Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography

We present a practical approach to intercompare a range of candidate digital elevation models (DEMs) based on predefined criteria and a statistically sound ranking approach. The presented approach integrates the randomized complete block design (RCBD) into a novel framework for DEM comparison. The method presented provides a flexible, statistically sound, and customizable tool for evaluating the quality of any raster—in this case, a DEM—by means of a ranking approach, which takes into account a confidence level and can use both quantitative and qualitative criteria. The users can design their own criteria for the quality evaluation in relation to their specific needs. The application of the RCBD method to rank six <inline-formula> <tex-math notation="LaTeX">$1''$ </tex-math></inline-formula> global DEMs, considering a wide set of study sites, covering different morphological and land cover settings, highlights the potentialities of the approach. We used a suite of criteria relating to the differences in the elevation, slope, and roughness distributions compared to reference DEMs aggregated from 1- to 5-m light detection and ranging (LiDAR)-derived DEMs. Results confirmed the significant superiority of Copernicus DEM (CopDEM) <inline-formula> <tex-math notation="LaTeX">$1''$ </tex-math></inline-formula> and its derivative forests and buildings removed DEM (FABDEM) as the overall best <inline-formula> <tex-math notation="LaTeX">$1''$ </tex-math></inline-formula> global DEMs. They are slightly better than Advanced Land Observing Satellite (ALOS) and clearly outperform NASADEM and SRTM, which are, in turn, much better than advanced spaceborne thermal emission and reflection radiometer (ASTER).
A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms

Entity resolution (ER) is the process of identifying records that refer to the same entities within one or across multiple databases. Numerous techniques have been developed to tackle ER challenges over the years, with recent emphasis placed on machine and deep learning methods for the matching phase. However, the quality of the benchmark datasets typically used in the experimental evaluations of learning-based matching algorithms has not been examined in the literature. To cover this gap, we propose four different approaches to assessing the difficulty and appropriateness of 13 established datasets: two theoretical approaches, which involve new measures of linearity and existing measures of complexity, and two practical approaches: the difference between the best non-linear and linear matchers, as well as the difference between the best learning-based matcher and the perfect oracle. Our analysis demonstrates that most of the popular datasets pose rather easy classification tasks. As a result, they are not suitable for properly evaluating learning-based matching algorithms. To address this issue, we propose a new methodology for yielding benchmark datasets. We put it into practice by creating four new matching tasks, and we verify that these new benchmarks are more challenging and therefore more suitable for further advancements in the field.
K-complex Detection Using Fourier Spectrum Analysis In EEG

K-complexes are an important marker of brain activity and are used both in clinical practice to perform sleep scoring, and in research. However, due to the size of electroencephalography (EEG) records, as well as the subjective nature of K-complex detection performed by somnologists, it is reasonable to automate K-complex detection. Previous works in this field of research have relied on the values of true positive rate and false positive rate to quantify the effectiveness of proposed methods, however this set of metrics may be misleading. The objective of the present research is to find a more accurate set of metrics and use them to develop a new method of K-complex detection, which would not rely on neural networks. Thus, the present article proposes two new methods for K-complex detection based on the fast Fourier transform. The results achieved demonstrated that the proposed methods offered a quality of K-complex detection that is either similar or superior to the quality of the methods demonstrated in previous works, including the methods employing neural networks, while requiring less computational power, meaning that K-complex detection does not require the use of neural networks. The proposed methods were evaluated using a new set of metrics, which is more representative of the quality of K-complex detection.
Absorbing Phase Transitions in Artificial Deep Neural Networks

Theoretical understanding of the behavior of infinitely-wide neural networks has been rapidly developed for various architectures due to the celebrated mean-field theory. However, there is a lack of a clear, intuitive framework for extending our understanding to finite networks that are of more practical and realistic importance. In the present contribution, we demonstrate that the behavior of properly initialized neural networks can be understood in terms of universal critical phenomena in absorbing phase transitions. More specifically, we study the order-to-chaos transition in the fully-connected feedforward neural networks and the convolutional ones to show that (i) there is a well-defined transition from the ordered state to the chaotics state even for the finite networks, and (ii) difference in architecture is reflected in that of the universality class of the transition. Remarkably, the finite-size scaling can also be successfully applied, indicating that intuitive phenomenological argument could lead us to semi-quantitative description of the signal propagation dynamics.
Sparse Graphs of Twin-width 2 Have Bounded Tree-width

