Reviewerly/exHarmony huggingface dataset:
Methods you can count on: A simulation experiment showing the advantages generalized linear modeling (GLM) over the linear regression of log-transformed count data

Count data are the oldest and most basic form of data in the field of archaeology. Count data, however, have long been known to defy the assumptions of many commonly used parametric statistical methods, such as linear regression, in generally failing to follow a normal distribution. Most archaeological analyses have dealt with this problem through the logarithmic transformation of count data for the purposes of normalization. Recently, other scientific fields have recognized major shortcomings associated with log transformation and they have adopted nonparametric alternatives, such as generalized linear modeling (GLM). With a few exceptions, the field of archaeology has not followed suit. This paper presents the results of a simulation experiment designed to compare the performance of linear regression of log-transformed data and GLM under some commonly occurring conditions for count data in the field of archaeology. The results of this simulation experiment confirm the striking superiority of GLM compared with the linear regression of log-transformed data, showing that the latter is particularly prone to false-negative results (i.e. Type II error). We argue that archaeologists, therefore, should join other scientific fields in more widely adopting GLM and its nonparametric counterparts in analyzing count data.
A multi-in and multi-out dendritic neuron model and its optimization

Artificial neural networks (ANNs), inspired by the interconnection of real neurons, have achieved unprecedented success in various fields such as computer vision and natural language processing. Recently, a novel mathematical ANN model, known as the dendritic neuron model (DNM), has been proposed to address nonlinear problems by more accurately reflecting the structure of real neurons. However, the single-output design limits its capability to handle multi-output tasks, significantly lowering its applications. In this paper, we propose a novel multi-in and multi-out dendritic neuron model (MODN) to tackle multi-output tasks. Our core idea is to introduce a filtering matrix to the soma layer to adaptively select the desired dendrites to regress each output. Because such a matrix is designed to be learnable, MODN can explore the relationship between each dendrite and output to provide a better solution to downstream tasks. We also model a telodendron layer into MODN to simulate better the real neuron behavior. Importantly, MODN is a more general and unified framework that can be naturally specialized as the DNM by customizing the filtering matrix. To explore the optimization of MODN, we investigate both heuristic and gradient-based optimizers and introduce a two-step training method for MODN. Extensive experimental results performed on 11 datasets on both binary and multi-class classification tasks demonstrate the effectiveness of MODN, with respect to accuracy, convergence, and generality.
Variations on a Hexagon: Iterative Design of Interactive Cyberphysical Tokens and Constraints

We describe iterations on the design of a hexagonal token and constraint tangible and cyberphysical interface. Our system has been co-designed both for use in tangible, embedded, embodied interaction and extended reality classroom contexts, and within broader applied generalizations. We survey some of the related literature in hexagonal, rectangular, and triangular tangible interfaces. We express a design space characterizing several aspects of these prior systems; and apply these to a number of additional virtual and physical iterations. We accompany our text with 3D printable, circuit board, and virtual models for a number of these interactors, usable in both tangible and purely virtual forms, including versions engaging four different embedded computing platforms (Raspberry Pi Zero, Raspberry Pi Pico, Adafruit Circuit Playground, and Blinks). We discuss experiences from use of these in classroom settings, and steps toward broader applications.
MorphMatrix: A Toolkit Facilitating Shape-Changing Interface Design

Shape-changing interfaces offer transformative potential for user interaction across numerous sectors, facilitating more intuitive and immersive experiences. In parallel, their significant design complexity and cost constrain the widespread adoption of these interfaces. To tackle these challenges, MorphMatrix is a toolkit that lowers the entry barrier for designing and implementing shape-changing interfaces. MorphMatrix includes a software system with customized GUI and simulation capabilities; a flexible physical framework adaptable for various application scenarios; and a kinetic system that governs interface transformations. We illustrate the potential of MorphMatrix through five diverse application scenarios, complemented by a user study validating its effectiveness and usability. MorphMatrix streamlines the design and construction of shape-changing interfaces, thus facilitating broader applicability and potential impacts across interactive computational systems.
UFPS: A unified framework for partially annotated federated segmentation in heterogeneous data distribution

Partially supervised segmentation is a label-saving method based on datasets with fractional classes labeled and intersectant. Its practical application in real-world medical scenarios is, however, hindered by privacy concerns and data heterogeneity. To address these issues without compromising privacy, federated partially supervised segmentation (FPSS) is formulated in this work. The primary challenges for FPSS are class heterogeneity and client drift. We propose a unified federated partially labeled segmentation (UFPS) framework to segment pixels within all classes for partially annotated datasets by training a comprehensive global model that avoids class collision. Our framework includes unified label learning (ULL) and sparse unified sharpness aware minimization (sUSAM) for class and feature space unification, respectively. Through empirical studies, we find that traditional methods in partially supervised segmentation and federated learning often struggle with class collision when combined. Our extensive experiments on real medical datasets demonstrate better deconflicting and generalization capabilities of UFPS.
Systematic literature review: Quantum machine learning and its applications

Quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations, as well as for large-scale information processing. These advantages are achieved through the use of quantum features, such as entanglement or superposition. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, scientific challenges are impossible to perform by classical computation due to computational complexity (more bytes than atoms in the observable universe) or the time it would take (thousands of years), and quantum computation is the only known answer. However, current quantum devices do not have yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications. The methodology follows the guidelines related to Systematic Literature Review methods, such as the one proposed by Kitchenham and other authors in the software engineering field. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms and shows their implementation using computational quantum circuits or ansatzs. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. One of the most relevant applications in the machine learning field is image classification. Many articles, especially within the classification, try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in quantum hardware is required for this potential to be achieved since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.
Parallel Light Fields: A Perspective and A Framework

Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired. Light field data enjoy a natural advantage over images or videos in 3D reconstruction, and are in great demand in applications such as virtual reality (VR) and augmented reality (AR). However, the high cost hardware and complicated synchronization issues in their array deployment severely hindered development of light field cameras. As a consequence, available light field data are much smaller than traditional images and videos in volume, and a lot of deep learning based computer vision algorithms that take light field images as input are difficult to be deployed.
The Relative Motion When Accelerating the Container of an Object Submerged in a Fluid

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ah Reum Yang, Yu Chen, Hee Ra Kim, Yu Jin Ahn, Jung Bog Kim; The Relative Motion When Accelerating the Container of an Object Submerged in a Fluid. Phys. Teach. 1 February 2024; 62 (2): 110–112. https://doi.org/10.1119/5.0109132 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Revolutionizing Education: Navigating the New Landscape Post-COVID-19: A Scoping Review

&lt;p style="text-align:justify"&gt;Education systems worldwide have been significantly disrupted due to the COVID-19 pandemic, creating an immediate need for a revamp of conventional teaching and learning techniques. To explore how this has affected the educational landscape, a scoping review was conducted. This scoping review aimed to examine the changes that occurred in the education field and to explore how it has transformed the educational landscape review. Using Arksey and O'Malley's methodology, 51 articles were selected for analysis from two leading databases: Scopus and Web of Science. All chosen articles were then subjected to thematic analysis. Three main aspects impacted by this global event were uncovered, which are technological advancements and digital transformation, changes in pedagogy and teaching methods, and mental health and well-being issues. This scoping review provides valuable insights into one of the most critical sectors affected by COVID-19, which can assist with planning future strategies for similar crises.&lt;/p&gt;
Extract, detect, eliminate: Enhancing reliability and performance of computational pathology through artifact processing pipelines

Cancer diagnosis involves obtaining tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing results in the introduction of artifacts, which are ultimately transferred to the digitized version of the glass slide, known as the whole slide image (WSI). Histological artifacts are diagnostically irrelevant areas and may result in wrong predictions by a deep learning (DL) algorithm. These WSIs are also typically large giga-pixel images, raising computational concerns for processing all WSIs simultaneously. Therefore, an artifact processing pipeline that can extract, detect, and exclude artifacts from WSI before running a diagnostic algorithm is much needed for a reliable computational pathology (CPATH) system. Artifact detection will not only ensure accurate CPATH predictions but also provide quality control. In this video, we demonstrate an artifact processing pipeline that can detect five notable artifacts: blood, folded tissue, blurry areas, damaged tissue, and air bubbles, and provide various post-processing outputs such as a segmentation mask for highlighting regions of interest, artifact report and artifact-refined WSI for further computational analysis. Equipping modern CPATH systems with the proposed artifact detection pipeline can ensure histologically relevant areas for diagnostic algorithms, stepping towards bringing automated diagnostics into clinical practice.
A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

With the enhancement of information volume, people are not satisfied with transmitting only a single secret image at a time but chase to hide multiple secret images in a single picture; however, the large-capacity steganographic scale can easily lead to the degradation of the quality of the image, which attracts the attention of eavesdroppers. In this paper, we propose a Chaotic mapping-enHanced imAge Steganography nEtwork (CHASE), which pioneers to hide colour images in grey images and reduces the difference between the container image and the cover image through the image permutation method, so as to enhance the security of the steganography. The method demonstrates excellent steganalysis resistance in experiments and introduces Generative Adversarial Networks (GANs) to improve the image fidelity in large-capacity steganographic scales. The fusion of chaotic mapping and GAN optimisation enables the steganographic network to simultaneously balance security and image quality. The experimental results show that CHASE can keep the secret image with good invisibility under large-capacity steganographic scales, and at the same time, it can reveal the secret image with high fidelity, and its steganalysis-resistant capability is much better than other state-of-the-art methods.
On clustering levels of a hierarchical categorical risk factor

