An Efficient Algorithm for Maximum Boolean Satisfiability Based on Unit Propagation, Linear Programming, and Dynamic Weighting

Maximum Boolean satisfiability (max-SAT) is the optimization counterpart of Boolean satisfiability (SAT), in which a variable assignment is sought to satisfy the maximum number of clauses in a logical formula. A branch-and-bound algorithm based on the Davis-Putnam-Logemann-Loveland procedure (DPLL) is one of the most efficient complete algorithms for solving max-SAT. In this paper, We propose and investigate a number of new strategies for max-SAT. Our first strategy is a set of unit propagation rules for max-SAT. As unit propaga-tion is a very efficient strategy for SAT, we show that it can be extended to max-SAT, and can greatly improve the performance of an extended DPLL-based algorithm. Our second strategy is an effective lookahead heuristic based on linear programming. We show that the LP heuristic can be made effective as the number of clauses increases. Our third strategy is a dynamic-weight variable ordering, which is based on a thorough analysis of two well-known existing branching rules. Based on the analysis of these strategies, we develop an integrated, constrainedness-sensitive max-SAT solver that is able to dynamically adjust strategies ac-cording to problem characteristics. Our experimental results on random max-SAT and some instances from the SATLIB show that our new solver outperforms most of the existing com-plete max-SAT solvers, with orders of magnitude of improvement in many cases

PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information. Firstly, we reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction, we propose a generative-based model for medical visual understanding by aligning visual information from a pre-trained vision encoder with a large language model. Secondly, we establish a scalable pipeline to construct a large-scale medical visual question-answering dataset, named PMC-VQA, which contains 227k VQA pairs of 149k images that cover various modalities or diseases. Thirdly, we pre-train our proposed model on PMC-VQA and then fine-tune it on multiple public benchmarks, e.g., VQA-RAD and SLAKE, outperforming existing work by a large margin. Additionally, we propose a test set that has undergone manual verification, which is significantly more challenging, even the best models struggle to solve

NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST's capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches.Comment: 21 pages,4 figure

Portable broadband cavity-enhanced spectrometer utilizing Kalman filtering: application to real-time, in situ monitoring of glyoxal and nitrogen dioxide

This article describes the development and field application of a portable broadband cavity enhanced spectrometer (BBCES) operating in the spectral range of 440-480 nm for sensitive, real-time, in situ measurement of ambient glyoxal (CHOCHO) and nitrogen dioxide (NO2). The instrument utilized a custom cage system in which the same SMA collimators were used in the transmitter and receiver units for coupling the LED light into the cavity and collecting the light transmitted through the cavity. This configuration realised a compact and stable optical system that could be easily aligned. The dimensions and mass of the optical layer were 676 × 74 × 86 mm3 and 4.5 kg, respectively. The cavity base length was about 42 cm. The mirror reflectivity at λ = 460 nm was determined to be 0.9998, giving an effective absorption pathlength of 2.26 km. The demonstrated measurement precisions (1σ) over 60 s were 28 and 50 pptv for CHOCHO and NO2 and the respective accuracies were 5% and 4%. By applying a Kalman adaptive filter to the retrieved concentrations, the measurement precisions of CHOCHO and NO2 were improved to 8 pptv and 40 pptv in 21 s

OH DETECTION USING OFF-AXIS INTEGRATED CAVITY OUTPUT SPECTROSCOPY (OA-ICOS)

Author Institution: Laboratoire de Physico-Chimie de l'Atmosphere, Universite du littoral Cote d'Opale, Dunkerque - France; Laboratoire de Physicochimie des Processus de Combustion et de l' Atmosphere, Universite des Sciences et Technologies de Lille; 59655 Villeneuve d'Ascq Cedex - FranceThe OA-ICOS cavity consisted of two 1{\textquotedbl} high reflectivity spherical mirrors with 1 m radius of curvature, separated by a 0.5-m long quartz coated stainless steel tube. The mirrors reflectivity was {\textgreater} 99.996\% at 1435 nm as specified by the manufacturer (Layertec, GmbH). The effective optical path length of the OA-ICOS approach was determined with direct absorption signal intensity of the pure H2O vapor line at 6965.80233 cm{}-1 and was found to be 1.263 km. \bigskip The OH radicals were generated with the help of a 2.45 GHz microwave discharge in water vapor flow under low pressure (1 mbar) to evaluate the developed OA-ICOS performance. The OH radical concentration of 7.28 e+13 OH/cm{}3 was determined using calibration with a close H2O absorption line at 6965.80 cm{}-1. The detection limit, deduced from the signal to noise ratio, was 3.86 e+11 OH/cm{}3. Experimental instrument detail and the preliminary measurement results will be presented and discussed

