Search

We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics., Comment: 8 pages

Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while improving inference speed up to 12x compared to other deep learning approaches on our tested benchmarks. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.

The Music Emotion Recognition (MER) field has seen steady developments in recent years, with contributions from feature engineering, machine learning, and deep learning. The landscape has also shifted from audio-centric systems to bimodal ensembles that combine audio and lyrics. However, a severe lack of public and sizeable bimodal databases has hampered the development and improvement of bimodal audio-lyrics systems. This article proposes three new audio, lyrics, and bimodal MER research datasets, collectively called MERGE, created using a semi-automatic approach. To comprehensively assess the proposed datasets and establish a baseline for benchmarking, we conducted several experiments for each modality, using feature engineering, machine learning, and deep learning methodologies. In addition, we propose and validate fixed train-validate-test splits. The obtained results confirm the viability of the proposed datasets, achieving the best overall result of 79.21% F1-score for bimodal classification using a deep neural network., Comment: 16 pages, 4 figures, 13 tables, submitted to IEEE Transactions on Affective Computing

Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks., Comment: 10 + 14 pages, 4 + 7 figures

This work presents phase envelope and speed of sound data for the (0.60 $CO_{2}$ + 0.40 $C_{3}H_{8}$) and (0.80 $CO_{2}$ + 0.20 $C_{3}H_{8}$) binary mixtures. Phase equilibria was measured using a cylindrical resonator working in the microwave band whereas an acoustic resonator was used for speed of sound measurements. The experimental results were compared with GERG-2008 equation of state, obtaining average absolute deviations by 0.24% in pressure for phase equilibria data and 0.025% for speed of sound data. Speed of sound values were used to derive perfect-gas heat capacities, acoustic virial coefficients, and second density virial coefficients. In addition, AGA8-DC92 equation of state performance was checked for the results derived from speeds of sound.

Heavy neutral leptons (HNLs), depending on their mass and mixing, can be efficiently produced in meson decays from the target or absorber in short- to medium-baseline accelerator neutrino experiments, leaving detectable signals through their decays inside the neutrino detectors. We show that the currently running ICARUS experiment at Fermilab can reconstruct the HNL mass and explore new HNL parameter space in the mass range of 70-190 MeV. The mass reconstruction is enabled by two ingredients: (i) simple two-body kinematics of HNL production from stopped kaon decays at the NuMI absorber, followed by HNL decay into a charged-lepton pair and neutrino at the detector, and (ii) high resolution of Liquid Argon Time Projection Chamber (LArTPC) detectors in reconstructing final state particles. Our mass reconstruction method is robust under realistic energy resolution and angular smearing of the charged leptons, and is applicable to any LArTPC detector. We also discuss the synergy between ICARUS and future facilities like DUNE near detector and PIP-II beam dump in probing the HNL parameter space., Comment: 14 pages, 6 figures, 1 table

A synthetic Mirnov coils diagnostic is presented and used to study the capabilities of the poloidal array of single-axis coils and the two helical arrays of tri-axial coils installed in the TJ-II stellarator. This tool integrates the plasma currents induced by Alfv\'en-like perturbations of the electric potential inside the plasma and provides the induced magnetic field oscillations anywhere outside of it. The simulated signals can then be analyzed in the same manner as the experimental ones, and a scan on the radial position and width of the potential perturbation is conducted to find the limiting values that produce identifiable signals. We find, not surprisingly, that core-localized modes are indistinguishable from one another; and that the identification of low-n, low-m modes is often subject to off-by-one errors. We also determine the optimal polarization basis in which to analyze the tri-axial coils signals and address the diagnostic performance when resolving components of gap modes such as HAEs. Additionally, selected cases have been analyzed with a simplified plasma response model, showing that plasma shielding of the mode currents may further deteriorate the accuracy of the mode identification method. The code for this synthetic diagnostic, that can be easily used to conduct similar analysis for other devices, is publicly available in http://github.com/pponsv/synth_mirnov .

Thermal soaring enables birds to perform cost-efficient flights during foraging or migration trips. Yet, although all soaring birds exploit vertical winds effectively, this group contains species that vary strongly in their morphologies. Aerodynamic rules dictate the costs and benefits of flight, but, depending on their ecological needs, species may use different behavioural strategies. To quantify these morphology-related differences in behavioural cross-country strategies, we compiled and analysed a large dataset, which includes data from over a hundred individuals from 12 soaring species recorded with high frequency tracking devices. We quantified the performance during thermalling and gliding flights, and the overall cross-country behaviour that is the combination of both. Our results confirmed aerodynamic theory across the 12 species; species with higher wing loading typically flew faster, and consequently turned on a larger radius, than lighter ones. Furthermore, the combination of circling radius and minimum sink speed determines the maximum benefits soaring birds can obtain from thermals. Also, we observed a spectrum of strategies regarding the adaptivity to thermal strength and uncovered a universal rule for cross-country strategies for all analysed species. Finally, our newly described behavioural rules can provide inspirations for technical applications, like the development of autopilot systems for autonomous robotic gliders., Comment: 31 pages, 5 figures. SI included (5 Supplementary Figures, 5 Supplementary Tables, Supplementary Text)

