Miracle-dz/UniFAR-FTUs huggingface dataset:
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold

In this work we propose a non-contrastive method for anomaly detection and segmentation in images, that benefits both from a modern machine learning approach and a more classic statistical detection theory. The method consists of three phases. First, features are extracted using a multi-scale image Transformer architecture. Then, these features are fed into a U-shaped Normalizing Flow that lays the theoretical foundations for the last phase, which computes a pixel-level anomaly map, and performs a segmentation based on the a contrario framework. This multiple-hypothesis testing strategy permits to derive robust automatic detection thresholds, which are crucial in many real-world applications, where an operational point is needed. The segmentation results are evaluated using the Intersection over Union (IoU) metric; and for assessing the generated anomaly maps we report the area under the Receiver Operating Characteristic curve (AUROC), and the area under the per-region-overlap curve (AUPRO). Extensive experimentation in various datasets shows that the proposed approach produces state-of-the-art results for all metrics and all datasets, ranking first in most MvTec-AD categories, with a mean pixel-level AUROC of 98.74%. Code and trained models are available at https:// github.com/mtailanian/uflow.
Compact object mergers: exploring uncertainties from stellar and binary evolution with sevn

Population-synthesis codes are an unique tool to explore the parameter space of massive binary star evolution and binary compact object (BCO) formation. Most population-synthesis codes are based on the same stellar evolution model, limiting our ability to explore the main uncertainties. Here, we present the new version of the code sevn, which overcomes this issue by interpolating the main stellar properties from a set of pre-computed evolutionary tracks. We describe the new interpolation and adaptive time-step algorithms of sevn, and the main upgrades on single and binary evolution. With sevn, we evolved 1.2 × 109 binaries in the metallicity range 0.0001 ≤ Z ≤ 0.03, exploring a number of models for electron-capture, core-collapse and pair-instability supernovae, different assumptions for common envelope, stability of mass transfer, quasi-homogeneous evolution, and stellar tides. We find that stellar evolution has a dramatic impact on the formation of single and BCOs. Just by slightly changing the overshooting parameter (λov = 0.4, 0.5) and the pair-instability model, the maximum mass of a black hole can vary from ≈60 to ≈100 M⊙. Furthermore, the formation channels of BCOs and the merger efficiency we obtain with sevn show significant differences with respect to the results of other population-synthesis codes, even when the same binary-evolution parameters are used. For example, the main traditional formation channel of BCOs is strongly suppressed in our models: at high metallicity (Z ≳ 0.01) only <20 per cent of the merging binary black holes and binary neutron stars form via this channel, while other authors found fractions >70 per cent.
Dark matter constraints from a joint analysis of dwarf Spheroidal galaxy observations with VERITAS

We present constraints on the annihilation cross section of weakly interacting massive particles dark matter based on the joint statistical analysis of four dwarf galaxies with VERITAS. These results are derived from an optimized photon weighting statistical technique that improves on standard imaging atmospheric Cherenkov telescope (IACT) analyses by utilizing the spectral and spatial properties of individual photon events. We report on the results of ∼230 hours of observations of five dwarf galaxies and the joint statistical analysis of four of the dwarf galaxies. We find no evidence of gamma-ray emission from any individual dwarf nor in the joint analysis. The derived upper limit on the dark matter annihilation cross section from the joint analysis is 1.35×10-23 cm3 s−1 at 1 TeV for the bottom quark (bb¯) final state, 2.85×10-24 cm3 s−1 at 1 TeV for the tau lepton (τ+τ-) final state and 1.32×10-25 cm3 s−1 at 1 TeV for the gauge boson (γγ) final state.
Query Complexity of Inversion Minimization on Trees

