Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS

The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (31.2 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure (BP) and heart rate (HR) associated with 150 ARDS patients admitted to five University of California academic health centers (containing 77,972 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Our deep learning model is able to achieve 0.81 area under the curve (AUC) to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients. Since our proposed model uses only the BP and HR, it would be possible to review data prior to the first reported cases in the U.S. to validate the presence or absence of COVID-19 in our communities prior to January 2020. In addition, by utilizing wearable devices, and monitoring vital signs of subjects in everyday settings it is possible to early-detect COVID-19 without visiting a hospital or a care site.
Multipole decomposition of redshift drift: Model-independent mapping of the expansion history of the Universe

We consider redshift drift in a general space-time as expressed in terms of physically interpretable multipole series. An important realization from the derived results is that redshift drift cannot in general be thought of as a direct probe of the average expansion rate of the Universe due to the presence of structure along the light beams from the astrophysical sources to the observer. We also predict the general presence of a dipolar and a quadrupolar offset in the detection of redshift drift for observers placed in locally anisotropic environments.
The Science of Crystallization: Microscopic Interfacial Phenomena

Preface Symbols 1. Introduction and philosophy 2. Interface energetics and molecular attachment kinetics 3. Dynamic interface shape effects in bulk crystals 4. The solute partitioning process 5. Thin film formation via vapour phase processing 6. Thermodynamics of bulk phases 7. Thermodynamics of interfaces 8. General kinetics and the nucleation process References Index.
Supersymmetric $\nu$-Inflaton Dark Matter

We present the supersymmetric extension of the unified model for inflation and Dark Matter studied in Ref. arXiv:1811.02302. The scenario is based on the incomplete decay of the inflaton field into right-handed (s)neutrino pairs. By imposing a discrete interchange symmetry on the inflaton and the right-handed (s)neutrinos, one can ensure the stability of the inflaton field at the global minimum today, while still allowing it to partially decay and reheat the Universe after inflation. Compatibility of inflationary predictions, BBN bounds and obtaining the right DM abundance for the inflaton Dark Matter candidate typically requires large values of its coupling to the neutrino sector, and we use supersymmetry to protect the inflaton from potentially dangerous large radiative corrections which may spoil the required flatness of its potential. In addition, the inflaton will decay now predominatly into sneutrinos during reheating, which in turn give rise both to the thermal bath made of Standard Model particles, and inflaton particles. We have performed a through analyses of the reheating process following the evolution of all the partners involved, identifying the different regimes in the parameter space for the final Dark Matter candidate. This as usual can be a WIMP-like inflaton particle or an oscillating condensate, but we find a novel regime for a FIMP-like candidate.
Affleck-Dine inflation in supergravity

Affleck-Dine inflation is a recently proposed model in which a single complex scalar field, nonminimally coupled to gravity, drives inflation and simultaneously generates the baryon asymmetry of universe via Affleck-Dine mechanism. In this paper we investigate the supersymmetric implementation of Affleck-Dine inflation in the use of two chiral superfields with appropriate superpotential and Kähler potential. The scalar potential has a similar form to the potential of original Affleck-Dine inflation, and it gives successful inflation and baryogenesis. We also consider the isocurvature perturbation evolving after crossing the horizon, and find that it is ignorable and hence consistent with the observations.
Exploring Simple Siamese Representation Learning

Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our "SimSiam" method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning. Code is made available.1
On Error Exponents of Encoder-Assisted Communication Systems

We consider a point–to–point communication system, where in addition to the encoder and the decoder, there is a helper that observes non–causally the realization of the noise vector and provides a (lossy) rate–<inline-formula> <tex-math notation="LaTeX">$ {R}_{{ \text { h}}}$ </tex-math></inline-formula> description of it to the encoder (<inline-formula> <tex-math notation="LaTeX">$ {R}_{{ \text { h}}} < \infty $ </tex-math></inline-formula>). In continuation to Lapidoth and Marti (2020), who derived the capacity of this model, here our focus is on error exponents. We consider both continuous–alphabet, additive white Gaussian channels and finite–alphabet, modulo–additive channels, and for each one of them, we study the cases of both fixed–rate and variable–rate noise descriptions by the helper. Our main finding is that, as long as the channel–coding rate, R, is below the helper–rate, <inline-formula> <tex-math notation="LaTeX">$ {R}_{{ \text { h}}}$ </tex-math></inline-formula>, the achievable error exponent is unlimited (i.e., it can be made arbitrarily large), and in some of the cases, it is even strictly infinite (i.e., the error probability can be made strictly zero). However, in the range of coding rates <inline-formula> <tex-math notation="LaTeX">$( {R}_{{ \text { h}}}, {R}_{{ \text { h}}}+ {C}_{0})$ </tex-math></inline-formula>, C<sub>0</sub> being the ordinary channel capacity (without help), the best achievable error exponent is finite and strictly positive, although there is a certain gap between our upper bound (converse bound) and lower bound (achievability) on the highest achievable error exponent. This means that the model of encoder–assisted communication is essentially equivalent to a model, where in addition to the noisy channel between the encoder and decoder, there is also a parallel noiseless bit–pipe of capacity <inline-formula> <tex-math notation="LaTeX">$ {R}_{{ \text { h}}}$ </tex-math></inline-formula>. We also extend the scope to the Gaussian multiple access channel (MAC) and characterize the rate sub–region, where the achievable error exponent is unlimited or even infinite.
Universal self-similar attractor in the bending-driven levelling of thin viscous films

