered from remote sensing much more common. The high-dimensional nature of these large scale data coupled with wavelength and spatial dependency necessitates high-dimensional and efficient computation methods to address these issues while producing results that are concise and easy to understand. The thesis addresses these issues by examining high-dimensional methods in the context of hyperspectral image classification, unmixing and wavelength correlation estimation. Chapter 2 re-examines the sparse Bayesian learning (SBL) of linear models of [88] in a high-dimensional setting with sparse signal. The hardthresholded version of the SBL estimator, under orthogonal design, achieves non-asymptotic error rate that is comparable to LASSO of [87]. We also establish in the chapter that with high-probability the estimator recovers the sparsity structure of the signal. The ability to recover sparsity structures in high dimensional settings is crucial for unmixing with high-dimensional libraries in the next chapter. In Chapter 3, the thesis investigates the application of SBL on the task of linear/bilinear unmixing and classification of hyperspectral images. The proposed model in this chapter uses latent Markov random fields to classify pixels and account for the spatial dependence between pixels. In the proposed model, the pixels belonging to the same group share the same mixture of pure endmembers. The task of unmixing and classification
Existence globale pour les systèmes de Maxwell-Bloch

(1)    μ∂tH + rotE = 0, ε∂tE − rotH = −∂tP, i∂tρ = [Ω − E · Γ,ρ]. Les variables d’espace-temps sont (t,x) ∈ R, les champs E et H sont à valeurs dans R. Les constantes μ et ε, strictement positives, sont respectivement la perméabilité magnétique et la permittivité électrique. Le couplage s’effectue via la polarisation P du milieu, champ à valeurs dans R donné par la loi constitutive : P = Tr (Γρ),
Total light deflection in the gravitational field of an axisymmetric body at rest with full mass and spin multipole structure

The tangent vector of the light trajectory at future infinity and the angle of total light deflection in the gravitational field of an isolated axisymmetric body at rest with full set of mass-multipoles and spin-multipoles is determined in harmonic coordinates in the 1PN and 1.5PN approximation of the post-Newtonian (PN) scheme. It is found that the evaluation of the tangent vector and of the angle of total light deflection caused by mass-multipoles and spin-multipoles leads directly and in a compelling way to Chebyshev polynomials of first and second kind, respectively. This fact allows to determine the upper limits of the total light deflection, which are strictly valid in the 1PN and 1.5PN approximation. They represent a criterion to identify those multipoles which contribute significantly to the total light deflection for a given astrometric accuracy. These upper limits are used to determine the total light deflection in the gravitational field of the Sun and giant planets of the solar system. It is found that the first few mass-multipoles with l \le 10 and the first few spin-multipoles with l \le 3 are sufficient for an accuracy on the nano-arcsecond level in astrometric angular measurements.
Rubin Observatory LSST Stars Milky Way and Local Volume Star Clusters Roadmap

The Vera C. Rubin Observatory will undertake the Legacy Survey of Space and Time, providing an unprecedented, volume-limited catalog of star clusters in the Southern Sky, including Galactic and extragalactic star clusters. The Star Clusters subgroup of the Stars, Milky Way and Local Volume Working Group has identified key areas where Rubin Observatory will enable significant progress in star cluster research. This roadmap represents our science cases and preparation for studies of all kinds of star clusters from the Milky Way out to distances of tens of megaparsecs.
Guided Deep Generative Model-Based Spatial Regularization for Multiband Imaging Inverse Problems

When adopting a model-based formulation, solving inverse problems encountered in multiband imaging requires to define spatial and spectral regularizations. In most of the works of the literature, spectral information is extracted from the observations directly to derive data-driven spectral priors. Conversely, the choice of the spatial regularization often boils down to the use of conventional penalizations (e.g., total variation) promoting expected features of the reconstructed image (e.g., piece-wise constant). In this work, we propose a generic framework able to capitalize on an auxiliary acquisition of high spatial resolution to derive tailored data-driven spatial regularizations. This approach leverages on the ability of deep learning to extract high level features. More precisely, the regularization is conceived as a deep generative network able to encode spatial semantic features contained in this auxiliary image of high spatial resolution. To illustrate the versatility of this approach, it is instantiated to conduct two particular tasks, namely multiband image fusion and multiband image inpainting. Experimental results obtained on these two tasks demonstrate the benefit of this class of informed regularizations when compared to more conventional ones.
Quaia, the Gaia-unWISE Quasar Catalog: An All-sky Spectroscopic Quasar Sample

