Nathan Dowlin

Bio: Nathan Dowlin is an academic researcher from Columbia University. The author has contributed to research in topics: Knot (mathematics) & Floer homology. The author has an hindex of 8, co-authored 20 publications receiving 1304 citations. Previous affiliations of Nathan Dowlin include Yale University & Princeton University.

CryptoNets: applying neural networks to encrypted data with high throughput and accuracy

Abstract: Applying machine learning to a problem which involves medical, financial, or other types of sensitive data, not only requires accurate predictions but also careful attention to maintaining data privacy and security. Legal and ethical requirements may prevent the use of cloud-based machine learning solutions for such tasks. In this work, we will present a method to convert learned neural networks to CryptoNets, neural networks that can be applied to encrypted data. This allows a data owner to send their data in an encrypted form to a cloud service that hosts the network. The encryption ensures that the data remains confidential since the cloud does not have access to the keys needed to decrypt it. Nevertheless, we will show that the cloud service is capable of applying the neural network to the encrypted data to make encrypted predictions, and also return them in encrypted form. These encrypted predictions can be sent back to the owner of the secret key who can decrypt them. Therefore, the cloud service does not gain any information about the raw data nor about the prediction it made. We demonstrate CryptoNets on the MNIST optical character recognition tasks. CryptoNets achieve 99% accuracy and can make around 59000 predictions per hour on a single PC. Therefore, they allow high throughput, accurate, and private predictions.

Manual for Using Homomorphic Encryption for Bioinformatics

Abstract: Biological data science is an emerging field facing multiple challenges for hosting, sharing, computing on, and interacting with large data sets. Privacy regulations and concerns about the risks of leaking sensitive personal health and genomic data add another layer of complexity to the problem. Recent advances in cryptography over the last five years have yielded a tool, homomorphic encryption, which can be used to encrypt data in such a way that storage can be outsourced to an untrusted cloud, and the data can be computed on in a meaningful way in encrypted form, without access to decryption keys. This paper introduces homomorphic encryption to the bioinformatics community, and presents an informal “manual” for using the Simple Encrypted Arithmetic Library (SEAL), which we have made publicly available for bioinformatic, genomic, and other research purposes.

The role of mergers in early-type galaxy evolution and black hole growth

Abstract: Models of galaxy formation invoke the major merger of gas-rich progenitor galaxies as the trigger for significant phases of black hole growth and the associated feedback that suppresses star formation to create red spheroidal remnants. However, the observational evidence for the connection between mergers and active galactic nucleus (AGN) phases is not clear. We analyze a sample of low-mass early-type galaxies known to be in the process of migrating from the blue cloud to the red sequence via an AGN phase in the green valley. Using deeper imaging from Sloan Digital Sky Survey Stripe 82, we show that the fraction of objects with major morphological disturbances is high during the early starburst phase, but declines rapidly to the background level seen in quiescent early-type galaxies by the time of substantial AGN radiation several hundred Myr after the starburst. This observation empirically links the AGN activity in low-redshift early-type galaxies to a significant merger event in the recent past. The large time delay between the merger-driven starburst and the peak of AGN activity allows for the merger features to decay to the background and hence may explain the weak link between merger features and AGN activity in the literature.

A spectral sequence from Khovanov homology to knot Floer homology

Abstract: A well-known conjecture of Rasmussen states that for any knot $K$ in $S^{3}$, the rank of the reduced Khovanov homology of $K$ is greater than or equal to the rank of the reduced knot Floer homology of $K$. This rank inequality is supposed to arise as the result of a spectral sequence from Khovanov homology to knot Floer homology. Using an oriented cube of resolutions construction for a homology theory related to knot Floer homology, we prove this conjecture.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Coevolution (Or Not) of Supermassive Black Holes and Host Galaxies

Abstract: Supermassive black holes (BHs) have been found in 85 galaxies by dynamical modeling of spatially resolved kinematics. The Hubble Space Telescope revolutionized BH research by advancing the subject from its proof-of-concept phase into quantitative studies of BH demographics. Most influential was the discovery of a tight correlation between BH mass and the velocity dispersion σ of the bulge component of the host galaxy. Together with similar correlations with bulge luminosity and mass, this led to the widespread belief that BHs and bulges coevolve by regulating each other's growth. Conclusions based on one set of correlations from in brightest cluster ellipticals to in the smallest galaxies dominated BH work for more than a decade. New results are now replacing this simple story with a richer and more plausible picture in which BHs correlate differently with different galaxy components. A reasonable aim is to use this progress to refine our understanding of BH-galaxy coevolution. BHs with masses of 105−106M...

Federated Machine Learning: Concept and Applications

Abstract: Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

Coevolution (Or Not) of Supermassive Black Holes and Host Galaxies: Supplemental Material

Abstract: This is the Supplemental Material to Kormendy and Ho 2013, ARAA, 51, 511 (arXiv:1304.7762). Section S1 summarizes indirect methods that are used to estimate black hole (BH) masses for galaxies with active nuclei (AGNs). Section S2 lists the observational criteria that are used to classify classical and pseudo bulges. The (pseudo)bulge classifications used in the main paper are not based on physical interpretation; rather, they are based on these observational criteria. Section S3 supplements the BH database in Section 5 of the main paper and Section S4 here. It discusses corrections to galaxy and BH parameters, most importantly to 2MASS K-band apparent magnitudes. It presents evidence that corrections are needed because 2MASS misses light at large radii when the images of galaxies subtend large angles on the sky or have shallow outer brightness gradients. Section S4 reproduces essentially verbatim the first part of Section 5 in the main paper, the BH database. It includes the list of BH and host-galaxy properties (Tables 2 and 3). Its most important purpose is to provide all of the notes on individual objects.

Deep learning for healthcare: review, opportunities and challenges.

Abstract: Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.