The Shannon entropy of a random variable X has much behaviour analogous to a signed measure. Previous work has explored this connection by defining a signed measure on abstract sets, which are taken to represent the information that different random variables contain. This construction is sufficient to derive many measure-theoretical counterparts to information quantities such as the mutual information $I(X; Y) = \mu(\tilde{X} \cap \tilde{Y})$, the joint entropy $H(X,Y) = \mu(\tilde{X} \cup \tilde{Y})$, and the conditional entropy $H(X|Y) = \mu(\tilde{X} \setminus \tilde{Y})$. Here we provide concrete characterisations of these abstract sets and a corresponding signed measure, and in doing so we demonstrate that there exists a much finer decomposition with intuitive properties which we call the logarithmic decomposition (LD). We show that this signed measure space has the useful property that its logarithmic atoms are easily characterised with negative or positive entropy, while also being consistent with Yeung's I-measure. We present the usability of our approach by re-examining the G\'acs-K\"orner common information and the Wyner common information from this new geometric perspective and characterising it in terms of our logarithmic atoms - a property we call logarithmic decomposability. We present possible extensions of this construction to continuous probability distributions before discussing implications for quality-led information theory. Lastly, we apply our new decomposition to examine the Dyadic and Triadic systems of James and Crutchfield and show that, in contrast to the I-measure alone, our decomposition is able to qualitatively distinguish between them., Comment: 25 pages, 10 figures, extended version of a conference paper

A robust face recognition model must be trained using datasets that include a large number of subjects and numerous samples per subject under varying conditions (such as pose, expression, age, noise, and occlusion). Due to ethical and privacy concerns, large-scale real face datasets have been discontinued, such as MS1MV3, and synthetic face generators have been proposed, utilizing GANs and Diffusion Models, such as SYNFace, SFace, DigiFace-1M, IDiff-Face, DCFace, and GANDiffFace, aiming to supply this demand. Some of these methods can produce high-fidelity realistic faces, but with low intra-class variance, while others generate high-variance faces with low identity consistency. In this paper, we propose a Triple Condition Diffusion Model (TCDiff) to improve face style transfer from real to synthetic faces through 2D and 3D facial constraints, enhancing face identity consistency while keeping the necessary high intra-class variance. Face recognition experiments using 1k, 2k, and 5k classes of our new dataset for training outperform state-of-the-art synthetic datasets in real face benchmarks such as LFW, CFP-FP, AgeDB, and BUPT. Our source code is available at: https://github.com/BOVIFOCR/tcdiff., Comment: SIBGRAPI 2024

Modeling of the collimator-detector response (CDR) in SPECT reconstruction enables improved resolution and more accurate quantitation, especially for higher energy imaging (e.g.Lu-177 and Ac-225). Such modeling, however, can pose a significant computational bottleneck when there are substantial components of septal penetration and scatter in the acquired data, since a direct convolution-based approach requires large 2D kernels. The present work presents an alternative method for fast and accurate CDR compensation using a linear operator built from 1D convolutions and rotations (1D-R). To enable open-source development and use of these models in image reconstruction, we release a SPECTPSFToolbox repository for the PyTomography project on GitHub.

Why humans fight has no easy answer. However, understanding better how humans fight could inform future interventions, hidden shifts and casualty risk. Fusion-fission describes the well-known grouping behavior of fish etc. fighting for survival in the face of strong opponents: they form clusters ('fusion') which provide collective benefits and a cluster scatters when it senses danger ('fission'). Here we show how similar clustering (fusion-fission) of human fighters provides a unified quantitative explanation for complex casualty patterns across decades of Israel-Palestine region violence, as well as the October 7 surprise attack -- and uncovers a hidden post-October 7 shift. State-of-the-art data shows this fighter fusion-fission in action. It also predicts future 'super-shock' attacks that will be more lethal than October 7 and will arrive earlier. It offers a multi-adversary solution. Our results -- which include testable formulae and a plug-and-play simulation -- enable concrete risk assessments of future casualties and policy-making grounded by fighter behavior., Comment: Comments welcome. Working paper

Understanding the dynamical behavior of complex systems from their underlying network architectures is a long-standing question in complexity theory. Therefore, many metrics have been devised to extract network features like motifs, centrality, and modularity measures. It has previously been proposed that network symmetries are of particular importance since they are expected to underly the synchronization of a system's units, which is ubiquitously observed in nervous system activity patterns. However, perfectly symmetrical structures are difficult to assess in noisy measurements of biological systems, like neuronal connectomes. Here, we devise a principled method to infer network symmetries from combined connectome and neuronal activity data. Using nervous system-wide population activity recordings of the \textit{C.elegans} backward locomotor system, we infer structures in the connectome called fibration symmetries, which can explain which group of neurons synchronize their activity. Our analysis suggests functional building blocks in the animal's motor periphery, providing new testable hypotheses on how descending interneuron circuits communicate with the motor periphery to control behavior. Our approach opens a new door to exploring the structure-function relations in other complex systems, like the nervous systems of larger animals., Comment: 32 pages, 7 figures

