Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Postoperative Nausea And Vomiting Using Machine Learning: A Model Development and Validation Study

Background: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction during the early postoperative period. The tools used for predicting PONV at present have not yielded satisfactory results. Therefore, prognostic tools for the prediction of early and delayed PONV were developed in this study with the aim of achieving satisfactory predictive performance. Methods: The retrospective data of adult patients admitted to the post-anesthesia care unit after undergoing surgical procedures under general anesthesia at the Sheba Medical Center, Israel, between September 1, 2018, and September 1, 2023, were used in this study. An ensemble model of machine learning algorithms trained on the data of 54848 patients was developed. The k-fold cross-validation method was used followed by splitting the data to train and test sets that optimally preserve the sociodemographic features of the patients, such as age, sex, and smoking habits, using the Bee Colony algorithm. Findings: Among the 54848 patients, early and delayed PONV were observed in 2706 (4.93%) and 8218 (14.98%) patients, respectively. The proposed PONV prediction tools could correctly predict early and delayed PONV in 84.0% and 77.3% of cases, respectively, outperforming the second-best PONV prediction tool (Koivuranta score) by 13.4% and 12.9%, respectively. Feature importance analysis revealed that the performance of the proposed prediction tools aligned with previous clinical knowledge, indicating their utility. Interpretation: The machine learning-based tools developed in this study enabled improved PONV prediction, thereby facilitating personalized care and improved patient outcomes.
A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power

In this work, we propose and assess the potential of generative artificial intelligence (AI) to generate public engagement around potential clean energy sources. Such an application could increase energy literacy -- an awareness of low-carbon energy sources among the public therefore leading to increased participation in decision-making about the future of energy systems. We explore the use of generative AI to communicate technical information about low-carbon energy sources to the general public, specifically in the realm of nuclear energy. We explored 20 AI-powered text-to-image generators and compared their individual performances on general and scientific nuclear-related prompts. Of these models, DALL-E, DreamStudio, and Craiyon demonstrated promising performance in generating relevant images from general-level text related to nuclear topics. However, these models fall short in three crucial ways: (1) they fail to accurately represent technical details of energy systems; (2) they reproduce existing biases surrounding gender and work in the energy sector; and (3) they fail to accurately represent indigenous landscapes -- which have historically been sites of resource extraction and waste deposition for energy industries. This work is performed to motivate the development of specialized generative tools and their captions to improve energy literacy and effectively engage the public with low-carbon energy sources.
GaussianHead: Impressive 3D Gaussian-based Head Avatars with Dynamic Hybrid Neural Field

Previous head avatar methods have mostly relied on fixed explicit primitives (mesh, point) or implicit surfaces (Sign Distance Function) and volumetric neural radiance field, it challenging to strike a balance among high fidelity, training speed, and resource consumption. The recent popularity of hybrid field has brought novel representation, but is limited by relying on parameterization factors obtained through fixed mappings. We propose GaussianHead: an head avatar algorithm based on anisotropic 3D gaus-sian primitives. We leverage canonical gaussians to represent dynamic scenes. Using explicit ”dynamic” tri-plane as an efficient container for parameterized head geometry, aligned well with factors in the underlying geometry and tri-plane, we obtain aligned canonical factors for the canonical gaussians. With a tiny MLP, factors are decoded into opacity and spherical harmonic coefficients of 3D gaus-sian primitives. Finally, we use efficient differentiable gaus-sian rasterizer for rendering. Our approach benefits significantly from our novel representation based on 3D gaus-sians, and the proper alignment transformation of underlying geometry structures and factors in tri-plane eliminates biases introduced by fixed mappings. Compared to state-of-the-art techniques, we achieve optimal visual results in tasks such as self-reconstruction, novel view synthesis, and cross-identity reenactment while maintaining high rendering efficiency (0.12s per frame)
The partitions whose members are finite and the permutations with at most $n$ non-fixed points of a set

We write $S_{\leq n}(A)$ and $\Part_{\fin}(A)$ for the set of permutations with at most $n$ non-fixed points, where $n$ is a natural number, and the set of partitions whose members are finite, respectively, of a set $A$. Among our results, we show, in the Zermelo-Fraenkel set theory, that $|\Part_{\fin}(A)| \nleq |S_{\leq n}(A)|$ for any infinite set $A$ and if $A$ can be linearly ordered, then $|S_{\leq n}(A)|<|\Part_{\fin}(A)|$ while the statement ``$|S_{\leq n}(A)|\leq|\Part_{\fin}(A)|$ for all infinite sets $A$"is not provable for $n\geq 3$.
hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications

