Evolutionary algorithms: Overview and applications to European transport

The present paper aims to analyse the research potential of Evolutionary Algorithms (EAs) in the light of their possible applications in the space-economy. For this purpose the first part of the paper will be devoted to an overview and illustration of EAs, also in comparison with other recent tools emerging form bio-computing, like Neural Networks (NNs). The second part of the paper will then focus on empirical applications concerning analyses and forecasts of European freight transport flows (at a regional level). In this context, the results stemming from an integrated approach combining EAs with NNs will be compared with those from conventional methodologies, like logit models, as well as with the "usual" NN models. We will analyze the sensitivity of various results by using different environmental policy on scenarios on European transport. The empirical experiments highlight the advantages and limitations of these approaches from both a methodological and empirical viewpoint, by offering a plausible range of values of outcomes that may be useful for planners and operators in this field.

«Rewording in melodious guile» W.B. Yeats’s The Song of the Happy Shepherd and its Evolution Towards a Musico-Literary Manifesto

This essay intends to explore how The Song of the Happy Shepherd elaborates on the notion of poetry as song, to contextualize it against the background of its (para)textual history and evolution, and emphasize its role as a musico-literary manifesto. Yeats’s Song is able to perform its variations on «the supreme theme of Art and Song» because its atavistically unifying ‘sooth’ is inborn to the very substance and features of its tropical mediation between poetry and song, thus making it neither classically «cracked» (l. 9) – i.e. burst asunder, fractured – like the merely «musical tune that Chronos sings» (l. 9), nor romantically ‘primeval and wild’ like The Song of the Shepherd in Thomas Moore’s To Joseph Atkinson, Esq. From Bermuda

BiSon-e: a lightweight and high-performance accelerator for narrow integer linear algebra computing on the edge

Linear algebra computational kernels based on byte and sub-byte integer data formats are at the base of many classes of applications, ranging from Deep Learning to Pattern Matching. Porting the computation of these applications from cloud to edge and mobile devices would enable significant improvements in terms of security, safety, and energy efficiency. However, despite their low memory and energy demands, their intrinsically high computational intensity makes the execution of these workloads challenging on highly resource-constrained devices. In this paper, we present BiSon-e, a novel RISC-V based architecture that accelerates linear algebra kernels based on narrow integer computations on edge processors by performing Single Instruction Multiple Data (SIMD) operations on off-The-shelf scalar Functional Units (FUs). Our novel architecture is built upon the binary segmentation technique, which allows to significantly reduce the memory footprint and the arithmetic intensity of linear algebra kernels requiring narrow data sizes. We integrate BiSon-e into a complete System-on-Chip (SoC) based on RISC-V, synthesized and Place Routed in 65nm and 22nm technologies, introducing a negligible 0.07% area overhead with respect to the baseline architecture. Our experimental evaluation shows that, when computing the Convolution and Fully-Connected layers of the AlexNet and VGG-16 Convolutional Neural Networks (CNNs) with 8-, 4-, and 2-bit, our solution gains up to 5.6×, 13.9× and 24× in execution time compared to the scalar implementation of a single RISC-V core, and improves the energy efficiency of string matching tasks by 5× when compared to a RISC-V-based Vector Processing Unit (VPU).This research was supported by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014-2020 with a grant of 50% of the total cost eligible, under the DRAC project [001-P-001723], and from the Spanish State Research Agency - Ministry of Science and Innovation (contract PID2019-107255GB). This research was also supported by the grant PRE2020-095272 funded by MCIN/AEI/ 10.13039/501100011033 and, by “ESF Investing in your future”.Peer ReviewedPostprint (author's final draft

Hot-Carrier Degradation in Power LDMOS: Selective LOCOS-Versus STI-Based Architecture

In this paper, we present an analysis of the degradation induced by hot-carrier stress in new generation power lateral double-diffused MOS (LDMOS) transistors. Two architectures with the same nominal voltage and comparable performance featuring a selective LOCOS and a shallow-trench isolation are investigated by means of constant voltage stress measurements and TCAD simulations. In particular, the on-resistance degradation in linear regime is experimentally extracted and numerically reproduced under different stress conditions. A similar amount of degradation has been reached by the two architectures, although different physical mechanisms contribute to the creation of the interface states. By using a recently developed physics-based degradation model, it has been possible to distinguish the damage due to collisions of single high-energetic electrons (single-particle events) and the contribution of colder electrons impinging on the silicon/oxide interface (multiple-particle events). A clear dominance of the single-electron collisions has been found in the case of LOCOS structure, whereas the multiple-particle effect plays a clear role in STI-based device at larger gate-voltage stress

Mix-GEMM: An efficient HW-SW architecture for mixed-precision quantized deep neural networks inference on edge devices

