Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Gas Adsorption Using Two-Dimensional Equations of State

Accurate description of gas adsorption on solids is gaining in importance as the number of engineering applications expands. In the present work, a general two-dimensional equation of state (2-D EOS) is presented and the fugacity equations are derived to describe the adsorbed phase for adsorption from gas mixtures. Model constants in the 2-D EOS are determined from pure adsorption equilibria data and then used to predict adsorption of mixtures on both homogeneous and heterogeneous surfaces. The 2-D van der Waals, Soave-Redlich-Kwong, Peng-Robinson, Eyring as well as a new 2-D EOS are evaluated for systems which include both ideal and nonideal adsorption behaviors. The predictions are compared with those of other methods, including the Langmuir model and ideal adsorbed solution theory. Various forms of the 2-D EOS are found to be capable of representing azeotropic behavior, and they provide overall the best predictive results (when based on information from pure-component isotherms).
The Effect of Solvent on the Phase of Titanium Dioxide Deposited by Aerosol-assisted CVD

Titanium (IV) isopropoxide (TTIP) in methanol, ethanol, hexane, dichloromethane, and isopropanol solvents is used to deposit titanium dioxide thin films on glass and steel substrates at 550 °C using aerosol-assisted (AA)CVD. X-ray diffraction (XRD), and Raman spectra of the as-deposited films show that using methanol as the carrier solvent produces exclusively rutile films on steel, and predominantly rutile on glass substrates, while the use of the other solvents produces exclusively anatase phase on the steel under the same conditions. TiO2 is also deposited by AACVD from a mixture of ethanol and methanol solvents. As little as 15% of methanol in ethanol produces rutile as the predominant phase. Using a dye-ink test, the titanium dioxide thin films produced with ethanol are shown to be more active photocatalysts than films produced with methanol. All the films show photo-induced superhydrophilicity but, surprisingly, films stored in the dark have a water contact angle above 100°.
Determining the nonequilibrium criticality of a Gardner transition via a hybrid study of molecular simulations and machine learning

Significance Understanding the nature of glass states remains as one of the grand challenges presently. A much-debated issue is whether or not a glass-to-glass transition, the Gardner transition, occurs in deeply annealed glass states, for which a number of clearly defined physical properties must follow, according to theories of phase transitions. Here, utilizing the current machine learning techniques, we show that finite-time and finite-size analyses of the massive numerical data, produced from molecular dynamics simulations of a hard-sphere glass model, support that the Gardner transition is a second-order phase transition in three dimensions. Our study also provides estimates of the critical exponents of the transition, which traditional approaches are unable to obtain. Apparent critical phenomena, typically indicated by growing correlation lengths and dynamical slowing down, are ubiquitous in nonequilibrium systems such as supercooled liquids, amorphous solids, active matter, and spin glasses. It is often challenging to determine if such observations are related to a true second-order phase transition as in the equilibrium case or simply a crossover and even more so to measure the associated critical exponents. Here we show that the simulation results of a hard-sphere glass in three dimensions are consistent with the recent theoretical prediction of a Gardner transition, a continuous nonequilibrium phase transition. Using a hybrid molecular simulation–machine learning approach, we obtain scaling laws for both finite-size and aging effects and determine the critical exponents that traditional methods fail to estimate. Our study provides an approach that is useful to understand the nature of glass transitions and can be generalized to analyze other nonequilibrium phase transitions.
Generalizing Koopman Theory to Allow for Inputs and Control

We develop a new generalization of Koopman operator theory that incorporates the e ects of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of nonlinear dynamical systems. Moreover, Koopman is intimately connected to dynamic mode decomposition (DMD), a method that discovers coherent, spatio-temporal modes from data, connects local-linear analysis to nonlinear operator theory, and importantly creates an equation-free architecture for the study of complex systems. For actuated systems, standard Koopman analysis and DMD are incapable of producing input-output models; moreover, the dynamics and the modes will be corrupted by external forcing. Our new theoretical developments extend Koopman operator theory to allow for systems with nonlinear input-output characteristics. We show how this generalization is rigorously connected to a recent development called dynamic mode decomposition with control. We demonstrate this new theory on nonlinear dynamical systems, including a standard susceptible-infectious-recovered model with relevance to the analysis of infectious disease data with mass vaccination (actuation).
Protein-Energy Malnutrition Increases Mortality in Patients Hospitalized With Bacterial Pneumonia: A Retrospective Nationwide Database Analysis

