Miracle-dz/UniFAR-FTUs huggingface dataset:
Filtered density function for large eddy simulation of turbulent reacting flows

A methodology termed the “filtered density function” (FDF) is developed and implemented for large eddy simulation (LES) of chemically reacting turbulent flows. In this methodology, the effects of the unresolved scalar fluctuations are taken into account by considering the probability density function (PDF) of subgrid scale (SGS) scalar quantities. A transport equation is derived for the FDF in which the effect of chemical reactions appears in a closed form. The influences of scalar mixing and convection within the subgrid are modeled. The FDF transport equation is solved numerically via a Lagrangian Monte Carlo scheme in which the solutions of the equivalent stochastic differential equations (SDEs) are obtained. These solutions preserve the Ito-Gikhman nature of the SDEs. The consistency of the FDF approach, the convergence of its Monte Carlo solution and the performance of the closures employed in the FDF transport equation are assessed by comparisons with results obtained by direct numerical simulation (DNS) and by conventional LES procedures in which the first two SGS scalar moments are obtained by a finite difference method (LES-FD). These comparative assessments are conducted by implementations of all three schemes (FDF, DNS and LES-FD) in a temporally developing mixing layer and a spatially developing planar jet under both non-reacting and reacting conditions. In non-reacting flows, the Monte Carlo solution of the FDF yields results similar to those via LES-FD. The advantage of the FDF is demonstrated by its use in reacting flows. In the absence of a closure for the SGS scalar fluctuations, the LES-FD results are significantly different from those based on DNS. The FDF results show a much closer agreement with filtered DNS results. © 1998 American Institute of Physics.
A novel simulation paradigm utilising MRI-derived phosphene maps for cortical prosthetic vision

Objective. We developed a realistic simulation paradigm for cortical prosthetic vision and investigated whether we can improve visual performance using a novel clustering algorithm. Approach. Cortical visual prostheses have been developed to restore sight by stimulating the visual cortex. To investigate the visual experience, previous studies have used uniform phosphene maps, which may not accurately capture generated phosphene map distributions of implant recipients. The current simulation paradigm was based on the Human Connectome Project retinotopy dataset and the placement of implants on the cortices from magnetic resonance imaging scans. Five unique retinotopic maps were derived using this method. To improve performance on these retinotopic maps, we enabled head scanning and a density-based clustering algorithm was then used to relocate centroids of visual stimuli. The impact of these improvements on visual detection performance was tested. Using spatially evenly distributed maps as a control, we recruited ten subjects and evaluated their performance across five sessions on the Berkeley Rudimentary Visual Acuity test and the object recognition task. Main results. Performance on control maps is significantly better than on retinotopic maps in both tasks. Both head scanning and the clustering algorithm showed the potential of improving visual ability across multiple sessions in the object recognition task. Significance. The current paradigm is the first that simulates the experience of cortical prosthetic vision based on brain scans and implant placement, which captures the spatial distribution of phosphenes more realistically. Utilisation of evenly distributed maps may overestimate the performance that visual prosthetics can restore. This simulation paradigm could be used in clinical practice when making plans for where best to implant cortical visual prostheses.
The generalized stochastic preference choice model

We propose a new discrete choice model, called the generalized stochastic preference (GSP) model, that incorporates non-rationality into the stochastic preference (SP) choice model, also known as the rank- based choice model. Our model can explain several choice phenomena that cannot be represented by any SP model such as the compromise and attraction effects, but still subsumes the SP model class. The GSP model is defined as a distribution over consumer types, where each type extends the choice behavior of rational types in the SP model. We build on existing methods for estimating the SP model and propose an iterative estimation algorithm for the GSP model that finds new types by solving a integer linear program in each iteration. We further show that our proposed notion of non-rationality can be incorporated into other choice models, like the random utility maximization (RUM) model class as well as any of its subclasses. As a concrete example, we introduce the non-rational extension of the classical MNL model, which we term the generalized MNL (GMNL) model and present an efficient expectation-maximization (EM) algorithm for estimating the GMNL model. Numerical evaluation on real choice data shows that the GMNL and GSP models can outperform their rational counterparts in out-of-sample prediction accuracy.
Comparing Rewinding and Fine-tuning in Neural Network Pruning

