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The $(2+1)$D Solid-On-Solid (SOS) model famously exhibits a roughening transition: on an $N\times N$ torus with the height at the origin rooted at $0$, the variance of $h(x)$, the height at $x$, is $O(1)$ at large inverse-temperature $\beta$, vs. $\asymp \log |x|$ at small $\beta$ (as in the Gaussian free field (GFF)). The former--rigidity at large $\beta$--is known for a wide class of $|\nabla\phi|^p$ models ($p=1$ being SOS) yet is believed to fail once the surface is on a slope (tilted boundary conditions). It is conjectured that the slope would destabilize the rigidity and induce the GFF-type behavior of the surface at small $\beta$. The only rigorous result on this is by Sheffield (2005): for these models of integer height functions, if the slope $\theta$ is irrational, then Var$(h(x))\to\infty$ with $|x|$ (with no known quantitative bound). We study a family of SOS surfaces at a large enough fixed $\beta$, on an $N\times N$ torus with a nonzero boundary condition slope $\theta$, perturbed by a potential $V$ of strength $\epsilon_\beta$ per site (arbitrarily small). Our main result is (a) the measure on the height gradients $\nabla h$ has a weak limit $\mu_\infty$ as $N\to\infty$; and (b) the scaling limit of a sample from $\mu_\infty$ converges to a full plane GFF. In particular, we recover the asymptotics Var$(h(x))\sim c\log|x|$. To our knowledge, this is the first example of a tilted $|\nabla\phi|^p$ model, or a perturbation thereof, where the limit is recovered at large $\beta$. The proof looks at random monotone surfaces that approximate the SOS surface, and shows that (i) these form a weakly interacting dimer model, and (ii) the renormalization framework of Giuliani, Mastropietro and Toninelli (2017) leads to the GFF limit. New ingredients are needed in both parts, including a nontrivial extension of [GMT17] from finite interactions to any long range summable interactions., Comment: 95 pages; 18 figures

Contrary to fermions and bosons, anyons are quasiparticles that keep a robust memory of particle exchanges via a braiding phase factor. This provides them with unique dynamical properties so far unexplored. When an anyon excitation is emitted toward a quantum point contact (QPC) in a fractional quantum Hall (FQH) fluid, this memory translates into tunneling events that may occur long after the anyon excitation has exited the QPC. Here, we use triggered anyon pulses incident on a QPC in a $\nu= 1/3$ FQH fluid to investigate anyon tunneling in the time domain. We observe that braiding increases the tunneling timescale, which is set by the temperature and the anyon scaling dimension that characterizes the edge state dynamics. This contrasts with the electron behavior where braiding is absent and the tunneling timescale is set by the temporal width of the generated electron pulses. Our experiment introduces time-domain measurements for characterizing the braiding phase and scaling dimension of anyons.

Feedforward monocular face capture methods seek to reconstruct posed faces from a single image of a person. Current state of the art approaches have the ability to regress parametric 3D face models in real-time across a wide range of identities, lighting conditions and poses by leveraging large image datasets of human faces. These methods however suffer from clear limitations in that the underlying parametric face model only provides a coarse estimation of the face shape, thereby limiting their practical applicability in tasks that require precise 3D reconstruction (aging, face swapping, digital make-up, ...). In this paper, we propose a method for high-precision 3D face capture taking advantage of a collection of unconstrained videos of a subject as prior information. Our proposal builds on a two stage approach. We start with the reconstruction of a detailed 3D face avatar of the person, capturing both precise geometry and appearance from a collection of videos. We then use the encoder from a pre-trained monocular face reconstruction method, substituting its decoder with our personalized model, and proceed with transfer learning on the video collection. Using our pre-estimated image formation model, we obtain a more precise self-supervision objective, enabling improved expression and pose alignment. This results in a trained encoder capable of efficiently regressing pose and expression parameters in real-time from previously unseen images, which combined with our personalized geometry model yields more accurate and high fidelity mesh inference. Through extensive qualitative and quantitative evaluation, we showcase the superiority of our final model as compared to state-of-the-art baselines, and demonstrate its generalization ability to unseen pose, expression and lighting., Comment: SIGGRAPH Asia 2024 Conference Paper. Project page: https://kelianb.github.io/SPARK/

Function names can greatly aid human reverse engineers, which has spurred development of machine learning-based approaches to predicting function names in stripped binaries. Much current work in this area now uses transformers, applying a metaphor of machine translation from code to function names. Still, function naming models face challenges in generalizing to projects completely unrelated to the training set. In this paper, we take a completely new approach by transferring advances in automated image captioning to the domain of binary reverse engineering, such that different parts of a binary function can be associated with parts of its name. We propose BLens, which combines multiple binary function embeddings into a new ensemble representation, aligns it with the name representation latent space via a contrastive learning approach, and generates function names with a transformer architecture tailored for function names. In our experiments, we demonstrate that BLens significantly outperforms the state of the art. In the usual setting of splitting per binary, we achieve an $F_1$ score of 0.77 compared to 0.67. Moreover, in the cross-project setting, which emphasizes generalizability, we achieve an $F_1$ score of 0.46 compared to 0.29., Comment: 23 pages, 5 figures. Tristan Benoit and Yunru Wang have made equally significant contributions to this work

