Miracle-dz/UniFAR-FTUs huggingface dataset:
A narrative review of the anatomy and function of the white matter tracts in language production and comprehension

Much is known about the role of cortical areas in language processing. The shift towards network approaches in recent years has highlighted the importance of uncovering the role of white matter in connecting these areas. However, despite a large body of research, many of these tracts’ functions are not well-understood. We present a comprehensive review of the empirical evidence on the role of eight major tracts that are hypothesized to be involved in language processing (inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, extreme capsule, middle longitudinal fasciculus, superior longitudinal fasciculus, arcuate fasciculus, and frontal aslant tract). For each tract, we hypothesize its role based on the function of the cortical regions it connects. We then evaluate these hypotheses with data from three sources: studies in neurotypical individuals, neuropsychological data, and intraoperative stimulation studies. Finally, we summarize the conclusions supported by the data and highlight the areas needing further investigation.
On the complexity of IgE: The role of structural flexibility and glycosylation for binding its receptors

It is well established that immunoglobulin E (IgE) plays a crucial role in atopy by binding to two types of Fcε receptors (FcεRI and FcεRII, also known as CD23). The cross-linking of FcεRI-bound IgE on effector cells, such as basophils and mast cells, initiates the allergic response. Conversely, the binding of IgE to CD23 modulates IgE serum levels and antigen presentation. In addition to binding to FcεRs, IgE can also interact with other receptors, such as certain galectins and, in mice, some FcγRs. The binding strength of IgE to its receptors is affected by its valency and glycosylation. While FcεRI shows reduced binding to IgE immune complexes (IgE-ICs), the binding to CD23 is enhanced. There is no evidence that galectins bind IgE-ICs. On the other hand, IgE glycosylation plays a crucial role in the binding to FcεRI and galectins, whereas the binding to CD23 seems to be independent of glycosylation. In this review, we will focus on receptors that bind to IgE and examine how the glycosylation and complexation of IgE impact their binding.
Fast data acquisition for three-dimensional shape measurement using fixed-pattern projection and temporal coding

The ﬁeld of image-based shape measurements us-ing structured illumination has been an active research area for several decades now. A lot of different methods are widely used e.g. for industrial inspection, surveillance as well as multimedia purposes. Usually, short measurement times and high correspondence accuracy are mutually exclusive properties, due to the limitations of the used projection technology. Using a standard stereo-photogrammetry approach, as well as a simple, yet powerful structured light projection concept based on a slide-projector, we demonstrate an acquisition rate of more than 700 dense and accurate 3D measurements per second.
(Poly)phenols in Human Breast Milk and their health benefits for the newborn

Human breast milk is the first food source available to newborns and is responsible for healthy growth and development during the first months of life. Human breast milk contains vitamins, hormones, cytokines, microbiota, and immune cells that are responsible for such healthy conditions. Nonetheless, knowledge of the vast array of molecules present in human breast milk and their potential health effects is still lacking. The effects of mothers diets on the molecules present in human breast milk are also generally unknown. The health benefits of (poly)phenols have been largely increasing but their presence in breast milk has been put largely aside. The impact of the mothers diet in the presence and quantification of these molecules in milk has also been overlooked. Above all, the potential benefits of (poly)phenols for newborns are just a vast emptiness of what is known about (poly)phenol research.
Cirrus Classification at Midlatitude from Systematic Lidar Observations

Systematic cirrus lidar measurements performed in the south of France during 2000 are analyzed statistically to search for cloud classes. The classes are based on cloud characteristics (cloud thickness, light backscattering efficiency, and its variance), cloud absolute geometric height, cloud height relative to the tropopause, and the temperature at the cloud level. The successive use of principal component analysis, cluster methods, and linear discriminant analysis allows the identification of four cirrus classes. Almost all the cirrus detections correspond to three classes with similar proportion of the total cirrus detected (around 30%). The absolute geometric height and the thickness are found to be the main discriminant variables. The first cirrus class corresponds to thin clouds above the local tropopause (absolute geometric height: 11.5 km), or at least around the tropopause, while another class corresponds also to thin clouds but at a lower altitude range in the troposphere (absolute geometric height: 8.6 km). The third class corresponds to thick clouds (thickness of 3.2 km) located below the tropopause, in an altitude range between the two first classes (absolute geometric height: 9.8 km). As expected, the high-altitude cirrus class is characterized with the lowest mean temperature. It is noted that the temperature is closely related to the altitude and so the role of temperature in the cirrus classes cannot be disentangled from the role of the altitude.
A modified FDTD algorithm for processing ultra-wide-band response

