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Earth Observation (EO) has traditionally involved the transmission of a large volume of raw data to map the Earth surface. This results in congestion to the satellite network and delays in the availability of the results, invalidating the approach for timing-sensitive applications. Instead, the computation resources at the satellites can be used as an edge layer for compressing the data and/or doing inferences. In this paper, we investigate satellite edge computing for vessel detection with a LEO satellite constellation. First, we distribute the computation and inference load among the neighbouring satellites of the one taking the images, based on the VHRShips data set and YOLOv8. This semantic and fragmented information is then routed to a remote ground monitor through the whole constellation. The average and peak Age of Information (AoI) are reformulated to measure the freshness of the aggregated information at the receiver in this image-capture scenario. We then dimension the network (number of orbital planes and satellites per orbital plane) for a given target age and covered area that quantify the level of achievement of the task. The results show that 20 orbital planes with 20 satellites are necessary to keep the peak AoI below 60 seconds with a compression ratio > 23000, i.e., a size reduction of 99.996%, and for a approximately 100% probability of coverage., Comment: This paper was presented at the ComSoc Challenge on 'Future 6G NTN Systems' held at ICCSPA'24, where it was awarded second place

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.

Detections of fast X-ray transients (FXTs) have been accrued over the last few decades. However, their origin has remained mysterious. There is now rapid progress thanks to timely discoveries and localisations with the Einstein Probe mission. Early results indicate that FXTs may frequently, but not always, be associated with gamma-ray bursts (GRBs). Here, we report on the multi-wavelength counterpart of FXT EP240414a, which has no reported gamma-ray counterpart. The transient is located 25.7~kpc in projection from a massive galaxy at $z=0.40$. We perform comprehensive photometric and spectroscopic follow-up. The optical light curve shows at least three distinct emission episodes with timescales of $\sim 1, 4$ and 15 days and peak absolute magnitudes of $M_R \sim -20$, $-21$, and $-19.5$, respectively. The optical spectrum at early times is extremely blue, inconsistent with afterglow emission. It may arise from the interaction of both jet and supernova shock waves with the stellar envelope and a dense circumstellar medium, as has been suggested for some Fast Blue Optical Transients (LFBOTs). At late times, the spectrum evolves to a broad-lined~Type~Ic supernova, similar to those seen in collapsar long-GRBs. This implies that the progenitor of EP240414a is a massive star creating a jet-forming supernova inside a dense envelope, resulting in an X-ray outburst with a luminosity of $\sim 10^{48}$ erg s$^{-1}$, and the complex observed optical/IR light curves. If correct, this argues for a causal link between the progenitors of long-GRBs, FXTs and LFBOTs., Comment: 36 pages, 13 figures, submitted to ApJ

The SaaS paradigm has popularized the usage of pricings, allowing providers to offer customers a wide range of subscription possibilities. This creates a vast configuration space for users, enabling them to choose the features and support guarantees that best suit their needs. Regardless of the reasons why changes in these pricings are made, the frequency of changes within the elements of pricings continues to increase. Therefore, for those responsible for the development and operation of SaaS, it would be ideal to minimize the time required to transfer changes in SaaS pricing to the software and underlying infrastructure, without compromising the quality and reliability.% of the service; %i.e., this development and operation should be Pricing-Driven. This work explores the support offered by the industry for this need. By modeling over 150 pricings from 30 different SaaS over six years, we reveal that the configuration space grows exponentially with the number of add-ons and linearly with the number of plans. We also evaluate 21 different feature toggling solutions, finding that feature toggling, particularly permission toggles, is a promising technique for enabling rapid adaptation to pricing changes. Our results suggest that developing automated solutions with minimal human intervention could effectively reduce the time-to-market for SaaS updates driven by pricing changes, especially with the adoption of a standard for serializing pricings., Comment: 15 pages. Accepted in the 22nd International Conference on Service-Oriented Computing (ICSOC'24)

The Two Higgs Doublet Model (2HDM) is a simple extension of the Standard Model (SM), which provides a rich and very interesting phenomenology. To remove the undesirable flavour-changing neutral currents (FCNCs), an additional $\mathcal Z_2$ symmetry is usually imposed into the 2HDM. However, FCNCs can be avoided in a much more general way by assuming a similar Yukawa structure for the two scalar doublets. But one can circumvent the issue of FCNCs by assuming similar Yukawa structure for the two scalar doublets too. The model with this intriguing feature is termed the Aligned Two Higgs Doublet Model (A2HDM). The phenomenological constraints on the A2HDM are much weaker than the ones on the usual $\mathcal Z_2$ models, opening a broader range of possible scenarios. Moreover, the A2HDM also provides a generic framework to study, as particular cases, the different varieties of $\mathcal Z_2$-symmetric 2HDMs. Here, we illustrate a global fit of the A2HDM using the package HEPfit. We study the possibility of having new scalar particles lighter than the SM Higgs. For this global fit we perform a Bayesian analysis, including stability and perturbativity bounds, flavour and electroweak precision observables, and scalar (and pseudoscalar) searches at LEP and LHC., Comment: Proceeding for ICHEP2024