Since the advent of mobile robots, obstacle detection has been a topic of great interest. It has also been a subject of study in neuroscience, where flying insects and bats could be considered two of the most interesting cases in terms of vision-based and sound-based mechanisms for obstacle detection, respectively. Currently, many studies focus on vision-based obstacle detection, but not many can be found regarding sound-based obstacle detection. This work focuses on the latter approach, which also makes use of a Spiking Neural Network to exploit the advantages of these architectures and achieve an approach closer to biology. The complete system was tested through a series of experiments that confirm the validity of the spiking architecture for obstacle detection. It is empirically demonstrated that, when the distance between the robot and the obstacle decreases, the output firing rate of the system increases in response as expected, and vice versa. Therefore, there is a direct relation between the two. Furthermore, there is a distance threshold between detectable and undetectable objects which is also empirically measured in this work. An in-depth study on how this system works at low level based on the Inter-Spike Interval concept was performed, which may be useful in the future development of applications based on spiking filters., Comment: 22 pages, 8 figures

While the power of quantum computers is commonly acknowledged to rise exponentially, it is often overlooked that the complexity of quantum noise mechanisms generally grows much faster. In particular, quantifying whether the instructions on a quantum processor are close to being unitary has important consequences concerning error rates, e.g., for the confidence in their estimation, the ability to mitigate them efficiently, or their relation to fault-tolerance thresholds in error correction. However, the complexity of estimating the coherence, or unitarity, of noise generally scales exponentially in system size. Here, we obtain an upper bound on the average unitarity of Pauli noise and develop a protocol allowing us to estimate the average unitarity of operations in a digital quantum device efficiently and feasibly for mid-size quantum systems. We demonstrate our results through both experimental execution on IQM Spark (TM), a 5-qubit superconducting quantum computer, and in simulation with up to 10 qubits, discussing the prospects for extending our technique to arbitrary scales., Comment: 11+21 pages, 10 figures

Exposure to cosmic radiation is a major concern in space exploration. On the Martian surface, a complex radiation field is present, formed by a constant influx of galactic cosmic radiation and the secondary particles produced by their interaction with the planet's atmosphere and regolith. In this work, a Martian environment model was developed using MCNP6 following the guidelines of the 1st Mars Space Radiation Modeling Workshop. The accuracy of the model was tested by comparing particle spectra and dose rate results with other model results and measurements from the Radiation Assessment Detector (RAD) onboard the Curiosity rover, taken between November 15, 2015, and January 15, 2016. The ICRP's voxel-type computational phantoms were then implemented into the code. Organ dose and effective dose equivalent were assessed for the same time period. The viability of a mission on the surface of Mars for extended periods of time under the assumed conditions was here investigated.

Gravitational waves can provide crucial insights about the environments in which black holes live. In this work, we use numerical relativity simulations to study the behaviour of self-interacting scalar (wave-like) dark matter clouds accreting onto isolated and binary black holes. We find that repulsive self-interactions smoothen the ``spike'' of an isolated black hole and saturate the density. Attractive self-interactions enhance the growth and result in more cuspy profiles, but can become unstable and undergo explosions akin to the superradiant bosenova that reduce the local cloud density. We quantify the impact of self-interactions on an equal-mass black hole merger by computing the dephasing of the gravitational-wave signal for a range of couplings. We find that repulsive self-interactions saturate the density of the cloud, thereby reducing the dephasing. For attractive self-interactions, the dephasing may be larger, but if these interactions dominate prior to the merger, the dark matter can undergo bosenova during the inspiral phase, disrupting the cloud and subsequently reducing the dephasing., Comment: 8 pages, 7 figures, 1 movie: https://youtu.be/ZeoMlUHknUM. Submitted to PRD. Comments welcome!

This work provides an optimization mechanism to ensure the compatibility of ReBCO (Rare-earth Barium Copper Oxide) high-temperature superconducting (HTS) tapes with non-planar stellarator coils. ReBCO coils enable higher field strengths and operating temperatures for the magnet systems of future fusion reactors but are sensitive to strain due to their brittle, ceramic functional layer. We have implemented a metric to optimize strain on stellarator coils made from ReBCO superconductors into the stellarator optimization framework \texttt{SIMSOPT} and used it to design new stellarator coil configurations. To ensure structural integrity of coils wound with HTS tape, we introduce a penalty on binormal curvature and torsion along a coil. It can be used to optimize the orientation of the winding path for a given coil filament or to jointly optimize orientation and coil filament. We apply the strain optimization to three cases. For the EPOS (Electrons and Positrons in an Optimized Stellarator) design, we combine the strain penalty with an objective for quasisymmetry into a single-stage optimization; this enables us to find a configuration with excellent quasisymmetry at the smallest possible size compatible with the use of ReBCO tape. For CSX (Columbia Stellarator eXperiment), in addition to HTS strain, we add a penalty to prevent net tape rotation to ease the coil winding process. If the strain is calculated for a coil at reactor scale, we find a considerable variation of the binormal and torsional strain over the cross section of the large winding pack (0.5\,m x 0.5\,m). By exploiting the overall orientation of the winding pack as a degree of freedom, we can reduce binormal and torsional strains below limits for every ReBCO stack., Comment: 14 pages, 14 figures

In this work, we explore the use and reliability of Large Language Models (LLMs) in musicology. From a discussion with experts and students, we assess the current acceptance and concerns regarding this, nowadays ubiquitous, technology. We aim to go one step further, proposing a semi-automatic method to create an initial benchmark using retrieval-augmented generation models and multiple-choice question generation, validated by human experts. Our evaluation on 400 human-validated questions shows that current vanilla LLMs are less reliable than retrieval augmented generation from music dictionaries. This paper suggests that the potential of LLMs in musicology requires musicology driven research that can specialized LLMs by including accurate and reliable domain knowledge.

