Miracle-dz/UniFAR-FTUs huggingface dataset:
Rift Segmentation: structural mapping of syn-rift successions between the Kerpini-Tsivlos and Mamoussia-Pirgaki Faults, Greece

ii Table of contents . iii List of figures . v List of tables . ix Chapter 1 – Introduction . 1 1.1 Geological framework . 3 1.1.1 Regional geology . 3 1.1.2 Structural and stratigraphic overview. 5 1.2 Previous work . 9 1.3 Thesis motivation and objectives . 15 Chapter 2 – Methodology . 16 2.1 Pre-fieldwork . 16 2.2 Fieldwork . 16 2.3 Post-fieldwork . 17 Chapter 3 – Field observations and interpretations . 19 3.
Delayed Bandits When Do Intermediate Observations Help?

We study a K -armed bandit with delayed feed-back and intermediate observations. We consider a model where intermediate observations have a form of a finite state, which is observed immediately after taking an action, whereas the loss is observed after an adversarially chosen delay. We show that the regime of the mapping of states to losses determines the complexity of the problem, irrespective of whether the mapping of actions to states is stochastic or adversarial. If the mapping of states to losses is adversarial, then the regret rate is of order (cid:112) ( K + d ) T (within log factors), where T is the time horizon and d is a fixed delay. This matches the regret rate of a K -armed bandit with delayed feedback and without intermediate observations, implying that intermediate observations are not helpful. However, if the mapping of states to losses is stochastic, we show that the regret grows at a rate of (cid:113)(cid:0) K + min {|S| , d } (cid:1) T (within log factors), implying that if the number |S| of states is smaller than the delay, then intermediate observations help. We also provide refined high-probability regret upper bounds for non-uniform delays, together with experimental validation of our algorithms.
Fast SoC thermal simulation with physics-aware U-Net

Fast thermal simulation for System on Chip (SoC) plays a crucial role in integrated circuit (IC) design industry, particularly as power density escalates with increasing computational requirements. It is imperative to assess thermal performance comprehensively during the design phase, utilizing a rapid and precise thermal simulator to expedite design iterations. In this paper, we introduce a fast, physics-aware thermal simulator that draws inspiration from Fourier’s law and the Fourier-Biot equation, which correspond to the first and second derivatives of the temperature map. Consequently, the learning objective evolves from merely translating images to approximating natural phenomena such as the thermal gradient and thermal laplacian. By replacing the image-based loss with thermal-aware loss, the proposed model achieves lower prediction error, higher data efficiency, and more physically accurate behavior. The present model demonstrates a significant improvement, achieving a 34% reduction in Maximum Temperature Error (MTE), showcasing the potential for integrating physics-aware learning into SoC thermal design.
Orbital selective order and $\mathbb{Z}_3$ Potts nematicity from a non-Fermi liquid

Motivated by systems where a high temperature non-Fermi liquid gives way to low temperature $\mathbb{Z}_3$ Potts nematic order, we studied a three-orbital Sachdev-Ye-Kitaev (SYK) model in the large-$N$ limit. In the single-site limit, this model exhibits a spontaneous orbital-selective transition which preserves average particle-hole symmetry, with two orbitals becoming insulators while the third orbital remains a non-Fermi liquid down to zero temperature. We extend this study to lattice models of three-orbital SYK dots, exploring uniform symmetry broken states on the triangular and cubic lattices. At high temperature, these lattice models exhibit an isotropic non-Fermi liquid metal phase. On the three-dimensional (3D) cubic lattice, the low temperature uniform $\mathbb{Z}_3$ nematic state corresponds to an orbital selective layered state which preserves particle-hole symmetry at small hopping and spontaneously breaks the particle-hole symmetry at large hopping. Over a wide range of temperature, the transport in this layered state shows metallic in-plane resistivity but insulating out-of-plane resistivity. On the 2D triangular lattice, the low temperature state with uniform orbital order is also a correlated $\mathbb{Z}_3$ nematic with orbital-selective transport but it remains metallic in both principal directions. We discuss a Landau theory with $\mathbb{Z}_3$ clock terms which captures salient features of the phase diagram and nematic order in all these models. We also present results on the approximate wavevector dependent orbital susceptibility of the isotropic non-Fermi liquid states.
Eigenstate switching of topologically ordered states using non-Hermitian perturbations

