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The integration of superconducting niobium and tantalum into superconducting quantum devices has been increasingly explored over the past few years. Recent developments have shown that two-level-systems (TLS) in the surface oxides of these superconducting films are a leading source of decoherence in quantum circuits, and understanding the surface oxidation kinetics of these materials is key to enabling scalability of these technologies. We analyze the nature of atmospheric oxidation of both niobium and a-tantalum surfaces at time scales relevant to fabrication, from sub-minute to two-week atmospheric exposure, employing a combination of x-ray photoelectron spectroscopy and transmission electron microscopy to monitor the growth of the surface oxides. The oxidation kinetics are modeled according to the Cabrera-Mott model of surface oxidation, and the model growth parameters are reported for both films. Our results indicate that niobium surface oxidation follows a consistent regime of inverse logarithmic growth for the entire time scale of the study, whereas a-Ta surface oxidation shows a clear transition between two inverse logarithmic growth regimes at time t = 1 hour, associated with the re-coordination of the surface oxide as determined by x-ray photoelectron spectroscopy analysis. Our findings provide a more complete understanding of the differences in atmospheric surface oxidation between Nb and a-Ta, particularly at short time scales, paving the way for the development of more robust fabrication control for quantum computing architectures., Comment: 15 pages, 5 figures, 4 embedded tables

Advancements in computing power have made it possible to numerically simulate large-scale fluid-mechanical and/or particulate systems, many of which are integral to core industrial processes. Among the different numerical methods available, the discrete element method (DEM) provides one of the most accurate representations of a wide range of physical systems involving granular and discontinuous materials. Consequently, DEM has become a widely accepted approach for tackling engineering problems connected to granular flows and powder mechanics. Additionally, DEM can be integrated with grid-based computational fluid dynamics (CFD) methods, enabling the simulation of chemical processes taking place, e.g., in fluidized beds. However, DEM is computationally intensive because of the intrinsic multiscale nature of particulate systems, restricting simulation duration or number of particles. Towards this end, NeuralDEM presents an end-to-end approach to replace slow numerical DEM routines with fast, adaptable deep learning surrogates. NeuralDEM is capable of picturing long-term transport processes across different regimes using macroscopic observables without any reference to microscopic model parameters. First, NeuralDEM treats the Lagrangian discretization of DEM as an underlying continuous field, while simultaneously modeling macroscopic behavior directly as additional auxiliary fields. Second, NeuralDEM introduces multi-branch neural operators scalable to real-time modeling of industrially-sized scenarios - from slow and pseudo-steady to fast and transient. Such scenarios have previously posed insurmountable challenges for deep learning models. Notably, NeuralDEM faithfully models coupled CFD-DEM fluidized bed reactors of 160k CFD cells and 500k DEM particles for trajectories of 28s. NeuralDEM will open many new doors to advanced engineering and much faster process cycles., Comment: Project page: https://nx-ai.github.io/NeuralDEM/

The observation of a resonance structure in the opening angle of the electron-positron pairs in the $^{7}$Li(p,\ee) $^{8}$Be reaction was claimed and interpreted as the production and subsequent decay of a hypothetical particle (X17). Similar excesses, consistent with this particle, were later observed in processes involving $^{4}$He and $^{12}$C nuclei with the same experimental technique. The MEG II apparatus at PSI, designed to search for the $\mu^+ \rightarrow \mathrm{e}^+ \gamma$ decay, can be exploited to investigate the existence of this particle and study its nature. Protons from a Cockroft-Walton accelerator, with an energy up to 1.1 MeV, were delivered on a dedicated Li-based target. The $\gamma$ and the e$^{+}$e$^{-}$ pair emerging from the $^8\mathrm{Be}^*$ transitions were studied with calorimeters and a spectrometer, featuring a broader angular acceptance than previous experiments. We present in this paper the analysis of a four-week data-taking in 2023 with a beam energy of 1080 keV, resulting in the excitation of two different resonances with Q-value \SI{17.6}{\mega\electronvolt} and \SI{18.1}{\mega\electronvolt}. No significant signal was found, and limits at \SI{90}{\percent} C.L. on the branching ratios (relative to the $\gamma$ emission) of the two resonances to X17 were set, $R_{17.6} < 1.8 \times 10^{-6} $ and $R_{18.1} < 1.2 \times 10^{-5} $., Comment: 11 pages, 9 figures, submitted to EPJC

