Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Hodgkin & Huxley-type model for action potentials in squid

By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused axons of squid we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a model which is simpler, more quantitative, and based more fundamentally on electrodiffusion principles. Our dynamical system, like that of HH, behaves as a 4-dimensional resonator exhibiting subthreshold oscillations. Speeds of propagating action potentials at 20 degrees Celsius are in very good agreement with the HH experimental value at 18.5 degrees Celsius. Unlike its HH counterpart it does not predict spike trains during prolonged constant-current stimulation, in agreement with recent experiments. Our resonator model predicts rebound spiking following prolonged hyperpolarizing stimulation, observed at 18.5 degrees Celsius by HH but not predicted at this temperature by their quantitative model. Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted at room temperature by our electrodiffusion model but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model. Such spiking in giant axons induced directly by brief hyperpolarizing stimulation at room temperature does not appear to have been investigated experimentally for either stationary membrane action potentials or their propagating counterparts.
Fuzzy dark matter soliton cores around supermassive black holes

We explore the effect of a supermassive black hole (SMBH) on the density profile of a fuzzy dark matter (FDM) soliton core at the centre of a dark matter (DM) halo. We numerically solve the Schrödinger–Poisson equations, treating the black hole as a gravitational point mass, and demonstrate that this additional perturbing term has a ‘squeezing’ effect on the soliton density profile, decreasing the core radius, and increasing the central density. In the limit of large black hole mass, the solution approaches one akin to the hydrogen atom, with radius inversely proportional to the black hole mass. By applying our analysis to two specific galaxies (M87 and the Milky Way) and pairing it with known observational limits on the amount of centrally concentrated DM, we obtain a constraint on the FDM particle mass, finding that the range 10−22.12 eV ≲ m ≲ 10−22.06 eV should be forbidden (taking into account additional factors concerning the lifetime of the soliton in the vicinity of a black hole). Improved observational mass measurements of the black hole and total enclosed masses will significantly extend the lower bound on the excluded FDM mass region, while self-consistent theoretical modelling of the soliton–black hole system can extend the upper bound.
Stop the Open Data Bus, We Want to Get Off

The subject of this report is the re-identification of individuals in the Myki public transport dataset released as part of the Melbourne Datathon 2018. We demonstrate the ease with which we were able to re-identify ourselves, our co-travellers, and complete strangers; our analysis raises concerns about the nature and granularity of the data released, in particular the ability to identify vulnerable or sensitive groups.
Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network

Graph Convolutional Networks (GCNs) have been widely studied for compact data representation and semi-supervised learning tasks. However, existing GCNs usually use a fixed neighborhood graph which is not guaranteed to be optimal for semi-supervised learning tasks. In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation. Based on this observation, we then propose a unified adaptive neighborhood feature propagation model and derive a novel Adaptive Neighborhood Graph Propagation Network (ANGPN) for data representation and semi-supervised learning. The aim of ANGPN is to conduct both graph construction and graph convolution simultaneously and cooperatively in a unified formulation and thus can learn an optimal neighborhood graph that best serves graph convolution for data representation and semi-supervised learning. One main benefit of ANGPN is that the learned (convolutional) representation can provide useful weakly supervised information for constructing a better neighborhood graph which meanwhile facilitates data representation and learning. Experimental results on four benchmark datasets demonstrate the effectiveness and benefit of the proposed ANGPN.
NGC 1261: A TIME-SERIES VI STUDY OF ITS VARIABLE STARS

Time-series VI CCD photometry of the globular cluster NGC 1261 is employed to study its variable star population. A membership analysis of most variables based on Gaia DR2 proper motions and colours was performed prior to the estimation of the mean cluster distance and metallicity. The light curves of the member RR Lyrae were Fourier decomposed to calculate their individual values of distance, [Fe/H], radius and mass. The I band P-L for RR Lyrae stars was also employed. Our best estimates of the metallicity and distance of this Oo I cluster are [Fe/H]ZW =−1.42 ± 0.05 dex and d = 17.2 ± 0.4 kpc. No mixture of fundamental and first overtone RR Lyrae stars in the either-or or bimodal region is seen in this cluster, as it seems to be the rule for Oo I clusters with a red horizontal branch. A multi-approach search in a region of about 10' × 10' around the cluster revealed no new variable stars within the limitations of our CCD photometry.
Causal discovery in heavy-tailed models

Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms only manifest themselves in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population case, the causal tail coefficient is shown to reveal the causal structure if the distribution follows a linear structural causal model. This holds even in the presence of latent common causes that have the same tail index as the observed variables. Based on a consistent estimator of the causal tail coefficient, we propose a computationally highly efficient algorithm that estimates the causal structure. We prove that our method consistently recovers the causal order and we compare it to other well-established and non-extremal approaches in causal discovery on synthetic and real data. The code is available as an open-access R package.
Invisible Units Detection and Estimation Based on Random Matrix Theory

Invisible units mainly refer to small-scale units that are not monitored by, and thus are not visible to utilities. Integration of these invisible units into power systems does significantly affect the way in which a distribution grid is planned and operated. This paper, based on random matrix theory (RMT), proposes a statistical, data-driven framework to handle the massive grid data, in contrast to its deterministic, model-based counterpart. Combining the RMT-based data-mining framework with conventional techniques, some heuristics are derived as the solution to the invisible units detection and estimation task: linear eigenvalue statistic indicators (LESs) are suggested as the main ingredients of the solution; according to the statistical properties of LESs, the hypothesis testing is formulated to conduct change point detection in the high-dimensional space. The proposed method is promising for anomaly detection and pertinent to current distribution networks—it is capable of detecting invisible power usage and fraudulent behavior while even being able to locate the suspect's location. Case studies, using both simulated data and actual data, validate the proposed method.
Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy

Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy. A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n = 8) and non-liver abdominal (n = 4) cancer. CT images were deformably registered to the corresponding MR images to generate deformed CT (dCT) images for treatment planning. Both cGAN and cycleGAN were trained using MR and dCT transverse slices. Four-fold cross-validation testing was conducted to generate sCT images for all patients. The HU prediction accuracy was evaluated by voxel-wise similarity metric between each dCT and sCT image for all 12 patients. dCT-based and sCT-based dose distributions were compared using gamma and dose-volume histogram (DVH) metric analysis for 8 liver patients. sCTcycleGAN achieved the average mean absolute error (MAE) of 94.1 HU, while sCTcGAN achieved 89.8 HU. In both models, the average gamma passing rates within all volumes of interest were higher than 95% using a 2%, 2 mm criterion, and 99% using a 3%, 3 mm criterion. The average differences in the mean dose and DVH metrics were within ±0.6% for the planning target volume and within ±0.15% for evaluated organs in both models. Results: demonstrated that abdominal sCT images generated by both cGAN and cycleGAN achieved accurate dose calculation for 8 liver radiotherapy plans. sCTcGAN images had smaller average MAE and achieved better dose calculation accuracy than sCTcyleGAN images. More abdominal patients will be enrolled in the future to further evaluate the two models.
Type-Directed Program Synthesis and Constraint Generation for Library Portability

Fast numerical libraries have been a cornerstone of scientific computing for decades, but this comes at a price. Programs may be tied to vendor specific software ecosystems resulting in polluted, non-portable code. As we enter an era of heterogeneous computing, there is an explosion in the number of accelerator libraries required to harness specialized hardware. We need a system that allows developers to exploit ever-changing accelerator libraries, without over-specializing their code. As we cannot know the behavior of future libraries ahead of time, this paper develops a scheme that assists developers in matching their code to new libraries, without requiring the source code for these libraries. Furthermore, it can recover equivalent code from programs that use existing libraries and automatically port them to new interfaces. It first uses program synthesis to determine the meaning of a library, then maps the synthesized description into generalized constraints which are used to search the program for replacement opportunities to present to the developer. We applied this approach to existing large applications from the scientific computing and deep learning domains. Using our approach, we show speedups ranging from 1.1x to over 10x on end to end performance when using accelerator libraries.
Helical phase inflation and its observational constraints