Finite-difference time-domain (FDTD) is an effective algorithm for resolving Maxwell equations directly in time domain. Although FDTD has obtained sufficient development, there still exists some improvement space for it, such as ultra-wide-band response and frequency-dependent nonlinearity. In order to resolve these troubles, a modified version of FDTD called complex-field frequency-decomposition (CFFD) FDTD method is introduced, in which the complex-field is adopted to eliminate pseudo-frequency components when computing nonlinearity and the frequency-decomposition is adopted to transform an ultra-wide-band response into a series of narrow-band responses when computing the interaction of ultra-short pulse with matters. Its successful applications in several typical situations and comparison with other methods sufficiently verify the uniqueness and superiority in processing ultra-wide-band response and frequency-dependent nonlinearity.
Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision

Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals’ stances. We analyze users’ attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines.
GEO-FPT: a model of the galaxy bispectrum at mildly non-linear scales

We present GEO-FPT (Geometric Fitted Perturbation Theory), a new model for the galaxy bispectrum anisotropic signal in redshift space, with functional form rooted in perturbation theory. It also models the dependence of the bispectrum with the geometric properties of the triangles in Fourier space, and has a broader regime of validity than state-of-the-art theoretical models based on perturbation theory. We calibrate the free parameters of this model using high-resolution dark matter simulations and perform stringent tests to show that GEO-FPT describes the galaxy bispectrum accurately up to scales of k ≃ 0.12hMpc-1 for different cosmological models, as well as for biased tracers of the dark matter field, considering a survey volume of 100 (Gpc h-1)3. In particular, a joint analysis of the power spectrum and bispectrum anisotropic signals, taking into account their full covariance matrix, reveals that the relevant physical quantities — the BAO peak position (along and across the line-of-sight), and the growth of structure parameters times the amplitude of dark matter fluctuations, fσ 8 — are recovered in an unbiased way, with an accuracy better than 0.4% and 2% respectively (which is our 2σ statistical limit of the systematic error estimate). In addition, the bispectrum signal breaks the fσ 8 degeneracy without detectable bias: f and σ 8 are recovered with better than 2.7% and 3.8% accuracy respectively (which is our 2σ statistical limit of the systematic error estimate). GEO-FPT boosts the applicability of the bispectrum signal of galaxy surveys beyond the current limitation of k ≲ 0.08hMpc-1 and makes the bispectrum a key statistic to unlock the information content from the mildly non-linear regime in the on-going and forthcoming galaxy redshift surveys.
Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation

Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full-head generation methods require a large number of 3D scans or multi-view images to train the model, resulting in expensive data acquisition costs. To address this issue, we propose Head3D, a method to generate full 3D heads with limited multi-view images. Specifically, our approach first extracts facial priors represented by tri-planes learned in EG3D, a 3D-aware generative model, and then proposes feature distillation to deliver the 3D frontal faces into complete heads without compromising head integrity. To mitigate the domain gap between the face and head models, we present a dual-discriminator to guide the frontal and back head generation, respectively. Our model achieves cost-efficient and diverse complete head generation with photo-realistic renderings and high-quality geometry representations. Extensive experiments demonstrate the effectiveness of our proposed Head3D, both qualitatively and quantitatively.
Planning with Sequence Models through Iterative Energy Minimization

Recent works have shown that sequence modeling can be effectively used to train reinforcement learning (RL) policies. However, the success of applying existing sequence models to planning, in which we wish to obtain a trajectory of actions to reach some goal, is less straightforward. The typical autoregressive generation procedures of sequence models preclude sequential refinement of earlier steps, which limits the effectiveness of a predicted plan. In this paper, we suggest an approach towards integrating planning with sequence models based on the idea of iterative energy minimization, and illustrate how such a procedure leads to improved RL performance across different tasks. We train a masked language model to capture an implicit energy function over trajectories of actions, and formulate planning as finding a trajectory of actions with minimum energy. We illustrate how this procedure enables improved performance over recent approaches across BabyAI and Atari environments. We further demonstrate unique benefits of our iterative optimization procedure, involving new task generalization, test-time constraints adaptation, and the ability to compose plans together. Project website: https://hychen-naza.github. io/projects/LEAP
Square Root LASSO: Well-posedness, Lipschitz stability and the tuning trade off

