Miracle-dz/UniFAR-FTUs huggingface dataset:
Nimodipine Promotes Functional Recovery After Spinal Cord Injury in Rats

Spinal cord injury (SCI) is a devastating condition that results in severe motor, sensory, and autonomic dysfunction. The L-/T-type calcium channel blocker nimodipine (NMD) exerts a protective effect on neuronal injury; however, the protective effects of long-term administration of NMD in subjects with SCI remain unknown. Thus, the aim of this study was to evaluate the role of long-term treatment with NMD on a clinically relevant SCI model. Female rats with SCI induced by 25 mm contusion were subcutaneously injected with vehicle or 10 mg/kg NMD daily for six consecutive weeks. We monitored the motor score, hind limb grip strength, pain-related behaviors, and bladder function in this study to assess the efficacy of NMD in rats with SCI. Rats treated with NMD showed improvements in locomotion, pain-related behaviors, and spasticity-like symptoms, but not in open-field spontaneous activity, hind limb grip strength or bladder function. SCI lesion areas and perilesional neuronal numbers, gliosis and calcitonin gene-related peptide (CGRP+) fiber sprouting in the lumbar spinal cord and the expression of K+–Cl− cotransporter 2 (KCC2) on lumbar motor neurons were also observed to further explore the possible protective mechanisms of NMD. NMD-treated rats showed greater tissue preservation with reduced lesion areas and increased perilesional neuronal sparing. NMD-treated rats also showed improvements in gliosis, CGRP+ fiber sprouting in the lumbar spinal cord, and KCC2 expression in lumbar motor neurons. Together, these results indicate that long-term treatment with NMD improves functional recovery after SCI, which may provide a potential therapeutic strategy for the treatment of SCI.
Design of Shallow Neural Network Based Plant Disease Detection System

— In this work, we proposed the use of a shallow neural network for plant disease detection. The study focuses on four major diseases that are known to attack some of the most cultivated crops globally. The diseases considered include Bacterial Blight, Anthracnose, Cercospora leaf spot and Alternaria Alternata. In developing the disease detection model, K-means algorithm was used for plant segmentation while color co-occurrence method was used for feature analysis. A shallow neural network trained on 145 training samples was used as a classifier. The detection accuracy of 98.34 %, 98.48%, 98.03% and 98.14% were recorded for Bacterial Blight, Anthracnose, Cercospora leaf spot and Alternaria Alternata diseases respectively. The overall detection accuracy of the model is 98.25%.
Image Captioning Techniques

- In the past decade, the need of generating descriptive sentences automatically for images has generated a rising interest in natural language processing and computer vision research. Image captioning is an important task that requires semantic understanding of images and the ability to generate description sentences with correct structure. In this study, we propose a hybrid system that employs the use of multilayer Convolutional Neural Network (CNN) to generate a vocabulary which describes images and Long Short-Term Memory (LSTM) for accurately structuring meaningful sentences using the generated keywords. The convolutional neural network compares the target image with a large dataset of training images, after which it generates an accurate description using trained captions. We have showcased the efficiency of our proposed model using the Flickr8K and Flickr30K datasets and we can show that their model gives superior results when compared to the state-of-the-art models utilizing the Bleu metric. The Bleu metric is an algorithm that evaluates the performance of a machine translation system by grading the quality of text translated from one natural language to another. The performance of this proposed model has been evaluated using standard evaluation matrices, which are found to be outperforming previous benchmark models.
Resource sharing on endogenous networks

We examine behavior in a voluntary resource sharing game that incorporates endogenous network formation; an incentive problem that is increasingly common in contemporary digital economies. By varying the information structure in a controlled laboratory experiment, we examine the underlying mechanisms of reciprocity that generate emergent patterns in linking and contribution decisions. Specifically, we vary whether players are given information about which other players in their group are sharing with them. Reduced-form estimates find significant effects of this information treatment on a number of key outcomes such as efficiency and balanced decentralization. To further understand the driving causes of these observed changes in behavior, we develop and estimate a discrete-choice framework, using computationally efficient panel methods to identify the structure of social preferences in this setting. We find that subjects react to this new information by focusing reciprocity, which helps players coordinate to reach mutually beneficial outcomes. We also find that direct and indirect reciprocity are not perfect substitutes, and this interaction further helps players support efficient outcomes.
Multi-loss ensemble deep learning for chest X-ray classification

Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train the deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. In this work, we benchmark various state-of-the-art loss functions that are suitable for multi-class classification, critically analyze model performance, and propose improved loss functions. We select a pediatric chest X-ray (CXR) dataset that includes images with no abnormality (normal), and those exhibiting manifestations consistent with bacterial and viral pneumonia. We construct prediction-level and model-level ensembles, respectively, to improve classification performance. Our results show that compared to the individual models and the state-of-the-art literature, the weighted averaging of the predictions for top-3 and top-5 model-level ensembles delivered significantly superior classification performance (p<0.05) in terms of MCC (0.9068, 95% confidence interval (0.8839, 0.9297)) metric. Finally, we performed localization studies to interpret model behaviors to visualize and confirm that the individual models and ensembles learned meaningful features and highlighted disease manifestations.
Flow Based Models For Manifold Data

Flow-based generative models typically deﬁne a latent space with dimensionality identical to the observational space. In many problems, however, the data does not populate the full ambient data-space that they natively reside in, rather inhabiting a lower-dimensional manifold. In such scenarios, ﬂow-based models are unable to represent data structures exactly as their density will always have support off the data manifold, potentially resulting in degradation of model performance. In addition, the requirement for equal latent and data space dimensionality can un-necessarily increase complexity for contemporary ﬂow models. Towards addressing these problems, we propose to learn a manifold prior that affords beneﬁts to both sample generation and representation quality. An auxiliary beneﬁt of our approach is the ability to identify the intrinsic dimension of the data distribution.
On the Estimation Bias in Double Q-Learning

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation. Its variants in the deep Q-learning paradigm have shown great promise in producing reliable value prediction and improving learning performance. However, as shown by prior work, double Q-learning is not fully unbiased and suffers from underestimation bias. In this paper, we show that such underestimation bias may lead to multiple non-optimal ﬁxed points under an approximate Bellman operator. To address the concerns of converging to non-optimal stationary solutions, we propose a simple but effective approach as a partial ﬁx for the underestimation bias in double Q-learning. This approach leverages an approximate dynamic programming to bound the target value. We extensively evaluate our proposed method in the Atari benchmark tasks and demonstrate its signiﬁcant improvement over baseline algorithms. an interesting fact that, under the effects of approximation error, double Q-learning may have multiple non-optimal ﬁxed points. The main cause of such non-optimal ﬁxed points is the underestimation bias of double Q-learning. Regarding this issue, we provide some analysis to characterize what kind of Bellman operators may suffer from the same problem, and how the agent may behave around these ﬁxed points. To address the potential risk of converging to non-optimal solutions, we propose doubly bounded Q-learning to reduce the underestimation in double Q-learning. The main idea of this approach is to leverage an abstracted dynamic programming as a second value estimator to rule out non-optimal ﬁxed points. The experiments show that the proposed method has shown great promise in improving
PeV Emission of the Crab Nebula: Constraints on the Proton Content in Pulsar Wind and Implications

Recently, two photons from the Crab Nebula with energy of approximately 1 PeV were detected by the Large High Altitude Air Shower Observatory (LHAASO), opening an ultrahigh-energy window for studying pulsar wind nebulae (PWNe). Remarkably, the LHAASO spectrum at the highest-energy end shows a possible hardening, which could indicate the presence of a new component. A two-component scenario with a main electron component and a secondary proton component has been proposed to explain the whole spectrum of the Crab Nebula, requiring a proton energy of 1046–1047 erg remaining in the present Crab Nebula. In this paper, we study the energy content of relativistic protons in pulsar winds using the LHAASO data of the Crab Nebula, considering the effect of diffusive escape of relativistic protons. Depending on the extent of the escape of relativistic protons, the total energy of protons lost in the pulsar wind could be 10–100 times larger than that remaining in the nebula presently. We find that the current LHAASO data allow up to (10–50)% of the spindown energy of pulsars being converted into relativistic protons. The escaping protons from PWNe could make a considerable contribution to the cosmic-ray flux of 10–100 PeV. We also discuss the leptonic scenario for the possible spectral hardening at PeV energies.
Radiatively driven, time dependent bipolar outflows

We study the radiatively driven fluid jets around a non-rotating black hole. The radiation arising from the inner compact corona and outer sub-Keplerian part of the disc accelerates the jets. We obtain the steady state, semi-analytical, radiatively driven outflow solutions. The thermodynamics of the outflow is described by a variable adiabatic index equation of state. We develop a TVD routine to investigate the time dependent behaviour of the radiatively driven bipolar outflow. We inject with flow variables from the steady state outflow solutions in the TVD code and allow the code to settle into steady state and match the numerical results with the steady state solution. The radiation arising out of the accretion disc can provide a wide range of jet solutions, depending upon parameters like the intensity of disc, location of the inner corona etc. We induce the time dependence of the radiation field by inducing oscillation of the inner corona of the accretion disc. The radiation field then makes the bipolar outflow time dependent. We show that a non-steady radiation field arising out of disc oscillations can generate the internal shocks closer to the jet base. Depending on the disc geometry, there might be transient shocks in the jet and there might be multiple non-stationary shocks in the jet, which are of much interest in jet physics.
Flux Limitation Mechanisms Arising in Multiscale Modelling of Cancer Invasion

