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The energy consumption and the compute performance of a fluid dynamic code have been investigated varying parallelization approach, arithmetic precision and clock speed. The code is based on a Lattice Boltzmann approximation, is written in Fortran and was executed on high-end GPUs of Leonardo Booster supercomputer. Tests were conducted on single server nodes (up to 4 GPUs in parallel). Performance metrics like the number of operations per second and energy consumption are reported, to quantify how smart coding approach and system adjustment can contribute to reduction of energy footprint while keeping the scientific throughput almost unaltered or with acceptable level of degradation. Results indicate that this application can be executed with 20% of energy saving and reduced thermal stress, at the cost of 5% more computing time. The paper presents preliminary conclusions, as it is a first step of a larger study dedicated to energy efficiency at scale.

The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical devices. OpenTitan stands out as a groundbreaking open-source RISC-V design with a comprehensive security toolkit as a standalone system-on-chip (SoC). OpenTitan includes Earl Grey, a fully implemented and silicon-proven SoC, and Darjeeling, announced but not yet fully implemented. Earl Grey targets standalone SoC implementations, while Darjeeling is for integrable implementations. The literature lacks a silicon-ready embedded implementation of an open-source Root of Trust, despite lowRISC's efforts on Darjeeling. We address the limitations of existing implementations by optimizing data transfer latency between memory and cryptographic accelerators to prevent under-utilization and ensure efficient task acceleration. Our contributions include a comprehensive methodology for integrating custom extensions and IPs into the Earl Grey architecture, architectural enhancements for system-level integration, support for varied boot modes, and improved data movement across the platform. These advancements facilitate deploying OpenTitan in broader SoCs, even without specific technology-dependent IPs, providing a deployment-ready research vehicle for the community. We integrated the extended Earl Grey architecture into a reference architecture in a 22nm FDX technology node, benchmarking the enhanced architecture's performance. The results show significant improvements in cryptographic processing speed, achieving up to 2.7x speedup for SHA-256/HMAC and 1.6x for AES accelerators compared to the baseline Earl Grey architecture.

Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use of Transformer neural networks, with a Masked Auto-Encoder architecture, as Foundation Models for SHM. We demonstrate the ability of these models to learn generalizable representations from multiple large datasets through self-supervised pre-training, which, coupled with task-specific fine-tuning, allows them to outperform state-of-the-art traditional methods on diverse tasks, including Anomaly Detection (AD) and Traffic Load Estimation (TLE). We then extensively explore model size versus accuracy trade-offs and experiment with Knowledge Distillation (KD) to improve the performance of smaller Transformers, enabling their embedding directly into the SHM edge nodes. We showcase the effectiveness of our foundation models using data from three operational viaducts. For AD, we achieve a near-perfect 99.9% accuracy with a monitoring time span of just 15 windows. In contrast, a state-of-the-art method based on Principal Component Analysis (PCA) obtains its first good result (95.03% accuracy) only considering 120 windows. On two different TLE tasks, our models obtain state-of-the-art performance on multiple evaluation metrics (R$^2$ score, MAE% and MSE%). On the first benchmark, we achieve an R$^2$ score of 0.97 and 0.85 for light and heavy vehicle traffic, respectively, while the best previous approach stops at 0.91 and 0.84. On the second one, we achieve an R$^2$ score of 0.54 versus the 0.10 of the best existing method., Comment: 16 pages, 4 tables, 9 figures