This paper establishes a rigorous connection between circuit representations and tensor factorizations, two seemingly distinct yet fundamentally related areas. By connecting these fields, we highlight a series of opportunities that can benefit both communities. Our work generalizes popular tensor factorizations within the circuit language, and unifies various circuit learning algorithms under a single, generalized hierarchical factorization framework. Specifically, we introduce a modular "Lego block" approach to build tensorized circuit architectures. This, in turn, allows us to systematically construct and explore various circuit and tensor factorization models while maintaining tractability. This connection not only clarifies similarities and differences in existing models, but also enables the development of a comprehensive pipeline for building and optimizing new circuit/tensor factorization architectures. We show the effectiveness of our framework through extensive empirical evaluations, and highlight new research opportunities for tensor factorizations in probabilistic modeling.

The integration of Semantic Communications (SemCom) and edge computing in space networks enables the optimal allocation of the scarce energy, computing, and communication resources for data-intensive applications. We use Earth Observation (EO) as a canonical functionality of satellites and review its main characteristics and challenges. We identify the potential of the space segment, represented by a low Earth orbit (LEO) satellite constellation, to serve as an edge layer for distributed intelligence. Based on that, propose a system architecture that supports semantic and goal-oriented applications for image reconstruction and object detection and localization. The simulation results show the intricate trade-offs among energy, time, and task-performance using a real dataset and State-of-the-Art (SoA) processing and communication parameters., Comment: Submitted for publication to IEEE Communications Magazine

A new system, called SELA -- Auditing Electronic System, has been developed to be applied to the Brazilian Electronic Voting Machine. The SELA was designed to use open hardware and software, making it widely known by society. The security of the auditing process is guaranteed by the application of a Fingerprint Algorithm, a Hash Function. This system is robust and requires minimal modifications to the Electronic Voting Machine. In this paper, SELA is described, and its use during the election process is analyzed. A comparison between SELA and the use of thermal printers as a secondary voting record system is also presented. The authors recommend a pilot implementation of SELA for the 2002 Brazilian Elections., Comment: 7 pages, in Portuguese language, 3 figures, prototype

Representation learning is an approach that allows to discover and extract the factors of variation from the data. Intuitively, a representation is said to be disentangled if it separates the different factors of variation in a way that is understandable to humans. Definitions of disentanglement and metrics to measure it usually assume that the factors of variation are independent of each other. However, this is generally false in the real world, which limits the use of these definitions and metrics to very specific and unrealistic scenarios. In this paper we give a definition of disentanglement based on information theory that is also valid when the factors of variation are not independent. Furthermore, we relate this definition to the Information Bottleneck Method. Finally, we propose a method to measure the degree of disentanglement from the given definition that works when the factors of variation are not independent. We show through different experiments that the method proposed in this paper correctly measures disentanglement with non-independent factors of variation, while other methods fail in this scenario.

Introduction. Here we reported the virological, entomological and epidemiological characteristics of the large autochthonous outbreak of dengue (DENV) occurred in a small village of the Lombardy region (Northern Italy) during summer 2023. Methods. After the diagnosis of the first autochthonous case on 18 August 2023, public health measures, including epidemiological investigation and vector control measures, were carried out. A serological screening for DENV antibodies detection was offered to the population. In the case of positive DENV IgM, a second sample was collected to detect DENV RNA and verify seroconversion. Entomological and epidemiological investigations were also performed. A modeling analysis was conducted to estimate the dengue generation time, transmission potential, distance of transmission, and assess diagnostic delays. Results. Overall, 416 subjects participated to the screening program and 20 were identified as DENV-1 cases (15 confirmed and 5 probable). In addition, DENV-1 infection was diagnosed in 24 symptomatic subjects referred to the local Emergency Room Department for suggestive symptoms and 1 case was identified through blood donation screening. The average generation time was estimated to be 18.3 days (95% CI: 13.1-23.5 days). R0 was estimated at 1.31 (95% CI: 0.76-1.98); 90% of transmission occurred within 500m. Entomological investigations performed in 46 pools of mosquitoes revealed the presence of only one positive pool for DENV-1. Discussion. This report highlights the importance of synergic surveillance, including virological, entomological and public health measures to control the spread of arboviral infections.

