Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric theory of images

This dissertation takes the first steps in the use of differential geometry in the study of images in pixel space. Unlike previous applications of geometry which focused on the surfaces of objects, this dissertation examines image manifolds. Previous work on image manifolds did not examine their intrinsic geometry. This work unifies many observations found in past work. Beyond that, many new results are obtained. In the first parts of this dissertation, the dimensions of some selected image manifolds are experimentally determined, yielding a ratio of pixel space dimension to image manifold dimension on the order of 100:1. This leads immediately to some new bounds on image source entropy. A novel approach to defining images as signals yields an analogous sampling theorem. The main repercussion of this approach is the discovery that sampling within reason is a geometric invariant. This work resurrects a classic signal interpolation method which has not been previously applied to images. With the geometric invariance of sampling in hand, more advanced aspects of image manifold geometry are explored. The first of these aspects is curvature, which is shown to be extremely high. The discovery of high curvature explains the well established but poorly understood fact that linear subspace coding methods applied to images are far from optimal. Some geometric theory concerning the use of geodesics to represent images is presented. More advanced aspects of image manifold structures are explored experimentally. Preliminary results in this area indicate that there may be a structural relation between images of crudely similar objects in similar poses. Supplementary to the theory, a novel approach to image warping is devised called multimorphing. Unlike traditional morphing, multimorphing allows one to combine several images.
Aspects of the diurnal cycle in a regional climate model

We analyze the ability of the regional climate model CLM to simulate the European summer climate with particular consideration of mean diurnal cycles of precipitation, surface variables, convective indices and vertical temperature and humidity profiles. We consider three sets of simulations using different convective parametrizations (Tiedtke, Tiedtke-CAPE and Kain-Fritsch), each covering 6 summer seasons (April-September) driven by the ERA-40 reanalysis. Analysis shows that the afternoon peak of precipitation occurs 3-7 hours too early in the model. This may be linked to the underestimation of convective inhibition energy (CIN). We also observe an underestimation of the diurnal temperature range and a cold bias of about 1-3 K, resulting in a too shallow boundary layer. In addition, the boundary layer is found to be too well mixed and the night-time inversion to be strongly underestimated. Finally, the diurnal evolution of Iberian profiles is found to be influenced by the Iberian thermal low in both model and observations: Between 850 and 750 hPa, the temperatures are lower at noon than at midnight. As an explanation, we propose night-time subsidence heating in layers above 900 hPa.
Aluminum Thin Film Nanostructure Traces in Pediatric EEG Net for MRI and CT Artifact Reduction

Magnetic resonance imaging (MRI) and continuous electroencephalogram (EEG) monitoring are essential in the clinical management of neonatal seizures. EEG electrodes, however, can significantly degrade the image quality of both MRI and CT due to substantial metallic artifacts and distortions. Thus, we developed a novel thin film trace EEG net (“NeoNet”) for improved MRI and CT image quality without compromising the EEG signal quality. The aluminum thin film traces were fabricated with an ultra-high-aspect ratio (up to 17,000:1, with dimensions 30 nm × 50.8 cm × 100 µm), resulting in a low density for reducing CT artifacts and a low conductivity for reducing MRI artifacts. We also used numerical simulation to investigate the effects of EEG nets on the B1 transmit field distortion in 3 T MRI. Specifically, the simulations predicted a 65% and 138% B1 transmit field distortion higher for the commercially available copper-based EEG net (“CuNet”, with and without current limiting resistors, respectively) than with NeoNet. Additionally, two board-certified neuroradiologists, blinded to the presence or absence of NeoNet, compared the image quality of MRI images obtained in an adult and two children with and without the NeoNet device and found no significant difference in the degree of artifact or image distortion. Additionally, the use of NeoNet did not cause either: (i) CT scan artifacts or (ii) impact the quality of EEG recording. Finally, MRI safety testing confirmed a maximum temperature rise associated with the NeoNet device in a child head-phantom to be 0.84 °C after 30 min of high-power scanning, which is within the acceptance criteria for the temperature for 1 h of normal operating mode scanning as per the FDA guidelines. Therefore, the proposed NeoNet device has the potential to allow for concurrent EEG acquisition and MRI or CT scanning without significant image artifacts, facilitating clinical care and EEG/fMRI pediatric research.
Relationship Between Rotating Night Shift Work and Anthropometric Markers of Overall and Central Adiposity