Humans perceive the world through multisensory integration, blending the information of different modalities to adapt their behavior. Contrastive learning offers an appealing solution for multimodal self-supervised learning. Indeed, by considering each modality as a different view of the same entity, it learns to align features of different modalities in a shared representation space. However, this approach is intrinsically limited as it only learns shared or redundant information between modalities, while multimodal interactions can arise in other ways. In this work, we introduce CoMM, a Contrastive MultiModal learning strategy that enables the communication between modalities in a single multimodal space. Instead of imposing cross- or intra- modality constraints, we propose to align multimodal representations by maximizing the mutual information between augmented versions of these multimodal features. Our theoretical analysis shows that shared, synergistic and unique terms of information naturally emerge from this formulation, allowing us to estimate multimodal interactions beyond redundancy. We test CoMM both in a controlled and in a series of real-world settings: in the former, we demonstrate that CoMM effectively captures redundant, unique and synergistic information between modalities. In the latter, CoMM learns complex multimodal interactions and achieves state-of-the-art results on the six multimodal benchmarks., Comment: 22 pages

We propose a source of purely electronic energy-entangled states implemented in a solid-state system with potential applications in quantum information protocols based on electron flying qubits. The proposed device relies on the standard tools of Electron Quantum Optics (EQO) and exploits entanglement of the Cooper pairs of a BCS superconductor. The latter is coupled via an adjustable quantum point contact to two opposite spin polarized electron wave-guides, which are driven by trains of Lorentzian pulses. This specific choice for the drive is crucial to inject purely electronic entangled-states devoid of spurious electron-hole pairs. In the Andreev regime, a perturbative calculation in the tunnel coupling confirms that entangled electrons states (EES) are generated at the output of the normal side. We introduce a quantity related to charge current cross-correlations which allows one to verify experimentally the entangled nature of the emitted state., Comment: 18 pages, 5 figures, comments are welcomed !

The effect of Diabatisation is reported in the excited argon isomers using the Diatomic-In-Molecules (DIM) method. In previous work using DIM, the lowest energy isomers of Ar$_N^*$ were shown as Ar$_3^*-$Ar$_{N-3}$, however, using the Hole-Particle-Psedopotential (HPP) method, it was shown that the excitation is localised over dimer not trimer; Ar$_2^*-$Ar$_{N-2}$. In this work we improve the DIM calculations by including previously ignored strongly avoided crossing between 3p4s and 3p4p $^{1,3}\Sigma$ states.

One of the most widely used active materials for phase-change memories (PCM), the ternary stoichiometric compound Ge$_2$Sb$_2$Te$_5$ (GST), has a low crystallization temperature of around 150$^\circ$C. One solution to achieve higher operating temperatures is to enrich GST with additional germanium (GGST). This alloy crystallizes into a polycrystalline mixture of two phases, GST and almost pure germanium. In a previous work [R. Bayle et al., J. Appl. Phys. 128, 185101 (2020)], this crystallization process was studied using a multi-phase field model (MPFM) with a simplified thermal field calculated by a separate solver. Here, we combine the MPFM and a phase-aware electro-thermal solver to achieve a consistent multi-physics model for device operations in PCM. Simulations of memory operations are performed to demonstrate its ability to reproduce experimental observations and the most important calibration curves that are used to assess the performance of a PCM cell., Comment: 20 pages, 27 figures, submitted to Journal of Applied Physics

We present a plausible and coherent view of the evolution of the protosolar disk that is consistent with the cosmochemical constraints and compatible with observations of other protoplanetary disks and sophisticated numerical simulations. The evidence that high-temperature condensates, CAIs and AOAs, formed near the protosun before being transported to the outer disk can be explained by either an early phase of vigorous radial spreading of the disk, or fast transport of these condensates from the vicinity of the protosun towards large disk radii via the protostellar outflow. The assumption that the material accreted towards the end of the infall phase was isotopically distinct allows us to explain the observed dichotomy in nucleosynthetic isotopic anomalies of meteorites and leads to intriguing predictions on the isotopic composition of refractory elements in comets. When the infall of material waned, the disk started to evolve as an accretion disk. Initially, dust drifted inwards, shrinking the radius of the dust component to ~ 45 au, probably about 1/2 of the width of the gas component. Then structures must have emerged, producing a series of pressure maxima in the disk which trapped the dust on My timescales. This allowed planetesimals to form at radically distinct times without changing significantly of isotopic properties. There was no late accretion of material onto the disk via streamers. The disk disappeared in ~5 Myr, as indicated by paleomagnetic data in meteorites. In conclusion, the evolution of the protosolar disk seems to have been quite typical in terms of size, lifetime, and dust behavior, suggesting that the peculiarities of the Solar system with respect to extrasolar planetary system probably originate from the chaotic nature of planet formation and not at the level of the parental disk., Comment: In press in Astronomy and Astrophysics

Generative large language models (LLMs) excel in natural language processing tasks, yet their inner workings remain underexplored beyond token-level predictions. This study investigates the degree to which these models decide the content of a paragraph at its onset, shedding light on their contextual understanding. By examining the information encoded in single-token activations, specifically the "\textbackslash n\textbackslash n" double newline token, we demonstrate that patching these activations can transfer significant information about the context of the following paragraph, providing further insights into the model's capacity to plan ahead.

Hymenoscyphus fraxineus is an invasive forest fungal pathogen that induces severe dieback in European ash populations. The spread of the disease has been closely monitored in France by the forest health survey system. We have developed a mechanisticstatistical model that describes the spread of the disease. It takes into account climate (summer temperature and spring rainfall), pathogen population dynamics (foliar infection, Allee effect induced by limited sexual partner encounters) and host density. We fitted this model using available disease reports. We estimated the parameters of our model, first identifying the appropriate ranges for the parameters, which led to a model reduction, and then using an adaptive multiple importance sampling algorithm for fitting. The model reproduces well the propagation observed in France over the last 20 years. In particular, it predicts the absence of disease impact in the south-east of the country and its weak development in the Garonne valley in south-west France. Summer temperature is the factor with the highest overall effect on disease spread, and explains the limited impact in southern France. Among the different temperature indices tested, the number of summer days with temperatures above 28{\textdegree}C gave the best qualitative behavior and the best fit. In contrast, the Allee effect and the heterogeneity of spring precipitation did not strongly affect the overall expansion of H. fraxineus in France and could be neglected in the modeling process. The model can be used to infer the average annual dispersal of H. fraxineus in France.