We present a thorough investigation of non-stabilizerness - a fundamental quantum resource that quantifies state complexity within the framework of quantum computing - in a one-dimensional U(1) lattice gauge theory. We show how non-stabilizerness is always extensive with volume, and has no direct relation to the presence of critical points. However, its derivatives typically display discontinuities across the latter: This indicates that non-stabilizerness is strongly sensitive to criticality, but in a manner that is very different from entanglement (that, typically, is maximal at the critical point). Our results indicate that error-corrected simulations of lattice gauge theories close to the continuum limit have similar computational costs to those at finite correlation length and provide rigorous lower bounds for quantum resources of such quantum computations., Comment: 5 pages + supplement. Comments welcome!!

Using integrability, we construct (to leading order in perturbation theory) the explicit form of twist-three light-ray operators in planar $\mathcal{N}=4$ SYM. This construction allows us to directly compute analytically continued CFT data at complex spin. We derive analytically the "magic'' decoupling zeroes previously observed numerically. Using the Baxter equation, we also show that certain Regge trajectories merge together into a single unifying Riemann surface. Perhaps more surprisingly, we find that this unification of Regge trajectories is not unique. If we organize twist-three operators differently into what we call "cousin trajectories'' we find infinitely more possible continuations. We speculate about which of these remarkable features of twist-three operators might generalize to other operators, other regimes and other theories., Comment: 52 pages, 15 figures

Generalizing the dihedral picture for G(M,M,2), we construct Hecke algebras (and categories) and asymptotic counterparts. We think of these as associated with the complex reflection group G(M,M,N)., Comment: 35 pages, many figures, comments welcome

The non-Hermitian skin effect (NHSE) is a distinctive topological phenomenon observed in nonHermitian systems. Recently, there has been considerable interest in exploring higher-order NHSE occurrences in two and three dimensions. In such systems, topological edge states collapse into a corner while bulk states remain delocalized. Through a Hermitian-non-Hermitian correspondence, this study predicts and experimentally observes the higher-order NHSE in an acoustic Kagome lattice possessing nonreciprocal hoppings. By rotating the frequency spectrum and employing complexfrequency excitation techniques, we observe the localization of acoustic energy towards a corner of the lattice in the topologically nontrivial phase, even when the source is located far from that corner. In contrast, the acoustic energy spreads out when excited at the frequencies hosting the bulk states. These observations are unequivocal evidence of the higher-order NHSE.

Several organic cations have been used to passivate perovskite films; however, selecting the optimal cation remains challenging. In this work, we carried out density functional theory calculations to understand the effects induced by 17 different organic cations on the passivation (P-cations) of thin two-dimensional P2MAn-1PbnI3n+1 perovskites films, where n = 1 and 2. We found that the interactions between different types of P-cations and the inorganic slab affect the length and angles of the bonds within the inorganic framework (PbI6-octahedra). In general, the binding mechanism includes the interactions of organic cations with the inorganic framework, which leads to the accumulation of electron density within the halides, indicative of Bronsted--Lowry acid-base interactions. Oxygenated groups facilitate additional H-bond formation through -OH and -COOH groups, promoting the localization of the electron density between layers and improving the energetic stability of the system. Based on the results and analysis, we found that three P-cations might have higher potential for real-life applications, namely 4-fluorophenylethylammonium (FPEA), phenylethylammonium (PEA) and butylammonium (BtA)., Comment: Keywords: Perovskites thin films; Organic cation passivation; MAPbI3-based layers; DFT; 2D materials

We introduce the notion of the \textit{Bruce-Roberts number} for holomorphic 1-forms relative to complex analytic varieties. Our main result shows that the Bruce-Roberts number of a 1-form $\omega$ with respect to a complex analytic hypersurface $X$ with an isolated singularity can be expressed in terms of the \textit{Ebeling--Gusein-Zade index} of $\omega$ along $X$, the \textit{Milnor number} of $\omega$ and the \textit{Tjurina number} of $X$. This result allows us to recover known formulas for the Bruce-Roberts number of a holomorphic function along $X$ and to establish connections between this number, the radial index, and the local Euler obstruction of $\omega$ along $X$. Moreover, we present applications to both global and local holomorphic foliations in complex dimension two., Comment: 31 pages. arXiv admin note: text overlap with arXiv:2107.01967 by other authors

Biological age scores are an emerging tool to characterize aging by estimating chronological age based on physiological biomarkers. Various scores have shown associations with aging-related outcomes. This study assessed the relation between an age score based on brain MRI images (BrainAge) and an age score based on metabolomic biomarkers (MetaboAge). We trained a federated deep learning model to estimate BrainAge in three cohorts. The federated BrainAge model yielded significantly lower error for age prediction across the cohorts than locally trained models. Harmonizing the age interval between cohorts further improved BrainAge accuracy. Subsequently, we compared BrainAge with MetaboAge using federated association and survival analyses. The results showed a small association between BrainAge and MetaboAge as well as a higher predictive value for the time to mortality of both scores combined than for the individual scores. Hence, our study suggests that both aging scores capture different aspects of the aging process.