Purpose This study aimed to investigate possible association between rotating night shift (RNS) work and anthropometric markers of overall and central adiposity among male workers. Methods Medical records of workers who underwent medical examination at the Occupational Medicine Unit affiliated with Alexandria Faculty of Medicine were reviewed in December 2021 to extract data about RNS, overall adiposity [body mass index (BMI) from 25 to 29.9 kg/m2 indicated overweight, while BMI ≥30 kg/m2 indicated obesity], central adiposity (waist circumference ≥94 cm). Univariate and multivariate logistic regression were done to model adiposity as a function of potential demographic, lifestyle, and occupational factors. Results Data of 647 male workers, including daytime workers (26.9%) and RNS workers (73.1%) who spent 1–5 years, 6–9 years, or ≥10 years working RNS, were analyzed. No association was found between RNS work and overall adiposity (BMI ≥30 kg/m2). In multivariate regression, age, residence, and smoking status were predictors of an increased BMI. Compared with young workers (<30 years old), the odds of an increased BMI (BMI ≥25 kg/m2) were 2.6 and 3.01 times higher among workers who were 30-<40, and 40-<50 years old, respectively [95% CI=(1.35, 5.05) and (1.19, 7.56), respectively]. Workers who lived in urban areas were less likely to have had an increased BMI (OR=0.34, 95% CI=0.16, 0.74). The odds of an increased BMI were 2.14 times higher in workers who never smoked compared with current smokers (95% CI=1.12, 4.11). No association was found between RNS and central adiposity. In multivariate regression, age was the sole predictor of central adiposity. Conclusion The study supports the idea that RNS work might not be associated with overall or central adiposity and highlights a possible association between central adiposity and age and association between BMI and age, residence, and smoking behavior. Longitudinal, larger studies are required to explore the relationship between RNS and adiposity.
Wavelength calibration of Fourier-transform emission spectra with applications to Fe I

Absolute calibration requirements for Fourier-transform and classical grating spectrometers are compared. The methods and precautions needed for good Fourier-transform spectrometer calibration are illustrated by application to the iron hollow-cathode spectrum. Relative wave numbers in the Fe i spectrum can be determined with a precision of 5.8 × 10−9 (±0.00014 cm−1) for lines of good signal-to-noise ratio. The accuracy of calibration is limited by the inadequacy of available standards, by pressure shifts, and by effects of illumination, all of which are found at the 0.0005-cm−1 level. A preliminary list of 300 well-determined Fe i lines in the range 17 350–26 140 cm−1 (5760–3830 A) is presented. The absolute accuracy is believed to be ±0.001 cm−1.
Probing the symmetry of nature. (CP Violation in B ! J=ˆ K L Decays)

In this work we present a measurement of the CP-violation parameter sin 2φ1 determined through the difference between the time-dependent decay rates for the B → J/ψ KL and B → J/ψ KL transitions. We also show improved selection criteria, which allow us to increase the reconstruction efficiency by 40% and decrease the statistical error in sin 2φ1 by 25%. In addition, we will present a detailed overview of the KL and J/ψ detection and reconstruction. Using 78 fb−1 of e+e− collision data, collected with the Belle detector at the KEKB accelerator, we reconstruct 2054 J/ψ KL candidates, with an estimated signal purity of 57 ± 3% . Using 1714 of these candidates, we measure sin 2φ1 = 0.51 ± 0.12(statistical)± 0.07(systematic) The measurements presented take advantage of the excellent design of the Belle KLM detector. They are in good agreement with the results from other experiments.
A Cell-Centered Pressure Based Method for Two/Three- Dimensional Unstructured Incompressible Navier-Stokes Solver