Neural network pruning is a popular technique for reducing inference costs by removing connections, neurons, or other structure from the network. In the literature, pruning typically follows a standard procedure: train the network, remove unwanted structure (pruning), and train the resulting network further to recover accuracy (fine-tuning). In this paper, we explore an alternative to fine-tuning: rewinding. Rather than continuing to train the resultant pruned network (fine-tuning), rewind the remaining weights to their values from earlier in training, and re-train the resultant network for the remainder of the original training process. We find that this procedure, which repurposes the strategy for finding lottery tickets presented by Frankle et al. (2019), makes it possible to prune networks further than is possible with fine-tuning for a given target accuracy, provided that the weights are rewound to a suitable point in training. We also find that there are wide ranges of suitable rewind points that achieve higher accuracy than fine-tuning across all tested networks. Based on these results, we argue that practitioners should explore rewinding as an alternative to fine-tuning for neural network pruning.
Cell Biology International

Oral squamous cell carcinoma (OSCC) is a common cancer with poor prognosis and high mortality. The role of CCN5 has attracted a great focus on the regulation of cancer progression. However, the biological function and mechanism of CCN5 in OSCC are still not well elucidated. The current study was designed to determine the effects of CCN5 on OSCC cell proliferation and apoptosis using two OSCC cell lines. Further, LY294002, a PI3K/AKT antagonist, was employed to explore the mechanism underlying the effects of CCN5 in the regulation of OSCC. The results showed that overexpression of CCN5 in TSCCa cells significantly reduced viable cell number, arrested cell cycle, and suppressed cell‐cycle regulators (cyclin D1, cyclin E, and CDK2). CCN5 overexpression increased the apoptotic ratio and Hoechst‐positive cell number, and altered the apoptotic‐related proteins (caspase‐3/9, Bax, and Bcl‐2). However, CCN5 silencing induced opposite effects on cell proliferation and apoptosis in Tca‐8113 cells. In addition, we observed that CCN5 knockdown increased the expression levels of PI3K (p85α and p110α) and phosphorylated AKT at serine 473 (p‐AKT Ser473) in Tca‐8113 cells. Inhibiting PI3K/AKT signaling with LY294002 rescued the apoptotic process in CCN5‐silenced OSCC cells. Finally, xenograft analysis showed that CCN5 represses tumorigenesis of OSCC cells. These findings together suggest that CCN5 functions as a tumor suppressor for OSCC cell development through inactivation of PI3K/AKT signaling pathway, providing a potential candidate for OSCC therapy.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

This study attempts to advance corpus-based exploration of sound iconicity, i.e. the existence of a non-arbitrary relationship between forms and meanings in language. We examine a number of phonesthemes , phonetic groupings proposed to be meaningful in the literature, with the aim of developing ways to validate their existence and their semantic content. Our ﬁrst experiment is a replication of Otis and Sagi (2008), who showed that sets of words containing phonesthemes are more semantically related to each other than sets of random words. We augment their results using the British National Corpus and the Semantic Vectors package for building a distributional semantic model. Our second experiment shows how the semantic content of at least some phonesthemes can be identiﬁed automatically using WordNet, thereby further reducing the room for intuitive judgments in this controversial ﬁeld.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

As much empirical work attests, people have a reliable tendency to match their conversational partner’s body movements, speech style, and patterns of language use – amongst other things. A speciﬁc version of this tendency, Structural priming , which occurs when prior exposure to a particular linguistic structure facilitates one’s subsequent processing of the same structure, has gained widespread acceptance. Pickering and Garrod (2004) propose that cross -person structural priming is a basic mechanism of conversational coordination – part of an automatic, resource-free alignment mechanism that is the basis for all successful human interaction. We present evidence to the contrary from two analyses of a corpus of ordinary conversation. The ﬁrst suggests that the level of structural (syntactic) matching is no different from chance, and the second that the observed statistical correlation between prime form and target form may be entirely associated with repetition of lexical form.
Computers in Biology and Medicine

The COVID-19 pandemic has extremely threatened human health, and automated algorithms are needed to segment infected regions in the lung using computed tomography (CT). Although several deep convolutional neural networks (DCNNs) have proposed for this purpose, their performance on this task is suppressed due to the limited local receptive field and deficient global reasoning ability. To address these issues, we propose a segmentation network with a novel pixel-wise sparse graph reasoning (PSGR) module for the segmentation of COVID-19 infected regions in CT images. The PSGR module, which is inserted between the encoder and decoder of the network, can improve the modeling of global contextual information. In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the encoder. Then, we convert the graph into a sparsely-connected one by keeping 𝐾 strongest connections to each uncertainly segmented pixel. Finally, the global reasoning is performed on the sparsely-connected graph. Our segmentation network was evaluated on three publicly available datasets and compared with a variety of widely-used segmentation models. Our results demonstrate that (1) the proposed PSGR module can capture the long-range dependencies effectively and (2) the segmentation model equipped with this PSGR module can accurately segment COVID-19 infected regions in CT images and outperform all other competing models.
A Note on Foldover of 2k − p Designs With Column Permutations

