On Optimization Modulo Theories, MaxSMT and Sorting Networks

Optimization Modulo Theories (OMT) is an extension of SMT which allows for finding models that optimize given objectives. (Partial weighted) MaxSMT --or equivalently OMT with Pseudo-Boolean objective functions, OMT+PB-- is a very-relevant strict subcase of OMT. We classify existing approaches for MaxSMT or OMT+PB in two groups: MaxSAT-based approaches exploit the efficiency of state-of-the-art MAXSAT solvers, but they are specific-purpose and not always applicable; OMT-based approaches are general-purpose, but they suffer from intrinsic inefficiencies on MaxSMT/OMT+PB problems. We identify a major source of such inefficiencies, and we address it by enhancing OMT by means of bidirectional sorting networks. We implemented this idea on top of the OptiMathSAT OMT solver. We run an extensive empirical evaluation on a variety of problems, comparing MaxSAT-based and OMT-based techniques, with and without sorting networks, implemented on top of OptiMathSAT and {\nu}Z. The results support the effectiveness of this idea, and provide interesting insights about the different approaches.Comment: 17 pages, submitted at Tacas 1

Night-time oxidation of surfactants at the air–water interface: effects of chain length, head group and saturation

Reactions of the key atmospheric night-time oxidant NO3 with organic monolayers at the air–water interface are used as proxies for the ageing of organic-coated aqueous aerosols. The surfactant molecules chosen for this study are oleic acid (OA), palmitoleic acid (POA), methyl oleate (MO) and stearic acid (SA) to investigate the effects of chain length, head group and degree of unsaturation on the reaction kinetics and products formed. Fully and partially deuterated surfactants were studied using neutron reflectometry (NR) to determine the reaction kinetics of organic monolayers with NO3 at the air–water interface for the first time. Kinetic modelling allowed us to determine the rate coefficients for the oxidation of OA, POA and MO monolayers to be (2.8 ± 0.7) × 10−8 cm2 molecule−1 s−1, (2.4 ± 0.5) × 10−8 cm2 molecule−1 s−1 and (3.3 ± 0.6) × 10−8 cm2 molecule−1 s−1, respectively. The corresponding uptake coefficients were found to be (2.1 ± 0.5) × 10−3, (1.7 ± 0.3) × 10−3 and (2.1 ± 0.4) × 10−3. For the much slower NO3-initiated oxidation of the saturated surfactant SA we found a loss rate of (5 ± 1) × 10−12 cm2 molecule−1 s−1 which we consider to be an upper limit for the reactive loss, and estimated an uptake coefficient of (5 ± 1) × 10−7. Our investigations demonstrate that NO3 will contribute substantially to the processing of unsaturated surfactants at the air–water interface during night-time given its reactivity is ca. two orders of magnitude higher than that of O3. Furthermore, the relative contributions of NO3 and O3 to the oxidative losses vary massively between species that are closely related in structure: NO3 reacts ca. 400 times faster than O3 with the common model surfactant oleic acid, but only ca. 60 times faster with its methyl ester MO. It is therefore necessary to perform a case-by-case assessment of the relative contributions of the different degradation routes for any specific surfactant. The overall impact of NO3 on the fate of saturated surfactants is slightly less clear given the lack of prior kinetic data for comparison, but NO3 is likely to contribute significantly to the loss of saturated species and dominate their loss during night-time. The retention of the organic character at the air–water interface differs fundamentally between the different surfactant species: the fatty acids studied (OA and POA) form products with a yield of ∼ 20% that are stable at the interface while NO3-initiated oxidation of the methyl ester MO rapidly and effectively removes the organic character (≤ 3% surface-active products). The film-forming potential of reaction products in real aerosol is thus likely to depend on the relative proportions of saturated and unsaturated surfactants as well as the head group properties. Atmospheric lifetimes of unsaturated species are much longer than those determined with respect to their reactions at the air–water interface, so that they must be protected from oxidative attack e.g. by incorporation into a complex aerosol matrix or in mixed surface films with yet unexplored kinetic behaviour

Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science

Recent advances in materials modelling, characterization and materials informatics suggest that deep integration of such methods can be a crucial aspect of the Industry 5.0 revolution, where the fourth industrial revolution paradigms are combined with the concepts of transition to a sustainable, human-centric and resilient industry. We pose a specific deep integration challenge beyond the ordinary multi-disciplinary modelling/characterization research approach in this short communication with research and innovation as drivers for scientific excellence. Full integration can be achieved by developing com-mon ontologies across different domains, enabling meaningful computational and experimental data integration and interoperability. On this basis, fine-tuning of adaptive materials modelling/characteriza-tion protocols can be achieved and facilitate computational and experimental efforts. Such interoperable and meaningful data combined with advanced data science tools (including machine learning and artifi-cial intelligence) become a powerful asset for materials scientists to extract complex information from the large amount of data generated by last generation characterization techniques. To achieve this ambi-tious goal, significant collaborative actions are needed to develop common, usable, and sharable digital tools that allow for effective and efficient twinning of data and workflows across the different materials modelling and characterization domains.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)

Enhancing Sensitivity Classification with Semantic Features using Word Embeddings

