Reviewerly/exHarmony huggingface dataset:
Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches

This proposed work implements various classification approaches to classify the H&E-stained Follicular Lymphoma tissue sample. As part of the process, k-means clustering is used to isolate cytological components like the nucleus extracellular and cytoplasmic regions from images. Then feature space vector is constructed by combining global feature extraction techniques like LBP, LDP, GLCM, and other local feature-extraction techniques. To classify FL images into their respective grades, feature space vectors obtained from feature extraction algorithms are input into a multiclass SVM. Moreover, classification accuracies were explicitly tested with different classifiers like CNN and other pre-trained deep learning networks that can directly operate on raw images without any preprocessing to classify the FL images into their respective grades. The efficacy of different classifiers is presented. This preliminary work provides a proof of concept for incorporating automated FL tissue diagnostic systems into future pathology workflows to supplement the pathologists'.
Gold-Sulfide Mineralization in Banded Iron Formation at Girar, Bundelkhand Craton, India: Inferences from Field Observations and Petrography

Gold mineralization associated with quartz veins and sulfide minerals has been reported in Banded Iron Formation (BIF) at Girar, Bundelkhand Craton, India. The BIF is deformed with the development of tight- isoclinal folds in high strain zones and hosts four types of quartz veins viz., (i) folded veins, (ii) band parallel veins, (iii) low angle cross-cutting veins, and (iv) high angle cross-cutting veins. Folded and band parallel veins are continuous with each other and spatially restricted to zones of tight-isoclinal folds in BIF. Shape of quartz aggregates in the folded and band parallel veins is interlobate, and these veins contain pyrite and chalcopyrite as dominant sulfide minerals along with gold. Microstructure of quartz grains and presence of fragments of BIF in sulfide mineralgold bearing folded and band parallel veins indicate that the formations of these veins were related to a single deformation episode. Spatial association of folded and band parallel veins to intensely folded zones in BIF is attributed to epigenetic nature of sulfide and gold mineralization.
Multi‐granularity re‐ranking for visible‐infrared person re‐identification

Abstract Visible‐infrared person re‐identification (VI‐ReID) is a supplementary task of single‐modality re‐identification, which makes up for the defect of conventional re‐identification under insufficient illumination. It is more challenging than single‐modality ReID because, in addition to difficulties in pedestrian posture, camera shooting angle and background change, there are also difficulties in the cross‐modality gap. Existing works only involve coarse‐grained global features in the re‐ranking calculation, which cannot effectively use fine‐grained features. However, fine‐grained features are particularly important due to the lack of information in cross‐modality re‐ID. To this end, the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm (termed QCMR) is proposed, including a Q‐nearest neighbour centre encoder (termed QNC) and a Multi‐granularity K‐reciprocal Encoder (termed MGK) for a more comprehensive feature representation. QNC converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality gap. MGK takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity measurement. Extensive experiments on two widely used VI‐ReID benchmarks, SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art results. Especially, the mAP of SYSU‐MM01 is increased by 5.9% in all‐search mode.
DMHC: Device-free multi-modal handwritten character recognition system with acoustic signal

Efficient typing in a mobile device is a long-standing problem because of its small-size touch screen. Methods for contactless solutions using acoustic sensors have been proposed recently, which are device-free and low cost. However, these methods are not practical enough as they use either the microphone of the mobile device for recording the sounds generated during writing or the speaker of the device for playing a frequency modulated ultrasonic signal and then the signal reflected from the hand of the user is analyzed, where the former process requires a quiet environment and the latter process requires the user to write a letter in a single stroke and large size. In order to overcome those challenges, we present DMHC, which is an effective and device-free multi-modal handwritten character recognition system. DMHC fuses two different acoustic modalities, which are ultrasonic and general audio signals. The main feature of DMHC is that we extract the latent interactions between the ultrasonic and general acoustic signals. Specifically, DMHC implements the noise-resistant handwritten character recognition system for reliably recognizing handwritten characters in real life. We implement our proposed module based on the self-attention method to multiple channel fusion. Extensive experimental results demonstrate the effectiveness and robustness of DMHC under various conditions, where the recognition accuracies of letters and words are found to be 97.4% and 95.3%, respectively.
The social-ecological system of the Kenai River Fishery (Alaska, USA)