RISC-V open-source systems are emerging in deployment scenarios where safety and security are critical. OpenTitan is an open-source silicon root-of-trust designed to be deployed in a wide range of systems, from high-end to deeply embedded secure environments. Despite the availability of various cryptographic hardware accelerators that make OpenTitan suitable for offloading cryptographic workloads from the main processor, there has been no accurate and quantitative establishment of the benefits derived from using OpenTitan as a secure accelerator. This paper addresses this gap by thoroughly analysing strengths and inefficiencies when offloading cryptographic workloads to OpenTitan. The focus is on three key IPs - HMAC, AES, and OpenTitan Big Number accelerator (OTBN) - which can accelerate four security workloads: Secure Hash Functions, Message Authentication Codes, Symmetric cryptography, and Asymmetric cryptography. For every workload, we develop a bare-metal driver for the OpenTitan accelerator and analyze its efficiency when computation is offloaded from a RISC-V application core within a System-on-Chip designed for secure Cyber-Physical Systems applications. Finally, we assess it against a software implementation on the application core. The characterization was conducted on a cycle-accurate RTL simulator of the System-on-Chip (SoC). Our study demonstrates that OpenTitan significantly outperforms software implementations, with speedups ranging from 4.3x to 12.5x. However, there is potential for even greater gains as the current OpenTitan utilizes a fraction of the accelerator bandwidths, which ranges from 16% to 61%, depending on the memory being accessed and the accelerator used. Our results open the way to the optimization of OpenTitan-based secure platforms, providing design guidelines to unlock the full potential of its accelerators in secure applications., Comment: 8 pages, 2 figures, accepted at CF'24 conference, pre camera-ready version

Modern RISC-V platforms control and monitor security-critical systems such as industrial controllers and autonomous vehicles. While these platforms feature a Root-of-Trust (RoT) to store authentication secrets and enable secure boot technologies, they often lack Control-Flow Integrity (CFI) enforcement and are vulnerable to cyber-attacks which divert the control flow of an application to trigger malicious behaviours. Recent techniques to enforce CFI in RISC-V systems include ISA modifications or custom hardware IPs, all requiring ad-hoc binary toolchains or design of CFI primitives in hardware. This paper proposes TitanCFI, a novel approach to enforce CFI in the RoT. TitanCFI modifies the commit stage of the protected core to stream control flow instructions to the RoT and it integrates the CFI enforcement policy in the RoT firmware. Our approach enables maximum reuse of the hardware resource present in the System-on-Chip (SoC), and it avoids the design of custom IPs and the modification of the compilation toolchain, while exploiting the RoT tamper-proof storage and cryptographic accelerators to secure CFI metadata. We implemented the proposed architecture on a modern RISC-V SoC along with a return address protection policy in the RoT, and benchmarked area and runtime overhead. Experimental results show that TitanCFI achieves overhead comparable to SoA hardware CFI solutions for most benchmarks, with lower area overhead, resulting in 1% of additional area occupation., Comment: 6 pages, 1 figure, accepted at DATE'24 conference, pre camera-ready version

Forward-modeling observables from galaxy simulations enables direct comparisons between theory and observations. To generate synthetic spectral energy distributions (SEDs) that include dust absorption, re-emission, and scattering, Monte Carlo radiative transfer is often used in post-processing on a galaxy-by-galaxy basis. However, this is computationally expensive, especially if one wants to make predictions for suites of many cosmological simulations. To alleviate this computational burden, we have developed a radiative transfer emulator using an artificial neural network (ANN), ANNgelina, that can reliably predict SEDs of simulated galaxies using a small number of integrated properties of the simulated galaxies: star formation rate, stellar and dust masses, and mass-weighted metallicities of all star particles and of only star particles with age <10 Myr. Here, we present the methodology and quantify the accuracy of the predictions. We train the ANN on SEDs computed for galaxies from the IllustrisTNG project's TNG50 cosmological magnetohydrodynamical simulation. ANNgelina is able to predict the SEDs of TNG50 galaxies in the ultraviolet (UV) to millimetre regime with a typical median absolute error of ~7 per cent. The prediction error is the greatest in the UV, possibly due to the viewing-angle dependence being greatest in this wavelength regime. Our results demonstrate that our ANN-based emulator is a promising computationally inexpensive alternative for forward-modeling galaxy SEDs from cosmological simulations.

