Reviewerly/exHarmony huggingface dataset:
Application of machine learning to experimental design in quantum mechanics

The recent advances in machine learning hold great promise for the fields of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system with which the agent is interacting, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system, and only some parameters of the evolution or the initial state are unknown. We present here a general machine learning technique that can optimize the precision of quantum sensors, exploiting the knowledge we have on the system through model-aware reinforcement learning. This framework has been implemented in the Python package qsensoropt, which is able to optimize a broad class of problems found in quantum metrology and quantum parameter estimation. The agent learns an optimal adaptive strategy that, based on previous outcomes, decides the next measurements to perform. We have explored some applications of this technique to NV centers and photonic circuits. So far, we have been able to certify better results than the current state-of-the-art controls for many cases. The machine learning technique developed here can be applied in all scenarios where the quantum system is well-characterized and relatively simple and small. In these cases, we can extract every last bit of information from a quantum sensor by appropriately controlling it with a trained neural network. The qsensoropt software is available on PyPI and can be installed with pip.
Interval Uncertainty Identification and Application of Strain Modes in Bridge Structures Based on Monitoring Big Data

In order to solve the problem of strain modal identification under strain monitoring signals with poor vibration modal information, this paper proposes a strain mode identification method with statistical stability significance. This method removes noise, vehicle-induced effects, and temperature effects from the original dynamic strain signal, retaining only vibration-related components, and obtaining a statistically stable high quality bridge strain power spectrum, thereby identifying high quality strain mode parameters. Furthermore, in order to verify the confidence level of the strain modes obtained by this method, this paper adopts the interval estimation method to estimate the power spectrum, natural frequency, damping ratio, and modal shape after statistical processing. The credibility of strain modes has been estimated by interval estimation. The confidence interval of 95% confidence for each modal parameter is obtained, achieving the confidence-level evaluation of corresponding variable modal parameter identification. In response to practical engineering problems, this paper evaluates the actual bridge data of Tongji Road Bridge in Shanghai, and explains the abnormal phenomena that occurred in the data evaluation based on the measured diseases, verifying the practicality of this method.
Research on tactile sensation by physical reservoir computing with a robot arm and a Ag<sub>2</sub>S reservoir

Abstract In recent years, physical reservoir computing has attracted much attention because of its low computational cost and low power consumption. In terms of social implementation of artificial intelligence, physical reservoir has a potential to meet the request, such as the need for AI robots to process information related to tactile sensation. It has been reported that a Ag 2 S polycrystalline thin film retains short-term memory and non-linearity when used as a physical reservoir. In this study, we applied the technique to tactile sensation by combining with a pressure sensor attached to a robot arm. In the object grasping task, a Ag 2 S physical reservoir enabled the objective recognition with the accuracy of 81.3%, although the task failed with linear regression of the direct output from the pressure sensor. We also demonstrate the potential of the system to detect anomalies in object grabbing.
Generative AI And Software Variability - A Research Vision

Generative Artificial Intelligence (GAI) promises groundbreaking automation technology - a potential which may raise the management of variability-intensive software systems to a new level of automation. Several activities in maintaining variability-intensive software systems, such as extracting feature traces to updating features consistently, are repetitive and performed mainly manually or semi-automatically. Exploiting the potentials of GAI in maintaining variability-intensive software systems opens a fundamentally new research perspective, where GAI shall solve repetitive and hard-to-automate tasks. In this vision paper, we propose and discuss increasing levels of maintaining variability-intensive software systems automatically enabled through the support of GAI. We sketch actions necessary to reach the next levels of automation while discussing the current state-of-the-art.
Data Introspection: An Embroidered PhD Trajectory in Unconventional Personal Data