Foldover is a commonly used follow-up strategy in experimental designs. All existing foldover designs were constructed by reversing the sign of columns of the initial design. We propose a new methodology by allowing the permutation of columns in foldover. Focusing on resolution IV designs, we show that almost all designs are better than existing results with respect to the minimum aberration criterion. While augmenting a design by a foldover with column permutations may result in a nonregular combined design, the proposed designs all have a resolution of 4.5 or higher, for which no two-factor interaction is fully aliased with any other two-factor interactions.
Statistical analysis of 26 yr of observations of decametric radio emissions from Jupiter

Jupiter is a complex and at the same time very powerful radio source in the decameter wavelength range. The emission is anisotropic, intrinsically variable at millisecond to hour timescales, and also modulated by various external processes at much longer periods, ranging from ~10 h to months or years (including Jovian day and year, solar activity and solar wind variations, and for ground-based observations, terrestrial day and year). As a consequence, long-term observations and their statistical study have proved to be necessary for disentangling and understanding the observed phenomena. We have built a database from the available 26 yr of systematic, daily observations conducted at the Nancay Decameter Array and recorded in digital format. This database contains all observed Jovian decametric emissions, classified with respect to the time-frequency morphology, their dominant circular polarization, and maximum frequency. We present the results of the first statistical analysis of this database. We confirm the earlier classification of Jovian decameter emissions in Io-A, -A′, -B, -C, -D and non-Io-A, -B, -C types, but we also introduce new emission types (Io-A′′ and Io-B′) and precise and characterize the non-Io-D type. We determine the contours of all emission types in the CML− Φ Io plane (Central Meridian Longitude in Jupiter’s System III coordinates versus Io Phase), provide representative examples of their typical time-frequency patterns, and the distribution of emission’s maximum frequency as a function of Λ Io (Io’s Longitude). Finally, we present a statistical analysis of the distributions of the occurrence rate, duration, intensity and polarization for each emission type. non-Io-DAM appears to be related to small-scale, possibly bursty auroral structures.
Controversies in cardiovascular medicine Anticoagulation in coronary intervention

Percutaneous coronary intervention (PCI) induces thrombin generation and is associated with the risk of acute, subacute, or long-term ischaemic events. Therefore, intravenous anticoagulation is recommended to minimize thrombotic complications. The intensity and duration of anticoagulation needed are dependent on the clinical presentation (elective PCI for stable coronary artery disease, PCI for non-ST elevation acute coronary syndromes, or primary PCI for ST-segment elevation myocardial infarction) and procedural features. As both ischaemic and periprocedural bleeding complications are associated with acute and long-term mortality, the optimal level of anticoagulation and the best agents are a matter of debate. Despite a number of limitations and the lack of large randomized clinical trials, unfractionated heparin (UFH) is still been used in the majority of interventions. Intravenous enoxaparin, a low-molecular-weight heparin, leads to a more predictable level of anticoagulation and has been compared with UFH in patients with elective PCI and primary PCI with favourable results. The direct thrombin inhibitor bivalirudin has been studied in numerous trials and consistently shown to reduce bleeding complications when compared with UFH with or without glycoprotein IIb/IIIa inhibitors. This review will summarize the current status of anticoagulation for PCI and the results of most recent trials and give recommendations for different clinical scenarios.
Addendum: Intrinsic flat stability of the positive mass theorem for graphical hypersurfaces of Euclidean space

Because of an error in the proof of Theorem 1.3 of Huang-Lee-Sormani, we provide an alternative proof that depends on recent results of Allen-Perales, in contrast to the use of Wenger's compactness theorem in the original proof. We also take the opportunity to flesh out some of the other arguments that were used in Huang-Lee-Sormani by proving some useful facts about point convergence in the Gromov-Hausdorff and intrinsic flat senses. In doing so, we clarify that all of the results of Huang-Lee-Sormani are true.
AAAI Press Formatting Instructions for Authors Using LaTeX -- A Guide

We design a highly efficient architecture called Gated Convolutional Network with Hybrid Connectivity (HCGNet), which is equipped with the combination of local residual and global dense connectivity to enjoy their individual superiorities as well as attention-based gate mechanism to assist feature recalibration. To adapt our hybrid connectivity, we further propose a novel module which includes a squeeze cell for obtaining the compact features from input and then a multi-scale excitation cell attached an update gate to model the global context features for capturing long-range dependency based on multi-scale information. We also locate a forget gate on residual connectivity to decay the reused features, which can be aggergated with newly global context features to form the output that can facilitate effective feature exploration as well as re-exploitation to some extent. Moreover, the number of our proposed modules under dense connectivity can quite fewer than classical DenseNet thus reducing considerable redundancy but with empirically better performance. On CIFAR-10/100 datasets, HCGNets significantly outperform state-of-the-art both human-designed and auto-searched networks with much fewer parameters. It can also consistently obtain better performance and interpretability than widely applied networks in practice on ImageNet dataset.
A Second look at Exponential and Cosine Step Sizes: Simplicity, Convergence, and Performance

