Miracle-dz/UniFAR-FTUs huggingface dataset:
Pressure-driven valence increase and metallization in the Kondo insulator 
Ce3Bi4Pt3

Author(s): Campbell, DJ; Brubaker, ZE; Roncaioli, C; Saraf, P; Xiao, Y; Chow, P; Kenney-Benson, C; Popov, D; Zieve, RJ; Jeffries, JR; Paglione, J | Abstract: © 2019 American Physical Society. We report the results of high-pressure X-ray diffraction, X-ray absorption, and electrical transport measurements of the Kondo insulator (KI) Ce3Bi4Pt3 up to 42 GPa, the highest pressure reached in the study of any Ce-based KI. We observe a smooth decrease in volume and movement toward intermediate Ce valence with pressure, both of which point to increased electron correlations. Despite this, temperature-dependent resistance data show the suppression of the interaction-driven ambient pressure insulating ground state. We also discuss potential ramifications of these results for the predicted topological KI state.
Critical Scaling of Diffusion Coefficients and Size of Rigid Clusters of Soft Athermal Particles Under Shear

We numerically investigate the self-diffusion coefficient and correlation length (i.e., the typical size of the collective motions) in sheared soft athermal particles. Here we find that the rheological flow curves on the self-diffusion coefficient are collapsed by the proximity to the jamming transition density. This feature is in common with the well-established critical scaling of flow curves on shear stress or viscosity. We furthermore reveal that the divergence of the correlation length governs the critical behavior of the diffusion coefficient, where the diffusion coefficient is proportional to the correlation length and the strain rate for a wide range of the strain rate and packing fraction across the jamming transition density.
Extended Models of Finite Automata

Many of the numerous automaton models proposed in the literature can be regarded as a finite automaton equipped with an additional storage mechanism. In this thesis, we focus on two such models, namely the finite automata over groups and the homing vector automata. A finite automaton over a group $ G $ is a nondeterministic finite automaton equipped with a register that holds an element of the group $ G $. The register is initialized to the identity element of the group and a computation is successful if the register is equal to the identity element at the end of the computation after being multiplied with a group element at every step. We investigate the language recognition power of finite automata over integer and rational matrix groups and reveal new relationships between the language classes corresponding to these models. We examine the effect of various parameters on the language recognition power. We establish a link between the decision problems of matrix semigroups and the corresponding automata. We present some new results about valence pushdown automata and context-free valence grammars. We also propose the new homing vector automaton model, which is a finite automaton equipped with a vector that can be multiplied with a matrix at each step. The vector can be checked for equivalence to the initial vector and the acceptance criterion is ending up in an accept state with the value of the vector being equal to the initial vector. We examine the effect of various restrictions on the model by confining the matrices to a particular set and allowing the equivalence test only at the end of the computation. We define the different variants of the model and compare their language recognition power with that of the classical models.
Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection

Today, many companies take advantage of viral marketing to promote their new products, and since there are several competing companies in many markets, Competitive Influence Maximization has attracted much attention. Two categories of studies exist in the literature. First, studies that focus on which nodes from the network to select considering the existence of the opponents. Second, studies that focus on the problem of budget allocation. In this work, we integrate these two lines of research and propose a new scenario where competition happens in two phases. First, parties identify the most influential nodes of the network. Then they compete over only these influential nodes by the amount of budget they allocate to each node. We provide a strong motivation for this integration. Also, in previous budget allocation studies, any fraction of the budget could be allocated to a node, which means that the action space was continuous. However, in our scenario, we consider that the action space is discrete. This consideration is far more realistic, and it significantly changes the problem. We model the scenario as a game and propose a novel framework for calculating the Nash equilibrium. Notably, building an efficient framework for this problem with such a huge action space and also handling the stochastic environment of influence maximization is very challenging. To tackle these difficulties, we devise a new payoff estimation method and a novel best response oracle to boost the efficiency of our framework. Also, we validate each aspect of our work through extensive experiments.
Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