We consider the following computational problem: Given a rooted tree and a ranking of its leaves, what is the minimum number of inversions of the leaves that can be attained by ordering the tree? This variation of the problem of counting inversions in arrays originated in mathematical psychology, with the evaluation of the Mann--Whitney statistic for detecting differences between distributions as a special case. We study the complexity of the problem in the comparison-query model, used for problems like sorting and selection. For many types of trees with $n$ leaves, we establish lower bounds close to the strongest known in the model, namely the lower bound of $\log_2(n! )$ for sorting $n$ items. We show: (a) $\log_2((\alpha(1-\alpha)n)! ) - O(\log n)$ queries are needed whenever the tree has a subtree that contains a fraction $\alpha$ of the leaves. This implies a lower bound of $\log_2((\frac{k}{(k+1)^2}n)! ) - O(\log n)$ for trees of degree $k$. (b) $\log_2(n! ) - O(\log n)$ queries are needed in case the tree is binary. (c) $\log_2(n! ) - O(k \log k)$ queries are needed for certain classes of trees of degree $k$, including perfect trees with even $k$. The lower bounds are obtained by developing two novel techniques for a generic problem $\Pi$ in the comparison-query model and applying them to inversion minimization on trees. Both techniques can be described in terms of the Cayley graph of the symmetric group with adjacent-rank transpositions as the generating set. Consider the subgraph consisting of the edges between vertices with the same value under $\Pi$. We show that the size of any decision tree for $\Pi$ must be at least: (i) the number of connected components of this subgraph, and (ii) the factorial of the average degree of the complementary subgraph, divided by $n$. Lower bounds on query complexity then follow by taking the base-2 logarithm.
Third order nonlinear study of ZnO nano particles under femto-second laser illumination

ZnO nano particles are synthesized at different percentages of Ag dopant by facile precipitation method. Two photon absorption (2PA) cross-section and nonlinear refractive index of ZnO nano particles are studied with 130 femto-second laser pulses at a repletion rate of 1 kHz with central wavelength of 532 nm by z-scan technique. The 2PA cross-section of ZnO nano particles are obtained by theoretical calculations, using rate equation approach. Both absorption cross-section and nonlinear refractive index increased with increasing the Ag dopant percentage. We have observed an increase of order of 104 in the nonlinear refractive index in the presence of femto-second laser excitation with reference to nano-second laser excitation.
Numerical simulation of dust devil‐scale vortices

A high‐resolution (2 m grid) large‐eddy simulation of the convective boundary layer is performed for the purpose of comparing physical characteristics of simulated convective vertical vortices to those of observed dust devils. This study is a necessary part of a larger effort to examine the possible formation and maintenance mechanisms for vertical vortices in the convective boundary layer. The case of convective vertical vortex formation in environments without imposed mean winds or wind shears is considered in this paper. Simulated vertical vortices and observations of dust devils compare favourably overall. Other findings include the detection of ‘bookend’ vortex patterns embedded within the convergence branches of the simulated cellular convective pattern, which is different from prior results in which vortices were found only at cell vertices. Lastly, the theoretical Rankine and Burgers–Rott vortex models are compared to the simulated vortices. It is found that the Burgers–Rott model matches the high‐resolution simulated vortex data better than the often used Rankine model. Copyright © 2005 Royal Meteorological Society
Sustainable Wireless Services With UAV Swarms Tailored to Renewable Energy Sources

Unmanned Aerial Vehicle (UAV) swarms are often required in off-grid scenarios, such as disaster-struck, war-torn or rural areas, where the UAVs have no access to the power grid and instead rely on renewable energy. Considering a main battery fed from two renewable sources, wind and solar, we scale such a system based on the financial budget, environmental characteristics, and seasonal variations. Interestingly, the source of energy is correlated with the energy expenditure of the UAVs, since strong winds cause UAV hovering to become increasingly energy-hungry. The aim is to maximize the cost efficiency of coverage at a particular location, which is a combinatorial optimization problem for dimensioning of the multivariate energy generation system under non-convex criteria. We have devised a customized algorithm by lowering the processing complexity and reducing the solution space through sampling. Evaluation is done with condensed real-world data on wind, solar energy, and traffic load per unit area, driven by vendor-provided prices. The implementation was tested in four locations, with varying wind or solar intensity. The best results were achieved in locations with mild wind presence and strong solar irradiation, while locations with strong winds and low solar intensity require higher Capital Expenditure (CAPEX) allocation.
Dark matter production via a non-minimal coupling to gravity