Twin-width is a structural width parameter introduced by Bonnet, Kim, Thomass\'e and Watrigant [FOCS 2020]. Very briefly, its essence is a gradual reduction (a contraction sequence) of the given graph down to a single vertex while maintaining limited difference of neighbourhoods of the vertices, and it can be seen as widely generalizing several other traditional structural parameters. Having such a sequence at hand allows to solve many otherwise hard problems efficiently. Our paper focuses on a comparison of twin-width to the more traditional tree-width on sparse graphs. Namely, we prove that if a graph $G$ of twin-width at most $2$ contains no $K_{t,t}$ subgraph for some integer $t$, then the tree-width of $G$ is bounded by a polynomial function of $t$. As a consequence, for any sparse graph class $\mathcal{C}$ we obtain a polynomial time algorithm which for any input graph $G \in \mathcal{C}$ either outputs a contraction sequence of width at most $c$ (where $c$ depends only on $\mathcal{C}$), or correctly outputs that $G$ has twin-width more than $2$. On the other hand, we present an easy example of a graph class of twin-width $3$ with unbounded tree-width, showing that our result cannot be extended to higher values of twin-width.
Statistical Physics Perspective on Economic Inequality

This article is a supplement to my main contribution to the Routledge Handbook of Complexity Economics (2023). On the basis of three recent papers, it presents an unconventional perspective on economic inequality from a statistical physics point of view. One section demonstrates empirical evidence for the exponential distribution of income in 67 countries around the world. The exponential distribution was not familiar to mainstream economists until it was introduced by physicists by analogy with the Boltzmann-Gibbs distribution of energy and subsequently confirmed in empirical data for many countries. Another section reviews the two-class structure of income distribution in the USA. While the exponential law describes the majority of population (the lower class), the top tail of income distribution (the upper class) is characterized by the Pareto power law, and there is no clearly defined middle class in between. As a result, the whole distribution can be very well fitted by using only three parameters. Historical evolution of these parameters and inequality trends are analyzed from 1983 to 2018. Finally, global inequality in energy consumption and CO2 emissions per capita is studied using the empirical data from 1980 to 2017. Global inequality, as measured by the Gini coefficient G, has been decreasing until around 2010, but then saturated at the level G=0.5. The saturation at this level was theoretically predicted on the basis of the maximal entropy principle, well before the slowdown of the global inequality decrease became visible in the data. This effect is attributed to accelerated mixing of the world economy due to globalization, which brings it to the state of maximal entropy and thus results in global economic stagnation. This observation has profound consequences for social and geopolitical stability and the efforts to deal with the climate change.
Resurgent Structure of the Topological String and the First Painlev\'e Equation

We present an explicit formula for the Stokes automorphism acting on the topological string partition function. When written in terms of the dual partition function, our formula implies that flat coordinates in topological string theory transform as quantum periods, and according to the Delabaere-Dillinger-Pham formula. We first show how the formula follows from the non-linear Stokes phenomenon of the Painlev\'e I equation, together with the connection between its $\tau$-function and topological strings on elliptic curves. Then, we show that this formula is also a consequence of a recent conjecture on the resurgent structure of the topological string, based on the holomorphic anomaly equations, and it is in fact valid for arbitrary Calabi-Yau threefolds
An Algorithm for Persistent Homology Computation Using Homomorphic Encryption

Topological Data Analysis (TDA) offers a suite of computational tools that provide quantified shape features in high dimensional data that can be used by modern statistical and predictive machine learning (ML) models. In particular, persistent homology (PH) takes in data (e.g., point clouds, images, time series) and derives compact representations of latent topological structures, known as persistence diagrams (PDs). Because PDs enjoy inherent noise tolerance, are interpretable and provide a solid basis for data analysis, and can be made compatible with the expansive set of well-established ML model architectures, PH has been widely adopted for model development including on sensitive data, such as genomic, cancer, sensor network, and financial data. Thus, TDA should be incorporated into secure end-to-end data analysis pipelines. In this paper, we take the first step to address this challenge and develop a version of the fundamental algorithm to compute PH on encrypted data using homomorphic encryption (HE).
A Study of High-Speed Aerodynamic Configurations for Increased Lateral-Directional Controllability

