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In nonequilibrium steady states of Markov jump processes, we derive exact Fluctuation-Response Relations (FRRs) that express the covariance between any pair of currents in terms of static responses in a notably simple form, thus generalizing the fluctuation-dissipation theorem far from equilibrium. We begin by considering perturbations in the symmetric part of the rates. We demonstrate that FRRs imply a hierarchy of thermodynamic bounds. These latter prove the recently conjectured Response Thermodynamic Uncertainty Relation (R-TUR), which bounds the ratio between any current's response and its variance by the entropy production rate (EPR). We furthermore strengthen this bound in two distinct ways, using partial EPR in one case and pseudo-EPR in the other. For perturbations in the antisymmetric part of the rates, we show that the ratio between any current's response and its variance is bounded by traffic, a metric representing the total number of transitions per unit time in the system. As an application, we use FRRs to explain the origin of positive correlations between currents in Coulomb-blockaded systems previously observed in experiments.

We construct global homotopies to compute differential Hochschild cohomologies in differential geometry. This relies on two different techniques: a symbol calculus from differential geometry and a coalgebraic version of the van Est theorem. Not only do we obtain an improved version of the Hochschild-Kostant-Rosenberg theorem but we also compute Hochschild cohomologies for related scenarios, e.g. for principal bundles, and invariant versions., Comment: 65 pages, comments are welcome!

Data and derived information about target victims has always been key for successful attacks, both during historical wars and modern cyber wars. Ours turns out to be an era in which modern cars generate a plethora of data about their drivers, and such data could be extremely attractive for offenders. This paper seeks to assess how well modern cars protect their drivers' data. It pursues its goal at a requirement level by analysing the gaps of the privacy policies of chief automakers such as BMW and Mercedes with respect to the General Data Protection Regulation (GDPR). It is found that both brands are still imprecise about how they comply with a number of GDPR articles, hence compliance often results non-verifiable. Most importantly, while BMW exhibits slightly broader compliance, both brands still fail to comply with a number of relevant articles of the regulation. An interpretation of these findings is a non-negligible likelihood that your car may turn against you should cyberwarfare break out.

We investigate the multipartite mutual information between $N$ discrete-state stochastic units interacting in a networks that is invariant under unit permutations. We show that when the system relaxes to fixed point attractors, multipartite correlations in the stationary state either do not scale extensively with $N$, or the extensive scaling is not robust to small perturbations of the system parameters. In particular, robust extensive scaling cannot occur in thermodynamic equilibrium. In contrast, mutual information scales extensively when the system relaxes to time-dependent attractors (e.g., limit cycles), which can occur only far from equilibrium. This demonstrates the essential role of dissipation in the generation and maintenance of multipartite correlations. We illustrate our theory with the nonequilibrium Potts model., Comment: 7 pages, 2 figures

Sensitivity and resolution of space telescopes are directly related to the size of the primary mirror. Enabling such future extremely large space telescopes or even arrays of those will require to drastically reduce the areal weight of the mirror system. Utilizing a thin parabolic polymeric membrane as primary mirror offers the prospect of very low weight and the flexible nature of those membranes allows compactly store them upon launch. Upon deployment the structure is unfolded and the mirror shape restored. Being an extremely thin structure, an active shape correction is required. Utilizing a thermal control of the surface via radiative coupling, localized shape changes are imprinted into the membrane telescope. In this paper we present the modelling and experimental test of the radiative adaptive optics. A detailed modeling of the influence function of the radiative shaping onto the membrane mirror has been carried out. Experimentally we have been radiatively actuated the shape of a prototype mirror in closed loop with a wavefront sensor and proven that we can control the mirrors surface figure to a ~15nm RMS precision., Comment: 13 pages

Most of the super-massive black holes in the Universe accrete material in an obscured phase. While it is commonly accepted that the "dusty torus" is responsible for the nuclear obscuration, its geometrical, physical, and chemical properties are far from being properly understood. In this paper, we take advantage of the multiple X-ray observations taken between 2007 and 2020, as well as of optical to far infra-red (FIR) observations of NGC 6300, a nearby ($z=0.0037$) Seyfert 2 galaxy. The goal of this project is to study the nuclear emission and the properties of the obscuring medium, through a multi-wavelength study conducted from X-ray to IR. We perform a simultaneous X-ray spectral fitting and optical-FIR spectral energy distribution (SED) fitting to investigate the obscuring torus. For the X-ray spectral fitting, physically motivated torus models, such as borus02, UXClumpy and XClumpy are used. The SED fitting is done using XCIGALE. Through joint analysis, we constrain the physical parameters of the torus and the emission properties of the accreting supermassive black hole. Through X-ray observations taken in the last 13 years, we have not found any significant line-of-sight column density variability for this source, but observed the X-ray flux dropping $\sim40-50\%$ in 2020 with respect to previous observations. The UXClumpy model predicts the presence of an inner ring of Compton-thick gaseous medium, responsible for the reflection dominated spectra above 10 keV. Through multi-wavelength SED fitting, we measure an Eddington accretion rate $\lambda_{\rm{Edd}}\sim2\times10^{-3}$, which falls in the range of the radiatively inefficient accretion solutions., Comment: Submitted to A&A, currently under review

Particle-producing targets in high-energy research facilities are often made from refractory metals, and they typically require dedicated cooling systems due to the challenging thermomechanical conditions they experience. However, direct contact of water with target blocks can induce erosion, corrosion, and embrittlement, especially of tungsten (W). One approach to overcoming this problem is cladding the blocks with tantalum (Ta). Unfortunately, Ta generates high decay heat when irradiated, raising safety concerns in the event of a loss-of-cooling accident. This study explored the capacity of niobium (Nb) and its alloys to form diffusion bonds with W and TZM (a molybdenum alloy with titanium and zirconium). This is because the Beam Dump Facility (BDF), a planned new fixed-target installation in CERN's North Area, uses these target materials. The bonding quality of pure Nb, Nb1Zr, and C103 (a Nb alloy with 10% hafnium and 1% titanium) with TZM and W obtained using hot isostatic pressing (HIP) was evaluated. The effects of different HIP temperatures and the introduction of a Ta interlayer were examined. Optical microscopy indicated promising bonding interfaces, which were further characterized using tensile tests and thermal-diffusivity measurements. Their performance under high-energy beam impact was validated using thermomechanical simulations. C103 exhibited higher interface strengths and safety factors than Ta2.5W, positioning it as a potential alternative cladding material for the BDF production target. The findings highlight the viability of Nb-based materials, particularly C103, for improving operational safety and efficiency in fixed-target physics experiments; however, considerations regarding the long half-life of 94Nb require further attention.

