Reviewerly/exHarmony huggingface dataset:
Conventional Cervical Cytology Image Dataset with Cell Outline Annotations

Here we describe our new Bialystok dataset of 162 Papanicolaou cervical smear images containing 2419 cells with annotations in form of cell cytoplasm maps. The images are fragments of whole slides, including artefacts and dense cell clusters. Cell classification annotations are created in accordance with the Bethesda system. It means that our dataset is coherent with the most widely used cytology reporting system, adaptable to different cell annotation methods. Additionally we perform segmentation with three common neural networks: U-Net, FPN and PSP-Net, and classification with VGG16 with four different classification cases. Then we compare our Bialystok dataset with another large outline annotation dataset Sipakmed qualitatively and as training data for deep learning. We train and test VGG16, on both datasets, including cross-testing. VGG16 reaches <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> of 99.09% on Sipakmed and 91.59% on Bialystok dataset. When cross-testing, the neural network trained on Sipakmed and tested on Bialystok reaches <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> of 87.83%. The neural network trained on Bialystok reaches <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> of 92.74 % when tested on Sipakmed. Results indicate Bialystok as more challenging for segmentation. The proposed dataset is useful for training deep learning methods to segment, detect or classify physiological and pathologic epithelial cells in whole slide smear images.
Variational Density Propagation Continual Learning

Deep Neural Networks (DNNs) deployed to the real world are regularly subject to out-of-distribution (OoD) data, various types of noise, and shifting conceptual objectives. This paper proposes a framework for adapting to data distribution drift modeled by benchmark Continual Learning datasets. We develop and evaluate a method of Continual Learning that leverages uncertainty quantification from Bayesian Inference to mitigate catastrophic forgetting. We expand on previous approaches by removing the need for Monte Carlo sampling of the model weights to sample the predictive distribution. We optimize a closed-form Evidence Lower Bound (ELBO) objective approximating the predictive distribution by propagating the first two moments of a distribution, i.e. mean and covariance, through all network layers. Catastrophic forgetting is mitigated by using the closed-form ELBO to approximate the Minimum Description Length (MDL) Principle, inherently penalizing changes in the model likelihood by minimizing the KL Divergence between the variational posterior for the current task and the previous task's variational posterior acting as the prior. Leveraging the approximation of the MDL principle, we aim to initially learn a sparse variational posterior and then minimize additional model complexity learned for subsequent tasks. Our approach is evaluated for the task incremental learning scenario using density propagated versions of fully-connected and convolutional neural networks across multiple sequential benchmark datasets with varying task sequence lengths. Ultimately, this procedure produces a minimally complex network over a series of tasks mitigating catastrophic forgetting.
On the Size of the Bijective Regions in Digital Rotations on the Hexagonal Grid

In this paper, we consider the digital rotations on the hexagonal and triangular grids, where the center of rotation is the midpoint of a given main pixel. Since there is one to one correspondence between the even (zero sum) trixels on the triangular grid and the hexels of the hexagonal grid (main cell with strict 2-neighbors in the triangular grid gets mapped to the main cell with the usual neighbors in the hexagonal grid), the results give us information on the size of the bijective regions after the digitized rotations on the hexagonal grid. First half of the paper considers digitized rotations of the strict 2-neighbors with the given main trixel on the triangular grid, where in the second half the same problem is discussed in a larger scale for all even trixels, and the minimum digital distance from the center of rotation to the first ‘error’ pixel (in terms of failure of bijectivity) is calculated, and this way the radius of the bijective region on the hexagonal grid is determined.
Computational Color Constancy-Based Backdoor Attacks