We present a collaboratively developed social-ecological model of the Kenai River Fishery. We developed the model through iterative interviews with stakeholders throughout the Kenai Peninsula using a novel participatory Fuzzy Cognitive Mapping process grounded in Ostrom's social-ecological systems framework. Individual social-ecological models, developed one-on-one with stakeholders, were combined into a single aggregated model representing the system's structure and function. We validated this aggregated model through subsequent interviews with stakeholders and focused literature reviews. The result is a model that can: 1) illustrate the breadth and interconnectedness of the Kenai River Fishery's social-ecological system; 2) be used to facilitate discussions around management of the fishery; and 3) be used to explore the components and interactions that move the system toward or away from sustainability. Using the model, we identify how the nature of salmon (migratory) and their habitat (large and unpredictable) leads to uncertainty about effective management strategies. This uncertainty, in addition to a large and diverse set of resource users, creates conflicting management goals that ultimately limit the governance system in making decisions that might increase the sustainability of the fishery.
Enhanced Pathology Image Quality with Restore–Generative Adversarial Network

Whole slide imaging is becoming a routine procedure in clinical diagnosis. Advanced image analysis techniques have been developed to assist pathologists in disease diagnosis, staging, subtype classification, and risk stratification. Recently, deep learning algorithms have achieved state-of-the-art performances in various imaging analysis tasks, including tumor region segmentation, nuclei detection, and disease classification. However, widespread clinical use of these algorithms is hampered by their performances often degrading due to image quality issues commonly seen in real-world pathology imaging data such as low resolution, blurring regions, and staining variation. Restore-Generative Adversarial Network (GAN), a deep learning model, was developed to improve the imaging qualities by restoring blurred regions, enhancing low resolution, and normalizing staining colors. The results demonstrate that Restore-GAN can significantly improve image quality, which leads to improved model robustness and performance for existing deep learning algorithms in pathology image analysis. Restore-GAN has the potential to be used to facilitate the applications of deep learning models in digital pathology analyses.
A Quantitative Comparison of Background Oriented and Conventional Schlieren Visualization

We apply background oriented schlieren (BOS) and conventional schlieren techniques to the same two flows--a turbulent heated jet and a laminar thermal plume--with the same field of view and magnification in order to directly qualitatively and quantitatively compare the two methods. While BOS is a simpler system to set up and use, the images reveal two distinct drawbacks. The flow is slightly out of focus by necessity, and so small-scale features are sacrificed. Second, visualization artifacts are present outside of the flow regions in the images. The second half of this paper analyzes these artifacts and we show that they are a direct result of camera sensor noise. Furthermore, the artifacts have a Gaussian distribution with a standard deviation proportional to the standard deviation of the sensor noise itself, which is also Gaussian distributed. Finally, we develop a method to remove the artifacts and show that it is significantly more effective than standard denoising techniques such as total variation regularization.
FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

Machine Learning is at the forefront of scientific progress in Healthcare and Medicine.To accelerate scientific discovery, it is important to have tools that allow progress iterations to be collaborative, reproducible, reusable and easily built upon without "reinventing the wheel" for each task.FuseMedML, or fuse, is a Python framework designed for accelerated Machine Learning (ML) based discovery in the medical domain.It is highly flexible and designed for easy collaboration, encouraging code reuse.Flexibility is enabled by a generic data object design where data is kept in a nested (hierarchical) Python dictionary (NDict), allowing to efficiently process and fuse information from multiple modalities.Functional components allow to specify input and output keys, to be read from and written to the nested dictionary.Easy code reuse is enabled through key components implemented as standalone packages under the main fuse repo using the same design principles.These include fuse.data-a flexible data processing pipeline, fuse.dl-reusable Deep Learning (DL) model architecture components and loss functions, and fuse.eval-a library for evaluating ML models.
DuaFace: Data uncertainty in angular based loss for face recognition

The Data Uncertainty inherently existed in feature continuous mapping space itself and the training dataset. In this paper, a general loss function DuaFace based on Data Uncertainty and Angular/cosine-margin-based loss is proposed to study the influence of Data Uncertainty on traditional Angular based loss functions. Correspondingly, insightful analysis on how incorporating Data Uncertainty estimation helps reducing the adverse effects of noisy samples and affects the process of feature learning are also provided. Moreover, extensive experiments conducted on Face Recognition demonstrate its superiority over state-of-the-arts.
Model-free forecasting of partially observable spatiotemporally chaotic systems

