Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Step Surface Damage Detection Scheme using Convolutional Neural Network and Artificial Neural Network

Surface damage on concrete is important as the damage can affect the structural integrity of the structure. This paper proposes a two-step surface damage detection scheme using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The CNN classifies given input images into two categories: positive and negative. The positive category is where the surface damage is present within the image, otherwise the image is classified as negative. This is an image-based classification. The ANN accepts image inputs that have been classified as positive by the ANN. This reduces the number of images that are further processed by the ANN. The ANN performs feature-based classification, in which the features are extracted from the detected edges within the image. The edges are detected using Canny edge detection. A total of 19 features are extracted from the detected edges. These features are inputs into the ANN. The purpose of the ANN is to highlight only the positive damaged edges within the image. The CNN achieves an accuracy of 80.7% for image classification and the ANN achieves an accuracy of 98.1% for surface detection. The decreased accuracy in the CNN is due to the false positive detection, however false positives are tolerated whereas false negatives are not. The false negative detection for both CNN and ANN in the two-step scheme are 0%.
Transfer Learning in Automated Gamma Spectral Identification

The models and weights of prior trained Convolutional Neural Networks (CNN) created to perform automated isotopic classification of time-sequenced gamma-ray spectra, were utilized to provide source domain knowledge as training on new domains of potential interest. The previous results were achieved solely using modeled spectral data. In this work we attempt to transfer the knowledge gained to the new, if similar, domain of solely measured data. The ability to train on modeled data and predict on measured data will be crucial in any successful data-driven approach to this problem space.
PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM

Registration methods for point clouds have become a key component of many SLAM systems on autonomous vehicles. However, an accurate estimate of the uncertainty of such registration is a key requirement to a consistent fusion of this kind of measurements in a SLAM filter. This estimate, which is normally given as a covariance in the transformation computed between point cloud reference frames, has been modelled following different approaches, among which the most accurate is considered to be the Monte Carlo method. However, a Monte Carlo approximation is cumbersome to use inside a time-critical application such as online SLAM. Efforts have been made to estimate this covariance via machine learning using carefully designed features to abstract the raw point clouds [1]. However, the performance of this approach is sensitive to the features chosen. We argue that it is possible to learn the features along with the covariance by working with the raw data and thus we propose a new approach based on PointNet [2]. In this work, we train this network using the KL divergence between the learned uncertainty distribution and one computed by the Monte Carlo method as the loss. We test the performance of the general model presented applying it to our target use-case of SLAM with an autonomous underwater vehicle (AUV) restricted to the 2-dimensional registration of 3D bathymetric point clouds.
A Comprehensive Analysis of Correlated Source Compression Using Edge Computing in Distributed Systems

This paper examines the theory pertaining to lossless compression of correlated sources located at the edge of a network. Importantly, communication between nodes is prohibited. In particular, a method that combines correlated source coding and matrix partitioning is explained. This technique is then made more flexible, by restricting the method to operate on two distinct groups of nodes. As a result, this new method allows for more freedom in compression performance, with consequent trade-off in node integrity validation. Specifically, it provides 2-3 times the compression savings when using a Hamming(7,4) with 4 nodes. It also decreases the complexity with regard to managing the nodes as they join/leave the network, while retaining the range within which the information can be losslessly decoded.
Learn to Schedule (LEASCH): A Deep Reinforcement Learning Approach for Radio Resource Scheduling in the 5G MAC Layer

Network management tools are usually inherited from one generation to another. This was successful since these tools have been kept in check and updated regularly to fit new networking goals and service requirements. Unfortunately, new networking services will render this approach obsolete and handcrafting new tools or upgrading the current ones may lead to complicated systems that will be difficult to maintain and improve. Fortunately, recent advances in AI have provided new promising tools that can help solving many network management problems. Following this interesting trend, the current article presents LEASCH, a deep reinforcement learning model able to solve the radio resource scheduling problem in the MAC layer of 5G networks. LEASCH is developed and trained in a sand-box and then deployed in a 5G network. It has been evaluated under different numerology settings. The experimental results show that it is both numerology-agnostic and efficient when compared to conventional baseline methods in many key performance indicators.
t|ket⟩: a retargetable compiler for NISQ devices

We present t|ket⟩, a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t|ket⟩ is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has been extensively benchmarked and outperforms most competitors in terms of circuit optimisation and qubit routing.
Learning regularization and intensity-gradient-based fidelity for single image super resolution