A characteristic signature of quark confinement is the concentration of the chromoelectric field between a static quark-antiquark pair in a flux tube. Here we report on lattice measurements of field distributions on smeared Monte Carlo ensembles in QCD with (2+1) HISQ flavors. We measure the field distributions for several distances between static quark-antiquark sources, ranging from 0.6 fm up to the distance where the color string is expected to break., Comment: 9 pages, 6 figures, talk presented at the 41st International Symposium on Lattice Field Theory (LATTICE2024), July 28th - August 3rd, 2024, The University of Liverpool

In this paper, the authors discuss neutron-proton equilibration process induced on the 70Zn + 70Zn system at 35 MeV/nucleon, comparing experimental results with the Anti-symmetrized Molecular Dynamics and the Constrained Molecular Dynamics model simulations (COMD). The comment contains observations on the improper use of the COMD model, including some misinterpretations of the obtained results., Comment: Comment on DOI 10.1103/PhysRevC.107.024601

We present the first demonstration of simultaneous phase space compression in two spatial dimensions of a positive muon beam, the first stage of the novel high-brightness muon beam under development by the muCool collaboration at the Paul Scherrer Institute. The keV-energy, sub-mm size beam would enable a factor 10$^5$ improvement in brightness for precision muSR, and atomic and particle physics measurements with positive muons. This compression is achieved within a cryogenic helium gas target with a strong density gradient, placed in a homogeneous magnetic field, under the influence of a complex electric field. In the next phase, the muon beam will be extracted into vacuum., Comment: Submission to SciPost

Accurate characterization of ferroelectric properties in polymer piezoelectrics is critical for optimizing the performance of flexible and wearable ultrasound transducers, such as screen-printed PVDF devices. Standard charge measurement techniques, like the Sawyer-Tower circuit, often fall short when applied to ferroelectric polymers due to low-frequency leakage. In this work, we present PEtra, an open-source and versatile piezoelectric loop tracer. PEtra employs a transimpedance amplifier (LMP7721, TI) to convert picoampere-level currents into measurable voltages, covering a frequency range of 0.1 Hz to 5 Hz for a gain setting of 10^7 V/A, and 0.1 Hz to 200 Hz for gain settings between 10^3 V/A to 10^6 V/A (10-fold increments). We demonstrate through simulations and experimental validations that PEtra achieves a sensitivity down to 2 pA, effectively addressing the limitations of traditional charge measurement methods. Compared to the Sawyer-Tower circuit, PEtra directly amplifies currents without the need for a reference capacitor. As a result, it is less susceptible to leakage and can operate at lower frequencies, improving measurement accuracy and reliability. PEtra's design is fully open source, offering researchers and engineers a versatile tool to drive advancements in flexible PVDF transducer technology.

While vision transformers are able to solve a wide variety of computer vision tasks, no pre-training method has yet demonstrated the same scaling laws as observed in language models. Autoregressive models show promising results, but are commonly trained on images that are cropped or transformed into square images, which distorts or destroys information present in the input. To overcome this limitation, we propose NARAIM, a vision model pre-trained with an autoregressive objective that uses images in their native aspect ratio. By maintaining the native aspect ratio, we preserve the original spatial context, thereby enhancing the model's ability to interpret visual information. In our experiments, we show that maintaining the aspect ratio improves performance on a downstream classification task., Comment: Accepted to NeurIPS, see https://openreview.net/forum?id=7Iuh8VWU66

