Reviewerly/exHarmony huggingface dataset:
Correcting solar radiation from reanalysis and analysis datasets with systematic and seasonal variations

Global Horizontal Irradiance (GHI), a component of solar radiation, is used to assess potential and estimate power output of solar thermal and PV applications. The reanalysis or analysis data are alternative to satellite data for solar resource assessment in places where latter is not available or costly, so there is need to evaluate their performance. The evaluation of estimated GHI (six-hourly data) from five global reanalysis and analysis datasets (NCEP-NCAR, NCEP-DOE, CFSR, NCEP-GFS and NCEP-FNL) is performed using annual, seasonal and monthly statistical analyses. The range of annual rMBE, rMAE, rRMSE and R is 13–41%, 23–41%, 44–66% and 0.938–0.965 respectively in the two inspected stations for all reanalysis and analysis datasets. Systematic errors were observed in estimated GHI which signifies need to apply bias correction to get data with acceptable uncertainty. Five bias correction methods based on Measure-Correlate-Predict, Linear-Adaptation (LA1 and LA2), CDF and Model Output Statistics (MOS) were applied to correct original GHI. The range of rMAE and rRMSE after bias-correction is 12–19% and 23–37% respectively for six-hourly data. The overall results of MOS method are best, followed by the linear-adaptation method. The corrected GHI from CFSR and NCEP-GFS datasets can be used for preliminary solar resource assessment.
Computational power of one- and two-dimensional dual-unitary quantum circuits

Quantum circuits that are classically simulatable tell us when quantum computation becomes less powerful than or equivalent to classical computation. Such classically simulatable circuits are of importance because they illustrate what makes universal quantum computation different from classical computers. In this work, we propose a novel family of classically simulatable circuits by making use of dual-unitary quantum circuits (DUQCs), which have been recently investigated as exactly solvable models of non-equilibrium physics, and we characterize their computational power. Specifically, we investigate the computational complexity of the problem of calculating local expectation values and the sampling problem of one-dimensional DUQCs, and we generalize them to two spatial dimensions. We reveal that a local expectation value of a DUQC is classically simulatable at an early time, which is linear in a system length. In contrast, in a late time, they can perform universal quantum computation, and the problem becomes a BQP-complete problem. Moreover, classical simulation of sampling from a DUQC turns out to be hard.
Recommending metamodel concepts during modeling activities with pre-trained language models

The design of conceptually sound metamodels that embody proper semantics in relation to the application domain is particularly tedious in model-driven engineering. As metamodels define complex relationships between domain concepts, it is crucial for a modeler to define these concepts thoroughly while being consistent with respect to the application domain. We propose an approach to assist a modeler in the design of metamodel by recommending relevant domain concepts in several modeling scenarios. Our approach does not require knowledge from the domain or to hand-design completion rules. Instead, we design a fully data-driven approach using a deep learning model that is able to abstract domain concepts by learning from both structural and lexical metamodel properties in a corpus of thousands of independent metamodels. We evaluate our approach on a test set containing 166 metamodels, unseen during the model training, with more than 5000 test samples. Our preliminary results show that the trained model is able to provide accurate top 5 lists of relevant recommendations for concept renaming scenarios. Although promising, the results are less compelling for the scenario of the iterative construction of the metamodel, in part because of the conservative strategy we use to evaluate the recommendations.
A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting

Biopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety.A prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation.The independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR =4.588, P=0.004), US features [margin (OR =2.916, P=0.019), shape (irregular vs. oval, OR =2.474, P=0.044; round vs. oval, OR =1.935, P=0.276), parallel orientation vs. not parallel (OR =2.204, P=0.040)], low suspicious lymph nodes (OR =7.664, P=0.019), and suspicious calcifications on mammography (MG) (OR =6.736, P=0.001). The C-index was good in the training [0.813, 95% confidence interval (95% CI), 0.733 to 0.893] and validation cohorts (0.765, 95% CI, 0.584 to 0.946). The calibration curves showed optimal agreement between the nomogram prediction and actual observations for the probability of malignancy. Also, the cutoff score was set to 100 for discriminating high and low risk. The model performed well in discerning different risk groups.We developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.
Morphology Matters: A Multilingual Language Modeling Analysis

