Reviewerly/exHarmony huggingface dataset:
Deterministic Construction of QFAs based on the Quantum Fingerprinting Technique

It is known that for some languages quantum finite automata are more efficient than classical counterparts. Particularly, a QFA recognizing the language $MOD_p$ has an exponential advantage over the classical finite automata. However, the construction of such QFA is probabilistic. In the current work, we propose a deterministic construction of the QFA for the language $MOD_p$. We construct a QFA for a promise problem $Palindrome_s$ and implement this QFA on the IBMQ simulator using qiskit library tools.
Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a high-performance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents. Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.
Investigation of Perturbation Theory with Variational Quantum Algorithm

Variational Quantum Algorithms are among the most promising systems to implement quantum computing under the Noisy-Intermediate Scale Quantum (NISQ) technology. In variational quantum algorithm, wavefunction represented by a parametrized ansatz and variational parameters are updated iteratively with a classical computer based on the measurement outputs from the quantum computer. In this paper, we investigate perturbation theory with these algorithms and prospect the possibility of using the variational quantum algorithm to simulate quantum dynamics in perturbation theory. We illustrate the use of algorithms with detailed examples which are in good agreement with analytical calculations
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling

Implicit Neural Representations (INR) have recently shown to be powerful tool for high-quality video compression. However, existing works are limiting as they do not explicitly exploit the temporal redundancy in videos, leading to a long encoding time. Additionally, these methods have fixed architectures which do not scale to longer videos or higher resolutions. To address these issues, we propose NIRVANA, which treats videos as groups of frames and fits separate networks to each group performing patch-wise prediction. This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video. The video representation is modeled autoregressively, with networks fit on a current group initialized using weights from the previous group's model. To further enhance efficiency, we perform quantization of the network parameters during training, requiring no post-hoc pruning or quantization. When compared with previous works on the benchmark UVG dataset, NIRVANA improves encoding quality from 37.36 to 37.70 (in terms of PSNR) and the encoding speed by 12X, while maintaining the same compression rate. In contrast to prior video INR works which struggle with larger resolution and longer videos, we show that our algorithm is highly flexible and scales naturally due to its patch-wise and autoregressive designs. Moreover, our method achieves variable bitrate compression by adapting to videos with varying inter-frame motion. NIRVANA achieves 6X decoding speed and scales well with more GPUs, making it practical for various deployment scenarios.
A Brief Analysis of the SwinIR Image Super-Resolution

SwinIR is a recent image restoration method based on the Swin Transformer architecture.In contrast to other traditional convolutional neural networks, SwinIR is capable of capturing sophisticated attention between image patches, leading to remarkable results.In this paper, we focus on the aspect of single-image super-resolution by SwinIR.We discuss the characteristics of the architecture of this algorithm and compare it to other deep learning methods.
Challenges in Open Data: A TRAJ Perspective

The editorial of the fifth issue of the Theoretical Roman Archaeology Journal focuses on the topic of open data. The editors consider both the challenges of using and disseminating open data within the field of Roman archaeology. We also reflect upon how TRAJ can improve on the inclusion of open-access data in the future.
A Supervised Method to Enhance Distance-based Neural Networks' Clustering Performance by Discovering Perfect Representative Neurons

Abstract Distance-based neural network clustering has an intrinsic assumption that a particular neuron in the network represents a cluster centroid. However, not all these neurons can perfectly represent the training data; these neurons can only represent part of the training samples. This paper proposes an effective training data splitting method (TDSM) to find perfect representative neurons and improve the clustering results in a distance-based neutral network without changing the original network's internal algorithm or the training data quality. The method allows a network with N neurons to be enlarged to a new m×N neurons network. These neurons represent m sub-networks, and each sub-network perfectly represents a part of the training set with the clustering qualification indicators (purity, Normalized Mutual Information, and Adjusted Rand Index), all equalling 1. Results are statistically validated with a t-test, and we demonstrate that TDSM performs better than the original clustering paradigm on some real data sets.
Penerapan Pembelajaran Mind Mapping Untuk Meningkatkan Hasil Belajar IPA Kelas VIII H SMPN I Gondang Mojokerto

This research aims to increase the activity and learning outcomes of class VIII H students on the material of the human respiratory organs. When learning shows that there are still many students who think that this material is not important, resulting in low learning outcome. One effort to improve learning outcomes is to evaluate the learning model that has been used so far. Furthermore, researchers apply the Mind Map learning model which is expected to increase learning activities and learning outcomes. This type of research is Classroom Action Research with the subject of class VIII H students as many as 31 students. The results showed that learning activities in the aspect of asking questions increased by 65.32% to 82.26%, the active aspect of discussing with groups by 76.61% increased to 85.48%, the aspect of summarizing teacher explanations was 68.54% increased to 83.06%, aspects in writing discussion results by 68.54% increased to 91.12, aspects in drawing mind mapping by 79.03% increased to 83.87%. So that there is an increase in the percentage of student learning activities from cycle I to cycle II.
Novel Architecture to Create and Maintain Personal Blockchains