Quantum computing has introduced novel perspectives for tackling and improving machine learning tasks. Moreover, the integration of quantum technologies together with well-known deep learning (DL) architectures has emerged as a potential research trend gaining attraction across various domains, such as Earth Observation (EO) and many other research fields. However, prior related works in EO literature have mainly focused on convolutional architectural advancements, leaving several essential topics unexplored. Consequently, this research investigates through three cases of study fundamental aspects of hybrid quantum machine models for EO tasks aiming to provide a solid groundwork for future research studies towards more adequate simulations and looking at the post-NISQ era. More in detail, we firstly (1) investigate how different quantum libraries behave when training hybrid quantum models, assessing their computational efficiency and effectiveness. Secondly, (2) we analyze the stability/sensitivity to initialization values (i.e., seed values) in both traditional model and quantum-enhanced counterparts. Finally, (3) we explore the benefits of hybrid quantum attention-based models in EO applications, examining how integrating quantum circuits into ViTs can improve model performance.

We re-examine by numerical simulation the phase structure of the three-dimensional Abelian lattice gauge theory (LGT) with $Z(2)$ gauge fields coupled to $Z(2)$-valued Higgs fields. Concretely, we explore two different order parameters which are able to distinguish the three phases of the theory: (i) the Fredenhagen-Marcu operator used to discriminate between deconfinement and confinement/Higgs phases and (ii) the Greensite-Matsuyama overlap operator proposed recently to distinguish confinement and Higgs phases. The latter operator is an analog of the overlap Edwards-Anderson order parameter for spin-glasses. According to it, the Higgs phase is realized as a glassy phase of the gauge system. For this reason standard tricks for simulations of spin-glass phases are utilized in this work, namely tempered Monte Carlo and averaging over replicas. In addition, we also present results for a certain definition of distance between Higgs field configurations. Finally, we calculate various gauge-invariant correlation functions in order to extract the corresponding masses., Comment: 24 pages, 20 figures, one table

We present lattice Monte Carlo results on the chromoelectric field created by a static quark-antiquark pair in the vacuum of QCD with 2+1 dynamical staggered fermions at physical masses. After isolating the nonperturbative, confining part of the field, we characterize its spatial profile for several values of the physical distances between the sources, ranging from about 0.5 fm up to the onset of string breaking. Moreover, we compare our results with a model of QCD vacuum as disordered chromomagnetic condensate., Comment: 11 pages, 8 figures

This study evaluates the performance of large language models (LLMs) as medical agents in Portuguese, aiming to develop a reliable and relevant virtual assistant for healthcare professionals. The HealthCareMagic-100k-en and MedQuAD datasets, translated from English using GPT-3.5, were used to fine-tune the ChatBode-7B model using the PEFT-QLoRA method. The InternLM2 model, with initial training on medical data, presented the best overall performance, with high precision and adequacy in metrics such as accuracy, completeness and safety. However, DrBode models, derived from ChatBode, exhibited a phenomenon of catastrophic forgetting of acquired medical knowledge. Despite this, these models performed frequently or even better in aspects such as grammaticality and coherence. A significant challenge was low inter-rater agreement, highlighting the need for more robust assessment protocols. This work paves the way for future research, such as evaluating multilingual models specific to the medical field, improving the quality of training data, and developing more consistent evaluation methodologies for the medical field., Comment: This work has been submitted to the IEEE for possible publication

We compute the real corrections to the impact factor for the production of a forward Higgs boson, retaining full top-mass dependence. We demonstrate that the rapidity divergence is the one predicted by the BFKL factorization and perform the explicit subtraction in the BFKL scheme. We show that the IR-structure of the impact factor is the expected one and that, in the infinite-top-mass approximation, the previously known result is recovered. We also verify that the impact factor vanishes when the transverse momenta of the $t$-channel Reggeon goes to zero, in agreement with its gauge-invariant definition, exploiting the $m_t \rightarrow \infty$ expansion up to the next-to-next-to-leading order., Comment: 29 pages, 6 figures