*† ‡ A cell-centered pressure based method is presented in this paper, and it is implemented in a new two/three-dimensional parallel unstructured CFD code to meet the challenges of physical problems with complex geometries and complicated boundary conditions while maintaining high computational efficiency. The method uses a second order upwind scheme in space and a second order backward scheme for time accuracy. The SIMPLE algorithm in conjunction with the Rhie and Chow approach solves the continuity equation, minimizing undesirable pressure oscillations. The code is parallelized using METIS domain decomposition with good load balancing across computational nodes. In order to demonstrate the accuracy and performance of the current cell-centered pressure based method, several test cases are presented for validation such as two-dimensional incompressible flow past a flat plate, two/three-dimensional driven cavity flow, and two/three-dimensional flow over a circular cylinder. Notably, an extensive qualitative and quantitative study of two-dimensional flow over a circular cylinder for low Reynolds number is also presented in this paper.
Effects of peripheral-layer viscosity on peristaltic transport of a bio-fluid

The effects of peripheral-layer viscosity on the flow characteristics of a bio-fluid due to peristaltic transport has been investigated. It is shown that, for a given pressure drop, the flow flux increases and the frictional force decreases as the viscosity of the peripheral-layer fluid decreases. However, for zero pressure drop, the flux does not depend upon this viscosity while the friction force decreases as it decreases. The analysis has been applied and compared with observed data.
Investigating physical states of molecular gas in the overlapping region of interacting galaxies NGC 4567/4568 using ALMA

We present ALMA observations of a diffuse gas tracer, CO(J = 1–0), and a warmer and denser gas tracer, CO(J = 3–2), in the overlapping region of interacting galaxies NGC 4567/4568, which are in the early stage of interaction. To understand the impact of galaxy interactions on molecular gas properties, we focus on interacting galaxies during the early stage and study their molecular gas properties. We investigate the physical states of a filamentary molecular structure at the overlapping region, which was previously reported. Utilizing new higher-resolution CO(J = 1–0) data, we identify molecular clouds within overlapping and disk regions. Although the molecular clouds in the filament have a factor of two higher average virial parameter (0.56 ± 0.14) than that in the overlapping region (0.28 ± 0.12) and in the disk region (0.26 ± 0.16), all the identified molecular clouds are gravitationally bound. These clouds in the filament also have a larger velocity dispersion than that in the overlapping region, suggesting that molecular gas and/or atomic gas with different velocities collide there. We calculate the ratio of the integrated intensity of CO(J = 3–2) and CO(J = 1–0) (= R3−2/1−0) on the molecular cloud scale. The maximum R3−2/1−0 is 0.17 ± 0.04 for all identified clouds. R3−2/1−0 for the molecular clouds in the filament is lower than that of the surrounding area. This result contradicts the predictions of previous numerical simulations, which suggested that the molecular gas on the collision front of galaxies is compressed and becomes denser. Our results imply that NGC 4567/4568 is in a very early stage of interaction; otherwise, the molecular clouds in the filament would not yet fulfill the conditions necessary to trigger star formation.
Interplay of many-body interactions and quasiperiodic disorder in the all-bands-flat diamond chain

