Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition Based on Fine-Tuned Channel–Spatial Attention Transformer

Facial expressions help individuals convey their emotions. In recent years, thanks to the development of computer vision technology, facial expression recognition (FER) has become a research hotspot and made remarkable progress. However, human faces in real-world environments are affected by various unfavorable factors, such as facial occlusion and head pose changes, which are seldom encountered in controlled laboratory settings. These factors often lead to a reduction in expression recognition accuracy. Inspired by the recent success of transformers in many computer vision tasks, we propose a model called the fine-tuned channel-spatial attention transformer (FT-CSAT) to improve the accuracy of recognition of FER in the wild. FT-CSAT consists of two crucial components: channel-spatial attention module and fine-tuning module. In the channel-spatial attention module, the feature map is input into the channel attention module and the spatial attention module sequentially. The final output feature map will effectively incorporate both channel information and spatial information. Consequently, the network becomes adept at focusing on relevant and meaningful features associated with facial expressions. To further improve the model's performance while controlling the number of excessive parameters, we employ a fine-tuning method. Extensive experimental results demonstrate that our FT-CSAT outperforms the state-of-the-art methods on two benchmark datasets: RAF-DB and FERPlus. The achieved recognition accuracy is 88.61% and 89.26%, respectively. Furthermore, to evaluate the robustness of FT-CSAT in the case of facial occlusion and head pose changes, we take tests on Occlusion-RAF-DB and Pose-RAF-DB data sets, and the results also show that the superior recognition performance of the proposed method under such conditions.
Nonparametric clustering of <scp>RNA</scp>‐sequencing data

Abstract Identification of clusters of co‐expressed genes in transcriptomic data is a difficult task. Most algorithms used for this purpose can be classified into two broad categories: distance‐based or model‐based approaches. Distance‐based approaches typically utilize a distance function between pairs of data objects and group similar objects together into clusters. Model‐based approaches are based on using the mixture‐modeling framework. Compared to distance‐based approaches, model‐based approaches offer better interpretability because each cluster can be explicitly characterized in terms of the proposed model. However, these models present a particular difficulty in identifying a correct multivariate distribution that a mixture can be based upon. In this manuscript, we review some of the approaches used to select a distribution for the needed mixture model first. Then, we propose avoiding this problem altogether by using a nonparametric MSL (maximum smoothed likelihood) algorithm. This algorithm was proposed earlier in statistical literature but has not been, to the best of our knowledge, applied to transcriptomics data. The salient feature of this approach is that it avoids explicit specification of distributions of individual biological samples altogether, thus making the task of a practitioner easier. We performed both a simulation study and an application of the proposed algorithm to two different real datasets. When used on a real dataset, the algorithm produces a large number of biologically meaningful clusters and performs at least as well as several other mixture‐based algorithms commonly used for RNA‐seq data clustering. Our results also show that this algorithm is capable of uncovering clustering solutions that may go unnoticed by several other model‐based clustering algorithms. Our code is publicly available on Github at https://github.com/Matematikoi/non_parametric_clustering
Open quantum system dynamics of X-states: Entanglement sudden death and sudden birth

In this paper, the origin of disentanglement for two specific sub-classes of [Formula: see text]-states namely maximally nonlocal mixed states (MNMSs) and maximally entangled mixed states (MEMSs) is investigated analytically for a physical system consisting of two spatially separated qubits interacting with a common vacuum bath. The phenomena of entanglement sudden death (ESD) and the entanglement sudden birth (ESB) are observed, but the characteristics of ESD and ESB are found to be different for the case of two-photon coherence and single photon coherence states. The role played by initial coherence for the underlying entanglement dynamics is investigated. Further, the entanglement dynamics of MNMSs and MEMSs under different environmental noises namely phase damping, amplitude damping and random telegraph noise (RTN) noise with respect to the decay and revival of entanglement is analyzed. It is observed that the single photon coherence states are more robust against the sudden death of entanglement, indicating the usability of such states in the development of technologies for the practical implementation of quantum information processing tasks.
Fusion of Appearance and Motion Features for Daily Activity Recognition from Egocentric Perspective

Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person's activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP-TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset).
MT$$^2$$AD: multi-layer temporal transaction anomaly detection in ethereum networks with GNN

Abstract In recent years, a surge of criminal activities with cross-cryptocurrency trades have emerged in Ethereum, the second-largest public blockchain platform. Most of the existing anomaly detection methods utilize the traditional machine learning with feature engineering or graph representation learning technique to capture the information in transaction network. However, these methods either ignore the timestamp information and the transaction flow direction information in transaction network or only consider single transaction network, the cross-cryptocurrency trading patterns in Ethereum are usually ignored. In this paper, we introduce a Multi-layer Temporal Transaction Anomaly Detection (MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD) model in Ethereum network with graph neural network. Specifically, for a given Ethereum token transaction network, we first extract its initial features including the structure subgraph and edge’s feature. Then, we model the temporal information in subgraph as a series of network snapshots according to the timestamp on each edge and time window. To capture the cross-cryptocurrency trading patterns, we combine the snapshots from multiple token transactions at a given timestamp, and we consider it as a new combined graph. We further use the graph convolution encoder with attention mechanism and pooling operation on this new graph to obtain the graph-level embedding, and we transform the anomaly detection on dynamic multi-layer Ethereum transaction networks as a graph classification task with these graph-level embeddings. MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD can integrate the transaction structure feature, edge’s feature and cross-cryptocurrency trading patterns into a framework to perform the anomaly detection with graph neural networks. Experiments on three real-world multi-layer transaction networks show that the proposed MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD (0.8789 Precision, 0.9375 Recall, 0.4987 FbMacro and 0.9351 FbWeighted) can achieve the best performance on most evaluation metrics in comparison with some competing approaches, and the effectiveness in consideration of multiple tokens is also demonstrated.
Institutions, digital assets, and implications for economic and financial performance

This paper provides a review of the literature on digital finance, cryptocurrency, and financial dependence in relation to economic development and growth. We summarize the evidence of how cryptocurrencies and digital finance impact growth. We show further that financial dependence and credit ratings are also critical to growth, along with language and culture. We introduce the papers in this special issue and elaborate on future research opportunities for these topics.
Assessment of ship position probability in the geometrical figure of position lines errors

Accurate and reliable determination of the ship position coordinates is one of the most important tasks for ensuring the safety of navigation at sea. A significant condition that contributes to the effective solution of this task is the use of redundancy in measurements of navigation parameters. The implementation of this redundancy allows the navigator or automatic navigation aid to control the trueness of measurements; eliminate gross and systematic errors; reduce the effect of random errors; apply various methods of analysis and algorithms for processing information in order to obtain the most accurate and reliable ship position. Adequate redundancy of navigation measurements makes it possible to obtain asymptotically unbiased estimates of coordinates, as well as to solve other navigation tasks based on redundant data. The results of a study on the probability of finding the true coordinates of the ship’s position in a geometric figure formed by random measurement errors are presented. It has been shown in the study that a single redundancy of measurements is not enough to achieve the required level of probability. Elements of classical combinatorics based on the Bernoulli scheme and including Newton’s binomial and Pascal’s triangle as tools are used as a mathematical apparatus for the study. These tools allow us to effectively analyze combinations of mathematical signs, such as plus and minus, of random measurement errors, reducing them into binary sequences, which are described by binomial coefficients. It has been argued in the paper that the low level of probability of finding the true values of the coordinates in the geometrical figure of measurement errors is a strong signal that any method of averaging the measurement results can lead to asymptotically biased estimates of the ship position coordinates. This is analogous to the influence of systematic errors on the coordinates within a certain time interval. From the point of view of navigational safety, a low probability can be qualified as a near-miss situation, which can lead to incorrect control of the ship position. The probabilistic criteria for the necessary redundancy of measurements to approach asymptotically unbiased estimates of ship position are presented in the paper.
Impact of entrepreneurial governance and ease of doing business on economic growth: Evidence from <scp>ECOWAS</scp> economies (2000–2019)