As a means to support the access of massive machine-type communication devices, grant-free access and non-orthogonal multiple access (NOMA) have received great deal of attention in recent years. In the grant-free transmission, each device transmits information without the granting process so that the base station needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to identify active devices from the superimposed received signal. An aim of this paper is to put forth a new type of AUD based on deep neural network (DNN). By feeding the training data in the properly designed DNN, the proposed AUD scheme learns the nonlinear mapping between the received NOMA signal and indices of active devices. As a result, the trained DNN can handle the whole AUD process, achieving an accurate detection of the active users. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches by a large margin in both AUD success probability and computational complexity.
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference

This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing. EdgeDRNN adopts the spiking neural network inspired delta network algorithm to exploit temporal sparsity in RNNs. It reduces off-chip memory access by a factor of up to 10x with tolerable accuracy loss. Experimental results on a 10 million parameter 2-layer GRURNN, with weights stored in DRAM, show that EdgeDRNN computes them in under 0.5 ms. With 2.42 W wall plug power on an entry level USB powered FPGA board, it achieves latency comparable with a 92W Nvidia 1080 GPU. It outperforms NVIDIA Jetson Nano, Jetson TX2 and Intel Neural Compute Stick 2 in latency by 6X. For a batch size of 1, EdgeDRNN achieves a mean effective throughput of 20.2 GOp/s and a wall plug power efficiency that is over 4X higher than all other platforms.
Unsupervised Adversarial Image Inpainting

We consider inpainting in an unsupervised setting where there is neither access to paired nor unpaired training data. The only available information is provided by the uncomplete observations and the inpainting process statistics. In this context, an observation should give rise to several plausible reconstructions which amounts at learning a distribution over the space of reconstructed images. We model the reconstruction process by using a conditional GAN with constraints on the stochastic component that introduce an explicit dependency between this component and the generated output. This allows us sampling from the latent component in order to generate a distribution of images associated to an observation. We demonstrate the capacity of our model on several image datasets: faces (CelebA), food images (Recipe-1M) and bedrooms (LSUN Bedrooms) with different types of imputation masks. The approach yields comparable performance to model variants trained with additional supervision.
Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders

Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic route to constructing this landscape, without the need for extensive a priori intuition into the relevant driving forces. In particular, autoencoders are powerful tools for dimensionality reduction, as they naturally force an information bottleneck and, thereby, a low-dimensional embedding of the essential features. While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at odds with the multi-basin free-energy landscapes that typically arise from the identification of meaningful collective variables. In this work, we incorporate this physical intuition into the prior by employing a Gaussian mixture variational autoencoder (GMVAE), which encourages the separation of metastable states within the embedding. The GMVAE performs dimensionality reduction and clustering within a single unified framework, and is capable of identifying the inherent dimensionality of the input data, in terms of the number of Gaussians required to categorize the data. We illustrate our approach on two toy models, alanine dipeptide, and a challenging disordered peptide ensemble, demonstrating the enhanced clustering effect of the GMVAE prior compared to standard VAEs. The resulting embeddings appear to be promising representations for constructing Markov state models, highlighting the transferability of the dimensionality reduction from static equilibrium properties to dynamics.
An Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants

Deep neural networks (DNNs) have emerged as a popular mathematical tool for function approximation due to their capability of modelling highly nonlinear functions. Their applications range from image classification and natural language processing to learning-based control. Despite their empirical successes, there is still a lack of theoretical understanding of the representative power of such deep architectures. In this work, we provide a theoretical analysis of the expressiveness of fully-connected, feedforward DNNs with 1-Lipschitz activation functions. In particular, we characterize the expressiveness of a DNN by its Lipchitz constant. By leveraging random matrix theory, we show that, given sufficiently large and randomly distributed weights, the expected upper and lower bounds of the Lipschitz constant of a DNN and hence their expressiveness increase exponentially with depth and polynomially with width, which gives rise to the benefit of the depth of DNN architectures for efficient function approximation. This observation is consistent with established results based on alternative expressiveness measures of DNNs. In contrast to most of the existing work, our analysis based on the Lipschitz properties of DNNs is applicable to a wider range of activation nonlinearities and potentially allows us to make sensible comparisons between the complexity of a DNN and the function to be approximated by the DNN. We consider this work to be a step towards understanding the expressive power of DNNs and towards designing appropriate deep architectures for practical applications such as system control.
Fluctuations of the spectrum in rotationally invariant random matrix ensembles