We study postinflationary scalar dark matter production via its non-minimal coupling to gravity. During the inflaton oscillation epoch, dark matter is produced resonantly for a sufficiently large non-minimal coupling ξ ≳ 5. We find that backreaction on the curvature and rescattering effects typically become important for the values of ξ above 30, which invalidate simple estimates of the production efficiency. At large couplings, the dark matter yield becomes almost independent of ξ, signifying approximate quasi-equilibrium in the inflaton-dark matter system. Although the analysis gets complicated by the presence of apparent negative energy in the Jordan frame, this behaviour can be regularized by introducing mild dark matter self-interaction. Using lattice simulations, we delineate parameter space leading to the correct dark matter relic abundance.
Photothermal and Thermo-optical Effects in 3D Arrays of Dielectric and Plasmonic Nanoantennas

Thermonanophotonics, i.e. the study of photothermal effects in optical nanoantennas, has recently attracted growing interest. While thermoplasmonic structures enable a broad range of applications, from imaging and optofluidics devices to medical and photochemical systems, dielectric nanoantennas open new opportunities for thermo-optical modulation and reconfigurable metasurfaces. However, computing both photo-thermal and thermo-optical effects in large arrays of nanoantennas remains a challenge. In this work, we implement a fast numerical method to compute the temperature increase of multi-dimensional arrays of optical antennas embedded in a uniform medium, accounting for self-heating, collective heating as well as thermo-optical effects. In particular, we demonstrate scalable computation of temperature in 3D networks with $10^5$ particles in less than 1 hour. Interestingly, by explicitly considering the role of discrete nanoparticles on light attenuation and photothermal conversion, this approach enables the optimization of complex temperature profiles in 3D arrays. Importantly, we compute for the first time the impact of thermo-optical effects beyond the single nanoantenna. Our results show that collective heating contributions amplify these effects in multi-dimensional arrays of both Silicon and Gold nanospheres, highlighting the importance of considering them in photothermal calculations. Overall, the proposed method opens new opportunities for the rapid assessment of complex photothermal effects in arrays of optical nanoantennas, supporting the development of advanced thermonanophotonic functionalities.
Factor-guided functional PCA for high-dimensional functional data

The literature on high-dimensional functional data focuses on either the dependence over time or the correlation among functional variables. In this paper, we propose a factor-guided functional principal component analysis (FaFPCA) method to consider both temporal dependence and correlation of variables so that the extracted features are as sufficient as possible. In particular, we use a factor process to consider the correlation among high-dimensional functional variables and then apply functional principal component analysis (FPCA) to the factor processes to address the dependence over time. Furthermore, to solve the computational problem arising from triple-infinite dimensions, we creatively build some moment equations to estimate loading, scores and eigenfunctions in closed form without rotation. Theoretically, we establish the asymptotical properties of the proposed estimator. Extensive simulation studies demonstrate that our proposed method outperforms other competitors in terms of accuracy and computational cost. The proposed method is applied to analyze the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, resulting in higher prediction accuracy and 41 important ROIs that are associated with Alzheimer's disease, 23 of which have been confirmed by the literature.
Leveraging Memory Effects and Gradient Information in Consensus-Based Optimization: On Global Convergence in Mean-Field Law