We consider a class of helical phase inflation models from the N=1 supergravity where the phase component of a complex field acts as an inflaton. This class of models avoids the eta problem in supergravity inflation due to the phase monodromy of the superpotential. We study the inflationary predictions of this class of models in the context of both standard and large extra dimensional brane cosmology, and find that they can easily accommodate the Planck 2018 and BICEP2 constraints. We find that the helical phase inflation has α-attractors and the attractors depend on one model parameter only.
Privacy Preserving Link Prediction with Latent Geometric Network Models

Link prediction is an important task in social network analysis, with a wide variety of applications ranging from graph search to recommendation. The usual paradigm is to propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some or all their connections private. Most link prediction heuristics, such as common neighbor, Jaccard coefficient, and Adamic-Adar, can leak private link information in making predictions. We present D P L P , a generic framework to protect differential privacy for these popular heuristics under the ranking objective. Under a recently-introduced latent node embedding model, we also analyze the trade-off between privacy and link prediction utility. Extensive experiments with eight diverse real-life graphs and several link prediction heuristics show that D P L P can trade off between privacy and predictive performance more effectively than several alternatives.
Pattern formation with pde2path -- a tutorial.

We explain some pde2path setups for pattern formation in 1D, 2D and 3D. A focus is on new pde2path functions for branch switching at steady bifurcation points of higher multiplicity, typically due to discrete symmetries, but we also review general concepts of pattern formation and their handling in pde2path, including localized patterns and homoclinic snaking, again in 1D, 2D and 3D, based on the demo sh (Swift-Hohenberg equation). Next, the demos schnakpat (a Schnakenberg reaction-diffusion system) and chemtax (a quasilinear RD system with cross-diffusion from chemotaxis) simplify and unify previous results in a simple and concise way, CH (Cahn-Hilliard) deals with mass constraints, hexex deals with (multiple) branch points of higher degeneracy in a scalar problem on a hexagonal domain, and shgc illustrates some global coupling. The demos acS, actor, schnakS and schnaktor (the Allen-Cahn and Schnakenberg models on spheres and tori) consider pattern formation on curved surfaces, cpol considers a problem of cell polarization described by bulk-surface coupling, and bruosc (Brusselator) explains how to augment autonomous systems by a time periodic forcing. Along the way we also comment on the choice of meshes and mesh adaptation, on time integration, and we give some examples of branch point continuation and Hopf point continuation to approximate stability boundaries.
Reasoning-Driven Question-Answering for Natural Language Understanding

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.
Macroscopic oscillations locked and synchronized on fixed energy levels by two cooperating drives

It was unknown whether the energy of a macroscopic object can be confined to a set of discrete values like the energy levels of microscopic systems. Here, through the numerical simulation and theoretical analysis based on an experimentally implementable model, we demonstrate that this phenomenon can occur to a mechanical oscillator, which is simply under the radiation pressure of a cavity field created by two driving lasers. Once the amplitudes and frequencies of the two drives are properly matched, the oscillator will stabilize on one of the fixed trajectories in its phase space. Then both amplitude and phase of its oscillation become frozen on the specific trajectory like an energy level. Above a certain drive amplitude, tiny variation of the oscillator's initial condition or in the drive amplitudes before reaching stability can affect its proceeding motion but, unlike the aperiodicity in chaotic motion, the oscillator will deterministically end up on one of such energy levels. This finding exemplifies a category of nonlinear dynamical processes, which is dissimilar to any other discovered in the past.
Spectrophotometric templates for core-collapse supernovae and their application in simulations of time-domain surveys

The design and analysis of time-domain sky surveys require the ability to simulate accurately realistic populations of core-collapse supernova (SN) events. We present a set of spectral time-series templates designed for this purpose, for both hydrogen-rich (Type II, IIn, and IIb) and stripped-envelope (Type Ib, Ic, and Ic-BL) core-collapse SNe. We use photometric and spectroscopic data for 67 core-collapse SNe from the literature, and for each generate a time-series spectral template. The techniques used to build the templates are fully data driven with no assumption of any parametric form or model for the light curves. The template-building code is open source, and can be applied to any transient for which well-sampled multiband photometry and multiple spectroscopic observations are available. We extend these spectral templates into the near-ultraviolet to λ ≃ 1600 Å using observer-frame ultraviolet photometry. We also provide a set of templates corrected for host galaxy dust extinction, and provide a set of luminosity functions that can be used with our spectral templates in simulations. We give an example of how these templates can be used by integrating them within the popular SN simulation package snana, and simulating core-collapse SNe in photometrically selected cosmological Type Ia SN samples, prone to contamination from core-collapse events.
Serverless Supercomputing: High Performance Function as a Service for Science

