Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking the industrial growth of modern China with high-resolution panchromatic imagery: A sequential convolutional approach

Due to insufficient or difficult to obtain data on development in inaccessible regions, remote sensing data is an important tool for interested stakeholders to collect information on economic growth. To date, no studies have utilized deep learning to estimate industrial growth at the level of individual sites. In this study, we harness high-resolution panchromatic imagery to estimate development over time at 419 industrial sites in the People's Republic of China using a multi-tier computer vision framework. We present two methods for approximating development: (1) structural area coverage estimated through a Mask R-CNN segmentation algorithm, and (2) imputing development directly with visible&infrared radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS). Labels generated from these methods are comparatively evaluated and tested. On a dataset of 2,078 50 cm resolution images spanning 19 years, the results indicate that two dimensions of industrial development can be estimated using high-resolution daytime imagery, including (a) the total square meters of industrial development (average error of 0.021 $\textrm{km}^2$), and (b) the radiance of lights (average error of 9.8 $\mathrm{\frac{nW}{cm^{2}sr}}$). Trend analysis of the techniques reveal estimates from a Mask R-CNN-labeled CNN-LSTM track ground truth measurements most closely. The Mask R-CNN estimates positive growth at every site from the oldest image to the most recent, with an average change of 4,084 $\textrm{m}^2$.
Sharing information across patient subgroups to draw conclusions from sparse treatment networks

: Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large sample approximations which are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.
BayBFed: Bayesian Backdoor Defense for Federated Learning

Federated learning (FL) is an emerging technology that allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks such as backdoor attacks. Consequently, a variety of defenses have recently been proposed, which have primarily utilized intermediary states of the global model (i.e., logits) or distance of the local models (i.e., L2−norm) with respect to the global model to detect malicious backdoors in FL. However, as these approaches directly operate on client updates (or weights), their effectiveness depends on factors such as clients’ data distribution or the adversary’s attack strategies. In this paper, we introduce a novel and more generic backdoor defense framework, called BayBFed, which proposes to utilize probability distributions over client updates to detect malicious updates in FL: BayBFed computes a probabilistic measure over the clients’ updates to keep track of any adjustments made in the updates, and uses a novel detection algorithm that can leverage this probabilistic measure to efficiently detect and filter out malicious updates. Thus, it overcomes the shortcomings of previous approaches that arise due to the direct usage of client updates; nevertheless, our probabilistic measure will include all aspects of the local client training strategies. BayBFed utilizes two Bayesian NonParametric (BNP) extensions: (i) a Hierarchical Beta-Bernoulli process to draw a probabilistic measure given the clients’ updates, and (ii) an adaptation of the Chinese Restaurant Process (CRP), referred by us as CRP-Jensen, which leverages this probabilistic measure to detect and filter out malicious updates. We extensively evaluate our defense approach on five benchmark datasets: CIFAR10, Reddit, IoT intrusion detection, MNIST, and FMNIST, and show that it can effectively detect and eliminate malicious updates in FL without deteriorating the benign performance of the global model.
Location Division Multiple Access for Near-Field Communications

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With dramatically increasing number of antennas, extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations to mitigate inter-user interferences. Simulation results verify the superiority of the proposed LDMA over classical SDMA in different scenarios.
Eight-stage pseudo-symplectic Runge-Kutta methods of order (4, 8)

Using simplifying assumptions that are related to the time reversal symmetry, a 1-dimensional family of 8-stage pseudo-symplectic Runge-Kutta methods of order (4, 8), i.e., methods of order 4 that preserve symplectic structure up to order 8, is derived. An example of 7-stage method of order (4, 9) is given.
Unifying Structure Reasoning and Language Pre-Training for Complex Reasoning Tasks

