Learning Robust Search Strategies Using a Bandit-Based Approach

Effective solving of constraint problems often requires choosing good or specific search heuristics. However, choosing or designing a good search heuristic is non-trivial and is often a manual process. In this paper, rather than manually choosing/designing search heuristics, we propose the use of bandit-based learning techniques to automatically select search heuristics. Our approach is online where the solver learns and selects from a set of heuristics during search. The goal is to obtain automatic search heuristics which give robust performance. Preliminary experiments show that our adaptive technique is more robust than the original search heuristics. It can also outperform the original heuristics.Comment: Published at the Proceedings of 32th AAAI Conference on Artificial Intelligence (AAAI'18

Solving Functional Constraints by Variable Substitution

Functional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ CSP-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other non-functional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints

Failure of classical elasticity in auxetic foams

A recent derivation [P.H. Mott and C.M. Roland, Phys. Rev. B 80, 132104 (2009).] of the bounds on Poisson's ratio, v, for linearly elastic materials showed that the conventional lower limit, -1, is wrong, and that v cannot be less than 0.2 for classical elasticity to be valid. This is a significant result, since it is precisely for materials having small values of v that direct measurements are not feasible, so that v must be calculated from other elastic constants. Herein we measure directly Poisson's ratio for four materials, two for which the more restrictive bounds on v apply, and two having values below this limit of 0.2. We find that while the measured v for the former are equivalent to values calculated from the shear and tensile moduli, for two auxetic materials (v < 0), the equations of classical elasticity give inaccurate values of v. This is experimental corroboration that the correct lower limit on Poisson's ratio is 0.2 in order for classical elasticity to apply.Comment: 9 pages, 2 figure

Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores

Modern business applications and scientific databases call for inherently dynamic data storage environments. Such environments are characterized by two challenging features: (a) they have little idle system time to devote on physical design; and (b) there is little, if any, a priori workload knowledge, while the query and data workload keeps changing dynamically. In such environments, traditional approaches to index building and maintenance cannot apply. Database cracking has been proposed as a solution that allows on-the-fly physical data reorganization, as a collateral effect of query processing. Cracking aims to continuously and automatically adapt indexes to the workload at hand, without human intervention. Indexes are built incrementally, adaptively, and on demand. Nevertheless, as we show, existing adaptive indexing methods fail to deliver workload-robustness; they perform much better with random workloads than with others. This frailty derives from the inelasticity with which these approaches interpret each query as a hint on how data should be stored. Current cracking schemes blindly reorganize the data within each query's range, even if that results into successive expensive operations with minimal indexing benefit. In this paper, we introduce stochastic cracking, a significantly more resilient approach to adaptive indexing. Stochastic cracking also uses each query as a hint on how to reorganize data, but not blindly so; it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decision-making. Thereby, we bring adaptive indexing forward to a mature formulation that confers the workload-robustness previous approaches lacked. Our extensive experimental study verifies that stochastic cracking maintains the desired properties of original database cracking while at the same time it performs well with diverse realistic workloads.Comment: VLDB201

Cytochrome P450 CYP1B1 interacts with 8-<i>methoxypsoralen</i> (8-MOP) and influences psoralen-Ultraviolet A (PUVA) sensitivity

Background: There are unpredictable inter-individual differences in sensitivity to psoralen-UVA (PUVA) photochemotherapy, used to treat skin diseases including psoriasis. Psoralens are metabolised by cytochrome P450 enzymes (P450), and we hypothesised that variability in cutaneous P450 expression may influence PUVA sensitivity. We previously showed that P450 CYP1B1 was abundantly expressed in human skin and regulated by PUVA, and described marked inter-individual differences in cutaneous CYP1B1 expression.Objectives: We investigated whether CYP1B1 made a significant contribution to 8-methoxypsoralen (8-MOP) metabolism, and whether individuality in CYP1B1 activity influenced PUVA sensitivity.Methods: We used E. coli membranes co-expressing various P450s and cytochrome P450 reductase (CPR) to study 8-MOP metabolism and cytotoxicity assays in CYP1B1-expressing mammalian cells to assess PUVA sensitivity.Results: We showed that P450s CYP1A1, CYP1A2, CYP1B1, CYP2A6 and CYP2E1 influence 8-MOP metabolism. As CYP1B1 is the most abundant P450 in human skin, we further demonstrated that: (i) CYP1B1 interacts with 8-MOP (ii) metabolism of the CYP1B1 substrates 7-ethoxyresorufin and 17-b-estradiol showed concentration-dependent inhibition by 8-MOP and (iii) inhibition of 7-ethoxyresorufin metabolism by 8-MOP was influenced by CYP1B1 genotype. The influence of CYP1B1 on PUVA cytotoxicity was further investigated in a Chinese hamster ovary cell line, stably expressing CYP1B1 and CPR, which was more sensitive to PUVA than control cells, suggesting that CYP1B1 metabolises 8-MOP to a more phototoxicmetabolite(s).Conclusion: Our data therefore suggest that CYP1B1 significantly contributes to cutaneous 8-MOP metabolism, and that individuality in CYP1B1 expression may influence PUVA sensitivity

