Reviewerly/exHarmony huggingface dataset:
27.4 Inverse Designed, Densely Integrated Classical and Quantum Photonics

Photonics - manipulation of flow of light on a chip - has many exciting applications, including new computing and communication platforms that are faster, more compact and more energy efficient, and a variety of sensors for medicine, autonomous vehicles, and the environment. Despite a great progress in photonics over the past few decades, photonic systems are nowhere near the level of integration and complexity comparable to those of electronic circuits, which prevents use of photonics in many applications. This lag in integration scale is in large part a result of how photonics is traditionally designed: by combining building blocks from a limited library of known designs, and by manual tuning of few parameters. Unfortunately, the resulting photonic circuits are often bulky, inefficient, and very sensitive to errors in manufacturing and the environment.
9.4 An In-depth Look at the Intel IPU E2000

The Intel® Infrastructure Processing Unit (Intel IPU) E2000 is Intel's first ASIC IPU device, a 200G product co-designed with Google and in production as of 2022. It features a rich packet processing pipeline, RDMA and storage capability including NVMe offload and an ARM Neoverse based compute complex enabling customer provided software to execute features ranging from complex packet processing pipelines to storag <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sup> transport to device management and telemetry. The device was fabricated in TSMC's 7nm process. Utilizing the combination of general-purpose acceleration and software running in the compute complex, this IPU enables a rich variety of services to be provided to the attached client. Attached clients can range from compute hosts for providing infrastructure as a service, storage disks for fronting a storage target, accelerators such as GPUs or FPGAs for fronting specialized processing functions, or even function as a pure SoC servicing custom appliances or downstream devices like smart switches. This broad deployment capability supports the rapid innovation necessary for the modern datacenter.
A 28nm 68MOPS 0.18\mu\mathrm{J}/\text{Op}$ Paillier Homomorphic Encryption Processor with Bit-Serial Sparse Ciphertext Computing

Cloud computing currently lies at the heart of tremendous emerging information applications, providing various reliable and high-performance services based on vast amounts of individual and organizational data. Paillier homomorphic encryption (PHE) [6] is one of the critical privacy computing techniques that enables the ciphertext to enjoy equivalent utility as the plaintext. In the PHE scheme shown on the upper left of Fig. 15.4.1, the client encrypts its plaintext into ciphertext and then sends it to the server, which performs the homomorphic evaluation and returns the encrypted results to the client. However, this process avoids the delivery of plaintext at the cost of several computing burdens, and we list three dominant challenges in the rest of Fig. 15.4.1. First, the ciphertext domain calculation needs expensive large integer modular arithmetic operations (e.g., modular multiplication (ModMul), modular inversion (Modlnv), and modular exponentiation (ModExp)) with several orders of magnitude higher energy and latency. Second, the client performs encryption and decryption featuring independent vector operations, while the server is expected to perform evaluations with multiply-and-accumulation (MAC) operations. Third, the diversity of tasks requires computing scalability to meet the latency and throughput demands in the cloud.
Linear-ResNet GAN-based anime style transfer of face images

Converting directly real-world images into high-quality anime styles using generative adversarial networks is one of the research hotspots in computer vision. The current popular AnimeGAN and WhiteBox anime generative adversarial networks are problematic when distortion of image features, loss of details on lines and textures are concerned. To address these problems, we introduce AnimationGAN. To preserve the details of images, we use the linear bottlenecks in the residual network, what is more, we also employ the hybrid attention mechanism to capture the salient information of images. In addition, we adopt optimized normalizations to improve the accuracy and learning rate of the model. The experimental results show that compared with AnimeGAN and Whitebox, the proposed AnimationGAN has smaller FID to cartoon(61.73), better IS(6.79) and faster network training speed(405 s per epoch). In summary, the generated animation image significantly improves line texture details and image feature retention with much faster network training speed.
Prediction of Cardiac Disorder using ML Technology

ML is a useful tool in the healthcare industry since it can determine whether locomotor disorders, heart ailments, and other conditions will exist or not. This project uses ML methods including logistic regression, backward elimination, and REFCV to estimate a person's risk of acquiring heart disease using a readily available Kaggle dataset. After that, a confusion matrix and cross validation are used to assess the outcomes. Early diagnosis of cardiovascular conditions can assist patients in altering their lifestyles to reduce the risk of complications, which may represent a significant medical advance.
Adoption of blockchain technology in the banking sector of Ghana: Opportunities and challenges

