Throughput-Distortion Computation Of Generic Matrix Multiplication: Toward A Computation Channel For Digital Signal Processing Systems

The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra libraries used in many computationally-demanding digital signal processing (DSP) systems. We propose an acceleration technique for GEMM based on dynamically adjusting the imprecision (distortion) of computation. Our technique employs adaptive scalar companding and rounding to input matrix blocks followed by two forms of packing in floating-point that allow for concurrent calculation of multiple results. Since the adaptive companding process controls the increase of concurrency (via packing), the increase in processing throughput (and the corresponding increase in distortion) depends on the input data statistics. To demonstrate this, we derive the optimal throughput-distortion control framework for GEMM for the broad class of zero-mean, independent identically distributed, input sources. Our approach converts matrix multiplication in programmable processors into a computation channel: when increasing the processing throughput, the output noise (error) increases due to (i) coarser quantization and (ii) computational errors caused by exceeding the machine-precision limitations. We show that, under certain distortion in the GEMM computation, the proposed framework can significantly surpass 100% of the peak performance of a given processor. The practical benefits of our proposal are shown in a face recognition system and a multi-layer perceptron system trained for metadata learning from a large music feature database.Comment: IEEE Transactions on Signal Processing (vol. 60, 2012

Progetto di un ricevitore software GPS su periferica USRP

Il lavoro mostra l'operazione di analisi del codice sorgente di un ricevitore software GPS (chiamato SoftRec), l'analisi del framework GNU Radio ed in particolare dell'interfaccia chiamata USRP. Le analisi portano alla realizzazione del progetto del porting di SoftRec sulla scheda USRP, nonché ad una prima implementazione, con lo studio delle relative problematiche

Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability

The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research. However, conventional AI models are heavily limited in handling complex biomedical structures (such as 2D or 3D protein and molecule structures) and providing interpretations for outputs, which hinders their practical application. As of late, Graph Machine Learning (GML) has gained considerable attention for its exceptional ability to model graph-structured biomedical data and investigate their properties and functional relationships. Despite extensive efforts, GML methods still suffer from several deficiencies, such as the limited ability to handle supervision sparsity and provide interpretability in learning and inference processes, and their ineffectiveness in utilising relevant domain knowledge. In response, recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery with limited training instances. However, a systematic definition for this burgeoning research direction is yet to be established. This survey presents a comprehensive overview of long-standing drug discovery principles, provides the foundational concepts and cutting-edge techniques for graph-structured data and knowledge databases, and formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug discovery. A thorough review of related KaGML works, collected following a carefully designed search methodology, are organised into four categories following a novel-defined taxonomy. To facilitate research in this promptly emerging field, we also share collected practical resources that are valuable for intelligent drug discovery and provide an in-depth discussion of the potential avenues for future advancements

Numerical models for the design and construction of new underground structures at CERN (HL-LHC), Point 5

The Large Hadron Collider (LHC) is the latest, most powerful, world’s largest underground particle accelerator realized on the CERN site. High-Luminosity LHC (HL-LHC) is a new project aimed to upgrade the LHC, at Point 1 (ATLAS in Switzerland) and Point 5 (CMS in France) in order to enhance scientific progress. This paper describes the design and construction issues developed at the Point 5 for the new underground structures, located near the existing LHC tunnel. The project requires new technical infrastructure: an additional shaft with a 12 m-diameter and 60 m-height, cavern with 270 m2 cross-section, approximately 500 meters of tunnels connected to the LHC tunnel, vertical linkage cores and additional technical buildings at the surface. The geological ground model of this site lies in an area covered by Quaternary moraine with two independent aquifers. The bedrock of Molasse comprises sub-horizontal lenses of heterogeneous sedimentary rock, that is known to locally retain hydrocarbons and to have a swelling behaviour. In order to investigate the heterogeneous behaviour of the rock mass composed of several layers with different strengths, numerical calculations have been performed, under a 2D plane strain condition with RS2 9.0 FEM-software.. The purpose of using the software was to design both the rock-supports and the concrete inner lining for the tunnels and the shaft. Data from a comprehensive monitoring system with pre-defined threshold values was compared to the 2D FEM results, confirming the importance of the observational method to verify the assumptions used in the numerical modelling. The execution of the underground works started in April 2018. The excavation of the main un-derground works has been successfully completed without any critical impact on the nearby ex-isting underground structures. The completion of the works is scheduled for September 2022

IEQ and energy improvement of existing buildings by prefabricated facade additions: the case of a student house in Athens