Radiation therapy is an important tool in the treatment of breast cancer and can play a crucial role in improving patient outcomes. For breast cancer, if the technique has been for a long time the use of 3DCRT, clinicians have seen the management evolve greatly in recent years. Field-in-field and IMRT approaches and more recently dynamic arctherapy are increasingly present. All of these approaches are constantly trying to improve tumour coverage and to preserve organs at risk by minimising the doses delivered to them. If arctherapy allows a considerable reduction of high doses received by healthy tissues, no one can deny that it also leads to an increase of low doses in tissues that would not have received any with other techniques. It is with this in mind that we propose a hybrid approach combining the robustness of the 3DCRT approach and the high technicality and efficiency of arctherapy. Statistical tests allow us to draw conclusions about the possibility of using the hybrid approach in certain cases (right breast, BMI > 23, age > 48, target volume >350cc, etc.). Indeed, depending on the breast laterality and patients morphological characteristics, hybridization may prove to be a therapeutic tool of choice in the management of breast cancer in radiotherapy. a)

The study of galaxy evolution hinges on our ability to interpret multi-wavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models which allow us to infer physical parameters from spectrophotometric data. In recent years, thanks to the wide and deep multi-waveband galaxy surveys, the volume of high quality data have significantly increased. Alongside the increased data, algorithms performing SED fitting have improved, including better modeling prescriptions, newer templates, and more extensive sampling in wavelength space. We present a comprehensive analysis of different SED fitting codes including their methods and output with the aim of measuring the uncertainties caused by the modeling assumptions. We apply fourteen of the most commonly used SED fitting codes on samples from the CANDELS photometric catalogs at z~1 and z~3. We find agreement on the stellar mass, while we observe some discrepancies in the star formation rate (SFR) and dust attenuation results. To explore the differences and biases among the codes, we explore the impact of the various modeling assumptions as they are set in the codes (e.g., star formation histories, nebular, dust, and AGN models) on the derived stellar masses, SFRs, and A_V values. We then assess the difference among the codes on the SFR-stellar mass relation and we measure the contribution to the uncertainties by the modeling choices (i.e., the modeling uncertainties) in stellar mass (~0.1dex), SFR (~0.3dex), and dust attenuation (~0.3mag). Finally, we present some resources summarizing best practices in SED fitting., Comment: 25 pages, 11 figures. Accepted for publication in ApJ

We study the problem of maximizing the number of full degree vertices in a spanning tree $T$ of a graph $G$; that is, the number of vertices whose degree in $T$ equals its degree in $G$. In cubic graphs, this problem is equivalent to maximizing the number of leaves in $T$ and minimizing the size of a connected dominating set of $G$. We provide an algorithm which produces (w.h.p.) a tree with at least $0.4591n$ vertices of full degree (and also, leaves) when run on a random cubic graph. This improves the previously best known lower bound of $0.4146 n$. We also provide lower bounds on the number of full degree vertices in the random regular graph $G(n,r)$ for $r \le 10$., Comment: 10 pages, 3 figures

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science., Comment: White paper from "From Data to Software to Science with the Rubin Observatory LSST" workshop

We present the results of a stellar-population analysis of Lyman-alpha emitting galaxies (LAES) in GOODS-N at 1.9 < z < 3.5 spectroscopically identified by the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). We provide a method for connecting emission-line detections from the blind spectroscopic survey to imaging counterparts, a crucial tool needed as HETDEX builds a massive database of ~1 million Lyman-alpha detections. Using photometric data spanning as many as 11 filters covering 0.4-4.5 microns from the Hubble and Spitzer Space Telescopes, we study the objects' global properties and explore which properties impact the strength of Lyman-alpha emission. We measure a median stellar mass of 0.8 (^+2.9_-0.5) x 10^9 Msol and conclude that the physical properties of HETDEX spectroscopically-selected LAEs are comparable to LAEs selected by previous deep narrow band studies. We find that stellar mass and star formation rate correlate strongly with the Lyman-alpha equivalent width. We then use a known sample of z>7 LAEs to perform a proto-study of predicting Lyman-alpha emission from galaxies in the Epoch of Reionization, finding agreement at the 1-sigma level between prediction and observation for the majority of strong emitters., Comment: 29 pages, 22 figures, Accepted to the Astrophysical Journal

Analogs of fundamental physical phenomena can be used in two ways. One way consists in reproducing specific aspects of classical or quantum gravity, of quantum fields in curved space or of other high-energy scenarios, on lower-energy corresponding systems. The ``reverse way'' consists in building fundamental physical theories, for instance, quantum gravity models, inspired by the lower-energy corresponding systems. Here we present the case of graphene and other Dirac materials., Comment: Invited Review, 27 pages, 5 figures. arXiv admin note: text overlap with arXiv:2005.11514