How to extract more and useful information for single image super resolution is an imperative and difficult problem. Learning-based method is a representative method for such task. However, the results are not so stable as there may exist big difference between the training data and the test data. The regularization-based method can effectively utilize the self-information of observation. However, the degradation model used in regularization-based method just considers the degradation in intensity space. It may not reconstruct images well as the degradation reflections in various feature space are not considered. In this paper, we first study the image degradation progress, and establish degradation model both in intensity and gradient space. Thus, a comprehensive data consistency constraint is established for the reconstruction. Consequently, more useful information can be extracted from the observed data. Second, the regularization term is learned by a designed symmetric residual deep neural-network. It can search similar external information from a predefined dataset avoiding the artificial tendency. Finally, the proposed fidelity term and designed regularization term are embedded into the regularization framework. Further, an optimization method is developed based on the half-quadratic splitting method and the pseudo conjugate method. Experimental results indicated that the subjective and the objective metric corresponding to the proposed method were better than those obtained by the comparison methods.
Analytic I-Love-C relations for realistic neutron stars

Recent observations of neutron stars (NS) with radio, X-rays and gravitational waves have begun to constrain the equation of state (EoS) for nuclear matter beyond the nuclear saturation density. To one's surprise, there exist approximate universal relations connecting certain bulk properties of NSs that are insensitive to the underlying EoS and having important applications on probing fundamental physics including nuclear and gravitational physics. To date, analytic works on universal relations for realistic NSs are lacking, which may lead to a better understanding of the origin of the universality. Here, we focus on the universal relations between the compactness(C), moment of inertia (I), and tidal deformability(related to the Love number), and derive analytic, approximate I-Love-C relations. To achieve this, we construct slowly-rotating/ tidally-deformed NS solutions analytically starting from an extended Tolman VII model that accurately describes non-rotating realistic NSs, allowing us to extract the moment of inertia and the tidal deformability on top of the compactness. We solve the field equations analytically by expanding them about the Newtonian limit and keeping up to 6th order in the stellar compactness. Based on these analytic solutions, we can mathematically demonstrate the O(10%) EoS variation in the I-C and Love-C relations and the O(1%) variation in the I-Love relation that have previously been found numerically. Our new analytic relations agree more accurately with numerical results for realistic NSs(especially the I-C and Love-C ones) than the analytic relations for constant density stars derived in previous work. Based on these analytic findings, we attribute a possible origin of the universality for the I-C and Love-C relations to the fact that the energy density of realistic NSs can be approximated as a quadratic function, as is the case for the Tolman VII solution.
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations

We consider the problem of robust matrix completion, which aims to recover a low rank matrix $L_*$ and a sparse matrix $S_*$ from incomplete observations of their sum $M=L_*+S_*\in\mathbb{R}^{m\times n}$. Algorithmically, the robust matrix completion problem is transformed into a problem of solving a system of nonlinear equations, and the alternative direction method is then used to solve the nonlinear equations. In addition, the algorithm is highly parallelizable and suitable for large scale problems. Theoretically, we characterize the sufficient conditions for when $L_*$ can be approximated by a low rank approximation of the observed $M_*$. And under proper assumptions, it is shown that the algorithm converges to the true solution linearly. Numerical simulations show that the simple method works as expected and is comparable with state-of-the-art methods.
Evaluation of error- and correlation-based loss functions for multitask learning dimensional speech emotion recognition

The choice of a loss function is a critical part in machine learning. This paper evaluates two different loss functions commonly used in regression-task dimensional speech emotion recognition — error-based and correlation-based loss functions. We found that using correlation-based loss function with concordance correlation coefficient (CCC) loss resulted in better performance than error-based loss functions with mean squared error (MSE) and mean absolute error (MAE). The evaluations were measured in averaged CCC among three emotional attributes. The results are consistent with two input feature sets and two datasets. The scatter plots of test prediction by those two loss functions also confirmed the results measured by CCC scores.
New incompressible symmetric tensor categories in positive characteristic