We study theoretically and numerically the bending-driven levelling of thin viscous films within the lubrication approximation. We derive Green’s function of the linearized thin-film equation and further show that it represents a universal self-similar attractor at long times. As such, the rescaled perturbation of the film profile converges in time towards the rescaled Green’s function, for any summable initial perturbation profile. In addition, for stepped axisymmetric initial conditions, we demonstrate the existence of another, short-term and one-dimensional-like self-similar regime. We also characterize the convergence time towards the long-term universal attractor in terms of the relevant physical and geometrical parameters, and provide the local hydrodynamic fields and global elastic energy in the universal regime as functions of time. Finally, we extend our analysis to the nonlinear thin-film equation through numerical simulations.
Improving Functional Connectome Fingerprinting with Degree-Normalization

BACKGROUND Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional MRI BOLD time series. The network representation of functional connectivity, called a Functional Connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine. METHODS Here, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks. RESULTS Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space. DISCUSSION The results suggest that low-dimensional functional fingerprints lie in part in weakly connected subnetworks of the brain, and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FCs fingerprinting studies.
Perturbation analysis of baroclinic torque in low-Mach-number flows

Abstract In this paper, we propose a series expansion of the baroclinic torque in low-Mach-number flows, so that the accuracy and universality of any buoyancy term could be examined analytically, and new types of buoyancy terms could be constructed and validated. We first demonstrate that the purpose of introducing a buoyancy term is to approximate the baroclinic torque, and straightforwardly the error of any buoyancy term could be defined with the deviation of its curl from the corresponding baroclinic torque. Then a regular perturbation method is introduced for the elliptic equation of the hydrodynamic pressure in low-Mach-number flows, resulting in a sequence of Poisson equations, whose solutions lead to the series representation of the baroclinic torque and the new types of buoyancy terms. It is found that the frame invariance of the momentum equation is maintained with one of the new types of buoyancy terms. With the error definition of buoyancy terms and the series representation of the baroclinic torque, the validity and accuracy of previous and new buoyancy terms are examined. Finally, numerical simulations confirm that, with a decreasing density variation or an increasing order of our new buoyancy term, the simplified equations can converge to the original low-Mach-number equations.
Faint Quasars Live in the Same Number Density Environments as Lyman Break Galaxies at z ∼ 4

Characterizing high-z quasar environments is key to understanding the coevolution of quasars and the surrounding galaxies. To restrict their global picture, we statistically examine the g-dropout galaxy overdensity distribution around 570 faint quasar candidates at z ∼ 4, based on the Hyper Suprime-Cam Subaru Strategic Program survey. We compare the overdensity significances of g-dropout galaxies around the quasars with those around g-dropout galaxies, and find no significant difference between their distributions. A total of 4 (22) out of the 570 faint quasars, , are found to be associated with the >4σ overdense regions within an angular separation of 1.8 (3.0) arcmin, which is the typical size of protoclusters at this epoch. This is similar to the fraction of g-dropout galaxies associated with the >4σ overdense regions. This result is consistent with our previous work in which and of luminous quasars detected in the Sloan Digital Sky Survey exist in the >4σ overdense regions within 1. ′8 and 3. ′0 separations, respectively. Therefore, we suggest that the galaxy number densities around quasars are independent of their luminosity, and most quasars do not preferentially appear in the richest protocluster regions at z ∼ 4. The lack of an apparent positive correlation between the quasars and the protoclusters implies that (i) the gas-rich major merger rate is relatively low in the protocluster regions, (ii) most high-z quasars may appear through secular processes, or (iii) some dust-obscured quasars exist in the protocluster regions.
Affine Pricing and Hedging of Collateralized Debt Obligations

This study deals with the pricing and hedging of single-tranche collateralized debt obligations (STCDOs). We specify an affine two-factor model in which a catastrophic risk component is incorporated. Apart from being analytically tractable, this model has the feature that it captures the dynamics of super-senior tranches, thanks to the catastrophic component. We estimate the factor model based on the iTraxx Europe data with six tranches and four different maturities, using a quasi-maximum likelihood (QML) approach in conjunction with the Kalman filter. We derive the model-based variance-minimizing strategy for the hedging of STCDOs with a dynamically rebalanced portfolio on the underlying swap index. We analyze the actual performance of the variance-minimizing hedge on the iTraxx Europe data. In order to assess the hedging performance further, we run a simulation analysis where normal and extreme loss scenarios are generated via the method of importance sampling. Both in-sample hedging and simulation analysis suggest that the variance-minimizing strategy is most effective for mezzanine tranches in terms of yielding less riskier hedging portfolios and it fails to provide adequate hedge performance regarding equity tranches.
Search for inelastic scattering of WIMP dark matter in XENON1T