The Gemini Planet Imager (GPI) is a high contrast imaging instrument that aims to detect and characterize extrasolar planets. GPI is being upgraded to GPI 2.0, with several subsystems receiving a re-design to improve its contrast. To enable observations on fainter targets and increase performance on brighter ones, one of the upgrades is to the adaptive optics system. The current Shack-Hartmann wavefront sensor (WFS) is being replaced by a pyramid WFS with an low-noise electron multiplying CCD (EMCCD). EMCCDs are detectors capable of counting single photon events at high speed and high sensitivity. In this work, we characterize the performance of the HN\"u 240 EMCCD from N\"uv\"u Cameras, which was custom-built for GPI 2.0. Through our performance evaluation we found that the operating mode of the camera had to be changed from inverted-mode (IMO) to non-inverted mode (NIMO) in order to improve charge diffusion features found in the detector's images. Here, we characterize the EMCCD's noise contributors (readout noise, clock-induced charges, dark current) and linearity tests (EM gain, exposure time) before and after the switch to NIMO., Comment: Proceeding of the SPIE Astronomical Telescopes+Instrumentation. 14 pages, 15 figures

For overdamped Langevin systems subjected to weak thermal noise and nonconservative forces, we establish a connection between Freidlin-Wentzell large deviations theory and stochastic thermodynamics. First, we derive a series expansion of the quasipotential around the detailed-balance solution, i.e. the system's free energy, and identify the condition for the linear response regime to hold even far from equilibrium. Second, we prove that the escape rate from dissipative fixed points of the macroscopic dynamics is bounded by the entropy production of trajectories that relax into, and escape from the attractors. These results provide the foundation to study the nonequilibrium thermodynamics of dissipative metastable states.

High-quality real-time view synthesis methods are based on volume rendering, splatting, or surface rendering. While surface-based methods generally are the fastest, they cannot faithfully model fuzzy geometry like hair. In turn, alpha-blending techniques excel at representing fuzzy materials but require an unbounded number of samples per ray (P1). Further overheads are induced by empty space skipping in volume rendering (P2) and sorting input primitives in splatting (P3). These problems are exacerbated on low-performance graphics hardware, e.g. on mobile devices. We present a novel representation for real-time view synthesis where the (P1) number of sampling locations is small and bounded, (P2) sampling locations are efficiently found via rasterization, and (P3) rendering is sorting-free. We achieve this by representing objects as semi-transparent multi-layer meshes, rendered in fixed layer order from outermost to innermost. We model mesh layers as SDF shells with optimal spacing learned during training. After baking, we fit UV textures to the corresponding meshes. We show that our method can represent challenging fuzzy objects while achieving higher frame rates than volume-based and splatting-based methods on low-end and mobile devices.

Josephson Traveling Wave Parametric Amplifiers (JTWPAs) are integral parts of many experiments carried out in quantum technologies. Being composed of hundreds of Josephson junction-based unit cells, such devices exhibit complex nonlinear behavior that typically cannot be fully explained with simple analytical models, thus necessitating the use of numerical simulators. A very useful characteristic of JTWPAs is the possibility of being biased by an external magnetic flux, allowing insitu control of the nonlinearity. It is therefore very desirable for numerical simulators to support this feature. Open-source numerical tools that allow to model JTWPA flux biasing, such as WRSPICE or PSCAN2, are based on time-domain approaches,which typically require long simulation times to get accurate results. In this work, we model the gain performance in a prototypical flux-tunable JTWPA by using JosephsonCircuits.jl,a recently developed frequency-domain open-source numerical simulator, which has the benefit of simulation times about 10,000 faster than time-domain methods. By comparing the numerical and experimental results, we validate this approach for modeling the flux dependent behavior of JTWPAs.

In order to preserve the leading role of the action principle in formulating all field theories one needs quantum field theory, with the associated BRST symmetry, and Feynman-DeWitt-Faddeev-Popov ghost fields. Such fields result from the fibre-bundle structure of the space of histories, but the physics-oriented literature used them formally because a rigorous theory of measure and integration was lacking. Motivated by this framework, this paper exploits previous work of Gill and Zachary, where the use of Banach spaces for the Feynman integral was proposed. The Henstock-Kurzweil integral is first introduced, because it makes it possible to integrate functions like the exponential of (i times x squared). The Lebesgue measure on R(infinity) is then built and used to define the measure on every separable Hilbert space. The subsequent step is the construction of a new Hilbert space KS2, which contains square-integrable functions on R**n as a continuous dense embedding, and contains both the test functions and their dual, the Schwartz space of distributions, as continuous embeddings. This space allows us to construct the Feynman path integral in a manner that maintains its intuitive and computational advantages. We also extend this space to KS2[H], where H is any separable Banach space. Last, the existence of a unique universal definition of time, tau(h), that we call historical time, is proved. We use tau(h) as the order parameter for our construction of Feynman's time ordered operator calculus, which in turn is used to extend the path integral in order to include all time dependent groups and semigroups with a kernel., Comment: 65 pages, Latex

It has been long known that the addition of linear friction on two-dimensional Navier-Stokes (NS) turbulence, often referred to as Ekman-Navier-Stokes (ENS) turbulence, induces strong intermittent fluctuations on small-scale vorticity. Such fluctuations are strong enough to be measurable at low-order statistics such as the energy or enstrophy spectrum. Simple heuristics lead to corrections in the spectrum which are proportional to the linear friction coefficient. In this work, we study the spectral correction by the implementation of a GPU-accelerated high-resolution numerical simulation of ENS covering a large range of Reynolds numbers. Among our findings, we highlight the importance of non-locality when comparing the expected results to the measured ones.