Government documents must be reviewed to identify any sensitive information they may contain, before they can be released to the public. However, traditional paper-based sensitivity review processes are not practical for reviewing born-digital documents. Therefore, there is a timely need for automatic sensitivity classification techniques, to assist the digital sensitivity review process. However, sensitivity is typically a product of the relations between combinations of terms, such as who said what about whom, therefore, automatic sensitivity classification is a difficult task. Vector representations of terms, such as word embeddings, have been shown to be effective at encoding latent term features that preserve semantic relations between terms, which can also be beneficial to sensitivity classification. In this work, we present a thorough evaluation of the effectiveness of semantic word embedding features, along with term and grammatical features, for sensitivity classification. On a test collection of government documents containing real sensitivities, we show that extending text classification with semantic features and additional term n-grams results in significant improvements in classification effectiveness, correctly classifying 9.99% more sensitive documents compared to the text classification baseline

Diagnosis, Clinical Features and Management of Interstitial Lung Diseases in Rheumatic Disorders: Still a Long Journey

: Interstitial lung disease (ILD) is one of the most frequent pulmonary complications of autoimmune rheumatic diseases (ARDs), and it is mainly associated with connective tissue diseases (CTDs) and rheumatoid arthritis (RA) [...]

From LTL and Limit-Deterministic B\"uchi Automata to Deterministic Parity Automata

Controller synthesis for general linear temporal logic (LTL) objectives is a challenging task. The standard approach involves translating the LTL objective into a deterministic parity automaton (DPA) by means of the Safra-Piterman construction. One of the challenges is the size of the DPA, which often grows very fast in practice, and can reach double exponential size in the length of the LTL formula. In this paper we describe a single exponential translation from limit-deterministic B\"uchi automata (LDBA) to DPA, and show that it can be concatenated with a recent efficient translation from LTL to LDBA to yield a double exponential, \enquote{Safraless} LTL-to-DPA construction. We also report on an implementation, a comparison with the SPOT library, and performance on several sets of formulas, including instances from the 2016 SyntComp competition

Insights from the classical MD simulations

Salt bridges and ionic interactions play an important role in protein stability, protein-protein interactions, and protein folding. Here, we provide the classical MD simulations of the structure and IR signatures of the arginine (Arg)–glutamate (Glu) salt bridge. The Arg-Glu model is based on the infinite polyalanine antiparallel two-stranded β-sheet structure. The 1 μs NPT simulations show that it preferably exists as a salt bridge (a contact ion pair). Bidentate (the end-on and side-on structures) and monodentate (the backside structure) configurations are localized [Donald et al., Proteins 79, 898–915 (2011)]. These structures are stabilized by the short +N–H⋯O− bonds. Their relative stability depends on a force field used in the MD simulations. The side-on structure is the most stable in terms of the OPLS-AA force field. If AMBER ff99SB-ILDN is used, the backside structure is the most stable. Compared with experimental data, simulations using the OPLS all-atom (OPLS-AA) force field describe the stability of the salt bridge structures quite realistically. It decreases in the following order: side-on > end-on > backside. The most stable side-on structure lives several nanoseconds. The less stable backside structure exists a few tenth of a nanosecond. Several short-living species (solvent shared, completely separately solvated ionic groups ion pairs, etc.) are also localized. Their lifetime is a few tens of picoseconds or less. Conformational flexibility of amino acids forming the salt bridge is investigated. The spectral signature of the Arg-Glu salt bridge is the IR-intensive band around 2200 cm−1. It is caused by the asymmetric stretching vibrations of the +N–H⋯O− fragment. Result of the present paper suggests that infrared spectroscopy in the 2000–2800 frequency region may be a rapid and quantitative method for the study of salt bridges in peptides and ionic interactions between proteins. This region is usually not considered in spectroscopic studies of peptides and proteins

Pirfenidone for the treatment of interstitial lung disease associated to rheumatoid arthritis: a new scenario is coming?

Introduction: Interstitial lung disease (ILD) is a frequent extra-articular manifestation of Rheumatoid arthritis (RA), but nowadays there are no randomized controlled clinical trials to support therapeutic guidelines. RA-ILD, especially with UIP pattern, shares some similarities with idiopathic pulmonary fibrosis, suggesting a possible role of antifibrotic therapy in these patients. To date, there are no published data supporting the use of pifenidone in RA-ILD. We describe for the first time two patients with a diagnosis of RA-ILD successfully treated with hydroxychloroquine and pirfenidone, without adverse events. Case presentation: Patient 1 and patient 2 were first diagnosed with IPF (UIP pattern at high-resolution computed tomography, no other signs or symptoms suggesting other forms of ILD, routine laboratory examinations and immunological texts negative). Patients started pirfenidone 2403 mg daily. Few months later, they referred to our multidisciplinary outpatient for arthritis. ACPA and RF were positive. A diagnosis of RA was performed and treatment with corticosteroids and hydroxychloroquine was started, in association with pirfenidone. In both cases we assessed the stabilization of articular and lung manifestations, without adverse events. Discussion: In absence of randomized controlled trials, the optimal treatment of RA-ILD has not been determined and remains challenging. When considering therapeutic options for RA-ILD, both pulmonary and extra-thoracic disease manifestations and degrees of activity should be assessed and taken into consideration. Future prospective research might change RA-ILD management, moving to a more personalized approach based on the identification of different phenotypes of the disease or to a combination of immunosuppressive and antifibrotic treatment

On Security and Sparsity of Linear Classifiers for Adversarial Settings

Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimization to investigate the link between regularization and security of linear classifiers, depending on the type of attack. We also analyze the relationship between the sparsity of feature weights, which is desirable for reducing processing cost, and the security of linear classifiers. We further propose a novel octagonal regularizer that allows us to achieve a proper trade-off between them. Finally, we empirically show how this regularizer can improve classifier security and sparsity in real-world application examples including spam and malware detection