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The increasing complexity of application requirements and the dynamic nature of the Cloud-Edge Continuum present significant challenges for efficient resource management. These challenges stem from the ever-changing infrastructure, which is characterized by additions, removals, and reconfigurations of nodes and links, as well as the variability of application workloads. Traditional centralized approaches struggle to adapt to these changes due to their static nature, while decentralized solutions face challenges such as limited global visibility and coordination overhead. This paper proposes a hybrid decentralized framework for dynamic application placement and resource management. The framework utilizes Graph Neural Networks (GNNs) to embed resource and application states, enabling comprehensive representation and efficient decision-making. It employs a collaborative multi-agent reinforcement learning (MARL) approach, where local agents optimize resource management in their neighborhoods and a global orchestrator ensures system-wide coordination. By combining decentralized application placement with centralized oversight, our framework addresses the scalability, adaptability, and accuracy challenges inherent in the Cloud-Edge Continuum. This work contributes to the development of decentralized application placement strategies, the integration of GNN embeddings, and collaborative MARL systems, providing a foundation for efficient, adaptive and scalable resource management.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 26 pages, 6 figures

We present an all-silicon source of near-infrared linearly-polarized single photons, fabricated by nanoscale positioning of a color center in a silicon-on-insulator microcavity. The color center consists of a single W center, created at a well-defined position by Si$^{+}$ ion implantation through a 150 nm-diameter nanohole in a mask. A circular Bragg grating cavity resonant with the W's zero-phonon line at 1217 nm is fabricated at the same location as the nanohole. Under above-gap continuous-wave excitation, a very clean photon antibunching behavior ($g{^2} \leq 0.06$) is observed over the entire power range, which highlights the absence of parasitic emitters. Purcell-enhancement of W's zero-phonon emission provides both a record-high photoluminescence count rate among Si color centers (ca $1.2 \times 10^{6}$ counts/s) and apparent Debye-Waller factor around 99%. We also demonstrate the triggered emission of single photons with 93% purity under weak pulsed laser excitation. At high pulsed laser power, we reveal a detrimental effect of repumping processes, that could be mitigated using selective pumping schemes in the future. These results represent a major step towards on-demand sources of indistinguishable near-infrared single photons within silicon photonics chips.

The average state dynamics of free fermions subject to random projective measurements of local site occupation numbers is governed by a Lindblad equation with dephasing noise. In the continuous limit, the equation of motion for the correlation matrix is mapped to a hydrodynamic equation for the Wigner function, which takes the form of a linear, nonlocal partial differential equation. As a main result, we derive the analytical solution to the hydrodynamic equation using the quasiparticle picture, showing that the Wigner dynamics emerges from stochastic sampling of classical run-and-tumble processes. As an application, we recover the crossover between ballistic and diffusive transport regimes., Comment: 9 pages, 3 figures

Two abelian varieties $A$ and $B$ over a number field $K$ are said to be strongly locally quadratic twists if they are quadratic twists at every completion of $K$. While it was known that this does not imply that $A$ and $B$ are quadratic twists over $K$, the only known counterexamples (necessarily of dimension $\geq 4$) are not geometrically simple. We show that, for every prime $p\equiv 13 \pmod{24}$, there exists a pair of geometrically simple abelian varieties of dimension $p-1$ over $\mathbb{Q}$ that are strongly locally quadratic twists but not quadratic twists. The proof is based on Galois cohomology computations and class field theory., Comment: 15 pages. Comments are welcome!

Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023., Comment: 325 pages-long review of recent topics of interest in the field of magnetic field effects on QED and QCD matter. To be susbmitted to PNPP

Dynamical phase transitions induced by local projective measurements have attracted a lot of attention in the past few years. It has been in particular argued that measurements may induce an abrupt change in the scaling law of the bipartite entanglement entropy. In this work we show that local projective measurements on a one-dimensional quadratic fermionic system induce a qualitative modification of the time growth of the entanglement entropy, changing from linear to logarithmic. However, in the stationary regime, the logarithmic behavior of the entanglement entropy does not survive in the thermodynamic limit and, for any finite value of the measurement rate, we numerically show the existence of a single area-law phase for the entanglement entropy. We give analytical arguments supporting our conclusions., Comment: 7 pages, 4 figures

This study investigates the temperature-induced hysteretic behavior of resistivity and magnetoresistance in electrodeposited bismuth films, with a focus on their application as absorbers in transition-edge sensors (TESs) for X-ray detection. Through a series of resistance versus temperature measurements from room temperature to a few Kelvins, we explore the change in the conductive behavior of bismuth electrodeposited on various substrates at the various temperatures. Our findings show for the first time both hysteretic and irreversible changes in resistivity as a function of temperature. Further, magnetoresistance measurements reveal notable variations in resistance behavior under different magnetic fields, highlighting the impact of magnetic fields on these films' electronic transport properties, with an indication of potential weak anti-localization effects at the lowest temperatures. This study not only provides a deeper understanding of bismuth's conductivity characteristics at low temperatures but also sheds light on the practical implications for developing more effective TESs for synchrotron X-ray facilities.