Stochastic Gradient Descent (SGD) is a popular tool in training large-scale machine learning models. Its performance, however, is highly variable, depending crucially on the choice of the step sizes. Accordingly, a variety of strategies for tuning the step sizes have been proposed. Yet, most of them lack a theoretical guarantee, whereas those backed by theories often do not shine in practice. In this paper, we study two heuristic step size schedules whose power has been repeatedly confirmed in practice: the exponential and the cosine step sizes. For the first time, we provide theoretical support for them: we prove their (almost) optimal convergence rates for stochastic optimization of smooth non-convex functions. Furthermore, if in addition, the Polyak-Łojasiewicz (PL) condition holds, they both automatically adapt to the level of noise, with a rate interpolating between a linear rate for the noiseless case and a sub-linear one for the noisy case. Finally, we conduct a fair and comprehensive empirical evaluation of real-world datasets with deep learning architectures. Results show that, even if only requiring at most two hyperparameters to tune, they best or match the performance of various finely-tuned state-of-the-art strategies.
Learning to Structure an Image with Few Colors and Beyond

Color and structure are the two pillars that combine to give an image its meaning. Interested in critical structures for neural network recognition, we isolate the influence of colors by limiting the color space to just a few bits, and find structures that enable network recognition under such constraints. To this end, we propose a color quantization network, ColorCNN, which learns to structure an image in limited color spaces by minimizing the classification loss. Building upon the architecture and insights of ColorCNN, we introduce ColorCNN+, which supports multiple color space size configurations, and addresses the previous issues of poor recognition accuracy and undesirable visual fidelity under large color spaces. Via a novel imitation learning approach, ColorCNN+ learns to cluster colors like traditional color quantization methods. This reduces overfitting and helps both visual fidelity and recognition accuracy under large color spaces. Experiments verify that ColorCNN+ achieves very competitive results under most circumstances, preserving both key structures for network recognition and visual fidelity with accurate colors. We further discuss differences between key structures and accurate colors, and their specific contributions to network recognition. For potential applications, we show that ColorCNNs can be used as image compression methods for network recognition.
DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes Supplementary Material

In the supplementary material, we provide further insights into the architectural design choices we make in order to leverage the potential of combining geodesic and Euclidean information. Furthermore, we present detailed network descriptions used for the ablation study as well as the ofﬁcial benchmarks. Finally, we show additional scores and qualitative results on all three benchmarks.
Nanomechanical Phenomena in Low-Dimensional Structures

This is a compilation thesis which investigates various mechanical phenomena in different low-dimensional nanoscale systems. In the first part, we consider a purely mechanical phenomenon: the sensitivity of the dispersion of a Love-type surface acoustic wave (SAW) to geometry and material parameters. We model a SAW sensor as a three-layer system with elastic, viscous, or viscoelastic layers. We find that viscoelasticity can remove the support for Love waves. We also derive analytic expressions for the wave velocity and attenuation in the limit of a thin middle layer. In the second part, we consider the interaction of SAWs with an electronic subsystem. We model a flat semi-infinite graphene sheet using 2D elasticity theory and consider Rayleigh-type SAWs. We investigate the resonant interaction of such SAWs with electronic edge states induced by a transverse magnetic field. When electronic relaxation is much faster than phonon absorption, we find that the SAWs attenuate. In the opposite limit, we show that nonlinear effects can lead to the formation of hypersonic solitons. In the third part, we demonstrate how interaction with an electronic subsystem can actuate nanomechanical vibrations. We study two different systems composed of a movable quantum dot (QD) in position dependent tunneling contact with two leads. In the first system, the leads are held at different temperatures and a spin-valve effect prevents electron exchange between them. We show that electron-electron interaction can mediate a heat flow which can actuate the QD position via a capacitive coupling. In the second system, both the leads and the QD are superconducting and the system has mirror symmetry. We find that an applied ac field can induce charge oscillations in the QD and parametrically excite vibrations of its position. The automatic synchronization of the oscillations in charge and position generates a supercurrent, the direction of which is a result of spontaneous symmetry breaking.