Towards Optimal Graph Coloring Using Rydberg Atoms

Quantum mechanics is expected to revolutionize the computing landscape in the near future. Among the many candidate technologies for building universal quantum computers, Rydberg atoms-based systems stand out for being capable of performing both quantum simulations and working as gate-based universal quantum computers while operating at room temperature through an optical system. Moreover, they can potentially scale up to hundreds of quantum bits (qubits). In this work, we solve a Graph Coloring problem by iteratively computing the solutions of Maximal Independent Set (MIS) problems, exploiting the Rydberg blockade phenomenon. Experimental results using a simulation framework on the CINECA Marconi-100 supercomputer demonstrate the validity of the proposed approach

Deep Learning for real-time neural decoding of grasp

Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this context, this work starts from a specific pre-existing dataset of neural recordings from monkey motor cortex and presents a Deep Learning-based approach to the decoding of neural signals for grasp type classification. Specifically, we propose here an approach that exploits LSTM networks to classify time series containing neural data (i.e., spike trains) into classes representing the object being grasped. The main goal of the presented approach is to improve over state-of-the-art decoding accuracy without relying on any prior neuroscience knowledge, and leveraging only the capability of deep learning models to extract correlations from data. The paper presents the results achieved for the considered dataset and compares them with previous works on the same dataset, showing a significant improvement in classification accuracy, even if considering simulated real-time decoding

CD19 Cell Count at Baseline Predicts B Cell Repopulation at 6 and 12 Months in Multiple Sclerosis Patients Treated with Ocrelizumab

Background: The kinetics of B cell repopulation in MS patients treated with Ocrelizumab is highly variable, suggesting that a fixed dosage and time scheduling might be not optimal. We aimed to investigate whether B cell repopulation kinetics influences clinical and radiological outcomes and whether circulating immune asset at baseline affects B cell repopulation kinetics. Methods: 218 MS patients treated with Ocrelizumab were included. Every six months we collected data on clinical and magnetic resonance imaging (MRI) activity and lymphocyte subsets at baseline. According to B cell counts at six and twelve months, we identified two groups of patients, those with fast repopulation rate (FR) and those with slow repopulation rate (SR). Results: A significant reduction in clinical and radiological activity was found. One hundred fifty-five patients had complete data and received at least three treatment cycles (twelve-month follow-up). After six months, the FR patients were 41/155 (26.45%) and 10/41 (29.27%) remained non-depleted after twelve months. FR patients showed a significantly higher percentage of active MRI scan at twelve months (17.39% vs. 2.53%; p = 0,008). Furthermore, FR patients had a higher baseline B cell count compared to patients with an SR (p = 0.02 and p = 0.002, at the six- and twelve-month follow-ups, respectively). Conclusion: A considerable proportion of MS patients did not achieve a complete CD19 cell depletion and these patients had a higher baseline CD19 cell count. These findings, together with the higher MRI activity found in FR patients, suggest that the Ocrelizumab dosage could be tailored depending on CD19 cell counts at baseline in order to achieve complete disease control in all patients

Designing Logic Tensor Networks for Visual Sudoku puzzle classification

Given the increasing importance of the neurosymbolic (NeSy) approach in artificial intelligence, there is a growing interest in studying benchmarks specifically designed to emphasize the ability of AI systems to combine low-level representation learning with high-level symbolic reasoning. One such recent benchmark is Visual Sudoku Puzzle Classification, that combines visual perception with relational constraints. In this work, we investigate the application of Logic Tensork Networks (LTNs) to the Visual Sudoku Classification task and discuss various alternatives in terms of logical constraint formulation, integration with the perceptual module and training procedure

Quality of care provided by Multiple Sclerosis Centers during Covid-19 pandemic: Results of an Italian multicenter patient-centered survey

Background: Covid-19 pandemic impacted on management of people with Multiple Sclerosis (pwMS). Level of satisfaction of pwMS regarding the care received by the staff of Multiple Sclerosis Centers (MSCs) during the pandemic was not fully investigated. In a large patient-centered multicenter study, the therapeutic adherence and quality of care of MSCs was assessed. Methods: In April-May 2021, an online survey was widespread by 16 Italian MSCs. Frequencies, percentages and/or means and standard deviations were calculated to describe the sample. ANOVAs were performed to evaluate the effect of sociodemographic and clinical variables on overall pwMS' rating of MSC assistance. Results: 1670 pwMS completed the survey (67.3% women). During the pandemic, 88% did not change their disease modifying therapy schedule, and 89.1% reached their MSCs with no or little difficulties. Even if only 1.3% of participants underwent a tele-health follow-up visit with their MSC staff, the 80.1% believed that tele-health services should be improved regardless of pandemic. 92% of participants were satisfied of how their MSC took charge of their needs; ANOVAs revealed an effect of disease duration on pwMS' level of satisfaction on MSCs management during the pandemic. Conclusions: The results revealed an efficient MSCs response to Covid-19 pandemic and provided the basis for the implementing of tele-health services that would further improve the taking charge of patients, particularly those with longer disease, higher disability, and/or living far from their MSC