Dexterous robotic manipulator teleoperation is widely used in many applications, either where it is convenient to keep the human inside the control loop, or to train advanced robot agents. So far, this technology has been used in combination with camera systems with remarkable success. On the other hand, only a limited number of studies have focused on leveraging haptic feedback from tactile sensors in contexts where camera-based systems fail, such as due to self-occlusions or poor light conditions like smoke. This study demonstrates the feasibility of precise pick-and-place teleoperation without cameras by leveraging tactile-based 3D object reconstruction in VR and providing haptic feedback to a blindfolded user. Our preliminary results show that integrating these technologies enables the successful completion of telemanipulation tasks previously dependent on cameras, paving the way for more complex future applications., Comment: 7 pages, 5 figures, This work has been submitted to the IEEE for possible publication

Robotic telemanipulation - the human-guided manipulation of remote objects - plays a pivotal role in several applications, from healthcare to operations in harsh environments. While visual feedback from cameras can provide valuable information to the human operator, haptic feedback is essential for accessing specific object properties that are difficult to be perceived by vision, such as stiffness. For the first time, we present a participant study demonstrating that operators can perceive the stiffness of remote objects during real-world telemanipulation with a dexterous robotic hand, when haptic feedback is generated from tactile sensing fingertips. Participants were tasked with squeezing soft objects by teleoperating a robotic hand, using two methods of haptic feedback: one based solely on the measured contact force, while the second also includes the squeezing displacement between the leader and follower devices. Our results demonstrate that operators are indeed capable of discriminating objects of different stiffness, relying on haptic feedback alone and without any visual feedback. Additionally, our findings suggest that the displacement feedback component may enhance discrimination with objects of similar stiffness., Comment: 8 pages,6 figures, "This work has been submitted to the IEEE for possible publication."

This paper introduces a family of entropy-conserving finite-difference discretizations for the compressible flow equations. In addition to conserving the primary quantities of mass, momentum, and total energy, the methods also preserve kinetic energy and pressure equilibrium. The schemes are based on finite-difference (FD) representations of the logarithmic mean, establishing and leveraging a broader link between linear and nonlinear two-point averages and FD forms. The schemes are locally conservative due to the summation-by-parts property and therefore admit a local flux form, making them applicable also in finite-volume and finite-element settings. The effectiveness of these schemes is validated through various test cases (1D Sod shock tube, 1D density wave, 2D isentropic vortex, 3D Taylor Green vortex) that demonstrate exact conservation of entropy along with conservation of the primary quantities and preservation of pressure equilibrium., Comment: 25 pages, 7 figures

Adaptive gradient methods have been increasingly adopted by deep learning community due to their fast convergence and reduced sensitivity to hyper-parameters. However, these methods come with limitations, such as increased memory requirements for elements like moving averages and a poorly understood convergence theory. To overcome these challenges, we introduce F-CMA, a Fast-Controlled Mini-batch Algorithm with a random reshuffling method featuring a sufficient decrease condition and a line-search procedure to ensure loss reduction per epoch, along with its deterministic proof of global convergence to a stationary point. To evaluate the F-CMA, we integrate it into conventional training protocols for classification tasks involving both convolutional neural networks and vision transformer models, allowing for a direct comparison with popular optimizers. Computational tests show significant improvements, including a decrease in the overall training time by up to 68%, an increase in per-epoch efficiency by up to 20%, and in model accuracy by up to 5%., Comment: There is an error in the literature review, in section 1. In particular, we noticed that there is a wrong citation, the [65], which has been erroneously associated with another author's claims

We calculate the screening masses of pseudoscalar mesons in a hot and strongly magnetized medium within the framework of the SU(3) Nambu-Jona--Lasinio model, using a magnetic field-independent regularization scheme. Inverse magnetic catalysis (IMC) is implemented through the use of a magnetic field-dependent coupling $G(B)$, fitted to reproduce lattice QCD results for the pseudocritical chiral transition temperature $T_c^B$. For the external homogeneous magnetic field considered, neutral screening masses separate in two types: perpendicular and parallel to the direction of the field, while for charged mesons only parallel energies can be defined for each Landau level. We obtain $m_{\mathrm{scr},\perp} > m_{\mathrm{scr},\parallel}$, as expected from causality. Thermally, all screening energies are almost constant until some critical temperature, whose behavior is correlated with $T_c^B$. They rapidly increase around this value, keeping a steady enhancement afterwards due to thermal excitation. Magnetically, neutral parallel masses are enhanced (suppressed) at high temperatures when considering $G(B)$ ($G$). Perpendicular ones display a non-linear magnetic behavior for $G$ (due to increasing $T_c^B$), while they always increase with $B$ for $G(B)$. Charged parallel energies are also always magnetically enhanced. In the high-temperature limit, we show that both neutral and charged screening energies converge to $2\pi T$. At $B=0$ the model overestimates the remaining quark interaction in this regime. At $B\neq 0$ we find that, when IMC is accounted for, the interaction is suppressed as $B$ increases, a fact that appears to be at odds with currently available lattice QCD results., Comment: 38 pages, 11 figures