In an age of data, we are increasingly defined by the data sets collected about us. Expressing identity in these data sets can be difficult as they are aggregated and collected according to specific values and beliefs that might not match our own. Through an elaborate process that I refer to as data introspection, I explored three data sets about myself (books I read, board games I played and my yoga practice) to create a feminist, hand-embroidered physicalisation of those data sets. Building on the principles of Data Feminism, I reflect on the process of creating that artefact, including the selection and cleaning of the data, the materiality of the artefact and the crafting process. The artefact exemplifies new ways to use unconventional personal data as a creative, feminist material for design.
The Body in Play: Dimensions of Embodiment in Design for Play

This paper offers an analysis of how design activities engage with the body in the context of teaching embodied methods, and specifically body-centric design for play. Based on the analysis, we propose an empirically grounded framework for describing the various engagements with and considerations around the body as it comes into play in coursework on the use of body-centric design methods.
A Method for Projecting Cloud Shadows Onto a Central Receiver Field to Predict Receiver Damage

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.
Meeting at Both Ends Information – Theoretic Semi-group Theory of Uncertain Reasoning

This paper provides a first-time ever unification of information theory, semi-group theory with the theory of uncertain reasoning, through functional perspective. Fundamentally, the threshold theorems for the inference functional were devised. Furthermore, numerical experiments are illustrated. The complex proofs in our paper are original results which emphasizes the credibility of the class of R&amp;eacute;nyi Generalized Entropies as measures of information, and that the field is open to extend an enhanced methodology regarding queuing networks with heavy tails.
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective  State Spaces

Attention mechanisms have been widely used to capture long-range dependencies among nodes in Graph Transformers. Bottlenecked by the quadratic computational cost, attention mechanisms fail to scale in large graphs. Recent improvements in computational efficiency are mainly achieved by attention sparsification with random or heuristic-based graph subsampling, which falls short in data-dependent context reasoning. State space models (SSMs), such as Mamba, have gained prominence for their effectiveness and efficiency in modeling long-range dependencies in sequential data. However, adapting SSMs to non-sequential graph data presents a notable challenge. In this work, we introduce Graph-Mamba, the first attempt to enhance long-range context modeling in graph networks by integrating a Mamba block with the input-dependent node selection mechanism. Specifically, we formulate graph-centric node prioritization and permutation strategies to enhance context-aware reasoning, leading to a substantial improvement in predictive performance. Extensive experiments on ten benchmark datasets demonstrate that Graph-Mamba outperforms state-of-the-art methods in long-range graph prediction tasks, with a fraction of the computational cost in both FLOPs and GPU memory consumption. The code and models are publicly available at https://github.com/bowang-lab/Graph-Mamba.
Exploring Novel Topological Descriptors: Geometric-Harmonic and Harmonic-Geometric Descriptors for HAC and HAP Conjugates

In this investigation, the exact formulas for geometric-harmonic (GH), neighborhood geometric-harmonic (NGH), harmonic-geometric (HG), and neighborhood harmonic-geometric (NHG) indices were systematically evaluated for hyaluronic acid-curcumin (HAC) and hyaluronic acid-paclitaxel (HAP) conjugates. Through this evaluation, a comprehensive quantitative assessment was conducted to elucidate the structural characteristics of these conjugates, highlighting the intricate geometric and harmonic relationships present within their molecular graphs. The study leveraged these indices to illuminate the complex interplay between geometric and harmonic properties, providing a novel perspective on the molecular architecture of HAC and HAP conjugates. This analytical approach not only sheds light on the structural nuances of these compounds but also offers a unique lens through which their potential in drug delivery applications can be assessed. Graphical analyses of the results further enhance the understanding of these molecular properties, presenting a detailed visualization that complements the quantitative findings. The integration of these topological descriptors into the study of HAC and HAP conjugates represents a significant advance in the field of medicinal chemistry, offering valuable insights for researchers engaged in the development of innovative drug delivery systems. The findings underscore the utility of these descriptors in characterizing the molecular topology of complex conjugates, setting the stage for further exploration of their applications in therapeutic contexts.
Structural constraints on the emergence of oscillations in multi-population neural networks

