Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomicity and Well Quasi-Order for Consecutive Orderings on Words and Permutations

Algorithmic decidability is established for two order-theoretic properties of downward closed subsets defined by finitely many obstructions in two infinite posets. The properties under consideration are: (a) being atomic, i.e. not being decomposable as a union of two downward closed proper subsets, or, equivalently, satisfying the joint embedding property; and (b) being well quasi-ordered. The two posets are: (1) words over a finite alphabet under the consecutive subword ordering; and (2) finite permutations under the consecutive subpermutation ordering. Underpinning the four results are characterisations of atomicity and well quasi-order for the subpath ordering on paths of a finite directed graph.
Proton Synchrotron Gamma-Rays and the Energy Crisis in Blazars

The origin of high-energy emission in blazars jets (i.e., leptonic versus hadronic) has been a longstanding matter of debate. Here, we focus on one variant of hadronic models where proton synchrotron radiation accounts for the observed steady γ-ray blazar emission. Using analytical methods, we derive the minimum jet power ( ) for the largest blazar sample analyzed to date (145 sources), taking into account uncertainties of observables and jet’s physical parameters. We compare against three characteristic energy estimators for accreting systems, i.e., the Eddington luminosity, the accretion disk luminosity, and the power of the Blandford–Znajek process, and find that is about 2 orders of magnitude higher than all energetic estimators for the majority of our sample. The derived magnetic field strengths in the emission region require either large amplification of the jet’s magnetic field (factor of 30) or place the γ-ray production site at sub-pc scales. The expected neutrino emission peaks at ∼0.1–10 EeV, with typical peak neutrino fluxes ∼10−4 times lower than the peak γ-ray fluxes. We conclude that if relativistic hadrons are present in blazar jets, they can only produce a radiatively subdominant component of the overall spectral energy distribution of the blazar’s steady emission.
Padovan numbers that are concatenations of two repdigits

Let $ (P_{n})_{n\ge 0} $ be the sequence of Padovan numbers defined by $ P_0=0 $, $ P_1 =1=P_2$, and $ P_{n+3}= P_{n+1} +P_n$ for all $ n\ge 0 $. In this paper, we find all Padovan numbers that are concatenations of two repdigits.
On rising magnetic flux tube and formation of sunspots in a deep domain

We investigate the rising flux tube and the formation of sunspots in an unprecedentedly deep computational domain that covers the whole convection zone with a radiative magnetohydrodynamics simulation. Previous calculations had shallow computational boxes (< 30 Mm) and convection zones at a depth of 200 Mm. By using our new numerical code R2D2, we succeed in covering the whole convection zone and reproduce the formation of the sunspot from a simple horizontal flux tube because of the turbulent thermal convection. The main findings are (1) The rising speed of the flux tube is larger than the upward convection velocity because of the low density caused by the magnetic pressure and the suppression of the mixing. (2) The rising speed of the flux tube exceeds 250 m/s at a depth of 18 Mm, while we do not see any clear evidence of the divergent flow 3 hr before the emergence at the solar surface. (3) Initially, the root of the flux tube is filled with the downflows and then the upflow fills the center of the flux tube during the formation of the sunspot. (4) The essential mechanisms for the formation of the sunspot are the coherent inflow and the turbulent transport. (5) The low-temperature region is extended to a depth of at least 40 Mm in the matured sunspot, with the high-temperature region in the center of the flux tube. Some of the findings indicate the importance of the deep computational domain for the flux emergence simulations.
CRNet: Cross-Reference Networks for Few-Shot Segmentation

Over the past few years, state-of-the-art image segmentation algorithms are based on deep convolutional neural networks. To render a deep network with the ability to understand a concept, humans need to collect a large amount of pixel-level annotated data to train the models, which is time-consuming and tedious. Recently, few-shot segmentation is proposed to solve this problem. Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a cross-reference network (CRNet) for few-shot segmentation. Unlike previous works which only predict the mask in the query image, our proposed model concurrently makes predictions for both the support image and the query image. With a cross-reference mechanism, our network can better find the co-occurrent objects in two images, thus helping the few-shot segmentation task. We also develop a mask refinement module to recurrently refine the prediction of the foreground regions. For the k-shot learning, we propose to finetune parts of networks to take advantage of multiple labeled support images. Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
Single-particle excitation and density excitation in Bose-Einstein condensates