The effects of kinetic-energy preservation errors due to Runge-Kutta (RK) temporal integrators have been analyzed for the case of large-eddy simulations of incompressible turbulent channel flow. Simulations have been run using the open-source solver Xcompact3D with an implicit spectral vanishing viscosity model and a variety of temporal Runge-Kutta integrators. Explicit pseudo-symplectic schemes, with improved energy preservation properties, have been compared to standard RK methods. The results show a marked decrease in the temporal error for higher-order pseudo-symplectic methods; on the other hand, an analysis of the energy spectra indicates that the dissipation introduced by the commonly used three-stage RK scheme can lead to significant distortion of the energy distribution within the inertial range. A cost-vs-accuracy analysis suggests that pseudo-symplectic schemes could be used to attain results comparable to traditional methods at a reduced computational cost., Comment: 13 pages, 11 figures. F.C. and G.C. contributed equally to this work

This study proposes a novel spatial discretization procedure for the compressible Euler equations which guarantees entropy conservation at a discrete level when an arbitrary equation of state is assumed. The proposed method, based on a locally-conservative discretization, guarantees also the spatial conservation of mass, momentum, and total energy and is kinetic energy-preserving. In order to achieve the entropy-conservation property for an arbitrary non-ideal gas, a general strategy is adopted based on the manipulation of discrete balance equations through the imposition of global entropy conservation and the use of a summation by parts rule. The procedure, which is extended to an arbitrary order of accuracy, conducts to a general form of the internal-energy numerical flux which results in a nonlinear function of thermodynamic and dynamic variables and still admits the mass flux as a residual degree of freedom. The effectiveness of the novel entropy-conservative formulation is demonstrated through numerical tests making use of some of the most popular cubic equations of state., Comment: 16 pages, 3 figures

We study in detail the Ramanujan smooth expansions, for arithmetic functions; we start with the most general ones, for which we supply the "$P-$local expansions", for arguments with all prime-factors $p\le P$ (namely, $P-$smooth arguments), that are also square-free; then, we supply general results for interesting subsets of arithmetic functions, regarding both their $P-$local and (global) Ramanujan smooth expansions., Comment: Plain TeX, 20 pages, plus 1 p.bib

Six years after the entry into force of the GDPR, European companies and organizations still have difficulties complying with it: the amount of fines issued by the European data protection authorities is continuously increasing. Personal data transfers are no exception. In this work we analyse the personal data transfers from more than 20000 Italian Public Administration (PA) entities to third countries. We developed "Minos", a user-friendly application which allows to navigate the web while recording HTTP requests. Then, we used the back-end of Minos to automate the analysis. We found that about 14% of the PAs websites transferred data out of the European Economic Area (EEA). This number is an underestimation because only visits to the home pages were object of the analysis. The top 3 destinations of the data transfers are Amazon, Google and Fonticons, accounting for about the 70% of the bad requests. The most recurrent services which are the object of the requests are cloud computing services and content delivery networks (CDNs). Our results highlight that, in Italy, a relevant portion of public administrations websites transfers personal data to non EEA countries. In terms of technology policy, these results stress the need for further incentives to improve the PA digital infrastructures. Finally, while working on refinements of Minos, the version here described is openly available on Zenodo: it can be helpful to a variety of actors (citizens, researchers, activists, policy makers) to increase awareness and enlarge the investigation., Comment: International Conference on Information Technology for Social Good (GoodIT '24), September 4-6, 2024, Bremen, Germany

Employing physically-consistent numerical methods is an important step towards attaining robust and accurate numerical simulations. When addressing compressible flows, in addition to preserving kinetic energy at a discrete level, as done in the incompressible case, additional properties are sought after, such as the ability to preserve the equilibrium of pressure that can be found at contact interfaces. This paper investigates the general conditions of the spatial numerical discretizations to achieve the pressure equilibrium preserving property (PEP). Schemes from the literature are analyzed in this respect, and procedures to impart the PEP property to existing discretizations are proposed. Additionally, new PEP numerical schemes are introduced through minor modifications of classical ones. Numerical tests confirmed the theory hereby presented and showed that the modifications, beyond the enforcement of the PEP property, have a generally positive impact on the performances of the original schemes., Comment: 15 pages, 2 figures

This study explores the impact of adversarial perturbations on Convolutional Neural Networks (CNNs) with the aim of enhancing the understanding of their underlying mechanisms. Despite numerous defense methods proposed in the literature, there is still an incomplete understanding of this phenomenon. Instead of treating the entire model as vulnerable, we propose that specific feature maps learned during training contribute to the overall vulnerability. To investigate how the hidden representations learned by a CNN affect its vulnerability, we introduce the Adversarial Intervention framework. Experiments were conducted on models trained on three well-known computer vision datasets, subjecting them to attacks of different nature. Our focus centers on the effects that adversarial perturbations to a model's initial layer have on the overall behavior of the model. Empirical results revealed compelling insights: a) perturbing selected channel combinations in shallow layers causes significant disruptions; b) the channel combinations most responsible for the disruptions are common among different types of attacks; c) despite shared vulnerable combinations of channels, different attacks affect hidden representations with varying magnitudes; d) there exists a positive correlation between a kernel's magnitude and its vulnerability. In conclusion, this work introduces a novel framework to study the vulnerability of a CNN model to adversarial perturbations, revealing insights that contribute to a deeper understanding of the phenomenon. The identified properties pave the way for the development of efficient ad-hoc defense mechanisms in future applications., Comment: 22 pages, 15 figures (including appendix)