Abstract Prior studies in multilingual language modeling (e.g., Cotterell et al., 2018; Mielke et al., 2019) disagree on whether or not inflectional morphology makes languages harder to model. We attempt to resolve the disagreement and extend those studies. We compile a larger corpus of 145 Bible translations in 92 languages and a larger number of typological features.1 We fill in missing typological data for several languages and consider corpus-based measures of morphological complexity in addition to expert-produced typological features. We find that several morphological measures are significantly associated with higher surprisal when LSTM models are trained with BPE-segmented data. We also investigate linguistically motivated subword segmentation strategies like Morfessor and Finite-State Transducers (FSTs) and find that these segmentation strategies yield better performance and reduce the impact of a language’s morphology on language modeling.
Multidimensional cluster states using a single spin-photon interface coupled strongly to an intrinsic nuclear register

Photonic cluster states are a powerful resource for measurement-based quantum computing and loss-tolerant quantum communication. Proposals to generate multi-dimensional lattice cluster states have identified coupled spin-photon interfaces, spin-ancilla systems, and optical feedback mechanisms as potential schemes. Following these, we propose the generation of multi-dimensional lattice cluster states using a single, efficient spin-photon interface coupled strongly to a nuclear register. Our scheme makes use of the contact hyperfine interaction to enable universal quantum gates between the interface spin and a local nuclear register and funnels the resulting entanglement to photons via the spin-photon interface. Among several quantum emitters, we identify the silicon-29 vacancy centre in diamond, coupled to a nanophotonic structure, as possessing the right combination of optical quality and spin coherence for this scheme. We show numerically that using this system a 2×5-sized cluster state with a lower-bound fidelity of 0.5 and repetition rate of 65 kHz is achievable under currently realised experimental performances and with feasible technical overhead. Realistic gate improvements put 100-photon cluster states within experimental reach.
A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions

Abstract The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or conceding the next goal at any time instance. In this work, we develop a comprehensive analysis framework for the EPV, providing soccer practitioners with the ability to evaluate the impact of observed and potential actions, both visually and analytically. The EPV expression is decomposed into a series of subcomponents that model the influence of passes, ball drives and shot actions on the expected outcome of a possession. We show we can learn from spatiotemporal tracking data and obtain calibrated models for all the components of the EPV. For the components related with passes, we produce visually-interpretable probability surfaces from a series of deep neural network architectures built on top of flexible representations of game states. Additionally, we present a series of novel practical applications providing coaches with an enriched interpretation of specific game situations. This is, to our knowledge, the first EPV approach in soccer that uses this decomposition and incorporates the dynamics of the 22 players and the ball through tracking data.
Resolution-based distillation for efficient histology image classification

Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based methodology for improving the computational efficiency of histology image classification. The proposed approach is robust when used with images that have reduced input resolution, and it can be trained effectively with limited labeled data. Moreover, our approach operates at either the tissue- or slide-level, removing the need for laborious patch-level labeling. Our method uses knowledge distillation to transfer knowledge from a teacher model pre-trained at high resolution to a student model trained on the same images at a considerably lower resolution. Also, to address the lack of large-scale labeled histology image datasets, we perform the knowledge distillation in a self-supervised fashion. We evaluate our approach on three distinct histology image datasets associated with celiac disease, lung adenocarcinoma, and renal cell carcinoma. Our results on these datasets demonstrate that a combination of knowledge distillation and self-supervision allows the student model to approach and, in some cases, surpass the teacher model's classification accuracy while being much more computationally efficient. Additionally, we observe an increase in student classification performance as the size of the unlabeled dataset increases, indicating that there is potential for this method to scale further with additional unlabeled data. Our model outperforms the high-resolution teacher model for celiac disease in accuracy, F1-score, precision, and recall while requiring 4 times fewer computations. For lung adenocarcinoma, our results at 1.25× magnification are within 1.5% of the results for the teacher model at 10× magnification, with a reduction in computational cost by a factor of 64. Our model on renal cell carcinoma at 1.25× magnification performs within 1% of the teacher model at 5× magnification while requiring 16 times fewer computations. Furthermore, our celiac disease outcomes benefit from additional performance scaling with the use of more unlabeled data. In the case of 0.625× magnification, using unlabeled data improves accuracy by 4% over the tissue-level baseline. Therefore, our approach can improve the feasibility of deep learning solutions for digital pathology on standard computational hardware and infrastructures.
Towards Unconstrained Joint Hand-Object Reconstruction From RGB Videos

Our work aims to obtain 3D reconstruction of hands and manipulated objects from monocular videos. Reconstructing hand-object manipulations holds a great potential for robotics and learning from human demonstrations. The supervised learning approach to this problem, however, requires 3D supervision and remains limited to constrained laboratory settings and simulators for which 3D ground truth is available. In this paper we first propose a learning-free fitting approach for hand-object reconstruction which can seamlessly handle two-hand object interactions. Our method relies on cues obtained with common methods for object detection, hand pose estimation and instance segmentation. We quantitatively evaluate our approach and show that it can be applied to datasets with varying levels of difficulty for which training data is unavailable.
Deep Convolutional Clustering-Based Time Series Anomaly Detection

This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional neural network-based deep autoencoder architecture. As a core novelty, we split the autoencoder latent space in discriminative and reconstructive latent features and introduce an auxiliary loss based on k-means clustering for the discriminatory latent variables. We employ a Top-K clustering objective for separating the latent space, selecting the most discriminative features from the latent space. We use the approach to the benchmark Tennessee Eastman data set to prove its applicability. We provide different ablation studies and analyze the method concerning various downstream tasks, including anomaly detection, binary and multi-class classification. The obtained results show the potential of the approach to improve downstream tasks compared to standard autoencoder architectures.
Mitaka: A Simpler, Parallelizable, Maskable Variant of Falcon

This work describes the Mitaka signature scheme: a new hash-and-sign signature scheme over NTRU lattices which can be seen as a variant of NIST finalist Falcon. It achieves comparable efficiency but is considerably simpler, online/offline, and easier to parallelize and protect against side-channels, thus offering significant advantages from an implementation standpoint. It is also much more versatile in terms of parameter selection. We obtain this signature scheme by replacing the FFO lattice Gaussian sampler in Falcon by the “hybrid” sampler of Ducas and Prest, for which we carry out a detailed and corrected security analysis. In principle, such a change can result in a substantial security loss, but we show that this loss can be largely mitigated using new techniques in key generation that allow us to construct much higher quality lattice trapdoors for the hybrid sampler relatively cheaply. This new approach can also be instantiated on a wide variety of base fields, in contrast with Falcon’s restriction to power-of-two cyclotomics. We also introduce a new lattice Gaussian sampler with the same quality and efficiency, but which is moreover compatible with the integral matrix Gram root technique of Ducas et al., allowing us to avoid floating point arithmetic. This makes it possible to realize the same signature scheme as Mitaka efficiently on platforms with poor support for floating point numbers. Finally, we describe a provably secure masking of Mitaka. More precisely, we introduce novel gadgets that allow provable masking at any order at much lower cost than previous masking techniques for Gaussian sampling-based signature schemes, for cheap and dependable side-channel protection.
Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (&lt;78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.
Accessing ground-state and excited-state energies in a many-body system after symmetry restoration using quantum computers