The continuous increase in computational power of GPUs, essential for advancements in areas like artificial intelligence and data processing, is driving the adoption of liquid cooling in data centers. Skived copper cold plates featuring parallel straight channels are a mature technology, but they lack design freedom due to manufacturing limitations. As chips become increasingly complex in their design with the transition towards heterogeneous integration, these parallel straight channels are not able to address critical areas of concentrated high heat flux (hotspots) on a chip. A single hotspot exceeding the upper temperature limit can cause the full chip to throttle and hence limit performance. In addition, this would require a reduction in coolant inlet temperature in the data center, causing an increase in electricity and water consumption. Ideally, areas of the cold plate in contact with hotspots of the chip need smaller channels to increase convective heat transfer, whereas areas with low heat flux may benefit from larger channels to compensate for the increased pressure drop. However, manual optimization of such a cooling design is challenging due to the nonlinearity of the problem. In this paper, we explore the usage of topology optimization as a method to tailor microfluidic cooling design to the power distribution of a chip to address the hotspot temperatures in high-power chips, using a platform called Glacierware. We compare the hotspot-aware, topology-optimized microfluidic design to straight channels of various widths to benchmark its performance. Evaluations of this optimized design show a 13% lower temperature rise or a 55% lower pressure than the best-performing straight channels, indicating highly competitive performance in industrial settings where both pressure and flow rate are constrained.

Speckle patterns manifesting from the interaction of coherent X-rays with matter offer a glimpse into the dynamics of nanoscale domains that underpin many emergent phenomena in quantum materials. While the dynamics of the average structure can be followed with time-resolved X-ray diffraction, the ultrafast evolution of local structures in nonequilibrium conditions have thus far eluded detection due to experimental limitations, such as insufficient X-ray coherent flux. Here we demonstrate a nonequilibrium speckle visibility experiment using a split-and-delay setup at an X-ray free-electron laser. Photoinduced electronic domain fluctuations of the magnetic model material Fe$_{3}$O$_{4}$ reveal changes of the trimeron network configuration due to charge dynamics that exhibit liquid-like fluctuations, analogous to a supercooled liquid phase. This suggests that ultrafast dynamics of electronic heterogeneities under optical stimuli are fundamentally different from thermally-driven ones.

The Monotonocity Principle states a monotonic relationship between a possibly non-linear material property and a proper corresponding boundary operator. The Monotonicity Principle (MP) has attracted great interest in the field of inverse problems, because of its fundamental role in developing real time imaging methods. Recently, with quite general assumptions, a MP in the presence of non linear materials has been established for elliptic PDE, such as those governing Electrical Resistance Tomography. Together with recently introduced imaging methods and algorithms based on MP, arises a fundamental question related to the Converse (of the MP). Indeed, the Converse of the MP is fundamental to define the theoretical limits of applicability of imaging methods and algorithms. Specifically, the Converse of the MP guarantees that the outer boundary of a nonlinear anomaly can be reconstructed by means of MP based imaging methods. In this paper, the Converse of the Monotonicity Principle for nonlinear anomaly embedded in a linear material is proved. The results is provided in a quite general setting for Electrical Resistance Tomography. Moreover, the nonlinear electrical conductivity of the anomaly, as function of the electric field, can be either bounded or not bounded from infinity and/or zero.

We investigate theoretically the buoyancy-driven motion of a viscous drop in a yield-stress material, incorporating elastic effects represented by the Saramito-Herschel-Bulkley constitutive equation. We solve the governing equations using an open-source finite volume solver and utilizing the volume of fluid technique to accurately capture the interface between the two fluids. To validate our numerical approach, we compare our results with data from previous experimental and numerical studies. We find quantitative agreement in terms of terminal velocities and drop shapes, affirming the accuracy of our model and its numerical solution. Notably, we observe that incorporating elastic effects into the modelling of the continuous phase is essential for predicting phenomena reported in experiments, such as the inversion of the flow field behind the sedimenting drop (i.e., the negative wake) or the formation of a teardrop shape. Due to the elastoviscoplastic nature of the continuous phase, we observe that small drops remain entrapped because the buoyancy force is insufficient to fluidize the surrounding material. We investigate entrapment conditions using two different protocols, which yield different outcomes due to the interplay between capillarity and elasto-plasticity. Finally, we conduct an extensive parametric analysis to evaluate the impact of rheological parameters (yield stress, elastic modulus, and interfacial tension) on the dynamics of sedimentation., Comment: 37 pages, 24 figures in the main article and 3 figures in the appendix

Using the notion of distribution-valued tensor, we discuss the junction conditions within the framework of f(Q)-gravity. We obtain the necessary and sufficient conditions for two distinct solutions of the field equations to be smoothly joined on a given separation hypersurface., Comment: in press

We construct deterministic particle solutions for linear and fast diffusion equations using a nonlocal approximation. We exploit the $2$-Wasserstein gradient flow structure of the equations in order to obtain the nonlocal approximating PDEs by regularising the corresponding internal energy with suitably chosen mollifying kernels, either compactly or globally supported. Weak solutions are obtained by the JKO scheme. From the technical point of view, we improve known commutator estimates, fundamental in the nonlocal-to-local limit, to include globally supported kernels which, in particular cases, allow us to justify the limit without any further perturbation needed. Furthermore, we prove geodesic convexity of the nonlocal energies in order to prove convergence of the particle solutions to the nonlocal equations towards weak solutions of the local equations. We overcome the crucial difficulty of dealing with the singularity of the first variation of the free energies at the origin. As a byproduct, we provide convergence rates expressed as a scaling relationship between the number of particles and the localisation parameter. The analysis we perform leverages the fact that globally supported kernels yield a better convergence rate compared to compactly supported kernels. Our result is relevant in statistics, more precisely in sampling Gibbs and heavy-tailed distributions.