The new open and royalty-free RISC-V ISA is attracting interest across the whole computing continuum, from microcontrollers to supercomputers. High-performance RISC-V processors and accelerators have been announced, but RISC-V-based HPC systems will need a holistic co-design effort, spanning memory, storage hierarchy interconnects and full software stack. In this paper, we describe Monte Cimone, a fully-operational multi-blade computer prototype and hardware-software test-bed based on U740, a double-precision capable multi-core, 64-bit RISC-V SoC. Monte Cimone does not aim to achieve strong floating-point performance, but it was built with the purpose of "priming the pipe" and exploring the challenges of integrating a multi-node RISC-V cluster capable of providing an HPC production stack including interconnect, storage and power monitoring infrastructure on RISC-V hardware. We present the results of our hardware/software integration effort, which demonstrate a remarkable level of software and hardware readiness and maturity - showing that the first generation of RISC-V HPC machines may not be so far in the future.

Galaxy populations show bimodality in a variety of properties: stellar mass, colour, specific star-formation rate, size, and S\'ersic index. These parameters are our feature space. We use an existing sample of 7556 galaxies from the Galaxy and Mass Assembly (GAMA) survey, represented using five features and the K-means clustering technique, showed that the bimodalities are the manifestation of a more complex population structure, represented by between 2 and 6 clusters. Here we use Self Organizing Maps (SOM), an unsupervised learning technique which can be used to visualize similarity in a higher dimensional space using a 2D representation, to map these five-dimensional clusters in the feature space onto two-dimensional projections. To further analyze these clusters, using the SOM information, we agree with previous results that the sub-populations found in the feature space can be reasonably mapped onto three or five clusters. We explore where the "green valley" galaxies are mapped onto the SOM, indicating multiple interstitial populations within the green valley population. Finally, we use the projection of the SOM to verify whether morphological information provided by GalaxyZoo users, for example, if features are visible, can be mapped onto the SOM-generated map. Voting on whether galaxies are smooth, likely ellipticals, or "featured" can reasonably be separated but smaller morphological features (bar, spiral arms) can not. SOMs promise to be a useful tool to map and identify instructive sub-populations in multidimensional galaxy survey feature space, provided they are large enough., Comment: 14 pages, 14 figures, accepted by MNRAS

We perform the dynamical system analysis of a cosmological model in the energy-momentum squared gravity (EMSG) of the form $f(T_{\mu\nu}T^{\mu\nu})=\alpha\ln({\lambda}T_{\mu\nu} T^{\mu\nu})$, which is known as energy-momentum log gravity (EMLG). In particular, we show that the analytical cosmological solution of EMLG presented by Akarsu {\it et al.} (Eur. Phys. J. C 79:846, 2019) is a future attractor. It includes new terms in the right-hand side of the Einstein field equations, which yield constant inertial mass density and provide a dynamical dark energy with a density passing below zero at large redshifts, accommodating a mechanism for screening $\Lambda$ in the past, suggested for alleviating some cosmological tensions. We show that the model gives rise to an entire class of new stable late-time solutions with $H\rightarrow\sqrt{(\Lambda+2\alpha)/3}$ as $a\rightarrow\infty$, where the new term is due to the constant effective inertial mass density that arises from EMLG contribution of dust, whereas $H\rightarrow\sqrt{\Lambda/3}$ as $a\rightarrow\infty$ in the $\Lambda$CDM model. We also show existence of new interesting features and trajectories that are absent in the $\Lambda$CDM model., Comment: 12 pages, 4 figures, 1 table

We describe the survey design, calibration, commissioning, and emission-line detection algorithms for the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). The goal of HETDEX is to measure the redshifts of over a million Ly$\alpha$ emitting galaxies between 1.88

This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units., Comment: Paper to be presented at the "Teaching ML" workshop at the European Conference of Machine Learning 2021. The Conclusions section includes a link to materials