Node-based programming languages are increasingly popular in media arts coding domains. These languages are designed to be accessible to users with limited coding experience, allowing them to achieve creative output without an extensive programming background. Using LLM-based code generation to further lower the barrier to creative output is an exciting opportunity. However, the best strategy for code generation for visual node-based programming languages is still an open question. In particular, such languages have multiple levels of representation in text, each of which may be used for code generation. In this work, we explore the performance of LLM code generation in audio programming tasks in visual programming languages at multiple levels of representation. We explore code generation through metaprogramming code representations for these languages (i.e., coding the language using a different high-level text-based programming language), as well as through direct node generation with JSON. We evaluate code generated in this way for two visual languages for audio programming on a benchmark set of coding problems. We measure both correctness and complexity of the generated code. We find that metaprogramming results in more semantically correct generated code, given that the code is well-formed (i.e., is syntactically correct and runs). We also find that prompting for richer metaprogramming using randomness and loops led to more complex code.

We investigate the dynamics of a two-degree-of-freedom mechanical system for energies slightly above a critical value. The critical set of the potential function is assumed to contain a finite number of saddle points. As the energy increases across the critical value, a disk-like component of the Hill region gets connected to other components precisely at the saddles. Under certain convexity assumptions on the critical set, we show the existence of a weakly convex foliation in the region of the energy surface where the interesting dynamics takes place. The binding of the foliation is formed by the index-$2$ Lyapunov orbits in the neck region about the rest points and a particular index-$3$ orbit. Among other dynamical implications, the transverse foliation forces the existence of periodic orbits, homoclinics, and heteroclinics to the Lyapunov orbits. We apply the results to the H\'enon-Heiles potential for energies slightly above $1/6$. We also discuss the existence of transverse foliations for decoupled mechanical systems, including the frozen Hill's lunar problem with centrifugal force, the Stark problem, the Euler problem of two centers, and the potential of a chemical reaction.

Leveraging the capabilities of Knowledge Distillation (KD) strategies, we devise a strategy to fight the recent retraction of face recognition datasets. Given a pretrained Teacher model trained on a real dataset, we show that carefully utilising synthetic datasets, or a mix between real and synthetic datasets to distil knowledge from this teacher to smaller students can yield surprising results. In this sense, we trained 33 different models with and without KD, on different datasets, with different architectures and losses. And our findings are consistent, using KD leads to performance gains across all ethnicities and decreased bias. In addition, it helps to mitigate the performance gap between real and synthetic datasets. This approach addresses the limitations of synthetic data training, improving both the accuracy and fairness of face recognition models., Comment: Accepted at ECCV 2024 Workshops

New constraints on the expansion rate of the Universe seem to favor evolving dark energy in the form of thawing quintessence models, i.e., models for which a canonical, minimally coupled scalar field has, at late times, begun to evolve away from potential energy domination. We scrutinize the evidence for thawing quintessence by exploring what it predicts for the equation of state. We show that, in terms of the usual Chevalier-Polarski-Linder parameters, ($w_0$, $w_a$), thawing quintessence is, in fact, only marginally consistent with a compilation of the current data. Despite this, we embrace the possibility that thawing quintessence is dark energy and find constraints on the microphysics of this scenario. We do so in terms of the effective mass $m^2$ and energy scale $V_0$ of the scalar field potential. We are particularly careful to enforce un-informative, flat priors on these parameters so as to minimize their effect on the final posteriors. While the current data favors a large and negative value of $m^2$, when we compare these models to the standard $\Lambda$CDM model we find that there is scant evidence for thawing quintessence., Comment: 18 pages, 14 figures. Comments welcome! Submitted to PRD

As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teacher framework to distill knowledge to a student network. In our approach, directed at face recognition use cases, we train four teachers on one specific ethnicity, leading to four highly specialized and biased teachers. Our strategy learns a project of these four teachers into a common space and distill that information to a student network. Our results highlighted increased performance and reduced bias for all our experiments. In addition, we further show that having biased/specialized teachers is crucial by showing that our approach achieves better results than when knowledge is distilled from four teachers trained on balanced datasets. Our approach represents a step forward to the understanding of the importance of ethnicity-specific features., Comment: Accepted at ECCV 2024 ABAW