Background. Technical debt (TD) has long been one of the key factors influencing the maintainability of software products. It represents technical compromises that sacrifice long-term software quality for potential short-term benefits. Objective. This work is to collectively investigate the practitioners' opinions on the various perspectives of TD from a large collection of articles. We find the topics and latent details of each, where the sentiments of the detected opinions are also considered. Method. For such a purpose, we conducted a grey literature review on the articles systematically collected from three mainstream technology forums. Furthermore, we adopted natural language processing techniques like topic modeling and sentiment analysis to achieve a systematic and comprehensive understanding. However, we adopted ChatGPT to support the topic interpretation. Results. In this study, 2,213 forum posts and articles were collected, with eight main topics and 43 sub-topics identified. For each topic, we obtained the practitioners' collective positive and negative opinions. Conclusion. We identified 8 major topics in TD related to software development. Identified challenges by practitioners include unclear roles and a lack of engagement. On the other hand, active management supports collaboration and mitigates the impact of TD on the source code.

Different approaches have been adopted in addressing the challenges of Artificial Intelligence (AI), some centred on personal data and others on ethics, respectively narrowing and broadening the scope of AI regulation. This contribution aims to demonstrate that a third way is possible, starting from the acknowledgement of the role that human rights can play in regulating the impact of data-intensive systems. The focus on human rights is neither a paradigm shift nor a mere theoretical exercise. Through the analysis of more than 700 decisions and documents of the data protection authorities of six countries, we show that human rights already underpin the decisions in the field of data use. Based on empirical analysis of this evidence, this work presents a methodology and a model for a Human Rights Impact Assessment (HRIA). The methodology and related assessment model are focused on AI applications, whose nature and scale require a proper contextualisation of HRIA methodology. Moreover, the proposed models provide a more measurable approach to risk assessment which is consistent with the regulatory proposals centred on risk thresholds. The proposed methodology is tested in concrete case-studies to prove its feasibility and effectiveness. The overall goal is to respond to the growing interest in HRIA, moving from a mere theoretical debate to a concrete and context-specific implementation in the field of data-intensive applications based on AI.

Context. Source code refactoring is a well-established approach to improving source code quality without compromising its external behavior. Motivation. The literature described the benefits of refactoring, yet its application in practice is threatened by the high cost of time, resource allocation, and effort required to perform it continuously. Providing refactoring recommendations closer to what developers perceive as relevant may support the broader application of refactoring in practice and drive prioritization efforts. Aim. In this paper, we aim to foster the design of a developer-based refactoring recommender, proposing an empirical study into the metrics that study the developer's willingness to apply refactoring operations. We build upon previous work describing the developer's motivations for refactoring and investigate how product and process metrics may grasp those motivations. Expected Results. We will quantify the value of product and process metrics in grasping developers' motivations to perform refactoring, thus providing a catalog of metrics for developer-based refactoring recommenders to use.

Context. In less than a year practitioners and researchers witnessed a rapid and wide implementation of Generative Artificial Intelligence. The daily availability of new models proposed by practitioners and researchers has enabled quick adoption. Textual GAIs capabilities enable researchers worldwide to explore new generative scenarios simplifying and hastening all timeconsuming text generation and analysis tasks. Motivation. The exponentially growing number of publications in our field with the increased accessibility to information due to digital libraries makes conducting systematic literature reviews and mapping studies an effort and timeinsensitive task Stemmed from this challenge we investigated and envisioned the role of GAIs in evidencebased software engineering. Future Directions. Based on our current investigation we will follow up the vision with the creation and empirical validation of a comprehensive suite of models to effectively support EBSE researchers

We present extensive photometric and spectroscopic observations of the nearby Type Ia supernova (SN) 2023wrk at a distance of about 40 Mpc. The earliest detection of this SN can be traced back to a few hours after the explosion. Within the first few days the light curve shows a bump feature, while the B - V color is blue and remains nearly constant. The overall spectral evolution is similar to that of an SN 1991T/SN 1999aa-like SN Ia, while the C II $\lambda6580$ absorption line appears to be unusually strong in the first spectrum taken at $t \approx -$15.4 days after the maximum light. This carbon feature disappears quickly in subsequent evolution but it reappears at around the time of peak brightness. The complex evolution of the carbon line and the possible detection of Ni III absorption around 4700 {\AA} and 5300 {\AA} in the earliest spectra indicate macroscopic mixing of fuel and ash. The strong carbon lines is likely related to collision of SN ejecta with unbound carbon, consistent with the predictions of pulsational delayed-detonation or carbon-rich circumstellar-matter interaction models. Among those carbon-rich SNe Ia with strong C II $\lambda6580$ absorption at very early times, the line-strength ratio of C II to Si II and the B-V color evolution are found to exhibit large diversity, which may be attributed to different properties of unbound carbon and outward-mixing $^{56}$Ni., Comment: Accepted for publication in the Astrophysical Journal (27 pages, 14 figures, 7 tables)

The 16 Myr-old A0V star HD 156623 in the Scorpius--Centaurus association hosts a high-fractional-luminosity debris disk, recently resolved in scattered light for the first time by the Gemini Planet Imager (GPI) in polarized intensity. We present new analysis of the GPI H-band polarimetric detection of the HD 156623 debris disk, with particular interest in its unique morphology. This debris disk lacks a visible inner clearing, unlike the majority of low-inclination disks in the GPI sample and in Sco-Cen, and it is known to contain CO gas, positioning it as a candidate ``hybrid'' or ``shielded'' disk. We use radiative transfer models to constrain the geometric parameters of the disk based on scattered light data and thermal models to constrain the unresolved inner radius based on the system's spectral energy distribution (SED). We also compute a measurement of the polarized scattering phase function, adding to the existing sample of empirical phase function measurements. We find that HD 156623's debris disk inner radius is constrained to less than 26.6 AU from scattered light imagery and less than 13.4 AU from SED modeling at a 99.7% confidence interval, and suggest that gas drag may play a role in retaining sub-blowout size dust grains so close to the star., Comment: 18 pages, 10 figures. Accepted for publication in AJ