We propose a method of constructing abelian envelopes of symmetric rigid monoidal Karoubian categories over an algebraically closed field $\bf k$. If ${\rm char}({\bf k})=p>0$, we use this method to construct generalizations ${\rm Ver}_{p^n}$, ${\rm Ver}_{p^n}^+$ of the incompressible abelian symmetric tensor categories defined in arXiv:1807.05549 for $p=2$ and by Gelfand-Kazhdan and Georgiev-Mathieu for $n=1$. Namely, ${\rm Ver}_{p^n}$ is the abelian envelope of the quotient of the category of tilting modules for $SL_2(\bf k)$ by the $n$-th Steinberg module, and ${\rm Ver}_{p^n}^+$ is its subcategory generated by $PGL_2(\bf k)$-modules. We show that ${\rm Ver}_{p^n}$ are reductions to characteristic $p$ of Verlinde braided tensor categories in characteristic zero, which explains the notation. We study the structure of these categories in detail, and in particular show that they categorify the real cyclotomic rings $\mathbb{Z}[2\cos(2\pi/p^n)]$, and that ${\rm Ver}_{p^n}$ embeds into ${\rm Ver}_{p^{n+1}}$. We conjecture that every symmetric tensor category of moderate growth over $\bf k$ admits a fiber functor to the union ${\rm Ver}_{p^\infty}$ of the nested sequence ${\rm Ver}_{p}\subset {\rm Ver}_{p^2}\subset\cdots$. This would provide an analog of Deligne's theorem in characteristic zero and a generalization of the result of arXiv:1503.01492, which shows that this conjecture holds for fusion categories, and then moreover the fiber functor lands in ${\rm Ver}_p$.
Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning

With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures. Although it has become possible to automatically detect anomalies and analyze root causes of failures with current methods, making decisions on what commands should be executed to recover from failures still depends on manual operation, which is quite time-consuming. Toward automatic recovery, we propose a method of estimating recovery commands by using Seq2Seq, a neural network model. This model learns complex relationships between logs obtained from equipment and recovery commands that operators executed in the past. When a new failure occurs, our method estimates plausible commands that recover from the failure on the basis of collected logs. We conducted experiments using a synthetic dataset and realistic OpenStack dataset, demonstrating that our method can estimate recovery commands with high accuracy.
Not quite black holes as dark matter

Primordial black holes that survive until the present have been considered as a dark matter candidate. In this paper we argue that primordial 2-2-hole remnants provide a more promising and testable option. 2-2-holes arise in quadratic gravity as a new family of classical solutions for ultracompact matter distributions and they possess the black hole exterior without an event horizon. They may serve as the endpoint of gravitational collapse, providing a resolution for the information loss problem. Intriguing thermodynamic behavior is found for these objects when sourced by a thermal gas. A large 2-2-hole radiates with a Hawking-like temperature and exhibits an entropy-area law. At a late stage, the evaporation slows down and essentially stops as the mass asymptotically approaches a minimal value. This remnant mass is determined by a fundamental scale in quadratic gravity. We study the cosmological and astrophysical implications of having these remnants as dark matter and derive the corresponding constraints. A distinctive phenomenon associated with remnant mergers occurs, predicting fluxes of high-energy astrophysical particles due to the spectacular evaporation of the merger product. Measurements of high-energy photon and neutrino fluxes could possibly bound the remnant mass to be not far above the Planck mass. Early-universe physics, on the other hand, requires that 2-2-holes quickly evolve into the remnant state after formation, putting an upper bound on the formation mass.
Grammars for Document Spanners

We propose a new grammar-based language for defining information-extractors from documents (text) that is built upon the well-studied framework of Document Spanners for extracting structured data from text. While previously studied formalisms for document spanners are mainly based on regular expressions, we use an extension of context-free grammars, called extraction grammars, to define the new class of context-free spanners. Extraction grammars are simply context-free grammars extended with variables that capture interval positions of the document, namely spans. While regular expressions are efficient for tokenizing and tagging, context-free grammars are also efficient for capturing structural properties. Indeed, we show that context-free spanners are strictly more expressive than their regular counterparts. We reason about the expressive power of our new class and present a pushdown-automata model that captures it. We show that extraction grammars can be evaluated with polynomial data complexity. Nevertheless, as the degree of the polynomial depends on the query, we present an enumeration algorithm for (a subset of) extraction grammars that, after cubic preprocessing, outputs the results sequentially, without repetitions, with a constant delay between every two consecutive ones.
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis

Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization problems can be tackled simultaneously by performing a single search process. The main objective to achieve this goal efficiently is to exploit synergies between the problems (tasks) to be optimized, helping each other via knowledge transfer (thereby being referred to as Transfer Optimization). Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration. As such, EM approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has shown a remarkable success when dealing with multiple discrete, continuous, single-, and/or multi-objective optimization problems. In this work we propose a novel algorithmic scheme for Multifactorial Optimization scenarios – the Multifactorial Cellular Genetic Algorithm (MFCGA) – that hinges on concepts from Cellular Automata to implement mechanisms for exchanging knowledge among problems. We conduct an extensive performance analysis of the proposed MFCGA and compare it to the canonical MFEA under the same algorithmic conditions and over 15 different multitasking setups (encompassing different reference instances of the discrete Traveling Salesman Problem). A further contribution of this analysis beyond performance benchmarking is a quantitative examination of the genetic transferability among the problem instances, eliciting an empirical demonstration of the synergies emerged between the different optimization tasks along the MFCGA search process
Influence-Based Community Partition With Sandwich Method for Social Networks

Community partition is an important problem in many areas, such as biology networks and social networks. The objective of this problem is to analyze the relationships among data via the network topology. In this article, we consider the community partition problem under the independent cascade (IC) model in social networks. We formulate the problem as a combinatorial optimization problem that aims at partitioning a given social network into disjoint $m$ communities. The objective is to maximize the sum of influence propagation of a social network through maximizing it within each community. The existing work shows that the influence maximization for community partition problem (IMCPP) is NP-hard. We first prove that the objective function of IMCPP under the IC model is neither submodular nor supermodular. Then, both supermodular upper bound and submodular lower bound are constructed and proved so that the sandwich framework can be applied. A continuous greedy algorithm and a discrete implementation are devised for upper and lower bound problems. The algorithm for both of the two problems gets a $1-1/e$ approximation ratio. We also present a simple greedy algorithm to solve the original objective function and apply the sandwich approximation framework to it to guarantee a data-dependent approximation factor. Finally, our algorithms are evaluated on three real datasets, which clearly verifies the effectiveness of our method in the community partition problem, as well as the advantage of our method against the other methods.
Well Responses of Clinical Symptoms and Prostate Specific Antigen Follow up in Advanced Stage Prostate Cancer Patients in One Year Managed by Hormonal Therapy

Purpose: To evaluate the responses of clinical symptoms and prostate specific antigen (PSA) follow up in advanced stage prostate cancer patients in one year of hormonal therapy Materials and Methods: We reviewed medical records of advanced prostate cancer patients consisting of age, clinical symptoms, digital rectal examination (DRE), PSA levels, Gleason Score (G S) – histopathology grading, bone scan and hormonal therapy, follow up of clinical symptoms and PSA levels in one year of hormonal therapy in Arifin Achmad Regional General Hospital, Pekanbaru, Riau Province, Indonesia. Statistical analysis of univariate was used. Approval on the study was obtained from the Ethical Review Board for Medicine and Health Research, Medical Faculty, University of Riau. Results: There were 36 advanced stage cancer patients in this study in which mostly (36.1%) in 51-60 and 61-70 year age groups. Most (52.8%) clinical symptom was urinary retension and most (69.4%) DRE was abnormal. Initial PSA levels were mostly (52.8%) > 100 ng /ml. All histopathology findings were adenocarsinoma and the Gleason Score (GS) with histopathology grading were mostly (63.9%) 8 -10 with poorly differentiated. Bone scan showed metastases in 69.4% patients. Management was mostly (63.9%) Androgen Deprivation Therapy. The clinical symptoms was least found until nine months and PSA levels also were normal in nine months of follow up after hormonal therapy. Conclusion: We found well responses of clinical symptoms and PSA follow up in advanced stage prostate cancer patients in one year managed by hormonal therapy.
Improved Hybrid Simulated Annealing and Particle Swarm Optimization for Maximum Power Point Tracking

This paper proposes an Improved Hybrid Simulated Annealing and Particle Swarm Optimization (ISA-PSO) which is applied in the maximum power point tracking (MPPT) of photovoltaic generation system. ISA-PSO combined with Simulated Annealing (SA) and Particle Swarm Optimization (PSO), using SA's escaping mechanism to improve the shortcomings of PSO slow convergence and easy to fall into the local optimal solution. The overall search ability is increase and the tracking time is reduced. This paper uses MATLAB to simulate MPPT. Through the simulation comparison with PSO, SA, SA-PSO, it can be verified that ISA-PSO scheme is better than other algorithms. In the temperature changing simulation, the efficiency is 97.03% and the tracking speed is reduced to 2.31 second. In the illumination changing simulation, the efficiency is 97.12% and the tracking speed is reduced to 2.27 second. Keywords- Maximum Power Point Tracking, Improved Hybrid Simulated Annealing and Particle Swarm Optimization, Photovoltaic Generation System.