Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing Forest Biomass Prediction Maps from Radar Backscatter by Sequential Regression with a Conditional Generative Adversarial Network

This paper studies construction of above-ground biomass (AGB) prediction maps from synthetic aperture radar (SAR) intensity images. The purpose is to improve traditional regression models based on SAR intensity, trained with a limited amount of AGB in situ measurements. Although it is costly to collect, data from airborne laser scanning (ALS) sensors are highly correlated with AGB. Therefore, we propose using AGB predictions based on ALS data as surrogate response variables for SAR data in a sequential modelling fashion. This increases the amount of training data dramatically. To model the regression function between SAR intensity and ALS-predicted AGB we propose to utilise a conditional generative adversarial network (cGAN), i.e. the Pix2Pix convolutional neural network. This enables the recreation of existing ALS-based AGB prediction maps. The generated synthesised ALS-based AGB predictions are evaluated qualitatively and quantitatively against ALS-based AGB predictions retrieved from a traditional non-sequential regression model trained in the same area. Results show that the proposed architecture manages to capture characteristics of the actual data. This suggests that the use of ALS-guided generative models is a promising avenue for AGB prediction from SAR intensity. Further research on this area has the potential of providing both large-scale and low-cost predictions of AGB.
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection

Random projection is a common technique for designing algorithms in a variety of areas, including information retrieval, compressive sensing and measuring of outlyingness. In this work, the original random projection outlyingness measure is modified and associated with a neural network to obtain an unsupervised anomaly detection method able to handle multimodal normality. Theoretical and experimental arguments are presented to justify the choice of the anomaly score estimator. The performance of the proposed neural network approach is comparable to a state-of-the-art anomaly detection method. Experiments conducted on the MNIST, Fashion-MNIST and CIFAR-10 datasets show the relevance of the proposed approach.
Covariant decomposition of the non-linear galaxy number counts and their monopole

We present a fully non-linear and relativistically covariant expression for the observed galaxy density contrast. Building on a null tetrad tailored to the cosmological observer's past light cone, we find a decomposition of the non-linear galaxy over-density into manifestly gauge-invariant quantities, each of which has a clear physical interpretation as a cosmological observable. This ensures that the monopole of the galaxy over-density field (the mean galaxy density as a function of observed redshift) is properly accounted for. We anticipate that this decomposition will be useful for future work on non-linearities in galaxy number counts, for example, deriving the relativistic expression for the galaxy bispectrum. We then specialise our results to conformal Newtonian gauge, with a Hubble parameter either defined globally or measured locally, illustrating the significance of the different contributions to the observed monopole of the galaxy density.
Scalable and Elastic LiDAR Reconstruction in Complex Environments Through Spatial Analysis

This paper presents novel strategies for spawning and fusing submaps within an elastic dense 3D reconstruction system. The proposed system uses spatial understanding of the scanned environment to control memory usage growth by fusing overlapping submaps in different ways. This allows the number of submaps and memory consumption to scale with the size of the environment rather than the duration of exploration. By analysing spatial overlap, our system segments distinct spaces, such as rooms and stairwells on the fly during exploration. Additionally, we present a new mathematical formulation of relative uncertainty between poses to improve the global consistency of the reconstruction. Performance is demonstrated using a multi-floor multi-room indoor experiment, a large-scale outdoor experiment and simulated datasets. Relative to our baseline, the presented approach demonstrates improved scalability and accuracy.
What Explains Gender Gap in Unpaid Household and Care Work in India?

Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been undertaken in the context of India. The present paper, using the recent Time Use Survey (2019) data, examines the socioeconomic and demographic factors associated with variation in time spent on unpaid household and care work among men and women. It analyses how much of the gender gap in the time allocated to unpaid work can be explained by differences in these factors. The findings show that women spend much higher time compared to men in unpaid household and care work. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results indicate that unobserved gender norms and practices most crucially govern the allocation of unpaid work within Indian households.
Local heat transfer distribution in a rib roughened rotating square duct with ribs on two opposite surfaces

An experimental investigation has been carried out to study the local heat transfer distribution in a square channel rotating about an axis normal to the free-stream direction in the presence of rib turbulators. The ribs are configured in a symmetric arrangement on two opposite surfaces with a rib angle of 90” to the mainstream flow. Experiments have been carried out for pitch-to-rib height ratios of 5 and 10 for a constant rib height-tohydraulic diameter ratio of 0.1. Experiments have been conducted for Reynolds numbers varying from 23000 to 41000 with the rotation number varying from 0 0.38 with water as the working medium. The test section in which the ribs are placed on the leading and trailing surfaces is considered as the base case (Orientation angle = O”, Coriolis force vector normal to the ribbed surfaces). The channel is turned in steps of 30” to vary the orientation angle from 0” to 90”. At all orientation angles other than zero, the heat transfer observed in case of pitch-to-rib height ratio of 5 is comparatively highter than that of a pitch-to-rib height ratio of 10. Hence, the pitch-to-rib height ratio of 10 configuration that is most preferred for the stationary case no longer appears to yield high heat transfer in the presence of rotation. Nomenclature A Area of cross section of copper cylinder ( m2 ) CP Specific heat of water ( kJ / kg K ) D Channel hydraulic diameter ( m ) e Rib height (m) h Heat transfer coefficient ( W / m2 K ) kf Thermal conductivity of water (W/m K) I Length of the copper cylinder ( m ) Nu(st) Nusselt number under stationary conditions Nu(rot) Nusselt number under conditions of rotation *Assistant Professor, Department of Mechanical Engineering **Associate Professor, Department of Mechanical Engineering Copyright
Neutrino-electron magnetohydrodynamics in an expanding universe

We derive a new model for neutrino-plasma interactions in an expanding universe that incorporates the collective effects of the neutrinos on the plasma constituents. We start from the kinetic description of a multispecies plasma in the flat Friedmann-Robertson-Walker metric, where the particles are coupled to neutrinos through the charged- and neutral-current forms of the weak interaction. We then derive the fluid equations and specialize our model to (a) the lepton epoch, where we consider a pair electron-positron plasma interacting with electron (anti)neutrinos, and (b) after the electron-positron annihilation, where we model an electron-proton plasma and take the limit of slow ions and inertialess electrons to obtain a set of neutrino-electron magnetohydrodynamics equations. In both models, the dynamics of the plasma is affected by the neutrino motion through a ponderomotive force and, as a result, new terms appear in the induction equation that can act as a source for magnetic field generation in the early Universe. A brief discussion on the possible applications of our model is proposed. DOI:
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning

An effective approach in meta-learning is to utilize multiple"train tasks"to learn a good initialization for model parameters that can help solve unseen"test tasks"with very few samples by fine-tuning from this initialization. Although successful in practice, theoretical understanding of such methods is limited. This work studies an important aspect of these methods: splitting the data from each task into train (support) and validation (query) sets during meta-training. Inspired by recent work (Raghu et al., 2020), we view such meta-learning methods through the lens of representation learning and argue that the train-validation split encourages the learned representation to be low-rank without compromising on expressivity, as opposed to the non-splitting variant that encourages high-rank representations. Since sample efficiency benefits from low-rankness, the splitting strategy will require very few samples to solve unseen test tasks. We present theoretical results that formalize this idea for linear representation learning on a subspace meta-learning instance, and experimentally verify this practical benefit of splitting in simulations and on standard meta-learning benchmarks.
Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., ‘0’ for bonafide versus ‘1’ for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.
Utilization of Biodiesel Wastes as a Bioresource for the Preparation of Activated Carbon