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.

Labor plays a major, albeit largely unrecognized role in the development of artificial intelligence. Machine learning algorithms are predicated on data-intensive processes that rely on humans to execute repetitive and difficult-to-automate, but no less essential, tasks such as labeling images, sorting items in lists, recording voice samples, and transcribing audio files. Online platforms and networks of subcontractors recruit data workers to execute such tasks in the shadow of AI production, often in lower-income countries with long-standing traditions of informality and lessregulated labor markets. This study unveils the resulting complexities by comparing the working conditions and the profiles of data workers in Venezuela, Brazil, Madagascar, and as an example of a richer country, France. By leveraging original data collected over the years 2018-2023 via a mixed-method design, we highlight how the cross-country supply chains that link data workers to core AI production sites are reminiscent of colonial relationships, maintain historical economic dependencies, and generate inequalities that compound with those inherited from the past. The results also point to the importance of less-researched, non-English speaking countries to understand key features of the production of AI solutions at planetary scale., Comment: Jack Qiu, Shinjoung Yeo, Richard Maxwell. The Handbook of Digital Labor, Wiley Blackwell, In press, ISBN10: 1119981808

We study the co-evolution of black holes (BHs) and their host galaxies in the ASTRID and Illustris-TNG300 cosmological simulations and the Dark Sage Semi-Analytic Model (SAM), focusing on the evolution of the BH mass - stellar mass ($M_{\rm BH}-M_*$) relation. Due to differences in the adopted sub-grid modeling of BH seeding, dynamics, and feedback, the models differ in their predicted redshift evolution of the $M_{\rm BH}-M_*$ relation. We find that it is the interplay between the star formation rate (SFR) and the black hole accretion rate (BHAR) which drives the evolution of the mean relation. We define a quantity $\mathcal{R}$, the ratio between the specific BHAR and SFR (i.e. $\mathcal{R} \equiv\ $sBHAR/sSFR), and demonstrate that it is $\mathcal{R}$ that governs the evolution of individual sources in the $M_{\rm BH}-M_*$ plane. The efficiency of BH growth versus stellar mass growth in the sSFR-sBHAR plane reflects the partitioning of gas between fueling star formation versus BH accretion. This partitioning depends on the implementation of BH dynamics and the nature of how AGN feedback quenches galaxies. In the cosmological simulations (ASTRID and Illustris-TNG300), the BHAR and SFR are intrinsically linked, resulting in a tight $M_{\rm BH}-M_*$ correlation, while the Dark Sage SAM produces a significantly larger scatter. We discuss these results in the context of recently discovered over-massive BHs and massive quenched galaxies at high redshift by the James Webb Space Telescope., Comment: 17 pages, 8 figures. Submitted to ApJ. Comments welcome

We analyse the maps of the Sunyaev-Zel'dovich (SZ) signal of local galaxy clusters ($z<0.1$) observed by the $Planck$ satellite in order to classify their dynamical state through morphological features. To study the morphology of the cluster maps, we apply a method recently employed on mock SZ images generated from hydrodynamical simulated galaxy clusters in THE THREE HUNDRED (THE300) project. Here, we report the first application on real data. The method consists in modelling the images with a set of orthogonal functions defined on circular apertures, the Zernike polynomials. From the fit we compute a single parameter, $\mathcal{C}$, that quantifies the morphological features present in each image. The link between the morphology of 2D images and the dynamical state of the galaxy clusters is well known, even if not obvious. We use mock $Planck$-like Compton parameter maps generated for THE300 clusters to validate our morphological analysis. These clusters, in fact, are properly classified for their dynamical state with the relaxation parameter, $\chi$, by exploiting 3D information from simulations. We find a mild linear correlation of $\sim 38\%$ between $\mathcal{C}$ and $\chi$ for THE300 clusters, mainly affected by the noise present in the maps. In order to obtain a proper dynamical-state classification for the $Planck$ clusters, we exploit the conversion from the $\mathcal{C}$ parameter derived in each $Planck$ map in $\chi$. A fraction of the order of $63\%$ of relaxed clusters is estimated in the selected $Planck$ sample. Our classification is then compared with those of previous works that have attempted to evaluate, with different indicators and/or other wavelengths, the dynamical state of the same $Planck$ objects. The agreement with the other works is larger than $58\%$., Comment: 15 pages, 7 figures, 2 tables. Submitted to A&A

