Cooperative Distribution Alignment via JSD Upper Bound

Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned distribution given only samples from each distribution. This task has many applications including generative modeling, unsupervised domain adaptation, and socially aware learning. Most prior works use adversarial learning (i.e., min-max optimization), which can be challenging to optimize and evaluate. A few recent works explore non-adversarial flow-based (i.e., invertible) approaches, but they lack a unified perspective and are limited in efficiently aligning multiple distributions. Therefore, we propose to unify and generalize previous flow-based approaches under a single non-adversarial framework, which we prove is equivalent to minimizing an upper bound on the Jensen-Shannon Divergence (JSD). Importantly, our problem reduces to a min-min, i.e., cooperative, problem and can provide a natural evaluation metric for unsupervised distribution alignment. We show empirical results on both simulated and real-world datasets to demonstrate the benefits of our approach. Code is available at https://github.com/inouye-lab/alignment-upper-bound.Comment: Accepted for publication in Advances in Neural Information Processing Systems 36 (NeurIPS 2022

Towards Practical Non-Adversarial Distribution Alignment via Variational Bounds

Distribution alignment can be used to learn invariant representations with applications in fairness and robustness. Most prior works resort to adversarial alignment methods but the resulting minimax problems are unstable and challenging to optimize. Non-adversarial likelihood-based approaches either require model invertibility, impose constraints on the latent prior, or lack a generic framework for alignment. To overcome these limitations, we propose a non-adversarial VAE-based alignment method that can be applied to any model pipeline. We develop a set of alignment upper bounds (including a noisy bound) that have VAE-like objectives but with a different perspective. We carefully compare our method to prior VAE-based alignment approaches both theoretically and empirically. Finally, we demonstrate that our novel alignment losses can replace adversarial losses in standard invariant representation learning pipelines without modifying the original architectures -- thereby significantly broadening the applicability of non-adversarial alignment methods

Structural reconstruction of the catalytic center of LiPDF through multiple scattering calculation with MXAN

Abstract Peptide deformylase (PDF, EC 3.5.1.27) is essential for the normal growth of eubacterium but not for mammalians. Recently, PDF has been studied as a target for new antibiotics. In this paper, X-ray absorption spectroscopy was employed to determine the local structure around the zinc ion of PDF from Leptospira Interrogans in dry powder, because it is very difficult to obtain the crystallized sample of Li PDF. We performed X-ray absorption near edge structure (XANES) calculation and reconstructed successfully the local geometry of the active center, and the results from calculations show that a water molecule (Wat1) has moved towards the zinc ion and lies in the distance range to coordinate with the zinc ion weakly. In addition, the sensitivity of theoretical spectra to the different ligand bodies was evaluated in terms of goodness-of-fit

Quantitative investigation of two metallohydrolases by X-ray absorption spectroscopy near-edge spectroscopy

The last several years have witnessed a tremendous increase in biological applications using X-ray absorption spectroscopy (BioXAS), thanks to continuous advancements in synchrotron radiation (SR) sources and detector technology. However, XAS applications in many biological systems have been limited by the intrinsic limitations of the Extended X-ray Absorption Fine Structure (EXAFS) technique e.g., the lack of sensitivity to bond angles. As a consequence, the application of the X-ray absorption near-edge structure (XANES) spectroscopy changed this scenario that is now continuously changing with the introduction of the first quantitative XANES packages such as Minut XANES (MXAN). Here we present and discuss the XANES code MXAN, a novel XANES-fitting package that allows a quantitative analysis of experimental data applied to Zn K-edge spectra of two metalloproteins: Leptospira interrogans Peptide deformylase (LiPDF) and acutolysin-C, a representative of snake venom metalloproteinases (SVMPs) from Agkistrodon acutus venom. The analysis on these two metallohydrolases reveals that proteolytic activities are correlated to subtle conformation changes around the zinc ion. In particular, this quantitative study clarifies the occurrence of the LiPDF catalytic mechanism via a two-water-molecules model, whereas in the acutolysin-C we have observed a different proteolytic activity correlated to structural changes around the zinc ion induced by pH variations

