Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of particle exposure, near-bed velocity and pressure fluctuations on incipient motion of particle-size mixtures

For the prediction of river bed destabilisation and fractional sediment transport of mixtures, we aim to solve two problems that are poorly understood. First, the flow and pressure fluctuations surrounding both the embedded and exposed particles must be parameterised for hydraulically smooth to rough flow. Second, an adequaterelationbetweenparticlesizeandparticleexposureshouldbebasedontheparticlesizedistributionand the (water-worked) bed structure. We use a recently developed force balance model for the threshold of motion of uniform sediments incorporating the effects of particle exposure, pressure fluctuations into the bed, very shallow flow and bed slope. The flow module is extended to non-uniform roughness of sediment mixtures. Our extended model predicts the critical Shields values of arbitrary mixtures directly as function of exposure and no longer needs empirical hiding-exposure relations. Several empirical and geometrical relations between particle size and exposure were tested. The results are compared to extensive datasets from the literature of incipient fractional transport rates. The modelled hiding-exposure relations are very sensitive to the relation between particle size and exposure, which differ for unimodal, skewed and bimodal mixtures. This is explained by the pore structure of these sediments.The existing relations fail particularly for the smaller particles in bimodal and skewed distributions.These small particles percolate through the pores so their exposure or embedding strongly depends on the fractional content and pore structure, in agreement with empirical data. We are working on a universalrelationforexposurecontainingparticlesizedistribution,porestructureandwater-working.Themodel reproduces data of uniform sediments well for the entire physically possible range of particle exposures and for hydraulically rough to nearly smooth conditions. Trends in existing data for mixtures are also reproduced but depend strongly on exposures that were not measured.
An End-to-End 12-Leading Electrocardiogram Diagnosis System Based on Deformable Convolutional Neural Network With Good Antinoise Ability

Electrocardiogram (ECG) is a tool to help judge heart activity. In recent years, the convolutional neural network (CNN) and various deep learning algorithms have been widely used in ECG diagnosis. CNN only considers the local feature. However, the ECG signal is susceptible to noise, and the waveform is complex, making it difficult for existing methods to get a good result. This article presents a novel neural network architecture for ECG diagnosis based on deformable CNN (Deform-CNN). The architecture makes good use of the feature-learning capability of deformable convolution to learn the time-domain and lead characteristics of multilead ECG signals. The proposed end-to-end method can achieve an overall diagnostic accuracy of 86.3% in the 12-lead ECG data of CPSC-2018, with good antinoise ability, which makes the method have a more competitive performance than other deep learning algorithms. The source code is publicly available at https://github.com/HeartbeatAI/Deform-CNN.
Meta-Learning Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks

Deep learning-based algorithms provide a promising solution to efficiently generate offloading decisions in mobile edge computing (MEC) networks. However, considering dynamic MEC devices or offloading tasks, most of them require large-scale training data and long training time to retrain the deep neural networks (DNNs). In this letter, we propose a MEta-Learning-based computation Offloading (MELO) algorithm for dynamic computation tasks in MEC networks. Specifically, it learns from historical MEC task scenarios and adapts to a new MEC task scenario with a few training samples. Numerical results show that the proposed algorithm can adapt to a new MEC task scenario and achieve 99% accuracy via 1-step fine-tuning using only 10 training samples.
Study the quantity of handling robots for picking operation considering charging

With the gradual expansion of e-commerce business scale, the warehouse picking operation is facing the production pressure of constant delivery time and increasing business volume. Therefore, enterprises need to adjust the resource allocation in the picking operation in time according to the order changes to ensure the order production efficiency. The handling robot is one of the resources that can be flexibly adjusted and directly affect the production time in the picking operation, so this paper considers adjusting the configuration of the handling robot. Due to the high unit price of the robot and the need to charge in the process of use, the enterprise also needs to balance the production efficiency and equipment cost. In addition, scheduling other robots during robot charging will also affect the number of robots. Therefore, this paper studies the number of handling robots in the picking operation considering charging, in order to ensure the production efficiency and reduce the cost of picking operation.
Driver Eye Location and State Estimation Based on a Robust Model and Data Augmentation