Topologically ordered phases have robust degenerate ground states against the local perturbations, providing a promising platform for fault-tolerant quantum computation. Despite of the non-local feature of the topological order, we find that local non-Hermitian perturbations can induce the transition between the topologically ordered ground states. In this work, we study the toric code in the presence of non-Hermitian perturbations. By controlling the non-Hermiticity, we show that non-orthogonal ground states can exhibit an eigenstate coalescence and have the spectral singularity, known as an exceptional point (EP). We explore the potential of the EPs in the control of topological order. Adiabatic encircling EPs allows for the controlled switching of eigenstates, enabling dynamic manipulation between the ground state degeneracy. Interestingly, we show a property of our scheme that arbitrary strengths of local perturbations can induce the EP and eigenstate switching. Finally, we also show the orientation-dependent behavior of non-adiabatic transitions (NAT) during the dynamic encirclement around an EP. Our work shows that control of the non-Hermiticity can serve as a promising strategy for fault-tolerant quantum information processing.
Federated Learning for Estimating Heterogeneous Treatment Effects

Machine learning methods for estimating heterogeneous treatment effects (HTE) facilitate large-scale personalized decision-making across various domains such as healthcare, policy making, education, and more. Current machine learning approaches for HTE require access to substantial amounts of data per treatment, and the high costs associated with interventions makes centrally collecting so much data for each intervention a formidable challenge. To overcome this obstacle, in this work, we propose a novel framework for collaborative learning of HTE estimators across institutions via Federated Learning. We show that even under a diversity of interventions and subject populations across clients, one can jointly learn a common feature representation, while concurrently and privately learning the specific predictive functions for outcomes under distinct interventions across institutions. Our framework and the associated algorithm are based on this insight, and leverage tabular transformers to map multiple input data to feature representations which are then used for outcome prediction via multi-task learning. We also propose a novel way of federated training of personalised transformers that can work with heterogeneous input feature spaces. Experimental results on real-world clinical trial data demonstrate the effectiveness of our method.
An efficient multimode vectorial nonlinear propagation solver beyond the weak guidance approximation

In this article, we present an efficient numerical model able to solve the vectorial nonlinear pulse propagation equation in circularly symmetric multimode waveguides. The algorithm takes advantage of the conservation of total angular momentum of light upon propagation and takes into account the vectorial nature of the propagating modes, making it particularly relevant for studies in ring-core fibers. While conventional propagation solvers exhibit a computational complexity scaling as N^4_{mode} where N_{mode} is the number of considered modes, the present solver scales as N^{3/2}_{mode}. As a first example, it is shown that orbital angular momentum modulation instability processes take place in ring-core fibers in realistic conditions. Finally, it is predicted that the modulation instability process is followed by the appearance of breather-like angular structures.
In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking

Deep learning-based methods monopolize the latest research in the field of thermal infrared (TIR) object tracking. However, relying solely on deep learning models to obtain better tracking results requires carefully selecting feature information that is beneficial to representing the target object and designing a reasonable template update strategy, which undoubtedly increases the difficulty of model design. Thus, recent TIR tracking methods face many challenges in complex scenarios. This paper introduces a novel Deep Bayesian Filtering (DBF) method to enhance TIR tracking in these challenging situations. DBF is distinctive in its dual-model structure: the system and observation models. The system model leverages motion data to estimate the potential positions of the target object based on two-dimensional Brownian motion, thus generating a prior probability. Following this, the observation model comes into play upon capturing the TIR image. It serves as a classifier and employs infrared information to ascertain the likelihood of these estimated positions, creating a likelihood probability. According to the guidance of the two models, the position of the target object can be determined, and the template can be dynamically updated. Experimental analysis across several benchmark datasets reveals that DBF achieves competitive performance, surpassing most existing TIR tracking methods in complex scenarios.
Measuring Mass Transfer Rates in Coalescing Neutron Star--White Dwarf Binaries with Deci-Hz Gravitational-wave Detectors