Abstract Strong connection between the single-particle excitation and the collective excitation stands out as one of the features of Bose–Einstein condensates (BECs). We discuss theoretically these single-particle and density excitations of BECs focusing on the exact properties of the one-body and two-body Green’s functions developed by Gavoret and Nozières. We also investigate these excitations by using the many-body approximation theory at non-zero temperatures. First, we revisited the earlier study presented by Gavoret and Nozières, involving the subsequent results given by Nepomnyashchii and Nepomnyashchii, in terms of the matrix formalism representation. This matrix formalism is an extension of the Nambu representation for the single-particle Green’s function of BECs to discuss the density and current response functions with reducing the complexity of calculations. We describe the exact low-energy properties of the correlation functions and the vertex functions, and discuss the correspondence of the spectra between the single-particle excitation and the density excitation in the low-energy and low-momentum limits. After deriving the exact low-energy structures of the one-body and two-body Green’s functions, we develop a many-body approximation theory of BECs with making the use of the matrix formalism for describing the single-particle Green’s function and the density response function at non-zero temperatures. We show how the peaks of the single-particle spectral function and the density response function behave with an increasing temperature. Many-body effect on the single-particle spectral function and the density response function is included within a random phase approximation, where satellite structures emerge both in the spectral function and density response function because of beyond-mean-field effects. Brief comments are also made on recent theories casting doubt upon the conventional wisdom of the BEC: the equivalence of the dispersion relations between the single-particle excitation and the collective excitation in the low-energy and low-momentum regime.
A Time-domain Monaural Speech Enhancement with Recursive Learning

In this paper, we propose a type of neural network with recursive learning in the time domain called RTNet for monaural speech enhancement, where the proposed network consists of three principal components. The first part is called stage recurrent neural network, which is introduced to effectively aggregate the deep feature dependencies across different stages with a memory mechanism and also remove the interference stage by stage. The second part is the convolutional auto-encoder. The third part consists of a series of concatenated gated linear units, which are capable of facilitating the information flow and gradually increasing the receptive fields. Recursive learning is adopted to improve the parameter efficiency and therefore, the number of trainable parameters is effectively reduced without sacrificing its performance. Numerous experiments are conducted on TIMIT corpus and experimental results demonstrate that the proposed network can achieve consistently better performance in terms of both PESQ and STOI scores than two state-of-the-art time domain-based baselines in different conditions. The code is provided at this https URL Andong-Li-speech/RTNet.
Input representation in recurrent neural networks dynamics

Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and its approximation performance. The recurrent part of these networks is not trained (e.g. via gradient descent), making them appealing for analytical studies, raising the interest of a vast community of researcher spanning from dynamical systems to neuroscience. It emerges that, even in the simple linear case, the working principle of these networks is not fully understood and the applied research is usually driven by heuristics. A novel analysis of the dynamics of such networks is proposed, which allows one to express the state evolution using the controllability matrix. Such a matrix encodes salient characteristics of the network dynamics: in particular, its rank can be used as an input-indepedent measure of the memory of the network. Using the proposed approach, it is possible to compare different architectures and explain why a cyclic topology achieves favourable results.
Model selection criteria of the standard censored regression model based on the bootstrap sample augmentation mechanism

The statistical regression technique is an extraordinarily essential data fitting tool to explore the potential possible generation mechanism of the random phenomenon. Therefore, the model selection or the variable selection is becoming extremely important so as to identify the most appropriate model with the most optimal explanation effect on the interesting response. In this paper, we discuss and compare the bootstrap-based model selection criteria on the standard censored regression model (Tobit regression model) under the circumstance of limited observation information. The Monte Carlo numerical evidence demonstrates that the performances of the model selection criteria based on the bootstrap sample augmentation strategy will become more competitive than their alternative ones, such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) etc. under the circumstance of the inadequate observation information. Meanwhile, the numerical simulation experiments further demonstrate that the model identification risk due to the deficiency of the data information, such as the high censoring rate and rather limited number of observations, can be adequately compensated by increasing the scientific computation cost in terms of the bootstrap sample augmentation strategies. We also apply the recommended bootstrap-based model selection criterion on the Tobit regression model to fit the real fidelity dataset.
Performance Evaluation of Machine Learning Algorithms for Hypertext Transfer Protocol Distributed Denial of Service Intrusion Detection