We investigate traces of powers of random matrices whose distributions are invariant under rotations (with respect to the Hilbert–Schmidt inner product) within a real-linear subspace of the space of [Formula: see text] matrices. The matrices, we consider may be real or complex, and Hermitian, antihermitian, or general. We use Stein’s method to prove multivariate central limit theorems, with convergence rates, for these traces of powers, which imply central limit theorems for polynomial linear eigenvalue statistics. In contrast to the usual situation in random matrix theory, in our approach general, nonnormal matrices turn out to be easier to study than Hermitian matrices.
Mass constraints for 15 protoplanetary discs from HD 1–0

Context. Hydrogen deuteride (HD) rotational line emission can provide reliable protoplanetary disc gas mass measurements, but this molecule is difficult to observe and detections have been limited to three T Tauri discs. No new data have been available since the Herschel Space Observatory mission ended in 2013. Aims. We set out to obtain new disc gas mass constraints by analysing upper limits on HD 1–0 emission in Herschel/PACS archival data from the DIGIT key programme. Methods. With a focus on the Herbig Ae/Be discs, whose stars are more luminous than T Tauris, we determined upper limits for HD in data previously analysed for its line detections. We studied the significance of these limits with a grid of models run with the DALI physical-chemical code, customised to include deuterium chemistry. Results. Nearly all the discs are constrained to Mgas ≤ 0.1 M⊙, ruling out global gravitational instability. A strong constraint is obtained for the HD 163296 disc mass, Mgas ≤ 0.067 M⊙, implying Δg/d ≤ 100. This HD-based mass limit is towards the low end of CO-based mass estimates for the disc, highlighting the large uncertainty in using only CO and suggesting that gas-phase CO depletion in HD 163296 is at most a factor of a few. The Mgas limits for HD 163296 and HD 100546, both bright discs with massive candidate protoplanetary systems, suggest disc-to-planet mass conversion efficiencies of Mp/(Mgas + Mp) ≈ 10–40% for present-day values. Near-future observations with SOFIA/HIRMES will be able to detect HD in the brightest Herbig Ae/Be discs within 150 pc with ≈ 10 h integration time.
The impact of filamentary accretion of subhaloes on the shape and orientation of haloes

Dark matter haloes are formed through hierarchical mergers of smaller haloes in large-scale cosmic environments, and thus anisotropic subhalo accretion through cosmic filaments has some impacts on halo structures. Recent studies using cosmological simulations have shown that the orientations of haloes correlate with the direction of cosmic filaments, and these correlations significantly depend on the halo mass. Using high-resolution cosmological N-body simulations, we quantified the strength of filamentary subhalo accretion for galaxy- and group-sized host haloes (Mhost = 5 × 1011–13 M⊙) by regarding the entry points of subhaloes as filaments and present statistical studies on how the shape and orientation of host haloes at redshift zero correlate with the strength of filamentary subhalo accretion. We confirm previous studies that found the host halo mass dependence of the alignment between orientations of haloes and filaments. We also show that, for the first time, the shape and orientation of haloes weakly correlate with the strength of filamentary subhalo accretion even if the host halo masses are the same. Minor-to-major axial ratios of haloes tend to decrease as their filamentary accretion gets stronger. Haloes with highly anisotropic accretion become more spherical or oblate, while haloes with isotropic accretion become more prolate or triaxial. For haloes with strong filamentary accretion, their major axes are preferentially aligned with the filaments, while their angular momentum vectors tend to be slightly more misaligned.
A priori error analysis of a numerical stochastic homogenization method

This paper provides an a priori error analysis of a localized orthogonal decomposition method (LOD) for the numerical stochastic homogenization of a model random diffusion problem. If the uniformly elliptic and bounded random coefficient field of the model problem is stationary and satisfies a quantitative decorrelation assumption in form of the spectral gap inequality, then the expected $L^2$ error of the method can be estimated, up to logarithmic factors, by $H+(\varepsilon/H)^{d/2}$; $\varepsilon$ being the small correlation length of the random coefficient and $H$ the width of the coarse finite element mesh that determines the spatial resolution. The proof bridges recent results of numerical homogenization and quantitative stochastic homogenization.
CMOS Position-Based Charge Qubits: Theoretical Analysis of Control and Entanglement