The Abstraction and Reasoning Corpus (ARC) is a set of procedural tasks that tests an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for humans, they are challenging for state-of-the-art AI. What makes building intelligent systems that can generalize to novel situations such as ARC difficult? We posit that the answer might be found by studying the difference of \emph{language}: While humans readily generate and interpret instructions in a general language, computer systems are shackled to a narrow domain-specific language that they can precisely execute. We present LARC, the \textit{Language-complete ARC}: a collection of natural language descriptions by a group of human participants who instruct each other on how to solve ARC tasks using language alone, which contains successful instructions for 88\% of the ARC tasks. We analyze the collected instructions as `natural programs', finding that while they resemble computer programs, they are distinct in two ways: First, they contain a wide range of primitives; Second, they frequently leverage communicative strategies beyond directly executable codes. We demonstrate that these two distinctions prevent current program synthesis techniques from leveraging LARC to its full potential, and give concrete suggestions on how to build the next-generation program synthesizers., Comment: equal contributions: (author 1,2) and (author 3,4,5). 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks

In Polymer Quantum Mechanics, a quantization scheme that naturally emerges from Loop Quantum Gravity, position and momentum operators cannot be both well-defined on the Hilbert space ( H_Poly ). It is henceforth deemed impossible to define standard creation and annihilation operators. In this letter we show that a q-oscillator structure, and hence q-deformed creation/annihilation operators, can be naturally defined on H_Poly , which is then mapped into the sum of many copies of the q-oscillator Hilbert space. This shows that the q-calculus is a natural calculus for Polymer Quantum Mechanics. Moreover, we show that the inequivalence of different superselected sectors of H Poly is of topological nature.

We present Ly$\alpha$ and ultraviolet-continuum (UV) luminosity functions (LFs) of galaxies and active galactic nuclei (AGN) at $z=2.0-3.5$ determined by the un-targetted optical spectroscopic survey of the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). We combine deep Subaru imaging with HETDEX spectra resulting in $11.4$ deg$^2$ of fiber-spectra sky coverage, obtaining $18320$ galaxies spectroscopically identified with Ly$\alpha$ emission, $2126$ of which host type 1 AGN showing broad (FWHM$~>1000$ km s$^{-1}$) Ly$\alpha$ emission lines. We derive the Ly$\alpha$ (UV) LF over 2 orders of magnitude covering bright galaxies and AGN in $\log L_\mathrm{Ly\alpha}/\mathrm{[erg~s^{-1}]}=43.3-45.5$ ($-27-21$) type 1 AGN increases from $z\sim2$ to $z\sim0$ as opposed to the evolution of bright ($M_\mathrm{UV}<-21$) type 1 AGN, suggesting the AGN downsizing in the rest-frame UV luminosity., Comment: 27 pages, 19 figures, accepted for publication in ApJ

Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing State-of-the-Art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline's implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design.

In this work, we first discuss the possibility that dark energy models with negative energy density values in the past can alleviate the $H_0$ tension, as well as the discrepancy with the baryon acoustic oscillation (BAO) Lyman-$\alpha$ data, both which prevail within the $\Lambda$CDM model. We then investigate whether two minimal extensions of the $\Lambda$CDM model, together or separately, can successfully realize such a scenario: (i) the spatial curvature, which, in the case of spatially closed universe, mimics a negative density source and (ii) simple-graduated dark energy (gDE), which promotes the null inertial mass density of the usual vacuum energy to an arbitrary constant--if negative, the corresponding energy density decreases with redshift similar to the phantom models, but unlike them crosses below zero at a certain redshift. We find that, when the Planck data are not included in the observational analysis, the models with simple-gDE predict interesting and some significant deviations from the $\Lambda$CDM model. In particular, a spatially closed universe along with a simple-gDE of positive inertial mass density, which work in contrast to each other, results in minor improvement to the $H_0$ tension. The joint dataset, including the Planck data, presents no evidence for a deviation from spatial flatness but almost the same evidence for a cosmological constant and the simple-gDE with an inertial mass density of order $\mathcal{O}(10^{-12})\,\rm eV^4$. The latter case predicts almost no deviation from the $\Lambda$CDM model up until today--so that it results in no improvement regarding the BAO Ly-$\alpha$ data--except that it slightly aggravates the $H_0$ tension. We also study via dynamical analysis the history of the Universe in the models, as the simple-gDE results in futures different than the de Sitter future of the $\Lambda$CDM model., Comment: 17 pages, 9 figures, 2 tables; matches the version published in Physical Review D