Implicit neural representations map a shape-specific latent code and a 3D coordinate to its corresponding signed distance (SDF) value. However, this approach only offers a single level of detail. Emulating low levels of detail can be achieved with shallow networks, but the generated shapes are typically not smooth. Alternatively, some network designs offer multiple levels of detail, but are limited to overfitting a single object. To address this, we propose a new shape modeling approach, which enables multiple levels of detail and guarantees a smooth surface at each level. At the core, we introduce a novel latent conditioning for a multiscale and bandwith-limited neural architecture. This results in a deep parameterization of multiple shapes, where early layers quickly output approximated SDF values. This allows to balance speed and accuracy within a single network and enhance the efficiency of implicit scene rendering. We demonstrate that by limiting the bandwidth of the network, we can maintain smooth surfaces across all levels of detail. At finer levels, reconstruction quality is on par with the state of the art models, which are limited to a single level of detail., Comment: Published in GCPR 2024 proceedings

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Semi-holography, originally proposed as a model for conducting lattice electrons coupled to a holographic critical sector, leads to an effective theory of non-Fermi liquids with only a few relevant interactions on the Fermi surface in the large $N$ limit. A refined version of such theories has only two effective couplings which give holographic and Fermi-liquid-like contributions to the self-energy, respectively. We show that a low co-dimension sub-manifold exists in the space of refined semi-holographic theories in which strange metallic behavior is manifested, and which can be obtained just by tuning the ratio of the two couplings. On this sub-manifold, the product of the spectral function and the temperature is approximately independent of the critical exponent, the Fermi energy, and the temperature at all frequencies and near the Fermi surface when expressed in terms of suitably scaled momentum and frequency variables. This quasi-universal behavior leads to linear-in-$T$ dc resistivity and Planckian dissipation over a large range of temperatures, and we also obtain $T^{-3}$ scaling of the Hall conductivity at higher temperatures. The quasi-universal spectral function also fits well with photoemission spectroscopic data without varying the critical exponent with the doping. Combining with the results for optical conductivity, we construct a generalized version of Drude phenomenology for strange-metallic behavior which satisfies non-trivial consistency tests. Finally, we discuss a possible dynamical mechanism for the fine-tuning of the ratio of the two couplings necessary to realize the strange metallic behavior in a typical state., Comment: 53 pages, 18 figures; version to appear in JHEP

RISTRETTO is a visible high-resolution spectrograph fed by an extreme adaptive optics (AO) system, to be proposed as a visitor instrument on ESO VLT. The main science goal of RISTRETTO is to pioneer the detection and atmospheric characterisation of exoplanets in reflected light, in particular the temperate rocky planet Proxima b. RISTRETTO will be able to measure albedos and detect atmospheric features in a number of exoplanets orbiting nearby stars for the first time. It will do so by combining a high-contrast AO system working at the diffraction limit of the telescope to a high-resolution spectrograph, via a 7-spaxel integral-field unit (IFU) feeding single-mode fibers. Further science cases for RISTRETTO include the study of accreting protoplanets such as PDS70b/c through spectrally-resolved H-alpha emission, and spatially-resolved studies of Solar System objects such as icy moons and the ice giants Uranus and Neptune. The project is in the manufacturing phase for the spectrograph sub-system, and the preliminary design phase for the AO front-end. Specific developments for RISTRETTO include a novel coronagraphic IFU combining a phase-induced amplitude apodizer (PIAA) to a 3D-printed microlens array feeding a bundle of single-mode fibers. It also features an XAO system with a dual wavefront sensor aiming at high robustness and sensitivity, including to pupil fragmentation. RISTRETTO is a pathfinder instrument in view of similar developments at the ELT, in particular the SCAO-IFU mode of ELT-ANDES and the future ELT-PCS instrument., Comment: 6 pages, 1 figure, SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

In Sunyaev-Zeldovich (SZ) cluster cosmology, two tools are needed to be able to exploit data from large scale surveys in the millimeter-wave domain. An accurate description of the IntraCluster Medium (ICM) pressure profile is needed along with the scaling relation connecting the SZ brightness to the mass. With its high angular resolution and large field of view, The NIKA2 camera, operating at 150 and 260 GHz, is perfectly suited for precise cluster SZ mapping. The SZ Large Program (LPSZ) of the NIKA2 collaboration is dedicated to the observation of a sample of 38 SZ-selected clusters at intermediate to high redshift and observed both in SZ and X-ray. The current status is that all LPSZ clusters have been observed and the analysis toward the final results is ongoing. We present in detail how NIKA2-LPSZ will obtain a robust estimation of the SZ-Mass scaling relation and how it will be used to obtain cosmological constraints., Comment: 4 pages, 3 figures, contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

Large-scale cosmic-ray detectors like the Giant Radio Array for Neutrino Detection (GRAND) are pushing the boundaries of our ability to identify air shower events. Existing trigger schemes rely solely on the timing of signals detected by individual antennas, which brings many challenges in distinguishing true air shower signals from background. This work explores novel event-level radio trigger methods specifically designed for GRAND, but also applicable to other systems, such as the Radio Detector (RD) of the Pierre Auger Observatory. In addition to an upgraded plane wave front reconstruction technique, we introduce orthogonal and complementary approaches that analyze the radio-emission footprint, the spatial distribution of signal strength across triggered antennas, to refine event selection. We test our methods on mock data sets constructed with simulated showers and real background noise measured with the GRAND prototype, to assess the performance potential in terms of sensitivity and background rejection in GRAND. Our preliminary results are a first step to identifying the most discriminating radio signal features at event-level, and optimizing the techniques for future implementation on experimental data., Comment: 8 pages, 5 figures, 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities - ARENA2024

