Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximal hypersurface in a D-dimensional dynamical spacetime

In this article, we set up a variational problem to arrive at the equation of the maximal hypersurface in the interior of a spherically symmetric evolving trapped region. In the first part of the article, we present the Lagrangian and the corresponding Euler-Lagrange equations that maximize the interior volume of a trapped region that is formed dynamically due to infalling matter in D-dimensions, with and without the cosmological constant. In the second part, we explore the properties of special radii, which we call Reinhart radii, that play a crucial role in approximating the maximal interior volume of a black hole. We derive a formula to locate these Reinhart radii in terms of coordinate invariants like area radius, principle values of the energy-momentum tensor, Misner-Sharp mass, and cosmological constant. Based on this formula, we estimate the location of Reinhart radii in various scenarios: (a) the case of static BTZ black holes in (2 + 1)-dimensions and for the Schwarzschild, Schwarzschild-de Sitter, and Schwarzschild-anti-de Sitter black holes in D-dimensions. We plot the location of the Reinhart radii in relation to the event horizon and cosmological horizon in a static D-dimensional scenario, (b) cosmological case: we prove that these Reinhart radii do not exist for homogeneous evolving dust for the zero and negative cosmological constant but exist in the presence of positive cosmological constant when the scale factor is greater than a critical value. We also show the relation between these Reinhart radii and Kodama vectors.
Large positive and negative Goos-Hänchen shifts from a weakly absorbing left-handed slab

We theoretically show that the Goos-Hanchen (GH) shifts [F. Goos and H. Hanchen, Ann. Phys. 1, 333 (1947)] of the TE- and TM-polarized beams reflected from a slab of the weakly absorbing left-handed material can be large positive near the resonant transmission if the absorption of the slab is sufficiently weak. The GH shift of the TM-polarized reflected beam can be large negative or positive near the Brewster angle.
The criterion on the Propulsive Balance of Oblique Detonation Engine

： The oblique detonation engine (ODE) has established a clear superiority for hypersonic flight because of its high thermal efficiency and compact structure. It has become the research hot spot all over the world in recent years. The aim of this study is to derive a criterion on the propulsive balance of ODE, from which we can find the key parameters governing the propulsive performance explicitly. A physical model of ODE is put forth, which consists of the inlet, the constant cross-section combustor and the divergent nozzle. The mathematical equations to calculate the thrust generated by the divergent nozzle and the pressure drag produced by the inlet are deduced. The net thrust of ODE is then obtained. The criterion shows clearly that the static temperature at the engine inlet exit is a very important parameter. The lower the inlet exit temperature is, the higher the specific impulse will be. The specific impulse of ODE with stoichiometric H 2 /air mixture and hydrocarbon/air mixture are calculated by using these equations. The results show that ODE can obtain positive net thrust from Ma8 to Ma15.
A hysteresis effect in the generation of field‐aligned irregularities by a high‐power radio wave

The anomalous self-absorption of a high-power radio wave measured during recent ionospheric modification experiments exhibits a hysteresis effect. These results are consistent with the assumption that the growth of field-aligned irregularities due to the interaction of a high-power pump wave and the ionospheric plasma occurs in two stages. During the first stage, striation growth is due to an instability which has a threshold independent of striation amplitude. When the striation level is sufficiently high, the threshold of the second stage is reached. The second-stage threshold is dependent on the striation amplitude. The state of background ionosphere is also found to be an important factor influencing the observed effects.
Medical Image Segmentation Review: The success of U-Net

—Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its ﬂexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deﬁciency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible ofﬁcial implementation. All information is gathered in https://github.com/NITR098/Awesome-U-Net repository.
Raman, green and infrared emission cross-sectionsof Er 3+ doped TZPPN tellurite glass