Recently, the n$^3$He collaboration reported a measurement of the parity-violating (PV) proton directional asymmetry $A_{\mathrm {PV}} = (1.55\pm 0.97~\mathrm {(st\ at)} \pm 0.24~\mathrm {(sys)})\times 10^{-8}$ in the capture reaction of ${}^3$He$(\vec {n},{\mathrm p}){}^3$H at meV incident neutron energies. The result increased the limited inventory of precisely measured and calculable PV observables in few-body systems required to further understand the structure of hadronic weak interaction. In this letter, we report the experimental and theoretical investigation of a parity conserving (PC) asymmetry $A_{\mathrm {PC}}$ in the same reaction (the first ever measured PC observable at meV neutron energies). As a result of S- and P-wave mixing in the reaction, the $A_{\mathrm {PC}}$ is inversely proportional to the neutron wavelength $\lambda$. The experimental value is $(\lambda\times A_{\mathrm {PC}})\equiv\beta= (-1.97 \pm 0.28~\mathrm{(stat)}\pm 0.12~\mathrm{(sys)}) \times 10^{-6}$ Amstrongs. We present results for a theoretical analysis of this reaction by solving the four-body scattering problem within the hyperspherical harmonic method. We find that in the ${}^3$He$(\vec {n},{\mathrm p}){}^3$H reaction, $A_{\mathrm {PC}}$ depends critically on the energy and width of the close $0^-$ resonant state of ${}^4$He, resulting in a large sensitivity to the spin-orbit components of the nucleon-nucleon force and even to the three-nucleon force. The analysis of the accurately measured $A_{\mathrm {PC}}$ and $A_{\mathrm {PV}}$ using the same few-body theoretical models gives essential information needed to interpret the PV asymmetry in the ${}^3$He$(\vec {n}, {\mathrm p}){}^3$H reaction., Comment: 8 pages, 3 figures

Large Language Models (LLMs) are becoming key in automating and assisting various software development tasks, including text-based tasks in requirements engineering but also in coding. Typically, these models are used to automate small portions of existing tasks, but we present a broader vision to span multiple steps from requirements engineering to implementation using existing libraries. This approach, which we call Semantic API Alignment (SEAL), aims to bridge the gap between a user's high-level goals and the specific functions of one or more APIs. In this position paper, we propose a system architecture where a set of LLM-powered ``agents'' match such high-level objectives with appropriate API calls. This system could facilitate automated programming by finding matching links or, alternatively, explaining mismatches to guide manual intervention or further development. As an initial pilot, our paper demonstrates this concept by applying LLMs to Goal-Oriented Requirements Engineering (GORE), via sub-goal analysis, for aligning with REST API specifications, specifically through a case study involving a GitHub statistics API. We discuss the potential of our approach to enhance complex tasks in software development and requirements engineering and outline future directions for research.

Effective execution of long-horizon tasks with dexterous robotic hands remains a significant challenge in real-world problems. While learning from human demonstrations have shown encouraging results, they require extensive data collection for training. Hence, decomposing long-horizon tasks into reusable primitive skills is a more efficient approach. To achieve so, we developed DexSkills, a novel supervised learning framework that addresses long-horizon dexterous manipulation tasks using primitive skills. DexSkills is trained to recognize and replicate a select set of skills using human demonstration data, which can then segment a demonstrated long-horizon dexterous manipulation task into a sequence of primitive skills to achieve one-shot execution by the robot directly. Significantly, DexSkills operates solely on proprioceptive and tactile data, i.e., haptic data. Our real-world robotic experiments show that DexSkills can accurately segment skills, thereby enabling autonomous robot execution of a diverse range of tasks.

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

In autonomous mobility-on-demand systems, effectively managing vehicle flows to mitigate induced congestion and ensure efficient operations is imperative for system performance and positive customer experience. Against this background, we study the potential of staggered routing, i.e., purposely delaying trip departures from a system perspective, in order to reduce congestion and ensure efficient operations while still meeting customer time windows. We formalize the underlying planning problem and show how to efficiently model it as a mixed integer linear program. Moreover, we present a matheuristic that allows us to efficiently solve large-scale real-world instances both in an offline full-information setting and its online rolling horizon counterpart. We conduct a numerical study for Manhattan, New York City, focusing on low- and highly-congested scenarios. Our results show that in low-congestion scenarios, staggering trip departures allows mitigating, on average, 94% of the induced congestion in a full information setting. In a rolling horizon setting, our algorithm allows us to reduce 90% of the induced congestion. In high-congestion scenarios, we observe an average reduction of 66% as the full information bound and an average reduction of 56% in our online setting. Surprisingly, we show that these reductions can be reached by shifting trip departures by a maximum of six minutes in both the low and high-congestion scenarios., Comment: 32 pages, 10 figures

The Scrum framework has gained widespread adoption in the industry for its emphasis on collaboration and continuous improvement. However, it has not reached a similar relevance in Software Engineering (SE) curricula. This work reports the experience of five editions of a SE course within an MSc. Degree in Computer Engineering. The course primary educational objective is to provide students with the skills to manage software development projects with Scrum. The course is based on the execution of a team project and on the definition of qualitative and quantitative means of assessment of the application of Scrum. The conduction of five editions of the course allowed us to identify several lessons learned about time budgeting and team compositions in agile student projects and its evidence of the applicability of the framework to software development courses., Comment: Submitted to LEARNER 2024 workshop

