Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal exponential random graph models of longitudinal brain networks after stroke

Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles (T) and edges (E) over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation (T) and between hemisphere integration (E), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.
Emergence and stability of spin-valley entangled quantum liquids in moiré heterostructures

Twisting moire heterostructures to the flatband regime allows for the formation of strongly correlated quantum states, since the dramatic reduction of the bandwidth can cause the residual electronic interactions to set the principal energy scale. An effective description for such correlated moire heterostructures, derived in the strong-coupling limit at integer filling, generically leads to spin-valley Heisenberg models. Here we explore the emergence and stability of spin liquid behavior in an SU(2)$^{\mathrm{spin}}\otimes$SU(2)$^{\mathrm{valley}}$ Heisenberg model upon inclusion of Hund's-induced and longer-ranged exchange couplings, employing a pseudofermion functional renormalization group approach. We consider two lattice geometries, triangular and honeycomb (relevant to different moire heterostructures), and find, for both cases, an extended parameter regime surrounding the SU(4) symmetric point where no long-range order occurs, indicating a stable realm of quantum spin liquid behavior. For large Hund's coupling, we identify the adjacent magnetic orders, with both antiferromagnetic and ferromagnetic ground states emerging in the separate spin and valley degrees of freedom. For both lattice geometries the inclusion of longer-ranged exchange couplings is found to have both stabilizing and destabilizing effects on the spin liquid regime depending on the sign of the additional couplings.
Some Results on Tighter Bayesian Lower Bounds on the Mean-Square Error

In random parameter estimation, Bayesian lower bounds (BLBs) for the mean-square error have been noticed to not be tight in a number of cases, even when the sample size, or the signal-to-noise ratio, grow to infinity. In this paper, we study alternative forms of BLBs obtained from a covariance inequality, where the inner product is based on the \textit{a posteriori} instead of the joint probability density function. We hence obtain a family of BLBs, which is shown to form a counterpart at least as tight as the well-known Weiss-Weinstein family of BLBs, and we extend it to the general case of vector parameter estimation. Conditions for equality between these two families are provided. Focusing on the Bayesian Cram\'er-Rao bound (BCRB), a definition of efficiency is proposed relatively to its tighter form, and efficient estimators are described for various types of common estimation problems, e.g., scalar, exponential family model parameter estimation. Finally, an example is provided, for which the classical BCRB is known to not be tight, while we show its tighter form is, based on formal proofs of asymptotic efficiency of Bayesian estimators. This analysis is finally corroborated by numerical results.
Direct Estimates of the Solar Coronal Magnetic Field Using Contemporaneous Extreme-ultraviolet, Radio, and White-light Observations

We report a solar coronal split-band type II radio burst that was observed on 2016 March 16 with the Gauribidanur Radio Spectro-Polarimeter in the frequency range ≈90–50 MHz, and the Gauribidanur RadioheliograPH at two discrete frequencies, viz. 80 and 53.3 MHz. Observations around the same epoch in extreme ultraviolet (EUV) and white light show that the above burst was associated with a flux-rope structure and a coronal mass ejection (CME), respectively. The combined height–time plot generated using EUV, radio, and white-light data suggests that the different observed features (i.e., the flux rope, type II burst, and the CME) are all closely associated. We constructed an empirical model for the coronal electron density distribution (Ne(r), where r is the heliocentric distance) from the above set of observations themselves and used it to estimate the coronal magnetic field strength (B) over the range of r values in which the respective events were observed. The B values are consistent with each other. They vary as B(r) = 2.61 × r−2.21 G in the range r ≈ 1.1–2.2R⊙. As far as we know, similar direct estimates of B in the near-Sun corona without assuming a model for Ne(r), and by combining cotemporal set of observations in two different regions (radio and white-light) of the electromagnetic spectrum, have rarely been reported. Further, the present work is a novel attempt where the characteristics of a propagating EUV flux-rope structure, considered to be the signature of a CME close to the Sun, have been used to estimate B(r) in the corresponding distance range.
Monomialization of a quasianalytic morphism.

