arXiv subjects

Rui Shi

Publications and source records attributed to Rui Shi.

At least 91 records · Page 5Linked to original sources

arXiv2022

The structural order of protein hydration water

The ability of water to dissolve biomolecules is crucial for our life. It has been shown that protein has a profound effect on the behavior of water in its hydration shell, which in turn affects the structure and function of the protein. However, there is still no consensus on whether protein promotes or destroys the structural order of water in its hydration shell until today, because of the lack of proper structural descriptor incorporating hydrogen-bond (H-bond) information for water at the protein/water interface. Here we performed all-atom molecular dynamics simulations of lysozyme protein in water and analyzed the H-bond structure of protein hydration water by using a newly developed structural descriptor. We find that the protein promotes local structural ordering of the hydration water while has a negligible effect on the strength of individual H-bond. These findings are fundamental to the structure and function of biomolecules and provide new insights into the hydration of protein in water.

cond-mat.soft↗

arXiv2022

Construction of a Large Diameter Reflective Half-Wave Plate Modulator for Millimeter Wave Applications

Polarization modulation is a powerful technique to increase the stability of measurements by enabling the distinction of a polarized signal from dominant slow system drifts and unpolarized foregrounds. Furthermore, when placed as close to the sky as possible, modulation can reduce systematic errors from instrument polarization. In this work, we introduce the design and preliminary drive system laboratory performance of a new 60 cm diameter reflective half-wave plate (RHWP) polarization modulator. The wave plate consists of a wire array situated in front of a flat mirror. Using \mbox{50 $μ$m} diameter wires with \mbox{175 $μ$m} spacing, the wave plate will be suitable for operation in the millimeter wavelength range with flatness of the wires and parallelism to the mirror held to a small fraction of a wavelength. The presented design targets the 77--108 GHz range. Modulation is performed by a rotation of the wave plate with a custom rotary drive utilizing an actively controlled servo motor.

astro-ph.IM↗

arXiv2022

Long-Timescale Stability in CMB Observations at Multiple Frequencies using Front-End Polarization Modulation

The Cosmology Large Angular Scale Surveyor (CLASS) is a telescope array observing the Cosmic Microwave Background (CMB) at frequency bands centered near 40, 90, 150, and 220 GHz. CLASS measures the CMB polarization on the largest angular scales to constrain the inflationary tensor-to-scalar ratio and the optical depth due to reionization. To achieve the long time-scale stability necessary for this measurement from the ground, CLASS utilizes a front-end, variable-delay polarization modulator on each telescope. Here we report on the improvements in stability afforded by front-end modulation using data across all four CLASS frequencies. Across one month of modulated linear polarization data in 2021, CLASS achieved median knee frequencies of 9.1, 29.1, 20.4, and 36.4 mHz for the 40, 90, 150, and 220 GHz observing bands. The knee frequencies are approximately an order of magnitude lower than achieved via CLASS pair-differencing orthogonal detector pairs without modulation.

astro-ph.IM↗

arXiv2022

Design and characterization of new 90 GHz detectors for the Cosmology Large Angular Scale Surveyor (CLASS)

The Cosmology Large Angular Scale Surveyor (CLASS) is a polarization-sensitive telescope array located at an altitude of 5,200 m in the Chilean Atacama Desert. CLASS is designed to measure "E-mode" (even parity) and "B-mode" (odd parity) polarization patterns in the Cosmic Microwave Background (CMB) over large angular scales with the aim of improving our understanding of inflation, reionization, and dark matter. CLASS is currently observing with three telescopes covering four frequency bands: one at 40 GHz (Q); one at 90 GHz (W1); and one dichroic system at 150/220 GHz (G). In these proceedings, we discuss the updated design and in-lab characterization of new 90 GHz detectors. The new detectors include design changes to the transition-edge sensor (TES) bolometer architecture, which aim to improve stability and optical efficiency. We assembled and tested four new detector wafers, to replace four modules of the W1 focal plane. These detectors were installed into the W1 telescope, and will achieve first light in the austral winter of 2022. We present electrothermal parameters and bandpass measurements from in-lab dark and optical testing. From in-lab dark tests, we also measure a median NEP of 12.3 $\mathrm{aW\sqrt{s}}$ across all four wafers about the CLASS signal band, which is below the expected photon NEP of 32 $\mathrm{aW\sqrt{s}}$ from the field. We therefore expect the new detectors to be photon noise limited.

astro-ph.IM↗

arXiv2022

What to expect from dynamical modelling of cluster haloes II. Investigating dynamical state indicators with Random Forest