We report the results of a search for the inelastic scattering of weakly interacting massive particles (WIMPs) in the XENON1T dark matter experiment. Scattering off $^{129}$Xe is the most sensitive probe of inelastic WIMP interactions, with a signature of a 39.6 keV de-excitation photon detected simultaneously with the nuclear recoil. Using an exposure of 0.89 tonne-years, we find no evidence of inelastic WIMP scattering with a significance of more than 2$\sigma$. A profile-likelihood ratio analysis is used to set upper limits on the cross-section of WIMP-nucleus interactions. We exclude new parameter space for WIMPs heavier than 100 GeV/c${}^2$, with the strongest upper limit of $3.3 \times 10^{-39}$ cm${}^2$ for 130 GeV/c${}^2$ WIMPs at 90\% confidence level.
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow often suffer from poor generalization. Here we present a recurrent architecture that learns a single step of an unrolled iterative alignment procedure for refining scene flow predictions. Inspired by classical algorithms, we demonstrate iterative convergence toward the solution using strong regularization. The proposed method can handle sizeable temporal deformations and suggests a slimmer architecture than competitive all-to-all correlation approaches. Trained on FlyingThings3D synthetic data only, our network successfully generalizes to real scans, outperforming all existing methods by a large margin on the KITTI self-supervised benchmark.1
Goldblatt-Thomason Theorems for Modal Intuitionistic Logics

We prove Goldblatt-Thomason theorems for frames and models of a wide variety of modal intuitionistic logics, including ones studied by Wolter and Zakharyaschev, Goldblatt, Fischer Servi, and Plotkin and Sterling. We use the framework of dialgebraic logic to describe most of these logics and derive results in a uniform way.
ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a <italic>soft</italic> autoweighted strategy and a doubly <italic>soft</italic> regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a <italic>proper</italic> weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula>-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula>. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability; 2) it automatically learns a proper weight for each view, thereby reducing the influence of noises; 3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works.
Wall slip in primitive chain network simulations of shear startup of entangled polymers and its effect on the shear stress undershoot

In some recent experiments on entangled polymers of stress growth in startup of fast shear flows an undershoot in the shear stress is observed following the overshoot, i.e., before approaching the steady state. Whereas tumbling of the entangled chain was proposed to be at its origin, here we investigate another possible cause for the stress undershoot, i.e., slippage at the interface between polymer and solid wall. To this end, we extend the primitive chain network model to include slip at the interface between entangled polymeric liquids and solid walls with grafted polymers. We determine the slip velocity at the wall, and the shear rate in the bulk, by imposing that the shear stress in the bulk polymers is equal to that resulting from the polymers grafted at the wall. After confirming that the predicted results for the steady state are reasonable, we examine the transient behavior. The simulations confirm that slippage weakens the magnitude of the stress overshoot, as reported earlier. The undershoot is also weakened, or even disappears, because of a reduced coherence in molecular tumbling. In other words, the disentanglement between grafted and bulk chains, occurring throughout the stress overshoot region, does not contribute to the stress undershoot.
Multi-probe study of excited states in $\mathrm{^{12}C}$: disentangling the sources of monopole strength between the Hoyle state and $E_{x} = 13$ MeV

Knowledge of the low-lying monopole strength in $\mathrm{^{12}C}$, the Hoyle state in particular, is crucial for our understanding of both the astrophysically important $3\alpha$ reaction and of $\alpha$-particle clustering. The $\mathrm{^{12}C}(\alpha, \alpha^{\prime})\mathrm{^{12}C}$ and $\mathrm{^{14}C}(p, t)\mathrm{^{12}C}$ reactions were employed to populate states in $^{12}$C. A self-consistent, simultaneous analysis of the inclusive spectra with lineshapes was performed, which accounted for distortion due to nuclear dynamics and experimental effects. Clear evidence was found for excess monopole strength at $E_{x} \sim 9$ MeV, particularly in the $\mathrm{^{12}C}(\alpha, \alpha^{\prime})\mathrm{^{12}C}$ reaction at $0^{\circ}$. This additional strength cannot be reproduced by the previously established monopole states between $E_{x} = 7$ and 13 MeV. An additional $0^{+}$ state at $E_{x} \sim 9$ MeV yielded a significantly improved fit of the data and is the leading candidate for the predicted breathing-mode excitation of the Hoyle state. Alternatively, the results may suggest that a more sophisticated, physically motivated parameterization of the astrophysically important monopole strengths in $\mathrm{^{12}C}$ is required.
Crowdsourcing airway annotations in chest computed tomography images

Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated scans for good performance. We investigate whether crowdsourcing can be used to gather airway annotations. We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall. After combining multiple crowd workers, we compare the measurements to those made by the experts in the original scans. Similar to our preliminary study, a large portion of the annotations were excluded, possibly due to workers misunderstanding the instructions. After excluding such annotations, moderate to strong correlations with the expert can be observed, although these correlations are slightly lower than inter-expert correlations. Furthermore, the results across subjects in this study are quite variable. Although the crowd has potential in annotating airways, further development is needed for it to be robust enough for gathering annotations in practice. For reproducibility, data and code are available online: http://github.com/adriapr/crowdairway.git.