In this paper, we study consensus-based optimisation (CBO), a versatile, flexible and customisable optimisation method suitable for performing nonconvex and nonsmooth global optimisations in high dimensions. CBO is a multi-particle metaheuristic, which is effective in various applications and at the same time amenable to theoretical analysis thanks to its minimalistic design. The underlying dynamics, however, is flexible enough to incorporate different mechanisms widely used in evolutionary computation and machine learning, as we show by analysing a variant of CBO which makes use of memory effects and gradient information. We rigorously prove that this dynamics converges to a global minimiser of the objective function in mean-field law for a vast class of functions under minimal assumptions on the initialisation of the method. The proof in particular reveals how to leverage further, in some applications advantageous, forces in the dynamics without loosing provable global convergence. To demonstrate the benefit of the herein investigated memory effects and gradient information in certain applications, we present numerical evidence for the superiority of this CBO variant in applications such as machine learning and compressed sensing, which en passant widen the scope of applications of CBO.
Primordial gravitational waves in a quantum model of big bounce

We quantise and solve the dynamics of gravitational waves in a quantum Friedmann-Lemaitre-Robertson-Walker spacetime filled with perfect fluid. The classical model is formulated canonically. The Hamiltonian constraint is de-parametrised by setting a fluid variable as the internal clock. The obtained reduced (i.e. physical) phase space is then quantised. Our quantisation procedure is implemented in accordance with two different phase space symmetries, namely, the Weyl-Heisenberg symmetry for the perturbation variables, and the affine symmetry for the background variables. As an appealing outcome, the initial singularity is removed and replaced with a quantum bounce. The quantum model depends on a free parameter that is naturally induced from quantisation and determines the scale of the bounce. We study the dynamics of the quantised gravitational waves across the bounce through three different methods (“thin-horizon”, analytical and numerical) which give consistent results and we determine the primordial power spectrum for the case of radiation-dominated universe. Next, we use the instantaneous radiation-matter transition transfer function to make approximate predictions for late universe and constrain our model with LIGO and Planck data. We also give an estimate of the quantum uncertainties in the present-day universe.
Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation

—Semi-supervised domain adaptation aims to classify data belonging to a target domain by utilizing a related label-rich source domain and very few labeled examples of the target domain. Here, we propose a novel framework, Pred&Guide, which leverages the inconsistency between the predicted and the actual class labels of the few labeled target examples to effectively guide the domain adaptation in a semi-supervised setting. Pred&Guide consists of three stages, as follows (1) First, in order to treat all the target samples equally, we perform unsupervised domain adaptation coupled with self-training; (2) Second is the label prediction stage, where the current model is used to predict the labels of the few labeled target examples, and (3) Finally, the correctness of the label predictions are used to effectively weigh source examples class-wise to better guide the domain adaptation process. Extensive experiments show that the proposed Pred&Guide framework achieves state-of-the-art results for two large-scale benchmark datasets, namely Ofﬁce-Home and DomainNet.
Heat properties for groups

We revisit Fourier’s approach to solve the heat equation on the circle in the context of (twisted) reduced group C*-algebras, convergence of Fourier series and semigroups associated to negative deﬁnite functions. We introduce some heat properties for countably inﬁnite groups and investigate when they are satisﬁed. Kazhdan’s property (T) is an obstruction to the weakest property, and our ﬁndings leave open the possibility that this might be the only one. On the other hand, many groups with the Haagerup property satisfy the strongest version. We show that this heat property implies that the associated heat problem has a unique solution regardless of the choice of the initial datum.
Are High-Redshift Damped Lyα Galaxies Lyman Break Galaxies?

We use deep HST STIS and NICMOS images of three spectroscopically confirmed galaxy counterparts of high-redshift damped Lyα (DLA) absorbers (one of which is a new discovery) to test the hypothesis that high-redshift DLA galaxies are Lyman break galaxies. If this hypothesis is correct, the emission properties of DLA galaxies must lie within the range of emission properties measured for Lyman break galaxies of similar absolute magnitude. This will be true regardless of selection biases in the sample of detected DLA galaxies. We test this prediction using several emission properties: half-light radius, radial profile (Sérsic n-parameter), optical to near-infrared color, morphology, Lyα emission equivalent width, and Lyα emission velocity structure. In all cases the measured values for the DLA galaxies lie within the range measured for the population of Lyman break galaxies. None of the measurements is in conflict with the prediction. We conclude that the measured emission properties of the three DLA galaxies studied here are consistent with the conjecture that high-redshift DLA galaxies are Lyman break galaxies. We show that this result does not conflict with the observation that the few high-redshift DLA galaxies discovered are mostly fainter than spectroscopically confirmed L* Lyman break galaxies.
Ultrafast Charge Transfer Enhancement in CdS–MoS2 via a Linker Molecule