Deep Neural Network (DNN) inference based on quantized narrow-precision integer data represents a promising research direction toward efficient deep learning computations on edge and mobile devices. On one side, recent progress of Quantization-Aware Training (QAT) frameworks aimed at improving the accuracy of extremely quantized DNNs allows achieving results close to Floating-Point 32 (FP32), and provides high flexibility concerning the data sizes selection. Unfortunately, current Central Processing Unit (CPU) architectures and Instruction Set Architectures (ISAs) targeting resource-constrained devices present limitations on the range of data sizes supported to compute DNN kernels.This paper presents Mix-GEMM, a hardware-software co-designed architecture capable of efficiently computing quantized DNN convolutional kernels based on byte and sub-byte data sizes. Mix-GEMM accelerates General Matrix Multiplication (GEMM), representing the core kernel of DNNs, supporting all data size combinations from 8- to 2-bit, including mixed-precision computations, and featuring performance that scale with the decreasing of the computational data sizes. Our experimental evaluation, performed on representative quantized Convolutional Neural Networks (CNNs), shows that a RISC-V based edge System-on-Chip (SoC) integrating Mix-GEMM achieves up to 1.3 TOPS/W in energy efficiency, and up to 13.6 GOPS in throughput, gaining from 5.3× to 15.1× in performance over the OpenBLAS GEMM frameworks running on a commercial RISC-V based edge processor. By performing synthesis and Place and Route (PnR) of the enhanced SoC in Global Foundries 22nm FDX technology, we show that Mix-GEMM only accounts for 1% of the overall area consumption.This research was supported by the ERDF Operational Program of Catalonia 2014-2020, with a grant from the Spanish State Research Agency [PID2019-107255GB] and with DRAC project [001-P-001723], by the grant [PID2019-107255G-C21] funded by MCIN/AEI/ 10.13039/501100011033, by the Generalitat de Catalunya [2017-SGR-1328], and by Lenovo-BSC Contract-Framework (2020). The Spanish Ministry of Economy, Industry and Competitiveness has partially supported M. Doblas through an FPU fellowship [FPU20-04076] and M. Moreto through a Ramon y Cajal fellowship [RYC-2016-21104].Peer ReviewedPostprint (author's final draft

Tensiomyography detects early hallmarks of bed-rest-induced atrophy before changes in muscle architecture.

In young and older people skeletal muscle mass is reduced after as little as seven days of disuse. The declines in muscle mass after such short periods are of high clinical relevance, particularly in older people who show higher atrophy rate, and a slower, or even a complete lack of muscle mass recovery after disuse. Ten men (24.3± 2.6 years) underwent 35 days of 6° head-down tilt bed rest followed by 30 days of recovery. During bed rest, a neutral energy balance was maintained, with three weekly passive physiotherapy sessions to minimise muscle soreness and joint stiffness. All measurements were performed in a hospital at days 1-10 (BR1-BR10), day 16 (BR16), 28 (BR28) and 35 (BR35) of bed rest, and day 1 (R+1), 3 (R+3) and 30 (R+30) after reambulation. Vastus medialis obliquus (VMO), vastus medialis longus (VML) and biceps femoris (BF) thickness (d) and pennation angle (Θ) were assessed by ultrasonography, while twitch muscle belly displacement (Dm) and contraction time (Tc) were assessed with tensiomyography. After bed rest, d and Θ decreased by 13-17% in all muscles (P<.001) and had recovered at R+30. Dm was increased by 42.3-84.4% (P<.001) at BR35 and preceded the decrease in d by 7, 5 and 3 days in VMO, VML and BF, respectively. Tc increased only in BF (32.1%; P<.001) and was not recovered at R+30. Tensiomyography can detect early bed-rest-induced changes in muscle with higher sensitivity before overt architectural changes and atrophy can be detected

Adaptable register file organization for vector processors

Contemporary Vector Processors (VPs) are de-signed either for short vector lengths, e.g., Fujitsu A64FX with 512-bit ARM SVE vector support, or long vectors, e.g., NEC Aurora Tsubasa with 16Kbits Maximum Vector Length (MVL1). Unfortunately, both approaches have drawbacks. On the one hand, short vector length VP designs struggle to provide high efficiency for applications featuring long vectors with high Data Level Parallelism (DLP). On the other hand, long vector VP designs waste resources and underutilize the Vector Register File (VRF) when executing low DLP applications with short vector lengths. Therefore, those long vector VP implementations are limited to a specialized subset of applications, where relatively high DLP must be present to achieve excellent performance with high efficiency. Modern scientific applications are getting more diverse, and the vector lengths in those applications vary widely. To overcome these limitations, we propose an Adaptable Vector Architecture (AVA) that leads to having the best of both worlds. AVA is designed for short vectors (MVL=16 elements) and is thus area and energy-efficient. However, AVA has the functionality to reconfigure the MVL, thereby allowing to exploit the benefits of having a longer vector of up to 128 elements microarchitecture when abundant DLP is present. We model AVA on the gem5 simulator and evaluate AVA performance with six applications taken from the RiVEC Benchmark Suite. To obtain area and power consumption metrics, we model AVA on McPAT for 22nm technology. Our results show that by reconfiguring our small VRF (8KB) plus our novel issue queue scheme, AVA yields a 2X speedup over the default configuration for short vectors. Additionally, AVA shows competitive performance when compared to a long vector VP, while saving 50% of area.Research reported in this publication is partially supported by CONACyT Mexico under Grant No. 472106, the Spanish State Research Agency - Ministry of Science and Innovation (contract PID2019-107255GB), and the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014-2020 with a grant of 50% of the total cost eligible, under the DRAC project [001-P-001723].Peer ReviewedPostprint (author's final draft