The telecommunications industry's deployment of billions of kilometers of optical fiber has created a vast global network that can be exploited for additional applications such as environmental sensing, quantum networking and international clock comparisons. However, for reasons such as the unidirectionality of long-haul fiber links, telecom fiber networks cannot always be adapted for important applications beyond data transmission. Fortunately, new multicore optical fibers create the opportunity for application coexistence with data traffic, creating expansive multifunctional networks. Towards that end, we propose and demonstrate the faithful transfer of ultrastable optical signals through multicore fiber in a way that is compatible with the unidirectionality of long-haul fiber optic systems, demonstrating a fractional frequency instability of 3x10-19 at 10,000 seconds. This opens the door towards intercontinental optical clock comparisons, with applications in fundamental physics and the redefinition of the second.

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessing the latent variables underlying such a data structure remains a challenge. In this work, we show that forward-backward experiments in diffusion-based models, where data is noised and then denoised to generate new samples, are a promising tool to probe the latent structure of data. We predict in simple hierarchical models that, in this process, changes in data occur by correlated chunks, with a length scale that diverges at a noise level where a phase transition is known to take place. Remarkably, we confirm this prediction in both text and image datasets using state-of-the-art diffusion models. Our results show how latent variable changes manifest in the data and establish how to measure these effects in real data using diffusion models., Comment: 11 pages, 6 figures

This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain. Decentralized AI distributes AI services among multiple entities without centralized oversight, fostering transparency and robustness. However, this structure introduces significant privacy challenges, as sensitive assets such as proprietary models and personal data may be exposed to untrusted participants. Cryptography-based privacy protection techniques such as zero-knowledge machine learning (zkML) suffers prohibitive computational overhead. To address the limitation, we propose leveraging Confidential Computing (CC). Confidential Computing leverages hardware-based Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data, ensuring that both model parameters and user data remain secure, even in decentralized, potentially untrusted environments. While TEEs face a few limitations, we believe they can bridge the privacy gap in decentralized AI. We explore how we can integrate TEEs into Atoma's decentralized framework.

We survey the recent history of the conformal Calabi-Yau problem consisting in determining the complex structures admitted by complete bounded minimal surfaces in $\mathbb{R}^3$. Moreover, we prove that for any minimally convex domain $\Omega$ in $\mathbb{R}^3$ and any compact Riemann surface $R$ there is a Cantor set $C$ in $R$ whose complement $R\setminus C$ is the complex structure of a complete proper minimal surface in $\Omega$., Comment: To appear in an issue of Pure Appl. Funct. Anal. dedicated to Nikolai Nadirashvili

Estimating steady state properties of open quantum systems is a crucial problem in quantum technology. In this work, we show how to derive in a scalable way using semi-definite programming certified bounds on the expectation value of an arbitrary observable of interest on the steady state of Lindbladian dynamics. We illustrate our method on a series of many-body systems, including a one-dimensional chain and a two-dimensional ladder, and benchmark it with state-of-the-art tensor-network approaches. For the tested models, only modest computational effort is needed to obtain certified bounds whose precision is comparable to variational methods, Comment: See also arXiv:2410.07384

We provide necessary and sufficient conditions for the uniqueness of the k-means set of a probability distribution. This uniqueness problem is related to the choice of k: depending on the underlying distribution, some values of this parameter could lead to multiple sets of k-means, which hampers the interpretation of the results and/or the stability of the algorithms. We give a general assessment on consistency of the empirical k-means adapted to the setting of non-uniqueness and determine the asymptotic distribution of the within cluster sum of squares (WCSS). We also provide statistical characterizations of k-means uniqueness in terms of the asymptotic behavior of the empirical WCSS. As a consequence, we derive a bootstrap test for uniqueness of the set of k-means. The results are illustrated with examples of different types of non-uniqueness and we check by simulations the performance of the proposed methodology., Comment: 41 pages, 14 figures

Optimization methods play a crucial role in modern machine learning, powering the remarkable empirical achievements of deep learning models. These successes are even more remarkable given the complex non-convex nature of the loss landscape of these models. Yet, ensuring the convergence of optimization methods requires specific structural conditions on the objective function that are rarely satisfied in practice. One prominent example is the widely recognized Polyak-Lojasiewicz (PL) inequality, which has gained considerable attention in recent years. However, validating such assumptions for deep neural networks entails substantial and often impractical levels of over-parametrization. In order to address this limitation, we propose a novel class of functions that can characterize the loss landscape of modern deep models without requiring extensive over-parametrization and can also include saddle points. Crucially, we prove that gradient-based optimizers possess theoretical guarantees of convergence under this assumption. Finally, we validate the soundness of our new function class through both theoretical analysis and empirical experimentation across a diverse range of deep learning models.