Coalescing neutron star--white dwarf (NS-WD) binaries are among the primary targets for upcoming space-borne gravitational wave (GW) detectors such as LISA, TaiJi, TianQin, etc. During close interaction, these binaries undergo mass transfer, emitting simultaneous X-rays and GWs. This offers a unique opportunity to measure mass transfer rates and study compact binary evolution. To analyze mass transfer rates, we employ the TaylorF2 frequency domain waveform model within the stationary phase approximation (SPA). Through this approach, we derive the GW phase induced during the mass transfer phase and perform Markov Chain Monte Carlo (MCMC) simulations to estimate the minimal detectable mass transfer rate given specific signal-to-noise ratios (SNRs). Our results suggest that for a NS-WD binary with a $0.5 \rm M_\odot$ white dwarf companion, we could measure mass transfer rates down to $10^{-7}\rm M_\odot , {\rm yr}^{-1}$ at SNR=20 and $10^{-9}\rm M_\odot , {\rm yr}^{-1}$ at SNR=1000. This measurement holds significance for studying compact binary evolution involving mass transfer and has potential applications in forecasting tidal disruption events.
The Maker-Breaker percolation game on a random board

The $(m,b)$ Maker-Breaker percolation game on $(\mathbb{Z}^2)_p$, introduced by Day and Falgas-Ravry, is played in the following way. Before the game starts, each edge of $\mathbb{Z}^2$ is removed independently with probability $1-p$. After that, Maker chooses a vertex $v_0$ to protect. Then, in each round Maker and Breaker claim respectively $m$ and $b$ unclaimed edges of $G$. Breaker wins if after the removal of the edges claimed by him the component of $v_0$ becomes finite, and Maker wins if she can indefinitely prevent Breaker from winning. We show that for any $p<1$, Breaker almost surely has a wining strategy for the $(1,1)$ game on $(\mathbb{Z}^2)_p$. This fully answers a question of Day and Falgas-Ravry, who showed that for $p = 1$ Maker has a winning strategy for the $(1,1)$ game. Further, we show that in the $(2,1)$ game on $(\mathbb{Z}^2)_p$ Maker almost surely has a winning strategy whenever $p>0.9402$, while Breaker almost surely has a winning strategy whenever $p<0.5278$. This shows that the threshold value of $p$ above which Maker has a winning strategy for the $(2,1)$ game on $\mathbb{Z}^2$ is non-trivial. In fact, we prove similar results in various settings, including other lattices and biases $(m,b)$. These results extend also to the most general case, which we introduce, where each edge is given to Maker with probability $\alpha$ and to Breaker with probability $\beta$ before the game starts.
Sharpened localization of the trailing point of the Pareto record frontier