Disease-Modifying Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis

Objective: This study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS). Methods: We retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results. Results: Of 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18-4.74, p = 0.015) with increased risk of severe COVID-19. Recent use (<1 month) of methylprednisolone was also associated with a worse outcome (OR = 5.24, 95% CI = 2.20-12.53, p = 0.001). Results were confirmed by the PS-weighted analysis and by all the sensitivity analyses. Interpretation: This study showed an acceptable level of safety of therapies with a broad array of mechanisms of action. However, some specific elements of risk emerged. These will need to be considered while the COVID-19 pandemic persists

COVID-19 Severity in Multiple Sclerosis: Putting Data Into Context

Background and objectives: It is unclear how multiple sclerosis (MS) affects the severity of COVID-19. The aim of this study is to compare COVID-19-related outcomes collected in an Italian cohort of patients with MS with the outcomes expected in the age- and sex-matched Italian population. Methods: Hospitalization, intensive care unit (ICU) admission, and death after COVID-19 diagnosis of 1,362 patients with MS were compared with the age- and sex-matched Italian population in a retrospective observational case-cohort study with population-based control. The observed vs the expected events were compared in the whole MS cohort and in different subgroups (higher risk: Expanded Disability Status Scale [EDSS] score > 3 or at least 1 comorbidity, lower risk: EDSS score ≤ 3 and no comorbidities) by the χ2 test, and the risk excess was quantified by risk ratios (RRs). Results: The risk of severe events was about twice the risk in the age- and sex-matched Italian population: RR = 2.12 for hospitalization (p < 0.001), RR = 2.19 for ICU admission (p < 0.001), and RR = 2.43 for death (p < 0.001). The excess of risk was confined to the higher-risk group (n = 553). In lower-risk patients (n = 809), the rate of events was close to that of the Italian age- and sex-matched population (RR = 1.12 for hospitalization, RR = 1.52 for ICU admission, and RR = 1.19 for death). In the lower-risk group, an increased hospitalization risk was detected in patients on anti-CD20 (RR = 3.03, p = 0.005), whereas a decrease was detected in patients on interferon (0 observed vs 4 expected events, p = 0.04). Discussion: Overall, the MS cohort had a risk of severe events that is twice the risk than the age- and sex-matched Italian population. This excess of risk is mainly explained by the EDSS score and comorbidities, whereas a residual increase of hospitalization risk was observed in patients on anti-CD20 therapies and a decrease in people on interferon

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon

A machine learning approach for an HPC use case: The jobs queuing time prediction

High-Performance Computing (HPC) domain provided the necessary tools to support the scientific and industrial advancements we all have seen during the last decades. HPC is a broad domain targeting to provide both software and hardware solutions as well as envisioning methodologies that allow achieving goals of interest, such as system performance and energy efficiency. In this context, supercomputers have been the vehicle for developing and testing the most advanced technologies since their first appearance. Unlike cloud computing resources that are provided to the end-users in an on-demand fashion in the form of virtualized resources (i.e., virtual machines and containers), supercomputers’ resources are generally served through State-of-the-Art batch schedulers (e.g., SLURM, PBS, LSF, HTCondor). As such, the users submit their computational jobs to the system, which manages their execution with the support of queues. In this regard, predicting the behaviour of the jobs in the batch scheduler queues becomes worth it. Indeed, there are many cases where a deeper knowledge of the time experienced by a job in a queue (e.g., the submission of check-pointed jobs or the submission of jobs with execution dependencies) allows exploring more effective workflow orchestration policies. In this work, we focused on applying machine learning (ML) techniques to learn from the historical data collected from the queuing system of real supercomputers, aiming at predicting the time spent on a queue by a given job. Specifically, we applied both unsupervised learning (UL) and supervised learning (SL) techniques to define the most effective features for the prediction task and the actual prediction of the queue waiting time. For this purpose, two approaches have been explored: on one side, the prediction of ranges on jobs’ queuing times (classification approach) and, on the other side, the prediction of the waiting time at the minutes level (regression approach). Experimental results highlight the strong relationship between the SL models’ performances and the way the dataset is split. At the end of the prediction step, we present the uncertainty quantification approach, i.e., a tool to associate the predictions with reliability metrics, based on variance estimation