We investigate the importances of various dynamical features in predicting the dynamical state (DS) of galaxy clusters, based on the Random Forest (RF) machine learning approach. We use a large sample of galaxy clusters from the Three Hundred Project of hydrodynamical zoomed-in simulations, and construct dynamical features from the raw data as well as from the corresponding mock maps in the optical, X-ray, and Sunyaev-Zel'dovich (SZ) channels. Instead of relying on the impurity based feature importance of the RF algorithm, we directly use the out-of-bag (OOB) scores to evaluate the importances of individual features and different feature combinations. Among all the features studied, we find the virial ratio, $η$, to be the most important single feature. The features calculated directly from the simulations and in 3-dimensions carry more information on the DS than those constructed from the mock maps. Compared with the features based on X-ray or SZ maps, features related to the centroid positions are more important. Despite the large number of investigated features, a combination of up to three features of different types can already saturate the score of the prediction. Lastly, we show that the most sensitive feature $η$ is strongly correlated with the well-known half-mass bias in dynamical modelling. Without a selection in DS, cluster halos have an asymmetric distribution in $η$, corresponding to an overall positive half-mass bias. Our work provides a quantitative reference for selecting the best features to discriminate the DS of galaxy clusters in both simulations and observations.

astro-ph.CO↗

arXiv2022

Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis

The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol is too expensive to implement in many real-world projects. In this study, we propose a method to refine human annotation, named Neural Annotation Refinement (NeAR). It is based on a learnable implicit function, which decodes a latent vector into represented shape. By integrating the appearance as an input of implicit functions, the appearance-aware NeAR fixes the annotation artefacts. Our method is demonstrated on the application of adrenal gland analysis. We first show that the NeAR can repair distorted golden standards on a public adrenal gland segmentation dataset. Besides, we develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR, where each case consists of a 3D shape of adrenal gland and its diagnosis label (normal vs. abnormal) assigned by experts. We show that models trained on the shapes repaired by the NeAR can diagnose adrenal glands better than the original ones. The ALAN dataset will be open-source, with 1,584 shapes for adrenal gland diagnosis, which serves as a new benchmark for medical shape analysis. Code and dataset are available at https://github.com/M3DV/NeAR.

cs.CV↗

arXiv2022

A machine learning approach to infer the accreted stellar mass fractions of central galaxies in the TNG100 simulation

We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using 2,710 galaxies with stellar mass $\log_{10}M_\ast/M_\odot>10.16$ from the TNG100 simulation. Galaxy size is the most important individual feature when calculated in 3-dimensions, which becomes less important after accounting for observational effects. For smaller galaxies, the rankings for features related to merger histories increase. When an entire set of halo and galaxy features are used, the prediction is almost unbiased, with root-mean-square error (RMSE) of $\sim$0.068. A combination of up to three features with different types (galaxy size, merger history and morphology) already saturates the power of prediction. If using observable features, the RMSE increases to $\sim$0.104, and a combined usage of stellar mass, galaxy size plus galaxy concentration achieves similar predictions. Lastly, when using galaxy density, velocity and velocity dispersion profiles as features, which approximately represent the maximum amount of information extracted from galaxy images and velocity maps, the prediction is not improved much. Hence the limiting precision of predicting $f_\mathrm{acc}$ is $\sim$0.1 with observables, and the multi-component decomposition of galaxy images should have similar or larger uncertainties. If the central black hole mass and the spin parameter of galaxies can be accurately measured in future observations, the RMSE is promising to be further decreased by $\sim$20%.

astro-ph.GA↗

arXiv2021

Detail-aware Deep Clothing Animations Infused with Multi-source Attributes

This paper presents a novel learning-based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations. In contrast to existing learning-based methods, which require numerous trained models for different garment topologies or poses and are unable to easily realize rich details, we use a unified framework to produce high fidelity deformations efficiently and easily. To address the challenging issue of predicting deformations influenced by multi-source attributes, we propose three strategies from novel perspectives. Specifically, we first found that the fit between the garment and the body has an important impact on the degree of folds. We then designed an attribute parser to generate detail-aware encodings and infused them into the graph neural network, therefore enhancing the discrimination of details under diverse attributes. Furthermore, to achieve better convergence and avoid overly smooth deformations, we proposed output reconstruction to mitigate the complexity of the learning task. Experiment results show that our proposed deformation method achieves better performance over existing methods in terms of generalization ability and quality of details.