For $d\ge2$ and iid $d$-dimensional observations $X^{(1)},X^{(2)},\dots$ with independent Exponential$(1)$ coordinates, we revisit the study by Fill and Naiman (Electron. J. Probab., 2020) of the boundary (relative to the closed positive orthant), or"frontier", $F_n$ of the closed Pareto record-setting (RS) region \[ \mbox{RS}_n:=\{0\le x\in{\mathbb R}^d:x\not\prec X^{(i)}\mbox{\ for all $1\le i\le n$}\} \] at time $n$, where $0\le x$ means that $0\le x_j$ for $1\le j\le d$ and $x\prec y$ means that $x_j<y_j$ for $1\le j\le d$. With $x_+:=\sum_{j=1}^d x_j$, let \[ F_n^-:=\min\{x_+:x\in F_n\}\quad\mbox{and}\quad F_n^+:=\max\{x_+:x\in F_n\}. \] Almost surely, there are for each $n$ unique vectors $\lambda_n\in F_n$ and $\tau_n\in F_n$ such that $F_n^+=(\lambda_n)_+$ and $F_n^-=(\tau_n)_+$; we refer to $\lambda_n$ and $\tau_n$ as the leading and trailing points, respectively, of the frontier. Fill and Naiman provided rather sharp information about the typical and almost sure behavior of $F^+$, but somewhat crude information about $F^-$, namely, that for any $\varepsilon>0$ and $c_n\to\infty$ we have \[ {\mathbb P}(F_n^- -\ln n\in (-(2+\varepsilon)\ln\ln\ln n,c_n))\to 1 \] (describing typical behavior) and almost surely \[ \limsup \frac{F_n^- - \ln n}{\ln \ln n} \le 0 \quad \mbox{and} \quad \liminf \frac{F_n^- - \ln n}{\ln \ln \ln n} \in [-2, -1]. \] In this paper we use the theory of generators (minima of $F_n$) together with the first- and second-moment methods to improve considerably the trailing-point location results to \[ F_n^- - (\ln n - \ln \ln \ln n) \overset{\mathrm{P}}{\longrightarrow} - \ln(d - 1) \] (describing typical behavior) and, for $d \ge 3$, almost surely \begin{align*}&\limsup [F_n^- - (\ln n - \ln \ln \ln n)] \leq -\ln(d - 2) + \ln 2 \\ \mbox{and }&\liminf [F_n^- - (\ln n - \ln \ln \ln n)] \ge - \ln d - \ln 2. \end{align*}
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization

Bilevel optimization aims to optimize an outer objective function that depends on the solution to an inner optimization problem. It is routinely used in Machine Learning, notably for hyperparameter tuning. The conventional method to compute the so-called hypergradient of the outer problem is to use the Implicit Function Theorem (IFT). As a function of the error of the inner problem resolution, we study the error of the IFT method. We analyze two strategies to reduce this error: preconditioning the IFT formula and reparameterizing the inner problem. We give a detailed account of the impact of these two modifications on the error, highlighting the role played by higher-order derivatives of the functionals at stake. Our theoretical findings explain when super efficiency, namely reaching an error on the hypergradient that depends quadratically on the error on the inner problem, is achievable and compare the two approaches when this is impossible. Numerical evaluations on hyperparameter tuning for regression problems substantiate our theoretical findings.
Source-receiver Marchenko redatuming on field data using an adaptive double-focusing method

We have developed an adaptive double-focusing method that is specifically designed for the field-data application of source-receiver Marchenko redatuming. Typically, the single-focusing Marchenko method is combined with a multidimensional deconvolution (MDD) to achieve redatuming. Our method replaces the MDD step by a second focusing step that naturally complements the single-focusing Marchenko method. Instead of performing the MDD method with the directionally decomposed Green’s functions that result from single-focusing, we now use the retrieved upgoing Green’s function and the retrieved downgoing focusing function to obtain a redatumed reflection response in the physical medium. Consequently, we only remove the strongest overburden effects instead of removing all of the overburden effects. However, the gain is a robust method that is less sensitive to imperfections in the data and a sparse acquisition geometry than the MDD method. In addition, it is computationally much cheaper, more straightforward to implement, and it can be parallelized over pairs of focal points, which makes it suitable for application to large data volumes. We evaluate the successful application of our method to 2D field data of the Santos Basin.
Substrate Scope Contrastive Learning: Repurposing Human Bias to Learn Atomic Representations