Deep neural networks (DNNs) have become an integral part of many computer vision tasks. However, training complex neural networks requires a large amount of computational resources. Therefore, many users outsource training to third parties. This introduces an attack vector for backdoor attacks. These attacks are described as attacks in which the neural network behaves as expected for benign inputs but acts maliciously when a backdoor trigger is present in the input. Triggers are small, preferably stealthy additions to the input. However, most of these triggers are based on the additive model, i.e., the trigger is simply added onto the image. Furthermore, optimized triggers are artificial, which means that it is difficult or impossible to reproduce them in the real-world, making them impractical to use in a real-world setting. In this work, we present a novel way of trigger injection for the classification problem. It is based on the von Kries model for image color correction, a frequently used component in all image processing pipelines. Our trigger uses a multiplicative rather than an additive model. This makes it harder to detect the injection by defensive methods. Second, the trigger is based on real-world phenomena of changing illumination. Finally, it can be made harder to spot by a human observer, when compared to some additive triggers. We test the performance of our attack strategy against various defense methods on several frequently used datasets, and achieve excellent results. Furthermore, we show that the malicious behavior of models trained on artificially colored images can be activated in real-world scenarios, further increasing the usefulness of our attack strategy.
High-Resolution Khalifa Satellite Imagery with AI-Powered Super Resolution Generative Adversarial Networks

KhalifaSat is an Earth observation satellite equipped with multispectral remote sensing capabilities, allowing it to analyze and detect various features like buildings and roads. However, the satellite's limited resolution sometimes leads to blurry image details. To address this issue, super resolution techniques have been employed, and one notable approach is using Generative Adversarial Networks (GANs). This study introduces a novel algorithm called Hybrid SRGAN, which simplifies the Super-Resolution Generative Adversarial Network (SRGAN) model while maintaining high accuracy. The proposed modifications involve eliminating batch normalization layers and using bicubic interpolation for resizing low-resolution images. By removing batch normalization, the training speed improves, and the model generalizes better across different image types. The performance of the Hybrid SRGAN model is evaluated using metrics like SSIM, PSNR, and BRISQUE, demonstrating its superior reconstruction quality and overall performance, particularly in the luminance channel (YCbCr), when compared to state-of-the-art algorithms. This suggests that the Hybrid SRGAN algorithm offers a more efficient and effective solution for enhancing image resolution and quality from the satellite's data.
Distributed Kalman Filtering for Temporal Resolution Enhancement of Thermal Images

This paper presents a novel method for enhancing temporal resolution of thermal image sequences. The proposed approach employs the Kalman filter, which exploits the spatio-temporal correlation of the underlying physical processes and leverages temporal information contained in rows of an image captured via rolling shutter readout, to successfully reconstruct intra-frame sequences. To address the computational complexity associated with large-scale Kalman filtering, we propose a distributed filtering scheme that takes advantage of the neighbourhood relationship in the underlying diffusion process. Simulation results demonstrate the capability of the method in enhancing temporal resolution while achieving acceptable Peak Signal-to-Noise Ratio (PSNR) of approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$30dB$</tex> . The distributed Kalman filter offers significant reductions in computational and storage costs, making it highly scalable for large images.
An Ablation Study on 360-Degree Saliency Estimation

Omni-directional multimedia is increasing in popularity thanks to the diffusion of head mounted displays for its fruition. The shift from the 2D to omni-directional paradigm implies a change in the way users interact with the visual content. In order to analyze how users explore this new type of stimulus, saliency maps can be exploited. Although several approaches have been proposed in the literature, a complete study on how the different image features contribute to the overall saliency is lacking. For this reason, this work considers a set of features commonly employed in the state-of-the-art, and analyzes their impact on saliency estimation. The performed analysis highlights that increasing the number of features does not necessarily imply better results, and saliency estimation gains from feature combination depending on the feature type.
Modeling the Effect of Relative Camera Pose Differences on Change Detection Tasks

Change detection algorithms have to be robust to unrelated changes in the observed scene such as changing lighting conditions or shot noise.When the camera is not static, image registration is often performed as a preprocessing step.Unfortunately, image resampling performed during registration can introduce additional artifacts which are a source of false positive detections.We present an approach to modeling changes resulting from observing the scene from a different pose.We reformulate the general problem to only moving the camera further away from the target object and model the effect of observing the scene from the new viewpoint using the camera's point spread function (PSF).Our proposed model significantly outperforms the baseline approach and we are able to characterize the performance of change detection algorithms more reliably.The proposed model can be applied to guide the user in acquiring good quality images at runtime for change detection or to design constraints on the camera pose to ensure required accuracy of change detection algorithms.
Improving YOLOv8 with Scattering Transform and Attention for Maritime Awareness