Eye state evaluation is crucial for vision-based driver fatigue detection. With the outbreak of COVID-19, many proposed models for eye location and state evaluation based on facial landmarks are unreliable due to mask coverings. In this paper, we proposed a robust facial landmark location model for eye location and state evaluation. First, we develop an existing lightweight face alignment model for eye key point locations that is robust in large poses. Then, to develop the performance of our model in a complex driving environment such as an environment with mask coverings, changing illumination, etc., we design a method to augment the training data set based on the original landmark data set without any extra cost. Finally, some facial landmarks around the eyes are extracted, and the eye aspect ratio (EAR) is introduced to evaluate the eye state based on eye key points. The experiment shows that our model achieves significantly improved landmark location performance on a driving simulation data set due to data augmentation. We tested our model on the BioID data set to measure the eye state evaluation performance, and the results showed that our model obtained satisfactory performance with an accuracy of approximately 97.7%. Further testing on the driving simulation data set shows that our model is robust in different driving scenarios with an average accuracy of approximately 93.9%.
A Mobility-Aware Caching Scheme in Heterogeneous Cellular Networks

User mobility is a challenging problem in heterogeneous cellular networks. In this paper, we try to turn the mobility challenge into an opportunity to reduce latency. We first define a model of an urban cellular network in which mobile users can move between different small cells. Then, by introducing a scheme called Cooperative LRU, we use user mobility to reduce the file download delays. In this method, the requested file which is cached in the current user’s cell is also cached in two adjacent cells. This means that the caching scheme in the current cell is reactive and in two other adjacent cells is proactive. Finally, we have a comparison between traditional methods and the introduced method and we examine the effect of different network and kinetic parameters on reducing latency.
Estimating Uniqueness of I-Vector-Based Representation of Human Voice

We study the individuality of the human voice with respect to a widely used feature representation of speech utterances, namely, the i-vector model. As a first step toward this goal, we compare and contrast uniqueness measures proposed for different biometric modalities. Then, we introduce a new uniqueness measure that evaluates the entropy of i-vectors while taking into account speaker level variations. Our measure operates in the discrete feature space and relies on accurate estimation of the distribution of i-vectors. Therefore, i-vectors are quantized while ensuring that both the quantized and original representations yield similar speaker verification performance. Uniqueness estimates are obtained from two newly generated datasets and the public VoxCeleb dataset. The first custom dataset contains more than one and a half million speech samples of 20,741 speakers obtained from TEDx Talks videos. The second one includes over twenty one thousand speech samples from 1,595 actors that are extracted from movie dialogues. Using this data, we analyzed how several factors, such as the number of speakers, number of samples per speaker, sample durations, and diversity of utterances affect uniqueness estimates. Most notably, we determine that the discretization of i-vectors does not cause a reduction in speaker recognition performance. Our results show that the degree of distinctiveness offered by i-vector-based representation may reach 43–70 bits considering 5-second long speech samples; however, under less constrained variations in speech, uniqueness estimates are found to reduce by around 30 bits. We also find that doubling the sample duration increases the distinctiveness of the i-vector representation by around 20 bits.
Exploring behaviour towards avatars and agents in immersive virtual environments with mixed-agency interactions

Immersive virtual environments (IVEs) in which multiple users navigate by walking and interact with each other in natural ways are perfectly suited for team applications from training to recreation. At the same time, they can solve scheduling conflicts by employing virtual agents in place of missing team members or additional participants of a scenario. While this idea has been long discussed in IVEs research there are no prior publications on social interactions in systems with multiple embodied users and agents. This paper presents an experiment at a work-in-progress stage that addresses the impact of perceived agency and control of a virtual character in a collaborative scenario with two embodied users and one virtual agent. Our future study will investigate whether users treat avatars and agents differently within a mixed-agency scenario, analysing several behavioural metrics and self-report of participants.
On the Aperiodic Autocorrelations of Rotated Binary Sequences

Binary sequences have found major applications in various technological domains such as radar and communication systems. A well-known computational problem is finding the lowest possible peak sidelobe level (PSL) among the set of a binary sequence <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula> and all binary sequences generated by rotations of <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>. In this letter, we present some useful properties of rotated binary sequences, which allowed us to project the aforementioned problem to a perfectly balanced parallelizable algorithm. The proposed algorithm, altogether with its graphical processing unit (GPU) implementation, is significantly faster than the existing instruments. Thus, we were able to exhaust the search space of all m-sequences with lengths <inline-formula> <tex-math notation="LaTeX">$2^{n}-1$ </tex-math></inline-formula>, for <inline-formula> <tex-math notation="LaTeX">$18 \leq n \leq 20$ </tex-math></inline-formula>. Then, we reveal a complete list of all PSL-optimal Legendre sequences, with or without rotations, for lengths up to 432100 - out of computational reach until now. The numerical experiments suggest that the PSL value of all PSL-optimal Legendre sequences, with or without rotations, and with lengths <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> greater than 235723, are strictly greater than <inline-formula> <tex-math notation="LaTeX">$\sqrt {N}$ </tex-math></inline-formula>.
Constant Envelope Precoding With Extended Degrees of Freedom Through Per-User Symbol Scaling