The abstraction of dynamical systems is a powerful tool that enables the design of feedback controllers using a correct-by-design framework. We investigate a novel scheme to obtain data-driven abstractions of discrete-time stochastic processes in terms of richer discrete stochastic models, whose actions lead to nondeterministic transitions over the space of probability measures. The data-driven component of the proposed methodology lies in the fact that we only assume samples from an unknown probability distribution. We also rely on the model of the underlying dynamics to build our abstraction through backward reachability computations. The nondeterminism in the probability space is captured by a collection of Markov Processes, and we identify how this model can improve upon existing abstraction techniques in terms of satisfying temporal properties, such as safety or reach-avoid. The connection between the discrete and the underlying dynamics is made formal through the use of the scenario approach theory. Numerical experiments illustrate the advantages and main limitations of the proposed techniques with respect to existing approaches., Comment: 6 pages

Software testing is a crucial phase in software development, enabling the detection of issues and defects that may arise during the development process. Addressing these issues enhances software applications' quality, reliability, user experience, and performance. Graphical User Interface (GUI) testing, one such technique, involves mimicking a regular user's interactions with an application to identify defects. However, GUI testing is often underutilized due to its perceived repetitiveness, error-proneness, and lack of immediate feedback on test quality. In recent years, gamification-incorporating game elements in non-game contexts to boost interest, motivation, and engagement-has gained traction in various fields, including software engineering and education. This paper presents GIPGUT: a prototype of a gamification plugin for IntelliJ IDEA, an Integrated Development Environment (IDE) that supports scripted GUI testing. The plugin enhances testers' engagement with typically monotonous and tedious tasks through achievements, rewards, and profile customization. A preliminary prototype evaluation was conducted with a small group of users to assess its usability and the impact of gamification on the GUI testing process. The results indicate high usability and positive reception of the gamification elements. However, due to the limited sample size of participants, further research is necessary to understand the plugin's effectiveness fully., Comment: 5 pages, 3 figures

Given the emergent reasoning abilities of large language models, information retrieval is becoming more complex. Rather than just retrieve a document, modern information retrieval systems advertise that they can synthesize an answer based on potentially many different documents, conflicting data sources, and using reasoning. We review recent literature and argue that the large language model has crucial flaws that prevent it from on its own ever constituting general intelligence, or answering general information synthesis requests. This review shows that the following are problems for large language models: hallucinations, complex reasoning, planning under uncertainty, and complex calculations. We outline how logical discrete graphical models can solve all of these problems, and outline a method of training a logical discrete model from unlabeled text., Comment: arXiv admin note: text overlap with arXiv:2402.06557

Given the emergent reasoning abilities of large language models, information retrieval is becoming more complex. Rather than just retrieve a document, modern information retrieval systems advertise that they can synthesize an answer based on potentially many different documents, conflicting data sources, and using reasoning. But, different kinds of questions have different answers, and different answers have different complexities. In this paper, we introduce a novel framework for analyzing the complexity of a question answer based on the natural deduction calculus as presented in Prawitz (1965). Our framework is novel both in that no one to our knowledge has used this logic as a basis for complexity classes, and also in that no other existing complexity classes to these have been delineated using any analogous methods either. We identify three decidable fragments in particular called the forward, query and planning fragments, and we compare this to what would be needed to do proofs for the complete first-order calculus, for which theorem-proving is long known to be undecidable.

Traditional and currently-prevalent pedagogies of design perpetuate ableist and exclusionary notions of what it means to be a designer. In this paper, we trace such historically exclusionary norms of design education, and highlight modern-day instances from our own experiences as design educators in such epistemologies. Towards imagining a more inclusive and sustainable future of design education, we present three case studies from our own experience as design educators in redesigning course experiences for blind and low-vision (BLV), deaf and hard-of-hearing (DHH) students, and students with other disabilities. In documenting successful and unsuccessful practices, we imagine what a pedagogy of care in design education would look like., Comment: Upcoming Publication, Design Research Society 2024

At the intersection of dynamical systems, control theory, and formal methods lies the construction of symbolic abstractions: these typically represent simpler, finite-state models whose behavior mimics that of an underlying concrete system but are easier to analyse. Building an abstraction usually requires an accurate knowledge of the underlying model: this knowledge may be costly to gather, especially in real-life applications. We aim to bridge this gap by building abstractions based on sampling finite length trajectories. To refine a controller built for the abstraction to one for the concrete system, we newly define a notion of probabilistic alternating simulation, and provide Probably Approximately Correct (PAC) guarantees that the constructed abstraction includes all behaviors of the concrete system and that it is suitable for control design, for arbitrarily long time horizons, leveraging scenario theory. Our method is then tested on several numerical benchmarks.