In this research, biodiesel wastes such as palm shells (PS) and Jatropha curcas fruit shells (JS) were used for the synthesis of activated carbon by chemical activation method using potassium hydroxide (KOH) as an activating agent. The effects of acidic treatment in hydrofluoric acid (HF), impregnation ratio, activation temperature and activation time on the adsorption capacity of methylene blue (MB) and iodine (I2) solution were examined. The results showed that HF-treated activated carbons exhibited high adsorption capacities by eliminating ash residues, which might fill up the pores. In addition, the adsorption capacities of methylene blue and iodine solution were also significantly influenced by the types of raw materials, the activation temperature and the activation time. The highest adsorption capacity of methylene blue of 257.07 mg/g and iodine of 847.58 mg/g were obtained from Jatropha curcas wastes.
Continuous Latent Process Flows

Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines. Fitting this type of data using statistical models with continuous dynamics is not only promising at an intuitive level but also has practical benefits, including the ability to generate continuous trajectories and to perform inference on previously unseen time stamps. Despite exciting progress in this area, the existing models still face challenges in terms of their representational power and the quality of their variational approximations. We tackle these challenges with continuous latent process flows (CLPF), a principled architecture decoding continuous latent processes into continuous observable processes using a time-dependent normalizing flow driven by a stochastic differential equation. To optimize our model using maximum likelihood, we propose a novel piecewise construction of a variational posterior process and derive the corresponding variational lower bound using trajectory re-weighting. Our ablation studies demonstrate the effectiveness of our contributions in various inference tasks on irregular time grids. Comparisons to state-of-the-art baselines show our model's favourable performance on both synthetic and real-world time-series data.
Quantum nature of time -- proposition of experimental verification

In quantum mechanics time usually appears as classical parameter which means that it is treated as being essentially different from spatial coordinates that are represented by operators. On the other hand, relativity theory demands to treat space and time on an equal footing and there are many approaches to the problem how to introduce time operator. In order to support these approaches we propose the experiment that will clearly expose the equivalence of space and time regarding the relation towards quantum interference. The proposed experiment can show that it is possible to have superposition of being created in two different moments in time, and by measuring standard interference pattern, it will be shown that there is interference in time. It is similar to the case of verification of the possibility of single quantum system to be in the state that is superposition of two different positions in space. If so, this would mean that there should be exactly the same formal approach to the space coordinates and time in quantum mechanics since they are all quantum in their nature.
On-board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing unit (Movidius stick) and a camera. The hardware employed to build the prototype is low-cost, easy to found and to use. Moreover, the dataset has been published on GitHub, made available to everyone. The results are promising and encouraging toward the employment of the proposed system in future missions, given that ESA has already moved the first steps of AI on board with the Phisat-1 satellite, launched on September 2020.
Predicting Depth from Semantic Segmentation using Game Engine Dataset

Depth perception is fundamental for robots to understand the surrounding environment. As the view of cognitive neuroscience, visual depth perception methods are divided into three categories, namely binocular, active, and pictorial. The first two categories have been studied for decades in detail. However, research for the exploration of the third category is still in its infancy and has got momentum by the advent of deep learning methods in recent years. In cognitive neuroscience, it is known that pictorial depth perception mechanisms are dependent on the perception of seen objects. Inspired by this fact, in this thesis, we investigated the relation of perception of objects and depth estimation convolutional neural networks. For this purpose, we developed new network structures based on a simple depth estimation network that only used a single image at its input. Our proposed structures use both an image and a semantic label of the image as their input. We used semantic labels as the output of object perception. The obtained results of performance comparison between the developed network and original network showed that our novel structures can improve the performance of depth estimation by 52% of relative error of distance in the examined cases. Most of the experimental studies were carried out on synthetic datasets that were generated by game engines to isolate the performance comparison from the effect of inaccurate depth and semantic labels of non-synthetic datasets. It is shown that particular synthetic datasets may be used for training of depth networks in cases that an appropriate dataset is not available. Furthermore, we showed that in these cases, usage of semantic labels improves the robustness of the network against domain shift from synthetic training data to non-synthetic test data.
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach

Critical role of Internet of Things (IoT) in various domains like smart city, healthcare, supply chain and transportation has made them the target of malicious attacks. Past works in this area focused on centralized Intrusion Detection System (IDS), assuming the existence of a central entity to perform data analysis and identify threats. However, such IDS may not always be feasible, mainly due to spread of data across multiple sources and gathering at central node can be costly. Also, the earlier works primarily focused on improving True Positive Rate (TPR) and ignored the False Positive Rate (FPR), which is also essential to avoid unnecessary downtime of the systems. In this paper, we first present an architecture for IDS based on hybrid ensemble model, named PHEC, which gives improved performance compared to state-of-the-art architectures. We then adapt this model to a federated learning framework that performs local training and aggregates only the model parameters. Next, we propose Noise-Tolerant PHEC in centralized and federated settings to address the label-noise problem. The proposed idea uses classifiers using weighted convex surrogate loss functions. Natural robustness of KNN classifier towards noisy data is also used in the proposed architecture. Experimental results on four benchmark datasets drawn from various security attacks show that our model achieves high TPR while keeping FPR low on noisy and clean data. Further, they also demonstrate that the hybrid ensemble models achieve performance in federated settings close to that of the centralized settings.
Central Limit Theorem for Cocycles over Hyperbolic Systems

We prove a Central Limit Theorem (CLT) in the non-commutative setting of random matrix products where the underlying process is driven by a subshift of finite type (SFT) with Markov measure. We use the martingale method introduced by Y. Benoist and J.F. Quint in the iid setting.
On shape sphere and rotation of three-body motion

For three-body problem, R.Montgomery [3] proved a reconstruction formula which calculates the overall rotation relating two similar triangle configurations if the initial triangular configuration is similar to the configuration formed at some later time. In this paper, we extend the formula so that it gives the angle of rotation for a single particle without requiring the similarity of initial and final configurations. The proof is different from that in [3] and uses fundamental calculus. Moreover, we answered a question proposed in [3].
Ju n 20 21 Finsler-based field theory-a mathematical foundation

The paper introduces a general mathematical framework for action based field theories on Finsler spacetimes. As most often fields on Finsler spacetime (e.g., the Finsler fundamental function or the resulting metric tensor) have a homogeneous dependence on the tangent directions of spacetime, we construct the appropriate configuration bundles whose sections are such homogeneous fields; on these configuration bundles, the tools of coordinate free calculus of variations can be consistently applied to obtain field equations. Moreover, we prove that general covariance of natural Finsler field Lagrangians leads to an averaged energy-momentum conservation law which, in the particular case of Lorentzian spacetimes, is equivalent to the usual, pointwise energy-momentum covariant conservation law.
On the global properties of Fourier multipliers in the nonharmonic analysis setting

In this paper, we investigate the global properties of Fourier multipliers in the setting of nonharmonic analysis of boundary value problems. We give necessary and sufficient conditions for a Fourier multiplier to be globally hypoelliptic and also to be globally solvable. As an application, we consider operators on [0, 1] with non-periodic boundary conditions and we obtain results that extend what is already known in the periodic case.
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition

One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints. However, the complexity of the recent State-Of-The-Art (SOTA) models for this task tends to be exceedingly sophisticated and over-parameterized. The low efficiency in model training and inference has increased the validation costs of model architectures in large-scale datasets. To address the above issue, recent advanced separable convolutional layers are embedded into an early fused Multiple Input Branches (MIB) network, constructing an efficient Graph Convolutional Network (GCN) baseline for skeleton-based action recognition. In addition, based on such the baseline, we design a compound scaling strategy to expand the model’s width and depth synchronously, and eventually obtain a family of efficient GCN baselines with high accuracies and small amounts of trainable parameters, termed EfficientGCN-Bx, where “x” denotes the scaling coefficient. On two large-scale datasets, i.e., NTU RGB+D 60 and 120, the proposed EfficientGCN-B4 baseline outperforms other SOTA methods, e.g., achieving 92.1% accuracy on the cross-subject benchmark of NTU 60 dataset, while being 5.82× smaller and 5.85× faster than MS-G3D, which is one of the SOTA methods. The source code in PyTorch version and the pretrained models are available at https://github.com/yfsong0709/EfficientGCNv1.