Explaining the Power-Law Distribution of Human Mobility Through Transportation Modality Decomposition

Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns.Peer reviewe

Optical properties of atmospheric fine particles near Beijing during the HOPE-J3A campaign

The optical properties and chemical composition of PM1.0 particles in a suburban environment (Huairou) near the megacity of Beijing were measured during the HOPE-J3A (Haze Observation Project Especially for Jing–Jin–Ji Area) field campaign. The campaign covered the period November 2014 to January 2015 during the winter coal heating season. The average values and standard deviations of the extinction, scattering, absorption coefficients, and the aerosol single scattering albedo (SSA) at λ = 470 nm during the measurement period were 201 ± 240, 164 ± 202, 37 ± 43 Mm−1, and 0.80 ± 0.08, respectively. The average values for the real and imaginary components of the effective complex refractive index (CRI) over the campaign were 1.40 ± 0.06 and 0.03 ± 0.02, while the average mass scattering and absorption efficiencies (MSEs and MAEs) of PM1.0 were 3.6 and 0.7 m2 g−1, respectively. Highly time-resolved air pollution episodes clearly show the dramatic evolution of the PM1.0 size distribution, extensive optical properties (extinction, scattering, and absorption coefficients), and intensive optical properties (SSA and CRI) during haze formation, development, and decline. Time periods were classified into three different pollution levels (clear, slightly polluted, and polluted) for further analysis. It was found that (1) the relative contributions of organic and inorganic species to observed aerosol composition changed significantly from clear to polluted days: the organic mass fraction decreased from 50 to 43 % while the proportion of sulfates, nitrates, and ammonium increased strongly from 34 to 44 %. (2) Chemical apportionment of extinction, calculated using the IMPROVE algorithm, tended to underestimate the extinction compared to measurements. Agreement with measurements was improved by modifying the parameters to account for enhanced absorption by elemental carbon (EC). Organic mass was the largest contributor (52 %) to the total extinction of PM1.0, while EC, despite its low mass concentration of ∼ 4 %, contributed about 17 % to extinction. When the air quality deteriorated, the contribution of nitrate aerosol increased significantly (from 15 % on clear days to 22 % on polluted days). (3) Under polluted conditions, the average MAEs of EC were up to 4 times as large as the reference MAE value for freshly generated black carbon (BC). The temporal pattern of MAE values was similar to that of the OC / EC ratio, suggesting that non-BC absorption from secondary organic aerosol also contributes to particle absorption

Optical properties of atmospheric fine particles near Beijing during the HOPE-J3A campaign

The optical properties and chemical composition of PM1.0 particles in a suburban environment (Huairou) near the megacity of Beijing were measured during the HOPE-J3A (Haze Observation Project Especially for Jing–Jin–Ji Area) field campaign. The campaign covered the period November 2014 to January 2015 during the winter coal heating season. The average values and standard deviations of the extinction, scattering, absorption coefficients, and the aerosol single scattering albedo (SSA) at λ  =  470 nm during the measurement period were 201 ± 240, 164 ± 202, 37 ± 43 Mm−1, and 0.80 ± 0.08, respectively. The average values for the real and imaginary components of the effective complex refractive index (CRI) over the campaign were 1.40 ± 0.06 and 0.03 ± 0.02, while the average mass scattering and absorption efficiencies (MSEs and MAEs) of PM1.0 were 3.6 and 0.7 m2 g−1, respectively. Highly time-resolved air pollution episodes clearly show the dramatic evolution of the PM1.0 size distribution, extensive optical properties (extinction, scattering, and absorption coefficients), and intensive optical properties (SSA and CRI) during haze formation, development, and decline. Time periods were classified into three different pollution levels (clear, slightly polluted, and polluted) for further analysis. It was found that (1) the relative contributions of organic and inorganic species to observed aerosol composition changed significantly from clear to polluted days: the organic mass fraction decreased from 50 to 43 % while the proportion of sulfates, nitrates, and ammonium increased strongly from 34 to 44 %. (2) Chemical apportionment of extinction, calculated using the IMPROVE algorithm, tended to underestimate the extinction compared to measurements. Agreement with measurements was improved by modifying the parameters to account for enhanced absorption by elemental carbon (EC). Organic mass was the largest contributor (52 %) to the total extinction of PM1.0, while EC, despite its low mass concentration of  ∼  4 %, contributed about 17 % to extinction. When the air quality deteriorated, the contribution of nitrate aerosol increased significantly (from 15 % on clear days to 22 % on polluted days). (3) Under polluted conditions, the average MAEs of EC were up to 4 times as large as the reference MAE value for freshly generated black carbon (BC). The temporal pattern of MAE values was similar to that of the OC / EC ratio, suggesting that non-BC absorption from secondary organic aerosol also contributes to particle absorption