This paper investigates the configurations needed to demonstrate positive lateral-directional controllability across the flight envelope of a hypersonic vehicle. We examined the NASA Space Shuttle Orbiter as a baseline reference configuration. The Orbiter had limited high-speed maneuvering capability; it relied on reaction-control jets to augment controllability due to a strong tendency for its aerodynamics to “control couple. ” We realize that many problems associated with the control of the hypersonic Orbiter are due to its slender configuration. This work relies upon the Evolved-Bihrle-Weissman chart as an accurate indicator of lateral-directional stability and controllability to explore variant configurations to shown how large wing tip fins may reduce its dependence on reaction control
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions

Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-task scenarios due to the insufficient task information provided only by rewards. Language-conditioned meta-RL improves the generalization capability by matching language instructions with the agent's behaviors. While both behaviors and language instructions have symmetry, which can speed up human learning of new knowledge. Thus, combining symmetry and language instructions into meta-RL can help improve the algorithm's generalization and learning efficiency. We propose a dual-MDP meta-reinforcement learning method that enables learning new tasks efficiently with symmetrical behav-iors and language instructions. We evaluate our method in mul-tiple challenging manipulation tasks, and experimental results show that our method can greatly improve the generalization and learning efficiency of meta-reinforcement learning. Videos are available at https://tumi6robot.wixsite. com/symmetry/.
Involutions of cohomology complex projective space with codimension-two fixed points

Suppose that M is a cohomology CPn which admits an involution fixing an orientable, codimension 2 submanifold of degree d. If n is even, then d is an odd divisor of n! and, if n is odd, then d is an odd divisor of n! For example, if n = 2 or 4, then d = 1. Other examples are given of situations where d must be 1.
Quantum Limits of Position and Polarizability Estimation in the Optical Near Field

Optical near fields are at the heart of various applications in sensing and imaging. We investigate dipole scattering as a parameter estimation problem and show that optical near-fields carry more information about the location and the polarizability of the scatterer than the respective far fields. This increase in information originates from and occurs simultaneously with the scattering process itself. Our calculations also yield the far-field localization limit for dipoles in free space.
Improved Decoding of Circuit Noise and Fragile Boundaries of Tailored Surface Codes

Realizing the full potential of quantum computation requires quantum error correction (QEC), with most recent breakthrough demonstrations of QEC using the surface code. QEC codes use multiple noisy physical qubits to encode information in fewer logical qubits, enabling the identification of errors through a decoding process. This process increases the logical fidelity (or accuracy) making the computation more reliable. However, most fast (efficient runtime) decoders neglect important noise characteristics, thereby reducing their accuracy. In this work, we introduce decoders that are both fast and accurate, and can be used with a wide class of QEC codes including the surface code. Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC. Using the surface code threshold as a performance metric, we observe a threshold at 0.94\% error probability for our decoders, outperforming the 0.82\% threshold for a standard minimum-weight perfect matching decoder. We also tested our belief-matching decoders in a theoretical case study of codes tailored to a biased noise model. We find that the decoders led to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code. Surprisingly, in the well-below threshold regime, the rectangular surface code becomes more resource-efficient than the tailored surface code, due to a previously unnoticed phenomenon that we call"fragile boundaries". Our decoders outperform all other fast decoders in terms of threshold and accuracy, enabling better results in current quantum error correction experiments and opening up new areas for theoretical case studies.
Table inference for combinatorial origin‐destination choices in agent‐based population synthesis