Tumour invasion is an essential stage of cancer progression. Its main drivers are diffusion and taxis, a directed movement along the gradient of a stimulus. Here we review models with flux limited diffusion and/or taxis which have applications in modelling of cell migration, particularly in cancer. Flux limitation ensures control upon propagation speeds, precluding unnaturally quick spread which is typical for traditional parabolic equations. We recall the main properties of models with flux limitation effects and discuss ways to construct them, concentrating on multiscale derivations from kinetic transport equations.
Dirac fermion optics and directed emission from single- and bilayer graphene cavities

High-mobility graphene hosting massless charge carriers with linear dispersion provides a promising platform for electron optics phenomena. Inspired by the physics of dielectric optical micro-cavities where the photon emission characteristics can be efﬁciently tuned via the cavity shape, we study corresponding mechanisms for trapped Dirac fermionic resonant states in deformed micro-disk graphene billiards and directed emission from those. In such graphene devices a back-gate voltage provides an additional tunable parameter to mimic different effective refractive indices and thereby the corresponding Fresnel laws at the boundaries. Moreover, cavities based on single-layer and double-layer graphene exhibit Klein- and anti-Klein tunneling, respectively, leading to distinct differences with respect to dwell times and resulting emission proﬁles of the cavity states. Moreover, we ﬁnd a variety of different emission characteristics depending on the position of the source where charge carriers are fed into the cavites. Combining quantum mechanical simulations with optical ray tracing and a corresponding phase-space analysis, we demonstrate strong conﬁnement of the emitted charge carriers in the mid ﬁeld of single-layer graphene systems and can relate this to a lensing effect. For bilayer graphene, trapping of the resonant states is more efﬁcient and the emission characteristics do less depend on the source position.
Dynamic Emotions of Supporters and Opponents of Anti-racism Movement from George Floyd Protests

Social media empowers citizens to raise the voice and expressed civil outrage leads to collective action to change the society. Since social media welcomes anyone regardless of the political ideology or perspectives, social media is where the supporters and opponents of specific issue discuss. This study attempts to empirically examine a recent antiracism movement initiated by the death of George Floyd with the lens of stance prediction and aspect-based sentiment analysis (ABSA). First, this study found the stance of the tweet and users do change over the course of the protest. Furthermore, there are more users who shifted the stance compared to those who maintained the stance. Second, both supporters and opponents expressed negative sentiment more on nine extracted aspects. This indicates that there was no significant difference of sentiment among supporters and opponents and raise a caution in predicting stance based on the sentiment. The contribution of the study is two-fold. First, ABSA was explored in the context of computational social science and second, stance prediction was first attempted at scale.
Distribution Knowledge Embedding for Graph Pooling

Graph-level representation learning is the pivotal step for downstream tasks that operate on the whole graph. The most common approach to this problem is graph pooling, where node features are typically averaged or summed to obtain the graph representations. However, pooling operations like averaging or summing inevitably cause severe information missing, which may severely downgrade the final performance. In this article, we argue what is crucial to graph-level downstream tasks includes not only the topological structure but also the distribution from which nodes are sampled. Therefore, powered by existing Graph Neural Networks (GNN), we propose a new plug-and-play pooling module, termed as Distribution Knowledge Embedding (DKEPool), where graphs are viewed as distributions on top of GNNs and the pooling goal is to summarize the entire distribution information instead of retaining a certain feature vector by simple predefined pooling operations. A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning. Structure learning follows a recursive neighborhood aggregation scheme to update node features where structure information is obtained. Distribution learning, on the other hand, omits node interconnections and focuses more on the distribution depicted by all the nodes. Extensive experiments on graph classification tasks demonstrate that the proposed DKEPool significantly and consistently outperforms the state-of-the-art methods. The code is avaliable at https://github.com/chenchkx/dkepool
Registry-Dependent Potential for Interfaces of Gold with Graphitic Systems

