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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 behaviour mimics the one 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. Adding the controller degrees of freedom, we newly define the notion of probabilistic alternating simulation, and provide probably approximately correct (PAC) guarantees that the constructed abstraction includes all behaviours of the concrete system and that it is suitable for control design, for arbitrarily long time horizons, leveraging the 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.