We propose 2d $\mathcal{N}=(0,2)$ dualities between SU(N) gauge theories with fundamental and antisymmetric chiral matter and Landau-Ginzburg theories with chiral and Fermi multiplets. Many of these dualities can be derived by topologically twisting 4d s-confining gauge theories on a two-sphere, with integer non-negative $R$ charges. We provide various checks of the dualities, showing that they descend from more "basic" dualities, similarly to analogous derivations in higher dimensions. The proof are based on the fact that the antisymmetric tensors can be traded with USp(2n) gauge theories with fundamental chirals, mimicking the higher dimensional mechanism known as tensor deconfinement. The quivers obtained in this way can be shown to be dual to LG models after applying other elementary "basic" dualities., Comment: 55 pages + appendices, 17 figures

We propose a new multirotor aerial vehicle class of designs composed of a multi-body structure in which a main body is connected by passive joints to links equipped with propellers. We have investigated some instances of such class, some of which are shown to achieve omnidirectionality while having a minimum number of inputs equal to the main body Degrees of Freedom DoF's, only uni-directional positive thrust propellers, and no internal forces generated at steady state. After dynamics are derived following the Euler-Lagrange approach, an I/O dynamic feedback linearization strategy is then used to show the controllability of any desired pose with stable zero dynamics. We finally verify the developed controller with closed-loop simulations.

Let $\mathbb{F}$ be a field and $\mathsf{G}$ a group. This work is inspired in the following problem: "{\it given a division (simple) $\mathsf{G}$-graded $\mathbb{F}$-algebra, is there any other division (simple) $\mathsf{G}$-graded $\mathbb{F}$-algebra such that the former can be $\mathsf{G}$-imbedded in the latter?}". In this work, we answer this question affirmatively for $\mathbb{F}$ algebraically closed, $\mathsf{G}$ finite abelian, and associative algebras of finite dimension. To prove this, we apply concepts and properties of Group Cohomology. We show $\mathcal{H}^2(H, \mathbb{F}^*)=\mathsf{res}^\mathsf{G}_H \left(\mathcal{H}^2(\mathsf{G}, \mathbb{F}^*)\right)$, where $H$ is a subgroup of $\mathsf{G}$ and $\mathsf{res}^\mathsf{G}_H$ is the restriction homomorphism. Posteriorly, we prove that, given any $H_1,H_2\leq\mathsf{G}$ and $\sigma_i\in\mathcal{Z}^2(H_i,\mathbb{F}^*)$, $i=1,2$, are equivalent: i) $\mathbb{F}^{\sigma_1}[H_1] \stackrel{\mathsf{G}}{\hookrightarrow} \mathbb{F}^{\sigma_2}[H_2]$; ii) $H_1\leq H_2$ and $[\sigma_1]=[\sigma_2]_{H_1}$; iii) $\mathsf{T}^{\mathsf{G}}(\mathbb{F}^{\sigma_2}[H_2])\subseteq \mathsf{T}^{\mathsf{G}}(\mathbb{F}^{\sigma_1}[H_1])$, where $\mathsf{T}^{\mathsf{G}}(\mathbb{F}^{\sigma_i}[H_i])$ is the $\mathsf{G}$T-ideal of graded identities of $\mathbb{F}^{\sigma_i}[H_i]$. Furthermore, we prove that, given $\mathfrak{A}$ and $\mathfrak{B}$ two finite dimensional simple $\mathsf{G}$-graded $\mathbb{F}$-algebras, if $\mathbb{F}$ is algebraically closed, $\mathsf{char}(\mathbb{F}) = 0$ or $\mathsf{char} (\mathbb{F})$ is coprime with the order of each finite subgroup of $\mathsf{G}$, and any subgroup of $\mathsf{G}$ is normal, then $\mathsf{T}^{\mathsf{G}}(\mathfrak{A})\subseteq \mathsf{T}^{\mathsf{G}}(\mathfrak{B})$ iff $\mathfrak{B} \stackrel{\mathsf{G}}{\hookrightarrow} \mathfrak{A}$.