Background Malnutrition is a less commonly recognized risk factor for various infections. It encompasses both undernutrition or protein-energy malnutrition (PEM) and overnutrition, including obesity. This study aimed to evaluate whether PEM impacts bacterial pneumonia (BP) and, if so, to quantify the degree of impact on inpatient outcomes. Methods This was a retrospective cohort study involving adult hospitalizations for BP using the nationwide inpatient database. Outcomes included comparing inpatient mortality, total hospital charges, length of hospital stay, as well as complications from bacterial pneumonia. Results The in-hospital mortality for adults with BP was 2.62%. Patients with PEM had a higher adjusted odds ratio (aOR) of inpatient mortality (adjusted odds ratio (aOR): 2.31, 95% confidence interval (CI): 2.14 - 2.48, p<0.001) as compared to non-PEM patients. PEM was also associated with higher odds of sepsis (aOR: 2.24, 95% CI: 2.04 - 2.46, p<0.001), septic shock (aOR: 3.29, 95% CI: 2.82 - 3.85, p<0.001), requiring mechanical ventilation (aOR: 2.51, 95% CI: 2.31 - 2.71, p<0.001), requiring vasopressors (aOR: 2.90, 95% CI: 2.20 - 3.83, p<0.001), acute respiratory distress syndrome (ARDS) (aOR: 1.63, 95% CI: 1.33 - 2.00, p<0.001), acute kidney failure (AKI) (aOR: 1.24, 95% CI: 1.18 - 1.29, p<0.001), deep vein thrombosis (DVT) (aOR: 1.80, 95% CI: 1.62 - 2.00, p<0.001), and pulmonary embolism (PE) (aOR: 1.25, 95% CI: 1.08 - 1.45, p=0.003). Conclusion The study concluded that PEM was an independent mortality predictor for those with BP, with an increased risk of systemic complications, as well as increased healthcare utilization costs.
Transient free‐surface flows in injection mold filling

Advancing and colliding flow fronts are important fluid mechanical problems in injection mold filling. These are small-scale, transient free-surface flows with a significant impact on the molecular orientation of molded parts. This paper describes a numerical technique for the simulation of transient free-surface flows. The algorithm combines a Galerkin/finite-element discretization of the governing equations with a predictor-corrector scheme for integration in time, and determines simultaneously the flow field and the free surface at every time step. The method is applied to the start-up of a fluid flow initially at rest and impingement of two flow fronts to form a weldline. Comparison of simulation with available experiments on the start-up problem shows very good agreement. Numerical tracking of fluid elements clarifies various aspects of flow-induced deformation of the material.
Non-operative Management of Adult Both Bone Forearm Fractures – A Case Report and Literature Review

Introduction: Adult both bone forearm fractures (BBFF) are common injuries that are typically treated with operative fixation given their instability. Non-displaced fractures can be theoretically treated non-operatively, but there is no literature demonstrating treatment outcomes of such fractures. Case Report: We present a case of non-displaced BBFF in a 23-year-old Caucasian male adult who was treated with cast immobilization and concomitant ultrasound stimulator use; this patient went on to have solid fracture healing without complication. Conclusion: Based on this case, we demonstrate that non-operative management of non-displaced BBFF in adult patients is an option if close follow-up is available. This is significant for the fields of both orthopedic and plastic surgery, as there is little concrete evidence of outcomes of such non-displaced fractures in hand surgery literature.
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization

We study online learning with bandit feedback (i.e. learner has access to only zeroth-order oracle) where cost/reward functions $\f_t$ admit a"pseudo-1d"structure, i.e. $\f_t(\w) = \loss_t(\pred_t(\w))$ where the output of $\pred_t$ is one-dimensional. At each round, the learner observes context $\x_t$, plays prediction $\pred_t(\w_t; \x_t)$ (e.g. $\pred_t(\cdot)=\langle \x_t, \cdot\rangle$) for some $\w_t \in \mathbb{R}^d$ and observes loss $\loss_t(\pred_t(\w_t))$ where $\loss_t$ is a convex Lipschitz-continuous function. The goal is to minimize the standard regret metric. This pseudo-1d bandit convex optimization problem (\SBCO) arises frequently in domains such as online decision-making or parameter-tuning in large systems. For this problem, we first show a lower bound of $\min(\sqrt{dT}, T^{3/4})$ for the regret of any algorithm, where $T$ is the number of rounds. We propose a new algorithm \sbcalg that combines randomized online gradient descent with a kernelized exponential weights method to exploit the pseudo-1d structure effectively, guaranteeing the {\em optimal} regret bound mentioned above, up to additional logarithmic factors. In contrast, applying state-of-the-art online convex optimization methods leads to $\tilde{O}\left(\min\left(d^{9.5}\sqrt{T},\sqrt{d}T^{3/4}\right)\right)$ regret, that is significantly suboptimal in $d$.
A closer look at temporal variability in dynamic online learning