Gravitational waves emitted by asymmetric rotating neutron stars are the primary targets of continuous gravitational-wave searches. Neutron stars in binary systems are particularly interesting due to the potential for non-axisymmetric deformations induced by a companion star. However, all-sky searches for unknown neutron stars in binary systems are very computationally expensive and this limits their sensitivity and/or breadth. In this paper we present results of a search for signals with gravitational-wave frequencies between $50$ and $150$~Hz, from systems with orbital periods between $100$ and $1\,000$ days and projected semi-major axes between $40$ and $200$~light-seconds. This parameter-space region has never been directly searched before. We do not detect any signal, and our results exclude gravitational-wave amplitudes above $1.25 \times 10^{-25}$ at $144.32$~Hz with $95\%$ confidence. Our improved search pipeline is more sensitive than any previous all-sky binary search by about $75\%$., Comment: 11 pages

Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization., Comment: 45 pages, 17 figures

We perform a search for continuous nearly monochromatic gravitational waves from the central compact objects associated with the supernova remnants Vela Jr. and G347.3 using LIGO O2 and O3 public data. Over $10^{18}$ different waveforms are considered, covering signal frequencies between 20-1300 Hz (20-400 Hz) for G347.3-0.5 (Vela Jr) and a very broad range of frequency derivatives. Thousands of volunteers donating compute cycles through the computing project Einstein@Home have made this endeavour possible. Following the Einstein@Home search, we perform multi-stage follow-ups of over 5 million waveforms. The selection threshold is set so that a signal could be confirmed using the first half of the LIGO O3 data. We find no significant signal candidate for either targets. Based on this null result, for G347.3-0.5, we set the most constraining upper limits to date on the amplitude of gravitational wave signals, corresponding to deformations below $10^{-6}$ in a large part of the search band. At the frequency of best strain sensitivity, near $161$ Hz, we set 90\%\ confidence upper limits on the gravitational wave intrinsic amplitude of $h_0^{90\%}\approx 6.2\times10^{-26}$. Over most of the frequency range our upper limits are a factor of 10 smaller than the indirect age-based upper limit. For Vela Jr., near $163$ Hz, we set $h_0^{90\%}\approx 6.4\times10^{-26}$. Over most of the frequency range our upper limits are a factor of 15 smaller than the indirect age-based upper limit. The Vela Jr. upper limits presented here are slightly less constraining than the most recent upper limits of \cite{ligo_o3a_c_v} but they apply to a broader set of signals.

We present novel predictions for rapidity and transverse-momentum distributions sensitive to the emission of a Higgs boson accompanied by a jet in proton collisions, calculated within the NLO fixed order in QCD and matched with the next-to leading energy-logarithmic accuracy. We also highlight first advancements in the extension of our analysis to the $Z$-boson case. We come out with the message that the improvement of fixed-order calculations on Higgs- and $Z$-boson plus jet distributions is a required step to reach the precision level of the description of observables relevant for Higgs and electroweak physics at current LHC energies and nominal FCC ones., Comment: 5 pages, 1 figure, proceedings of the 31st International Workshop on Deep Inelastic Scattering (DIS2024), 8-12 April 2024, Grenoble, France

Precision physics in the Higgs sector has been one of the main challenges of particle physics in the recent years. The pure fixed-order calculations entering the collinear factorization framework, which have been pushed up to next-cube-leading-order, are not able to describe the entire kinematic spectrum. In particular sectors, they have to be necessarily enhanced by all-order resummations. In the so-called semi-hard regime, large energy-type logarithms spoil the perturbative convergence of the series and must be resummed to all orders. This resummation is a core ingredient for a correct description of the inclusive hadroproduction of a forward Higgs boson in the limit of small Bjorken $x$, as well as for a precision study of inclusive forward emissions of a Higgs boson in association with a backward identified object. A complete resummation for these processes can be achieved at the at next-to-leading logarithmic accuracy thanks to the Balitsky-Fadin-Kuraev-Lipatov approach. In the present work we present and discuss a series of recent phenomenological results within a partial next-to-leading accuracy. They include the analysis of rapidity and azimuthal-angle differential rates for Higgs plus jet and Higgs plus charm reactions in forward and ultraforward directions of rapidity at the LHC., Comment: 5 pages, 1 figure, proceedings of the 31st International Workshop on Deep Inelastic Scattering (DIS2024), 8-12 April 2024, Grenoble, France

