Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure

Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization problems: given raw input data and an evaluator to guide the process, the idea is to automatically learn a policy able to return feasible and high-quality outputs. Recent works have shown promising results but the latter were mainly evaluated on the travelling salesman problem (TSP) and similar abstract variants such as Split Delivery Vehicle Routing Problem (SDVRP). In this paper, we analyze how and whether recent neural architectures can be applied to graph problems of practical importance. We thus set out to systematically "transfer" these architectures to the Power and Channel Allocation Problem (PCAP), which has practical relevance for, e.g., radio resource allocation in wireless networks. Our experimental results suggest that existing architectures (i) are still incapable of capturing graph structural features and (ii) are not suitable for problems where the actions on the graph change the graph attributes. On a positive note, we show that augmenting the structural representation of problems with Distance Encoding is a promising step toward the still-ambitious goal of learning multi-purpose autonomous solvers

Different MRI patterns in MS worsening after stopping fingolimod

Objective To analyze MRI images in patients with MS who experienced worsening of neurologic status (WNS) after stopping fingolimod (FTY).MethodsIn this retrospective study, demographic, clinical, and radiologic data of patients with MS who experienced WNS after stopping FTY were retrospectively collected. We introduced the "\u3b4Expanded Disability Status Scale (EDSS)-ratio" to identify patients who, after FTY withdrawal, showed an inflammatory flare-up exceeding the highest lifetime disease activity level. Patients with \u3b4EDSS-ratio > 1 were enrolled in the study.ResultsEight patients were identified. The mean (SD) age of the 8 (7 female) patients was 35.3 (4.9) years. The mean FTY treatment duration was 3.1 (0.8) years. The mean FTY discontinuation-WNS interval was 4 (0.9) months. The 4 patients with \u3b4EDSS-ratio 65 2 developed severe monophasic WNS (EDSS score above 8.5), characterized by clinical features and MRI findings not typical of MS, which we classified as "tumefactive demyelination pattern" (TDL) and "Punctuated pattern" (PL). Conversely, patients whose \u3b4EDSS-ratio was between 1 and 2 had clinical features and brain MRI compatible with a more typical, even if aggressive, MS relapse. In patients with TDL and PL, the flare-up of inflammatory activity led to severe tissue damage resulting in T2 but also T1 lesion volume increase at 6-month follow-up.ConclusionsPeculiar MRI features (TDL and PL), different from a typical MS flare-up, might occur in some patients who experienced WNS after stopping FTY. Further studies, also involving immunologic biomarkers, are necessary to investigate TDL or PL pathophysiology

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

The polynomial robust knapsack problem

This paper introduces a new optimization problem, namely the Polynomial Robust Knapsack Problem. It generalises the Robust Knapsack formulation to encompass possible relations between subsets of items having every possible cardinality. This allows to better describe the utility function for the decision maker, at the price of increasing the complexity of the problem. Thus, in order to solve realistic instances in a reasonable amount of time, two heuristics are proposed. The first one applies machine learning techniques in order to quickly select the majority of the items, while the second makes use of genetic algorithms to solve the problem. A set of simulation examples is finally presented to show the effectiveness of the proposed approaches