The evolution of blockchain technology has drastically altered the provision of banking services.Businesses are currently transitioning to a paperless environment.Robots are taking over laborintensive jobs, Artificial Intelligence (AI) is assisting in tracking any massive disaster, and biotech is assisting in curing multiple illnesses in a matter of seconds.Blockchain, one of the most important inventions of our time, is poised to assist individuals in reaching new heights of innovation in almost all domains.However, like other technologies, blockchain is confronting unique hurdles that must be overcome before widespread adoption particularly in Ghana and in Africa as a whole.This paper sought to examine the opportunities and challenges of the adoption of blockchain technology in Ghana.A qualitative technique in data gathering was employed for the purpose of this paper.The study used an interview-based technique, aimed to identify and communicate the problems that the banking sector in Ghana experiences in adopting blockchain technology.The interview took place in Ghana's capital, Accra.One representative from the Ghanaian Central Bank and nine from Commercial banks were interviewed.The paper concludes that the implementation of blockchain technology in Ghana's banking sector is being hampered by a lack of international norms or laws, high energy costs, a lack of intermediaries, poverty, corruption, a lack of infrastructure, and a lack of technical support.
A new Gaussian curvature of the image surface based variational model for haze or fog removal

Outdoor images are usually affected by haze which limits the visibility and reduces the contrast of the images. Removal of haze from real-world images is always a challenging task. Recently, many mathematical models have been proposed for the effective removal of haze from real-world images. However, these models may produce staircase effects or lower the image contrast or smooth the edges of the object. In this paper, we propose a model based on Gaussian curvature for the de-hazing of images. The atmospheric veil estimate is included based on dark channel prior (DCP), which can significantly reduce the artifacts on the edge of the image and increase the accuracy. The transmission map then changes to a high-quality map to reduce haze or fog from gray and color images. DCP combined with Gaussian curvature is done for the first time for image de-hazing/de-fogging. The augmented Lagrangian method is used to find the minimizer of the proposed functional, which will be a system of partial differential equations. To get fast convergence, fast Fourier transforms (FFT) is used to solve the system of PDEs. The performance of the proposed model is compared with other state-of-the-art models qualitatively and quantitatively. The proposed model is tested on various real and synthetic images which show better efficiency in staircase effects reduction, haze/fog removal, image contrast, corners, and sharp edges conservation respectively.
Methodological Approaches to the Formation of Cognitive Universal Educational Activities in Younger Schoolchildren

The article highlights the methods of working with primary schoolchildren, which contribute to the formation of their cognitive universal educational activities. The mastery of a set of universal educational activities by younger schoolchildren, in turn, is considered in the Federal State Educational Standard for Primary General Education as the most important requirement - the formation of meta-subject learning outcomes. The article provides interesting examples of tasks offered to students in the lessons of the world around them, which ensure the development of their basic logical operations, initial research activities, the ability to work with information presented in various forms, as well as modeling and design skills.
Modeling Educational Space in a Modern Elementary School

The article discusses the idea of modeling the educational space of a modern elementary school within the framework of the working program of upbringing of an educational organization. A detailed analysis of the main structural components of the Approximate working program of education is given: target, content, organizational. It highlights the targets set out in it, which are formulated in accordance with the updated version of the Federal State Educational Standard for Primary General Education. On this basis, the ways of constructing a working program of education for elementary school are determined.
Organization of Design and Research Activities of Younger Schoolchildren

The article analyzes the conditions for the effective formation of design and research activities in elementary school. As a priority form, it is proposed to conduct classes based on the principle of integrating the theoretical and practical components of extracurricular activities. The subject of classes is given, which allows the teacher to plan the work of schoolchildren on projects in the process of studying the course of the Russian language, its stages are indicated. The important role of involving students in research activities for the formation of their cognitive activity and the development of the need and ability for creative search is emphasized.
Rain Water Harvesting Technology: Drinking water fulfillment and water conservation nearby landfill area

Abstract The drawback of groundwater quality occurs due to minimum treatment of waste management in the landfill. The research objectives are to know the groundwater quality around the landfill study site, rainwater potential for drinking water purposes, and decreasing runoff due to rainwater harvesting technology implementation. The well water samples were collected from eight wells surrounding the landfill. Rainwater samples were collected from rainwater harvesting technology installation. Water quality parameters consist of total coliform, pH, and Total Dissolved Solids (TDS). Water conservation was observed by infiltration and precipitation. Total coliforms of well water are higher than rainwater. Furthermore, based on acidity and TDS values, well water is higher (pH 6.5-8.5 and TDS 188.8 + 128.7 ppm, respectively) than rainwater (pH 6.1 + 0.2 and TDS 8.4 + 1.4 ppm, respectively). Based on three parameters, rainwater is more proper for drinking water than well water in the study site. Rainwater harvesting technology is able to reduce runoff by 58.42% with the rainwater catchment area around 7,095 m 2 . Waste management in landfills should concern with groundwater pollution. Rainwater harvesting technology is a potential solution for drinking water supply in water crisis areas.
Video super-resolution via nonlocal deformable alignment and frame recursive progressive fusion network