Abstract Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. We commonly rely on methods such as the random effects approach to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers’ compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. Our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, we obtain a better differentiation between high-risk and low-risk companies.
Factors affecting Thai EFL students’ behavioral intentions toward mobile-assisted language learning

Introduction Recently, researchers have begun to pay more attention to topics related to the adoption of mobile devices for supporting second or foreign language learning. Mobile-assisted language learning (MALL) is now prevalent among language learners and educators because of its convenient and enjoyable features. This study combined and extended the Technology Acceptance Model (TAM) and Expectation Confirmation Theory (ECT) to investigate the factors influencing English as a Foreign Language (EFL) students’ behavioral intentions to use MALL at two universities in Bangkok, Thailand. Methods Quantitative methods were utilized in this study and the researchers obtained a total of 507 valid responses by using three-step sampling. After using confirmatory factor analysis (CFA) to determine that the study had enough construct validity, structural equation modeling (SEM) was applied to test the research’s hypotheses. Results The findings revealed that all 15 hypotheses were supported, except that social influence cannot significantly influence behavioral intention. Discussion and implication By acquiring a deeper understanding of the factors that impact the behavioral intentions of language learners to utilize MALL, developers and providers can improve their capacity to design more enjoyable and effective applications that align with customer expectations and enhance financial gains. By understanding students’ behavioral intentions towards MALL, educators can efficiently raise awareness of its benefits and provide effective training, enabling students to utilize available resources and enhance their language learning experience.
Breast Tumor Classification with Enhanced Transfer Learning Features and Selection Using Chaotic Map-Based Optimization

Abstract Among women, breast cancer remains one of the most dominant cancer types. In the year 2022, around 2,87,800 new cases were diagnosed, and 43,200 women faced mortality due to this disease. Analysis and processing of mammogram images is vital for its earlier identification and thus helps in reducing mortality rates and facilitating effective treatment for women. Accordingly, several deep-learning techniques have emerged for mammogram classification. However, it is still challenging and requires promising solutions. This study proposed a newer automated computer-aided implementation for breast cancer classification. The work starts with enhancing the mammogram contrast using a haze-reduced adaptive technique followed by augmentation. Afterward, EfficientNet-B4 pre-trained architecture is trained for both original and enhanced sets of mammograms individually using static hyperparameters’ initialization. This provides an output of 1792 feature vectors for each set and then fused using a serial mid-value-based approach. The final feature vectors are then optimized using a chaotic-crow-search optimization algorithm. Finally, the obtained significant feature vectors were classified with the aid of machine learning algorithms. The evaluation is made using INbreast and CBIS-DDSM databases. The proposed framework attained a balanced computation time with a maximum classification performance of 98.459 and 96.175% accuracies on INbreast and CBIS-DDSM databases, respectively.
Path integral quantum algorithm for simulating non-Markovian quantum dynamics in open quantum systems

Research in open quantum system dynamics has received growing interest in recent years, and its ongoing investigation has uncovered many intriguing physics departing from closed systems. A particularly useful application of the open quantum system theory is to simulate quantum dynamical processes in condensed phase materials. As the quantum degree of freedom is constantly under the influence of its thermal environment, the accurate description of the dynamics often requires non-Markovian time evolution at finite temperature. Such calculation is usually quite challenging on classical computers as extensive memory storage is required. In this work, we focus on a quantum system linearly coupled to its harmonic bath and present a path integral based quantum algorithm that time-evolves the reduced density matrix under finite temperature non-Markovian dynamics. To treat the nonunitary time evolution, the Sz.-Nagy dilation scheme is used for the conversion to unitary dynamics that can be implementable on gate-based quantum computers. The modified Hadamard test is then used to retrieve all the information of the reduced density matrix. Complexity analysis shows that the memory requirement has exponential reduction on the quantum machine whereas the runtime complexity stays roughly the same as for the classical computer. It points to the possibility of using this algorithm to simulate multilevel and multisite non-Markovian quantum dynamics that are beyond the reach of classical computers. The algorithm makes no ad hoc assumptions and extends naturally beyond Markovian and weak coupling regimes. We validated the algorithm on the quantum simulator with the spin-boson model and demonstrated its excellent agreement with the classical computer benchmark.
Immune subtyping of melanoma whole slide images using multiple instance learning

Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into ‘high’ or ‘low immune’ subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into ‘high’ and ‘low immune’ subgroups with significantly different melanoma specific survival outcomes (log rank test, P < 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.