To describe the spatial trajectory of an overdamped Brownian particle, inertial effects can be neglected. Yet, at the energetic level of stochastic thermodynamics, changes in kinetic energy must be considered to accurately predict the heat exchanged with the thermal bath. On the other hand, in the presence of external driving forces, one would expect the effects of kinetic energy fluctuations to be reduced, as thermal noise becomes comparatively less relevant. Here, we investigate the competition between the kinetic energy and the external work contributions to the heat statistics of passive and active overdamped Brownian particles subject to external driving forces. We find that kinetic energy effects cause fluctuations in the exchanged heat to become non-Gaussian. To evaluate the relevance of these effects, we compute the excess kurtosis and the Pearson correlation. For fixed parameter values adapted from experiments, we identify a crossover transition from a regime in which the stochastic heat of overdamped particles is dominated by external work, where kinetic energy changes can be safely ignored, to a regime dominated by kinetic energy effects. Our results also provide a quantitative analytical way to assess how deep into a particular regime the system is., Comment: 13 pages, 4 figures

We present ground-based photometric observations of secondary eclipses of the hottest known planet KELT-9b using MuSCAT2 and Sinistro. We detect secondary eclipse signals in $i$ and $z_{\rm s}$ with eclipse depths of $373^{+74}_{-75}$ ppm and $638^{+199}_{-178}$, respectively. We perform an atmospheric retrieval on the emission spectrum combined with the data from HST/WFC3, Spitzer, TESS, and CHEOPS to obtain the temperature profile and chemical abundances, including TiO and VO, which have been thought to produce temperature inversion structures in the dayside of ultra-hot Jupiters. While we confirm a strong temperature inversion structure, we find low abundances of TiO and VO with mixing ratios of $\rm{log(TiO)}=-7.80^{+0.15}_{-0.30}$ and $\rm{log(VO)}=-9.60^{+0.64}_{-0.57}$, respectively. The low abundances of TiO and VO are consistent with theoretical predictions for such an ultra-hot atmosphere. In such low abundances, TiO and VO have little effect on the temperature structure of the atmosphere. The abundance of ${\rm e}^{-}$, which serves as a proxy for ${\rm H}^{-}$ ions in this study, is found to be high, with $\rm{log(e^-)}=-4.89\pm{0.06}$. These results indicate that the temperature inversion in KELT-9 b's dayside atmosphere is likely not caused by TiO/VO, but rather by the significant abundance of ${\rm H}^{-}$ ions. The best-fit model cannot fully explain the observed spectrum, and chemical species not included in the retrieval may introduce modeling biases. Future observations with broader wavelength coverage and higher spectral resolution are expected to provide more accurate diagnostics on the presence and abundances of TiO/VO. These advanced observations will overcome the limitations of current data from HST and photometric facilities, which are constrained by narrow wavelength coverage and instrumental systematics., Comment: 16 pages, 9 figures, 4 tables, Accepted for publication in PASJ

We introduce G-Style, a novel algorithm designed to transfer the style of an image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting is a powerful 3D representation for novel view synthesis, as -- compared to other approaches based on Neural Radiance Fields -- it provides fast scene renderings and user control over the scene. Recent pre-prints have demonstrated that the style of Gaussian Splatting scenes can be modified using an image exemplar. However, since the scene geometry remains fixed during the stylization process, current solutions fall short of producing satisfactory results. Our algorithm aims to address these limitations by following a three-step process: In a pre-processing step, we remove undesirable Gaussians with large projection areas or highly elongated shapes. Subsequently, we combine several losses carefully designed to preserve different scales of the style in the image, while maintaining as much as possible the integrity of the original scene content. During the stylization process and following the original design of Gaussian Splatting, we split Gaussians where additional detail is necessary within our scene by tracking the gradient of the stylized color. Our experiments demonstrate that G-Style generates high-quality stylizations within just a few minutes, outperforming existing methods both qualitatively and quantitatively.

The implementation of machine learning in Internet of Things devices poses significant operational challenges due to limited energy and computation resources. In recent years, significant efforts have been made to implement simplified ML models that can achieve reasonable performance while reducing computation and energy, for example by pruning weights in neural networks, or using reduced precision for the parameters and arithmetic operations. However, this type of approach is limited by the performance of the ML implementation, i.e., by the loss for example in accuracy due to the model simplification. In this article, we present adaptive resolution inference (ARI), a novel approach that enables to evaluate new tradeoffs between energy dissipation and model performance in ML implementations. The main principle of the proposed approach is to run inferences with reduced precision (quantization) and use the margin over the decision threshold to determine if either the result is reliable, or the inference must run with the full model. The rationale is that quantization only introduces small deviations in the inference scores, such that if the scores have a sufficient margin over the decision threshold, it is unlikely that the full model would have a different result. Therefore, we can run the quantized model first, and only when the scores do not have a sufficient margin, the full model is run. This enables most inferences to run with the reduced precision model and only a small fraction requires the full model, so significantly reducing computation and energy while not affecting model performance. The proposed ARI approach is presented, analyzed in detail, and evaluated using different data sets for floating-point and stochastic computing implementations. The results show that ARI can significantly reduce the energy for inference in different configurations with savings between 40% and 85%.