Growing data volumes and velocities are driving exciting new methods across the sciences in which data analytics and machine learning are increasingly intertwined with research. These new methods require new approaches for scientific computing in which computation is mobile, so that, for example, it can occur near data, be triggered by events (e.g., arrival of new data), or be offloaded to specialized accelerators. They also require new design approaches in which monolithic applications can be decomposed into smaller components, that may in turn be executed separately and on the most efficient resources. To address these needs we propose funcX---a high-performance function-as-a-service (FaaS) platform that enables intuitive, flexible, efficient, scalable, and performant remote function execution on existing infrastructure including clouds, clusters, and supercomputers. It allows users to register and then execute Python functions without regard for the physical resource location, scheduler architecture, or virtualization technology on which the function is executed---an approach we refer to as "serverless supercomputing. " We motivate the need for funcX in science, describe our prototype implementation, and demonstrate, via experiments on two supercomputers, that funcX can process millions of functions across more than 65000 concurrent workers. We also outline five scientific scenarios in which funcX has been deployed and highlight the benefits of funcX in these scenarios.
A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics

Historically, scalability has been a major challenge for the successful application of semidefinite programming in fields such as machine learning, control, and robotics. In this article, we survey recent approaches to this challenge, including those that exploit structure (e.g., sparsity and symmetry) in a problem, those that produce low-rank approximate solutions to semidefinite programs, those that use more scalable algorithms that rely on augmented Lagrangian techniques and the alternating-direction method of multipliers, and those that trade off scalability with conservatism (e.g., by approximating semidefinite programs with linear and second-order cone programs). For each class of approaches, we provide a high-level exposition, an entry point to the corresponding literature, and examples drawn from machine learning, control, or robotics. We also present a list of software packages that implement many of the techniques discussed in the review. Our hope is that this article will serve as a gateway to the rich and exciting literature on scalable semidefinite programming for both theorists and practitioners.
Output Reach Control Problem with Applications to Motion Planning for Robotic Systems

Output Reach Control Problem with Applications to Motion Planning for Robotic Systems Zachary Kroeze Doctor of Philosophy Graduate Department of Electrical & Computer Engineering University of Toronto 2019 As electronic devices become more embedded into everyday products, an increasing number of consumer devices are being equipped with control systems. These control systems are sometimes required to achieve complex specifications such as safety constraints or switching between distinct tasks. Among a wide range of techniques to deal with the control design of complex systems, one is to partition the state space into polytopic regions, and solve a control problem on each region such that the collective behaviour of the hybrid system solves the original complex control specification. In this dissertation we consider such a methodology where the control problem on each polytopic region is called the Reach Control Problem (RCP). The RCP is to find a state feedback to make the closed-loop trajectories of an affine control system defined on a polytope reach and exit a prescribed facet of the polytope in finite time. There is already an extensive literature on the RCP. This dissertation extends this literature by considering the Output Reach Control Problem (ORCP). The ORCP is to control the output trajectory of the control system on a simplex in the output space, in contrast to control the entire state trajectory. We also extend the applications of the RCP by developing a unified framework for control with complex specifications. This dissertation contains four distinct contributions. The first contribution provides an extension of classical linear regulator theory to affine systems and exosystems. This extension then provides the basis to develop a framework for the output reach control problem with disturbances. The second contribution leverages existing literature on viability theory to convert the ORCP in the output space on a simplex to a RCP in the full state space on a polytope. The third contribution is to propose a unified framework for solving control problems with complex specifications. Our framework is targeted at robotic systems. Finally, our fourth contribution is a set of so called motion primitives which comprise one of the layers of our aforementioned framework. These motion primitives are based on our approach to the ORCP.