This paper presents initial results from the ESA-funded ``SUPPPPRESS'' project, which aims to develop high-performance liquid-crystal coronagraphs for direct imaging of Earth-like exoplanets in reflected light. The project focuses on addressing the significant challenge of polarization leakage in vector vortex coronagraphs (VVCs). We utilize newly manufactured multi-grating, liquid-crystal VVCs, consisting of a two- or three-element stack of vortex and grating patterns, to reduce this leakage to the $10^{-10}$ contrast level. We detail the experimental setups, including calibration techniques with polarization microscopes and Mueller matrix ellipsometers to enhance the direct-write accuracy of the liquid-crystal patterns. The performance testing of these coronagraph masks will be conducted on the THD2 high-contrast imaging testbed in Paris., Comment: 14 pages, 10 figures

Despite their apparent simplicity, devices like smart light bulbs and electrical plugs are often perceived as exempt from rigorous security measures. However, this paper challenges this misconception, uncovering how vulnerabilities in these seemingly innocuous devices can expose users to significant risks. This paper extends the findings outlined in previous work, introducing a novel attack scenario. This new attack allows malicious actors to obtain sensitive credentials, including the victim's Tapo account email and password, as well as the SSID and password of her local network. Furthermore, we demonstrate how these findings can be replicated, either partially or fully, across other smart devices within the same IoT ecosystem, specifically those manufactured by Tp-Link. Our investigation focused on the Tp-Link Tapo range, encompassing smart bulbs (Tapo L530E, Tapo L510E V2, and Tapo L630), a smart plug (Tapo P100), and a smart camera (Tapo C200). Utilizing similar communication protocols, or slight variants thereof, we found that the Tapo L530E, Tapo L510E V2, and Tapo L630 are susceptible to complete exploitation of all attack scenarios, including the newly identified one. Conversely, the Tapo P100 and Tapo C200 exhibit vulnerabilities to only a subset of attack scenarios. In conclusion, by highlighting these vulnerabilities and their potential impact, we aim to raise awareness and encourage proactive steps towards mitigating security risks in smart device deployment.

The discovery of pulsations in (at least) six ultraluminous X-ray sources (ULXs) has shown that neutron stars can accrete at (highly) super-Eddington rates, challenging the standard accretion theories. M51 ULX-7, with a spin signal of $P\simeq2.8$ s, is the pulsating ULX (PULX) with the shortest known orbital period ($P_\mathrm{orb}\simeq2$ d) and has been observed multiple times by XMM-Newton, Chandra, and NuSTAR. We report on the timing and spectral analyses of three XMM-Newton observations of M51 ULX-7 performed between the end of 2021 and the beginning of 2022, together with a timing re-analysis of XMM-Newton, Chandra, and NuSTAR archival observations. We investigated the spin signal by applying accelerated search techniques and studied the power spectrum through the fast Fourier transform, looking for (a)periodic variability in the source flux. We analysed the energy spectra of the 2021-2022 observations and compared them to the older ones. We report the discovery of a recurrent, significant ($>$3$\sigma$) broad complex at mHz frequencies in the power spectra of M51 ULX-7. We did not detect the spin signal, setting a 3$\sigma$ upper limit on the pulsed fraction of $\lesssim10\%$ for the single observation. The complex is significantly detected also in five Chandra observations performed in 2012. M51 ULX-7 represents the second PULX for which we have a significant detection of mHz-QPOs at super-Eddington luminosities. These findings suggest that one should avoid using the observed QPO frequency to infer the mass of the accretor in a ULX. The absence of spin pulsations when the broad complex is detected suggests that the mechanism responsible for the aperiodic modulation also dampens the spin signal's pulsed fraction. If true, this represents an additional obstacle in the detection of new PULXs, suggesting an even larger occurrence of PULXs among ULXs., Comment: 14 pages (12 main text + Appendix), 5 figures. Accepted for publication on A&A

We develop a framework describing the dynamics and thermodynamics of open non-ideal reaction-diffusion systems, which embodies Flory-Huggins theories of mixtures and chemical reaction network theories. Our theory elucidates the mechanisms underpinning the emergence of self-organized dissipative structures in these systems. It evaluates the dissipation needed to sustain and control them, discriminating the contributions from each reaction and diffusion process with spatial resolution. It also reveals the role of the reaction network in powering and shaping these structures. We identify particular classes of networks in which diffusion processes always equilibrate within the structures, while dissipation occurs solely due to chemical reactions. The spatial configurations resulting from these processes can be derived by minimizing a kinetic potential, contrasting with the minimization of the thermodynamic free energy in passive systems. This framework opens the way to investigating the energetic cost of phenomena such as liquid-liquid phase separation, coacervation, and the formation of biomolecular condensates.

There are conflicting reports in the literature regarding the finite-size scaling of the Liouvillian gap and dynamical fluctuations at discontinuous phase transitions, with various studies reporting either exponential or power-law behavior. We clarify this issue by employing large deviation theory. We distinguish two distinct classes of discontinuous phase transitions that have different dynamical properties. The first class is associated with phase coexistence, i.e., the presence of multiple stable attractors of the system dynamics (e.g., local minima of the free energy functional) in a finite phase diagram region around the phase transition point. In that case, one observes asymptotic exponential scaling related to stochastic switching between attractors (though the onset of exponential scaling may sometimes occur for very large system sizes). In the second class, there is no phase coexistence away from the phase transition point, while at the phase transition point itself there are infinitely many attractors. In that case, one observes power-law scaling related to the diffusive nature of the system relaxation to the stationary state., Comment: 20 pages, 20 figures. Accepted to Phys. Rev. E

In this paper we study the effect of the Hardy potential on existence, uniqueness and optimal summability of solutions of the mixed local-nonlocal elliptic problem $$-\Delta u + (-\Delta)^s u - \gamma \frac{u}{|x|^2}=f \text{ in } \Omega, \ u=0 \text{ in } \mathbb{R}^n \setminus \Omega,$$ where $\Omega$ is a bounded domain in $\mathbb{R}^n$ containing the origin and $\gamma> 0$. In particular, we will discuss the existence, non-existence and uniqueness of solutions in terms of the summability of $f$ and of the value of the parameter $\gamma$.

