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

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

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

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

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

Evolving rules for document classification

We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Strong Anisotropy in Liquid Water upon Librational Excitation using Terahertz Laser Fields

Tracking the excitation of water molecules in the homogeneous liquid is challenging due to the ultrafast dissipation of rotational excitation energy through the hydrogen-bonded network. Here we demonstrate strong transient anisotropy of liquid water through librational excitation using single-color pump-probe experiments at 12.3 THz. We deduce a third order response of chi^3 exceeding previously reported values in the optical range by three orders of magnitude. Using a theory that replaces the nonlinear response with a material response property amenable to molecular dynamics simulation, we show that the rotationally damped motion of water molecules in the librational band is resonantly driven at this frequency, which could explain the enhancement of the anisotropy in the liquid by the external Terahertz field. By addition of salt (MgSO4), the hydration water is instead dominated by the local electric field of the ions, resulting in reduction of water molecules that can be dynamically perturbed by THz pulses

An efficient k.p method for calculation of total energy and electronic density of states

An efficient method for calculating the electronic structure in large systems with a fully converged BZ sampling is presented. The method is based on a k.p-like approximation developed in the framework of the density functional perturbation theory. The reliability and efficiency of the method are demostrated in test calculations on Ar and Si supercells