With the recent success of artificial intelligence in neuroscience, a number of deep learning (DL) models were proposed for classification, anomaly detection, and pattern recognition tasks in electroencephalography (EEG). EEG is a multi-channel time-series that provides information about the individual brain activity for diagnostics, neuro-rehabilitation, and other applications (including emotions recognition). Two main issues challenge the existing DL-based modeling methods for EEG: the high variability between subjects and the low signal-to-noise ratio making it difficult to ensure a good quality in the EEG data. In this paper, we propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction. We properly designed their architectures using the blocks of the well-known EEGNet as the encoder, and proposed a loss function based on dynamic time warping. We tested the models on the public Dataset 2a - BCI Competition IV, where EEG was collected from 9 subjects and 22 channels. hvEEGNet was found to reconstruct the EEG data with very high-fidelity, outperforming most previous solutions (including our vEEGNet-ver3 ). Furthermore, this was consistent across all subjects. Interestingly, hvEEGNet made it possible to discover that this popular dataset includes a number of corrupted EEG recordings that might have influenced previous literature results. We also investigated the training behaviour of our models and related it with the quality and the size of the input EEG dataset, aiming at opening a new research debate on this relationship. In the future, hvEEGNet could be used as anomaly (e.g., artefact) detector in large EEG datasets to support the domain experts, but also the latent representations it provides could be used in other classification problems and EEG data generation.
Proposal to PAC 51: Color Transparency in Maximal Rescattering Kinematics

With the current highest beam energy at Jefferson Lab and traditional methods, we have exhausted our sensitivity for observing the onset of proton color transparency in a nucleus in A(e,e'p) parallel scattering kinematics for up to $Q^{2}$ = 14 GeV$^{2}$ . One of the disadvantages in A(e,e'p) experiments is that even if a point-like color singlet is produced at such $Q^{2}$, its expansion is unconstrained over the full radius of the nuclei, with the potential to significantly reduce the size of the color transparency effect. Therefore, in order to be sensitive to the effects of color transparency, we enhance the sensitivity of the measurement to the production of a point-like color neutral object prior to the onset of wave-function expansion. In this experiment, we propose a color transparency measurement in maximal rescattering ("dirty") kinematics in deuterium where final-state interactions (FSIs) are known to be huge effects, thereby enhancing our sensitivity to a reduction in FSIs indicative of color transparency. The kinematics in exclusive processes in deuterium can be precisely chosen such that the inter-nucleon distances of the struck and spectator nucleon lead to well-controlled FSIs.
Quantifying Hippocampal Shape Asymmetry in Alzheimer's Disease Using Optimal Shape Correspondences

Hippocampal atrophy in Alzheimer's disease (AD) is asymmetric and spatially inhomogeneous. While extensive work has been done on volume and shape analysis of atrophy of the hippocampus in AD, less attention has been given to hippocampal asymmetry specifically. Previous studies of hippocampal asymmetry are limited to global volume or shape measures, which don't localize shape asymmetry at the point level. In this paper, we propose to quantify localized shape asymmetry by optimizing point correspondences between left and right hippocampi within a subject, while simultaneously favoring a compact statistical shape model of the entire sample. To account for related variables that have impact on AD and healthy subject differences, we build linear models with other confounding factors. Our results on the OASIS3 dataset demonstrate that compared to using volumetric information, shape asymmetry reveals fine-grained, localized differences that indicate the hippocampal regions of most significant shape asymmetry in AD patients.
Hydrodynamic correlation and spectral functions of perfect cubic crystals

We investigate the collective dynamics of the perfect cubic crystal by deriving from the hydrodynamic equations the time-dependent correlation and the spectral functions characterizing the fluctuations of mass and momentum densities. We show that the seven hydrodynamic modes of the perfect crystal can be identified from the resonances of these spectral functions. The comparison with those of a fluid is discussed. Using the numerical values of the thermodynamic, elastic, and transport coefficients computed in our previous paper (J Mabillard and P Gaspard 2024 J. Stat. Mech. 023208) for a system of hard spheres, the theoretical expressions for the correlation and spectral functions are compared to the same functions directly computed using molecular dynamics simulations. The excellent agreement between theory and simulation provides strong support for the microscopic hydrodynamic theory of perfect crystals based on the local-equilibrium approach. This work sheds light on the fundamental mechanisms governing the collective behavior of matter in the solid state.
Cauchy problem for singular-degenerate porous medium type equations: well-posedness and Sobolev regularity