The aim of this paper is to evaluate and illustrate the energy saving potential and Indoor Environmental Quality (IEQ) performances of a fa\ue7ade addition on existing and low energy performing buildings. Different technical solutions are proposed and all IEQ indicators\u2019 simulation results are presented for the case of a students\u2019 building block of the 80\u2019s located in Athens. The building is the demonstrator of the \u201cPro-GET-onE\u201d Horizon 2020 project, that aims to demonstrate the attractiveness and the energy efficiency of a renovation strategy based on new fa\ue7ade additions combining inteGrated Efficient Technologies (GETs). The research project proposes the highest transformation of the existing building\u2019s shell with external added volumes, which generate energy efficient buffer zones and at the same time increase the building\u2019s volume (with balconies, sunspaces and extra rooms). This strategy gives also the possibility to increase IEQ performance, in different ways depending on the architectural solutions, the selected materials and the adopted technological solutions. As a general statement, the facade addition solution leads to an increase of the thermo-hygrometric conditions (both for the cold winter season and the summer period), of the facade sound insulation and consequently the acoustic comfort, and of the indoor air quality. The lighting and the visual comfort are a critical point due to the enlargement of the existing surface of the rooms: specific light enhancement techniques have been studied to optimize indoor light, therefore minimizing the drawbacks of fa\ue7ade expansions, and will be suggested for the final design of the case study. The detailed analysis of individual units (additions) led to the formulation of hypotheses for targeted energy retrofitting interventions in different options; with different scenarios of integrated RES technologies, these options have been analysed both separately and in combination, to assess the technical, the energy feasibility and the IEQ performance in each scenario

Allogeneic Stem Cell Transplantation Compared with Chemotherapy for Poor-Risk Hodgkin Lymphoma

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Information management in the early stages of the COVID-19 pandemic

This paper reviews the information management aspects of the early months of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Coronavirus 19 outbreak. It shows that the transition from epidemic to pandemic was caused partly by poor management of information that was publicly available in January 2020. The approach combines public domain epidemic data with economic, demographic, health, social and political data and investigates how information was managed by governments. It includes case studies of early-stage information management, from countries with high and low COVID-19 impacts (as measured by deaths per million). The reasons why the information was not acted upon appropriately include “dark side” information behaviours Stone et al. (2019). Many errors and misjudgements could have been avoided by using learnings from previous epidemics, particularly the 1918-19 flu epidemic, when international travel (mainly of troops in World War 1) was a prime mode of spreading. It concludes that if similar outbreaks are not to turn into pandemics, much earlier action is needed, mainly closing borders and locking-down. The research is based on what was known at the time of writing, when the pandemic’s exact origin was uncertain, when some statistics about actions and results were unavailable and when final results were unknown. Governments faced with early warning signs or pandemics must act much faster. This is one of the first analyses of information management practices relating to the pandemic’s early stages

Nirmatrelvir treatment of SARS-CoV-2-infected mice blunts antiviral adaptive immune responses

Alongside vaccines, antiviral drugs are becoming an integral part of our response to the SARS-CoV-2 pandemic. Nirmatrelvir-an orally available inhibitor of the 3-chymotrypsin-like cysteine protease-has been shown to reduce the risk of progression to severe COVID-19. However, the impact of nirmatrelvir treatment on the development of SARS-CoV-2-specific adaptive immune responses is unknown. Here, by using mouse models of SARS-CoV-2 infection, we show that nirmatrelvir administration blunts the development of SARS-CoV-2-specific antibody and T cell responses. Accordingly, upon secondary challenge, nirmatrelvir-treated mice recruited significantly fewer memory T and B cells to the infected lungs and mediastinal lymph nodes, respectively. Together, the data highlight a potential negative impact of nirmatrelvir treatment with important implications for clinical management and might help explain the virological and/or symptomatic relapse after treatment completion reported in some individuals

Developing the next generation of renewable energy technologies:an overview of low-TRL EU-funded research projects

A cluster of eleven research and innovation projects, funded under the same call of the EU’s H2020 programme, are developing breakthrough and game-changing renewable energy technologies that will form the backbone of the energy system by 2030 and 2050 are, at present, at an early stage of development. These projects have joined forces at a collaborative workshop, entitled ‘ Low-TRL Renewable Energy Technologies’, at the 10th Sustainable Places Conference (SP2022), to share their insights, present their projects’ progress and achievements to date, and expose their approach for exploitation and market uptake of their solutions.</p