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.
Decoding Sentiments: Virtue or Vice through Multilingual Paragraph Analysis

This work provides a comprehensive overview of recent developments in sentiment analysis methodologies. It explores innovative approaches, including the integration of rule-based sentiment dictionaries, machine learning techniques, and deep learning solutions for financial sentiment analysis. Emphasis is placed on key preprocessing steps such as tokenization, lowercasing, stop words removal, and punctuation elimination. Feature extraction techniques like Bag-of-Words, Word2Vec, and TF-IDF are discussed, highlighting their roles in representing textual information. The abstract delves into model selection, covering traditional machine learning models like Naive Bayes, Support Vector Machines, and Random Forests, as well as deep learning models such as Recurrent Neural Networks, Long Short-Term Memory networks, and BERT. The abstract explains these algorithms in detail, emphasizing their application in sentiment analysis. Training the model through supervised learning and evaluating its performance using metrics like accuracy, precision, recall, and F1 score are outlined. Additionally, a structured approach to paraphrasing is introduced, underlining its significance in creating meaningful representations of text.
Stakeholders’ Perceptions on EFL Teacher Professionalism: A Phenomenological Study at a Bachelor Programme in Nepal

Teaching English as a foreign language (EFL) in the twenty-first century has witnessed several changes, which have sparked tremendous changes in the teachers’ standpoints and the conceptualizations of professionalism. In this context, this study explored the stakeholders’ perceptions of EFL teacher professionalism. For this, the phenomenological research design was adopted. The study site was a Bachelor's Programme on a community campus in Nepal. The informants comprised the real stakeholders of the programme that comprised three EFL teachers, nine students, two administrators, and one member of the campus management committee. To collect the informants’ lived experiences, the researcher used an unstructured interview in a naturalistic setting. The results revealed that EFL teachers’ professionalism can be measured by their roles and skills; teacher-student relationships; and teachers as administrators and change agents. This implies that EFL teachers, like the teachers of other subjects, should be loyal, committed, accountable, and responsible as the success or failure of the autonomous programmes mainly depends on the teachers.
The Influence Of Health Promotion Using Lecture Method Using Comic Media On Dental And Oral Health Knowledge In Class III Students At State Primary School 1 Pangkajene District Sidenreng Rappang

Introduction: Dental and oral health has a big influence on preventing cavities or caries, gingivitis, perioldolitis, and also preventing bad breath. Diseases affecting the teeth and mouth, causing complaints of pain. School-aged children 6-12 years old are the age category at risk of developing cavities or caries. Age 6-12 years is the age when milk teeth begin to replace permanent teeth. One of the causes of the prevalence of dental and oral diseases in Indonesian children is the low level of knowledge about dental and oral health. Objective: to determine the effect of health promotion using the lecture method using kolmik media on dental and oral health knowledge in class III students at Pangkajene 1 Elementary School, Sidenreng Rapang Regency. Method: This type of research is quantitative research with a research design using a pre-experimental design, namely Olne Group Pretest and Polstest. The sample used was 31 people taken using the Total Sampling technique. Results: Before being given health promotion using the lecture method using comic media with a number of 54 with poor criteria and after being given health promotion using the lecture method using comic media with a number of 80 with good criteria. There was a level of increase in knowledge, namely 26. And the significance value was using the Paired T Test. namely 0.01 &lt; 0.05 so that the null hypothesis (H0) is rejected and the alternative hypothesis (Ha) is accepted
Unconstrained Face Recognition Using Infrared Images