High-pressure phase transition of a natural pigeonite

High-pressure and room-temperature single-crystal X-ray diffraction (XRD) studies have been performed on crystals of a natural pigeonite sample with composition ca. Wo10En43Fs47 using diamond anvil cells. The unit-cell parameters were determined at 18 different pressures up to about 6 GPa. A first-order P21/c-C2/c phase transition was found between 3.5 and 3.6 GPa, associated with the disappearance of the b-type reflections (h + k= odd) and a strong discontinuity (about 1.7%) in the unit-cell volume. At the transition, a small hysteresis (~0.3 GPa) was observed. A third-order Birch-Murnaghan equation of state (BM3-EoS) fit to the 10 P-V data of the low-P phase yielded V0 = 431.93(2) Å3, KT0 = 96.8(8) GPa and K' = 8.5(6). A second-order Birch-Murnaghan EoS fit to the 8 P-V data (between 3.6 and 6 GPa) of the C2/c high-P phase yielded V0 = 423.6(1) Å3and KT0 = 112.4(8), indicating that the high-P C2/c phase is significantly stiffer than the low-P phase. In a separated experiment with crystals of the same sample, intensity data were collected and crystal structures were refined at 13 pressures up to 9.4 GPa. The M 1-O and M2-O mean bond lengths of the low-P P21/c phase decrease by 0.7 and 2.1%, respectively. The two non-equivalent A and B tetrahedral chains become more kinked with pressure, with a reduction of their angle by 2.2 and 5.1% respectively. At the transition the A-chain changes sense of rotation and both chains become equivalent and more kinked, with a further reduction of their angle by 2.5% up to 9.4 GPa. Strain calculations have been performed and the evolution of the spontaneous strain and the order parameter variation with pressure are discussed, considering geometrical parameters of the structure and comparing our results with the available data for other compositions

Towards NLP-based Processing of Honeypot Logs

Honeypots are active sensors deployed to obtain information about attacks. In their search for vulnerabilities, attackers generate large volumes of logs, whose analysis is time consuming and cumbersome. We here evaluate whether Natural Language Processing (NLP) approaches can provide meaningful representations to find common traits in attackers' activity. We consider a widely used SSH/Telnet honeypot to record more than 200,000 sessions, including 61,000 unique shell scripts, some containing sequences of more than 100 Bash commands. We first parse the sessions to separate Bash commands, options and parameters. Next, we project each session in a metric space opposing two common tools used in NLP: Bag of Words and Word2Vec. Last, we leverage a clustering algorithm to aggregate the sessions while offering an instrumental representation of the clustering process. In the end, we obtain few tens of clusters that we analyze to explain the attackers' goals, i.e., obtain system information, inject malicious accounts, download and run executables, etc. Our work is a first step towards automatically identifying attack patterns on honeypots, thus effectively supporting security activities

High-pressure phase transition of a natural pigeonite

High-pressure and room-temperature single-crystal X-ray diffraction (XRD) studies have been performed on crystals of a natural pigeonite sample with composition ca. Wo10En43Fs47 using diamond anvil cells. The unit-cell parameters were determined at 18 different pressures up to about 6 GPa. A first-order P21/c-C2/c phase transition was found between 3.5 and 3.6 GPa, associated with the disappearance of the b-type reflections (h + k= odd) and a strong discontinuity (about 1.7%) in the unit-cell volume. At the transition, a small hysteresis (~0.3 GPa) was observed. A third-order Birch-Murnaghan equation of state (BM3-EoS) fit to the 10 P-V data of the low-P phase yielded V0 = 431.93(2) Å3, KT0 = 96.8(8) GPa and K' = 8.5(6). A second-order Birch-Murnaghan EoS fit to the 8 P-V data (between 3.6 and 6 GPa) of the C2/c high-P phase yielded V0 = 423.6(1) Å3and KT0 = 112.4(8), indicating that the high-P C2/c phase is significantly stiffer than the low-P phase. In a separated experiment with crystals of the same sample, intensity data were collected and crystal structures were refined at 13 pressures up to 9.4 GPa. The M 1-O and M2-O mean bond lengths of the low-P P21/c phase decrease by 0.7 and 2.1%, respectively. The two non-equivalent A and B tetrahedral chains become more kinked with pressure, with a reduction of their angle by 2.2 and 5.1% respectively. At the transition the A-chain changes sense of rotation and both chains become equivalent and more kinked, with a further reduction of their angle by 2.5% up to 9.4 GPa. Strain calculations have been performed and the evolution of the spontaneous strain and the order parameter variation with pressure are discussed, considering geometrical parameters of the structure and comparing our results with the available data for other compositions