The spectroscopic properties of Er3‏+ ion in tellurite glass of molar composition 76TeO2∙10ZnO∙9.0PbO∙1.0PbF2∙3.0Na2O∙1.0Er2O3 was investigated experimentally. The three phenomenological intensity parameters Ωk (k = 2, 4, 6) were determined from the absorption spectral intensities using the Judd-Ofelt (J-O) theory. Several radiative properties such as spontaneous transition probabilities, fluorescence branching ratios and radiative life times were determined by using these intensity parameters. The special attention was attributed to the visible emissions that could be obtained by pumping using a blue laser diode. The stimulated emission cross-section and CIE chromaticity coordinates were calculated. The latter were used to evaluate green light emitting by Er:TZPPN glass. Subsequently, the stimulated emission cross-section, around 1.5 μm, was calculated from McCumber theory. Gain cross-section for laser transition 4I13/2→ 4I15/2 of Er3+-ions was obtained. In comparison with other Er-doped laser glasses, the calculated parameters show that Er:TZPPN glass satisfies the fundamental spectral condition for laser emission around 1.5 μm. Moreover the Raman gain coefficient of the present glass was obtained from Raman scattering experiments using 532 nm excitation [(532 nm Laser type Diode-pumped, solid state (DPSS)]. The developed glass showed the widest bandwidths of gain cross section from 249 to 1,106 cm−1.
Hypernetworks for Zero-shot Transfer in Reinforcement Learning

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
How nanoporous silicon-polypyrrole hybrids flex their muscles in aqueous electrolytes: 
In operando
 high-resolution x-ray diffraction and electron tomography-based micromechanical computer simulations

Macroscopic strain experiments revealed that Si crystals traversed by parallel, channel-like nanopores functionalized with the muscle polymer polypyrrole exhibit large and reversible electrochemo-mechanical actuation in aqueous electrolytes. On the microscopical level this system still bears open questions, as to how the electrochemical expansion and contraction of PPy acts on to np-Si pore walls and how the collective motorics of the pore array emerges from the single-nanopore behavior. An analysis of in operando X-ray diffraction experiments with micromechanical finite element simulations, based on a 3D reconstruction of the nanoporous medium by TEM tomography, shows that the in-plane mechanical response is dominantly isotropic despite the anisotropic elasticity of the single crystalline host matrix. However, the structural anisotropy originating from the parallel alignment of the nanopores lead to significant differences between the in- and out-of-plane electromechanical response. This response is not describable by a simple 2D arrangement of parallel cylindrical channels. Rather, the simulations highlight that the dendritic shape of the Si pore walls, including pore connections between the main channels, cause complex, inhomogeneous stress-strain fields in the crystalline host. Time-dependent X-ray scattering on the dynamics of the actuator properties hint towards the importance of diffusion limitations, plastic deformation and creep in the nanoconfined polymer upon (counter-)ion adsorption and desorption, the very pore-scale processes causing the macroscopic electroactuation. From a more general perspective, our study demonstrates that the combination of TEM tomography-based micromechanical modeling with high-resolution X-ray scattering experiments provides a powerful approach for in operando analysis of nanoporous composites from the single-nanopore up to the porous-medium scale.
Stochastic Steffensen method

Is it possible for a first-order method, i.e., only first derivatives allowed, to be quadratically convergent? For univariate loss functions, the answer is yes -- the Steffensen method avoids second derivatives and is still quadratically convergent like Newton method. By incorporating an optimal step size we can even push its convergence order beyond quadratic to $1+\sqrt{2} \approx 2.414$. While such high convergence orders are a pointless overkill for a deterministic algorithm, they become rewarding when the algorithm is randomized for problems of massive sizes, as randomization invariably compromises convergence speed. We will introduce two adaptive learning rates inspired by the Steffensen method, intended for use in a stochastic optimization setting and requires no hyperparameter tuning aside from batch size. Extensive experiments show that they compare favorably with several existing first-order methods. When restricted to a quadratic objective, our stochastic Steffensen methods reduce to randomized Kaczmarz method -- note that this is not true for SGD or SLBFGS -- and thus we may also view our methods as a generalization of randomized Kaczmarz to arbitrary objectives.
Multi-Modal Few-Shot Temporal Action Detection via Vision-Language Meta-Adaptation

Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection (TAD) to new classes. The former adapts a pretrained vision model to a new task represented by as few as a single video per class, whilst the latter requires no training examples by exploiting a semantic description of the new class. In this work, we introduce a new multi-modality few-shot (MMFS) TAD problem, which can be considered as a marriage of FS-TAD and ZS-TAD by leveraging few-shot support videos and new class names jointly. To tackle this problem, we further introduce a novel MUlti-modality PromPt mETa-learning (MUPPET) method. This is enabled by efficiently bridging pretrained vision and language models whilst maximally reusing already learned capacity. Concretely, we construct multi-modal prompts by mapping support videos into the textual token space of a vision-language model using a meta-learned adapter-equipped visual semantics tokenizer. To tackle large intra-class variation, we further design a query feature regulation scheme. Extensive experiments on ActivityNetv1.3 and THUMOS14 demonstrate that our MUPPET outperforms state-of-the-art alternative methods, often by a large margin. We also show that our MUPPET can be easily extended to tackle the few-shot object detection problem and again achieves the state-of-the-art performance on MS-COCO dataset. The code will be available in https://github.com/sauradip/MUPPET
BadPrompt: Backdoor Attacks on Continuous Prompts

The prompt-based learning paradigm has gained much research attention recently. It has achieved state-of-the-art performance on several NLP tasks, especially in the few-shot scenarios. While steering the downstream tasks, few works have been reported to investigate the security problems of the prompt-based models. In this paper, we conduct the first study on the vulnerability of the continuous prompt learning algorithm to backdoor attacks. We observe that the few-shot scenarios have posed a great challenge to backdoor attacks on the prompt-based models, limiting the usability of existing NLP backdoor methods. To address this challenge, we propose BadPrompt, a lightweight and task-adaptive algorithm, to backdoor attack continuous prompts. Specially, BadPrompt first generates candidate triggers which are indicative for predicting the targeted label and dissimilar to the samples of the non-targeted labels. Then, it automatically selects the most effective and invisible trigger for each sample with an adaptive trigger optimization algorithm. We evaluate the performance of BadPrompt on five datasets and two continuous prompt models. The results exhibit the abilities of BadPrompt to effectively attack continuous prompts while maintaining high performance on the clean test sets, outperforming the baseline models by a large margin. The source code of BadPrompt is publicly available at https://github.com/papersPapers/BadPrompt.
An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea

The crossMoDA challenge aims to automatically segment the vestibular schwannoma (VS) tumor and cochlea regions of unlabeled high-resolution T2 scans by leveraging labeled contrast-enhanced T1 scans. The 2022 edition extends the segmentation task by including multi-institutional scans. In this work, we proposed an unpaired cross-modality segmentation framework using data augmentation and hybrid convolutional networks. Considering heterogeneous distributions and various image sizes for multi-institutional scans, we apply the min-max normalization for scaling the intensities of all scans between -1 and 1, and use the voxel size resampling and center cropping to obtain fixed-size sub-volumes for training. We adopt two data augmentation methods for effectively learning the semantic information and generating realistic target domain scans: generative and online data augmentation. For generative data augmentation, we use CUT and CycleGAN to generate two groups of realistic T2 volumes with different details and appearances for supervised segmentation training. For online data augmentation, we design a random tumor signal reducing method for simulating the heterogeneity of VS tumor signals. Furthermore, we utilize an advanced hybrid convolutional network with multi-dimensional convolutions to adaptively learn sparse inter-slice information and dense intra-slice information for accurate volumetric segmentation of VS tumor and cochlea regions in anisotropic scans. On the crossMoDA2022 validation dataset, our method produces promising results and achieves the mean DSC values of 72.47% and 76.48% and ASSD values of 3.42 mm and 0.53 mm for VS tumor and cochlea regions, respectively.
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network