In this study, a formal definition, robustness analysis and discussion on the control of a position-based semiconductor charge qubit are presented. Such a qubit can be realized in a chain of coupled quantum dots, forming a register of charge-coupled transistor-like devices, and is intended for CMOS implementation in scalable quantum computers. We discuss the construction and operation of this qubit, its Bloch sphere, and relation with maximally localized Wannier functions which define its position-based nature. We then demonstrate how to build a tight-binding model of single and multiple interacting qubits from first principles of the Schrödinger formalism. We provide all required formulae to calculate the maximally localized functions and the entries of the Hamiltonian matrix in the presence of interaction between qubits. We use three illustrative examples to demonstrate the electrostatic interaction of electrons and discuss how to build a model for many-electron (qubit) system. To conclude this study, we show that charge qubits can be entangled through electrostatic interaction.
Phase transitions for infinite products of large non-Hermitian random matrices

Products of $M$ i.i. d. non-Hermitian random matrices of size $N \times N$ relate Gaussian fluctuation of Lyapunov and stability exponents in dynamical systems (finite $N$ and large $M$) to local eigenvalue universality in random matrix theory (finite $M$ and large $N$). The remaining task is to study local eigenvalue statistics as $M$ and $N$ tend to infinity simultaneously, which lies at the heart of understanding two kinds of universal patterns. For products of i.i. d. complex Ginibre matrices, truncated unitary matrices and spherical ensembles, as $M+N\to \infty$ we prove that local statistics undergoes a transition when the relative ratio $M/N$ changes from $0$ to $\infty$: Ginibre statistics when $M/N \to 0$, normality when $M/N\to \infty$, and new critical phenomena when $M/N\to \gamma \in (0, \infty)$.
Deep Learning for 3D Point Clouds: A Survey

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.
Motile cilia hydrodynamics: entrainment versus synchronization when coupling through flow

Coordinated motion of cilia is a fascinating and vital aspect of very diverse forms of eukaryotic life, enabling swimming and propulsion of fluid across cellular epithelia. There are many questions still unresolved, and broadly they fall into two classes. (i) The mechanism of how cilia physically transmit forces onto each other. It is not known for many systems if the forces are mainly of hydrodynamical origin, or if elastic forces within the cytoskeleton are important. (ii) In those systems where we know that forces are purely hydrodynamical, we do not have a framework for linking our understanding of how each cilium behaves in isolation to the collective properties of two or more cilia. In this work, we take biological data of cilia dynamics from a variety of organisms as an input for an analytical and numerical study. We calculate the relative importance of external flows versus internal cilia flows on cilia coupling. This study contributes to both the open questions outlined above: firstly, we show that it is, in general, incorrect to infer cilium–cilium coupling strength on the basis of experiments with external flows, and secondly, we show a framework to recapitulate the dynamics of single cilia (the waveform) showing classes that correspond to biological systems with the same physiological activity (swimming by propulsion, versus forming collective waves). This article is part of the Theo Murphy meeting issue ‘Unity and diversity of cilia in locomotion and transport’.
Cilia in the developing zebrafish ear

The inner ear, which mediates the senses of hearing and balance, derives from a simple ectodermal vesicle in the vertebrate embryo. In the zebrafish, the otic placode and vesicle express a whole suite of genes required for ciliogenesis and ciliary motility. Every cell of the otic epithelium is ciliated at early stages; at least three different ciliary subtypes can be distinguished on the basis of length, motility, genetic requirements and function. In the early otic vesicle, most cilia are short and immotile. Long, immotile kinocilia on the first sensory hair cells tether the otoliths, biomineralized aggregates of calcium carbonate and protein. Small numbers of motile cilia at the poles of the otic vesicle contribute to the accuracy of otolith tethering, but neither the presence of cilia nor ciliary motility is absolutely required for this process. Instead, otolith tethering is dependent on the presence of hair cells and the function of the glycoprotein Otogelin. Otic cilia or ciliary proteins also mediate sensitivity to ototoxins and coordinate responses to extracellular signals. Other studies are beginning to unravel the role of ciliary proteins in cellular compartments other than the kinocilium, where they are important for the integrity and survival of the sensory hair cell. This article is part of the Theo Murphy meeting issue ‘Unity and diversity of cilia in locomotion and transport’.