Audio-language models (ALMs) process sounds to provide a linguistic description of sound-producing events and scenes. Recent advances in computing power and dataset creation have led to significant progress in this domain. This paper surveys existing datasets used for training audio-language models, emphasizing the recent trend towards using large, diverse datasets to enhance model performance. Key sources of these datasets include the Freesound platform and AudioSet that have contributed to the field's rapid growth. Although prior surveys primarily address techniques and training details, this survey categorizes and evaluates a wide array of datasets, addressing their origins, characteristics, and use cases. It also performs a data leak analysis to ensure dataset integrity and mitigate bias between datasets. This survey was conducted by analyzing research papers up to and including December 2023, and does not contain any papers after that period.

In the era of 6G, developing and managing software requires cutting-edge software engineering (SE) theories and practices tailored for such complexity across a vast number of connected edge devices. Our project aims to lead the development of sustainable methods and energy-efficient orchestration models specifically for edge environments, enhancing architectural support driven by AI for contemporary edge-to-cloud continuum computing. This initiative seeks to position Finland at the forefront of the 6G landscape, focusing on sophisticated edge orchestration and robust software architectures to optimize the performance and scalability of edge networks. Collaborating with leading Finnish universities and companies, the project emphasizes deep industry-academia collaboration and international expertise to address critical challenges in edge orchestration and software architecture, aiming to drive significant advancements in software productivity and market impact.

Radiance field methods have achieved photorealistic novel view synthesis and geometry reconstruction. But they are mostly applied in per-scene optimization or small-baseline settings. While several recent works investigate feed-forward reconstruction with large baselines by utilizing transformers, they all operate with a standard global attention mechanism and hence ignore the local nature of 3D reconstruction. We propose a method that unifies local and global reasoning in transformer layers, resulting in improved quality and faster convergence. Our model represents scenes as Gaussian Volumes and combines this with an image encoder and Group Attention Layers for efficient feed-forward reconstruction. Experimental results demonstrate that our model, trained for two days on four GPUs, demonstrates high fidelity in reconstructing 360 deg radiance fields, and robustness to zero-shot and out-of-domain testing. Our project Page: https://apchenstu.github.io/LaRa/., Comment: Project Page: https://apchenstu.github.io/LaRa/

W2 (CXOGlb J002415.8-720436) is a cataclysmic variable (CV) in the Galactic globular cluster 47 Tucanae. Its modulation was discovered within the CATS@BAR project. The source shows all the properties of magnetic CVs, but whether it is a polar or an intermediate polar is still a matter of debate. This paper investigates the spectral and temporal properties of the source, using all archival X-ray data from Chandra and eROSITA Early Data Release, to establish whether the source falls within the category of polars or intermediate polars. We fitted Chandra archival spectra with three different models: a power law, a bremsstrahlung and an optically thin thermal plasma. We also explored the temporal properties of the source with searches for pulsations with a power spectral density analysis and a Rayleigh test ($Z_n^2$). W2 displays a mean luminosity of $\sim 10^{32}$ erg s$^{-1}$ over a 20-year span, despite lower values in a few epochs. The source is not detected in the latest observation, taken with Chandra in 2022, and we infer an X-ray luminosity $\leq 7 \times 10^{31}$ erg s$^{-1}$. The source spectral shape does not change over time and can be equally well fitted with each of the three models, with a best-fit photon index of 1.6 for the power law and best-fit temperatures of 10 keV for both the bremsstrahlung and the thermal plasma models. We confirm the previously detected period of 8649 s, ascribed to the binary orbital period, and found a cycle-to-cycle variability associated with this periodicity. No other significant pulsation is detected. Considering the source orbital period, luminosity, spectral characteristics, long-term evolution and strong cycle-to-cycle variability, we suggest that W2 is a magnetic CV of the polar type., Comment: 13 pages, 8 figures. Accepted for publication on A&A

We report on the multi-year evolution of the population of X-ray sources in the nuclear region of NGC 3621 based on Chandra, XMM-Newton and Swift observations. Among these, two sources, X1 and X5, after their first detection in 2008, seem to have faded below the detectability threshold, a most interesting fact as X1 is associated with the AGN of the galaxy. Two other sources, X3 and X6 are presented for the first time, the former showing a peculiar short-term variability in the latest available dataset, suggesting an egress from eclipse, hence belonging to the handful of known eclipsing ultra-luminous X-ray sources. One source, X4, previously known for its "heart-beat", i.e. a characteristic modulation in its signal with a period of $\approx1$ h, shows a steady behaviour in the latest observation. Finally, the brightest X-ray source in NGC 3621, here labelled X2, shows steady levels of flux across all the available datasets but a change in its spectral shape, reminiscent of the behaviours of Galactic disk-fed X-ray binaries., Comment: 11 pages, 10 figures, 3 tables, 2 appendices available online. Accepted for publication on A&A

The Quantum approximate optimization algorithm (QAOA) is a leading hybrid classical-quantum algorithm for solving complex combinatorial optimization problems. QAOA-in-QAOA (QAOA^2) uses a divide-and-conquer heuristic to solve large-scale Maximum Cut (MaxCut) problems, where many subgraph problems can be solved in parallel. In this work, an implementation of the QAOA2 method for the scalable solution of the MaxCut problem is presented, based on the Classiq platform. The framework is executed on an HPE-Cray EX supercomputer by means of the Message Passing Interface (MPI) and the SLURM workload manager. The limits of the Goemans-Williamson (GW) algorithm as a purely classical alternative to QAOA are investigated to understand if QAOA^2 could benefit from solving certain sub-graphs classically. Results from large-scale simulations of up to 33 qubits are presented, showing the advantage of QAOA in certain cases and the efficiency of the implementation, as well as the adequacy of the workflow in the preparation of real quantum devices. For the considered graphs, the best choice for the sub-graphs does not significantly improve results and is still outperformed by GW., Comment: Equal Contribution