We study the effects of quasiperiodic Aubry-Andr\'e (AA) disorder and interactions on a one-dimensional all-band-flat (ABF) diamond chain. We consider the application of disorder in two ways: a symmetric one, where the same disorder is applied to the top and bottom sites of a unit cell, and an antisymmetric one, where the disorder applied to the top and bottom sites are of equal magnitude but with opposite signs. The single-particle wave-packet dynamics for the clean system and when the disorder is applied symmetrically show quantum caging; in the antisymmetric case, the wave-packet spreads over the entire lattice. These results agree with our previous work, where compact localization was observed in the case of the clean system and for symmetrically disordered diamond lattices. In the presence of nearest-neighbour interactions, nonergodic phases are observed in the case of a clean system and symmetrical disorder; at higher disorder strengths, we find an MBL-like phase in the symmetric case. However, many-body non-equilibrium dynamics of the system from carefully engineered initial states exhibit quantum caging. In the antisymmetric case, a nonergodic mixed phase, a thermal phase and an MBL-like phases, respectively, are observed at low, intermediate and high disorder strengths. We observe an absence of caging and initial state dependence (except at the intermediate disorder strength) in the study of non-equilibrium dynamics.
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space; (2) unconstrained DP-SCO in $\ell_p^d$ space; (3) DP-SCO with heavy-tailed data over a constrained and bounded set in $\ell_p^d$ space. For problem (1), for both convex and strongly convex loss functions, we propose methods whose outputs could achieve (expected) excess population risks that are only dependent on the Gaussian width of the constraint set rather than the dimension of the space. Moreover, we also show the bound for strongly convex functions is optimal up to a logarithmic factor. For problems (2) and (3), we propose several novel algorithms and provide the first theoretical results for both cases when $1
Data-Enabled Policy Optimization for the Linear Quadratic Regulator

Policy optimization (PO), an essential approach of reinforcement learning for a broad range of system classes, requires significantly more system data than indirect (identification-followed-by-control) methods or behavioral-based direct methods even in the simplest linear quadratic regulator (LQR) problem. In this paper, we take an initial step towards bridging this gap by proposing the data-enabled policy optimization (DeePO) method, which requires only a finite number of sufficiently exciting data to iteratively solve the LQR problem via PO. Based on a data-driven closed-loop parameterization, we are able to directly compute the policy gradient from a batch of persistently exciting data. Next, we show that the nonconvex PO problem satisfies a projected gradient dominance property by relating it to an equivalent convex program, leading to the global convergence of DeePO. Moreover, we apply regularization methods to enhance the certainty-equivalence and robustness of the resulting controller and show an implicit regularization property. Finally, we perform simulations to validate our results.
The asymptotics of massive fields on stationary spherically symmetric black holes for all angular momenta

We study the massive scalar field equation $\Box_g \phi = m^2 \phi$ on a stationary and spherically symmetric black hole $g$ (including in particular the Schwarzschild and Reissner--Nordstr\"om black holes in the full sub-extremal range) for solutions $\phi$ projected on a fixed spherical harmonic. Our problem involves the scattering of an attractive long-range potential (Coulomb-like) and thus cannot be treated perturbatively. We prove precise (point-wise) asymptotic tails of the form $t^{-5/6} f(t)+ O(t^{-1+\delta})$, where $f(t)$ is an explicit oscillating profile. Our asymptotics appear to be the first rigorous decay result for a massive scalar field on a black hole. Establishing these asymptotics is also an important step in retrieving the assumptions used in work of the third author regarding the interior of dynamical black holes and Strong Cosmic Censorship.
Interstellar Objects and Exocomets

In this chapter we review our knowledge of our galaxy's cometary population outside our Oort Cloud - exocomets and Interstellar Objects (ISOs). We start with a brief overview of planetary system formation, viewed as a general process around stars. We then take a more detailed look at the creation and structure of exocometary belts, as revealed by the unprecedented combination of theoretical and observational advances in recent years. The existence and characteristics of individual exocomets orbiting other stars is summarized, before looking at the mechanisms by which they may be ejected into interstellar space. The discovery of the first two ISOs is then described, along with the surprising differences in their observed characteristics. We end by looking ahead to what advances may take place in the next decade.
GlyphDraw: Learning to Draw Chinese Characters in Image Synthesis Models Coherently