Hybrid systems, which take advantage of low material dimensionality, have great potential for designing nanoscale devices. Quantum dots (QDs) -- a 0D nanostructure -- can be combined with 2D monolayers to achieve success in photovoltaics and photocatalytic water splitting. In such colloidal systems, ligand molecules such as cysteine play an important role in device performance. The role of the ligand molecule in these QD heterostructures is poorly understood. In this study, time-dependent density functional theory (TD-DFT) is employed in order to explore how the ligand affect the charge transfer at the ultra-fast timescale. We study the charge transfer dynamics in CdS-MoS2 heterostructures both with and without an organic linker molecule. We find that the ligand molecule enhances the ultrafast charge transfer, and that electrons are preferentially transferred from CdS to MoS2 as band alignment would predict. The electronic dynamics and time-evolved projection character are sensitive to the ionic temperature and excitation density.
Toward $N$ to $N\pi$ matrix elements from lattice QCD

QCD matrix elements of axial and vector currents between nucleons are required for the Monte Carlo reconstruction of the energy of neutrinos that are detected in long baseline oscillation experiments in the quasi-elastic regime. The cleanest approach for determining the axial matrix elements is lattice QCD. However, the extraction of these from the corresponding correlation functions is complicated by very large excited state contributions, that are related to transitions from the nucleon to a nucleon-pion pair. In this pilot study with a pion mass $m_\pi = 429~ \mathrm{MeV}$, we demonstrate for the first time that these contributions can be removed by including five-(anti)quark operators into the basis of interpolators used to create the nucleon. The same techniques will be needed to compute transition matrix elements between the nucleon and nucleon-pion scattering states that are relevant in the resonance production regime.
Energy Consumption of Automated Program Repair

Automated program repair (APR) aims to automatize the process of repairing software bugs in order to reduce the cost of maintaining software programs. Moreover, the success (given by the accuracy metric) of APR approaches has increased in recent years. However, no previous work has considered the energy impact of repairing bugs automatically using APR. The field of green software research aims to measure the energy consumption required to develop, maintain, and use software products. This paper combines, for the first time, the APR and Green software research fields. We have as main goal to define the foundation for measuring the energy consumption of the APR activity. We measure the energy consumption of ten traditional program repair tools for Java and ten fine-tuned Large-Language Models (LLM) on source code trying to repair real bugs from Defects4J, a set of real buggy programs. The initial results from this experiment show the existing trade-off between energy consumption and the ability to correctly repair bugs: Some APR tools are capable of achieving higher accuracy by spending less energy than other tools.
Formation of Optimal Interbank Lending Networks under Liquidity Shocks

We formulate a model of the banking system in which banks control both their supply of liquidity, through cash holdings, and their exposures to risky interbank loans. The value of interbank loans jumps when banks suffer liquidity shortages, which can be caused by the arrival of large enough liquidity shocks. In two distinct settings, we compute the unique optimal allocations of capital. In the first, banks seek only to maximize their own utility -- in a decentralized manner. Second, a central planner aims to maximize the sum of all banks' utilities. Both of the resulting financial networks exhibit a `core-periphery' structure. However, the optimal allocations differ -- decentralized banks are more susceptible to liquidity shortages, while the planner ensures that banks with more debt hold greater liquidity. We characterize the behavior of the planner's optimal allocation as the size of the system grows. Surprisingly, the `price of anarchy' is of constant order. Finally, we derive capitalization requirements that cause the decentralized system to achieve the planner's level of risk. In doing so, we find that systemically important banks must face the greatest losses when they suffer liquidity crises -- ensuring that they are incentivized to avoid such crises.