Background. Software quality assurance is essential during software development and maintenance. Static Analysis Tools (SAT) are widely used for assessing code quality. Architectural smells are becoming more daunting to address and evaluate among quality issues. Objective. We aim to understand the relationships between static analysis warnings (SAW) and architectural smells (AS) to guide developers/maintainers in focusing their efforts on SAWs more prone to co-occurring with AS. Method. We performed an empirical study on 103 Java projects totaling 72 million LOC belonging to projects from a vast set of domains, and 785 SAW detected by four SAT, Checkstyle, Findbugs, PMD, SonarQube, and 4 architectural smells detected by ARCAN tool. We analyzed how SAWs influence AS presence. Finally, we proposed an AS remediation effort prioritization based on SAW severity and SAW proneness to specific ASs. Results. Our study reveals a moderate correlation between SAWs and ASs. Different combinations of SATs and SAWs significantly affect AS occurrence, with certain SAWs more likely to co-occur with specific ASs. Conversely, 33.79% of SAWs act as "healthy carriers", not associated with any ASs. Conclusion. Practitioners can ignore about a third of SAWs and focus on those most likely to be associated with ASs. Prioritizing AS remediation based on SAW severity or SAW proneness to specific ASs results in effective rankings like those based on AS severity.

In this research note, we revisit the bandits with expert advice problem. Under a restricted feedback model, we prove a lower bound of order $\sqrt{K T \ln(N/K)}$ for the worst-case regret, where $K$ is the number of actions, $N>K$ the number of experts, and $T$ the time horizon. This matches a previously known upper bound of the same order and improves upon the best available lower bound of $\sqrt{K T (\ln N) / (\ln K)}$. For the standard feedback model, we prove a new instance-based upper bound that depends on the agreement between the experts and provides a logarithmic improvement compared to prior results.

Coherent photon sources are key elements in different applications, ranging from quantum sensing to quantum computing. In the context of circuit quantum electrodynamics, there have been multiple proposals for potential coherent sources of photons, but a well established candidate is still missing. The possibility of designing and engineering superconducting circuits behaving like artificial atoms supports the realization of quantum optics protocols, including microwave photons generation. Here we propose and theoretically investigate a new design that allows a tunable photon injection directly on-chip. The scheme is based on initiating a population inversion in a superconducting circuit that will act as the photon source of one or multiple target resonators. The key novelty of the proposed layout consists in replacing the usual capacitive link between the source and the target cavity with a tunable coupler, with the advantage of having on-demand control on the injected steady-state photons. We validate the dynamical control of the generated coherent states under the effect of an external flux threading the tunable coupler and discuss the possibility of employing this scheme also in the context of multiple bosonic reservoirs., Comment: 12 pages, 4 figures

Can a deep neural network be approximated by a small decision tree based on simple features? This question and its variants are behind the growing demand for machine learning models that are *interpretable* by humans. In this work we study such questions by introducing *interpretable approximations*, a notion that captures the idea of approximating a target concept $c$ by a small aggregation of concepts from some base class $\mathcal{H}$. In particular, we consider the approximation of a binary concept $c$ by decision trees based on a simple class $\mathcal{H}$ (e.g., of bounded VC dimension), and use the tree depth as a measure of complexity. Our primary contribution is the following remarkable trichotomy. For any given pair of $\mathcal{H}$ and $c$, exactly one of these cases holds: (i) $c$ cannot be approximated by $\mathcal{H}$ with arbitrary accuracy; (ii) $c$ can be approximated by $\mathcal{H}$ with arbitrary accuracy, but there exists no universal rate that bounds the complexity of the approximations as a function of the accuracy; or (iii) there exists a constant $\kappa$ that depends only on $\mathcal{H}$ and $c$ such that, for *any* data distribution and *any* desired accuracy level, $c$ can be approximated by $\mathcal{H}$ with a complexity not exceeding $\kappa$. This taxonomy stands in stark contrast to the landscape of supervised classification, which offers a complex array of distribution-free and universally learnable scenarios. We show that, in the case of interpretable approximations, even a slightly nontrivial a-priori guarantee on the complexity of approximations implies approximations with constant (distribution-free and accuracy-free) complexity. We extend our trichotomy to classes $\mathcal{H}$ of unbounded VC dimension and give characterizations of interpretability based on the algebra generated by $\mathcal{H}$., Comment: To appear at COLT 2024

We extend the evolution mapping approach, originally proposed by Sanchez (2022) to describe non-linear matter density fluctuations, to statistics of the cosmic velocity field. This framework classifies cosmological parameters into shape parameters, which determine the shape of the linear matter power spectrum, $P_L(k, z)$, and evolution parameters, which control its amplitude at any given redshift. Evolution mapping leverages the fact that density fluctuations in cosmologies with identical shape parameters but different evolution parameters exhibit remarkably similar non-linear evolutions when expressed as a function of the clustering amplitude. We use a suite of N-body simulations sharing identical shape parameters but spanning a wide range of evolution parameters. Using an efficient method for estimating the volume-weighted velocity field based on the Voronoi tesselation of the simulation particles, we study the non-linear evolution of the power spectra of the velocity divergence, $P_{\theta\theta}(k)$, and its cross-power spectrum with the density field, $P_{\delta\theta}(k)$. By analysing snapshots at redshifts where the linear matter perturbations have the same amplitude, we demonstrate that evolution mapping accurately applies to $P_{\theta\theta}(k)$ and $P_{\delta\theta}(k)$. Deviations at small scales can be modelled in terms of differences in the suppression factor, $g(a) = D(a)/a$, akin to those observed for the density field. Evolution mapping simplifies the description of the cosmological dependence of non-linear density and velocity statistics, streamlining the sampling of large cosmological parameter spaces for the analysis of cosmological observables., Comment: 10 pages, 4 figures, including an appendix. v2 has minor changes from peer review suggestions

The precision of currents in Markov networks is bounded by dissipation via the so-called thermodynamic uncertainty relation (TUR). In our work, we demonstrate a similar inequality that bounds the precision of the static current response to perturbations of kinetic barriers. Perturbations of such type, which affect only the system kinetics but not the thermodynamic forces, are highly important in biochemistry and nanoelectronics. We prove that our inequality cannot be derived from the standard TUR. Instead, it implies the standard TUR and provides an even tighter bound for dissipation. We also provide a procedure for obtaining the optimal response precision for a given model., Comment: 6 pages, 4 figures