The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable success in diverse image segmentation scenarios. Despite its advancements, SAM encountered limitations in handling some complex low-level segmentation tasks like camouflaged object and medical imaging. In response, in 2023, we introduced SAM-Adapter, which demonstrated improved performance on these challenging tasks. Now, with the release of Segment Anything 2 (SAM2), a successor with enhanced architecture and a larger training corpus, we reassess these challenges. This paper introduces SAM2-Adapter, the first adapter designed to overcome the persistent limitations observed in SAM2 and achieve new state-of-the-art (SOTA) results in specific downstream tasks including medical image segmentation, camouflaged (concealed) object detection, and shadow detection. SAM2-Adapter builds on the SAM-Adapter's strengths, offering enhanced generalizability and composability for diverse applications. We present extensive experimental results demonstrating SAM2-Adapter's effectiveness. We show the potential and encourage the research community to leverage the SAM2 model with our SAM2-Adapter for achieving superior segmentation outcomes. Code, pre-trained models, and data processing protocols are available at http://tianrun-chen.github.io/SAM-Adaptor/, Comment: arXiv admin note: text overlap with arXiv:2304.09148

Critical infrastructure refers to essential physical and cyber systems vital to the functioning and stability of societies and economies. These systems include key sectors such as healthcare, energy, and water supply, which are crucial for societal and economic stability and are increasingly becoming prime targets for malicious actors, including state-sponsored hackers, seeking to disrupt national security and economic stability. This paper reviews literature on critical infrastructure security, focusing on penetration testing and exploit development. It explores four main questions: the characteristics of critical infrastructure, the role and challenges of penetration testing, methodologies of exploit development, and the contribution of these practices to security and resilience. The findings of this paper reveal inherent vulnerabilities in critical infrastructure and sophisticated threats posed by cyber adversaries. Penetration testing is highlighted as a vital tool for identifying and addressing security weaknesses, allowing organizations to fortify their defenses. Additionally, understanding exploit development helps anticipate and mitigate potential threats, leading to more robust security measures. The review underscores the necessity of continuous and proactive security assessments, advocating for integrating penetration testing and exploit development into regular security protocols. By doing so, organizations can preemptively identify and mitigate risks, enhancing the overall resilience of critical infrastructure. The paper concludes by emphasizing the need for ongoing research and collaboration between the public and private sectors to develop innovative solutions for the evolving cyber threat landscape. This comprehensive review aims to provide a foundational understanding of critical infrastructure security and guide future research and practices.

Object-centric (OC) representations, which represent the state of a visual scene by modeling it as a composition of objects, have the potential to be used in various downstream tasks to achieve systematic compositional generalization and facilitate reasoning. However, these claims have not been thoroughly analyzed yet. Recently, foundation models have demonstrated unparalleled capabilities across diverse domains from language to computer vision, marking them as a potential cornerstone of future research for a multitude of computational tasks. In this paper, we conduct an extensive empirical study on representation learning for downstream Visual Question Answering (VQA), which requires an accurate compositional understanding of the scene. We thoroughly investigate the benefits and trade-offs of OC models and alternative approaches including large pre-trained foundation models on both synthetic and real-world data, and demonstrate a viable way to achieve the best of both worlds. The extensiveness of our study, encompassing over 600 downstream VQA models and 15 different types of upstream representations, also provides several additional insights that we believe will be of interest to the community at large.

