Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibiotic Treatment, Duration of Infectiousness, and Disease Transmission

Humans, domestic animals, orchard crops, and ornamental plants are commonly treated with antibiotics in response to bacterial infection. By curing infectious individuals, antibiotic therapy might limit the spread of contagious disease among hosts. But an antibiotic`s suppression of within-host pathogen density might also reduce the probability that the host is otherwise removed from infectious status prior to therapeutic recovery. When rates of both recovery via treatment and other removal events (e.g., isolation or mortality) depend directly on within-host pathogen density, antibiotic treatment can relax the overall removal rate sufficiently to increase between-host disease transmission. To explore this dependence, a deterministic within-host dynamics drives the infectious host's time-dependent probability of disease transmission, as well as the probabilistic duration of the infectious period. At the within-host scale, the model varies (1) inoculum size, (2) bacterial self-regulation, (3) the time between infection and initiation of therapy, and (4) antibiotic efficacy. At the between-host scale the model varies (5) the size/susceptibility of groups randomly encountered by an infectious host. Results identify conditions where antibiotic treatment can increase duration of a host`s infectiousness, and consequently increase the expected number of new infections. At lower antibiotic efficacy, treatment might convert a rare, serious bacterial disease into a common, but treatable infection.
Joint Energy Harvest and Information Transfer for Energy Beamforming in Backscatter Multiuser Networks

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. However, existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the restrictive relationship between the energy harvesting rate and achievable rate with estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max-min optimization problem by maximizing the minimum achievable rate for all backscatter tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the achievable rate for maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Our numerical results indicate that our scheme significantly outperforms state-of-the-art energy beamforming schemes. Additionally, the achievable rate of our scheme approaches more than 90% of the rate limit achieved via beamforming with perfect CSI for both receivers.
Search for globular clusters associated with the Milky Way dwarf galaxies using Gaia DR2

We report the result of searching for globular clusters (GCs) around 55 Milky Way (MW) satellite dwarf galaxies within the distance of 450 kpc from the Galactic Centre except for the Large and Small Magellanic Clouds and the Sagittarius dwarf. For each dwarf, we analyse the stellar distribution of sources in Gaia DR2, selected by magnitude, proper motion, and source morphology. Using the kernel density estimation of stellar number counts, we identify 11 possible GC candidates. Cross-matched with existing imaging data, all 11 objects are known either GCs or galaxies and only Fornax GC 1–6 among them are associated with the targeted dwarf galaxy. Using simulated GCs, we calculate the GC detection limit $M_{\rm V}^{\rm lim}$ that spans the range from $M_{\rm V}^{\rm lim}\sim -7$ for distant dwarfs to $M_{\rm V}^{\rm lim}\sim 0$ for nearby systems. Assuming a Gaussian GC luminosity function, we compute that the completeness of the GC search is above 90 per cent for most dwarf galaxies. We construct the 90 per cent credible intervals/upper limits on the GC specific frequency SN of the MW dwarf galaxies: 12 < SN < 47 for Fornax, SN < 20 for the dwarfs with −12 < MV < −10, SN < 30 for the dwarfs with −10 < MV < −7, and SN < 90 for the dwarfs with MV > −7. Based on SN, we derive the probability of galaxies hosting GCs given their luminosity, finding that the probability of galaxies fainter than MV = −9 to host GCs is lower than 0.1.
Canonical correlation analysis in high dimensions with structured regularization

Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ 2 penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this article we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.
Self-semi-supervised Learning to Learn from NoisyLabeled Data

The remarkable success of today's deep neural networks highly depends on a massive number of correctly labeled data. However, it is rather costly to obtain high-quality human-labeled data, leading to the active research area of training models robust to noisy labels. To achieve this goal, on the one hand, many papers have been dedicated to differentiating noisy labels from clean ones to increase the generalization of DNN. On the other hand, the increasingly prevalent methods of self-semi-supervised learning have been proven to benefit the tasks when labels are incomplete. By 'semi' we regard the wrongly labeled data detected as un-labeled data; by 'self' we choose a self-supervised technique to conduct semi-supervised learning. In this project, we designed methods to more accurately differentiate clean and noisy labels and borrowed the wisdom of self-semi-supervised learning to train noisy labeled data.
Quantum simulation of light-front parton correlators

The physics of high-energy colliders relies on the knowledge of different non-perturbative parton correlators, such as parton distribution functions, that encode the information on universal hadron structure and are thus the main building blocks of any factorization theorem of the underlying process in such collision. These functions are given in terms of gauge-invariant light-front operators, they are non-local in both space and real time, and are thus intractable by standard lattice techniques due to the well-known sign problem. In this paper, we propose a quantum algorithm to perform a quantum simulation of these type of correlators, and illustrate it by considering a space-time Wilson loop. We discuss the implementation of the quantum algorithm in terms of quantum gates that are accessible within actual quantum technologies such as cold atoms setups, trapped ions or superconducting circuits.
A Study of Policy Gradient on a Class of Exactly Solvable Models

Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return. In this paper, we explore the evolution of the policy parameters, for a special class of exactly solvable POMDPs, as a continuous-state Markov chain, whose transition probabilities are determined by the gradient of the distribution of the policy's value. Our approach relies heavily on random walk theory, specifically on affine Weyl groups. We construct a class of novel partially observable environments with controllable exploration difficulty, in which the value distribution, and hence the policy parameter evolution, can be derived analytically. Using these environments, we analyze the probabilistic convergence of policy gradient to different local maxima of the value function. To our knowledge, this is the first approach developed to analytically compute the landscape of policy gradient in POMDPs for a class of such environments, leading to interesting insights into the difficulty of this problem.
Loss Bounds for Approximate Influence-Based Abstraction

Sequential decision making techniques hold great promise to improve the performance of many real-world systems, but computational complexity hampers their principled application. Influence-based abstraction aims to gain leverage by modeling local subproblems together with the 'influence' that the rest of the system exerts on them. While computing exact representations of such influence might be intractable, learning approximate representations offers a promising approach to enable scalable solutions. This paper investigates the performance of such approaches from a theoretical perspective. The primary contribution is the derivation of sufficient conditions on approximate influence representations that can guarantee solutions with small value loss. In particular we show that neural networks trained with cross entropy are well suited to learn approximate influence representations. Moreover, we provide a sample based formulation of the bounds, which reduces the gap to applications. Finally, driven by our theoretical insights, we propose approximation error estimators, which empirically reveal to correlate well with the value loss.
Federated LQR: Learning through Sharing

In many multi-agent reinforcement learning applications such as flocking, multi-robot applications and smart manufacturing, distinct agents share similar dynamics but face different objectives. In these applications, an important question is how the similarities amongst the agents can accelerate learning in spite of the agents' differing goals. We study a distributed LQR (Linear Quadratic Regulator) tracking problem which models this setting, where the agents, acting independently, share identical (unknown) dynamics and cost structure but need to track different targets. In this paper, we propose a communication-efficient, federated model-free zeroth-order algorithm that provably achieves a convergence speedup linear in the number of agents compared with the communication-free setup where each agent's problem is treated independently. We support our arguments with numerical simulations of both linear and nonlinear systems.
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions

Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics. To compute event salience without any annotations, we adopt Barthes’ definition of event salience and propose several unsupervised methods that require only a pre-trained language model. Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find fine-tuning a language model on narrative texts is a key factor in improving the proposed methods.
VLA ammonia observations of L1287

The present work aims at studying the dense gas of the molecular cloud LDN 1287 (L1287), which harbors a double FU Ori system, an energetic molecular outflow and a still-forming cluster of deeply embedded low-mass, young stellar objects, showing a high level of fragmentation. We present optical Halpha and [SII], and VLA NH$_3$ (1,1) and (2,2) observations with an angular resolution of ~3.5''. The observed NH$_3$ spectra have been analyzed with the HfS tool, fitting simultaneously three different velocity components. The NH$_3$ emission from L1287 comes from four different structures: a core associated with RNO 1, a guitar-shaped core (the "Guitar") and two interlaced filaments (the Blue and Red Filaments) roughly centered towards the binary FU Ori system RNO 1B/C and its associated cluster. Regarding the Guitar Core, there are clear signatures of gas infall onto a central mass that has been estimated to be ~2.1 $M_\odot$. Regarding the two filaments, they have radii ~0.03 pc, masses per unit length ~50 $M_\odot$ pc$^{-1}$, and are near isothermal equilibrium. A central cavity, probably related with the outflow, and also traced by the Halpha and [SII] emission, is identified, with several young stellar objects near its inner walls. Both filaments show clear signs of perturbation by the high-velocity gas of the outflows driven by one or several young stellar objects of the cluster. The Blue and Red filaments are coherent in velocity and have nearly subsonic gas motions, except at the position of the embedded sources. Velocity gradients across the Blue Filament can be interpreted either as infalling material onto the filament or rotation. Velocity gradients along the filaments are interpreted as infall motions towards a gravitational well at the intersection of both filaments.
Galaxy morphology rules out astrophysically relevant Hu-Sawicki 
f(R)
 gravity

$f(R)$ is a paradigmatic modified gravity theory that typifies extensions to General Relativity with new light degrees of freedom and hence screened fifth forces between masses. These forces produce observable signatures in galaxy morphology, caused by a violation of the weak equivalence principle due to a differential impact of screening among galaxies' mass components. We compile statistical datasets of two morphological indicators -- offsets between stars and gas in galaxies and warping of stellar disks -- and use them to constrain the strength and range of a thin-shell-screened fifth force. This is achieved by applying a comprehensive set of upgrades to past work (Desmond et al 2018a,b): we construct a robust galaxy-by-galaxy Bayesian forward model for the morphological signals, including full propagation of uncertainties in the input quantities and marginalisation over an empirical model describing astrophysical noise. Employing more stringent data quality cuts than previously we find no evidence for a screened fifth force of any strength $\Delta G/G_\text{N}$ in the Compton wavelength range $0.3-8$ Mpc, setting a $1\sigma$ bound of $\Delta G/G_\text{N}<0.8$ at $\lambda_C=0.3$ Mpc that strengthens to $\Delta G/G_\text{N}<3\times10^{-5}$ at $\lambda_C=8$ Mpc. These are the tightest bounds to date beyond the Solar System by over an order of magnitude. For the Hu-Sawicki model of $f(R)$ with $n=1$ we require a background scalar field value $f_{R0} < 1.4 \times 10^{-8}$, forcing practically all astrophysical objects to be screened. We conclude that this model can have no relevance to astrophysics or cosmology.
Better Sign Language Translation with STMC-Transformer

Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR) system to extract sign language glosses from videos. Then, a translation system generates spoken language translations from the sign language glosses. This paper focuses on the translation system and introduces the STMC-Transformer which improves on the current state-of-the-art by over 5 and 7 BLEU respectively on gloss-to-text and video-to-text translation of the PHOENIX-Weather 2014T dataset. On the ASLG-PC12 corpus, we report an increase of over 16 BLEU. We also demonstrate the problem in current methods that rely on gloss supervision. The video-to-text translation of our STMC-Transformer outperforms translation of GT glosses. This contradicts previous claims that GT gloss translation acts as an upper bound for SLT performance and reveals that glosses are an inefficient representation of sign language. For future SLT research, we therefore suggest an end-to-end training of the recognition and translation models, or using a different sign language annotation scheme.
Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Distributional Adjustment

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to resilience (and hence profitability) of future wind farms to a changing climate, given that current wind turbines have lifespans of up to thirty years. In this work, we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a cluster-wise minimization of the Kullback-Leibler divergence. Future wind abundance will be determined for Saudi Arabia, a country with recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profit over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $273,000 for the wind energy market for the optimal locations for wind farming in the country.
Acyclic polynomials of graphs.

For each nonnegative integer $i$, let $a_i$ be the number of $i$-subsets of $V(G)$ that induce an acyclic subgraph of a given graph $G$. We define $A(G,x) = \sum_{i \geq 0} a_i x^i$ (the generating function for $a_i$) to be the acyclic polynomial for $G$. We investigate here the nature and location of its roots.
Nonuniversal Interstellar Density Spectra Probed by Pulsars

Galactic interstellar turbulence affects density distribution and star formation. We introduce a new method of measuring interstellar turbulent density spectra by using the dispersion measures (DMs) of a large sample of pulsars. Without the need of invoking multiple tracers, we obtain nonuniversal density spectra in the multiphase interstellar medium over different ranges of length scales. By comparing the analytical structure function of DMs with the observationally measured one in different areas of sky, we find a shallow density spectrum arising from the supersonic turbulence in cold interstellar phases, and a Kolmogorov-like density spectrum in the diffuse warm ionized medium (WIM). Both spectra extend up to hundreds of parsecs. On larger scales, we identify, for the first time, a steep density spectrum in the diffuse WIM extending up to several kiloparsecs. Our results show that the DMs of pulsars can provide unique new information on the interstellar turbulence.
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search

Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks. However, these methods are affected by their the correlation between pretraining domain and target domain and rely on massive fine-tuning relevance labels. Directly applying pretraining methods to specific domains may result in suboptimal search quality because specific domains may have domain adaption problems, such as the COVID domain. This paper presents a search system to alleviate the special domain adaption problem. The system utilizes the domain-adaptive pretraining and few-shot learning technologies to help neural rankers mitigate the domain discrepancy and label scarcity problems. Besides, we also integrate dense retrieval to alleviate traditional sparse retrieval's vocabulary mismatch obstacle. Our system performs the best among the non-manual runs in Round 2 of the TREC-COVID task, which aims to retrieve useful information from scientific literature related to COVID-19. Our code is publicly available at this https URL.
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process

A computational method based on the non-linear Gaussian process (GP), known as deep Gaussian processes (deep GPs) for uncertainty quantification & propagation in modelling of flow through heterogeneous porous media is presented. The method is also used for reducing dimensionality of model output and consequently emulating highly complex relationship between hydrogeological properties and reduced order fluid velocity field in a tractable manner. Deep GPs are multi-layer hierarchical generalisations of GPs with multiple, infinitely wide hidden layers that are very efficient models for deep learning and modelling of high-dimensional complex systems by tackling the complexity through several hidden layers connected with non-linear mappings. According to this approach, the hydrogeological data is modelled as the output of a multivariate GP whose inputs are governed by another GP such that each single layer is either a standard GP or the Gaussian process latent variable model. A variational approximation framework is used so that the posterior distribution of the model outputs associated to given inputs can be analytically approximated. In contrast to the other dimensionality reduction, methods that do not provide any information about the dimensionality of each hidden layer, the proposed method automatically selects the dimensionality of each hidden layer and it can be used to propagate uncertainty obtained in each layer across the hierarchy. Using this, dimensionality of the full input space consists of both geometrical parameters of modelling domain and stochastic hydrogeological parameters can be simultaneously reduced without the need for any simplifications generally being assumed for stochastic modelling of subsurface flow problems. It allows estimation of the flow statistics with greatly reduced computational efforts compared to other stochastic approaches such as Monte Carlo method.
Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space – the motion matrix – which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model’s applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.