Ship recognition and georeferencing using monitoring cameras are crucial to many applications in maritime situational awareness. Although deep learning algorithms are available for ship recognition tasks, there is a need for innovative approaches that attain higher precision rates irrespective of ship sizes, types, or physical hardware limitations. Furthermore, their deployment in maritime environments requires embedded systems capable of image processing, with balanced accuracy, reduced latency and low energy consumption. To achieve that, we build upon the foundations of the standard YOLOv8 and present a novel architecture that improves the segmentation and georeferencing of ships in the context of maritime awareness using a real-world dataset (ShipSG). Our architecture synergizes global and local features in the image for improved ship segmentation and georeferencing. The 2D scattering-transform enhances the YOLOv8 backbone by extracting global structural features from the image. The addition of convolutional block attention module (CBAM) in the head allows focusing on relevant spatial and channel-wise regions. We achieve mAP of 75.46%, comparable to larger YOLOv8 models at a much faster inference speed, 59.3 milliseconds per image, when deployed on the NVIDIA Jetson Xavier AGX as target embedded system. We applied the modified network to georeference the segmented ship masks, with a georeferencing distance error of 18 meters, which implies comparable georeferencing performance to non-embedded approaches.
Cricket Shot Classification using Transformer

Sports activity recognition is crucial for monitoring player performance and team progress, encompassing a wide range of athletic competitions with unique dynamics. Detecting events in sports videos is challenging due to factors like abrupt motions, similar attire, varying viewpoints, and changing player postures. Cricket, in particular, presents a difficult problem with closely overlapping behaviours. Existing approaches rely on hand-crafted features and limited public data. In this paper, we employed a transformer model with DenseNet, achieving superior results on the cricket activity dataset. Compared to the state-of-the-art, our approach achieved an accuracy of 96.5%, recall of 95%, precision, and Fl score of 95%.
Comparative Analysis of Image Encoders and Compression Effects on Machine Task Performance

In recent years, there has been a sharp increase in automated analysis of images by machines. It is expected that the volume of images consumed by machines will greatly exceed the volume of images consumed by humans. The utilities of traditional image coding algorithms for machine consumption are not sufficiently explored. To address this issue, we conducted a study of compression efficiency for machine consumption of JPEG, JPEG XL, WebP, AVIF, HEVC, and VVC codecs. Test results show that the use of classical image codecs such as JPEG and WebP could lead to unsatisfactory performance of convolutional neural networks in object detection accuracy, especially at lower bitrates. State-of-the-art codecs VVC, JPEG XL, and AVIF could be more suitable for those use cases, despite the increased computational complexity.
Which Way to Go Now? Financing Economic Growth in the Sustainable Development Era

Our paper uses a novel methodology to reexamine the relationship between financial development and economic growth in the era of sustainable development. Our empirical procedure deals with both functional-form misspecification bias as well as bias from endogenous regressors. It also provides an estimate of the growth-finance relationship for every country-year observation, allowing us to examine the relationship of interest for various country groups, using a global sample of 133 countries during 1960–2015. Our results indicate that countries with weak institutions and a smaller-than-average banking sector will reap more benefits from bank-based financial systems. The impact of financial development on economic performance has enormous policy implications for international institutions that provide policy support to countries in their pursuit of achieving the 2030 Agenda for Sustainable Development.
Pascal's New Wager: AI Is Not Explainable, But Governable

I would like to update you with the following news
Study on the Modernization of Supply Chain Management of the Luxury Industry in the Context of the Digital Economy

As a field with both traditional and modern characteristics, the luxury industry has been profoundly affected by the wave of the digital economy. The luxury industry, characterized by tradition and modernity, has been profoundly affected by the wave of the digital economy. The luxury industry has relied heavily on the traditional offline sales model, but with the outbreak of the COVID-19 epidemic, offline sales worldwide have been severely impacted, and the prosperity of the industry once had faded. Under such circumstances, a transformation of supply chain management in the luxury industry is imminent. This study first introduces the development status of the luxury industry in the context of the digital economy; then analyzes the problems and shortcomings of the traditional supply chain model; and finally examines the application of digital tools such as AI, blockchain technology and sustainability in the luxury supply chain. Using a case analysis approach, we aim to provide insights into innovative supply chain management applications in the luxury industry and to reveal how these tools can improve the supply chain’s efficiency, transparency and sustainability through real-life case studies.
Mathematical tools

