Reviewerly/exHarmony huggingface dataset:
Dolomite cement microstratigraphy: A record of brine evolution and ore precipitation mechanisms, upper Knox Group, Tennessee and Kentucky, USA

Abstract Trace element changes in fluids associated with ore-forming events in sedimentary basins may be recorded by contemporaneous cements, especially zoned carbonate minerals (microstratigraphy). Cement analysis using advanced mapping and analytical techniques including scanning electron microscopy cathodoluminescence (SEM-CL), charge contrast imaging, high-resolution X-ray computed tomography (XCT), and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) documents geochemical changes associated with Mississippi Valley–type mineralization in solution-collapse breccias of the Cambrian–Ordovician Knox Group (Tennessee and Kentucky, USA). Dolomite cement zonation coincident with changes in Fe and Mn can be observed with optical microscope CL in bands as narrow as 5 µm, whereas panchromatic SEM-CL reveals microfractures and cement subzones coincident with changes in La and Ce concentrations in bands as narrow as 0.1 µm. XCT scans image a high-density (Fe-rich) dolomite zone at the onset of late sulfide precipitation. The transition from pre-ore to ore-stage cementation is marked by increased Fe, Mn, Zn, Cd, Ga, Pb, and Sr and decreased La and Ce concentrations. Fine-scale metal depletion cycles during this transition may record metal precipitation from brine in response to the availability of reduced sulfur. Except for Fe and Mn, post-ore dolomite zones generally have low metal concentrations. Thus, dolomite microstratigraphy tracks systematic changes in brine metal concentrations modified by episodes of localized sulfide mineral precipitation.
Robot Calligraphy Based on Footprint Model and Brush Trajectory Extraction

In this paper, a method based on footprint model and brush trajectory extraction is proposed to make robots write calligraphy. As the footprint model is important for robot calligraphy, especially for the width control of stroke. A footprint model is first proposed based on a binary linear regression algorithm. The relationship between controllable parameters of robot and the width of stroke is established in our model. Thus, the model is suitable for robotic writing. Then, a skeleton-based stroke generation method is proposed for simulating the actual writing process. The writing trajectory of stroke is extracted by the skeleton tracking algorithm, and the normal angle is computed to reconstruct the brush stroke with footprint model and brush trajectory. A writing optimization method based on calligraphic rules is proposed to make strokes conform to calligraphic characteristics. Finally, the Non-Uniform Rational B-Spline (NURBS) algorithm is used for robotic writing path planning so that actual writing could be performed. Our approach shows excellent performance in writing typical strokes and Chinese characters.
When I use a word . . . . Information

Information in common parlance means knowledge communicated about particular facts, subjects, or events; a fact, for example, is a piece of information. However, in computing science information has a different meaning, given by information theory, and is the degree of choice exercised when a particular symbol or message is chosen from a range of possible symbols or messages, the choice being probabilistically determined. The latest outcome of this is the development of GPTs (Generative Pre-trained Transformers), or chatbots, which may facilitate the writing of medical and other scientific texts. The extent to which this may be advantageous or otherwise is not yet clear, but there will certainly be unintended consequences.
Multivariate functional data clustering using adaptive density peak detection

Clustering for multivariate functional data is a challenging problem since the data are represented by a set of curves and functions belonging to an infinite-dimensional space. In this article, we propose a novel clustering method for multivariate functional data using an adaptive density peak detection technique. It is a quick cluster center identification algorithm based on the two measures of each functional data observation: the functional density estimate and the distance to the closest observation with a higher functional density. We suggest two types of functional density estimators for multivariate functional data. The first one is a functional k$$ k $$ -nearest neighbor density estimator based on (a) an L2 distance between raw functional curves, or (b) a semimetric of multivariate functional principal components. The second one is a k$$ k $$ -nearest neighbor density estimator based on multivariate functional principal scores. Our clustering method is computationally fast since it does not need an iterative process. The flexibility and advantages of the method are examined by comparing it with other existing clustering methods in simulation studies. A user-friendly R package FADPclust is developed for public use. Finally, our method is applied to a real case study in lung cancer research.
Multi-phase attention network for face super-resolution