This paper introduces the Quantified Boolean Bayesian Network (QBBN), which provides a unified view of logical and probabilistic reasoning. The QBBN is meant to address a central problem with the Large Language Model (LLM), which has become extremely popular in Information Retrieval, which is that the LLM hallucinates. A Bayesian Network, by construction, cannot hallucinate, because it can only return answers that it can explain. We show how a Bayesian Network over an unbounded number of boolean variables can be configured to represent the logical reasoning underlying human language. We do this by creating a key-value version of the First-Order Calculus, for which we can prove consistency and completeness. We show that the model is trivially trained over fully observed data, but that inference is non-trivial. Exact inference in a Bayesian Network is intractable (i.e. $\Omega(2^N)$ for $N$ variables). For inference, we investigate the use of Loopy Belief Propagation (LBP), which is not guaranteed to converge, but which has been shown to often converge in practice. Our experiments show that LBP indeed does converge very reliably, and our analysis shows that a round of LBP takes time $O(N2^n)$, where $N$ bounds the number of variables considered, and $n$ bounds the number of incoming connections to any factor, and further improvements may be possible. Our network is specifically designed to alternate between AND and OR gates in a Boolean Algebra, which connects more closely to logical reasoning, allowing a completeness proof for an expanded version of our network, and also allows inference to follow specific but adequate pathways, that turn out to be fast.

We study the mass spectrum of light pseudoscalar and vector mesons in the presence of an external uniform magnetic field $\vec B$, considering the effects of the mixing with the axial vector meson sector. The analysis is performed within a two-flavor NJL-like model which includes isoscalar and isovector couplings together with a flavor mixing 't Hooft-like term. The effect of the magnetic field on charged particles is taken into account by retaining the Schwinger phases carried by quark propagators, and expanding the corresponding meson fields in proper Ritus-like bases. The spin-isospin and spin-flavor decomposition of meson mass states is also analyzed. For neutral pion masses it is shown that the mixing with axial vector mesons improves previous theoretical results, leading to a monotonic decreasing behavior with $B$ that is in good qualitative agreement with LQCD calculations, both for the case of constant or $B$-dependent couplings. Regarding charged pions, it is seen that the mixing softens the enhancement of their mass with $B$. As a consequence, the energy becomes lower than the one corresponding to a pointlike pion, improving the agreement with LQCD results. The agreement is also improved for the magnetic behavior of the lowest $\rho^+$ energy state, which does not vanish for the considered range of values of $B$ -- a fact that can be relevant in connection with the occurrence of meson condensation for strong magnetic fields., Comment: 62 pages, 6 figures

This work presents an analysis of the effectiveness of using standard shallow feed-forward networks to mimic the behavior of the attention mechanism in the original Transformer model, a state-of-the-art architecture for sequence-to-sequence tasks. We substitute key elements of the attention mechanism in the Transformer with simple feed-forward networks, trained using the original components via knowledge distillation. Our experiments, conducted on the IWSLT2017 dataset, reveal the capacity of these "attentionless Transformers" to rival the performance of the original architecture. Through rigorous ablation studies, and experimenting with various replacement network types and sizes, we offer insights that support the viability of our approach. This not only sheds light on the adaptability of shallow feed-forward networks in emulating attention mechanisms but also underscores their potential to streamline complex architectures for sequence-to-sequence tasks., Comment: Accepted at AAAI24(https://aaai.org/aaai-conference/)

We analyze the open dynamics of quantum systems conditioned on no jumps being detected. We first obtain general results relating the no-jump probability and the waiting-time distributions to the conditional evolution of specific system observables. These results are applied to single-qubit models, whose conditional dynamics is quite involved and shows a rich set of physical behaviors. Furthermore, we obtain general expressions for the no-jump dynamics of non-interacting fermionic-bosonic chains undergoing Gaussian-preserving dynamics. We show that the conditional dynamics is determined by a non-linear Riccati-type differential equation for the correlation matrix. Finally, we apply our results to chains of hopping particles under inhomogeneous jump rates and boundary driven systems in presence of pairing terms., Comment: 22 pages, 10 figures

How subjective experience (i.e., consciousness) arises out of objective material processes has been called the hard problem. The neuroscience of consciousness has set out to find the sufficient conditions for consciousness and theoretical and empirical endeavours have placed a particular focus on the cortex and subcortex, whilst discounting the cerebellum. However, when looking at neuroimaging research, it becomes clear there is substantial evidence that cerebellar, cortical and subcortical functions are correlated with consciousness. Neurostimulation evidence suggests that alterations in any part of the brain may provoke alterations in experience, but the most extreme changes are provoked via the subcortex. I then evaluate neuropsychological evidence and find abnormality in any part of the brain may provoke changes in experience; but only damage to the oldest regions seem to completely obliterate experience. Finally, I review congenital and experimental decorticate cases, and find that behavioral evidence of experience is largely compatible with the absence of the cortex. The evidence, taken together, indicates that the body, subcortex and environment are sufficient for behaviours that suggest bastic experiences. I then emphasise both the importance of the individual's developmental trajectory and the interdependencies between different neural systems.

In this paper we determine the conductor exponent of non-special Ciani quartics at primes of potentially good reduction in terms of the Ciani invariants. As an intermediate step in order to do so, we provide a reconstruction algorithm to construct Ciani quartics with given invariants. We also discuss how to descend the provided model to be defined over the same field as the invariants.