Investigation of zinc-containing peptide deformylase from Leptospira interrogans by X-ray absorption near-edge spectroscopy

Peptide deformylase (PDF, EC 3.5.1.27) is essential for the normal growth of eubacterium but not for mammalians. Recently, PDF has been studied as a target for new antibiotics. Its activity is strongly dependent on the bound metal ion. The crystallographic studies did not show any significant structural difference upon various bound metal ions. In this paper, X-ray absorption spectroscopy was employed to determine the local structure around the zinc ion of PDF from Leptospira interrogans in dry powder. XANES (X-ray absorption near-edge structure) calculations were performed and the local geometry of the active center was reconstructed successfully. By comparing with the crystal structure of an enzyme-product complex, the results from calculations show that a water molecule has moved towards the zinc ion and lies in the distance range to coordinate with the zinc ion weakly

Evaluating Brush Movements for Chinese Calligraphy:A Computer Vision Based Approach

Chinese calligraphy is a popular, highly esteemed art form in the Chinese cultural sphere and worldwide. Ink brushes are the traditional writing tool for Chinese calligraphy and the subtle nuances of brush movements have a great impact on the aesthetics of the written characters. However, mastering the brush movement is a challenging task for many calligraphy learners as it requires many years’ practice and expert supervision. This paper presents a novel approach to help Chinese calligraphy learners to quantify the quality of brush movements without expert involvement. Our approach extracts the brush trajectories from a video stream; it then compares them with example templates of reputed calligraphers to produce a score for the writing quality. We achieve this by first developing a novel neural network to extract the spatial and temporal movement features from the video stream. We then employ methods developed in the computer vision and signal processing domains to track the brush movement trajectory and calculate the score. We conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly accurate in identifying brush movements, yielding an average accuracy of 90%, and the generated score is within 3% of errors when compared to the one given by human experts

FINCH: A Blueprint for Accessible and Scientifically Valuable Remote Sensing Satellite Missions

Satellite remote sensing missions have grown in popularity over the past fifteen years due to their ability to cover large swaths of land at regular time intervals, making them suitable for monitoring environmental trends such as greenhouse gas emissions and agricultural practices. As environmental monitoring becomes central in global efforts to combat climate change, accessible platforms for contributing to this research are critical. Many remote sensing missions demand high performance of payloads, restricting research and development to organizations with sufficient resources to address these challenges. Atmospheric remote sensing missions, for example, require extremely high spatial and spectral resolutions to generate scientifically useful results. As an undergraduate-led design team, the University of Toronto Aerospace Team’s Space Systems Division has performed an extensive mission selection process to find a feasible and impactful mission focusing on crop residue mapping. This mission profile provides the data needed to improve crop residue retention practices and reduce greenhouse gas emissions from soil, while relaxing performance requirements relative to many active atmospheric sensing missions. This is accompanied by the design of FINCH, a 3U CubeSat with a hyperspectral camera composed of custom and commercial off-the-shelf components. The team’s custom composite payload, the FINCH Eye, strives to advance performance achieved at this form factor by leveraging novel technologies while keeping design feasibility for a student team a priority. Optical and mechanical design decisions and performance are detailed, as well as assembly, integration, and testing considerations. Beyond its design, the FINCH Eye is examined from operational, timeline, and financial perspectives, and a discussion of the supporting firmware, data processing, and attitude control systems is included. Insight is provided into open-source tools that the team has developed to aid in the design process, including a linear error analysis tool for assessing scientific performance, an optical system tradeoff analysis tool, and data processing algorithms. Ultimately, the team presents a comprehensive case study of an accessible and impactful satellite optical payload design process, in hopes of serving as a blueprint for future design teams seeking to contribute to remote sensing research