We present a semi-anisotropic interfacial potential (SAIP) designed to classically describe the interaction between gold and two-dimensional (2D) carbon allotropes such as graphene, fullerenes, or hydrocarbon molecules. The potential is able to accurately reproduce dispersion-corrected density functional theory (DFT+D3) calculations performed over selected configurations: a flat graphene sheet, a benzene molecule, and a C60 fullerene, physisorbed on the Au(111) surface. The effects of bending and hydrogen passivation on the potential terms are discussed. The presented SAIP provides a noticeable improvement in the state-of-the-art description of Au–C interfaces. Furthermore, its functional form is suitable to describe the interfacial interaction between other 2D and bulk materials.
Spreading of fake news, competence and learning: kinetic modelling and numerical approximation

The rise of social networks as the primary means of communication in almost every country in the world has simultaneously triggered an increase in the amount of fake news circulating online. The urgent need for models that can describe the growing infodemic of fake news has been highlighted by the current pandemic. The resulting slowdown in vaccination campaigns due to misinformation and generally the inability of individuals to discern the reliability of information is posing enormous risks to the governments of many countries. In this research using the tools of kinetic theory, we describe the interaction between fake news spreading and competence of individuals through multi-population models in which fake news spreads analogously to an infectious disease with different impact depending on the level of competence of individuals. The level of competence, in particular, is subject to evolutionary dynamics due to both social interactions between agents and external learning dynamics. The results show how the model is able to correctly describe the dynamics of diffusion of fake news and the important role of competence in their containment. This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.
A Riemannian Approach to Multivariate Geostatistical Modeling

In geosciences, the use of classical Euclidean methods is unsuitable for treating and analyzing some types of data, as this may not belong to a vector space. This is the case for correlation matrices, belonging to a subfamily of symmetric positive definite matrices, which in turn form a cone shape Riemannian manifold. We propose two novel applications for dealing with the problem of accounting with the non-linear behavior usually presented on multivariate geological data by exploiting the manifold features of correlations matrices. First, we employ an extension for the linear model of coregionalization (LMC) that alters the linear mixture, which is assumed fixed on the domain, and making it locally varying according to the local strength in the dependency of the coregionalized variables. The main challenge, once this relaxation on the LMC is assumed, is to solve appropriately the interpolation of the different known correlation matrices throughout the domain, in a reliable and coherent fashion. The present work adopts the non-euclidean framework to achieve our objective by locally averaging and interpolating the correlations between the variables, retaining the intrinsic geometry of correlation matrices. A second application deals with the problem of clustering of multivariate data.
Hydrodynamic limit of Exclusion Processes with slow boundaries on hypercubes

We study the hydrodynamic limit of SSEP with slow boundaries on hypercubes in dimension at least two. The hydrodynamic limit equation is shown to be a heat equation with three different types of boundary conditions according to the slowness of the boundary dynamics.The proof is based on Yau's relative entropy method.
Competence-Aware Path Planning Via Introspective Perception

Robots deployed in the real world over extendedperiods of time need to reason about unexpected failures, learn to predict them, and to proactively take actions to avoid future failures. Existing approaches for competence-aware planning are either model-based, requiring explicit enumeration of known failure sources, or purely statistical, using state- and location-specific failure statistics to infer competence. We instead propose a structured model-free approach to competence-aware planning by reasoning about plan execution failures due to errors in perception, without requiring a priori enumeration of failure sources or requiring location-specific failure statistics. We introduce competence-aware path planning via introspective perception (CPIP), a Bayesian framework to iteratively learn and exploit task-level competence in novel deployment environments. CPIP factorizes the competence-aware planning problem into two components. First, perception errors are learned in a model-free and location-agnostic setting via introspective perception prior to deployment in novel environments. Second, during actual deployments, the prediction of task-level failures is learned in a context-aware setting. Experiments in a simulation show that the proposed CPIP approach outperforms the frequentist baseline in multiple mobile robot tasks, and is further validated via real robot experiments in environments with perceptually challenging obstacles and terrain.
Equivariant Neural Network for Factor Graphs

Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution. An algorithm that performs inference on a factor graph should ideally be equivariant or invariant to permutations of global indices of nodes, variable orderings within a factor, and variable assignment orderings. However, existing neural network-based inference procedures fail to take advantage of this inductive bias. In this paper, we precisely characterize these isomorphic properties of factor graphs and propose two inference models: Factor-Equivariant Neural Belief Propagation (FE-NBP) and Factor-Equivariant Graph Neural Networks (FE-GNN). FE-NBP is a neural network that generalizes BP and respects each of the above properties of factor graphs while FE-GNN is an expressive GNN model that relaxes an isomorphic property in favor of greater expressivity. Empirically, we demonstrate on both real-world and synthetic datasets, for both marginal inference and MAP inference, that FE-NBP and FE-GNN together cover a range of sample complexity regimes: FE-NBP achieves state-of-the-art performance on small datasets while FE-GNN achieves state-of-the-art performance on large datasets.