Recently, face recognition (FR) has become an important research topic due to increase in video surveillance. However, the surveillance images may have vague non-frontal faces, especially with the unidentifiable face pose or unconstrained environment such as bad illumination and dark environment. As a result, most FR algorithms would not show good performance when they are applied on these images. On the contrary, it is common at surveillance field that only Single Sample per Person (SSPP) is available for identification. In order to resolve such issues, visible spectrum infrared images were used which can work in entirely dark condition without having any light variations. Furthermore, to effectively improve FR for both the low-quality SSPP and unidentifiable pose problem, an approach to synthesize 3D face modeling and pose variations is proposed in this paper. A 2D frontal face image is used to generate a 3D face model. Then several virtual face test images with different poses are synthesized from this model. A well-known Surveillance Camera’s Face (SCface) database is utilized to evaluate the proposed algorithm by using PCA, LDA, KPCA, KFA, RSLDA, LRPP-GRR, deep KNN and DLIB deep learning. The effectiveness of the proposed method is verified through simulations, where increase in average recognition rates up to 10%, 27.69%, 14.62%, 25.38%, 57.46%, 57.43, 37.69% and 63.28%, respectively, for SCface database as observed.
Journeying through Query Verse: A MERN-Stack Implementation for Dynamic Web-based Inquiry

Abstract: The recognition of the need for a senior-junior question-answer web application emerged from interactions within educational institutions. Numerous instances of juniors seeking guidance from seniors led to the realization of a gap in information dissemination. The necessity to bridge the gap between students' queries and available answers became evident, Research into existing platforms revealed a limited number of solutions, predominantly Q&amp;A websites and educational forums. Existing online platforms offer general Q&amp;A interactions but fail to cater to the unique mentorship dynamics between seniors and juniors. These platforms lack features tailored to fostering these specific relationships. The implementation of a seniorjunior question-answer web application is essential to formalize the mentorship process. Literature reviews unveiled gaps in platforms catering to such relationships, highlighting the need for a specialized solution. The proposed solution seeks to address these gaps.
Technology in community development: A comparative review of USA and African Projects

This paper conducts a comprehensive comparative review of technology-driven community development initiatives in the United States and various regions across Africa. The study aims to shed light on the diverse approaches and outcomes of projects leveraging technology to empower communities in different socio-cultural and economic contexts. The review begins by examining the landscape of community development in both the USA and Africa, emphasizing the challenges faced by urban and rural communities alike. It establishes a foundation for understanding the increasing role of technology in addressing these challenges, framing it as a dynamic and evolving field with vast potential for positive impact. Key findings from the comparative analysis are distilled, emphasizing the importance of context-specific approaches in technology-driven community development. Projects that demonstrate a nuanced understanding of local needs, cultural dynamics, and infrastructural realities exhibit greater success. The review underscores that successful implementation requires more than the transfer of technology; it demands a deep integration into the fabric of community life. The paper also touches upon overarching insights, highlighting the transformative power of technology when thoughtfully applied to community development. It acknowledges the significance of community engagement, adaptability, and a holistic understanding of local nuances as critical factors influencing project success. In concluding remarks, the paper encourages continued research and collaboration. It recognizes the ongoing journey towards harnessing technology's potential for global community empowerment and emphasizes the importance of cross-cultural partnerships, interdisciplinary collaboration, and knowledge sharing. Ultimately, the paper seeks to contribute to the ongoing discourse on technology in community development, offering insights that can inform future projects, policies, and academic pursuits in this dynamic field.
CROSS STYLE IMAGE SYNTHESIS THROUGH SEMI COUPLED DICTIONARY LEARNING AND GENERALIZED CROSS VALIDATION ALGORITHM

In various Computer vision applications for better visualization, interpretation and better recognition, we frequently want to change an image in one style into another one which is a large research area of compressive sensing. Algorithms on compressed sensing are in demand to create better reconstruction of the images while taking less computational time and requiring less storage capacity. In the present work attempts are put in the same direction and brought an algorithm which includes semi coupled dictionary learning (SCDL) model of compressed sensing with Singular Value Decomposition (SVD) based Generalized Cross Validation (GCV) algorithm. Using this algorithm one can find the different value of regularization parameters for different sizes of images and that helps to get better reconstruction of the images with ease while doing experiments on cross style image synthesis problems.