Soft robots are known for their ability to perform tasks with great adaptability, enabled by their distributed, non-uniform stiffness and actuation. Bending is the most fundamental motion for soft robot design, but creating robust, and easy-to-fabricate soft bending joint with tunable properties remains an active problem of research. In this work, we demonstrate an inflatable actuation module for soft robots with a defined bending plane enabled by forced partial wrinkling. This lowers the structural stiffness in the bending direction, with the final stiffness easily designed by the ratio of wrinkled and unwrinkled regions. We present models and experimental characterization showing the stiffness properties of the actuation module, as well as its ability to maintain the kinematic constraint over a large range of loading conditions. We demonstrate the potential for complex actuation in a soft continuum robot and for decoupling actuation force and efficiency from load capacity. The module provides a novel method for embedding intelligent actuation into soft pneumatic robots.

With his formal analysis in 1951, the physicist Pyotr Kapitza demonstrated that an inverted pendulum with an externally vibrating base can be stable in its upper position, thus overcoming the force of gravity. Kapitza's work is an example that an originally unstable system can become stable after a minor perturbation of its properties or initial conditions is applied. Inspired by his ideas, we show how non-Foster circuits can be stabilized with the application of external \textit{electrical vibration}, i.e., time modulations. Non-Foster circuits are highly appreciated in the engineering community since their bandwidth characteristics are not limited by passive-circuits bounds. Unfortunately, non-Foster circuits are usually unstable and they must be stabilized prior to operation. Here, we focus on the study of non-Foster $L(t)C$ circuits with time-varying inductors and time-invariant negative capacitors. We find an intrinsic connection between Kapitza's inverted pendulum and non-Foster $L(t)C$ resonators. Moreover, we show how positive time-varying modulations of $L(t)>0$ can overcome and stabilize non-Foster negative capacitances $C<0$. These findings open up an alternative manner of stabilizing electric circuits with the use of time modulations, and lay the groundwork for application of, what we coin \textit{Vibrational Electromagnetics}, in more complex media., Comment: 10 pages (7 pages main text, 3 pages supplementary materials), 4 figures

We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin foil coupled with high-speed infrared thermography. The measurement technique is challenged by the thermal inertia of the foil, the high frequency of turbulent fluctuations, and the measurement noise of the infrared camera. We discuss in detail the advantages and drawbacks of all the design choices that were made, thereby providing a successful implementation strategy to obtain high-quality data. This experimental approach could be useful for experimental studies employing wall-based measurements of turbulence, such as flow control applications in wall-bounded turbulence.

Super-resolution (SR) is a promising cost-effective downscaling methodology for producing high-resolution climate information from coarser counterparts. A particular application is downscaling regional reanalysis outputs (predictand) from the driving global counterparts (predictor). This study conducts an intercomparison of various SR downscaling methods focusing on temperature and using the CERRA reanalysis (5.5 km resolution, produced with a regional atmospheric model driven by ERA5) as example. The method proposed in this work is the Swin transformer and two alternative methods are used as benchmark (fully convolutional U-Net and convolutional and dense DeepESD) as well as the simple bicubic interpolation. We compare two approaches, the standard one using the full domain as input and a more scalable tiling approach, dividing the full domain into tiles that are used as input. The methods are trained to downscale CERRA surface temperature, based on temperature information from the driving ERA5; in addition, the tiling approach includes static orographic information. We show that the tiling approach, which requires spatial transferability, comes at the cost of a lower performance (although it outperforms some full-domain benchmarks), but provides an efficient scalable solution that allows SR reduction on a pan-European scale and is valuable for real-time applications.

Adversarial training can be used to learn models that are robust against perturbations. For linear models, it can be formulated as a convex optimization problem. Compared to methods proposed in the context of deep learning, leveraging the optimization structure allows significantly faster convergence rates. Still, the use of generic convex solvers can be inefficient for large-scale problems. Here, we propose tailored optimization algorithms for the adversarial training of linear models, which render large-scale regression and classification problems more tractable. For regression problems, we propose a family of solvers based on iterative ridge regression and, for classification, a family of solvers based on projected gradient descent. The methods are based on extended variable reformulations of the original problem. We illustrate their efficiency in numerical examples.

Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA are not really complex, and the way they are built distorts our perception of progress in this field. For example, we find that in these benchmarks, most queries (up to 98% for some query types) can be reduced to simpler problems, e.g., link prediction, where only one link needs to be predicted. The performance of state-of-the-art CQA models drops significantly when such models are evaluated on queries that cannot be reduced to easier types. Thus, we propose a set of more challenging benchmarks, composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs. In a systematic empirical investigation, the new benchmarks show that current methods leave much to be desired from current CQA methods.

Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups within the population. Population-adjustment methods are increasingly used to adjust for differences in effect modifiers between study populations and to produce population-adjusted estimates in a relevant target population for decision-making. It is also widely understood that marginal and conditional estimands for non-collapsible effect measures, such as odds ratios or hazard ratios, do not in general coincide even without effect modification. However, the consequences of both non-collapsibility and effect modification together are little-discussed in the literature. In this paper, we set out the definitions of conditional and marginal estimands, illustrate their properties when effect modification is present, and discuss the implications for decision-making. In particular, we show that effect modification can result in conflicting treatment rankings between conditional and marginal estimates. This is because conditional and marginal estimands correspond to different decision questions that are no longer aligned when effect modification is present. For time-to-event outcomes, the presence of covariates implies that marginal hazard ratios are time-varying, and effect modification can cause marginal hazard curves to cross. We conclude with practical recommendations for decision-making in the presence of effect modification, based on pragmatic comparisons of both conditional and marginal estimates in the decision target population. Currently, multilevel network meta-regression is the only population-adjustment method capable of producing both conditional and marginal estimates, in any decision target population., Comment: 30 pages, 8 figures

The SIDDHARTA-2 collaboration built a new cadmium-zinc-telluride (CZT, CdZnTe)-based X-ray detection system, used for the first time in the DA$\Phi$NE electron-positron collider at INFN-LNF. The aim of this work is to show that these detectors present optimal long- and short-term linearity and stability to perform precise spectroscopic measurements in a collider environment. The spectra used as references for calibration are reported, and the results about the linearity and stability studies are presented. It is also discussed and showed what is the proper function to describe all the effects that alter the Gaussian shape in semiconductors, particularly evident in the CZT case. Good residuals and resolutions were obtained for all the calibrations. In a test run with the source and the collider beam on, it was demonstrated that the calibrations made with beam off are optimal also when the beam is on, and the actual systematics in a physics run were estimated. These promising results show the potentialities of this detector in the high rate environment of a particle collider, and pave the way for the use of CZT detectors in kaonic atoms researches and in accelerators, with applications for particle and nuclear physics., Comment: 15 pages, 9 figures, 3 tables

This paper introduces an approach to analyze nonhomogeneous Poisson processes (NHPP) observed with noise, focusing on previously unstudied second-order characteristics of the noisy process. Utilizing a hierarchical Bayesian model with noisy data, we estimate hyperparameters governing a physically motivated NHPP intensity. Simulation studies demonstrate the reliability of this methodology in accurately estimating hyperparameters. Leveraging the posterior distribution, we then infer the probability of detecting a certain number of events within a given radius, the $k$-contact distance. We demonstrate our methodology with an application to observations of fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment's FRB Project (CHIME/FRB). This approach allows us to identify repeating FRB sources by bounding or directly simulating the probability of observing $k$ physically independent sources within some radius in the detection domain, or the $\textit{probability of coincidence}$ ($P_{\text{C}}$). The new methodology improves the repeater detection $P_{\text{C}}$ in 86% of cases when applied to the largest sample of previously classified observations, with a median improvement factor (existing metric over $P_{\text{C}}$ from our methodology) of $\sim$ 3000., Comment: 24 pages, 8 figures, submitted to the Annals of Applied Statistics. Feedback/comments welcome

In this paper, we propose the $\textit{geometric invariance hypothesis (GIH)}$, which argues that when training a neural network, the input space curvature remains invariant under transformation in certain directions determined by its architecture. Starting with a simple non-linear binary classification problem residing on a plane in a high dimensional space, we observe that while an MLP can solve this problem regardless of the orientation of the plane, this is not the case for a ResNet. Motivated by this example, we define two maps that provide a compact $\textit{architecture-dependent}$ summary of the input space geometry of a neural network and its evolution during training, which we dub the $\textbf{average geometry}$ and $\textbf{average geometry evolution}$, respectively. By investigating average geometry evolution at initialization, we discover that the geometry of a neural network evolves according to the projection of data covariance onto average geometry. As a result, in cases where the average geometry is low-rank (such as in a ResNet), the geometry only changes in a subset of the input space. This causes an architecture-dependent invariance property in input-space curvature, which we dub GIH. Finally, we present extensive experimental results to observe the consequences of GIH and how it relates to generalization in neural networks.