Video super-resolution (VSR) is a process of high-resolution reconstruction of low-resolution video. To address the problems of the previous VSR methods with poor temporal consistency and unsatisfactory SR results, we proposed a nonlocal deformable alignment and frame recursive progressive fusion (RPF) network combining sliding window and recursive methods, which uses nonlocal operations to align sequential frame features and later applies recursion to temporally model the hidden information and alignment features of the previous moment, thus improving temporal consistency. The RPF unit is used to fully fuse the hidden information with the currently aligned features, acquiring more supporting information to be obtained from adjacent frames, resulting in better SR results. The results were evaluated on the three public VSR datasets of Vid4, udm10, and Vimeo-90K, and the experimental results show that the proposed method can achieve state-of-the-art performance on VSR task.
MHAMD-MST-CNN: multiscale head attention guided multiscale density maps fusion for video crowd counting via multi-attention spatial-temporal CNN

Video-based crowd counting and density estimation (CCDE) is vital for crowd monitoring. The existing solutions lack in addressing issues like cluttered background and scale variation in crowd videos. To this end, a multiscale head attention-guided multiscale density maps fusion for video-based CCDE via multi-attention Spatial-Temporal CNN (MHAMD-MST-CNN) is proposed. The MHAMD-MST-CNN has three modules: a multi attention spatial stream (MASS), a multi attention temporal stream (MATS), and a final density map generation (FDMG) module. The spatial head attention modules (SHAMs) and temporal head attention modules (THAMs) are designed to eliminate the background influence from the MASS and the MATS, respectively, by mapping the multiscale spatial or temporal features to head maps. The multiscale de-backgrounded features are utilised by the density map generation (DMG) modules to generate multiscale density maps to deal with scale variation due to perspective distortion. The multiscale density maps are fused and fed into the FDMG module to obtain the final crowd density map. The MHAMD-MST-CNN has been trained and validated on three publicly available benchmark datasets: the Venice, the Mall, and the UCSD. The MHAMD-MST-CNN provides competitive results as compared with the state-of-the-arts in terms of mean absolute error (MAE) and root mean squared error (RMSE).
SYNTHETIC DATA GENERATION AND CLASSIFICATION OF HISTOPATHOLOGICAL IMAGES

Histopathology involves the analysis of microscopic tissue images for diagnosing and studying the progress of diseases, such as cancers. Recently, Artificial Intelligence algorithms reached encouraging success in diagnosing diseases related to these medical images. However, research in this area can be hampered by several problems. Indeed, due to the sensitive nature of medical data, it is challenging to access real datasets, making it impossible to train Deep Learning models. Moreover, real datasets often contain biases or imbalances that hinder the generalization of the results on new unseen data. Variational Autoencoders are a popular class of probabilistic generative models that enable consistent training and a useful latent representation of the original input. However, there are theoretical and practical obstacles that hinder their generative potential. Here, we consider different approaches to address the challenges of synthetic data generation of histopathology images and discuss the potential impact in improving the performance of diagnosis models.
A Multi-Start Gradient Combination Method for High-Performance Global Search

The gradient descent is one of the most common methods to modify the gradient parameters of the neural network, however, it may fall into the local optimum in the training process. As we all know, genetic algorithm can find the global optimal solution through global search, but it may not be efficient, always takes a lot of running time. These two methods are complementary in the running time and the cost, which inspired us to consider the combination of gradient descent and genetic algorithm. In this paper, a multi-start combinatorial optimization method based on the genetic algorithm and the gradient descent is proposed. First, the initial point is selected through the genetic algorithm. Then the combination of multi-start and gradient descent is used, which can quickly achieve the global search and improve the performance with relatively less running time and cost. Compared with the traditional genetic algorithm and gradient descent method, this proposed algorithm has a better performance in computing the global optimum efficiently.
Fixed points of the subset sum pseudorandom number generators

Abstract We give upper bounds on the power moments of the number of fixed points of a family of subset sum pseudorandom number generators, introduced by Rueppel (Analysis and design of stream ciphers, Springer-Verlag, Berlin, 1986).
Selection Informative Units for Extractive Summarization