New intelligent network approach for monitoring physiological parameters : the case of Benin

Benin health system is facing many challenges as: (i) affordable high-quality health care to a growing population providing need, (ii) patients’ hospitalization time reduction, (iii) and presence time of the nursing staff optimization. Such challenges can be solved by remote monitoring of patients. To achieve this, five steps were followed. 1) Identification of the Wireless Body Area Network (WBAN) systems’ characteristics and the patient physiological parameters’ monitoring. 2) The national Integrated Patient Monitoring Network (RIMP) architecture modeling in a cloud of Technocenters. 3) Cross-analysis between the characteristics and the functional requirements identified. 4) Each Technocenter’s functionality simulation through: a) the design approach choice inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the communication technology and different architectures of sensor networks choice studying. 5) An estimate of the material resources of the national RIMP according to physiological parameters. A National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, was designed for Beninese health system. The implementation of the RNIMP will contribute to improve patients’ care in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance

Carbon nanotubes in the Coulomb blockade regime

Quantum transport through finite-length single wall carbon nanotubes was investigated theoretically in the Coulomb blockade regime. The spin-degenerate state of the nanotube is found to play a crucial role, and is responsible for the experimentally observed alternation in the heights of the conductance spectrum as electrons are added to the nanotubes. We also show that the relaxation of the energy eigenstates, which takes place as the electrons tunnel to and from the nanotubes, is responsible for the current saturation as a function of bias voltage polarity.published_or_final_versio

Benchmarking Symbolic Execution Using Constraint Problems -- Initial Results

Symbolic execution is a powerful technique for bug finding and program testing. It is successful in finding bugs in real-world code. The core reasoning techniques use constraint solving, path exploration, and search, which are also the same techniques used in solving combinatorial problems, e.g., finite-domain constraint satisfaction problems (CSPs). We propose CSP instances as more challenging benchmarks to evaluate the effectiveness of the core techniques in symbolic execution. We transform CSP benchmarks into C programs suitable for testing the reasoning capabilities of symbolic execution tools. From a single CSP P, we transform P depending on transformation choice into different C programs. Preliminary testing with the KLEE, Tracer-X, and LLBMC tools show substantial runtime differences from transformation and solver choice. Our C benchmarks are effective in showing the limitations of existing symbolic execution tools. The motivation for this work is we believe that benchmarks of this form can spur the development and engineering of improved core reasoning in symbolic execution engines

HDAC inhibitors increase NRF2-signaling in tumour cells and blunt the efficacy of co-adminstered cytotoxic agents

The NRF2 signalling cascade provides a primary response against electrophilic chemicals and oxidative stress. The activation of NRF2-signaling is anticipated to have adverse clinical consequences; NRF2 is activated in a number of cancers and, additionally, its pharmacological activation by one compound can reduce the toxicity or efficiency of a second agent administered concomitantly. In this work, we have analysed systematically the ability of 152 research, pre-clinical or clinically used drugs to induce an NRF2 response using the MCF7-AREc32 NRF2 reporter. Ten percent of the tested drugs induced an NRF2 response. The NRF2 activators were not restricted to classical cytotoxic alkylating agents but also included a number of emerging anticancer drugs, including an IGF1-R inhibitor (NVP-AEW541), a PIM-1 kinase inhibitor (Pim1 inhibitor 2), a PLK1 inhibitor (BI 2536) and most strikingly seven of nine tested HDAC inhibitors. These findings were further confirmed by demonstrating NRF2-dependent induction of endogenous AKR genes, biomarkers of NRF2 activity. The ability of HDAC inhibitors to stimulate NRF2-signalling did not diminish their own potency as antitumour agents. However, when used to pre-treat cells, they did reduce the efficacy of acrolein. Taken together, our data suggest that the ability of drugs to stimulate NRF2 activity is common and should be investigated as part of the drug-development process