We prove a monomialization theorem for mappings in general classes of infinitely differentiable functions that are called quasianalytic. Examples include Denjoy-Carleman classes (of interest in real analysis), the class of infinitely differentiable functions which are definable in a given polynomially bounded o-minimal structure (in model theory), as well as the classes of real- or complex-analytic functions, and algebraic functions over any field of characteristic zero. The monomialization theorem asserts that mapping in a quasianalytic class can be transformed to a mapping whose components are monomials with respect to suitable local coordinates, by sequences of simple modifications of the source and target (local blowings-up and power substitutions in the real cases, in general, and local blowings-up alone in the algebraic or analytic cases). Monomialization is a version of resolution of singularities for a mapping. It is not possible, in general, to monomialize by global blowings-up, even in the real analytic case.
Superconducting order of 
Sr2RuO4
 from a three-dimensional microscopic model

We compute and compare even- and odd-parity superconducting order parameters of strontium ruthenate ($\mathrm{Sr}_2\mathrm{RuO}_4$) in the limit of weak interactions, resulting from a fully microscopic three-dimensional model including spin-orbit coupling. We find that odd-parity helical and even-parity $d$-wave order are favored for smaller and larger values of the Hund's coupling parameter $J$, respectively. Both orders are found compatible with specific heat data and the recently-reported nuclear magnetic resonance (NMR) Knight shift drop [A. Pustogow et al. Nature 574, 72 (2019)]. The chiral $p$-wave order, numerically very competitive with helical order, sharply conflicts with the NMR experiment.
Exploring Semantic Segmentation on the DCT Representation

Typical convolutional networks are trained and conducted on RGB images. However, images are often compressed for memory savings and efficient transmission in real-world applications. In this paper, we explore methods for performing semantic segmentation on the discrete cosine transform (DCT) representation defined by the JPEG standard. We first rearrange the DCT coefficients to form a preferred input type, then we tailor an existing network to the DCT inputs. The proposed method has an accuracy close to the RGB model at about the same network complexity. Moreover, we investigate the impact of selecting different DCT components on segmentation performance. With a proper selection, one can achieve the same level accuracy using only 36% of the DCT coefficients. We further show the robustness of our method under the quantization errors. To our knowledge, this paper is the first to explore semantic segmentation on the DCT representation.
Symplectic fillings of asymptotically dynamically convex manifolds I

We consider exact fillings with vanishing first Chern class of asymptotically dynamically convex (ADC) manifolds. We construct two structure maps on the positive symplectic cohomology and prove that they are independent of the filling for ADC manifolds. The invariance of the structure maps implies that the vanishing of symplectic cohomology and the existence of symplectic dilations are properties independent of the filling for ADC manifolds. Using them, various topological applications on symplectic fillings are obtained, including the uniqueness of diffeomorphism types of fillings for many contact manifolds. We use the structure maps to define the first symplectic obstructions to Weinstein fillability. In particular, we show that for all dimension 4k+3,k⩾1 , there exist infinitely many contact manifolds that are exactly fillable, almost Weinstein fillable but not Weinstein fillable. The invariance of the structure maps generalizes to strong fillings with vanishing first Chern class. We show that any strong filling with vanishing first Chern class of a class of manifolds, including (S2n−1,ξstd),∂(T∗L×Cn) with L simply connected, must be exact and have unique diffeomorphism type. As an application of the proof, we show that the existence of symplectic dilation implies uniruledness. In particular, any affine exotic Cn with nonnegative log Kodaira dimension is a symplectic exotic Cn .
SPICE-Based Multiphysics Model to Analyze the Dynamics of Ferroelectric Negative-Capacitance–Electrostatic MEMS Hybrid Actuators

We propose a Simulation Program with Integrated Circuit Emphasis (SPICE)-based multiphysics framework to model ferroelectric negative-capacitance–electrostatic microelectromechanical systems (MEMS) hybrid actuators. Our approach couples the nonlinear dynamics of both the ferroelectric capacitor and the MEMS actuator. Using this framework, we examine the dynamic response and the energy consumed during pull-in switching of the hybrid actuator. We predict a significant reduction in the dynamic pull-in and pull-out voltages and the energy consumed by the hybrid actuator compared with the standalone MEMS actuator. We also predict that the pull-in time of the hybrid actuator is, however, larger than that of the standalone actuator. Nevertheless, we show that one can tradeoff a small part of the reduction in actuation voltage to achieve identical pull-in times in the hybrid and standalone MEMS actuators while still consuming substantially lower energy in the former compared with the latter. Our analysis approach is compatible with standard circuit simulators and is, hence, suitable for analysis and evaluation of various heterogeneous systems consisting of hybrid MEMS actuators and other electronic devices.
MixNet : Mixed Depthwise Convolutional Kernels

Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked. In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on this observation, we propose a new mixed depthwise convolution (MDConv), which naturally mixes up multiple kernel sizes in a single convolution. As a simple drop-in replacement of vanilla depthwise convolution, our MDConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection. By integrating MDConv into AutoML search space, we have further developed a new family of models, named as MixNets, which significantly outperform previous models including MobileNetV2 [19] (ImageNet top-1 accuracy +4.2%), ShuffleNetV2 [15] (+3.5%), MnasNet [25] (+1.3%), ProxylessNAS [2] (+2.2%), and FBNet [26] (+2.0%). In particular, our MixNet-L achieves a new state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile settings (<600M FLOPS). Code is at https://github. com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet.
Gibbs energy of ices III, V and VI: wholistic thermodynamics and elasticity of the water phase diagram to 2300 MPa

Gibbs energy representations for ice III, V and VI are reported. These were constructed using new measurements of volumes at high pressure over a range of low temperatures combined with calculated vibrational energies grounded in statistical physics. The collection of representations including ice Ih and water (released as the open source SeaFreeze framework) allow accurate determinations of thermodynamics properties (phase boundaries, density, heat capacity, bulk modulus, thermal expansivity, chemical potentials) and seismic wave velocities over the entire range of conditions encountered in hydrospheres in our solar system (220 - 500K to 2300 MPa). These comprehensive representations allow exploration of the rich spectrum of thermodynamic behavior in the H2O system. Although the results are broadly applicable in science and engineering, their use in habitability analysis in water-rich planetary bodies of our solar system and beyond is particularly relevant.
Ab initio dielectric response function of diamond and other relevant high pressure phases of carbon

The electronic structure and dielectric properties of the diamond, body centered cubic diamond (bc8), and hexagonal diamond (lonsdaleite) phases of carbon are computed using density functional theory and many-body perturbation theory with the emphasis on the excitonic picture of the solid phases relevant in the regimes of high-pressure physics and warm dense matter. We also discuss the capabilities of reproducing the inelastic x-ray scattering spectra in comparison with the existing models in light of recent x-ray scattering experiments on carbon and carbon bearing materials in the Megabar range.
Idealized Einstein-Podolsky-Rosen states from non–phase-matched parametric down-conversion

Entanglement of high dimensional states is becoming increasingly important for quantum communication and computing. The most common source of entangled photons is spontaneous parametric down conversion (SPDC), where the degree of frequency and momentum entanglement is determined by the non-linear interaction volume. Here we show that by reducing the length of a highly non-linear material to the micrometer scale it is possible to relax the longitudinal phase matching condition and reach record levels of transverse wavevector entanglement. From a micro-sized layer of lithium niobate we estimate the number of entangled angular modes to be over 1200. The entanglement is measured both directly using correlation measurements and indirectly using stimulated emission tomography. The high entanglement of the state generated can be used to massively increase the quantum information capacity of photons, but it also opens up the possibility to improve the resolution of many quantum imaging techniques.
Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAM

We present NOLBO, a variational observation model estimation for 3D multi-object from 2D single shot. Previous probabilistic instance-level understandings mainly consider the single-object image, not single shot with multi-object; relations between objects and the entire scene are out of their focus. The objectness of each observation also hardly join their model. Therefore, we propose a method to approximate the Bayesian observation model of scene-level 3D multi-object understanding. By exploiting variational auto-encoder (VAE), we estimate latent variables from the entire scene, which follow tractable distributions and concurrently imply 3D full shape and pose. To perform object-oriented data association and probabilistic simultaneous localization and mapping (SLAM), our observation models can easily be adopted to probabilistic inference by replacing object-oriented features with latent variables.
Deep Temporal Analysis for Non-Acted Body Affect Recognition

In the field of body affect recognition, the majority of literature is based on experiments performed on datasets where trained actors simulate emotional reactions. These acted and unnatural expressions differ from the more challenging genuine emotions, thus leading to less valuable results. In this article, a solution for basic non-acted emotion recognition based on 3D skeleton and Deep Neural Networks (DNNs) is provided. The proposed work introduces three majors contributions. First, temporal local movements performed by subjects are examined frame-by-frame, unlike the current state-of-the-art in non-acted body affect recognition where only static or global body features are considered. Second, an original set of global and time-dependent features for body movement description is provided. Third, this is one of the first works to use deep learning methods in the current non-acted body affect recognition literature. Due to the novelty of the topic, only the UCLIC dataset is currently considered the benchmark for comparative tests. On the latter, the proposed method outperforms all the competitors.
Making Maps Available for Play

