Reviewerly/exHarmony huggingface dataset:
The Shift in Power from Conventional to Digital and Virtual Microscopy

Since the eleventh century, manipulating lenses to magnify an object for enhanced viewing, launched the evolution of optical microscopes in, but not limited to the fields of clinical and medical sciences. The strides in technological advancements have transformed the use and application of traditional microscopy in the teaching and learning environment of medical sciences. The aim of this chapter is to discuss our experience in the transition from traditional microscopy to digital and virtual learning techniques in the study of histology within the clinical and medical sciences disciplines at a tertiary level. Limitations of traditional microscopy include high cost, maintenance, colour fading and the need for a dedicated laboratory. Moreover, economic constraints in low- and middle-income countries make it impossible to provide microscopes to all learners within a class. In a class restricted by the number of optical microscopes available to teach a histology practical, digital and virtual microscopy has opened up the classroom beyond its physical location. Microscopes interfaced with large monitors and software have enabled images to be streamed to the entire class whilst simultaneously reducing the cost of buying each student a microscope. Pathologists worldwide can now simultaneously consult on a patient’s outcome with the convenience of a virtual hub of histopathology tissue sections. The diagnostic accuracy of histological assessment of digitized virtual slides is similar to traditional glass slides. Additionally, studies have shown that grasping the concept of histology and improved understanding and recall of cellular structure and function are attributed to digital and virtual microscopy. Furthermore, when challenged with the COVID-19 pandemic where access to laboratories and lecture venues had been severely restricted, histology teaching and learning continued to thrive. This chapter will highlight the benefits of virtual visualization from digitized histological slides over traditional microscopy such as the multimodal communication of images. Virtual microscopy is a visual stimulus that is an undeniable tool for improved teaching and learning of histology at the tertiary level of biomedicine. The positive pedagogic outcome of this type of visualization and learning is the outcome of the students’ assessment.
The Development of the Concept of Isostasy

The idea that loads on the Earth’s surface may be regionally rather than locally supported can be traced back to Gilbert and Barrell’s work at the turn of the twentieth century. Their work was carried out at a time when the U.S. Coast and Geodetic Survey promoted the use of local, rather than regional, models of isostasy. The geodesists, with the exception of Putnam, showed that regional models of isostasy were not required to explain geodetic data.
New Politico-Economic Axes of the Separation of Powers

The contribution of antitrust de lege lata to the separation of powers is rather limited. A role should nevertheless be granted to specific legislation, regulation or practices and, possibly, antitrust de lege ferenda, especially with respect to the control of the digital infrastructure of democracy, the prohibition of distortions of the electoral and democratic process, the conclusion of certain governmental contracts with large or, a fortiori, dominant platforms, as well as the regulation and deconcentration or decentralization of artificial intelligence and the Metaverse. Artificial intelligence and, notably, machine learning based on data face a cycle of concentration accentuating the need for regulation. Deconcentration or decentralization may be envisaged, imposed or incentivized. Furthermore, limits are especially necessary when substantial autonomous powers are granted to forms of artificial intelligence. The Metaverse, for its part, may reshape society, politics, and economy around the globe in the future. From an antitrust perspective, the Metaverse poses at least two main issues: antitrust on the Metaverse and antitrust in the Metaverse.
Post-processing automatic transcriptions with machine learning for verbal fluency scoring

To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers. Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain "gold standard" transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions. For both animal and letter fluency tasks, we achieved good separation of valid versus invalid utterances. Verbal fluency scores calculated based on automatic transcriptions showed high correlation with those calculated after manual correction. Many techniques for scoring verbal fluency word lists require accurate transcriptions with word timings. We show that machine learning methods can be applied to improve off-the-shelf ASR for this purpose. These automatically derived scores may be satisfactory for some applications. Low correlations among some of the scores indicate the need for improvement in automatic speech recognition before a fully automatic approach can be reliably implemented.
PC-Reg: A pyramidal prediction–correction approach for large deformation image registration

Deformable image registration plays an important role in medical image analysis. Deep neural networks such as VoxelMorph and TransMorph are fast, but limited to small deformations and face challenges in the presence of large deformations. To tackle large deformations in medical image registration, we propose PC-Reg, a pyramidal Prediction and Correction method for deformable registration, which treats multi-scale registration akin to solving an ordinary differential equation (ODE) across scales. Starting with a zero-initialized deformation at the coarse level, PC-Reg follows the predictor–corrector regime and progressively predicts a residual flow and a correction flow to update the deformation vector field through different scales. The prediction in each scale can be regarded as a single step of ODE integration. PC-Reg can be easily extended to diffeomorphic registration and is able to alleviate the multiscale accumulated upsampling and diffeomorphic integration error. Further, to transfer details from full resolution to low scale, we introduce a distillation loss, where the output is used as the target label for intermediate outputs. Experiments on inter-patient deformable registration show that the proposed method significantly improves registration not only for large but also for small deformations.
Advances in Quantum Machine Learning and Deep Learning for Image Classification: A Survey