cs.CV↗

arXiv2021

Microscopic understanding of ion solvation in water

Solvation of ions is ubiquitous on our planet. Solvated ions have a profound effect on the behavior of ionic solutions, which is crucial in nature and technology. Experimentally, ions have been classified into "structure makers" or "structure breakers", depending on whether they slow down or accelerate the solution dynamics. Theoretically, the dynamics of ions has been explained by a dielectric friction model combining hydrodynamics and charge-dipole interaction in the continuum description. However, both approaches lack a microscopic structural basis, leaving the microscopic understanding of salt effects unclear. Here we elucidate unique microscopic features of solvation of spherical ions by computer simulations. We find that increasing the ion electric field causes a sharp transitional decrease in the hydration-shell thickness, signaling the ion mobility change from the Stokes to dielectric friction regime. The dielectric friction regime can be further divided into two due to the competition between the water-water hydrogen bonding and ion-water electrostatic interactions: Whether the former or latter prevails determines whether the water dynamics are accelerated or decelerated. In the ion-water interaction predominant regime, a specific combination of ion size and charge stabilizes the hydration shell via orientational-symmetry breaking, reminiscent of the Thomson problem for the electron configuration of atoms. Notably, the hydration-shell stability is much higher for a composite coordination number than a prime one, a prime-number effect on solvent dynamics. These findings are fundamental to the structure breaker/maker concept and provide new insights into the solvent structure and dynamics beyond the continuum model, paving the way towards a microscopic theory of ionic solutions.

cond-mat.soft↗

arXiv2021

MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.

cs.CV↗

arXiv2021

Unfolding the Neutron Spectra from a Water-Pumping-Injection Multi-layered Concentric Sphere Neutron Spectrometer Using a Self-Adaptive Differential Evolution Algorithm

A self-adaptive differential evolution neutron spectrum unfolding algorithm (SDENUA) was established in this paper to unfold the neutron spectra obtained from a Water-pumping-injection Multi-layered concentric sphere Neutron Spectrometer (WMNS). Specifically, the neutron fluence bounds were estimated to accelerate the algorithm convergence, the minimum error between the optimal solution and the input neutron counts with relative uncertainties was limited to 10-6 to avoid useless calculation. Furthermore, the crossover probability and scaling factor were controlled self-adaptively. FLUKA Monte Carlo was used to simulate the readings of the WMNS under (1) a spectrum of Cf-252 and (2) its spectrum after being moderated, (3) a spectrum used for BNCT, and (4) a reactor spectrum, and the measured neutron counts unfolded by using the SDENUA. The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA, which does not require complex parameter tuning and the priori default spectrum. Results indicate that the solutions of the SDENUA are more in agreement with the IAEA spectra than that of the MAXED and GRAVEL in UMG 3.1, and the errors of the final results calculated by SDENUA are under 12%. The established SDENUA has potential applications for unfolding spectra from the WMNS.

physics.ins-det↗

arXiv2021

Research on Fitness Function of Two Evolution Algorithms Used for Neutron Spectrum Unfolding

When evolution algorithms are used to unfold the neutron energy spectrum, fitness function design is an important fundamental work for evaluating the quality of the solution, but it has not attracted much attention. In this work, we investigated the performance of eight fitness functions attached to the genetic algorithm (GA) and the differential evolution algorithm (DEA) used for unfolding four neutron spectra selected from the IAEA 403 report. Experiments show that the fitness functions with a maximum in the GA can limit the ability of the population to percept the fitness change, but the ability can be made up in the DEA. The fitness function with a feature penalty term helps to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-squared result shows the balance between the algorithm and the spectra. The results also show that the DEA has good potential for neutron energy spectrum unfolding. The purposes of this work are to provide evidence for structuring and modifying the fitness functions and to suggest some genetic operations that should receive attention when using the fitness function to unfold neutron spectra.

cs.NE↗

arXiv2020

Normed ideal perturbation of irreducible operators in semifinite von Neumann factors

In [10], Halmos proved an interesting result that the set of irreducible operators is dense in $\mathcal B(\mathcal H)$ in the sense of Hilbert-Schmidt approximation. In a von Neumann algebra $\mathcal M$ with separable predual, an operator $a\in \mathcal M$ is said to be {irreducible in} $\mathcal M$ if $W^*(a)$ is an irreducible subfactor of $\mathcal M$, i.e., $W^*(a)'\cap \mathcal M={\mathbb C} \cdot I$. In this paper, let $Φ(\cdot)$ be a $\Vert\cdot\Vert$-dominating, unitarily invariant norm (see Definition 2.1), where by $\Vert\cdot\Vert$ we denote the operator norm. We prove that in every semifinite von Neumann factor $\mathcal M$ with separable predual, if the norm $Φ(\cdot)$ satisfies a natural restriction introduced in (1.1), then irreducible operators are $Φ(\cdot)$-norm dense in $\mathcal M$. In particular, the operator norm $\Vert\cdot\Vert$ and the $\max\{\Vert\cdot\Vert, \Vert\cdot\Vert_p\}$-norm (for each $p>1$) naturally satisfy the condition in (1.1), where $τ$ is a faithful, normal, semifinite, tracial weight and $\Vert x\Vert_p=τ(|x|^p)^{1/p}$ for all $x\in \mathcal M \cap L^{p}(\mathcal M,τ)$ (see [18, Preliminaries]). This can be viewed as a (stronger) analogue of a theorem of Halmos in [10], proved with different techniques developed in semifinite, properly infinite von Neumann factors. Meanwhile, for every $\Vert\cdot\Vert$-dominating, unitarily invariant norm $Φ(\cdot)$, we develop another method to prove that each normal operator in $\mathcal M$ is a sum of an irreducible operator in $\mathcal M$ and an arbitrarily small $Φ(\cdot)$-norm perturbation, where the $Φ(\cdot)$-norm isn't restricted by (1.1). Particularly, the $Φ(\cdot)$-norm can be the $\max\{\Vert\cdot\Vert, \Vert\cdot\Vert_1\}$-norm.