Abstract In this chapter, we introduce some mathematical tools used throughout the rest of the textbook, to provide readers from different backgrounds with a good understanding of our notation and terminology. These mathematical tools include the binary system, Boolean functions, and sets of logic gates universal for classical computation. We then introduce the notion of a decision problem, a mathematical problem whose solution is either yes or no and discuss how a computation solves it by exploiting two physical resources: time and space. We conclude by presenting several examples of decision problems, including satisfiability, MAXCUT, traveling salesman, and the hidden subgroup problem
Superconducting qubits

Abstract This chapter presents the principles of superconducting qubits. We start our discussion with a presentation of the Josephson effect. We then introduce the Cooper pair box and the transmon, the most widespread superconducting qubit. We study how to execute single-qubit gates by driving a superconducting qubit with electromagnetic pulses, and how to read out the state of a qubit by a transmission measurement. The last part of the chapter is devoted to the presentation of some two-qubit gates. Specifically, we illustrate the implementation of the iSWAP and square root of iSWAP gates and of the cross-resonance gate. The last section discusses tunable couplers
Dedication

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
Preface

Extract About one hundred years ago, several scientists, including Planck, Bohr, De Broglie, Schrödinger, Pauli, Dirac, and Heisenberg, made significant contributions to the discovery of a new physical theory called quantum mechanics. Initially developed to address specific problems, such as the black body radiation and atomic absorption spectra, quantum mechanics gradually extended to other physical phenomena, correctly predicting the collective behavior of interacting particles. The success of quantum mechanics allowed us to construct electronic devices of immense practical relevance, including the transistor, underpinning semiconductor-based computers, and the laser, an essential component in optical communication. Although modern computers contain some quantum components, their computational processes are still governed by classical laws and formalized by a mathematical model known as the Turing machine [1]. For this reason, we call them “classical computers". For the past forty years, universities and research centers have been investigating new types of computational models based on the principles of quantum mechanics to determine whether it is possible to build a machine more powerful than Turing machines.
Early quantum algorithms

Abstract This chapter presents early quantum algorithms. We first describe Deutsch’s and Bernstein-Vazirani, some of the first quantum algorithms that showed a query advantage over their classical counterparts. We then present Grover’s algorithm, a quantum algorithm that finds an item in an unstructured database with a quadratic speed-up over classical algorithms. We explain the quantum Fourier transform, an important subroutine in various quantum algorithms. The chapter concludes with a detailed presentation of two of the most prominent applications of the quantum Fourier transform: the period finding algorithm and Shor’s algorithm for prime factorization.
Soybean Gyan - a mobile application for effective soybean knowledge dissemination

Soybean Gyan Mobile App is developed for soybean growers to take right decisions in the real-time field conditions. It contains comprehensive knowledge on soybean crop management on one platform. The design anddevelopment of the mobile App is discussed in the paper. The salient features of the App are presented in detail. The functionality of the App and overall look and feel of the App is given. The app is available for free download from Google play store. It is a user friendly App that can cater to the needs of all the stakeholders like farmers, agricultural advisors, agriculture students or research scholars and line department officials. This app is being used by many as indicated by the number of downloads (over 10000) within a short time.
Diffuser Bed Construction Challenges on the Musaimeer Pump Station and Outfall Project

Musaimeer Pump Station and Outfall project (MPSO) is a strategically important drainage asset initiated, developed, and completed by Ashghal in 2022. This project accommodates ground and storm water received from the drainage networks of 270 km2 of urban areas in southern Doha, along with storm protection for four soccer stadiums used during the FIFA 2022 World Cup. The key elements of this project were a pump station with a capacity of 19.7 m³ / s, one of the longest ground and storm water tunnels in the world with a total length of 10.2 km and the diffuser bed structure measuring 194 m x 40 m. The diffuser bed structure is connected to the outfall tunnel by a riser shaft and is the structure used to disperse the ground and storm water in a controlled manner and in accordance with environmental standards. This technical paper will detail the entire process from dredging and material stockpiling, riser shaft drilling and construction, diffuser bed fabrication onshore, transportation to final location, installation on the sea floor, backfilling and protection works and finally commissioning of the entire project.
Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review

&lt;abstract&gt; &lt;p&gt;Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems.&lt;/p&gt; &lt;/abstract&gt;