As a recently-identified Mott system, PbCrO3 remains largely unexplored, especially for its band structure, leading to many contentious issues on its structural, electronic, and magnetic properties. Here we present a comprehensive study of two different Pb1-{\delta}CrO3 ({\delta} = 0 and 0.15) samples with involving atomic deficiency prepared under pressure. By means of the state-of-the-art diffraction techniques, crystal structure of PbCrO3 is definitively determined to adopt the pristine Pm-3m symmetry, rather than other previously misassigned structures of M2-Pm-3m and Pmnm. The two materials exhibit a similar charge-transfer-type insulating band structure, and the charge-transfer effect splits both Cr2p and Pb4f orbitals, rationalizing doublet splitting of the associated spectral lines. Nearly identical nominal cationic valence states of Cr4+ and Pb2+ are identified for this oxide system, hence calling into question the validity of recently-proposed charge disproportionation mechanisms. Besides, Pb0.85CrO3 exhibits an anomalously higher N\'eel temperature of ~240 K than that of PbCrO3 (i.e., ~200 K), likely due to the deficiency-induced enhancements of Cr: 3d-O:2p orbital overlap and magnetic exchange. These findings provide many solid evidences to look into the fundamental properties of this important material system.

Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A key advantage of VAD is its unsupervised nature, which eliminates the need for costly and time-consuming labeled data collection. However, despite its potential for real-world applications, the literature has given limited focus to resource-efficient VAD, particularly for deployment on edge devices. This work addresses this gap by leveraging lightweight neural networks to reduce memory and computation requirements, enabling VAD deployment on resource-constrained edge devices. We benchmark the major VAD algorithms within this framework and demonstrate the feasibility of edge-based VAD using the well-known MVTec dataset. Furthermore, we introduce a novel algorithm, Partially Shared Teacher-student (PaSTe), designed to address the high resource demands of the existing Student Teacher Feature Pyramid Matching (STFPM) approach. Our results show that PaSTe decreases the inference time by 25%, while reducing the training time by 33% and peak RAM usage during training by 76%. These improvements make the VAD process significantly more efficient, laying a solid foundation for real-world deployment on edge devices., Comment: 13 pages, 6 figures

We present a method for prediction of a person's hairstyle from a single image. Despite growing use cases in user digitization and enrollment for virtual experiences, available methods are limited, particularly in the range of hairstyles they can capture. Human hair is extremely diverse and lacks any universally accepted description or categorization, making this a challenging task. Most current methods rely on parametric models of hair at a strand level. These approaches, while very promising, are not yet able to represent short, frizzy, coily hair and gathered hairstyles. We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system. Previous classification approaches have been restricted by poorly labeled data that lacks diversity, imposing constraints on the usefulness of any resulting enrollment system. We use only synthetic data to train our models. This allows for explicit control of diversity of hairstyle attributes, hair colors, facial appearance, poses, environments and other parameters. It also produces noise-free ground-truth labels. We introduce a novel hairstyle taxonomy developed in collaboration with a diverse group of domain experts which we use to balance our training data, supervise our model, and directly measure fairness. We annotate our synthetic training data and a real evaluation dataset using this taxonomy and release both to enable comparison of future hairstyle prediction approaches. We employ an architecture based on a pre-trained feature extraction network in order to improve generalization of our method to real data and predict taxonomy attributes as an auxiliary task to improve accuracy. Results show our method to be significantly more robust for challenging hairstyles than recent parametric approaches.

Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, surprisingly consisting only of a handful of molecular species, thus significantly increasing their identifiability in biological systems. This optimal size emerges from a tradeoff between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways., Comment: 18 pages with SI in total, including 8 maintext and 5 SI figures

In this work, we propose a machine learning approach to address one aspect of the quantum marginal problem: finding a global density matrix compatible with a given set of quantum marginals. Our method involves combining a quantum marginal imposition technique with convolutional denoising autoencoders. The loss function ensures that the output density matrix satisfies essential properties such as hermiticity, positivity, and normalization. Through extensive numerical simulations, we demonstrate the effectiveness of our approach, achieving a high success rate and accuracy. Our model can serve as an initial guess generator for solving semidefinite programs (SDPs) associated with the quantum marginal problem, potentially accelerating convergence and improving accuracy. This work highlights the potential of machine learning techniques to address complex problems in quantum mechanics.

We show that the static tidal Love number of Schwarzschild black holes in four dimensions and in the vacuum vanishes at any order in the external tidal force. We also identify the underlying non-linear symmetry which is responsible for this result and becomes manifest when the black hole metric is written in axsymmetric static Weyl coordinates., Comment: 13 pages, LaTeX file

We explore the concept of negative pressure and its relevance in a variety of physical contexts: the expansion of the universe, mixture theory, cavitation, and the capillary effect in plants. Using thermodynamic arguments, we discuss the intricate connection between negative pressure and negative thermal expansion. We highlight the fact that metastable states and competing phases are often associated with the emergence of negative pressure. We also propose a new link between the effective Gr\"uneisen parameter and nucleation theory., Comment: 15 pages, 5 figs, comments are welcome