This work focuses on the setting of dynamic regret in the context of online learning with full information. In particular, we analyze regret bounds with respect to the temporal variability of the loss functions. By assuming that the sequence of loss functions does not vary much with time, we show that it is possible to incur improved regret bounds compared to existing results. The key to our approach is to use the loss function (and not its gradient) during the optimization process. Building on recent advances in the analysis of Implicit algorithms, we propose an adaptation of the Implicit version of Online Mirror Descent to the dynamic setting. Our proposed algorithm is adaptive not only to the temporal variability of the loss functions, but also to the path length of the sequence of comparators when an upper bound is known. Furthermore, our analysis reveals that our results are tight and cannot be improved without further assumptions. Next, we show how our algorithm can be applied to the setting of learning with expert advice or to settings with composite loss functions. Finally, when an upper bound to the path-length is not fixed beforehand we show how to combine a greedy strategy with existing strongly-adaptive algorithms to compete optimally against different sequences of comparators simultaneously.
Thermal evolution of relativistic hyperonic compact stars with calibrated equations of state

A set of unified relativistic mean-field equations of state for hyperonic compact stars recently built in [M. Fortin, Ad. R. Raduta, S. Avancini, and C. Providencia, Phys. Rev. D {\bf 101}, 034017 (2020)] is used to study the thermal evolution of non-magnetized and non-rotating spherically-symmetric isolated and accreting neutron stars under different hypothesis concerning proton $S$-wave superfluidity. These equations of state have been obtained in the following way: the slope of the symmetry energy is in agreement with experimental data; the coupling constants of $\Lambda$ and $\Xi$-hyperons are determined from experimental hypernuclear data; uncertainties in the nucleon-$\Sigma$ interaction potential are accounted for; current constraints on the lower bound of the maximum neutron star mass are satisfied. Within the considered set of equations of state, the presence of hyperons is essential for the description of the cooling/heating curves. One of the conclusions we reach is that the criterion of best agreement with observational data leads to different equations of states and proton $S$-wave superfluidity gaps when applied separately for isolated neutron stars and accreting neutron stars in quiescence. This means that at least in one situation the traditional simulation framework that we employ is not complete and/or the equations of state are inappropriate. Another result is that, considering equations of state which do not allow for nucleonic dUrca or allow for it only in very massive NS, the low luminosity of SAX J1808 requires a repulsive $\Sigma$-hyperon potential in symmetric nuclear matter in the range $U_\Sigma^{(N)}\approx 10-30$ MeV. This range of values for $U_\Sigma^{(N)} $ is also supported by the criterion of best agreement with all available data from INS and XRT.
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. Under this setting, neural network training performance may vary significantly according to the data distribution and even hurt training convergence. Most of the previous work has focused on a difference in the distribution of labels or client shifts. Unlike those settings, we address an important problem of FL, e.g., different scanners/sensors in medical imaging, different scenery distribution in autonomous driving (highway vs. city), where local clients store examples with different distributions compared to other clients, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg, as well as the state-of-the-art for non-iid data (FedProx) on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg. Code is available at https://github.com/med-air/FedBN.
Developing parsimonious ensembles using predictor diversity within a reinforcement learning framework

Heterogeneous ensembles that can aggregate an unrestricted number and variety of base predictors can effectively address challenging prediction problems. In particular, accurate ensembles that are also parsimonious, i.e., consist of as few base predictors as possible, can help reveal potentially useful knowledge about the target problem domain. Although ensemble selection offers a potential approach to achieving these goals, the currently available algorithms are limited in their abilities. In this paper, we present several algorithms that incorporate ensemble diversity into a reinforcement learning (RL)-based ensemble selection framework to build accurate and parsimonious ensembles. These algorithms, as well as several baselines, are rigorously evaluated on datasets from diverse domains in terms of the predictive performance and parsimony of their ensembles. This evaluation demonstrates that our diversity-incorporated RL-based algorithms perform better than the others for constructing simultaneously accurate and parsimonious ensembles. These algorithms can eventually aid the interpretation or reverse engineering of predictive models assimilated into effective ensembles. To enable such a translation, an implementation of these algorithms, as well the experimental setup they are evaluated in, has been made available at https://github.com/GauravPandeyLab/lens-learning-ensembles-using-reinforcement-learning.
IMF: An Iterative Max-Flow Method for Localizability Detection

—Determining whether nodes can be localized, called localizability detection, is a basic work in network localization. We present a novel approach for localizability detection given a set of nodes and their connectivity relationships. The key idea is transforming the problem of detecting three disjoint paths into a max-ﬂow problem, which can be efﬁciently solved by existing algorithms. Furthermore, we prove the correctness of our algorithm.
Charge-dependent oxygen vacancy diffusion in Al2O3-based resistive-random-access-memories