Blockchain has been touted as a revolutionary technology. However, despite the excitement, blockchain has not been adopted in many fields. Many are hesitant to adopt blockchain technology due to privacy concerns, barriers to use, or lack of practical use cases. In this work, we outline a potential blockchain use case for tracking financial transactions across multiple financial institutions. We show the downsides of traditional centralized approaches and that blockchain approaches fail to give all the privacy and accessibility required for this use case. Thus we propose a novel blockchain architecture to support our use case. This novel architecture combines the ease of use of public blockchains with the privacy of private blockchains by allowing users to create personal blockchains. We believe this novel personal blockchain architecture will lead to more blockchain adoption, particularly in use cases handling private data.
Exploring Transformer Backbones for Image Diffusion Models

We present an end-to-end Transformer based Latent Diffusion model for image synthesis. On the ImageNet class conditioned generation task we show that a Transformer based Latent Diffusion model achieves a 14.1FID which is comparable to the 13.1FID score of a UNet based architecture. In addition to showing the application of Transformer models for Diffusion based image synthesis this simplification in architecture allows easy fusion and modeling of text and image data. The multi-head attention mechanism of Transformers enables simplified interaction between the image and text features which removes the requirement for crossattention mechanism in UNet based Diffusion models.
Modified noise kernels in Gaussian process modelling of energy surfaces

We explore the use of non homogenous noise kernels in Gaussian process modelling to improve the potential energy curve models describing stochastic electronic structure data. We use the same noise kernels on energy curves describing deterministic electronic structure data by creating non-homogenous noise model. We observe, as well as agreement between the noise of the model and the stochastic data, a strong regularisation of the curves when using artificial noises on deterministic data which improves Gaussian processes in the over fitting regime.
The Improvement of Decision Tree Construction Algorithm Based On Quantum Heuristic Algorithms

This work is related to the implementation of a decision tree construction algorithm on a quantum simulator. Here we consider an algorithm based on a binary criterion. Also, we study the improvement capability with quantum heuristic QAOA. We implemented the classical and the quantum version of this algorithm to compare built trees.
NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation

Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense gaze data generation with view consistency and accurate gaze direction. Moreover, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, so it can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by manipulating the latent code belonging to different face attributions in an unsupervised manner. Extensive experiments on several benchmarks demonstrate the effectiveness of our method in domain generalization and domain adaptation for gaze estimation tasks.
Trust Management in the Internet of Everything

Digitalization is leading us towards a future where people, processes, data and things are not only interacting with each other, but might start forming societies on their own. In these dynamic systems enhanced by artificial intelligence, trust management on the level of human-to-machine as well as machine-to-machine interaction becomes an essential ingredient in supervising safe and secure progress of our digitalized future. This tutorial paper discusses the essential elements of trust management in complex digital ecosystems, guiding the reader through the definitions and core concepts of trust management. Furthermore, it explains how trust-building can be leveraged to support people in safe interaction with other (possibly autonomous) digital agents, as trust governance may allow the ecosystem to trigger an auto-immune response towards untrusted digital agents, protecting human safety.
NISQ-ready community detection based on separation-node identification

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.
Neural Volumetric Blendshapes: Computationally Efficient Physics-Based Facial Blendshapes

Computationally weak systems and demanding graphical applications are still mostly dependent on linear blendshapes for facial animations. The accompanying artifacts such as self-intersections, loss of volume, or missing soft tissue elasticity can be avoided by using physics-based animation models. However, these are cumbersome to implement and require immense computational effort. We propose neural volumetric blendshapes, an approach that combines the advantages of physics-based simulations with realtime runtimes even on consumer-grade CPUs. To this end, we present a neural network that efficiently approximates the involved volumetric simulations and generalizes across human identities as well as facial expressions. Our approach can be used on top of any linear blendshape system and, hence, can be deployed straightforwardly. Furthermore, it only requires a single neutral face mesh as input in the minimal setting. Along with the design of the network, we introduce a pipeline for the challenging creation of anatomically and physically plausible training data. Part of the pipeline is a novel hybrid regressor that densely positions a skull within a skin surface while avoiding intersections. The fidelity of all parts of the data generation pipeline as well as the accuracy and efficiency of the network are evaluated in this work. Upon publication, the trained models and associated code will be released.
Sparse Johnson--Lindenstrauss transforms and analysis of their extreme singular values

The Johnson--Lindenstrauss (JL) lemma is a powerful tool for dimensionality reduction in modern algorithm design. The lemma states that any set of high-dimensional points in a Euclidean space can be flattened to lower dimensions while approximately preserving pairwise Euclidean distances. Random matrices satisfying this lemma are called JL transforms (JLTs). Inspired by existing $s$-hashing JLTs with exactly $s$ nonzero elements on each column, the present work introduces an ensemble of sparse matrices encompassing so-called $s$-hashing-like matrices whose expected number of nonzero elements on each column is~$s$. The independence of the sub-Gaussian entries of these matrices and the knowledge of their exact distribution play an important role in their analyses. Using properties of independent sub-Gaussian random variables, these matrices are demonstrated to be JLTs, and their smallest and largest singular values are estimated non-asymptotically using a technique from geometric functional analysis. As the dimensions of the matrix grow to infinity, these singular values are proved to converge almost surely to fixed quantities (by using the universal Bai--Yin law), and in distribution to the Gaussian orthogonal ensemble (GOE) Tracy--Widom law after proper rescalings. Understanding the behaviors of extreme singular values is important in general because they are often used to define a measure of stability of matrix algorithms. For example, JLTs were recently used in derivative-free optimization algorithmic frameworks to select random subspaces in which are constructed random models or poll directions to achieve scalability, whence estimating their smallest singular value in particular helps determine the dimension of these subspaces.
Equivariant Light Field Convolution and Transformer