Previous general super-resolution methods do not perform well in restoring the details structure information of face images. Prior and attribute-based face super-resolution methods have improved performance with extra trained results. However, they need an additional network and extra training data are challenging to obtain. To address these issues, we propose a Multi-phase Attention Network (MPAN). Specifically, our proposed MPAN builds on integrated residual attention groups (IRAG) and a concatenated attention module (CAM). The IRAG consists of residual channel attention blocks (RCAB) and an integrated attention module (IAM). Meanwhile, we use IRAG to bootstrap the face structures. We utilize the CAM to concentrate on informative layers, hence improving the network's ability to reconstruct facial texture features. We use the IAM to focus on important positions and channels, which makes the network more effective at restoring key face structures like eyes and mouths. The above two attention modules form the multi-phase attention mechanism. Extensive experiments show that our MPAN has a significant competitive advantage over other state-of-the-art networks on various scale factors using various metrics, including PSNR and SSIM. Overall, our proposed Multi-phase Attention mechanism significantly improves the network for recovering face HR images without using additional information.
Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data

Synthetic clinical images could augment real medical image datasets, a novel approach in otolaryngology-head and neck surgery (OHNS). Our objective was to develop a generative adversarial network (GAN) for tympanic membrane images and to validate the quality of synthetic images with human reviewers. Our model was developed using a state-of-the-art GAN architecture, StyleGAN2-ADA. The network was trained on intraoperative high-definition (HD) endoscopic images of tympanic membranes collected from pediatric patients undergoing myringotomy with possible tympanostomy tube placement. A human validation survey was administered to a cohort of OHNS and pediatrics trainees at our institution. The primary measure of model quality was the Frechet Inception Distance (FID), a metric comparing the distribution of generated images with the distribution of real images. The measures used for human reviewer validation were the sensitivity, specificity, and area under the curve (AUC) for humans' ability to discern synthetic from real images. Our dataset comprised 202 images. The best GAN was trained at 512x512 image resolution with a FID of 47.0. The progression of images through training showed stepwise "learning" of the anatomic features of a tympanic membrane. The validation survey was taken by 65 persons who reviewed 925 images. Human reviewers demonstrated a sensitivity of 66%, specificity of 73%, and AUC of 0.69 for the detection of synthetic images. In summary, we successfully developed a GAN to produce synthetic tympanic membrane images and validated this with human reviewers. These images could be used to bolster real datasets with various pathologies and develop more robust deep learning models such as those used for diagnostic predictions from otoscopic images. However, caution should be exercised with the use of synthetic data given issues regarding data diversity and performance validation. Any model trained using synthetic data will require robust external validation to ensure validity and generalizability.
Little fast, little slow, should I stay or should I go? Adapting cognitive control to local-global temporal prediction across typical development

Adaptive cognitive control (CC), the ability to adjust goal-directed behavior according to changing environmental demand, can be instantiated bottom-up by implicit knowledge, including temporal predictability of task-relevant events. In S1-S2 tasks, either local (trial-by-trial hazard expectation) or global (block-by-block expectation) temporal information can induce prediction, allowing for proactive action control. Recent developmental evidence showed that adaptive CC based on global temporal prediction emerges earlier than when it is based on the local one only. However, very little is known about how children learn to dynamically adjust behavior on the fly according to changing global predictive information. Addressing this issue is nevertheless crucial to unravel the mechanisms underlying adaptive CC flexibility. Here we used a modified version of the Dynamic Temporal Prediction task to investigate how typically developing younger (6-8 years) and older children (9-11 years), adolescents (12-15 years) and adults (21-31 years) use global prediction to shape adaptive CC over time. Specifically, the short-long percentage of S2 preparatory intervals was manipulated list-wide to create a slow-fast-slow-fast fixed block sequence and test how efficiently the response speed adapted accordingly. Overall, results revealed that in all groups behavioral performance is successfully adjusted as a function of global prediction in the late phase of the task (block 3 to 4). Remarkably, only adolescents and adults exhibit an early adaptation of adaptive CC (block 1 to 2), while children younger than 11 show sluggish ability in inferring implicit changes in global predictive rules. This age-related dissociation suggests that, although being present from an early age, adaptive CC based on global predictive information needs more developmental space to become flexible in an efficient way. In the light of a neuroconstructivist approach, we suggest that bottom-up driven implicit flexibility may represent a key prerequisite for the development of efficient explicit cognitive control.
A robust kinship verification scheme using face age transformation