We study the problem of uniformizing quasi-projective varieties with logcanonical compactifications. More precisely, given a complex projective variety X with log-canonical singularities, we give criteria for X to be isomorphic to a Baily-Borel-Mok compactification of a ball quotient, asking on the one hand the equality case in a suitable Miyaoka-Yau (MY) inequality, and on the other hand some adequate assumptions on the singularities. We also give as a result of independent interest that log-resolutions of log-canonical singularities have their fibers connected by chains of special varieties in the sense of Campana; this is used in the proof to control the behaviour of the period map near the exceptional divisors of such resolutions. We also show that it is necessary to assume that the singularities are at least logcanonical: some examples of Deligne-Mostow-Deraux can be manipulated to provide examples of singular varieties satisfying the equality case in MY, while not being isomorphic to such Baily-Borel-Mok compactifications.

The inner disk of the young star PDS 70 may be a site of rocky planet formation, with two giant planets detected further out. Solids in the inner disk may inform us about the origin of this inner disk water and nature of the dust in the rocky planet-forming regions. We aim to constrain the chemical composition, lattice structure, and grain sizes of small silicate grains in the inner disk of PDS 70, observed both in JWST/MIRI MRS and Spitzer IRS. We use a dust fitting model, called DuCK, based on a two-layer disk model. We use Gaussian Random Field and Distribution of Hollow Spheres models to obtain two sets of dust opacities. The third set of opacities is obtained from aerosol spectroscopy. We use stoichiometric amorphous silicates, forsterite, and enstatite in our analysis. We also used iron-rich and magnesium-rich amorphous silicate and fayalite dust species to study the iron content. The Gaussian Random Field opacity agrees well with the observed spectrum. In both MIRI and Spitzer spectra, amorphous silicates are the dominant dust species. Crystalline silicates are dominated by iron-poor olivine. We do not find strong evidence for enstatite. Moreover, the MIRI spectrum indicates larger grain sizes than the Spitzer spectrum, indicating a time-variable small grain reservoir. The inner PDS 70 disk is dominated by a variable reservoir of optically thin warm amorphous silicates. We suggest that the small grains detected in the inner PDS 70 disk are likely transported inward from the outer disk as a result of filtration and fragmentation at the ice line. In addition, the variation between MIRI and Spitzer data can be explained by the grain growth over 15 years and a dynamical inner disk where opacity changes occur resulting from the highly variable hot innermost dust reservoir., Comment: 18 pages, 13 figures, Accepted by A&A

Recent observations have begun probing the early phases of disc formation, but little data yet exists on disc structure and morphology of Class 0 objects. Using simulations, we are able to lay out predictions of disc morphologies expected in future surveys of young discs. Based on detailed simulations of ab initio star formation by core collapse, we predict that early discs must be eccentric. In this letter, we study the morphology and, in particular, the eccentricity of discs formed in non-ideal magnetohydrodynamic (MHD) collapse simulations. We attempt to show that discs formed by cloud collapse are likely to be eccentric. We ran non-ideal MHD collapse simulations in the adaptive mesh refinement code RAMSES with radiative transfer. We used state-of-the-art analysis methods to measure the disc eccentricity. We find that despite no asymmetry in the initial conditions, the discs formed are eccentric, with eccentricities on the order of 0.1. These results may have important implications for protoplanetary disc dynamics and planet formation. The presence of eccentricity in young discs that is not seen at later stages of disc evolution is in tension with current viscous eccentricity damping models. This implies that there may be an as-yet undiscovered circularisation mechanism in circumstellar discs., Comment: accepted for publication in Astronomy & Astrophysics, 7 pages, 8 figures

The influence of the surface pre-deformation on jerky flow caused by the Portevin-Le Chatelier (PLC) effect was investigated using flat tensile specimens of an Al-Mg alloy. Although jerky flow represents a macroscopic plastic instability, the underlying mechanisms stem from self-organization of dislocations, which pertains to deformation processes at mesoscopic scales. To provide a comprehensive approach, the investigation was carried out by coupling tensile tests, digital image correlation and acoustic emission techniques, each targeting a particular range of scales. Thin superficial layers were pre-deformed using surface mechanical attrition technique (SMAT). It was found that the observed effects depend on which surfaces are processed. Overall, the treated samples exhibited an enhanced yield strength without deterioration of ductility in comparison with the initial material. These changes in the general mechanical behavior are likely to be correlated with the changes in jerky flow. Using digital image correlation, a tendency to delocalization of bursts of plastic flow was found for both the PLC bands and smaller-scale strain heterogeneities outside the bands, the latter having been little studied so far. Unexpectedly, SMAT occurred to modify plastic flow drastically at this scale. In addition to clarification of the role played by the surface in the PLC effect, these findings provide new insights into relationships between deformation processes at distinct scales., Comment: 28 pages, 12 figures

When performing radio detection of ultra-high energy astroparticles, the planar wavefront model is often used as a first step to evaluate the arrival direction of primary particles, by adjusting the wavefront orientation based on the peak of signal traces of individual antennas. The benefits of this approach are however limited by the lack of a good evaluation of its robustness. In order to mitigate this limitation, we derive in this work an analytical solution for the arrival direction as well as the corresponding analytical reconstruction uncertainty. Because it is fast and robust, this reconstruction method can be used as a proxy for more complex estimators, or implemented online for low-level triggering., Comment: Submitted to NIMA