As this paper has expounded, the techniques against DDoS attacks borrow greatly from the already tested traditional techniques. However, no technique has proven to be perfect towards the full detection and prevention of DDoS attacks. Intrusion detection system (IDS) using machine learning approach is one of the implemented solutions against harmful attacks. However, achieving high detection accuracy with minimum false positive rate remains issue that still need to be addressed. Consequently, this study carried out an experimental evaluation on various machine learning algorithms such as Random forest J48, Naïve Bayes, IBK and Multilayer perception on HTTP DDoS attack dataset. The dataset has a total number of 17512 instances which constituted normal (10256) and HTTP DDoS (7256) attack with 21 features. The implemented Performance evaluation revealed that Random Forest algorithm performed best with an accuracy of 99.94% and minimum false positive rate of 0.001%.
Deep Learning for Image-based Cervical Cancer Detection and Diagnosis — A Survey

Cervical cancer is the fourth most common type of cancer found in females with a record of 570,000 incidences and 311,000 deaths in the year 2018 worldwide. It is caused by a virus known as Human Papilloma Virus (HPV). Screening if done early can reduce this prevalence. However, manual screening methods are not efficient in the detection of cervical cancer as a result of some factors. This, however, results in misdiagnosis and over-treatment. Therefore, researchers proposed screening cervical automatically by using traditional and deep learning techniques. This paper aims to review past work that has been done particularly in the deep learning domain and discusses future directions in the automated detection of cervical cancer. It is believed that this will ensure proper diagnosis and could potentially reduce the prevalence of cervical cancer.
Machine Learning Classification Algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis

Exponential growth experienced in Internet usage has paved the way to exploit users of the Internet, a phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false-positive rate and low accuracy have been a setback in phishing detection. In this research 17 different supervised learning techniques such as RandomForest, Systematically Developed Forest (SysFor), Spectral Areas and Ratios Classifier (SPAARC), Reduces Error Pruning Tree (RepTree), RandomTree, Logic Model Tree (LMT), Forest by Penalizing Attributes (ForestPA), JRip, PART, Nearest Neighbor with Generalization (NNge), One Rule (OneR), AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, Library for Support Vector Machine (LibSVM), and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms was rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quite several classifiers also exist which if properly explored will yield more accurate results for phishing detection. RandomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false-positive rate for phishing classification which suggests that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.
Top-k Nearest Keyword Search in Public Transportation Networks

Top-k nearest keyword search is important for various applications. However, the existing methods are only applicable to static graphs, not public transportation networks. This is because unlike static graph, public transportation network is a temporal graph where the path in the temporal graph must satisfy the time constraint. Thus, the path which is reachable in the static graph, may not reachable in the temporal graph. Therefore, the methods applicable to static graphs cannot be applied to temporal graphs. In this paper, to solve the top-k nearest neighbor keyword search on public transportation networks, we propose two indexes and two algorithms called Temporal Forward Search (TFS) and Temporal Forward-Backward Search (TFBS) to improve the efficiency. Extensive experiments on the real-world datasets were conducted to show the efficiency of our proposed methods.
A Cluster Model Applied to Power Consumption

The main impact of this paper is to compare two methods of grouping; Kmean and Usage function, for promote the effect of two classifications. According to the simulation results, respectively, using those two cluster methods to divide power data into 10 groups, the result is Usage function can significantly improve the C5.0 classification and prediction, but also can enhance the classification and prediction of CART. We hope through the effective grouping method to efficient distribution and use of electricity.
A Preliminary Study of Loop-Time Delays in Iot Platforms: The Thingspeak Case

IoT platforms play an important role on modern measurement systems because they allow the ingestion and processing of huge amounts of data (big data). Usually, these platforms run as a service on the cloud and are accessed through open programming interfaces based on ubiquitous internet protocols (such as HTTP). Data analysis is done in batch, from time to time, or when a pre-configured event is triggered. Most of the platforms can also actuate on the physical world by issuing messages, thus closing the loop ingest-analyze-actuate. The continuous investment on IoT platforms has made them extremely reliable and performant, wondering if they can be used to control physical processes. The paper contributes to this discussion by evaluating the loop-time - defined as the delay between ingestion and actuation - of the ThingSpeak platform. The measuring methodology is explained, results are presented, and conclusions are extracted.
Research on Structurally Integrated Phased Array for Wireless Communications