Image classification, which is a fundamental element of Computer Vision (CV) and Artificial Intelligence (AI), has been researched intensively in numerous domains and embedded in many products. However, with the exponential increase in the number of images and the complexity of the required tasks, deep-learning classification algorithms demand more intensive resources and computational power to train the models and update the massive number of parameters. Quantum computing is a new research technology with a promising capability of exponential speed up and operational parallelization with its unique phenomena including superposition and entanglement. Researchers have already started utilizing Quantum Deep Learning (QDL) and Quantum Machine Learning (QML) in image classification. Yet, to our knowledge, there exists no comprehensive published literature review on quantum image classification. Therefore, this paper analyzes the advances in this field by dividing the studies based on a unique taxonomy, discussing the limitations, summarizing essential aspects of each research, and finally, emphasizing the gaps, challenges, and recommendations. One of the key challenges presented in the paper is that quantum computers are in the Noisy Intermediate-Scale Quantum (NISQ) era, where they contain a limited number of noisy qubits, therefore challenging complex quantum classifiers and complex images from advanced datasets. This research recommends constructing a novel quantum image encoding method that adapts to the available number of qubits and enables RGB images as a critical contribution to the existing research.
Coherence dynamics in Simon's quantum algorithm

Abstract Quantum coherence plays a pivotal role in quantum algorithms. We study the coherence dynamics of the evolved states in Simon's quantum algorithm based on Tsallis relative α entropy and <?CDATA $l_{1,p}$ ?> norm. We prove that the coherence of the first register and the second register both rely on the dimension N of the state spaces of the n qubit systems, and increase with the increase of N . We show that the oracle operator O does not change the coherence. Moreover, we study the coherence dynamics in Simon's quantum algorithm and prove that overall the coherence is in production when <?CDATA $N>4$ ?> and in depletion when <?CDATA $N<4$ ?> .
RS-RCNN: an indoor window detection algorithm for autonomous spraying robot

Abstract The limitations of deep learning detection algorithms based on convolutional neural networks stem from the local characteristics inherent in convolutional operations. To overcome this issue, an RS-RCNN (ResNet_50 + Swin Transformer RCNN) object detection algorithm is proposed as an extension of Faster-RCNN. This algorithm incorporates the fusion of ResNet_50 and Swin Transformer networks as backbone networks for feature extraction. Moreover, it integrates the AAM_HRFPN (Attention Aggregation Module High resolution network) multi-feature fusion network and incorporates a linear attention mechanism. By employing the SIoU loss calculation method, the proposed RS-RCNN network enhances the representation of global context information and local semantic information, while improving fusion efficiency and detection accuracy. Compared to typical object detection networks, the proposed network achieves an AP value of 0.877, which is 7.4% points higher than the original network. The successful application of this method presents a novel solution for robot detection of non-spraying areas.
MF-Box: multifidelity and multiscale emulation for the matter power spectrum

ABSTRACT We introduce MF-Box, an extended version of MFEmulator, designed as a fast surrogate for power spectra, trained using N-body simulation suites from various box sizes and particle loads. To demonstrate MF-Box’s effectiveness, we design simulation suites that include low-fidelity (LF) suites (L1 and L2) at 256 and $100 \, \rm {Mpc\, ~}h^{-1}$, each with 1283 particles, and a high-fidelity (HF) suite with 5123 particles at $256 \, \rm {Mpc\, ~}h^{-1}$, representing a higher particle load compared to the LF suites. MF-Box acts as a probabilistic resolution correction function, learning most of the cosmological dependencies from L1 and L2 simulations and rectifying resolution differences with just three HF simulations using a Gaussian process. MF-Box successfully emulates power spectra from our HF testing set with a relative error of $\lt 3~{{\ \rm per\ cent}}$ up to $k \simeq 7 \, h\rm {Mpc}{^{-1}}$ at z ∈ [0, 3], while maintaining a cost similar to our previous multifidelity approach, which was accurate only up to z = 1. The addition of an extra LF node in a smaller box significantly improves emulation accuracy for MF-Box at $k \gt 2 \, h\rm {Mpc}{^{-1}}$, increasing it by a factor of 10. We conduct an error analysis of MF-Box based on computational budget, providing guidance for optimizing budget allocation per fidelity node. Our proposed MF-Box enables future surveys to efficiently combine simulation suites of varying quality, effectively expanding the range of emulation capabilities while ensuring cost efficiency.
A weakly supervised NMF method to decipher molecular subtype-related dynamic patterns in breast DCE-MR images

Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important imaging modality for breast cancer diagnosis. Intratumoral heterogeneity causes a major challenge in the interpretation of breast DCE-MRI. Previous studies have introduced decomposition methods on DCE-MRI to reveal intratumoral heterogeneity by analyzing distinct dynamic patterns within each tumor. However, these methods estimated the dynamic patterns and their corresponding component coefficients in an unsupervised manner, without considering any clinically relevant information.Approach. To decipher molecular subtype-related dynamic patterns, we propose a weakly supervised nonnegative matrix factorization method (WSNMF), which is able to decompose the pixel kinetics of DCE-MRI with image-level subtype labels. The WSNMF is developed based on a discriminant nonnegative matrix factorization (NMF) to utilize coarse-grained subtype information, in which between- and within-class scatters are defined on the mean vector of component coefficients over all pixels in each tumor, rather than directly on the vector of component coefficients of each pixel.Main results. Experiments demonstrated that the dynamic patterns identified by WSNMF had superior performance in distinguishing between luminal A and the other subtype tumors. The classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). WSNMF yielded better classification performance (AUC = 0.822) than other heterogeneity analysis methods, including two partitioning-based methods (KPC with AUC = 0.697 and TTP with AUC = 0.760) and two unsupervised decomposition-based methods (PCA with AUC = 0.774 and NMF with AUC = 0.797).Significance. Our method adds a valuable new perspective into DCE-MRI decomposition-based heterogeneity analysis by taking advantage of intrinsic tumor characteristics to improve the diagnosis of breast cancer.
A geometric framework for multiclass ensemble classifiers

Abstract Ensemble classifiers have been investigated by many in the artificial intelligence and machine learning community. Majority voting and weighted majority voting are two commonly used combination schemes in ensemble learning. However, understanding of them is incomplete at best, with some properties even misunderstood. In this paper, we present a group of properties of these two schemes formally under a geometric framework. Two key factors, every component base classifier’s performance and dissimilarity between each pair of component classifiers are evaluated by the same metric—the Euclidean distance. Consequently, ensembling becomes a deterministic problem and the performance of an ensemble can be calculated directly by a formula. We prove several theorems of interest and explain their implications for ensembles. In particular, we compare and contrast the effect of the number of component classifiers on these two types of ensemble schemes. Some important properties of both combination schemes are discussed. And a method to calculate the optimal weights for the weighted majority voting is presented. Empirical investigation is conducted to verify the theoretical results. We believe that the results from this paper are very useful for us to understand the fundamental properties of these two combination schemes and the principles of ensemble classifiers in general. The results are also helpful for us to investigate some issues in ensemble classifiers, such as ensemble performance prediction, diversity, ensemble pruning, and others.
An update on RSCVD at the Forum on Interlending in the UK

For three of the past four years we’ve had the honor of speaking to the Forum for Interlending in the UK about our work on integrating Open Access into Interlibrary Loan.
Mineral Prospectivity Mapping of Tungsten Polymetallic Deposits Using Machine Learning Algorithms and Comparison of Their Performance in the Gannan Region, China

Abstract The current study aims at assessing the capabilities of five machine learning models in terms of mapping tungsten polymetallic prospectivity in the Gannan region, China. The five models include logistic regression (LR), support vector machine (SVM), random forest, convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Three types of data sets including geochemical data, lithostratigraphic contacts, and faults were employed to generate 16 evidential maps that were used to build the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performance of the models was evaluated through receiver operating characteristic and K‐fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb, and Cd, implying that they are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict the maximum number of tungsten polymetallic deposits within the minimum area based on the prediction‐area plot analysis, while the SVM model can capture the least amount of tungsten polymetallic deposits within the largest area. Finally, 18 prospective areas were delineated according to the prediction results of the machine learning models, which provided important guidance for tungsten polymetallic exploration in the study area.
Model-based trajectory stitching for improved behavioural cloning and its applications