We present a new, all-sky quasar catalog, Quaia, that samples the largest comoving volume of any existing spectroscopic quasar sample. The catalog draws on the 6,649,162 quasar candidates identified by the Gaia mission that have redshift estimates from the space observatory’s low-resolution blue photometer/red photometer spectra. This initial sample is highly homogeneous and complete, but has low purity, and 18% of even the bright (G < 20.0) confirmed quasars have discrepant redshift estimates (∣Δz/(1 + z)∣ > 0.2) compared to those from the Sloan Digital Sky Survey (SDSS). In this work, we combine the Gaia candidates with unWISE infrared data (based on the Wide-field Infrared Survey Explorer survey) to construct a catalog useful for cosmological and astrophysical quasar studies. We apply cuts based on proper motions and colors, reducing the number of contaminants by approximately four times. We improve the redshifts by training a k-Nearest Neighbor model on SDSS redshifts, and achieve estimates on the G < 20.0 sample with only 6% (10%) catastrophic errors with ∣Δz/(1 + z)∣ > 0.2 (0.1), a reduction of approximately three times (approximately two times) compared to the Gaia redshifts. The final catalog has 1,295,502 quasars with G < 20.5, and 755,850 candidates in an even cleaner G < 20.0 sample, with accompanying rigorous selection function models. We compare Quaia to existing quasar catalogs, showing that its large effective volume makes it a highly competitive sample for cosmological large-scale structure analyses. The catalog is publicly available at 10.5281/zenodo.10403370.
Single and double quantum transitions in spin-mixed states under photo-excitation

Electronic spins associated with the Nitrogen-Vacancy (NV) center in diamond offer an opportunity to study spin-related phenomena with extremely high sensitivity owing to their high degree of optical polarization. Here, we study both single- and double-quantum transitions (SQT and DQT) in NV centers between spin-mixed states, which arise from magnetic fields that are non-collinear to the NV axis. We demonstrate the amplification of the ESR signal from both these types of transition under laser illumination. We obtain hyperfine-resolved X-band ESR signal as a function of both excitation laser power and misalignment of static magnetic field with the NV axis. This combined with our analysis using a seven-level model that incorporates thermal polarization and double quantum relaxation allows us to comprehensively analyze the polarization of NV spins under off-axis fields. Such detailed understanding of spin-mixed states in NV centers under photo-excitation can help greatly in realizing NV-diamond platform's potential in sensing correlated magnets and biological samples, as well as other emerging applications, such as masing and nuclear hyperpolarization.
Towards the extraction of robust sign embeddings for low resource sign language recognition

Isolated Sign Language Recognition (SLR) has mostly been applied on datasets containing signs executed slowly and clearly by a limited group of signers. In real-world scenarios, however, we are met with challenging visual conditions, coarticulated signing, small datasets, and the need for signer independent models. To tackle this difficult problem, we require a robust feature extractor to process the sign language videos. One could expect human pose estimators to be ideal candidates. However, due to a domain mismatch with their training sets and challenging poses in sign language, they lack robustness on sign language data and image-based models often still outperform keypoint-based models. Furthermore, whereas the common practice of transfer learning with image-based models yields even higher accuracy, keypoint-based models are typically trained from scratch on every SLR dataset. These factors limit their usefulness for SLR. From the existing literature, it is also not clear which, if any, pose estimator performs best for SLR. We compare the three most popular pose estimators for SLR: OpenPose, MMPose and MediaPipe. We show that through keypoint normalization, missing keypoint imputation, and learning a pose embedding, we can obtain significantly better results and enable transfer learning. We show that keypoint-based embeddings contain cross-lingual features: they can transfer between sign languages and achieve competitive performance even when fine-tuning only the classifier layer of an SLR model on a target sign language. We furthermore achieve better performance using fine-tuned transferred embeddings than models trained only on the target sign language. The embeddings can also be learned in a multilingual fashion. The application of these embeddings could prove particularly useful for low resource sign languages in the future.
Recent Results from Belle and Belle II

