Graph Convolutional Networks (GCNs) for Molecular Property Prediction in Drug Development

Molecular property prediction is key to drug development. The rising of deep learning techniques provides new possibilities to learn the molecular properties directly from chemical data. In particular, graph convolutional networks have been introduced into the field and made significant enhancements compared to traditional methods. The first part of this paper serves as a study to explore and evaluate this emerging method while the second part demonstrates that graph convolution networks can be further improved by incorporating attention mechanism, another influential deep learning idea.No embargoAcademic Major: Computer and Information Scienc

From hadrons to quarks in neutron stars: a review

We review the equation of state of matter in neutron stars from the solid crust through the liquid nuclear matter interior to the quark regime at higher densities. We focus in detail on the question of how quark matter appears in neutron stars, and how it affects the equation of state. After discussing the crust and liquid nuclear matter in the core we briefly review aspects of microscopic quark physics relevant to neutron stars, and quark models of dense matter based on the Nambu--Jona-Lasinio framework, in which gluonic processes are replaced by effective quark interactions. We turn then to describing equations of state useful for interpretation of both electromagnetic and gravitational observations, reviewing the emerging picture of hadron-quark continuity in which hadronic matter turns relatively smoothly, with at most only a weak first order transition, into quark matter with increasing density. We review construction of unified equations of state that interpolate between the reasonably well understood nuclear matter regime at low densities and the quark matter regime at higher densities. The utility of such interpolations is driven by the present inability to calculate the dense matter equation of state in QCD from first principles. As we review, the parameters of effective quark models -- which have direct relevance to the more general structure of the QCD phase diagram of dense and hot matter -- are constrained by neutron star mass and radii measurements, in particular favoring large repulsive density-density and attractive diquark pairing interactions. We describe the structure of neutron stars constructed from the unified equations of states with crossover. Lastly we present the current equations of state -- called "QHC18" for quark-hadron crossover -- in a parametrized form practical for neutron star modeling.Comment: v2, 42 pages, 36 figures, 3 tables; to be published in Reports on Progress in Physics; new sections for cooling, X-ray analyses, and gravitational waves are added; the results for tidal deformability are included; equations of state and the numerical tables are updated; v3, typos corrected in eq.

Advanced Underwater Image Restoration in Complex Illumination Conditions

Underwater image restoration has been a challenging problem for decades since the advent of underwater photography. Most solutions focus on shallow water scenarios, where the scene is uniformly illuminated by the sunlight. However, the vast majority of uncharted underwater terrain is located beyond 200 meters depth where natural light is scarce and artificial illumination is needed. In such cases, light sources co-moving with the camera, dynamically change the scene appearance, which make shallow water restoration methods inadequate. In particular for multi-light source systems (composed of dozens of LEDs nowadays), calibrating each light is time-consuming, error-prone and tedious, and we observe that only the integrated illumination within the viewing volume of the camera is critical, rather than the individual light sources. The key idea of this paper is therefore to exploit the appearance changes of objects or the seafloor, when traversing the viewing frustum of the camera. Through new constraints assuming Lambertian surfaces, corresponding image pixels constrain the light field in front of the camera, and for each voxel a signal factor and a backscatter value are stored in a volumetric grid that can be used for very efficient image restoration of camera-light platforms, which facilitates consistently texturing large 3D models and maps that would otherwise be dominated by lighting and medium artifacts. To validate the effectiveness of our approach, we conducted extensive experiments on simulated and real-world datasets. The results of these experiments demonstrate the robustness of our approach in restoring the true albedo of objects, while mitigating the influence of lighting and medium effects. Furthermore, we demonstrate our approach can be readily extended to other scenarios, including in-air imaging with artificial illumination or other similar cases

Categorizing Flight Paths using Data Visualization and Clustering Methodologies

This work leverages the U.S. Federal Aviation Administration's Traffic Flow Management System dataset and DV8, a recently developed tool for highly interactive visualization of air traffic data, to develop clustering algorithms for categorizing air traffic by their varying flight paths. Two clustering methodologies, a spatial-based geographic distance model, and a vector-based cosine similarity model, are demonstrated and compared for their clustering effectiveness. Examples of their applications reveal successful, realistic clustering based on automated clustering result determination and human-in-the-loop processes, with geographic distance algorithms performing better for enroute portions of flight paths and cosine similarity algorithms performing better for near-terminal operations, such as arrival paths. A point extraction technique is applied to improve computation efficiency.Comment: Published in the 9th International Conference on Research in Air Transportation (ICRAT'20): https://www.icrat.org/previous-conferences/9th-international-conference/papers