Abstract Behavioural cloning (BC) is a commonly used imitation learning method to infer a sequential decision-making policy from expert demonstrations. However, when the quality of the data is not optimal, the resulting behavioural policy also performs sub-optimally once deployed. Recently, there has been a surge in offline reinforcement learning methods that hold the promise to extract high-quality policies from sub-optimal historical data. A common approach is to perform regularisation during training, encouraging updates during policy evaluation and/or policy improvement to stay close to the underlying data. In this work, we investigate whether an offline approach to improving the quality of the existing data can lead to improved behavioural policies without any changes in the BC algorithm. The proposed data improvement approach - Model-Based Trajectory Stitching (MBTS) - generates new trajectories (sequences of states and actions) by ‘stitching’ pairs of states that were disconnected in the original data and generating their connecting new action. By construction, these new transitions are guaranteed to be highly plausible according to probabilistic models of the environment, and to improve a state-value function. We demonstrate that the iterative process of replacing old trajectories with new ones incrementally improves the underlying behavioural policy. Extensive experimental results show that significant performance gains can be achieved using MBTS over BC policies extracted from the original data. Furthermore, using the D4RL benchmarking suite, we demonstrate that state-of-the-art results are obtained by combining MBTS with two existing offline learning methodologies reliant on BC, model-based offline planning (MBOP) and policy constraint (TD3+BC).
Using Electronic Health Record Data to Measure the Latent Tuberculosis Infection Care Cascade in Safety-Net Primary Care Clinics

•Latent tuberculosis care cascades can be created using electronic medical record data.•Care cascades can identify gaps in latent tuberculosis screening, testing, and treatment.•There is potential to improve tuberculosis screening guideline implementation in primary care.•Preferred diagnostic test is not used for most tuberculosis infection testing.•Latent tuberculosis infection treatment prescribing is low, especially preferred regimens. IntroductionPrevention of tuberculosis disease through diagnosis and treatment of latent tuberculosis infection is critical for achieving tuberculosis elimination in the U.S. Diagnosis and treatment of latent tuberculosis infection in safety-net primary care settings that serve patients at risk for tuberculosis may increase uptake of this prevention effort and accelerate progress toward elimination. Optimizing tuberculosis prevention in these settings requires measuring the latent tuberculosis infection care cascade (testing, diagnosis, and treatment) and identifying gaps to develop solutions to overcome barriers. We used electronic health record data to describe the latent tuberculosis infection care cascade and identify gaps among a network of safety-net primary care clinics.MethodsElectronic health record data for patients seen in the OCHIN Clinical Network, the largest network of safety-net clinics in the U.S., between 2012 and 2019 were extracted. electronic health record data were used to measure the latent tuberculosis infection care cascade: patients who met tuberculosis screening criteria on the basis of current recommendations were tested for tuberculosis infection, diagnosed with latent tuberculosis infection, and prescribed treatment for latent tuberculosis infection. Outcomes were stratified by diagnostic test and treatment regimen.ResultsAmong 1.9 million patients in the analytic cohort, 43.5% met tuberculosis screening criteria, but only 21.4% were tested for latent tuberculosis infection; less than half (40.4%) were tested using an interferon-gamma release assay. Among those with a valid result, 10.5% were diagnosed with latent tuberculosis infection, 29.1% of those were prescribed latent tuberculosis infection treatment, and only 33.6% were prescribed a recommended rifamycin-based regimen.ConclusionsElectronic health record data can be used to measure the latent tuberculosis infection care cascade. A large proportion of patients in this safety-net clinical network are at high risk for tuberculosis infection. Addressing identified gaps in latent tuberculosis infection testing and treatment may have a direct impact on improving tuberculosis prevention in primary care clinics and accelerate progress toward elimination.
An image encryption algorithm based on the double time-delay Lorenz system

The traditional image encryption technology has the disadvantages of low encryption efficiency and low security. According to the characteristics of image information, an image encryption algorithm based on double time-delay chaos is proposed by combining the delay chaotic system with traditional encryption technology. Because of the infinite dimension and complex dynamic behavior of the delayed chaotic system, it is difficult to be simulated by AI technology. Furthermore time delay and time delay position have also become elements to be considered in the key space. The proposed encryption algorithm has good quality. The stability and the existence condition of Hopf bifurcation of Lorenz system with double delay at the equilibrium point are studied by nonlinear dynamics theory, and the critical delay value of Hopf bifurcation is obtained. The system intercepts the pseudo-random sequence in chaotic state and encrypts the image by means of scrambling operation and diffusion operation. The algorithm is simulated and analyzed from key space size, key sensitivity, plaintext image sensitivity and plaintext histogram. The results show that the algorithm can produce satisfactory scrambling effect and can effectively encrypt and decrypt images without distortion. Moreover, the scheme is not only robust to statistical attacks, selective plaintext attacks and noise, but also has high stability.