We are currently developing an innovative and visually-driven programming language called Omega.Although the Omega code is stored in text files, these files are not intended for manual editing or traditional printing.Furthermore, parsing these files using a context-free grammar is not possible.The parsing of the code and the facilitation of user-friendly manual editing both necessitate a global knowledge of the codebase.Strictly speaking, code visualization is not an integral part of the Omega language; instead, this task is delegated to the editing tools.Thanks to the global knowledge of the code, the editing process becomes remarkably straightforward, with numerous automatic completion features that enhance usability.Omega leverages a visual-oriented approach to encompass all fundamental aspects of software engineering.It offers robust features, including safe static typing, design by contracts, rules for accessing slots, operator definitions, and more,all presented in an intuitively and visually comprehensible manner, eliminating the need for obscure syntax.

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect highly inclined extensive air showers (EAS) with down-going and up-going trajectories. Several working groups in the GRAND collaboration are developing methods to reconstruct the incoming direction, core position, primary energy, and composition of the showers. The reconstruction pipeline -- currently under development in the France/IAP working group -- relies on a model of spherical wavefront emission for arrival times, which is possible because the radio signals are generated far away from the antenna stations. The amplitude distribution of the signals at the antenna level is described by an Angular Distribution Function that considers various asymmetries in the data, including geomagnetic effects. In this contribution, we present preliminary results from testing our EAS reconstruction procedure using realistic mock observations., Comment: V2: Fixed typo in axis labels of Fig.5 and collaboration author list updated

In this paper, we consider event structures and their probabilistic and quantum extensions as originally defined by Winskel. If these structures have already been part of sophisticated computational models, they have rarely been directly studied as an immediate model of execution traces of programs. This paper offers such an analysis. We propose a simple imperative operational framework and show how to derive soundness and adequacy results with event structures considered as a semantics. We show how event structures naturally handle non-deterministic, probabilistic and quantum effects.

Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development -- from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction Engineering -- a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction Engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research.

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

By means of a specific heat, susceptibility and high-pressure electrical resistivity study, we show that the local magnetic moments of the intercalated V ions in V$_{5}$S$_{8}$ realize a prototype of Kondo lattice system, where an antiferromagnetic order of the moments coexists with a Fermi liquid in the VS$_{2}$ layers with intermediate heavy Fermion properties. The antiferromagnetic order and the Fermi-liquid behavior are simultaneously suppressed at a critical pressure, $P_c =10$ GPa, signature of a quantum critical point, which supports a Kondo lattice scenario and raises the question whether, in the paramagnetic phase at higher pressures, the heavy quasiparticles survive or form a non-Fermi liquid phase governed by the Kondo interaction., Comment: 5 pages, 4 figures

One of the main challenges of N-Version Programming is development cost: it requires paying multiple teams to develop variants of the same system. To address this issue, we propose the automated generation of variants using large language models. We design, develop and evaluate Gal\'apagos: a tool for generating program variants using LLMs, validating their correctness and equivalence, and using them to assemble N-Version binaries. We evaluate Gal\'apagos by creating N-Version components of real-world C code. Our original results show that Gal\'apagos can produce program variants that are proven to be functionally equivalent, even when the variants are written in a different programming language. Our systematic diversity measurement indicate that functionally equivalent variants produced by Gal\'apagos, are statically different after compilation, and present diverging internal behavior at runtime. We demonstrate that the variants produced by Gal\'apagos can protect C code against real miscompilation bugs which affect the Clang compiler. Overall, our paper shows that producing N-Version software can be drastically automated by advanced usage of practical formal verification and generative language models.

Clay minerals are abundant natural materials used widely in coatings, construction materials, ceramics, as well as being a component of drilling fluids. Here, we present the effect of steady and oscillatory preshear on organophilic modified clay gels in synthetic oil. Both platelet and needle-like particles are used as viscosifiers in drilling fluid formulations. For both particles the plateau modulus exhibits a similar concentration dependence, G_P ~ c^3.9, whereas the yield strain is {\gamma}_y ~ c^(-1) for the platelets and {\gamma}_y ~ c^-1.7 for the needles. Mixtures of the two follow an intermediate behavior: at low concentrations their elasticity and yield strain follows that of needle particles while at higher concentrations it exhibits a weaker power law dependence. Furthermore, upon varying the preshear history, the gel viscoelastic properties can be significantly tuned. At lower (higher) clay concentrations, preshear at specific oscillatory strain amplitudes or steady shear rates, may induce a hardening (softening) of the dispersions and, at all concentrations, a lowering of the shear strain. Hence, in needle dispersions preshear resulted in changes in the volume fraction dependence of the elastic modulus from G_P ~ c^3.9 to G_P ~ c^2.5 and of the yield strain from {\gamma}_y ~ c^-1.7 to {\gamma}_y ~ c^-1. However, small angle X-ray scattering showed not much structural changes, within the q-range covered. Our findings indicate ways to design colloidal organoclay dispersions with a mechanical response that can be tuned at will.