Real-life comparison of Pirfenidone and Nintedanib in patients with Idiopathic Pulmonary Fibrosis: a 24-month assessment.

Background: Real-life data on the use of pirfenidone and nintedanib to treat patients with idiopathic pulmonary fibrosis (IPF) are still scarce. Methods: We compared the efficacy of either pirfenidone (n=78) or nintedanib (n=28) delivered over a 24-month period in patients with IPF, followed at two regional clinic centers in Italy, with a group of patients who refused the treatment (n=36), and who were considered to be controls. All patients completed regular visits at 1- to 3-month intervals, where primary [forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO)] and secondary outcomes (side effects, treatment compliance, and mortality) were recorded. Results: Over time, the decline in FVC and DLCO was significantly higher (p=0.0053 and p=0.037, respectively) in controls when compared with the combined treated group, with no significant difference between the two treated groups. Compared to patients with less advanced disease (GAP (Gender, Age, Physiology) stage I), those in GAP stages II and III showed a significantly higher decline in both FVC and DLCO irrespective of the drug taken. Side effects were similarly reported in patients receiving pirfenidone and nintedanib (5% and 7%, respectively), whereas mortality did not differ among the three groups. Conclusion: This real-life study demonstrated that both pirfenidone and nintedanib were equally effective in reducing the decline of FVC and DLCO versus non-treated patients after 24 months of treatment; however, patients with more advanced disease were likely to show a more rapid decline in respiratory function

Transcriptomic Analysis of Single Isolated Myofibers Identifies miR-27a-3p and miR-142-3p as Regulators of Metabolism in Skeletal Muscle

Summary: Skeletal muscle is composed of different myofiber types that preferentially use glucose or lipids for ATP production. How fuel preference is regulated in these post-mitotic cells is largely unknown, making this issue a key question in the fields of muscle and whole-body metabolism. Here, we show that microRNAs (miRNAs) play a role in defining myofiber metabolic profiles. mRNA and miRNA signatures of all myofiber types obtained at the single-cell level unveiled fiber-specific regulatory networks and identified two master miRNAs that coordinately control myofiber fuel preference and mitochondrial morphology. Our work provides a complete and integrated mouse myofiber type-specific catalog of gene and miRNA expression and establishes miR-27a-3p and miR-142-3p as regulators of lipid use in skeletal muscle. : Chemello et al. characterize coding mRNAs and non-coding microRNAs expressed by myofibers of hindlimb mouse muscles, identifying complex interactions between these molecules that modulate mitochondrial functions and muscle metabolism. They demonstrate that specific short non-coding RNAs influence the contractile fiber composition of skeletal muscles by modulating muscle metabolism. Keywords: single myofiber, skeletal muscle metabolism, mitochondria, miRNAs, lipid

Un lampo obliquo. Luigi Bernardi, i suoi libri e il suo immaginario

In occasione del decennale della morte di Luigi Bernardi (Ozzano dell’Emilia, 1953 - Bologna, 2013), il volume - a cura di Filippo Milani e Alberto Sebastiani - raccoglie gli atti relativi all’evento di inaugurazione del Fondo “Luigi Bernardi”, che si è svolto il 17 gennaio 2020 presso gli spazi del Dipartimento di Filologia classica e Italianistica dell’Università di Bologna, poche settimane prima che la pandemia sconvolgesse le vite di tutte e tutti noi. Si è trattato della prima iniziativa volta a valorizzare il Fondo archivistico e librario dello scrittore emiliano, che merita di certo ulteriori studi e approfondimenti, volti a indagare sia l’attività creativa di Bernardi sia quella di editore, traduttore, promotore culturale e scopritore di talenti letterari e fumettistici, soprattutto nell’ambito del genere noir. In quell’occasione era stata allestita anche una mostra virtuale - tuttora accessibile - che offre una prima visione panoramica sugli interessi dell’autore e sulla composizione della sua biblioteca