Recent pre-trained language models (PLMs) equipped with foundation reasoning skills have shown remarkable performance on downstream complex tasks. However, the significant structure reasoning skill has been rarely studied, which involves modeling implicit structure information within the text and performing explicit logical reasoning over them to deduce the conclusion. This paper proposes a unified learning framework that combines explicit structure reasoning and language pre-training to endow PLMs with the structure reasoning skill. It first identifies several elementary structures within contexts to construct structured queries and performs step-by-step reasoning along the queries to identify the answer entity. The fusion of textual semantics and structure reasoning is achieved by using contextual representations learned by PLMs to initialize the representation space of structures, and performing stepwise reasoning on this semantic representation space. Experimental results on four datasets demonstrate that the proposed model achieves significant improvements in complex reasoning tasks involving diverse structures, and shows transferability to downstream tasks with limited training data and effectiveness for complex reasoning of KGs modality.
Towards a Measure of Trustworthiness to Evaluate CNNs During Operation

–Due to the black box nature of Convolutional neural networks (CNNs), the continuous validation of CNNs during operation is infeasible. As a result this makes it diﬃcult for developers and regulators to gain conﬁdence in the deployment of autonomous systems employing CNNs. It is critical for safety during operation to know when a CNN’s predictions are trustworthy or suspicious. The basic approach is to use the model’s output conﬁdence score to assess if predictions are trustworthy or suspicious. However, the model’s conﬁdence score is a result of computations com-ing from a black box, therefore lacks transparency and makes it challenging to credit trustworthiness to predictions. We introduce the trustworthiness score (TS), a simple metric that provides a more transparent and eﬀective way of providing conﬁdence in CNNs predictions. The metric quantiﬁes the trustworthiness in a prediction by checking for the existence of certain features in the predictions made by the CNN. The TS metric can also be utilised to check for suspiciousness in a frame by scanning for the existence of untrustworthy pre-dictions.We conduct a case study using YOLOv5 on persons detection to demonstrate our method and usage of the trustworthiness score. The case study shows that using our method consistently improves the precision of predictions compared to relying on model conﬁdence alone, for both approving of trustworthy predictions ( ∼ 20% improvement) and detecting suspicious frames ( ∼ 5% improvement).
MATT: Multimodal Attention Level Estimation for e-learning Platforms

This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals obtained from the behavior and physiological processes that have been related to modeling cognitive load such as faces gestures (e.g., blink rate, facial actions units) and user actions (e.g., head pose, distance to the camera). The multimodal system uses the following modules based on Convolutional Neural Networks (CNNs): Eye blink detection, head pose estimation, facial landmark detection, and facial expression features. First, we individually evaluate the proposed modules in the task of estimating the student's attention level captured during online e-learning sessions. For that we trained binary classifiers (high or low attention) based on Support Vector Machines (SVM) for each module. Secondly, we find out to what extent multimodal score level fusion improves the attention level estimation. The mEBAL database is used in the experimental framework, a public multi-modal database for attention level estimation obtained in an e-learning environment that contains data from 38 users while conducting several e-learning tasks of variable difficulty (creating changes in student cognitive loads).
Pushing a non-Newtonian fluid in a Hele-Shaw cell: From fingers to needles

We make a theoretical study of the finger behavior of a simple fluid displacing a non-Newtonian fluid confined in a Hele–Shaw cell. We study the Saffman–Taylor instability when the viscosity of the displaced fluid changes with shear. Our results predict a decrease of the finger width that goes to zero for large values of the velocity. An analytical treatment allows the predictions of the dynamics in radial growth.
Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection

Community detection is an essential task in network analysis as it helps identify groups and patterns within a network. High-speed community detection algorithms are necessary to ana-lyze large-scale networks in a reasonable amount of time. Researchers have made signiﬁcant contributions in the development of high-speed community detection algorithms, particularly in the area of label-propagation based disjoint community detection. These algorithms have been proven to be highly effective in analyzing large-scale networks in a reasonable amount of time. However, it is important to evaluate the performance and accuracy of these existing methods to determine which algorithm is best suited for a particular type of network and speciﬁc research problem. In this report, we investigate the RAK, COPRA, and SLPA, three label-propagation-based static community discovery techniques. We pay close attention to each algorithm’s minute details as we implement both its single-threaded and multi-threaded OpenMP-based variants, making any necessary adjustments or optimizations and obtaining the right parameter values. The RAK algorithm is found to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with 12 threads was 6.75x faster than the sequential non-strict RAK. The COPRA algorithm works well with a single label for road networks and max labels of 4-16 for other classes of graphs. The SLPA algorithm performs well with increasing memory size, but overall doesn’t offer a favourable return on investment. The RAK algorithm is recommended for label-propagation based disjoint community detection.
Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data. While numerous approaches have been proposed to improve GNNs in terms of the Weisfeiler-Lehman (WL) test, generally there is still a lack of deep understanding of what additional power they can systematically and provably gain. In this paper, we take a fundamentally different perspective to study the expressive power of GNNs beyond the WL test. Specifically, we introduce a novel class of expressivity metrics via graph biconnectivity and highlight their importance in both theory and practice. As biconnectivity can be easily calculated using simple algorithms that have linear computational costs, it is natural to expect that popular GNNs can learn it easily as well. However, after a thorough review of prior GNN architectures, we surprisingly find that most of them are not expressive for any of these metrics. The only exception is the ESAN framework, for which we give a theoretical justification of its power. We proceed to introduce a principled and more efficient approach, called the Generalized Distance Weisfeiler-Lehman (GD-WL), which is provably expressive for all biconnectivity metrics. Practically, we show GD-WL can be implemented by a Transformer-like architecture that preserves expressiveness and enjoys full parallelizability. A set of experiments on both synthetic and real datasets demonstrates that our approach can consistently outperform prior GNN architectures.
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images

Visual question answering (VQA) is an important and challenging multimodal task in computer vision. Recently, a few efforts have been made to bring VQA task to aerial images, due to its potential real-world applications in disaster monitoring, urban planning, and digital earth product generation. However, not only the huge variation in the appearance, scale and orientation of the concepts in aerial images, but also the scarcity of the well-annotated datasets restricts the development of VQA in this domain. In this paper, we introduce a new dataset, HRVQA, which provides collected 53512 aerial images of 1024*1024 pixels and semi-automatically generated 1070240 QA pairs. To benchmark the understanding capability of VQA models for aerial images, we evaluate the relevant methods on HRVQA. Moreover, we propose a novel model, GFTransformer, with gated attention modules and a mutual fusion module. The experiments show that the proposed dataset is quite challenging, especially the specific attribute related questions. Our method achieves superior performance in comparison to the previous state-of-the-art approaches. The dataset and the source code will be released at https://hrvqa.nl/.
Autoencoding Hyperbolic Representation for Adversarial Generation

With the recent advance of geometric deep learning, neural networks have been extensively used for data in non-Euclidean domains. In particular, hyperbolic neural networks have proved successful in processing hierarchical information of data. However, many hyperbolic neural networks are numerically unstable during training, which precludes using complex architectures. This crucial problem makes it difficult to build hyperbolic generative models for real and complex data. In this work, we propose a hyperbolic generative network in which we design novel architecture and layers to improve stability in training. Our proposed network contains three parts: first, a hyperbolic autoencoder (AE) that produces hyperbolic embedding for input data; second, a hyperbolic generative adversarial network (GAN) for generating the hyperbolic latent embedding of the AE from simple noise; third, a generator that inherits the decoder from the AE and the generator from the GAN. We call this network the hyperbolic AE-GAN, or HAEGAN for short. The architecture of HAEGAN fosters expressive representation in the hyperbolic space, and the specific design of layers ensures numerical stability. Experiments show that HAEGAN is able to generate complex data with state-of-the-art structure-related performance.
Increasing the efficiency of controllable laser thermal cleavage of insulating materials