This paper studies well-posedness and parameter sensitivity of the Square Root LASSO (SR-LASSO), an optimization model for recovering sparse solutions to linear inverse problems in finite dimension. An advantage of the SR-LASSO (e.g., over the standard LASSO) is that the optimal tuning of the regularization parameter is robust with respect to measurement noise. This paper provides three point-based regularity conditions at a solution of the SR-LASSO: the weak, intermediate, and strong assumptions. It is shown that the weak assumption implies uniqueness of the solution in question. The intermediate assumption yields a directionally differentiable and locally Lipschitz solution map (with explicit Lipschitz bounds), whereas the strong assumption gives continuous differentiability of said map around the point in question. Our analysis leads to new theoretical insights on the comparison between SR-LASSO and LASSO from the viewpoint of tuning parameter sensitivity: noise-robust optimal parameter choice for SR-LASSO comes at the"price"of elevated tuning parameter sensitivity. Numerical results support and showcase the theoretical findings.
Polar Coded Integrated Data and Energy Networking: A Deep Neural Network Assisted End-to-End Design

Wireless sensors are everywhere. To address their energy supply, we proposed an end-to-end design for polar-coded integrated data and energy networking (IDEN), where the conventional signal processing modules, such as modulation/demodulation and channel decoding, are replaced by deep neural networks (DNNs). Moreover, the input-output relationship of an energy harvester (EH) is also modelled by a DNN. By jointly optimizing both the transmitter and the receiver as an autoencoder (AE), we minimize the bit-error-rate (BER) and maximize the harvested energy of the IDEN system, while satisfying the transmit power budget constraint determined by the normalization layer in the transmitter. Our simulation results demonstrate that the DNN aided end-to-end design conceived outperforms its conventional model-based counterpart both in terms of the harvested energy and the BER.
Joint embedding in Hierarchical distance and semantic representation learning for link prediction

The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail entities, nor even make good use of hierarchical level information. Thus, in this paper, we propose a novel knowledge graph embedding model for the link prediction task, namely, HIE, which models each triplet (\textit{h}, \textit{r}, \textit{t}) into distance measurement space and semantic measurement space, simultaneously. Moreover, HIE is introduced into hierarchical-aware space to leverage rich hierarchical information of entities and relations for better representation learning. Specifically, we apply distance transformation operation on the head entity in distance space to obtain the tail entity instead of translation-based or rotation-based approaches. Experimental results of HIE on four real-world datasets show that HIE outperforms several existing state-of-the-art knowledge graph embedding methods on the link prediction task and deals with complex relations accurately.
Adaptive Riemannian Metrics on SPD Manifolds

Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity of encoding underlying structural correlation in data. To reflect the non-Euclidean geometry of SPD manifolds, many successful Riemannian metrics have been proposed. However, existing fixed metric tensors might lead to sub-optimal performance for SPD matrices learning, especially for SPD neural networks. To remedy this limitation, we leverage the idea of pullback and propose adaptive Riemannian metrics for SPD manifolds. Moreover, we present comprehensive theories for our metrics. Experiments on three datasets demonstrate that equipped with the proposed metrics, SPD networks can exhibit superior performance.
Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes

We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians. Because lung cancer has a low prevalence in primary care, we also address the problem of classification under highly imbalanced classes. Specifically, we use large Transformer-based pretrained language models (PLMs) and investigate: 1) how \textit{soft prompt-tuning} -- an NLP technique used to adapt PLMs using small amounts of training data -- compares to standard model fine-tuning; 2) whether simpler static word embedding models (WEMs) can be more robust compared to PLMs in highly imbalanced settings; and 3) how models fare when trained on notes from a small number of patients. We find that 1) soft-prompt tuning is an efficient alternative to standard model fine-tuning; 2) PLMs show better discrimination but worse calibration compared to simpler static word embedding models as the classification problem becomes more imbalanced; and 3) results when training models on small number of patients are mixed and show no clear differences between PLMs and WEMs. All our code is available open source in \url{https://bitbucket.org/aumc-kik/prompt_tuning_cancer_prediction/}.
The Tornadic Supercell on the Kanto Plain on 6 May 2012: Polarimetric Radar and Surface Data Assimilation with EnKF and Ensemble-Based Sensitivity Analysis