Osseointegration is one of the key conditions for long term successful dental implantation. To this end, titanium alloys undergo plethora of surface treatments able to sustain osteogenic differentiation. For these surface treatments, femtosecond laser (FSL) can generate precise and reproducible surface patterns on titanium, avoiding thermal damage and chemical pollution. We recently identify that laser-induced periodic surface structure (LIPSS) with radial orientation, generated on model (flat) titanium surface, has a high osteogenic potential. However, nano-texturing is time consuming. In the present study, we aimed to reduce the texturing time of radial LIPSS, as well as processing of large commercially available dental screws by ways of laser beam engineering. Our objectives were to maintain at least osteogenic properties demonstrated on model surfaces by adjusting laser beam diameters and to demonstrate maintenance of performance with a dental screw texturing process not exceeding 1 minute.We first textured model surfaces with radial LIPSS by laser beams of different diameters, with surface impacts of 24$\mu$m, 80$\mu$m or 180$\mu$m, named as R24, R80 or R180 respectively. Osteogenic performance of human mesenchymal stem cells (hMSCs) were compared; seeded on polished control surfaces and textured surfaces and subjected to osteogenic evaluation by cell/matrix imaging, qRT-PCR and mineral deposition quantification. All textured surfaces showed greater osteogenic potential than the control surfaces, with significantly higher efficacy on R180 surface. Therefore, R180 pattern with large beam impacts was chosen for texturing on a dental screw and its osteogenic activity was compared to that of a non-textured screw. Interestingly, R180 required only 40 seconds to be textured on whole screws, on which it preserved a high osteogenic potential. Thus, by using FSL technology, we have improved the osteogenic potential of a topographic pattern while optimizing and scaling up its processing time on a medical device.

Recently, Conditional Neural Fields (NeFs) have emerged as a powerful modelling paradigm for PDEs, by learning solutions as flows in the latent space of the Conditional NeF. Although benefiting from favourable properties of NeFs such as grid-agnosticity and space-time-continuous dynamics modelling, this approach limits the ability to impose known constraints of the PDE on the solutions -- e.g. symmetries or boundary conditions -- in favour of modelling flexibility. Instead, we propose a space-time continuous NeF-based solving framework that - by preserving geometric information in the latent space - respects known symmetries of the PDE. We show that modelling solutions as flows of pointclouds over the group of interest $G$ improves generalization and data-efficiency. We validated that our framework readily generalizes to unseen spatial and temporal locations, as well as geometric transformations of the initial conditions - where other NeF-based PDE forecasting methods fail - and improve over baselines in a number of challenging geometries.

Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a shared backbone Neural Field (NeF) to reconstruct the sample. However, existing CNF architectures face limitations when using this latent downstream in tasks requiring fine grained geometric reasoning, such as classification and segmentation. We posit that this results from lack of explicit modelling of geometric information (e.g. locality in the signal or the orientation of a feature) in the latent space of CNFs. As such, we propose Equivariant Neural Fields (ENFs), a novel CNF architecture which uses a geometry-informed cross-attention to condition the NeF on a geometric variable, a latent point cloud of features, that enables an equivariant decoding from latent to field. We show that this approach induces a steerability property by which both field and latent are grounded in geometry and amenable to transformation laws: if the field transforms, the latent representation transforms accordingly - and vice versa. Crucially, this equivariance relation ensures that the latent is capable of (1) representing geometric patterns faitfhully, allowing for geometric reasoning in latent space, (2) weight-sharing over similar local patterns, allowing for efficient learning of datasets of fields. We validate these main properties in a range of tasks including classification, segmentation, forecasting and reconstruction, showing clear improvement over baselines with a geometry-free latent space.

We investigate the inclusive production of fully charmed tetraquarks, $T_{4c}(0^{++})$ or $T_{4c}(2^{++})$ radial excitations, in high-energy proton collisions. We build our study upon the collinear fragmentation of a single parton in a variable-flavor number scheme, suited to describe the tetraquark formation mechanism from moderate to large transverse-momentum regimes. To this extent, we derive a novel set of DGLAP-evolving collinear fragmentation functions, named TQ4Q1.0 determinations. They encode initial-scale inputs corresponding to both gluon and heavy-quark fragmentation channels, defined within the context of quark-potential and spin-physics inspired models, respectively. We work within the NLL/NLO$^+$ hybrid factorization and make use of the JETHAD numeric interface along with the symJETHAD symbolic calculation plugin. With these tools, we provide predictions for high-energy observables sensitive to $T_{4c}$ plus jet emissions at center-of-mass energies ranging from 14 TeV at the LHC to the 100 TeV nominal energy of the FCC., Comment: 56 pages, 13 figures, version published in EPJC