Learning molecular representation is a critical step in molecular machine learning that significantly influences modeling success, particularly in data-scarce situations. The concept of broadly pre-training neural networks has advanced fields such as computer vision, natural language processing, and protein engineering. However, similar approaches for small organic molecules have not achieved comparable success. In this work, we introduce a novel pre-training strategy, substrate scope contrastive learning, which learns atomic representations tailored to chemical reactivity. This method considers the grouping of substrates and their yields in published substrate scope tables as a measure of their similarity or dissimilarity in terms of chemical reactivity. We focus on 20,798 aryl halides in the CAS Content Collection spanning thousands of publications to learn a representation of aryl halide reactivity. We validate our pre-training approach through both intuitive visualizations and comparisons to traditional reactivity descriptors and physical organic chemistry principles. The versatility of these embeddings is further evidenced in their application to yield prediction, regioselectivity prediction, and the diverse selection of new substrates. This work not only presents a chemistry-tailored neural network pre-training strategy to learn reactivity-aligned atomic representations, but also marks a first-of-its-kind approach to benefit from the human bias in substrate scope design.
Extreme Miscalibration and the Illusion of Adversarial Robustness

Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify. Adversarial Training (AT) is often used to increase model robustness. However, we have discovered an intriguing phenomenon: deliberately or accidentally miscalibrating models masks gradients in a way that interferes with adversarial attack search methods, giving rise to an apparent increase in robustness. We show that this observed gain in robustness is an illusion of robustness (IOR), and demonstrate how an adversary can perform various forms of test-time temperature calibration to nullify the aforementioned interference and allow the adversarial attack to find adversarial examples. Hence, we urge the NLP community to incorporate test-time temperature scaling into their robustness evaluations to ensure that any observed gains are genuine. Finally, we show how the temperature can be scaled during \textit{training} to improve genuine robustness.
Development of a Generalizable Data-driven Turbulence Model: Conditioned Field Inversion and Symbolic Regression

This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are essential for many engineering tasks. Traditional RANS models usually struggle with this task, so recent efforts have focused on data-driven methods such as field inversion and machine learning (FIML) to correct this issue by adjusting the baseline equations. However, these FIML methods often reduce accuracy in attached boundary layers. To address this issue, we developed a"conditioned field inversion"technique. This method adjusts the corrective factor \b{eta} (used by FIML) in the shear-stress transport (SST) model. It multiplies \b{eta} with a shield function f_d that is off in the boundary layer and on elsewhere. This maintains the accuracy of the baseline model for the attached flows. We applied both conditioned and classic field inversion to the NASA hump and a curved backward-facing step (CBFS), creating two datasets. These datasets were used to train two models: SR-CND (from our new method) and SR-CLS (from the traditional method). The SR-CND model matches the SR-CLS model in predicting separated flows in various scenarios, such as periodic hills, the NLR7301 airfoil, the 3D SAE car model, and the 3D Ahmed body, and significantly outperforms the baseline SST model. Importantly, the SR-CND model maintains accuracy in the attached boundary layers, whereas the SR-CLS model does not. Therefore, the proposed method improves separated flow predictions while maintaining the accuracy of the original model for attached flows, offering a better way to create data-driven turbulence models.
Friedmann-Robertson-Walker spacetimes from the perspective of geometric algebra

The intention of our paper is to provide a pedagogical application of geometric algebra to a particularly well-investigated system: We formulate the geometric and dynamical properties of Friedmann-Robertson-Walker spacetimes within the language of geometric algebra and re-derive the Friedmann-equations as the central cosmological equations. Through the geometric algebra-variant of the Raychaudhuri equations, we comment on the evolution of spacetime volumes, before illustrating conformal flatness as a central property of Friedmann-cosmologies. An important aspect of spacetime symmetries are the associated conservation laws, for which we provide a geometric algebra formulation of the Lie-derivatives, of the Killing equation and of conserved quantities in Friedmann-Robertson-Walker spacetimes. Finally, we discuss the gravitational dynamics of scalar fields, with their particular relevance in cosmology, for cosmic inflation, and for dark energy.
From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions

Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constrained by limited datasets, which are either small in scale, synthesized, or only contain well-structured documents. In this paper, we address these limitations by introducing a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. As part of this work, we introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines. Lastly, we expand the notion of text segmentation to a more practical “smart chaptering” task that involves the segmentation of unstructured content, the generation of meaningful segment titles, and a potential real-time application of the models.