Motivated by models for biofilm growth, we consider Cauchy problems for quasilinear reaction diffusion equations where the diffusion coefficient has a porous medium type degeneracy as well as a singularity. We prove results on the well-posedness and Sobolev regularity of solutions. The proofs are based on m-accretive operator theory, kinetic formulations and Fourier analytic techniques.
Random zero currents of sections of Hermitian line bundles over compact Riemannian manifolds

This paper is concerned with zero currents of random section of a Hermitian line bundle $E$ over a compact oriented Riemannian manifold. Given a metric connection, heat flow yields a natural 1-parameter family of probability measures on the space of smooth sections $\Gamma E$. It is shown that the corresponding family of random zero currents connects the curvature of the bundle to the ground state zero current.
Optimizing Camera Configurations for Multi-View Pedestrian Detection

Jointly considering multiple camera views (multi-view) is very effective for pedestrian detection under occlusion. For such multi-view systems, it is critical to have well-designed camera configurations, including camera locations, directions, and fields-of-view (FoVs). Usually, these configurations are crafted based on human experience or heuristics. In this work, we present a novel solution that features a transformer-based camera configuration generator. Using reinforcement learning, this generator autonomously explores vast combinations within the action space and searches for configurations that give the highest detection accuracy according to the training dataset. The generator learns advanced techniques like maximizing coverage, minimizing occlusion, and promoting collaboration. Across multiple simulation scenarios, the configurations generated by our transformer-based model consistently outperform random search, heuristic-based methods, and configurations designed by human experts, shedding light on future camera layout optimization.
Implicit Learning of Scene Geometry From Poses for Global Localization

Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations (X, Y, Z coordinates) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets.
Feedback cooling of an insulating high-Q diamagnetically levitated plate

Levitated systems in vacuum have many potential applications ranging from new types of inertial and magnetic sensors through to fundamental issues in quantum science, the generation of massive Schrödinger cats, and the connections between gravity and quantum physics. In this work, we demonstrate the passive, diamagnetic levitation of a centimeter-sized massive oscillator, which is fabricated using a method that ensures that the material, though highly diamagnetic, is an electrical insulator. Electrical conductors moving in a magnetic field experience eddy damping—which can severely reduce their motional quality factor. By chemically coating a powder of microscopic graphite beads with silica and embedding the coated powder in high-vacuum compatible wax, we form a centimeter-sized thin square plate which magnetically levitates over a checkerboard magnet array. The insulating coating reduces eddy damping by almost an order of magnitude compared to uncoated graphite with the same particle size. These plates exhibit a different equilibrium orientation from pyrolytic graphite due to their isotropic magnetic susceptibility. We measure the motional quality factor to be Q∼1.58×105 for an approximately centimeter-sized composite resonator with a mean particle size of 12 μm. Furthermore, we apply delayed feedback to cool the vertical motion of frequency ∼19 Hz and achieve center-of-mass temperature decrease by three orders of magnitude.
Comparison of Deterministic and Nondeterministic Decision Trees for Decision Tables with Many-valued Decisions from Closed Classes

In this paper, we consider classes of decision tables with many-valued decisions closed relative to removal of attributes (columns) and changing sets of decisions assigned to rows. For tables from an arbitrary closed class, we study a function $\mathcal{H}^{\infty}_{\psi ,A}(n)$ that characterizes the dependence in the worst case of the minimum complexity of deterministic decision trees on the minimum complexity of nondeterministic decision trees. Note that nondeterministic decision trees for a decision table can be interpreted as a way to represent an arbitrary system of true decision rules for this table that cover all rows. We indicate the condition for the function $\mathcal{H}^{\infty}_{\psi ,A}(n)$ to be defined everywhere. If this function is everywhere defined, then it is either bounded from above by a constant or is greater than or equal to $n$ for infinitely many $n$. In particular, for any nondecreasing function $\varphi$ such that $\varphi (n)\geq n$ and $\varphi (0)=0$, the function $\mathcal{H}^{\infty}_{\psi ,A}(n)$ can grow between $\varphi (n)$ and $\varphi (n)+n$. We indicate also conditions for the function $\mathcal{H}^{\infty}_{\psi,A}(n)$ to be bounded from above by a polynomial on $n$.
There are no unconditional Schauder frames of translates in $L^p(\mathbb{R})$, $1 \le p \le 2$

It is known that a system formed by translates of a single function cannot be an unconditional Schauder basis in the space $L^p(\mathbb{R})$ for any $1 \le p<\infty$. To the contrary, there do exist unconditional Schauder frames of translates in $L^p(\mathbb{R})$ for every $p>2$. The existence of such a system for $1<p \leq 2$, however, has remained an open problem. In this paper the problem is solved in the negative: we prove that none of the spaces $L^p(\mathbb{R})$, $1 \le p \le 2$, admits an unconditional Schauder frame of translates.
Data Management For Large Language Models: A Survey