Cold, dense quark matter

This thesis discusses the properties of cold quark matter as exists in the core of massive neutron stars, with baryon densities several times of nuclear saturation density, n_0 ≈ 0.16 fm^-3, at zero temperature. Specifically, we study effective quark models, the symmetry pattern of different quark matter phases, the collective modes associated with spontaneous chiral symmetry breaking, a possible realization of quark-hadron continuity in the color-flavor locked (CFL) quark superfluid, and the implications of these issues to quark matter equations of state, and thus to neutron star structure including the mass-radius (M-R) relation. In Chapter 1, we present a general overview of quark matter described by quantum chromodynamics (QCD) in the context of dense neutron star cores. We discuss the physical motivation for studying quark matter, and how the fundamental symmetry and symmetry breaking patterns of QCD guide the construction of phenomenological quark models – in particular, the Nambu–Jona-Lasinio (NJL) model. We briefly review the modern understanding of the QCD phase diagram studied via such effective quark models, and give an overview of recent progress in constructing quark-hadron crossover equation of states using quark model and nuclear matter models, and how neutron star observations constrain the parameter spaces, thus providing insight into quark matter. After the introduction, we next focus on details of the NJL model and how it can be made to reflect the QCD symmetries in Chapter 2. We describe quark matter using effective local interactions, and demonstrate how the spontaneous breaking of chiral symmetry is realized through such interactions. We work through a Hubbard-Stratonovich transformation and derive the effective quark-meson theory in a vacuum with chiral condensate, and note its structural connection to the sigma model. We then describe diquark pairing in the NJL model, which breaks chiral symmetry at high density as well. Lastly we explore the problem of meson condensation in quark matter, which is relevant to the both neutron star M-R relation and the cooling process; we show that the physics of quark matter meson condensation is very tightly connected to hadronic meson condensation studies, and discuss the criteria of condensation instability caused by quark-meson interactions. The next part of this thesis, Chapter 3, turns to the issue of connecting the chiral symmetry breaking in the vacuum and in high density color superconductors – the interplay of chiral and diquark condensates in the effective quark model. By using a schematic NJL model, we solve the phase diagram at zero temperature, and demonstrate a continuous evolution of the Goldstone bosons, i.e., the pions, from their vacuum q ̄q form to their diquark qq form. We identify all the collective modes associated with the chiral and diquark condensates and calculate the pion self-energy, deriving a generalized Gell-Mann–Oakes–Renner (GMOR) relation. We thus establish a picture of continuous chiral symmetry breaking from vacuum to high density quark matter, and discuss its implications and connection to the quark-hadron continuity conjecture. In Chapter 4 we focus on a possible realization of quark-hadron continuity in the color-flavor-locked (CFL) superfluid phase, where the CFL diquark condensates screen color charges of elementary excitations, a novel feature of the SU(3) color-flavor structure. We construct the dressing scheme inspired by the non-linear sigma model, and derive an effective theory in terms of baryons and mesons, a gauge-invariant theory that originally started with quarks and gluons. Such a mapping is a direct realization of the quark hadron continuity in both the fermion sector and the boson sector, suggesting that we may study the properties of CFL quark matter in an entirely gauge-invariant manner at lower energies. The mapping scheme also brings up the relation between the effective baryon-meson Lagrangian’s couplings, elementary excitations and collective modes, and those of quark and nuclear matter as a potential research topic, which contributes to our further understanding of the ground state of dense matter at several times n_0. Finally, in Chapter 5 we turn back to the effective quark model and try to connect it to both nuclear matter and the more fundamental QCD. We demonstrate that the explicit single gluon exchange energy can help understanding the magnitude and density dependence of the constrained value of the phenomenological vector repulsion necessary to support massive neutron stars, with a moderate strong coupling constant and gluon mass at some 5n_0. We also estimate the effect of higher order effects of introducing quark chiral masses and CFL pairing into the quark Green’s functions. Our calculation yields an approximately flavor-symmetric vector repulsion that is a monotonous decreasing function of density, which we parametrize for use in future studies of neutron star equations of state. We also discuss the potential connections of this calculation to the concept of quark-hadron continuity, based on the similarity of the quark model to chiral baryon models

Indexing Metric Spaces for Exact Similarity Search

With the continued digitalization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity and variety. Many studies address volume or velocity, while much fewer studies concern the variety. Metric space is ideal for addressing variety because it can accommodate any type of data as long as its associated distance notion satisfies the triangle inequality. To accelerate search in metric space, a collection of indexing techniques for metric data have been proposed. However, existing surveys each offers only a narrow coverage, and no comprehensive empirical study of those techniques exists. We offer a survey of all the existing metric indexes that can support exact similarity search, by i) summarizing all the existing partitioning, pruning and validation techniques used for metric indexes, ii) providing the time and storage complexity analysis on the index construction, and iii) report on a comprehensive empirical comparison of their similarity query processing performance. Here, empirical comparisons are used to evaluate the index performance during search as it is hard to see the complexity analysis differences on the similarity query processing and the query performance depends on the pruning and validation abilities related to the data distribution. This article aims at revealing different strengths and weaknesses of different indexing techniques in order to offer guidance on selecting an appropriate indexing technique for a given setting, and directing the future research for metric indexes

Rationale-Enhanced Language Models are Better Continual Relation Learners

Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations. To address the issue, we introduce rationale, i.e., the explanations of relation classification results generated by large language models (LLM), into CRE task. Specifically, we design the multi-task rationale tuning strategy to help the model learn current relations robustly. We also conduct contrastive rationale replay to further distinguish analogous relations. Experimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models.Comment: Accepted at EMNLP 202