Kinship verification determines the existence of a kin relationship between people through facial image analysis, and can be used in various real-life applications, such as finding missing family members, analyzing social media, and genealogy research. Recently, many convolutional neural network (CNN)-based kinship verification methods have been proposed, owing to the good image-processing performance of the CNNs. Nevertheless, insufficient labeled data and age differences in the kinship images, make kinship verification quite difficult. To mitigate these limitations, herein, we first propose a face age transformation model to generate facial images of various age groups. Then, we construct a cross-age kinship verification model constructed using the generated images as a training dataset. To show the effectiveness of the proposed scheme, we conducted various comparative experiments with other models using popular kinship datasets and confirmed that our proposed method exhibited an improved verification accuracy.
Research on Chinese traditional opera costume recognition based on improved YOLOv5

Abstract In order to protect the cultural heritage of opera costumes, establish visual labels for opera costumes, accelerate the establishment of a database for opera costumes, and increase the dissemination of opera culture, we propose an improved You Only Look Once (YOLO) v5-based opera costume recognition model for opera costumes with a wide range of styles, rich colors, and complex stage environments. By adding Coordinate Attention (CA) mechanism to the backbone of YOLOv5, the network can focus on more interesting information when extracting features; replacing the original feature pyramid module with a weighted bidirectional feature pyramid module in the Neck part to achieve efficient fusion of features; replacing the original loss function GIOU with DIOU to improve the detection accuracy and convergence speed. The average detection accuracy of the improved YOLOv5 model reaches 86.3% and its inference speed reaches 28 ms per frame through experiments on the homemade Chinese costume dataset, which improves the average detection accuracy by 3.1% compared with the original model, and has good robustness in detecting complex scenes such as covered targets, light-colored costumes, cross targets, dense targets and different angles. The model meets the requirements for accuracy and real-time costume recognition in complex theatrical environments.
From centralized to Federated Learning: Exploring performance and end-to-end resource consumption

Machine Learning (ML) is increasingly implemented in a distributed fashion to harness the data abundance generated in the mobile client devices. Contrary to cloud-based centralized learning (CL), distributed schemes like Federated Learning (FL) shift the computational load to the user-equipment. Trying to compare and characterize the performance of the two schemes, existing literature focuses solely on model accuracy, neglecting the impact of training tasks on the underlying network resources. In this work, we introduce a realistic measurement-based model to thoroughly capture the cloud-to-client bandwidth and energy footprint of ML training on the network, while assessing the achieved accuracy, in the light of different data/model ratios. Our model is implemented in an FL simulation framework that allows trace-driven mobile users to conduct image classification tasks, under a varying exposure to training data heterogeneity. Having hyper-parameters tuned inline with network resources, our experiments unveil how the data to model ratio regulates both bandwidth expenditure and the client/infrastructure energy consumption, in cellular and wireless environments. Further simulation results confirm CL's superiority over FL under data heterogeneity and capture how FL trades-off accuracy and convergence time; thus, shedding light into so-far open questions that shape (distributed) learning solutions in networked environments.
Semantic inconsistency measures using 3-valued logics

AI systems often need to deal with inconsistencies. One way of getting information about inconsistencies is by measuring the amount of information in the knowledgebase. In the past 20 years numerous inconsistency measures have been proposed. Many of these measures are syntactic measures, that is, they are based in some way on the minimal inconsistent subsets of the knowledgebase. Very little attention has been given to semantic inconsistency measures, that is, ones that are based on the models of the knowledgebase where the notion of a model is generalized to allow an atom to be assigned a truth value that denotes contradiction. In fact, only one nontrivial semantic inconsistency measure, the contension measure, has been in wide use. The purpose of this paper is to define a class of semantic inconsistency measures based on 3-valued logics. First, we show which 3-valued logics are useful for this purpose. Then we show that the class of semantic inconsistency measures can be developed using a graphical framework similar to the way that syntactic inconsistency measures have been studied. We give several examples of semantic inconsistency measures and show how they apply to three useful 3-valued logics. We also investigate the properties of these inconsistency measures and show their computation for several knowledgebases.
Demonstration of 90 000 superconductive bump connections for massive quantum computing

Abstract We developed fabrication and bonding technology of superconductive bump for massive quantum computing. A bonded test vehicle demonstrates supercurrent through 90 000 series daisy-chain. Proposed bump consists of lead-indium alloy. The bumps land onto gold pad on the opposite chip and alloy with the gold pad for supercurrent. The primary advantage of the proposed bump is the minimized damage to the quantum chip. Plasma cleaning process which may degrade sensitive components is only required for bump chips and not for quantum chips. Lower bonding temperature of 100 degrees Celsius for the proposed bump also keeps sensitive components away from degradation in high temperature.
Optimization of Digital Information Management of Financial Services Based on Artificial Intelligence in the Digital Financial Environment