Context. Risk analysis assesses potential risks in specific scenarios. Risk analysis principles are context-less; the same methodology can be applied to a risk connected to health and information technology security. Risk analysis requires a vast knowledge of national and international regulations and standards and is time and effort-intensive. A large language model can quickly summarize information in less time than a human and can be fine-tuned to specific tasks. Aim. Our empirical study aims to investigate the effectiveness of Retrieval-Augmented Generation and fine-tuned LLM in risk analysis. To our knowledge, no prior study has explored its capabilities in risk analysis. Method. We manually curated 193 unique scenarios leading to 1283 representative samples from over 50 mission-critical analyses archived by the industrial context team in the last five years. We compared the base GPT-3.5 and GPT-4 models versus their Retrieval-Augmented Generation and fine-tuned counterparts. We employ two human experts as competitors of the models and three other human experts to review the models and the former human experts' analysis. The reviewers analyzed 5,000 scenario analyses. Results and Conclusions. Human experts demonstrated higher accuracy, but LLMs are quicker and more actionable. Moreover, our findings show that RAG-assisted LLMs have the lowest hallucination rates, effectively uncovering hidden risks and complementing human expertise. Thus, the choice of model depends on specific needs, with FTMs for accuracy, RAG for hidden risks discovery, and base models for comprehensiveness and actionability. Therefore, experts can leverage LLMs as an effective complementing companion in risk analysis within a condensed timeframe. They can also save costs by averting unnecessary expenses associated with implementing unwarranted countermeasures.

Quantum computing, albeit readily available as hardware or emulated on the cloud, is still far from being available in general regarding complex programming paradigms and learning curves. This vision paper introduces $Classi|Q\rangle$, a translation framework idea to bridge Classical and Quantum Computing by translating high-level programming languages, e.g., Python or C++, into a low-level language, e.g., Quantum Assembly. Our idea paper serves as a blueprint for ongoing efforts in quantum software engineering, offering a roadmap for further $Classi|Q\rangle$ development to meet the diverse needs of researchers and practitioners. $Classi|Q\rangle$ is designed to empower researchers and practitioners with no prior quantum experience to harness the potential of hybrid quantum computation. We also discuss future enhancements to $Classi|Q\rangle$, including support for additional quantum languages, improved optimization strategies, and integration with emerging quantum computing platforms.

Major shifts in the global system of science and technology are destabilizing the global status order and demonstrating the capacity for emerging countries like China and India to exert greater influence. In order to measure changes in the global scientific system, we develop a framework to assess the hierarchical position of countries in the international scientific collaboration network. Using a machine-learning model to identify the leaders of 5,966,623 scientific teams that collaborated across international borders, we show that Chinese scientists substantially narrowed their leadership deficit with scientists from the US, UK, and EU between 1990 and 2023 in absolute terms. Consequently, China and the US are on track to reach an equal number of team leaders engaged in bilateral collaborations between 2027 and 2028. Nevertheless, Chinese progress has been considerably slower in per-collaborator terms: after adjusting for the number of non-leaders from each country, our models do not predict parity between the US and China until after 2087. These dynamics extend to 11 critical technology areas central to ongoing diplomacy between the two nations, such AI, Semiconductors, and Advanced Communications, and to China's scientific leadership with respect to the European Union and the United Kingdom. Thus, while China's elite scientists are achieving leadership in the international scientific community, China's scientific enterprise continues to face developmental constraints. We conclude by reviewing several steps that Chinese science is taking to overcome these constraints, by increasing its engagement in scientific training and research in signatory nations to the Belt and Road Initiative.

We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. Most existing approaches predict volumetric density to render multi-view consistent images. By employing volumetric rendering using neural radiance fields, they inherit a key limitation: the generated geometry is noisy and unconstrained, limiting the quality and utility of the output meshes. To address this issue, we propose GeoGen, a new SDF-based 3D generative model trained in an end-to-end manner. Initially, we reinterpret the volumetric density as a Signed Distance Function (SDF). This allows us to introduce useful priors to generate valid meshes. However, those priors prevent the generative model from learning details, limiting the applicability of the method to real-world scenarios. To alleviate that problem, we make the transformation learnable and constrain the rendered depth map to be consistent with the zero-level set of the SDF. Through the lens of adversarial training, we encourage the network to produce higher fidelity details on the output meshes. For evaluation, we introduce a synthetic dataset of human avatars captured from 360-degree camera angles, to overcome the challenges presented by real-world datasets, which often lack 3D consistency and do not cover all camera angles. Our experiments on multiple datasets show that GeoGen produces visually and quantitatively better geometry than the previous generative models based on neural radiance fields.

We provide a characterization of parallelizable compact complex manifolds and their quotients using holomorphic symmetric differentials. In particular we show that compact complex manifolds of Kodaira dimension 0 having strongly semiample cotangent bundle are parallelizable manifolds, while compact complex manifolds of Kodaira dimension 0 having weakly semiample cotangent bundle are quotients of parallelizable manifolds. The main constructions used involve considerations about semiampleness of vector bundles, which are themselves of interest. As a byproduct we prove that compact manifolds having Kodaira dimension 0 and weakly sermiample cotangent bundle have infinite fundamental group, and we conjecture that this should be the case for all compact complex manifolds not of general type with weakly semiample cotangent bundle.

This article demonstrates the effectiveness of employing a deep learning model in an optimization pipeline. Specifically, in a generic exact algorithm for a NP problem, multiple heuristic criteria are usually used to guide the search of the optimum within the set of all feasible solutions. In this context, neural networks can be leveraged to rapidly acquire valuable information, enabling the identification of a more expedient path in this vast space. So, after the explanation of the tackled traveling salesman problem, the implemented branch and bound for its classical resolution is described. This algorithm is then compared with its hybrid version termed "graph convolutional branch and bound" that integrates the previous branch and bound with a graph convolutional neural network. The empirical results obtained highlight the efficacy of this approach, leading to conclusive findings and suggesting potential directions for future research., Comment: Submitted to European Journal of Operational Research