This paper discusses various methods of increasing the efficiency of controllable thermal cleavage of insulating materials (oxide glasses, quartz, sapphire, etc.) by means of the radiation of CO2 lasers: noncontact production of microdefects for setting up a microcrack in the material being processed, control of the motion of a laser microcrack to make it possible to increase both the speed and thickness of the high-accuracy cutting of an insulating material by laser thermal cleavage, and thermal cleavage in mutually intersecting directions to obtain miniature items. © 2004 Optical Society of America
Following dynamic processes by X-ray tomographic microscopy with sub-second temporal resolution

Several non‐destructive imaging techniques offer the possibility to observe rapid phenomena in real time, yet most of these techniques fail when it comes to bulky samples and micrometer precision in three dimensions. Therefore there is clearly a need to develop approaches that address such conditions. We identified the large potential that lies in synchrotron‐based x‐rays as a probe and developed a direct‐space tomographic instrument suitable to provide sub‐second temporal resolution with several‐micrometers spatial resolution. Selected applications from the field of biology and material science are shown in order to demonstrate the unique capabilities in generating three‐dimensional images with very high quality making image segmentation and analysis possible for samples that could, until now, only be studied in two dimensions due to the occurrence of rapid structural changes.
Optical tweezers and optical spanners with Laguerre-Gaussian modes

Abstract We numerically model the axial trapping forces within optical tweezers arising from Laguerre–Gaussian laser modes. For an 8 μm diameter sphere suspended in water, the higher-order modes produce an axial trapping force several times larger than that of the fundamental. Partial absorption results in a transfer of the orbital angular momentum from the Laguerre–Gaussian mode to the trapped particle. This results in the rotation of the particle by what may be called an optical spanner (wrench). For an absorption coefficient of α = 5700 m−1 and a laser power of 10 mW, we find that an 8 μm diameter sphere would acquire an angular acceleration of 10 × 104 rad s−2 and a limiting angular velocity of 0.2 rad s−1.
Correcting echo-integration data for transducer motion

When calculating biomass estimates using acoustic echo-integration it is necessary to account for the change in pointing direction of the transducer between transmission and reception. It is shown that a single correction function can be obtained for a wide range of circular transducers, thus removing the need for the correction to be recalculated for each individual transducer. This correction function is also applicable to those transducers whose beam pattern approximates that of a circular transducer.
Piecewise Linear Behavior of RC Beams with Openings

A method is presented in this paper to predict the load‐deflection response of a reinforced‐concrete beam containing a large opening. The load‐deflection curve has been idealized by a piecewise linear relation in which each transition point represents the formation of a plastic hinge. The method of successive elastic analyses is used to trace the formation of hinges until a mechanism is formed. Deflections corresponding to each plastic hinge formation are calculated by linear superposition of deflections caused by flexural action on the entire beam to those produced by shear at the opening. The former is calculated using the conjugate segment analogy while the latter is obtained by treating the chord members as double cantilevers. A comparison of theoretical predictions with test data available on 12 simply supported, three two‐span continuous, and five three‐span continuous beams shows good agreement in load‐deflection relations and modes of failure as well as maximum deflections at calculated service lo.
Magnetospheric resonances at low and middle latitudes

We present results from a numerical study of structure and dynamics of dispersive Alfvén waves in the near‐Earth magnetosphere containing proton radiation belt (near L = 1.5 dipole magnetic shell). The interest in this problem is motivated by numerous observations of magnetic oscillations with frequencies in the range of 0.1–4.0 Hz detected on the ground at low and middle latitudes. In a number of studies these oscillations interpreted as shear Alfvén waves standing inside the so‐called ionospheric Alfvén resonator. We present results from two‐dimensional, time‐dependent simulations of the reduced two‐fluid MHD model performed in the dipole magnetic field geometry with the realistic parameters of the magnetospheric plasma. These simulations show that these pulsations can be produced by the fundamental mode of the global field line resonator, spanning the entire magnetic field line in the low or middle magnetosphere. Simulations also show that even the waves with the highest considered frequencies (2.44 Hz) are not trapped inside the ionospheric resonator. Therefore, if these waves will be generated by some ionospheric source, then they can reach the equatorial magnetosphere and interact with energetic protons in the proton radiation belt.