In silico evolution instantiates the processes of heredity, variation, and differential reproductive success (the three "ingredients" for evolution by natural selection) within digital populations of computational agents. Consequently, these populations undergo evolution, and can be used as virtual model systems for studying evolutionary dynamics. This experimental paradigm -- used across biological modeling, artificial life, and evolutionary computation -- complements research done using in vitro and in vivo systems by enabling experiments that would be impossible in the lab or field. One key benefit is complete, exact observability. For example, it is possible to perfectly record all parent-child relationships across simulation history, yielding complete phylogenies (ancestry trees). This information reveals when traits were gained or lost, and also facilitates inference of underlying evolutionary dynamics. The Phylotrack project provides libraries for tracking and analyzing phylogenies in in silico evolution. The project is composed of 1) Phylotracklib: a header-only C++ library, developed under the umbrella of the Empirical project, and 2) Phylotrackpy: a Python wrapper around Phylotracklib, created with Pybind11. Both components supply a public-facing API to attach phylogenetic tracking to digital evolution systems, as well as a stand-alone interface for measuring a variety of popular phylogenetic topology metrics. Underlying design and C++ implementation prioritizes efficiency, allowing for fast generational turnover for agent populations numbering in the tens of thousands. Several explicit features (e.g., phylogeny pruning and abstraction, etc.) are provided for reducing the memory footprint of phylogenetic information.

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to influence phylogenetic structure. Here, we set out to assess (1) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure, (2) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure, and (3) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Further, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With such work, phylogenetic analysis could provide a versatile toolkit to study large-scale evolving populations.

Continuing generation of novelty, complexity, and adaptation are well-established as core aspects of open-ended evolution. However, it has yet to be firmly established to what extent these phenomena are coupled and by what means they interact. In this work, we track the co-evolution of novelty, complexity, and adaptation in a case study from the DISHTINY simulation system, which is designed to study the evolution of digital multicellularity. In this case study, we describe ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. We contextualize the evolutionary history of these morphologies with measurements of complexity and adaptation. Our case study suggests a loose -- sometimes divergent -- relationship can exist among novelty, complexity, and adaptation.

The paper aims to investigate relevant computational issues of deep neural network architectures with an eye to the interaction between the optimization algorithm and the classification performance. In particular, we aim to analyze the behaviour of state-of-the-art optimization algorithms in relationship to their hyperparameters setting in order to detect robustness with respect to the choice of a certain starting point in ending on different local solutions. We conduct extensive computational experiments using nine open-source optimization algorithms to train deep Convolutional Neural Network architectures on an image multi-class classification task. Precisely, we consider several architectures by changing the number of layers and neurons per layer, in order to evaluate the impact of different width and depth structures on the computational optimization performance.

In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock volatility is a critical challenge that has attracted both academics and investors. While previous studies have used multimodal deep learning-based models to obtain a general view of ECCs for volatility predicting, they often fail to capture detailed, complex information. Our research introduces a novel framework: \textbf{ECC Analyzer}, which utilizes large language models (LLMs) to extract richer, more predictive content from ECCs to aid the model's prediction performance. We use the pre-trained large models to extract textual and audio features from ECCs and implement a hierarchical information extraction strategy to extract more fine-grained information. This strategy first extracts paragraph-level general information by summarizing the text and then extracts fine-grained focus sentences using Retrieval-Augmented Generation (RAG). These features are then fused through multimodal feature fusion to perform volatility prediction. Experimental results demonstrate that our model outperforms traditional analytical benchmarks, confirming the effectiveness of advanced LLM techniques in financial analysis., Comment: 9 pages, 1 figures, 2 tables

The Lunar Gravitational-wave Antenna (LGWA) is a proposed array of next-generation inertial sensors to monitor the response of the Moon to gravitational waves (GWs). Given the size of the Moon and the expected noise produced by the lunar seismic background, the LGWA would be able to observe GWs from about 1 mHz to 1 Hz. This would make the LGWA the missing link between space-borne detectors like LISA with peak sensitivities around a few millihertz and proposed future terrestrial detectors like Einstein Telescope or Cosmic Explorer. In this article, we provide a first comprehensive analysis of the LGWA science case including its multi-messenger aspects and lunar science with LGWA data. We also describe the scientific analyses of the Moon required to plan the LGWA mission.