Fluctuations of observables provide unique insights into the nature of physical systems, and their study stands as a cornerstone of both theoretical and experimental science. Generalized fluctuations, or cumulants, provide information beyond the mean and variance of an observable. In this letter, we develop a systematic method to determine the asymptotic behavior of cumulants of local observables as the region becomes large. Our analysis reveals that the expansion is closely tied to the geometric characteristics of the region and its boundary, with coefficients given by convex moments of the connected correlation function: the latter is integrated against intrinsic volumes of convex polytopes built from the coordinates, which can be interpreted as averaged shadows. A particular application of our method shows that, in two dimensions, the leading behavior of odd cumulants of conserved quantities is topological, specifically depending on the Euler characteristic of the region. We illustrate these results with the paradigmatic strongly-interacting system of two-dimensional quantum Hall state at filling fraction $1/2$, by performing Monte-Carlo calculations of the skewness (third cumulant) of particle number in the Laughlin state., Comment: 4+8 pages, 3+3 figures

The stability of liquid film flows are important in many industrial applications. In the dip-coating process, a liquid film is formed over a substrate extracted at a constant speed from a liquid bath. We studied the linear stability of this film considering different thicknesses $\hat{h}$ for four liquids, spanning a large range of Kapitza numbers ($\rm Ka$). By solving the Orr-Sommerfeld eigenvalue problem with the Chebyshev-Tau spectral method, we calculated the neutral curves, investigated the instability mechanism and computed the absolute/convective threshold. The instability mechanism was studied through the analysis of vorticity distribution and the kinetic energy balance of the perturbations. It was found that liquids with low $\rm Ka$ (e.g. corn oil, $\text{Ka}$ = 4) have a smaller area of stability than a liquid at high $\rm Ka$ (e.g. Liquid Zinc, $\rm Ka$ = 11525). Surface tension has both a stabilizing and a destabilizing effect, especially for large $\rm Ka$. For long waves, it curves the vorticity lines near the substrate, reducing the flow under the crests. For short waves, it fosters vorticity production at the interface and creates a region of intense vorticity near the substrate. In addition, we discovered that the surface tension contributes to both the production and dissipation of perturbation's energy depending on the $\rm Ka$ number. In terms of absolute/convective threshold, we found a window of absolute instability in the $\text{Re}-\hat{h}$ space, showing that the Landau-Levich-Derjaguin solution ($\hat{h}=0.945 \text{Re}^{1/9}\text{Ka}^{-1/6}$) is always convectively unstable. Moreover, we show that for $\text{Ka}<17$, the Derjaguin's solution ($\hat{h}=1$) is always convectively unstable.

KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.18^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 29 pages, 12 figures

Whether or not several Creole languages which developed during the early modern period can be considered genetic descendants of European languages has been the subject of intense debate. This is in large part due to the absence of evidence of intermediate forms. This work introduces a new open corpus, the Moly\'e corpus, which combines stereotypical representations of three kinds of language variation in Europe with early attestations of French-based Creole languages across a period of 400 years. It is intended to facilitate future research on the continuity between contact situations in Europe and Creolophone (former) colonies., Comment: 8 main pages and 3 pages of references

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in neuroimaging is represented by brain age, which has been shown to be a good indicator for different conditions, such as Alzheimer's Disease. Using brain age for weakly supervised pre-training of DL models in transfer learning settings has also recently shown promising results, especially when dealing with data scarcity of different conditions. On the other hand, anatomical information of brain MRIs (e.g. cortical thickness) can provide important information for learning good representations that can be transferred to many downstream tasks. In this work, we propose AnatCL, an anatomical foundation model for brain MRIs that i.) leverages anatomical information in a weakly contrastive learning approach, and ii.) achieves state-of-the-art performances across many different downstream tasks. To validate our approach we consider 12 different downstream tasks for the diagnosis of different conditions such as Alzheimer's Disease, autism spectrum disorder, and schizophrenia. Furthermore, we also target the prediction of 10 different clinical assessment scores using structural MRI data. Our findings show that incorporating anatomical information during pre-training leads to more robust and generalizable representations. Pre-trained models can be found at: https://github.com/EIDOSLAB/AnatCL., Comment: Updated version; added ablation study

Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective training set. In this work, we implement and train a MLP to obtain an accurate description of the potential energy surface and property predictions for organic compounds, as both single molecules and in the condensed phase. We devise a dual descriptor, based on the atomic cluster expansion (ACE), that couples an information-rich short-range description with a coarser long-range description that captures weak intermolecular interactions. We employ uncertainty-guided active learning for the training set generation, creating a dataset that is comparatively small for the breadth of application and consists of alcohols, alkanes, and an adipate. Utilizing that MLP, we calculate densities of those systems of varying chain lengths as a function of temperature, obtaining a discrepancy of less than 4% compared with experiment. Vibrational frequencies calculated with the MLP have a root mean square error of less than 1 THz compared to DFT. The heat capacities of condensed systems are within 11% of experimental findings, which is strong evidence that the dual descriptor provides an accurate framework for the prediction of both short-range intramolecular and long-range intermolecular interactions.

Turbulence in classical fluids is characterized by persistent structures that emerge from the chaotic landscape. We investigate the analogous process in fully kinetic plasma turbulence by using high-resolution, direct numerical simulations in two spatial dimensions. We observe the formation of long-living vortices with a profile typical of macroscopic, magnetically dominated force-free states. Inspired by the Harris pinch model for inhomogeneous equilibria, we describe these metastable solutions with a self-consistent kinetic model in a cylindrical coordinate system centered on a representative vortex, starting from an explicit form of the particle velocity distribution function. Such new equilibria can be simplified to a Gold-Hoyle solution of the modified force-free state. Turbulence is mediated by the long-living structures, accompanied by transients in which such vortices merge and form self-similarly new metastable equilibria. This process can be relevant to the comprehension of various astrophysical phenomena, going from the formation of plasmoids in the vicinity of massive compact objects to the emergence of coherent structures in the heliosphere., Comment: 12 pages, 5 figures (Main Text), 3 figures (Appendices)