This is a kind of survey on properties of correlations of two very general arithmetic functions, mainly from the point of view of Ramanujan expansions. In fact, our previous papers on these links had, as a focus, the "Ramanujan coefficients" of these correlations and the resulting "R.e.e.f.", i.e., Ramanujan exact explicit formula. This holds, actually, under a variety of sufficient conditions, mainly under two conditions of convergence involving correlations' "Eratosthenes Transform", namely what we call "Delange Hypothesis" and "Wintner Assumption" (the former implying the latter). We proved Hardy-Littlewood Conjecture on $2k-$twin primes, in particular, from the first of these two (that implies convergence of classic Ramanujan expansion, whence the R.e.e.f.); more recently, we gave a more general proof, from second condition, entailing the R.e.e.f. again, but this time from another method of summation for Ramanujan expansions, we detailed in "A smooth summation of Ramanujan expansions"; in which paper (see 8th ver.) we also started to give few elementary methods for correlations. Which we deepen here, adding recent, elementary and entirely new ones., Comment: We have entirely reorganized everything

Statistical physics and dynamical systems theory are key tools to study high-impact geophysical events such as temperature extremes, cyclones, thunderstorms, geomagnetic storms and many more. Despite the intrinsic differences between these events, they all originate as temporary deviations from the typical trajectories of a geophysical system, resulting in well-organised, coherent structures at characteristic spatial and temporal scales. While statistical extreme value analysis techniques are capable to provide return times and probabilities of occurrence of certain geophysical events, they are not apt to account for their underlying physics. Their focus is to compute the probability of occurrence of events that are large or small with respect to some specific observable (e.g. temperature, precipitation, solar wind), rather than to relate rare or extreme phenomena to the underlying anomalous geophysical regimes. This paper outlines this knowledge gap, presenting some related challenges, new formalisms and briefly commenting on how stochastic approaches tailored to the study of extreme geophysical events can help to advance their understanding., Comment: This perspective paper has issued from discussions held during the UNDERPIN symposium held in Erice, Italy in 2022

The supervised training of a deep neural network on a given dataset consists in the unconstrained minimization of the finite sum of continuously differentiable functions, commonly referred to as loss with respect to the samples. These functions depend on the network parameters and most of the times are non-convex. We develop CMA Light, a globally convergent mini-batch gradient method to tackle this problem. We consider the recently introduced Controlled Minibatch Algorithm (CMA) framework and we overcome its main bottleneck, removing the need for at least one evaluation of the whole objective function per iteration. We prove globally convergence of CMA Light under mild assumptions and we discuss extensive computational results on the same experimental test-bed used for CMA, showing that CMA Light requires less computational effort than most of the state-of-the-art optimizers. Eventually, we present early results on a large-scale Image Classification task.

This paper proposes a hierarchy of numerical fluxes for the compressible flow equations which are kinetic-energy and pressure equilibrium preserving and asymptotically entropy conservative, i.e., they are able to arbitrarily reduce the numerical error on entropy production due to the spatial discretization. The fluxes are based on the use of the harmonic mean for internal energy and only use algebraic operations, making them less computationally expensive than the entropy-conserving fluxes based on the logarithmic mean. The use of the geometric mean is also explored and identified to be well-suited to reduce errors on entropy evolution. Results of numerical tests confirmed the theoretical predictions and the entropy-conserving capabilities of a selection of schemes have been compared., Comment: 9 pages, 4 figures

We study the equation $(x-4r)^3 + (x-3r)^3 + (x-2r)^3+(x-r)^3 + x^3 + (x+r)^3+(x+2r)^3 + (x+3r)^3 + (x+4r)^3 = y^p$, which is a natural continuation of previous works carried out by A. Arg\'{a}ez-Garc\'{i}a and the fourth author (perfect powers that are sums of cubes of a three, five and seven term arithmetic progression). Under the assumptions $0 < r \leq 10^6$, $p \geq 5 $ a prime and $\gcd(x, r) = 1$, we show that solutions must satisfy $xy=0$. Moreover, we study the equation for prime exponents $2$ and $3$ in greater detail. Under the assumptions $r>0$ a positive integer and $\gcd(x, r) = 1$ we show that there are infinitely many solutions for $p=2$ and $p=3$ via explicit constructions using integral points on elliptic curves. We use an amalgamation of methods in computational and algebraic number theory to overcome the increased computational challenge. Most notable is a significant computational efficiency obtained through appealing to Bilu, Hanrot and Voutier's Primitive Divisor Theorem and the method of Chabauty, as well as employing a Thue equation solver earlier on., Comment: 12 pages

Research on power values of power sums has gained much attention of late, partially due to the explosion of refinements in multiple advanced tools in (computational) Number Theory in recent years. In this survey, we present the key tools and techniques employed thus far in the (explicit) resolution of Diophantine problems, as well as an overview of existing results. We also state some open problems that naturally arise in the process., Comment: Added additional references and open problems. This collaboration was formed from the Women in Numbers Europe 4 workshop

A case study of the non-equilibrium dynamics of open quantum systems in the markovian approximation is presented for two dynamical models based on a single harmonic oscillator in an external field. Specified through distinct forms of ohmic damping, their quantum Langevin equations are derived from an identical set of physical criteria, namely the canonical commutator between position and momentum, the Kubo formula, the virial theorem and the quantum equilibrium variance. The associated Lindblad equations are derived but only one of them is completely positive. Transforming those into Fokker-Planck equations for the Wigner functions, both models are shown to evolve towards the same Gibbs state, for a vanishing external field. The phenomenological differences between the models are illustrated through their quantum relaxations and through the phase diagrammes derived from their re-interpretation as mean-field approximations of an interacting many-body system., Comment: 4 figures