Surface porosity has been found to be an important property for small bodies. Some asteroids and comets can exhibit an extremely high surface porosity in the first millimeter layer. This layer may be produced by various processes and maintained by the lack of an atmosphere. However, the influence of porosity on the spectro-photometric properties of small body surfaces is not yet fully understood. In this study, we looked into the effect of porosity on the spectro-photometric properties of Phobos regolith spectroscopic simulants; created by mixing the simulants with ultra-pure water, producing ice-dust particles, and then sublimating the water. The reflectance spectroscopic properties in the visible and near-infrared (0.5-4.2 $\mu$m) show no strong variations between the porous and compact samples. However, one simulant exhibits a bluing of the slope after increasing porosity, providing possible insights into the differences between the blue and red units observed on Phobos. In the mid-infrared range, a contrast increase of the 10-$\mu$m emissivity plateau due to silicates is observed. Photometry reveals a modification in the phase reddening behavior between the compact powder and the sublimation residue for both simulants. However, the observed behavior is different between the simulants, suggesting that the phase reddening may be dependent on the composition of the simulants. The phase curve also appears to be modified by the addition of porosity, with a higher contribution of forward scattering observed for the sublimation residue. The derivation of the Hapke parameters indicates an increase in roughness for the porous sample, but no significant modification of the opposition effect. This study aims to provide new insights into the understanding of porosity by using two Phobos simulants in the context of the upcoming JAXA/Martian Moons eXploration mission., Comment: Submitted to Icarus

Measurements of Higgs boson processes by the ATLAS and CMS experiments at the LHC use Simplified Template Cross Sections (STXS) as a common framework for the combination of measurements in different decay channels and their further interpretation, e.g. to measure Higgs couplings. The different Higgs production processes are measured in predefined kinematic regions -- the STXS bins -- requiring precise theory predictions for each individual bin. In gluon-fusion Higgs production a main division is into 0-jet, 1-jet, and $\geq 2$-jet bins, which are further subdivided in bins of the Higgs transverse momentum $p_T^H$. Requiring a fixed number of jets induces logarithms $\ln p_T^{\mathrm{cut}}/Q$ in the cross section where $p_T^{\mathrm{cut}}$ is the jet-$p_T$ threshold and $Q\sim p_T^H\sim m_H$ the hard-interaction scale. These jet-veto logarithms can be resummed to all orders in perturbation theory to achieve the highest possible perturbative precision. We provide state-of-the art predictions for the $p_T^H$ spectrum in exclusive $H+$1-jet production and the corresponding $H+$1-jet STXS bins in the kinematic regime $p_T^{\mathrm{cut}} \ll p_T^H\sim m_H$. We carry out the resummation at NNLL$'$ accuracy, using theory nuisance parameters to account for the few unknown ingredients at this order, and match to full NNLO. We revisit the jet-veto factorization for this process and find that it requires refactorizing the total soft function into a global and soft-collinear contribution in order to fully account for logarithms of the signal jet radius. The leading nonglobal logarithms are also included, though they are numerically small for the region of phenomenological interest., Comment: 52 pages, 13 figures

Quantum metrology offers significant improvements in several quantum technologies. In this work, we propose a Gaussian quantum metrology protocol assisted by initial position-momentum correlations (PM). We employ a correlated Gaussian wave packet as a probe to examine the dynamics of Quantum Fisher Information (QFI) and purity based on PM correlations to demonstrate how to estimate the PM correlations and, more importantly, to unlock its potential applications such as a resource to enhance quantum thermometry. In the low-temperature regime, we find an improvement in the thermometry of the surrounding environment when the original system exhibits a non-null initial correlation (correlated Gaussian state). In addition, we explore the connection between the loss of purity and the gain in QFI during the process of estimating the effective environment coupling and its effective temperature.

Financial fraud is the cause of multi-billion dollar losses annually. Traditionally, fraud detection systems rely on rules due to their transparency and interpretability, key features in domains where decisions need to be explained. However, rule systems require significant input from domain experts to create and tune, an issue that rule induction algorithms attempt to mitigate by inferring rules directly from data. We explore the application of these algorithms to fraud detection, where rule systems are constrained to have a low false positive rate (FPR) or alert rate, by proposing RIFF, a rule induction algorithm that distills a low FPR rule set directly from decision trees. Our experiments show that the induced rules are often able to maintain or improve performance of the original models for low FPR tasks, while substantially reducing their complexity and outperforming rules hand-tuned by experts., Comment: Published as a conference paper at RuleML+RR 2024

Quantum computing has tremendous potential to overcome some of the fundamental limitations present in classical information processing. Yet, today's technological limitations in the quality and scaling prevent exploiting its full potential. Quantum computing based on superconducting quantum processing units (QPUs) is among the most promising approaches towards practical quantum advantage. In this article the basic technological approach of IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. In particular, the focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We also present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.