AbstractA tornadic supercell and associated low-level mesocyclone (LMC) observed on the Kanto Plain, Japan, on 6 May 2012 were predicted with a nonhydrostatic mesoscale model with a horizontal resolution of 350 m through assimilation of surface meteorological data (horizontal wind, temperature, and relative humidity) of high spatial density and C-band Doppler radar data (radial velocity and rainwater estimated from reflectivity and specific differential phase) with a local ensemble transform Kalman filter. With assimilation of both surface and radar data, a strong LMC was successfully predicted near the path of the actual tornado. When either surface or radar data were not assimilated, however, the LMC was not predicted. Therefore, both surface and radar data were essential for successful LMC forecasts. The factors controlling the strength of the predicted LMC, defined as a low-level maximum vertical vorticity, were clarified by an ensemble-based sensitivity analysis (ESA), which is a new approach for ana.
First-order optimization on stratified sets

We consider the problem of minimizing a differentiable function with locally Lipschitz continuous gradient on a stratified set and present a first-order algorithm designed to find a stationary point of that problem. Our assumptions on the stratified set are satisfied notably by the determinantal variety (i.e., matrices of bounded rank), its intersection with the cone of positive-semidefinite matrices, and the set of nonnegative sparse vectors. The iteration map of the proposed algorithm applies a step of projected-projected gradient descent with backtracking line search, as proposed by Schneider and Uschmajew (2015), to its input but also to a projection of the input onto each of the lower strata to which it is considered close, and outputs a point among those thereby produced that maximally reduces the cost function. Under our assumptions on the stratified set, we prove that this algorithm produces a sequence whose accumulation points are stationary, and therefore does not follow the so-called apocalypses described by Levin, Kileel, and Boumal (2022). We illustrate the apocalypse-free property of our method through a numerical experiment on the determinantal variety.
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects

We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a single geometry and articulation decoder for multiple object categories. Despite training on multiple categories, our decoder achieves a comparable reconstruction accuracy to methods that train bespoke decoders separately for each category. Combined with our stereo image encoder we infer the 3D shape, 6D pose, size, joint type, and the joint state of multiple unknown objects in a single forward pass. Our method achieves a 20.4% absolute improvement in mAP 3D IOU50 for novel instances when compared to a two-stage pipeline. Inference time is fast and can run on a NVIDIA TITAN XP GPU at 1 HZ for eight or less objects present. While only trained on simulated data, CARTO transfers to real-world object instances. Code and evaluation data is available at: carto. cs. uni - freiburg. de
Continuum Swarm Tracking Control: A Geometric Perspective in Wasserstein Space

We consider a setting in which one swarm of agents is to service or track a second swarm, and formulate an optimal control problem which trades off between the competing objectives of servicing and motion costs. We consider the continuum limit where large-scale swarms are modeled in terms of their time-varying densities, and where the Wasserstein distance between two densities captures the servicing cost. We show how this non-linear infinite-dimensional optimal control problem is intimately related to the geometry of Wasserstein space, and provide new results in the case of absolutely continuous densities and constant-in-time references. Specifically, we show that optimal swarm trajectories follow Wasserstein geodesics, while the optimal control tradeoff determines the time-schedule of travel along these geodesics. We briefly describe how this solution provides a basis for a model-predictive control scheme for tracking time-varying and real-time reference trajectories as well.
Structural identifiability in linear time-invariant systems

A matrix-operator representation of parameter sensitivities is used to provide an algebraic "structural" analysis of local parameter identifiability in linear time-invariant ordinary differential equation systems. Necessary conditions for identifiability depend only upon the system matrices, no integrals must be computed, and arbitrary parametrization may be used. Relations to insensitivity are discussed, and design techniques are suggested which use the nonidentifiable (insensitive) subspace to systematically reduce the number of exciting parameters in individually designed parameter identification experiments. Finally, sufficiency conditions for zero-state identifiability are examined in terms of control inputs which continually excite the natural modes of the parameter sensitivities.