Distributed sensor arrays capable of detecting multiple spatially distributed stimuli are considered an important element in the realisation of exteroceptive and proprioceptive soft robots. This paper expands upon the previously presented idea of decoupling the measurements of pressure and location of a local indentation from global deformation, using the overall stretch experienced by a soft capacitive e-skin. We employed machine learning methods to decouple and predict these highly coupled deformation stimuli, collecting data from a soft sensor e-skin which was then fed to a machine learning system comprising of linear regressor, gaussian process regressor, SVM and random forest classifier for stretch, force, detection and localisation respectively. We also studied how the localisation and forces are affected when two forces are applied simultaneously. Soft sensor arrays aided by appropriately chosen machine learning techniques can pave the way to e-skins capable of deciphering multi-modal stimuli in soft robots.

We discuss the possibility of defining an emergent local temperature in extended quantum many-body systems evolving out of equilibrium. For the most simple case of free-fermionic systems, we give an explicit formula for the effective temperature in the case of, not necessarily unitary, Gaussian preserving dynamics. In this framework, we consider the hopping fermions on a one-dimensional lattice submitted to randomly distributed projective measurements of the local occupation numbers. We show from the average over many quantum trajectories that the effective temperature relaxes exponentially towards infinity., Comment: 13 pages, 1 figure

In 1995, Richard Stanley introduced the chromatic symmetric function $X_G$ of a graph $G$ and proved that, when written in terms of the elementary symmetric functions, it reveals the number of acyclic orientations of $G$ with a given number of sinks. In this paper, we generalize this result to signed graphs, that is, to graphs whose edges are labeled with $+$ or $-$ and whose colorings and orientations can interact with their signs. Additionally, we introduce a non-homogeneous basis which detects the number of sinks and which not only gives a Stanley-type result for signed graphs but gives an analogous result of this form for unsigned graphs as well.

Fragile (i.e., non-robust) test execution is a common challenge for automated GUI-based testing of web applications as they evolve. Despite recent progress, there is still room for improvement since test execution failures caused by technical limitations result in unnecessary maintenance costs that limit its effectiveness and efficiency. One of the most reported technical challenges for web-based tests concerns how to reliably locate a web element used by a test script. This paper proposes the novel concept of Visually Overlapping Nodes (VON) that reduces fragility by utilizing the phenomenon that visual web elements (observed by the user) are constructed from multiple web-elements in the Document Object Model (DOM) that overlaps visually. We demonstrate the approach in a tool, VON Similo, which extends the state-of-the-art multi-locator approach (Similo) that is also used as the baseline for an experiment. In the experiment, a ground truth set of 1163 manually collected web element pairs, from different releases of the 40 most popular websites on the internet, are used to compare the approaches' precision, recall, and accuracy. Our results show that VON Similo provides 94.7% accuracy in identifying a web element in a new release of the same SUT. In comparison, Similo provides 83.8% accuracy. These results demonstrate the applicability of the visually overlapping nodes concept/tool for web element localization in evolving web applications and contribute a novel way of thinking about web element localization in future research on GUI-based testing.

We present a Wigner function-based approach for the particle density evolution in fermionic and bosonic open quantum many-body systems, including the effects of dephasing. In particular, we focus on chains of non-interacting particles coupled to Lindblad baths. The dissipative processes, described by linear and quadratic jump operators, are modulated by inhomogeneous couplings. Following a semi-classical approach, we find the differential equation governing the Wigner function evolution, which can be solved in closed form in some particular cases. We check the accuracy of the Wigner approach in different scenarios (i.e. Gaussian jump rates), describing the density evolution and the transport phenomena in terms of classical quasi-particles., Comment: 12 pages, 8 figures

In this work, we consider minimizing the average of a very large number of smooth and possibly non-convex functions, and we focus on two widely used minibatch frameworks to tackle this optimization problem: Incremental Gradient (IG) and Random Reshuffling (RR). We define ease-controlled modifications of the IG/RR schemes, which require a light additional computational effort {but} can be proved to converge under {weak} and standard assumptions. In particular, we define two algorithmic schemes in which the IG/RR iteration is controlled by using a watchdog rule and a derivative-free linesearch that activates only sporadically to guarantee convergence. The two schemes differ in the watchdog and the linesearch, which are performed using either a monotonic or a non-monotonic rule. The two schemes also allow controlling the updating of the stepsize used in the main IG/RR iteration, avoiding the use of pre-set rules that may drive the stepsize to zero too fast, reducing the effort in designing effective updating rules of the stepsize. We prove convergence under the mild assumption of Lipschitz continuity of the gradients of the component functions and perform extensive computational analysis using different deep neural architectures and a benchmark of varying-size datasets. We compare our implementation with both a full batch gradient method (i.e. L-BFGS) and an implementation of IG/RR methods, proving that our algorithms require a similar computational effort compared to the other online algorithms and that the control on the learning rate may allow a faster decrease of the objective function., Comment: Add author, add references, correct typos, improve imoplementation