Transformations in the input space of Deep Neural Networks (DNN) lead to unintended changes in the feature space. Almost perceptually identical inputs, such as adversarial examples, can have significantly distant feature representations. On the contrary, Out-of-Distribution (OOD) samples can have highly similar feature representations to training set samples. Our theoretical analysis for DNNs trained with a categorical classification head suggests that the inflexible logit space restricted by the classification problem size is one of the root causes for the lack of $\textit{robustness}$. Our second observation is that DNNs over-fit to the training augmentation technique and do not learn $\textit{nuance invariant}$ representations. Inspired by the recent success of prototypical and contrastive learning frameworks for both improving robustness and learning nuance invariant representations, we propose a training framework, $\textbf{Supervised Contrastive Prototype Learning}$ (SCPL). We use N-pair contrastive loss with prototypes of the same and opposite classes and replace a categorical classification head with a $\textbf{Prototype Classification Head}$ (PCH). Our approach is $\textit{sample efficient}$, does not require $\textit{sample mining}$, can be implemented on any existing DNN without modification to their architecture, and combined with other training augmentation techniques. We empirically evaluate the $\textbf{clean}$ robustness of our method on out-of-distribution and adversarial samples. Our framework outperforms other state-of-the-art contrastive and prototype learning approaches in $\textit{robustness}$.
Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox disease outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease syndromes are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performances in the image-based diagnosis of COVID-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep-learning models, VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2, along with integrated channel and spatial attention mechanisms, and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying human monkeypox and other diseases with a validation accuracy of 83.89%.
Null controllability of damped nonlinear wave equation

In this paper, we study the null controllability of nonlinear wave systems. Firstly, by combing Gerlerkin method and a fixed point theorem, we give the null controllability for semi-linear wave equation with nonlinear function dependents on velocities under the geometric control condition. Secondly, based on a new iterative method, we prove the null controllability for a class of quasi-linear wave system constructively. Finally, we give a control result for a class of fully nonlinear wave system as application. 2010 Mathematics Subject Classification. 35L70, 93B05, 35L05
Application of a WRF Mesoscale Data Assimilation System to Springtime Severe Weather Events 2007–09

AbstractAn ensemble-based data assimilation system using the Weather Research and Forecasting Model (WRF) has been used to initialize forecasts of prolific severe weather events from springs 2007 to 2009. These experiments build on previous work that has shown the ability of ensemble Kalman filter (EnKF) data assimilation to produce realistic mesoscale features, such as drylines and convectively driven cold pools, which often play an important role in future convective development. For each event in this study, severe weather parameters are calculated from an experimental ensemble forecast started from EnKF analyses, and then compared to a control ensemble forecast in which no ensemble-based data assimilation is performed. Root-mean-square errors for surface observations averaged across all events are generally smaller for the experimental ensemble over the 0–6-h forecast period. At model grid points nearest to tornado reports, the ensemble-mean significant tornado parameter (STP) and the probability that.
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation

We approach autonomous drone-based reforestation with a collaborative multi-agent reinforcement learning (MARL) setup. Agents can communicate as part of a dynamically changing network. We explore collaboration and communication on the back of a high-impact problem. Forests are the main resource to control rising CO2 conditions. Unfortunately, the global forest volume is decreasing at an unprecedented rate. Many areas are too large and hard to traverse to plant new trees. To efficiently cover as much area as possible, here we propose a Graph Neural Network (GNN) based communication mechanism that enables collaboration. Agents can share location information on areas needing reforestation, which increases viewed area and planted tree count. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show how communication enables collaboration and increases collective performance, planting precision and the risk-taking propensity of individual agents.
Discontinuous Shear Thickening in Biological Tissue Rheology

During embryonic morphogenesis, tissues undergo dramatic deformations in order to form functional organs. Similarly, in adult animals, living cells and tissues are continually subjected to forces and deformations. Therefore, the success of embryonic development and the proper maintenance of physiological functions rely on the ability of cells to withstand mechanical stresses as well as their ability to flow in a collective manner. During these events, mechanical perturbations can originate from active processes at the single cell level, competing with external stresses exerted by surrounding tissues and organs. However, the study of tissue mechanics has been somewhat limited to either the response to external forces or to intrinsic ones. In this work, we use an active vertex model of a 2D confluent tissue to study the interplay of external deformations that are applied globally to a tissue with internal active stresses that arise locally at the cellular level due to cell motility. We elucidate in particular the way in which this interplay between globally external and locally internal active driving determines the emergent mechanical properties of the tissue as a whole. For a tissue in the vicinity of a solid-fluid jamming/unjamming transition, we uncover a host of fascinating rheological phenomena, including yielding, shear thinning, continuous shear thickening (CST) and discontinuous shear thickening (DST). These model predictions provide a framework for understanding the recently observed nonlinear rheological behaviors {\it in vivo}.