Structurally integrated antenna is a kind of highly integrated microwave device with a load-bearing function, and it is usually installed on the structural surface of the air, water and ground vehicles. This paper presents the design, fabrication and testing of a novel structurally integrated Ka-band active antenna for airborne 5G wireless communications. The proposed antenna is mainly composed of three parts: a package layer, a control and signal process layer and a RF layer. In the RF layer, the microstrip antenna array, tile transmitting (Tx) modules, micro-channel heat sinks and a stripline feeding network are highly integrated into a functional block with a thickness of 2.8 mm. Electromechanical co-design methods are developed to design the active antenna array with the superstrates, and two schemes for designing micro-channel heat sinks are evaluated to obtain a uniform temperature distribution. The RF layer is fabricated by using the low-temperature cofired ceramic process, and the three layers are assembled to form the full-size antenna prototype. The mechanical and electromagnetic experiments are carried out, and the results demonstrate the feasibility of the structurally integrated active antenna for airborne wireless communications.
A Fully Automatic Framework to Localize Esophageal Tumor for Radiation Therapy

Automatic localization of esophageal tumors is an important part of target volume planning in radiotherapy. Currently, the main localization method is manual localization. Traditional manual positioning is time-consuming and inaccurate for the following reasons. First of all, esophageal neoplasms are irregular in shape. The second, the tumor image was insufficiently contrasted with the surrounding tissue. Also, the tumor area is highly heterogeneous. To solve these problems, this paper proposes an automatic positioning framework combining single point multi-box detector (SSD) with the optimized VGG16 deep learning network. The optimized algorithm network has achieved good results in our esophageal tumor localization experiment. The experimental data consists of 96 esophageal VMAT plans and training set consists of 60 patients, the remaining 36 patient data sets were used as the test set. We trained with 5000 slices and tested with 1000. The experiment result showed the tumor areas of 820 CT slices were effectively located, and the accuracy rate of intersection greater than and (IoU)[6] value was 82%. These promising results suggest that the target area of esophageal tumor can be well located in our optimized framework, which can improve the efficiency and quality of plan making of esophageal tumor radiotherapy.
Time-Varying Lyapunov Functions for Tracking Control of Mechanical Systems With and Without Frictions

In this paper, the tracking control problem is investigated for time-varying nonlinear mechanical systems with and without frictions. A reduced-order dynamic and a traditional time-invariant Lyapunov design method is utilized for the case without frictions. Based on the traditional results, a new class of time-varying Lyapunov functions is provided for the case with time-varying frictions. It is shown that the tracking errors of the closed-loop system will converge to the neighborhood of the origin, which is determined by the feedback parameters. Numerical examples are presented for illustration.
An Approach of Face Recognition Applied for Smarthome Using System – on – Chip Technology

This paper presents a real-time face recognition system consisting of face detection and recognition using an System-on-Chip (SoC) Technology. Our system provides an integration of hardware and software solution for face recognition that receives video input from a camera, detects the locations of the faces using the Haar Like Feature proposed by Viola - Jones, subsequently recognizes each face using Local Binary Pattern Histogram (LBPH) Algorithm. Beside, a subsystem of using Histogram of Oriented Gradients (HOG) feature combine with Support Vector Machine (SVM) classifier to recognize human body before face detection. Experimental results shows maximum execution time not exceeded 220 ms and success rate of face recognition up to 80% within 1 meter around camera on SoC Cyclone V from Terasic DE10-NANO development board.
QoS-Aware Call Admission Control (QA-CAC) Scheme for LTE Networks.

Quality of Service (QoS) provisioning is a critical challenge in any wireless broadband (WiBB) network. LTE being a WiBB network technology aimed at providing adequate network resources for speedy transmission of applications with varying QoS requirements. It uses radio resource management (RRM) techniques such as call admission control (CAC) for resource utilization and to guarantee these QoS requirements. In this paper, a novel call admission control scheme is proposed to guarantee the QoS of calls and also increase the throughput of Real-time (RT) calls. The scheme allocates maximum bandwidth requirements to both RT and NRT calls at the point of admission. It then degrades all admitted NRT calls when a call arrives and there are insufficient resources to admit the requested call. Several simulation experiments were conducted with the aid of Vienna LTE system level simulator and the results reveal that the proposed scheme achieved superior performance in terms of throughput and blocking rate of RT traffic compared to the benchmark scheme