Multiple Instance Learning (MIL) models have proven effective for cancer prognosis from Whole Slide Images. However, the original MIL formulation incorrectly assumes the patches of the same image to be independent, leading to a loss of spatial context as information flows through the network. Incorporating contextual knowledge into predictions is particularly important given the inclination for cancerous cells to form clusters and the presence of spatial indicators for tumors. State-of-the-art methods often use attention mechanisms eventually combined with graphs to capture spatial knowledge. In this paper, we take a novel and transversal approach, addressing this issue through the lens of regularization. We propose Context-Aware Regularization for Multiple Instance Learning (CARMIL), a versatile regularization scheme designed to seamlessly integrate spatial knowledge into any MIL model. Additionally, we present a new and generic metric to quantify the Context-Awareness of any MIL model when applied to Whole Slide Images, resolving a previously unexplored gap in the field. The efficacy of our framework is evaluated for two survival analysis tasks on glioblastoma (TCGA GBM) and colon cancer data (TCGA COAD).

The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. In this paper, we focus on machine translation (MT), a task that has been shown to benefit from in-context translation examples. However no systematic studies have been published on how best to select examples, and mixed results have been reported on the usefulness of similarity-based selection over random selection. We provide a study covering multiple LLMs and multiple in-context example retrieval strategies, comparing multilingual sentence embeddings. We cover several language directions, representing different levels of language resourcedness (English into French, German, Swahili and Wolof). Contrarily to previously published results, we find that sentence embedding similarity can improve MT, especially for low-resource language directions, and discuss the balance between selection pool diversity and quality. We also highlight potential problems with the evaluation of LLM-based MT and suggest a more appropriate evaluation protocol, adapting the COMET metric to the evaluation of LLMs. Code and outputs are freely available at https://github.com/ArmelRandy/ICL-MT.

(Abridged) Observational surveys of entire star-forming regions have provided evidence of power-law correlations between the disc properties and the stellar mass, especially the disc mass (${M_d \propto M_*}^{\lambda_m}$) and the accretion rate ($\dot M \propto {M_*}^{\lambda_{acc}}$). Whether the secular disc evolution affects said correlations is still debated: while the purely viscous scenario has been probed, other mechanisms could impact differently. We study the evolution of the slopes $\lambda_m$ and $\lambda_{acc}$ in the wind-driven and hybrid case and compare it to the viscous prediction, using a combination of analytical calculations and numerical simulations (performed with the 1D population synthesis code Diskpop, that we also present and release). Assuming $M_d(0) \propto {M_*}^{\lambda_{m, 0}}$ and $\dot M(0) \propto {M_*}^{\lambda_{acc, 0}}$ as initial conditions, we find that viscous and hybrid accretion preserve the shape of the correlations and evolve their slope; on the other hand, MHD winds change the shape of the correlations, bending them according to the scaling of the accretion timescale with the stellar mass. We also show how a spread in the initial conditions conceals this behaviour. We then analyse the impact of disc dispersal, and find that the currently available sample sizes ($\sim 30$ discs at 5 Myr) introduce stochastic oscillations in the slopes evolution, which dominate over the physical signatures. Increasing the sample size could mitigate this issue: $\sim 140$ discs at 5 Myr, corresponding to the complete Upper Sco sample, would give small enough error bars to use the evolution of the slopes as a proxy for the driving mechanism of disc evolution. Finally, we discuss how the observational claim of steepening slopes necessarily leads to an initially steeper $M_d - M_*$ correlation with respect to $\dot M - M_*$., Comment: 14 pages, 5 figures and 3 tables (plus Appendix). Accepted for publication in A&A. Includes Diskpop and popcorn software release

We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has $\sim 6$ million trainable parameters with training times $\lesssim 24$ hours. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of $\sim 6$s., Comment: Submitted to MLST

We demonstrate that the plasma analogy is generally unreliable at predicting the edge properties of quantum Hall (QH) states, as it fails to account for the local edge velocity. This discrepancy arises from a fundamental difference between QH droplets and Coulomb gases (CGs): the former are incompressible liquids subject to area-preserving deformations, while the latter are governed by electrostatics and thus involve conformal transformations. We illustrate this QH/CG mismatch across various examples and show its impact on physical quantities, measurable in both solid-state samples and quantum simulators. Specifically, we discuss fluctuations of local observables and their connection to state-of-the-art microwave absorption experiments., Comment: 9 pages + SM, RevTeX, 6 figures

We introduce Java-Class-Hijack, a novel software supply chain attack that enables an attacker to inject malicious code by crafting a class that shadows a legitimate class that is in the dependency tree. We describe the attack, provide a proof-of-concept demonstrating its feasibility, and replicate it in the German Corona-Warn-App server application. The proof-of-concept illustrates how a transitive dependency deep within the dependency tree can hijack a class from a direct dependency and entirely alter its behavior, posing a significant security risk to Java applications. The replication on the Corona-Warn-App demonstrates how compromising a small JSON validation library could result in a complete database takeover., Comment: 6 pages, added a mitigation chapter