In this article, we summarize two agnostic approaches in the framework of spatially curved Friedmann-Robertson-Walker (FRW) cosmologies discussed in detail in (Kerachian et al., 2020, 2019). The first case concerns the dynamics of a fluid with an unspecified barotropic equation of state (EoS), for which the only assumption made is the non-negativity of the fluid's energy density. The second case concerns the dynamics of a non-minimally coupled real scalar field with unspecified positive potential. For each of these models, we define a new set of dimensionless variables and a new evolution parameter. In the framework of these agnostic setups, we are able to identify several general features, like symmetries, invariant subsets and critical points, and provide their cosmological interpretation., Comment: 11 pages, 2 figures, 1 table

We discuss the representations of the algebra of quantization, the canonical commutation relations, in a scalar quantum field theory with spontaneously broken U(1) internal symmetry, when a topological defect of the vortex type is formed via the condensation of Nambu-Goldstone particles. We find that the usual thermodynamic limit is not necessary in order to have the inequivalent representations needed for the existence of physically disjoint phases of the system. This is a new type of inequivalence, due to the nontrivial topological structure of the phase space, that appears at finite volume. We regard this as a first step towards a unifying view of topological and thermodynamic phases, and offer here comments on the possible application of this scenario to quantum gravity., Comment: 15 pages, 2 figures

The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of low-power, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of cores, leading to minimum energy consumption. Depending on the kernel to be executed, the energy optimal scaling configuration is not trivial. While recent work has focused on general-purpose systems to learn and predict the best execution target in terms of the execution time of a snippet of code or kernel (e.g. offload OpenCL kernel on multicore CPU or GPU), in this work we focus on static compile-time features to assess if they can be successfully used to predict the minimum energy configuration on PULP, an ultra-low-power architecture featuring an on-chip cluster of RISC-V processors. Experiments show that using machine learning models on the source code to select the best energy scaling configuration automatically is viable and has the potential to be used in the context of automatic system configuration for energy minimisation.

Bayesian model comparison frameworks can be used when fitting models to data in order to infer the appropriate model complexity in a data-driven manner. We aim to use them to detect the correct number of major episodes of star formation from the analysis of the spectral energy distributions (SEDs) of galaxies, modeled after 3D-HST galaxies at z ~ 1. Starting from the published stellar population properties of these galaxies, we use kernel density estimates to build multivariate input parameter distributions to obtain realistic simulations. We create simulated sets of spectra of varying degrees of complexity (identified by the number of parameters), and derive SED fitting results and evidences for pairs of nested models, including the correct model as well as more simplistic ones, using the BAGPIPES codebase with nested sampling algorithm MultiNest. We then ask the question: is it true - as expected in Bayesian model comparison frameworks - that the correct model has larger evidence?} Our results indicate that the ratio of evidences (the Bayes factor) is able to identify the correct underlying model in the vast majority of cases. The quality of the results improves primarily as a function of the total S/N in the SED. We also compare the Bayes factors obtained using the evidence to those obtained via the Savage-Dickey Density Ratio (SDDR), an analytic approximation which can be calculated using samples from regular Markov Chain Monte Carlo methods. We show that the SDDR ratio can satisfactorily replace a full evidence calculation provided that the sampling density is sufficient., Comment: 18 pages, 10 figures

Serious Games (SGs) represent a new approach to improve learning processes more effectively and economically than traditional methods. This paper aims to present a SG approach for the electro-mobility context, in order to encourage the use of electric light vehicles. The design of the SG is based on the typical elements of the classic "game" with a real gameplay with different purposes. In this work, the proposed SG aims to raise awareness on environmental issues caused by mobility and actively involve users, on improving livability in the city and on real savings using alternative means to traditional vehicles. The objective of the designed tool is to propose elements of fun and entertainment for tourists or users of electric vehicles in the cities, while giving useful information about the benefits of using such vehicles, discovering touristic and interesting places in the city to discover. In this way, the user is stimulated to explore the artistic and historical aspects of the city through an effective learning process: he/she is encouraged to search the origins and the peculiarities of the monuments., Comment: This paper has been presented at 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)