We explore the possibility to perform symmetry restoration with the variation after projection technique on a quantum computer followed by additional postprocessing. The final goal is to develop configuration interaction techniques based on many-body trial states preoptimized on a quantum computer. We show how the projection method used for symmetry restoration can prepare optimized states that could then be employed as initial states for quantum or hybrid quantum-classical algorithms. We use the quantum phase estimation and the quantum Krylov approaches for the postprocessing. The latter method combined with the quantum variation after projection leads to very fast convergence toward the ground-state energy. The possibility to access excited states energies is also discussed. Illustrations of the different techniques are made using the pairing Hamiltonian.
Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions

A long-term goal of AI research is to build intelligent agents that can communicate with humans in natural language, perceive the environment, and perform real-world tasks.Visionand-Language Navigation (VLN) is a fundamental and interdisciplinary research topic towards this goal, and receives increasing attention from natural language processing, computer vision, robotics, and machine learning communities.In this paper, we review contemporary studies in the emerging field of VLN, covering tasks, evaluation metrics, methods, etc.Through structured analysis of current progress and challenges, we highlight the limitations of current VLN and opportunities for future work.This paper serves as a thorough reference for the VLN research community. 1
Slicing-Volume: Hybrid 3D/2D Multi-target Selection Technique for Dense Virtual Environments

3D selection in dense VR environments (e.g., point clouds) is extremely challenging due to occlusion and imprecise mid-air input modalities (e.g., 3D controllers and hand gestures). In this paper, we propose "Slicing-Volume", a hybrid selection technique that enables simultaneous 3D interaction in mid-air, and a 2D pen-and-tablet metaphor in VR. Inspired by well-known slicing plane techniques in data visualization, our technique consists of a 3D volume that encloses target objects in mid-air, which are then projected to a 2D tablet view for precise selection on a tangible physical surface. While slicing techniques and tablets-in-VR have been previously explored, in this paper, we evaluated the potential of this hybrid approach to improve accuracy in highly occluded selection tasks, comparing different multimodal interactions (e.g., Mid-air, Virtual Tablet and Real Tablet). Our results showed that our hybrid technique significantly improved overall accuracy of selection compared to Mid-air selection only, thanks to the added haptic feedback given by the physical tablet surface, rather than the added visualization given by the tablet view.
Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space

Support vector machines have actually consulted with significant success in various real-world learning jobs. The Support Vector Machine (SVM) is a thoroughly utilized classifier. Along with yet, obtaining the finest outcomes along with SVMs needs an understanding of their procedures as well as the different implies a consumer can influence their preciseness. We supply the individual with a fundamental understanding of the concept behind SVMs and also concentrate on their usage in technique. This paper is concentrated on the useful concerns being used to support vector machines to identify information that is currently supplied as functions in some fixed-dimensional vector space.
Non-Markovian disentanglement dynamics in double-giant-atom waveguide-QED systems

We study the disentanglement dynamics of two giant atoms coupled to a common one-dimensional waveguide. We focus on the non-Markovian retarded effect in the disentanglement of the two giant atoms by taking the photon transmission time into account. By solving the time-delayed equations of motion for the probability amplitudes, we obtain the evolution of the entanglement of the two giant atoms, which are initially in the maximally entangled states in the single-excitation space. It is found that the retardation-induced non-Markovianity leads to nonexponential decay and revivals of entanglement. Concretely, we consider separate-, braided-, and nested-coupling configurations, and find that the disentanglement dynamics in these configurations exhibits different features. We demonstrate that the steady-state entanglement depends on the time delay under certain conditions in these three coupling configurations. We also study the dependence of the disentanglement of the two giant atoms on both the detuning effect and the initial-state phase effect. In addition, we consider the disentanglement dynamics of the two giant atoms, which are initially in the state superposed by zero-excitation and two-excitation components. This work will pave the way for the generation of stationary entanglement between two giant atoms, which may have potential applications in the construction of large-scale quantum networks based on the giant-atom waveguide-QED systems.