In this letter, we propose a novel constant envelope precoding (CEP) design for multi-user multi-input single-output (MU-MISO) downlink, where we exploit the extra degrees of freedom (DoFs) by allowing individual rotation and scaling for each constellation point. By jointly optimizing the transmitting signal matrix and the complex scaling factors, the signal-to-interference-and-noise-ratio (SINR) at the receiver is maximized. The formulated optimization problem is divided into two sub-problems and solved by the alternating optimization approach. We further propose a simplified gradient descent algorithm and extend our algorithm to frame-level precoding design. Simulation results show that the proposed algorithms outperform the existing CEP algorithms in terms of both symbol error rate (SER) and computational complexity.
Optimal Detection of Multiple Symbol-Slotted Random Access-Based Packet Transmissions

Random access (RA) protocols are suitable for the support of massive connectivity especially in machine-type communications, such as the Internet of Things (IoT). In this context, ALOHA protocols (both slotted and unslotted) have been re-introduced as promising enablers due to a potentially high throughput when enhanced via successive interference cancellation (SIC). Meanwhile, SIC has become the most popular solution for RA detection problems. In this letter, we first point out the weaknesses of SIC and then propose a novel method based on joint detection of all randomly scheduled data packets. The proposed method is based on the joint computation of a-posteriori probabilities for all data symbols and packet localizations, such that optimal detection for RA is obtained.
A Deep Learning-Based Framework for Damage Detection With Time Series

Natural hazards have caused damages to buildings and infrastructures and economic losses in many countries. Immediate emergency response requires accurate damage detection. In recent studies, advances in deep learning approaches have put forward the development of data-driven damage detection. However, these proposed approaches can successfully classify damaged samples into different damage states without quantifying them. Therefore, we propose a deep learning-based framework for damage detection, which comprises signal processing and damage recognition modules. The signal processing module is introduced to denoise the noisy signals collected from sensors and process the denoised signals via fast Fourier transform. The damage recognition module is characterized by a novel network with the architecture of encoder-decoder-encoder. We train the network with only undamaged samples to capture the pattern in the undamaged state. In the testing stage, given an unknown damage state, we feed damaged samples into the trained network to quantify it by outputting the corresponding probability of damage. We conduct experiments to verify the validity and feasibility of the proposed framework with a dataset of a building subjected to excitations. Our approach can map samples from various damage states to a specific probability of damage, which increases as the intensity of excitation increases.
Fault-Tolerant Routing in DCell Networks

Data center networks (hereinafter DCNs) have become increasingly crucial to various cloud applications, as the advance of cloud computing. The k-dimensional DCell based on n-port switches $(k\geq 0$ and $n\geq 2),D_{k,n}$, is a suitable DCN with high network capacity. In this paper, we study a fault-free path join any two fault-free different vertices of $D_{k,n}$ with a faulty vertex set $F\subset V(D_{k,n})$ and $|F|\leq n+k-2$. Besides, we study the maximal length of the paths, less than or equal to $2^{k+1}+3$, where it is studied that the diameter of $D_{k,n}$ has an upper bound $2^{k+1}-1$. This paper also demonstrates that the fault diameter of $D_{k,n}$ is less than or equal to $2^{k+1}+3$. These results are fundamental for fault-tolerant in DCell networks.
Developing a Flipped Learning model for Teaching EAP Students Struggling with Multi-Level Challenges in a College Context

This article aims to understand students’ experiences regarding the implementation of flipped learning (FL) as a modern blended learning technique in teaching English for academic purposes (EAP) in a community college context in Toronto. Based on students’ views, blended learning theories, and several previous studies, the study also aims to develop a holistic contextualized flipped learning model that helps both students and teachers in the context of EAP to cope with the challenges of a multilevel EAP classroom. The study is guided by the epistemology and philosophy of the interpretive paradigm as an underpinning stance. Accordingly, the qualitative approach has been selected for determining the strategy and methods of sampling, and data collection and data analysis. Results revealed that students’ views are compatible with the theoretical views in validating the utilization of flipped learning as a modern technique in the context of EAP. However, results revealed that the development of a holistic model includes a further component-online engagement as an extension component to the model. The study offers a set of recommendations and implications for EAP teachers and instructors within the area of ELT for classroom practice.
CSI-Free vs CSI-Based Multi-Antenna WET for Massive Low-Power Internet of Things

Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in a setup with WET in the downlink, and periodic or Poisson-traffic Wireless Information Transfer (WIT) in the uplink. When CSI is available, we show that a maximum ratio transmission beamformer is close to optimum whenever the farthest node experiences at least 3 dB of power attenuation more than the remaining devices. On the other hand, although the adopted CSI-free mechanism is not capable of providing average harvesting gains, it does provide greater WET/WIT diversity with lower energy requirements when compared with the CSI-based scheme. Our numerical results evidence that the CSI-free scheme performs favorably under periodic traffic conditions, but it may be deficient in case of Poisson traffic, specially if the setup is not optimally configured. Finally, we show the prominent performance results when the uplink transmissions are periodic, while highlighting the need of a minimum mean square error equalizer rather than zero-forcing for information decoding.
Chlor(trifluorphosphan)gold(I), eine einfache flüchtige Goldverbindung

(PF 3 )AuCl was prepared from PF 3 and Au 2 Cl 6 in SOCl 2 as a solvent. It is more stable than the analogous CO complex. It has a vapor pressure of about 10 −4 mbar at room temperature. Its IR, NMR and mass spectra are reported
Convolutional Neural Network Feature Extraction Using Covariance Tensor Decomposition

This work describes a new method to extract image features using tensor decomposition to model data. Given a set of sample images, we extract patches from images, compute the covariance tensor for all patches, decompose with the Tucker model, and obtain the most critical features from a tensor core. To extract features, we factorize the covariance tensor (CovTen) into its core and propose a new interpretation of the resultant tensor structure, which holds relevant features in a block-wise arrangement (also named filters, weights, or kernels). This tensorial representation allows preserving the spatial structure, learning multichannel filters, and establishing linear dependence between dimensions, reducing the dimensional complexity (the curse of dimensionality). Thus, the proposed method generates filters by a single feed-forward step using a few samples per class as low as 1. Besides, in kernel generation, labels are not needed. The obtained features were extensively tested using a convolutional neural network for classification. All tests were conducted under the VGG architecture conventions. The experiments helped us identify the proposed method’s advantages versus traditional convolutional neural networks in inference capacity and kernels initialization. We also performed experiments to select hyperparameters (nonLinearity, max pooling, samples, filter size) according to their performance. The inference capacity results showed an increased classification accuracy around 67% with CIFAR 10, 64% with CIFAR 100, and 98% with MNIST, using 10,100,1000 samples with a single feed-forward training. On the other hand, the initialization experiments showed the feature extraction capability versus available initializers (He random, He uniform, Glorot, random), confirming linear tensor constraints’ usefulness to generate features. Using the method as kernel initializer returns comparable findings with state of the art around 91% with CIFAR 10, 72% with CIFAR 100, and 99% with MNIST.
A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

The pine wilt disease (PWD) is one of the most dangerous and destructive diseases to coniferous forests. The rapid spread trend and strong destruction directly threaten the security of forests. The complex spread pattern and the hard labor process of diagnosis call for an effective way to detect the infected areas. In this paper, an airborne edge-computing and lightweight deep learning based system are designed for PWD detection by using imagery sensors. Unmanned aerial vehicle (UAV) is firstly utilized to realize a large-scale coverage of forests, which can substantially reduce the hard labor. Except for infected trees, a large number of irrelevant images are also acquired by the UAV, which will overload the burden of process and transmission. Then a lightweight improved YOLOv4-Tiny based method (named as YOLOv4-Tiny-3Layers) is proposed to filter these uninterested images by leveraging the computation capability of edge computing, which can realize a fast coarse-grained detection with a low missing rate. Finally, all the remaining images are transmitted to the ground workstation for the final fine-grained detection. Experimental results show that the proposed system can implement a fast detection with superior performance as compared to other methods, which helps to detect the infected pine trees in a quick manner.
Brain structural alterations in schizophrenia and their non-affected relatives: a voxel-based morphometric study

To assess differences and similarities in brain character, we compared regional grey matter (GM) density across whole brain in schizophrenia, their un-affected first-degree relatives and healthy controls, using voxel-based morphometry. Findings showed slight GM density reductions for relatives in left middle temporal gyri, compared to controls, and for patients with schizophrenia (SCZ) in right temporal pole -middle and –superior gyrus, compared with relatives. There was no GM density reduction presented for SCZ in any region. Findings also presented excessive GM density for SCZ in frontal gyrus, left precuneus, and for relatives in right superamarginal gyrus extending to Rolandic operculum, right inferior frontal gyrus, and left inferior temporal gyrus. Compared patients with relatives, right superior occipital lobe and part of cerebellum exhibited higher density while right temporal pole and bilateral medial frontal lobe showed lower density in patients. The scores of negative symptoms in SCZ negative correlated with the GM density in calcarine (r=-0.763, p=0.046) and left putamen (r=-0.882, p=0.009), and the illness duration for left putamen (r=-0.876, p=0.01). There was no any correlation between the regional GM density across whole brains and doses of medication treatment in SCZ.