Maps in video games have grown into complex interactive systems alongside video games themselves. What map systems have done and currently do have not been cataloged or evaluated. We trace the history of game map interfaces from their paper-based inspiration to their current smart phone-like appearance. Read-only map interfaces enable players to consume maps, which is sufficient for wayfinding. Game cartography interfaces enable players to persistently modify maps, expanding the range of activity to support planning and coordination. We employ thematic analysis on game cartography interfaces, contributing a near-exhaustive catalog of games featuring such interfaces, a set of properties to describe and design such interfaces, a collection of play activities that relate to cartography, and a framework to identify what properties promote the activities. We expect that designers will find the contributions enable them to promote desired play experiences through game map interface design.
Curiosity Did Not Kill the Robot

Learning from human interaction data is a promising approach for developing robot interaction logic, but behaviors learned only from offline data simply represent the most frequent interaction patterns in the training data, without any adaptation for individual differences. We developed a robot that incorporates both data-driven and interactive learning. Our robot first learns high-level dialog and spatial behavior patterns from offline examples of human--human interaction. Then, during live interactions, it chooses among appropriate actions according to its curiosity about the customer's expected behavior, continually updating its predictive model to learn and adapt to each individual. In a user study, we found that participants thought the curious robot was significantly more humanlike with respect to repetitiveness and diversity of behavior, more interesting, and better overall in comparison to a non-curious robot.
Reducing global reductions in large-scale distributed training

Current large-scale training of deep neural networks typically employs synchronous stochastic gradient descent that incurs large communication overhead. Instead of optimizing reduction routines as done in recent studies, we propose algorithms that do not require frequent global reductions. We first show that reducing the global reduction frequency works as an effective regularization technique that improves generalization of adaptive optimizers. We then propose an algorithm that reduces the global reduction frequency by employing local reductions on a subset of learners. In addition, to maximize the effect of reduction on convergence, we introduce reduction momentum that further accelerates convergence. Our experiment with the CIFAR-10 dataset shows that for the K-step averaging algorithm extremely sparse reductions help bridge the generalization gap. With 6 GPUs, in comparison to regular synchronous implementations, our implementation reduces more than 99% of global reductions. Further, we show that with 32 GPUs, our implementation reduces the number of global reductions by half. Experimenting with the ImageNet-1K dataset, we show that combining local reductions with global reductions and applying reduction momentum can further reduce global reductions by up to 62% for the same validation accuracy achieved in comparison to K-step averaging. With 400 GPUs global reduction frequency is reduced to once per 102K samples.
Coagulotoxic e ff ects by brown snake ( Pseudonaja ) and taipan ( Oxyuranus ) venoms , and the e ffi cacy of a new antivenom

Snakebite is a neglected tropical disease that disproportionately affects the poor. Antivenom is the only specific and effective treatment for snakebite, but its distribution is severely limited by several factors, including the prohibitive cost of some products. Papua New Guinea (PNG) is a snakebite hotspot but the high costs of Australian antivenoms (thousands of dollars per treatment) makes it unaffordable in PNG. A more economical taipan antivenom has recently been developed at the Instituto Clodomiro Picado (ICP) in Costa Rica for PNG and is currently undergoing clinical trials for the treatment of envenomations by coastal taipans (Oxyuranus scutellatus). In addition to potentially having the capacity to neutralise the effects of envenomations of non-PNG taipans, this antivenom may have the capacity to neutralise coagulotoxins in venom from closely related brown snakes (Pseudonaja spp.) also found in PNG. Consequently, we investigated the cross-reactivity of taipan antivenom across the venoms of all Oxyuranus and Pseudonaja species. In addition, to ascertain differences in venom biochemistry that influence variation in antivenom efficacy, we tested for relative cofactor dependence. We found that the new ICP taipan antivenom exhibited high selectivity for Oxyuranus venoms and only low to moderate cross-reactivity with any Pseudonaja venoms. Consistent with this genus level distinction in antivenom efficacy were fundamental differences in the venom biochemistry. Not only were the Pseudonaja venoms significantly more procoagulant, but they were also much less dependent upon the cofactors calcium and phospholipid. There was a strong correlation between antivenom efficacy, clotting time and cofactor dependence. This study sheds light on the structure-function relationships of the procoagulant toxins within these venoms and may have important clinical implications including for the design of next-generation antivenoms.