We reignited the debate that developing countries still struggle to make the most of entrepreneurship due to institutional imbalances. The aim of this study is to empirically examine impact of entrepreneurial governance and ease of doing business on economic growth in a panel of 15 ECOWAS countries. The empirical evidence is based on secondary data from 2000 to 2019 estimated using the Pooled OLS Regression OLS estimator. Using institutional theory, we examined the chain of causality from institutions to entrepreneurship and from entrepreneurship to economic growth. Four findings emerged. Firstly, the regulatory pillar, represented by the indicators of ease of doing business (incorporation procedure, time required to set up a business, and cost of the business), is negatively and significantly related to economic growth. But the indicators of cost of doing business and start‐up costs are more detrimental to business and economic growth. Secondly, the normative pillar represented by entrepreneurial governance indicators (government integrity and business freedom) is positively and significantly associated with growth. But the impact of government integrity on economic growth is greater than that of business freedom. Third, the interaction between regulatory and normative pillars is negative and significant. Finally, the cognitive pillar shows mixed results. For example, indicators of investment, trade openness, education, and financial development have positive and significant effects on economic growth, while government size, inflation, and population have negative and significant effects on economic growth. We discuss policy implications for stakeholders in ECOWAS economies.
Visual Communication of Probabilistic Information to Enhance Decision Support

Abstract When hazardous weather is forecast, communicating probabilistic information (PI) can improve trust, confidence, and understanding of forecast information, resulting in improved decision-making by emergency managers and public audiences. With probabilistic forecast tools modernizing forecast operations, the National Weather Service is calling on regional offices to increase the use of PI. However, communicating PI can be challenging since the information is intrinsically more complex than single-value deterministic forecasts that do not include a measure of uncertainty. We suggest that effective PI visualization not only includes the PI graphic but also communicates potential impacts and issues preventative guidance to limit exposure to weather-related hazards. Decision support tools like PI benefit from, if not require, effective visual communication that capitalizes on the efficiency of the visual system to extract information, decrease the time to interpret information, and increase the understanding of uncertainties. Furthermore, PI visuals need to be accessible to disabled and neurodivergent audiences. To enhance the visual communication of PI, we synthesize literature from graphic design and social science to identify guiding principles for effective visual communication and provide a one-page printout quick guide. To showcase how forecasters can incorporate guiding principles in the local context, we provide examples built from readily usable templates to demonstrate how probabilistic forecast information extracted from tools like the National Blend of Models can be used to enhance the visual communication of PI to support more informed decision-making.
Remote Sensing Neural Radiance Fields for Multi-View Satellite Photogrammetry

Neural radiance fields (NeRFs) combining machine learning with differentiable rendering have arisen as one of the most promising approaches for novel view synthesis and depth estimates. However, NeRFs only applies to close-range static imagery and it takes several hours to train the model. The satellites are hundreds of kilometers from the earth. Satellite multi-view images are usually captured over several years, and the scene of images is dynamic in the wild. Therefore, multi-view satellite photogrammetry is far beyond the capabilities of NeRFs. In this paper, we present a new method for multi-view satellite photogrammetry of Earth observation called remote sensing neural radiance fields (RS-NeRFs). It aims to generate novel view images and accurate elevation predictions quickly. For each scene, we train an RS-NeRF using high-resolution optical images without labels or geometric priors and apply image reconstruction losses for self-supervised learning. Multi-date images exhibit significant changes in appearance, mainly due to cars and varying shadows, which brings challenges to satellite photogrammetry. Robustness to these changes is achieved by the input of solar ray direction and the vehicle removal method. NeRFs make it intolerable by requiring a very long time to train an easy scene. In order to significantly reduce the training time of RS-NeRFs, we build a tiny network with HashEncoder and adopted a new sampling technique with our custom CUDA kernels. Compared with previous work, our method performs better on novel view synthesis and elevation estimates, taking several minutes.
Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm

To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA.
Error Guarantees for Least Squares Approximation with Noisy Samples in Domain Adaptation

Given n samples of a function f : D → C in random points drawn with respect to a measure ϱS we develop theoretical analysis of the L2(D, ϱT )-approximation error.For a parituclar choice of ϱS depending on ϱT , it is known that the weighted least squares method from finite dimensional function spaces Vm, dim(Vm) = m < ∞ has the same error as the best approximation in Vm up to a multiplicative constant when given exact samples with logarithmic oversampling.If the source measure ϱS and the target measure ϱT differ we are in the domain adaptation setting, a subfield of transfer learning.We model the resulting deterioration of the error in our bounds.Further, for noisy samples, our bounds describe the bias-variance trade off depending on the dimension m of the approximation space Vm.All results hold with high probability.For demonstration, we consider functions defined on the d-dimensional cube given in unifom random samples.We analyze polynomials, the half-period cosine, and a bounded orthonormal basis of the non-periodic Sobolev space H 2 mix .Overcoming numerical issues of this H 2 mix basis, this gives a novel stable approximation method with quadratic error decay.Numerical experiments indicate the applicability of our results.
Uncertain Pose Estimation during Contact Tasks using Differentiable Contact Features

For many robotic manipulation and contact tasks, it is crucial to accurately estimate uncertain object poses, for which certain geometry and sensor information are fused in some optimal fashion.Previous results for this problem primarily adopt sampling-based or end-to-end learning methods, which yet often suffer from the issues of efficiency and generalizability.In this paper, we propose a novel differentiable framework for this uncertain pose estimation during contact, so that it can be solved in an efficient and accurate manner with gradient-based solver.To achieve this, we introduce a new geometric definition that is highly adaptable and capable of providing differentiable contact features.Then we approach the problem from a bi-level perspective and utilize the gradient of these contact features along with differentiable optimization to efficiently solve for the uncertain pose.Several scenarios are implemented to demonstrate how the proposed framework can improve existing methods.
Rates of approximation by ReLU shallow neural networks

Neural networks activated by the rectified linear unit (ReLU) play a central role in the recent development of deep learning. The topic of approximating functions from Hölder spaces by these networks is crucial for understanding the efficiency of the induced learning algorithms. Although the topic has been well investigated in the setting of deep neural networks with many layers of hidden neurons, it is still open for shallow networks having only one hidden layer. In this paper, we provide rates of uniform approximation by these networks. We show that ReLU shallow neural networks with m hidden neurons can uniformly approximate functions from the Hölder space W∞r([−1,1]d) with rates O((log⁡m)12+dm−rdd+2d+4) when r<d/2+2. Such rates are very close to the optimal one O(m−rd) in the sense that d+2d+4 is close to 1, when the dimension d is large.
Multi-view convolutional vision transformer for 3D object recognition

With the rapid development of three-dimensional (3D) vision technology and the increasing application of 3D objects, there is an urgent need for 3D object recognition in the fields of computer vision, virtual reality, and artificial intelligence robots. The view-based method projects 3D objects into two-dimensional (2D) images from different viewpoints and applies convolutional neural networks (CNN) to model the projected views. Although these methods have achieved excellent recognition performance, there is not sufficient information interaction between the features of different views in these methods. Inspired by the recent success achieved by vision transformer (ViT) in image recognition, we propose a hybrid network by taking advantage of CNN to extract multi-scale local information of each view, and of transformer to capture the relevance of multi-scale information between different views. To verify the effectiveness of our multi-view convolutional vision transformer (MVCVT), we conduct experiments on two public benchmarks, ModelNet40 and ModelNet10, and compare with those of some state-of-the-art methods. The final results show that MVCVT has competitive performance in 3D object recognition.