Recent breakthroughs in the ﬁeld of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions. Although the synthesis performance is fascinating, one signiﬁcant limitation of current image generation models is their insufﬁcient ability to generate coherent text within images, particularly for complex glyph structures like Chinese characters. To address this problem, we introduce GlyphDraw, a general learning framework aiming at endowing image generation models with the capacity to generate images embedded with coherent text. To the best of our knowledge, this is the ﬁrst work in the ﬁeld of image synthesis to address the generation of Chinese characters. We ﬁrst sophisticatedly design the image-text dataset’s construction strategy, then build our model speciﬁcally on a diffusion-based image generator and carefully modify the network structure to allow the model to learn drawing Chinese characters with the help of glyph and position information. Furthermore, we maintain the model’s open-domain image synthesis capability by preventing catastrophic forgetting by using a variety of training techniques. Extensive qualitative and quantitative experiments demonstrate that our method not only produces accurate Chinese characters as in prompts, but also naturally blends the generated text into the background. Please refer to our project page.
General theory for discrete symmetry-breaking equilibrium states

Spontaneous symmetry-breaking in phase transitions occurs when the system Hamiltonian is symmetric under a certain transformation, but the equilibrium states observed in nature are not. Here, we prove that when a discrete symmetry is spontaneously broken in a quantum system, then the time evolution necessarily conserves two additional and non-commuting quantities, besides the one linked to the symmetry. This implies the existence of equilibrium states consisting in superpositions of macroscopic configurations. Then, we propose an experimental realization of such equilibrium states with the current state-of-the art in quantum technologies. Through numerical calculations, we show that they survive as very long-lived pre-thermal states, even very far away from the thermodynamic limit. Finally, we also show that a small symmetry-breaking perturbation in the Hamiltonian stabilizes the conservation of one of the two former quantities, implying that symmetry-breaking equilibrium states become stable even in small quantum systems.
Microwave Quantum Illumination with Correlation-To-Displacement Conversion

Entanglement is vulnerable to degradation in a noisy sensing scenario, but surprisingly, the quantum illumination protocol has demonstrated that its advantage can survive. However, designing a measurement system that realizes this advantage is challenging since the information is hidden in the weak correlation embedded in the noise at the receiver side. Recent progress in a correlation-to-displacement conversion module provides a route towards an optimal protocol for practical microwave quantum illumination. In this work, we extend the conversion module to accommodate experimental imperfections that are ubiquitous in microwave systems. To mitigate loss, we propose amplification of the return signals. In the case of ideal amplification, the entire six-decibel error-exponent advantage in target detection error can be maintained. However, in the case of noisy amplification, this advantage is reduced to three-decibel. We analyze the quantum advantage under different scenarios with a Kennedy receiver in the final measurement. In the ideal case, the performance still achieves the optimal one over a fairly large range with only on-off detection. Empowered by photon number resolving detectors, the performance is further improved and also analyzed in terms of receiver operating characteristic curves. Our findings pave the way for the development of practical microwave quantum illumination systems.
Solving morphological analogies: from retrieval to generation

Analogical inference is a remarkable capability of human reasoning, and has been used to solve hard reasoning tasks. Analogy based reasoning (AR) has gained increasing interest from the artificial intelligence community and has shown its potential in multiple machine learning tasks such as classification, decision making and recommendation with competitive results. We propose a deep learning (DL) framework to address and tackle two key tasks in AR: analogy detection and solving. The framework is thoroughly tested on the Siganalogies dataset of morphological analogical proportions (APs) between words, and shown to outperform symbolic approaches in many languages. Previous work have explored the behavior of the Analogy Neural Network for classification (ANNc) on analogy detection and of the Analogy Neural Network for retrieval (ANNr) on analogy solving by retrieval, as well as the potential of an autoencoder (AE) for analogy solving by generating the solution word. In this article we summarize these findings and we extend them by combining ANNr and the AE embedding model, and checking the performance of ANNc as an retrieval method. The combination of ANNr and AE outperforms the other approaches in almost all cases, and ANNc as a retrieval method achieves competitive or better performance than 3CosMul. We conclude with general guidelines on using our framework to tackle APs with DL.