math.OA↗

arXiv2020

Updated constraints on superconducting cosmic strings from the astronomy of fast radio bursts

In this article we update constraints on superconducting cosmic strings (SCSs) in light of the recent observational developments of fast radio bursts (FRBs) astronomy. Assuming strings follow an exponential distribution characterized by current, we show that two parameters in our context, which are the characteristic tension ($Gμ$) and a parameter which describes the aforementioned exponential distribution ($I_c$), can be constrained by FRB experiments. Particularly, we investigate data sets from Parkes and ASKAP. We looked through a parameter space where $Gμ\sim [10^{-17}, 10^{-12}]$ and $I_c\sim [10^{-1}, 10^{2}]$ GeV, and found our results show that Parkes jointly with ASKAP can constrain the parameter space for SCSs.

astro-ph.HE↗

arXiv2019

Approximate equivalence of representations of AH algebras into semifinite von Neumann factors

In this paper, we prove a non-commutative version of the Weyl-von Neumann theorem for representations of unital, separable AH algebras into countably decomposable, semifinite, properly infinite, von Neumann factors, where an AH algebra means an approximately homogeneous ${\rm C}^{\ast}$-algebra. We also prove a result for approximate summands of representations of unital, separable AH algebras into finite von Neumann factors.

math.OA↗

arXiv2019

A Fully Stochastic Second-Order Trust Region Method

A stochastic second-order trust region method is proposed, which can be viewed as a second-order extension of the trust-region-ish (TRish) algorithm proposed by Curtis et al. (INFORMS J. Optim. 1(3) 200-220, 2019). In each iteration, a search direction is computed by (approximately) solving a trust region subproblem defined by stochastic gradient and Hessian estimates. The algorithm has convergence guarantees for stochastic minimization in the fully stochastic regime, meaning that guarantees hold when each stochastic gradient is required merely to be an unbiased estimate of the true gradient with bounded variance and when the stochastic Hessian estimates are bounded uniformly in norm. The algorithm is also equipped with a worst-case complexity guarantee in the nearly deterministic regime, i.e., when the stochastic gradient and Hessian estimates are very close in expectation to the true gradients and Hessians. The results of numerical experiments for training convolutional neural networks for image classification and training a recurrent neural network for time series forecasting are presented. These results show that the algorithm can outperform a stochastic gradient approach and the first-order TRish algorithm in practice.

math.OC↗

arXiv2019

Distinct signature of two local structural motifs of liquid water in the scattering function

Liquids generally become more ordered upon cooling. However, it has been a long-standing debate on whether such structural ordering in liquid water takes place continuously or discontinuosly: continuum vs. mixture models. Here, by computer simulations of three popular water models and analysis of recent scattering experiment data, we show that, in the structure factor of water, there are two overlapped peaks hidden in the apparent "first diffraction peak", one of which corresponds to the neighboring O-O distance as in ordinary liquids and the other to the longest periodicity of density waves in a tetrahedral structure. This unambiguously proves the coexistence of two local structural motifs. Our findings not only provide key clues to settle long-standing controversy on the water structure but also allow experimental access to the degree and range of structural ordering in liquid water.

cond-mat.soft↗

arXiv2019

Crystalline clusters in mW water: stability, growth, and grain boundaries

With numerical simulations of the mW model of water, we investigate the energetic stability of crystalline clusters for both Ice I (cubic and hexagonal ice) and for the metastable Ice 0 phase as a function of the cluster size. Under a large variety of forming conditions, we find that the most stable cluster changes as a function of size: at small sizes the Ice 0 phase produces the most stable clusters, while at large sizes there is a crossover to Ice I clusters. We further investigate the growth of crystalline clusters with the seeding technique and study the growth patterns of different crystalline clusters. While energetically stable at small sizes, the growth of metastable phases (cubic and Ice 0) is hindered by the formation of coherent grain boundaries. A five-fold symmetric twin boundary for cubic ice, and a newly discovered coherent grain boundary in Ice 0, that promotes cross nucleation of cubic ice. Our work reveals that different local structures can compete with the stable phase in mW water, and that the low energy cost of particular grain boundaries might play an important role in polymorph selection.

cond-mat.soft↗