We report the latest status of the physics activities at the Belle and Belle II experiments including determinations of the CKM-triangle parameters, tests of the lepton-flavor universality, a search for the B+→K+νν¯ decay, a measurement of the Ωc0 lifetime, and searches for invisible new particles.
A Comprehensive Simulation of Solar Wind Formation from the Solar Interior: Significant Cross-field Energy Transport by Interchange Reconnection near the Sun

The physical connection between thermal convection in the solar interior and the solar wind remains unclear due to their significant scale separation. Using an extended version of the three-dimensional radiative magnetohydrodynamic code RAMENS, we perform the first comprehensive simulation of the solar wind formation, starting from the wave excitation and the small-scale dynamo below the photosphere. The simulation satisfies various observational constraints as a slow solar wind emanating from the coronal hole boundary. The magnetic energy is persistently released in the simulated corona, showing a hot upward flow at the interface between open and closed fields. To evaluate the energetic contributions from Alfvén wave and interchange reconnection, we develop a new method to quantify the cross-field energy transport in the simulated atmosphere. The measured energy transport from closed coronal loops to open field accounts for approximately half of the total. These findings suggest a significant role of the supergranular-scale interchange reconnection in solar wind formation.
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning

We present CausalVLR (Causal Visual-Linguistic Reasoning), an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods for various visual-linguistic reasoning tasks, such as VQA, image/video captioning, medical report generation, model generalization and robustness, etc. These methods have been included in the toolbox with PyTorch implementations under NVIDIA computing system. It not only includes training and inference codes, but also provides model weights. We believe this toolbox is by far the most complete visual-linguitic causal reasoning toolbox. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new causal reasoning methods. Code and models are available at https://github.com/HCPLab-SYSU/CausalVLR. The project is under active development by HCP-Lab's contributors and we will keep this document updated.
Biomedical Language Models are Robust to Sub-optimal Tokenization

As opposed to general English, many concepts in biomedical terminology have been designed in recent history by biomedical professionals with the goal of being precise and concise. This is often achieved by concatenating meaningful biomedical morphemes to create new semantic units. Nevertheless, most modern biomedical language models (LMs) are pre-trained using standard domain-specific tokenizers derived from large scale biomedical corpus statistics without explicitly leveraging the agglutinating nature of biomedical language. In this work, we first find that standard open-domain and biomedical tokenizers are largely unable to segment biomedical terms into meaningful components. Therefore, we hypothesize that using a tokenizer which segments biomedical terminology more accurately would enable biomedical LMs to improve their performance on downstream biomedical NLP tasks, especially ones which involve biomedical terms directly such as named entity recognition (NER) and entity linking. Surprisingly, we find that pre-training a biomedical LM using a more accurate biomedical tokenizer does not improve the entity representation quality of a language model as measured by several intrinsic and extrinsic measures such as masked language modeling prediction (MLM) accuracy as well as NER and entity linking performance. These quantitative findings, along with a case study which explores entity representation quality more directly, suggest that the biomedical pre-training process is quite robust to instances of sub-optimal tokenization.
Revealing the spatial extent of patent citations in the UK: How far does knowledge really spillover?

Access to external knowledge sources through localized knowledge spillovers is an important determinant of the innovative capabilities of firms. However, the geographical extent of knowledge spillovers is not well understood. In this article we use patent citations in the UK as a proxy of knowledge flows and analyze the spatial extent of knowledge spillovers relative to the distribution of existing knowledge creation. We find that local, regional and country specific institutional factors play an important role in influencing the probability of knowledge spillovers and that most knowledge spillovers are exhausted within an extended commuting boundary. It is also shown that these effects have increased over time and that the spatial extent of knowledge spillovers varies by industry.