3D reconstruction and novel view rendering can greatly benefit from geometric priors when the input views are not sufficient in terms of coverage and inter-view baselines. Deep learning of geometric priors from 2D images often requires each image to be represented in a $2D$ canonical frame and the prior to be learned in a given or learned $3D$ canonical frame. In this paper, given only the relative poses of the cameras, we show how to learn priors from multiple views equivariant to coordinate frame transformations by proposing an $SE(3)$-equivariant convolution and transformer in the space of rays in 3D. This enables the creation of a light field that remains equivariant to the choice of coordinate frame. The light field as defined in our work, refers both to the radiance field and the feature field defined on the ray space. We model the ray space, the domain of the light field, as a homogeneous space of $SE(3)$ and introduce the $SE(3)$-equivariant convolution in ray space. Depending on the output domain of the convolution, we present convolution-based $SE(3)$-equivariant maps from ray space to ray space and to $\mathbb{R}^3$. Our mathematical framework allows us to go beyond convolution to $SE(3)$-equivariant attention in the ray space. We demonstrate how to tailor and adapt the equivariant convolution and transformer in the tasks of equivariant neural rendering and $3D$ reconstruction from multiple views. We demonstrate $SE(3)$-equivariance by obtaining robust results in roto-translated datasets without performing transformation augmentation.
THE PATHETIC DIMENSION OF HYBRID IMAGES IN ENVIRONMENTAL EDUCATION

Environmental Education is one of the most relevant domains for sustainable development and sustainability, as it encourages reflection on environmental issues, making people aware of the preservation of natural resources.The languages that make use of iconic signs, such as the photographic image, for example, are effective pedagogical instruments to be used by environmental education.Communication through images is universal, capturing people's attention, who are increasingly influenced by iconic signs.This capture of attention becomes more evident when the image contains pathetic elements, that is, elements that seek to persuade people through emotion.This research sought to reflect on the potential of the hybrid photographic image with an environmental theme and a pathetic content as a means of raising awareness of the environmental cause.The hybrid image themed "Queimada" was produced.Hybrid images are fusions of digital photographic images treated and edited to achieve the purpose of communication intensely, generating contents of high expressiveness.The research was qualitative, with the purpose of carrying out a bibliographic review of the critical fortune on the use of the hybrid image in environmental education.In addition to bibliographical and documental research, pre-production, photographic production and post-production technical procedures were developed and carried out for the design of a portfolio of hybrid images for educational use.It is concluded that hybrid images can be used to effectively attract people's attention -especially students -to the environmental issue, pathetically sensitizing them and persuading them about the pressing need for sustainable action.
Peningkatan Kemampuan Menulis Puisi Pada Siswa Kelas VIII-1 MTS Negeri 2 Bungo Melalui Model Pembelajaran Menulis Terbimbing Strategi Inventaris Diksi

This study aims to look at the Ability to Write Poetry in Class VIII-1 Students of MTS Negeri 2 Bungo Through the Guided Writing Learning Model Diction Inventory Strategy. The method used is class action research with a cycle model design, namely the stages of (1) planning, (2) implementation, (3) observation, and (4) reflection. As for the research results obtained by learning using the guided writing learning model the diction inventory strategy can improve learning outcomes. In the first cycle the level of student learning completeness reached 62.85% with good learning abilities. Meanwhile in cycle II the level of student learning completeness reached 82.85% with a very good level of learning ability.
The Importance of Human Capital Development in Ensuring Sustainable Economic Growth

Annotation: Today's dynamically developing world is moving towards the formation of a knowledge society, an innovative economy, and a market for goods of intellectual activity. In such a society, the development of science and fundamental education represent a profitable investment in a special production that creates knowledge as a commodity with a certain value. The global economy is characterized by the demand for innovation not by individual entrepreneurs, but by national economies. The knowledge economy is a new stage in the development of the economy, in which knowledge plays a decisive role, and its production is a source of growth
Substantial and Qualificative Indefinite Additive Syntaxema Options

Annotation: Syntactic units in indefinite pronouns were analyzed separately by syntax. Their categorical differential syntacticsemantic features were revealed, and on the basis of the features, depending on the position of indefinite pronouns in a sentence, substantial and qualificative indefinite additive syntactic variants were distinguished from noncategorical featutes. Various transformation methods have been used to define additive syntax variants. Addition and subtraction methods were used.