Data plays a fundamental role in the training of Large Language Models (LLMs). Effective data management, particularly in the formulation of a well-suited training dataset, holds significance for enhancing model performance and improving training efficiency during pretraining and supervised fine-tuning phases. Despite the considerable importance of data management, the current research community still falls short in providing a systematic analysis of the rationale behind management strategy selection, its consequential effects, methodologies for evaluating curated datasets, and the ongoing pursuit of improved strategies. Consequently, the exploration of data management has attracted more and more attention among the research community. This survey provides a comprehensive overview of current research in data management within both the pretraining and supervised fine-tuning stages of LLMs, covering various noteworthy aspects of data management strategy design: data quantity, data quality, domain/task composition, etc. Looking toward the future, we extrapolate existing challenges and outline promising directions for development in this field. Therefore, this survey serves as a guiding resource for practitioners aspiring to construct powerful LLMs through effective data management practices. The collection of the latest papers is available at https://github.com/ZigeW/data_management_LLM.
Interlacing Polynomial Method for Matrix Approximation via Generalized Column and Row Selection

This paper delves into the spectral norm aspect of the Generalized Column and Row Subset Selection (GCRSS) problem. Given a target matrix $\mathbf{A}$, the objective of GCRSS is to select a column submatrix $\mathbf{B}_{:,S}$ from the source matrix $\mathbf{B}$ and a row submatrix $\mathbf{C}_{R,:}$ from the source maitrx $\mathbf{C}$, with the aim of minimizing the spectral norm of the residual matrix $(\mathbf{I}_n-\mathbf{B}_{:,S}\mathbf{B}_{:,S}^{\dagger})\mathbf{A}(\mathbf{I}_d-\mathbf{C}_{R,:}^{\dagger} \mathbf{C}_{R,:})$. By employing the interlacing polynomials method, we show that the largest root of the expected characteristic polynomial of the residual matrix serves as an upper bound on the smallest spectral norm of the residual matrix. We estimate this root for two specific GCRSS scenarios, one where $r=0$, simplifying the problem to the Generalized Column Subset Selection (GCSS) problem, and the other where $\mathbf{B}=\mathbf{C}=\mathbf{I}_d$, reducing the problem to the submatrix selection problem. In the GCSS scenario, we connect the expected characteristic polynomials to the convolution of multi-affine polynomials, leading to the derivation of the first provable reconstruction bound on the spectral norm of the residual matrix for the GCSS problem. In the submatrix selection scenario, we show that for any sufficiently small $\varepsilon>0$ and any square matrix $\mathbf{A}\in\mathbb{R}^{d\times d}$, there exist two subsets $S\subset [d]$ and $R\subset [d]$ of sizes $O(d\cdot \varepsilon^2)$ such that $\Vert\mathbf{A}_{S,R}\Vert_2\leq \varepsilon\cdot \Vert\mathbf{A}\Vert_2$. Unlike previous studies that have produced comparable results for very special cases where the matrix is either a zero-diagonal or a positive semidefinite matrix, our results apply universally to any matrix $\mathbf{A}$.
EFFECT OF PLANTING DATE ON SEED SIZE TUBER YIELD OF POTATO IN NILGIRIS

A field experiment was conducted during summer seasons of 2003 and 2004 at CPRS, Muthorai with three dates of planting i.e. April 21 st , May 22 nd and June 21 st using randomised block design with four replications using three cvs. , viz., Kufri Giriraj, Kufri Jyoti and Kufri Swarna. April 21 st planting produced more number of seed size tubers and yield in cvs. Kufri Giriraj and Kufri Jyoti, whereas June 21 st planting performed well with cv. Kufri Swarna by producing higher number of seed size tubers and yield. In cv. Kufri Jyoti among the three dates of planting, April 21 st planting produced highest yield of seed size tubers (51.25q ha-1) followed by June 21st planting (50.23q/ha-1). In case of cv. Kufri Giriraj, maximum seed size tuber yield (46.5 q/ha) was obtained in April 21 st planting followed by 21 st June planting (32.71q/ha). Whereas, cv. Kufri Swarna produced higher seed size tuber yield (21.68q ha1) in 21st June planting. Number of Seed size tubers also followed the similar trend as that of yield. The interaction effect between date of planting and varieties were significant with respect to grade wise tuber yield. The results showed that the crop in Nilgiris should be planted around 21 st April for achieving the highest yield and number of seed size tubers in cvs. Kufri Jyoti and Kufri Giriraj while Kufri Swarna may be planted on 21 st June.