A key challenge in agent‐based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin‐destination matrices that are sampled using coarse travel surveys. This is because fine‐grained trip profiles are scarce and fragmented due to privacy and cost reasons. The discrepancy between data and sampling resolutions renders agent traits nonidentifiable due to the combinatorial space of data‐consistent individual attributes. This problem is pertinent to any agent‐based inference setting where the latent state is discrete. Existing approaches have used continuous relaxations of the underlying location assignments and subsequent ad hoc discretisation thereof. We propose a framework to efficiently navigate this space offering improved reconstruction and coverage as well as linear‐time sampling of the ground truth origin‐destination table. This allows us to avoid factorially growing rejection rates and poor summary statistic consistency inherent in discrete choice modelling. We achieve this by introducing joint sampling schemes for the continuous intensity and discrete table of agent trips, as well as Markov bases that can efficiently traverse this combinatorial space subject to summary statistic constraints. Our framework's benefits are demonstrated in multiple controlled experiments and a large‐scale application to agent work trip reconstruction in Cambridge, UK.
Unruh entropy of Schwarzschild black hole

The entropy produced by the Unruh radiation is estimated and compared to the entropy of a Schwarzschild black hole. We simulate a spherical system of mass $ M $ by set of Unruh horizons and estimate the total entropy of the outgoing radiation. Dependence on mass and spin of the emitted particles is taken into account. The obtained results can be easily extended to any other intrinsic degrees of freedom of outgoing particles. The ratio of Unruh entropy to the Schwarzschild black hole entropy is derived in exact analytical form. For large black holes this ratio is highly sensitive to quantum numbers of emitted quanta, e.g., spin $ s $, for which it varies from $ 0\% $ for $ s = 0 $ to $ 19\% $ for $ s = 5/2 $.
Characterisation of three-body loss in ${}^{166}$Er and optimised production of large Bose-Einstein condensates

Ultracold gases of highly magnetic lanthanide atoms have enabled the realisation of dipolar quantum droplets and supersolids. However, future studies could be limited by the achievable atom numbers and hindered by high three-body loss rates. Here we study density-dependent atom loss in an ultracold gas of ${}^{166}$Er for magnetic fields below $4\ \textrm{G}$, identifying six previously unknown features which display both a strong temperature dependence and also sensitivity to the polarisation and intensity of the light used to optically trap the atoms. This detailed knowledge of the loss landscape allows us to optimise the production of dipolar BECs with more than $2 \times 10^5$ atoms and points towards optimal strategies for the study of large-atom-number dipolar gases in the droplet and supersolid regimes.
Scattering, random phase and wave turbulence

We start from the remark that in wave turbulence theory, exemplified by the cubic twodimensional Schr{\"o}dinger equation (NLS) on the real plane, the regularity of the resonant manifold is linked with dispersive properties of the equation and thus with scattering phenomena. In contrast with classical analysis starting with a dynamics on a large periodic box, we propose to study NLS set on the real plane using the dispersive effects, by considering the time evolution operator in various time scales for deterministic and random initial data. By considering periodic functions embedded in the whole space by gaussian truncation, this allows explicit calculations and we identify two different regimes where the operators converges towards the kinetic operator but with different form of convergence.
DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://github.com/SCLBD/DeepfakeBench.
GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm

Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitable initialization and in the presence of a high outlier rate or in the case of small overlap though the task of rigid registration still presents great challenges. The advent of deep learning in computer vision has brought new drive to research on this topic, since it provides the possibility to learn expressive feature-representations and provide one-shot estimates instead of depending on time-consuming iterations of conventional robust methods. Yet, the rotation and permutation invariant nature of point clouds poses its own challenges to deep learning, resulting in loss of performance and low generalization capability due to sensitivity to outliers and characteristics of 3D scans not present during network training. In this work, we present a novel fast and light-weight network architecture using the attention mechanism to augment point descriptors at inference time to optimally suit the registration task of the specific point clouds it is presented with. Employing a fully-connected graph both within and between point clouds lets the network reason about the importance and reliability of points for registration, making our approach robust to outliers, low overlap and unseen data. We test the performance of our registration algorithm on different registration and generalization tasks and provide information on runtime and resource consumption. The code and trained weights are available at https://github.com/mordecaimalignatius/GAFAR/.
The geometric tensor for classical states

We use the Liouville eigenfunctions to define a classical version of the geometric tensor and study its relationship with the classical adiabatic gauge potential (AGP). We focus on integrable systems and show that the imaginary part of the geometric tensor is related to the Hannay curvature. The singularities of the geometric tensor and the AGP allows us to link the transition from Arnold-Liouville integrability to chaos with some of the mathematical formalism of quantum phase transitions.