The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering (Q$\&$A), and stock movement prediction (binary classification), with a notable gap in the application of LLMs for financial risk prediction. Addressing this gap, in this paper, we introduce \textbf{RiskLabs}, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely combines different types of financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data surrounding ECC release dates. Our approach involves a multi-stage process: initially extracting and analyzing ECC data using LLMs, followed by gathering and processing time-series data before the ECC dates to model and understand risk over different timeframes. Using multimodal fusion techniques, RiskLabs amalgamates these varied data features for comprehensive multi-task financial risk prediction. Empirical experiment results demonstrate RiskLab's effectiveness in forecasting both volatility and variance in financial markets. Through comparative experiments, we demonstrate how different data sources contribute to financial risk assessment and discuss the critical role of LLMs in this context. Our findings not only contribute to the AI in finance application but also open new avenues for applying LLMs in financial risk assessment., Comment: 24 pages, 7 figures, 5 tables, 1 algorithm

This paper presents the proof-of-concept of the first automatic voice assistant ever built in Wolof language, the main vehicular language spoken in Senegal. This voicebot is the result of a collaborative research project between Orange Innovation in France, Orange Senegal (aka Sonatel) and ADNCorp, a small IT company based in Dakar, Senegal. The purpose of the voicebot is to provide information to Orange customers about the Sargal loyalty program of Orange Senegal by using the most natural mean to communicate: speech. The voicebot receives in input the customer's oral request that is then processed by a SLU system to reply to the customer's request using audio recordings. The first results of this proof-of-concept are encouraging as we achieved 22\% of WER for the ASR task and 78\% of F1-score on the NLU task., Comment: in French language

Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth estimation have gained significant interest in recent years, owing to the potential benefits of this methodology in overcoming the limitations of active depth sensing systems. Moreover, due to the low cost and size of monocular cameras, researchers have focused their attention on monocular depth estimation (MDE), which consists in estimating a dense depth map from a single RGB video frame. State of the art MDE models typically rely on vision transformers (ViT) architectures that are highly deep and complex, making them unsuitable for fast inference on devices with hardware constraints. Purposely, in this paper, we address the problem of exploiting ViT in MDE on embedded devices. Those systems are usually characterized by limited memory capabilities and low-power CPU/GPU. We propose METER, a novel lightweight vision transformer architecture capable of achieving state of the art estimations and low latency inference performances on the considered embedded hardwares: NVIDIA Jetson TX1 and NVIDIA Jetson Nano. We provide a solution consisting of three alternative configurations of METER, a novel loss function to balance pixel estimation and reconstruction of image details, and a new data augmentation strategy to improve the overall final predictions. The proposed method outperforms previous lightweight works over the two benchmark datasets: the indoor NYU Depth v2 and the outdoor KITTI.

Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A common solution to overcome this lack of data is to employ graphic engines to produce synthetic proxies; however, those data do not often reflect real-world images, resulting in poor performance of the trained models at the inference step. In this paper we propose a novel training pipeline that incorporates Diffusion4D (D4D), a customized 4-channels diffusion model able to generate realistic RGBD samples. We show the effectiveness of the developed solution in improving the performances of deep learning models on the monocular depth estimation task, where the correspondence between RGB and depth map is crucial to achieving accurate measurements. Our supervised training pipeline, enriched by the generated samples, outperforms synthetic and original data performances achieving an RMSE reduction of (8.2%, 11.9%) and (8.1%, 6.1%) respectively on the indoor NYU Depth v2 and the outdoor KITTI dataset.