We theoretically study an oxygen vacancy (VO) diffusion in Al2O3-based resistive-random-access-memories (ReRAMs). We find that the activation energy of VO diffusion in Al2O3 strongly depends on the charge state of VO. In ReRAM, the charge state of VO can be easily changed by applying voltage and the lowest activation energy is observed at q = 2+. The operation voltage on Al2O3-based ReRAM is close to the activation energy at q = 2+, indicating that VO diffuses with doubly positive state. Moreover, the activation energy at q = 0 is close to that observed in bulk Al2O3, which explains the discrepancy between previous experimental and theoretical studies.
Cross-domain Time Series Forecasting with Attention Sharing

Recent years have witnessed deep neural networks gaining increasing popularity in the ﬁeld of time series forecasting. A primary reason of their success is their ability to effectively capture complex temporal dynamics across multiple related time series. However, the advantages of these deep forecasters only start to emerge in the presence of a sufﬁcient amount of data. This poses a challenge for typical forecasting problems in practice, where one either has a small number of time series, or limited observations per time series, or both. To cope with the issue of data scarcity, we propose a novel domain adaptation framework, Domain Adaptation Forecaster (DAF), that leverages the statistical strengths from another relevant domain with abundant data samples (source) to improve the performance on the domain of interest with limited data (target). In particular, we pro-pose an attention-based shared module with a domain discriminator across domains as well as private modules for individual domains. This allows us to jointly train the source and target domains by generating domain-invariant latent features while retraining domain-speciﬁc features. Extensive experiments on various domains demonstrate that our proposed method outperforms state-of-the-art baselines on synthetic and real-world datasets.
Demystifying Inductive Biases for β-VAE Based Architectures

The performance of $\beta$-Variational-Autoencoders ($\beta$-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled. On the other hand, there are theoretical arguments suggesting the impossibility of unsupervised disentanglement. In this work, we shed light on the inductive bias responsible for the success of VAE-based architectures. We show that in classical datasets the structure of variance, induced by the generating factors, is conveniently aligned with the latent directions fostered by the VAE objective. This builds the pivotal bias on which the disentangling abilities of VAEs rely. By small, elaborate perturbations of existing datasets, we hide the convenient correlation structure that is easily exploited by a variety of architectures. To demonstrate this, we construct modified versions of standard datasets in which (i) the generative factors are perfectly preserved; (ii) each image undergoes a mild transformation causing a small change of variance; (iii) the leading \textbf{VAE-based disentanglement architectures fail to produce disentangled representations whilst the performance of a non-variational method remains unchanged}. The construction of our modifications is nontrivial and relies on recent progress on mechanistic understanding of $\beta$-VAEs and their connection to PCA. We strengthen that connection by providing additional insights that are of stand-alone interest.
CQNet: Complex Input Quantized Neural Network designed for Massive MIMO CSI Feedback

The Massive Multiple Input Multiple Output (MIMO) system is a core technology of the next generation communication. With the growing complexity of CSI in massive MIMO system, traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical. Recently, numerous deep learning based CSI feedback approaches demonstrate the efﬁciency and potential. However, the existing methods lack a reasonable interpretation of the deep learning model and the accuracy of the model decreases signiﬁcantly as the CSI compression rate increases. In this paper, from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07% in both outdoor and indoor scenarios. In addition, this paper also investigates the reasons for the decrease in model accuracy at large compression rates and proposes a strategy to embed a quantization layer to achieve effective compression, by which the original accuracy loss of 67.19% on average is reduced to 21.96% on average, and the compression rate is increased by 8 times on the original benchmark. Codes are available at github 1 .
Kronecker-factored Quasi-Newton Methods for Convolutional Neural Networks

Second-order methods have the capability of accelerating optimization by using much richer curvature information than ﬁrst-order methods. However, most are impractical in a deep learning setting where the number of training parameters is huge. In this paper, we propose KF-QN-CNN, a new Kronecker-factored quasi-Newton method for training convolutional neural networks (CNNs), where the Hessian is approximated by a layer-wise block diagonal matrix and each layer’s diagonal block is further approximated by a Kronecker product corresponding to the structure of the Hessian restricted to that layer. New damping and Hessian-action techniques for BFGS are designed to deal with the non-convexity and the particularly large size of Kronecker matrices in CNN models and convergence results are proved for a variant of KF-QN-CNN under relatively mild conditions. KF-QN-CNN has memory requirements comparable to ﬁrst-order methods and much less per-iteration time complexity than traditional second-order methods. Compared with state-of-the-art ﬁrst-and second-order methods on several CNN models, KF-QN-CNN consistently exhibited superior performance in all of our tests.