By exciting a series of $1\mathrm{s}^{2}\, ^{1}\mathrm{S}_{0} \to 1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ transitions in helium-like nitrogen ions with linearly polarized monochromatic soft X-rays at the Elettra facility, we found a change in the angular distribution of the fluorescence sensitive to the principal quantum number $n$. In particular it is observed that the ratio of emission in directions parallel and perpendicular to the polarization of incident radiation increases with higher $n$. We find this $n$-dependence to be a manifestation of the Hanle effect, which served as a practical tool for lifetime determinations of optical transitions since its discovery in 1924. In contrast to traditional Hanle effect experiments, in which one varies the magnetic field and considers a particular excited state, we demonstrate a 'soft X-ray Hanle effect' which arises in a static magnetic field but for a series of excited states. By comparing experimental data with theoretical predictions, we were able to determine lifetimes ranging from hundreds of femtoseconds to tens of picoseconds of the $1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ levels, which find excellent agreement with atomic-structure calculations. We argue that dedicated soft X-ray measurements could yield lifetime data that is beyond current experimental reach and cannot yet be predicted with sufficient accuracy., Comment: 7 pages, 4 figures

This work provides a complete rheological characterization of molybdenum disulfide (MoS2) inks in the presence of electric fields. Several concentrations of MoS2 are studied and dispersed in a viscoelastic fluid. The lubrication effects are present in the ink when the MoS2 concentration is higher than 0.10% w/w. The dielectric properties show the impossibility of a positive electrorheological effect for all MoS2-inks studied. The formation of vortices and electromigration of MoS2 particles occur under the influence of an external electric field. These two phenomena affect the rheological behavior of MoS2-inks under shear flow condition. Relatively to the extensional rheology experiments, the particle migration and the vortex formation promote anisotropy on the rheological properties of the inks which affects the relaxation time, the formation of beads-on-a-string and the uniaxial elongational flow condition is no longer valid. When the electric field strength is 1.5 kV/mm, the formation of Taylor's cone is observed and independent of MoS2 concentration.

Antarctic ice shelves play a vital role in preserving the physical conditions of the Antarctic cryosphere and the Southern Ocean, and beyond. By serving as a buttressing force, ice shelves prevent sea-level rise by restraining the flow of continental ice and glaciers to the sea. Sea-level rise impacts the global environment in multiple ways, including flooding habitats, eroding coastlines, and contaminating soil and groundwater. It is therefore essential to monitor the stability of Antarctic ice shelves, for which a variety of complementary data sources is required. We have developed RIS-Vis, a novel data visualization platform to monitor Antarctic ice shelves. Although focused on the Ross Ice Shelf (RIS), RIS-Vis could be readily scaled to monitor other ice shelves around Antarctica, and elsewhere. Currently, RIS-Vis is capable of analyzing and visualizing seismic, geodetic, and weather data to provide meaningful information for Antarctic cryosphere research. RIS-Vis was built using Python libraries including Obspy, APScheduler, and the Plotly Dash framework, and uses SQLite as the backing database. Visualizations developed on RIS-Vis include filtered seismic waveforms, spectrograms, and power spectral densities, geodetic-based ice-shelf flow, and meteorological variables such as atmospheric temperature and pressure. The dashboard visualization platform abstracts away the time-intensive analysis process of raw data and allows scientists to better concentrate on RIS science.

We propose an empirical approach centered on the spectral dynamics of weights -- the behavior of singular values and vectors during optimization -- to unify and clarify several phenomena in deep learning. We identify a consistent bias in optimization across various experiments, from small-scale ``grokking'' to large-scale tasks like image classification with ConvNets, image generation with UNets, speech recognition with LSTMs, and language modeling with Transformers. We also demonstrate that weight decay enhances this bias beyond its role as a norm regularizer, even in practical systems. Moreover, we show that these spectral dynamics distinguish memorizing networks from generalizing ones, offering a novel perspective on this longstanding conundrum. Additionally, we leverage spectral dynamics to explore the emergence of well-performing sparse subnetworks (lottery tickets) and the structure of the loss surface through linear mode connectivity. Our findings suggest that spectral dynamics provide a coherent framework to better understand the behavior of neural networks across diverse settings.

Active galactic nuclei (AGN) are among the main candidates for ultra-high-energy cosmic ray (UHECR) sources. However, while theoretical and some phenomenological works favor AGNs as the main sources, recent works have shown that using the very-high-energy (VHE) $\gamma$-ray flux as a proxy for the UHECR flux leads to a bad agreement with data. In this context, the energy spectrum and composition data are hardly fitted. At the same time, the arrival directions map is badly described and a spurious dipole direction is produced. In this work, we propose a possible solution to these contradictions. Using the observed $\gamma$-ray flux as a proxy may carry the implicit assumption of beamed UHECR emission and, consequently, its beam will remain collimated up to its detection on Earth. We show that assuming an isotropic UHECR emission and correcting the $\gamma$-ray emission proxy by Doppler boosting can overcome the problem. The combined fit of the spectrum and composition is improved by $3.56\sigma$, while the predicted arrival directions agree much better with the data. In particular, a spurious direction of the dipole can be reduced from $10.3 \ (5.4)\sigma$ away from the data to $2.2 \ (1.5)\sigma$ for $E > 8$ EeV ($E > 32$ EeV). We also show that this effect is particularly important when including AGNs of different classes in the same analysis, such as radio galaxies and blazars.