At present, society has entered the era of digital finance, and the information management system (IMS) of financial services has been developing rapidly, so the security of data has become particularly important. Firstly, some security techniques in IMS of financial services are introduced. Secondly, this study analyzes how to combine secure muti-party computation with blockchain technology to enhance the security of IMS. Finally, the feasibility and reliability of the scheme are verified by a comparative test. The experimental results reveal that the evaluation index score of the optimized scheme is higher than that of the traditional scheme. Meanwhile, in the comparative experiment of information data encryption, it can be seen that the running time of all schemes will improve with the increase of data. However, the increase rate of the optimized model in this study is much slower than that of the traditional model.
Quantum control of tunable-coupling transmons using dynamical invariants of motion

Abstract We analyze the implementation of a fast nonadiabatic CZ gate between two transmon qubits with tunable coupling. The gate control method is based on a theory of dynamical invariants which leads to reduced leakage and robustness against decoherence. The gate is based on a description of the resonance between the <?CDATA $\vert 11\rangle$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo fence="false" stretchy="false">|</mml:mo> <mml:mn>11</mml:mn> <mml:mo fence="false" stretchy="false">⟩</mml:mo> </mml:math> and <?CDATA $\vert 20\rangle$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo fence="false" stretchy="false">|</mml:mo> <mml:mn>20</mml:mn> <mml:mo fence="false" stretchy="false">⟩</mml:mo> </mml:math> using an effective Hamiltonian with the six lowest energy states. A modification of the protocol allows us to take into account the higher-order perturbative corrections of this effective model. This enables a gate fidelity several orders of magnitude higher than other quasiadiabatic protocols, with gate times that approach the theoretical limit.
Local2Global: a distributed approach for scaling representation learning on graphs

Abstract We propose a decentralised “ local2global ” approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or “ patches ”) and training local representations for each patch independently. In a second step, we combine the local representations into a globally consistent representation by estimating the set of rigid motions that best align the local representations using information from the patch overlaps, via group synchronization. A key distinguishing feature of local2global relative to existing work is that patches are trained independently without the need for the often costly parameter synchronization during distributed training. This allows local2global to scale to large-scale industrial applications, where the input graph may not even fit into memory and may be stored in a distributed manner. We apply local2global on data sets of different sizes and show that our approach achieves a good trade-off between scale and accuracy on edge reconstruction and semi-supervised classification. We also consider the downstream task of anomaly detection and show how one can use local2global to highlight anomalies in cybersecurity networks.
Decentralized Multi-authority ABE for $$\textsf{NC}^1$$ from BDH

Decentralized multi-authority attribute-based encryption ( $$\textsf{MA}$$ - $$\textsf{ABE}$$ ) is a strengthening of standard ciphertext-policy attribute-based encryption so that there is no trusted central authority: Any party can become an authority and there is no requirement for any global coordination other than the creation of an initial set of common reference parameters. Essentially, any party can act as an authority for some attribute by creating a public key of its own and issuing private keys to different users that reflect their attributes. This paper presents the first $$\textsf{MA}$$ - $$\textsf{ABE}$$ proven secure under the standard search variant of bilinear Diffie–Hellman (CBDH) and in the random oracle model. Our scheme supports all access policies captured by $$\textsf{NC}^1$$ circuits. All previous constructions were proven secure in the random oracle model and additionally were based on decision assumptions such as the DLIN assumption, non-standard q-type assumptions, or subspace decision assumptions over composite-order bilinear groups.
How augmented reality influences student learning and inquiry styles: A study of 1-1 physics remote AR tutoring

While augmented reality (AR) technology is being considered by educators for its potential to help students visualize abstract concepts, currently there are barriers from the high cost of developing complex AR applications. In this study we investigate how the complexity of AR content impacts student learning in remote 1-1 tutoring scenarios, where an instructor uses an AR headset while teaching physics concepts to a remote student. This approach brings the benefits of augmented reality into the already-existing educational practices of 1-1 remote instruction, without requiring AR devices for every student. We present a system for AR-based physics instruction and perform a between-subjects study, measuring how student learning and inquiry behaviors differ between two experimental conditions that vary in the complexity of AR content. Through quantitative and qualitative analysis, our results show that students who are tutored with more complex AR content learn better and show a wider variety of inquiry styles. Furthermore, AR visual representations appear to stimulate students to think about a wide range of scientific ideas, to make deeper connections between scientific concepts, and encourage students to have a more active learning style with increased transitions between inquiry activities. We discuss possible reasons and wider implications for these findings.