An Extractive Multi-Document Summarizer must select the most informative units and prevents duplication in extraction. In order to achieve this goal, a new technique, called “comprising at least one Representative Term at the Highest Frequency”, called RTHF, is proposed in this work. The units which include representative terms, but with low frequencies are not considered for extraction (selection of the most informative units). On the other hand, these units which provide RTHF feature, precede other similar units in ranking (prevents duplication). The heuristic behind the RTHF is explained by probability. RTHF was experimented on a previously developed and tested paragraph- based Extractive Multi-Document Summarizer. The results show that it enhances the original system by 0.8% ~ 3.2% (Average-F values of ROUGE metrics).
One-step implementation of a multi-target-qubit controlled-phase gate with photonic qubits encoded via eigenstates of the photon-number parity operator

In recent years, quantum state engineering and quantum information processing using microwave fields and photons have received increasing attention. In addition, multiqubit gates play an important role in quantum information processing. In this work, we propose to encode a photonic qubit via two arbitrary orthogonal eigenstates (with eigenvalues 1 and -1, respectively) of the photon-number parity operator. With such encoding, we then present a single-step method to realize a multi-target-qubit controlled-phase gate with one photonic qubit simultaneously controlling n-1 target photonic qubits, by employing n microwave cavities coupled to one superconducting flux qutrit. This proposal can be applied not only to implement nonhybrid multi-target-qubit controlled-phase gates using photonic qubits with various encodings, but also to realize hybrid multi-target-qubit controlled-phase gates using photonic qubits with different encodings. The gate realization requires only a single-step operation. The gate operation time does not increase with the number of target qubits. Because the qutrit remains in the ground state during the entire operation, decoherence from the qutrit is greatly suppressed. As an application, we show how to apply this gate to generate a multicavity Greenberger-Horne-Zeilinger (GHZ) entangled state with general expression. Depending on the specific encodings, we further discuss the preparation of several nonhybrid and hybrid GHZ entangled states of multiple cavities. We numerically investigate the circuit-QED experimental feasibility of creating a three-cavity spin-coherent hybrid GHZ state. This proposal can be extended to accomplish the same tasks in a wide range of physical systems, such as multiple microwave or optical cavities coupled to a three-level natural or artificial atom.
AESTHETIC FOUNDATIONS AND VALUE CRITERIA OF MODERN MUSIC STUDIES

The purpose of this study is to analyze and theoretically substantiate the complex multidimensional value communicative system inherent in musical art in its dynamic movement and holistic form, to clarify its structure and functional formations, its main internal and external relationships, as well as to establish some ways of influencing the course of artistic processes in society, and, to a certain extent, clarifying the ability to manage them without invading them. It is shown that, for all the subjectivity of perception in the art of music, style favors the formation of conditions for objective value judgments and conclusions. The latter, in turn, form an informational thesaurus that influences the historical dynamics of public musical consciousness.
Submodule codes as spherical codes in buildings

Abstract We give a generalization of subspace codes by means of codes of modules over finite commutative chain rings. We define a new class of Sperner codes and use results from extremal combinatorics to prove the optimality of such codes in different cases. Moreover, we explain the connection with Bruhat–Tits buildings and show how our codes are the buildings’ analogue of spherical codes in the Euclidean sense.
A Survey of Quantum Entanglement Routing Protocols—Challenges for Wide‐Area Networks

Abstract In this paper, the key challenges (loss due to distance, entanglement routing, multi‐commodities) for the coming quantum internet, relying on entanglement of quantum bits (for short, qubits) on top of an existing network, are analyzed. A unifying framework enabling to compare the various entanglement distribution, purification, and routing protocols published so far is presented. With regard to entanglement routing, the introduction of different time windows will be essential in order to cope efficiently with the main challenges like complex route calculation and fidelity estimation on the one hand, actual entanglement route selection and entangled photon generation on the other hand. For a roll‐out on top of existing transmission networks, all the research publications for the last 20 years start to cover pretty well the global scheme. Nevertheless, open questions remain, like the actual advantage of some task execution prior to the online quantum path selection, or the design of algorithms approximating the multi‐commodities flow optimization problem, or the issue of dealing with a processing time not much longer than the qubit life time.
Vocational Environment of the Art School

A number of state documents in the field of education emphasize the importance of organizing an effective system of career guidance for students. The relevance of this work is due to several reasons, one of which is the strengthening of the dynamics of the labor market and the market of professions. Today, futurologists find it difficult to say which of the professions will be in demand in the future. Today’s students should have a clear understanding of what the expression “lifelong education” means and be ready to change their profession. One of the modern approaches to creating an effective career guidance system is the environmental approach, that is, the use of the career guidance potential of the society. This approach is especially relevant for organizations of additional education for children.