Context. The explosive burning that drives nova eruptions results in unique nucleosynthesis that heavily over-produces certain isotopes relative to the solar abundance. However, novae are often ignored when considering the chemical evolution of our Galaxy due to their low ejecta masses. Aims. In this work, we use previously computed synthetic nova populations and the galactic chemical evolution code OMEGA+ to assess the impact that novae have on the evolution of stable elemental and isotopic abundances. Methods. We combine populations of novae computed using the binary population synthesis code binary_c with the galactic chemical evolution code OMEGA+ and detailed, white dwarf mass-dependent nova yields to model the nucleosynthetic contributions of novae to the evolution of the Milky Way. We consider three different nova yield profiles, each corresponding to a different set of nova yield calculations. Results. Despite novae from low-mass white dwarfs (WDs) dominating nova ejecta contributions, we find that novae occurring on massive WDs are still able to contribute significantly to many isotopes, particularly those with high mass numbers. We find that novae can produce up to 35% of the Galactic 13C and 15N mass by the time the model Galaxy reaches [Fe/H] = 0, and earlier in the evolution of the Galaxy (between [Fe/H] = -2 and -1) novae may have been the dominant source of 15N. Predictions for [13C/Fe], [15N/Fe], 12C/13C, and 14N/15N abundances ratios vary by up to 0.2 dex at [Fe/H] = 0 and by up to 0.7 dex in [15N/Fe] and 14N/15N between [Fe/H] = -2 and -1 (corresponding approximately to Galactic ages of 170 Myr and 1 Gyr in our model). The Galactic evolution of other stable isotopes (excluding Li) is not noticeably affected by including novae.

In this article, I present a Tauberian equivalence of the Riemann hypothesis within the framework of the theory of regular arithmetic functions, a branch of Tauberian theory that extends the theory of functions with good variation introduced in 2016. The central element of this study is the function $\Phi(x)=x\left\lfloor \frac{1}{x}\right\rfloor$, which allows for the extension of Ingham's summation method far beyond the prime number theorem by linking it to the Riemann hypothesis. I thus demonstrate the following equivalence $$RH\Longleftrightarrow\alpha\left(\Phi\right)=\frac{1}{2}$$ where $\alpha\left(\Phi\right)$ represents the index of good variation of $\Phi$, an essential characteristic of functions of good variation. This equivalence of the Riemann hypothesis is a potential new contribution absent from recent comprehensive catalogs of equivalent of the Riemann hypothesis in mathematical literature., Comment: 44 pages. The changes mainly concern a correction in the proof of lemma 3.5.1 (a typo: \mu(k) was missing in a sum) and a more substantial revision of the proof of the (d) in Lemma 4.1

Call a finite relational structure $k$-Slupecki if its only surjective $k$-ary polymorphisms are essentially unary, and Slupecki if it is $k$-Slupecki for all $k \geq 2$. We present conditions, some necessary and some sufficient, for a reflexive digraph to be Slupecki. We prove that all digraphs that triangulate a 1-sphere are Slupecki, as are all the ordinal sums $m \oplus n$ ($m,n \geq 2$). We prove that the posets $P = m \oplus n \oplus k$ are not 3-Slupecki for $m,n,k \geq 2$, and prove there is a bound $B(m,k)$ such that $P$ is 2-Slupecki if and only if $n > B(m,k)+1$; in particular there exist posets that are 2-Slupecki but not 3-Slupecki.

Detecting planet signatures in protoplanetary disks is fundamental to understanding how and where planets form. In this work, we report dust and gas observational hints of planet formation in the disk around 2MASS-J16120668-301027, as part of the ALMA Large Program "AGE-PRO: ALMA survey of Gas Evolution in Protoplanetary disks". The disk was imaged with the Atacama Large Millimeter/submillimeter Array (ALMA) at Band 6 (1.3 mm) in dust continuum emission and four molecular lines: $^{12}$CO(J=2-1), $^{13}$CO(J=2-1), C$^{18}$O(J=2-1), and H$_2$CO(J=3$_{(3,0)}$-2$_{(2,0)}$). Resolved observations of the dust continuum emission (angular resolution of $\sim 150$ mas, 20 au) show a ring-like structure with a peak at $0.57 ^{\prime \prime}$ (75 au), a deep gap with a minimum at 0.24$^{\prime \prime}$ (31 au), an inner disk, a bridge connecting the inner disk and the outer ring, along with a spiral arm structure, and a tentative detection (to $3\sigma$) of a compact emission at the center of the disk gap, with an estimated dust mass of $\sim 2.7-12.9$ Lunar masses. We also detected a kinematic kink (not coincident with any dust substructure) through several $^{12}$CO channel maps (angular resolution $\sim$ 200 mas, 30 au), located at a radius of $\sim 0.875^{\prime \prime}$ (115.6 au). After modeling the $^{12}$CO velocity rotation around the protostar, we identified a tentative rotating-like structure at the kink location with a geometry similar to that of the disk. We discuss potential explanations for the dust and gas substructures observed in the disk, and their potential connection to signatures of planet formation., Comment: 24 pages, 15 figures

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Multitask learning is a widely recognized technique in the field of computer vision and deep learning domain. However, it is still a research question in remote sensing, particularly for hyperspectral imaging. Moreover, most of the research in the remote sensing domain focuses on small and single-task-based annotated datasets, which limits the generalizability and scalability of the developed models to more diverse and complex real-world scenarios. Thus, in this study, we propose a multitask deep learning model designed to perform multiple classification and regression tasks simultaneously on hyperspectral images. We validated our approach on a large hyperspectral dataset called TAIGA, which contains 13 forest variables, including three categorical variables and ten continuous variables with different biophysical parameters. We design a sharing encoder and task-specific decoder network to streamline feature learning while allowing each task-specific decoder to focus on the unique aspects of its respective task. Additionally, a dense atrous pyramid pooling layer and attention network were integrated to extract multi-scale contextual information and enable selective information processing by prioritizing task-specific features. Further, we computed multitask loss and optimized its parameters for the proposed framework to improve the model performance and efficiency across diverse tasks. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods. We trained our model across 10 seeds/trials to ensure robustness. Our proposed model demonstrates higher mean performance while maintaining lower or equivalent variability. To make the work reproducible, the codes will be available at https://github.com/Koushikey4596/Multitask-Deep-Learning-Model-for-Taiga-datatset.