Reservoir computing is a powerful tool for forecasting turbulence because its simple architecture has the computational efficiency to handle high-dimensional systems. Its implementation, however, often requires full state-vector measurements and knowledge of the system nonlinearities. We use nonlinear projector functions to expand the system measurements to a high dimensional space and then feed them to a reservoir to obtain forecasts. We demonstrate the application of such reservoir computing networks on spatiotemporally chaotic systems, which model several features of turbulence. We show that using radial basis functions as nonlinear projectors enables complex system nonlinearities to be captured robustly even with only partial observations and without knowing the governing equations. Finally, we show that when measurements are sparse or incomplete and noisy, such that even the governing equations become inaccurate, our networks can still produce reasonably accurate forecasts, thus paving the way towards model-free forecasting of practical turbulent systems.
An Improved DDPG and Its Application in Spacecraft Fault Knowledge Graph

We construct a spacecraft performance-fault relationship graph of the control system, which can help space robots locate and repair spacecraft faults quickly. In order to improve the performance-fault relationship graph, we improve the Deep Deterministic Policy Gradient (DDPG) algorithm, and propose a relationship prediction method that combines representation learning reasoning with deep reinforcement learning reasoning. We take the spacecraft performance-fault relationship graph as the agent learning environment and adopt reinforcement learning to realize the optimal interaction between the agent and the environment. Meanwhile, our model uses a deep neural network to construct a complex value function and strategy function, which makes the agent have excellent perceptual decision-making ability and accurate value judgment ability. We evaluate our model on a performance-fault relationship graph of the control system. The experimental results show that our model has high prediction speed and accuracy, which can completely infer the optimal relationship path between entities to complete the spacecraft performance-fault relationship graph.
X-Wines: A Wine Dataset for Recommender Systems and Machine Learning

In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.
Research on How Executive Connections Affect Enterprise Digital Transformation: Empirical Evidence from China

In the context of the digital economy, the external connection of executives provides enterprises with a good idea to amplify their potential for digital transformation with the help of external forces. Therefore, we conduct a theoretical exploration and an empirical analysis of the relationship between executive connections and enterprise digital transformation. As the research sample, we use the A-share manufacturing companies listed in China from 2012 to 2021. According to sufficient verifications, we discover that executive connections can effectively support digital transformation. From the perspective of each subdivision dimension, executive business connections, executive technical connections, and executive financial connections can significantly promote digital transformation, among which executive technical connections have the greatest favorable impact. However, the impact of executive political connections on digital transformation is not obvious. Additionally, executive connections primarily foster enterprise digital transformation by reducing enterprise asset specificity. The results of the boundary mechanism test demonstrate that the external environmental dynamics and the internal dynamic capabilities reinforce the positive effect of executive connections on digital transformation. These findings contribute to a deeper understanding of the role of executive connections in digital transformation and provide practical guidance for firms to accelerate digital transformation.
A Semantic Model for Enhancing Data-Driven Open Banking Services

In current Open Banking services, the European Payment Services Directive (PSD2) allows the secure collection of bank customer information, on their behalf and with their consent, to analyze their financial status and needs. The PSD2 directive has lead to a massive number of daily transactions between Fintech entities which require the automatic management of the data involved, generally coming from multiple and heterogeneous sources and formats. In this context, one of the main challenges lies in defining and implementing common data integration schemes to easily merge them into knowledge-base repositories, hence allowing data reconciliation and sophisticated analysis. In this sense, Semantic Web technologies constitute a suitable framework for the semantic integration of data that makes linking with external sources possible and enhances systematic querying. With this motivation, an ontology approach is proposed in this work to operate as a semantic data mediator in real-world open banking operations. According to semantic reconciliation mechanisms, the underpinning knowledge graph is populated with data involved in PSD2 open banking transactions, which are aligned with information from invoices. A series of semantic rules is defined in this work to show how the financial solvency classification of client entities and transaction concept suggestions can be inferred from the proposed semantic model.
Enhancing the Generalization for Text Classification through Fusion of Backward Features

Generalization has always been a keyword in deep learning. Pretrained models and domain adaptation technology have received widespread attention in solving the problem of generalization. They are all focused on finding features in data to improve the generalization ability and to prevent overfitting. Although they have achieved good results in various tasks, those models are unstable when classifying a sentence whose label is positive but still contains negative phrases. In this article, we analyzed the attention heat map of the benchmarks and found that previous models pay more attention to the phrase rather than to the semantic information of the whole sentence. Moreover, we proposed a method to scatter the attention away from opposite sentiment words to avoid a one-sided judgment. We designed a two-stream network and stacked the gradient reversal layer and feature projection layer within the auxiliary network. The gradient reversal layer can reverse the gradient of features in the training stage so that the parameters are optimized following the reversed gradient in the backpropagation stage. We utilized an auxiliary network to extract the backward features and then fed them into the main network to merge them with normal features extracted by the main network. We applied this method to the three baselines of TextCNN, BERT, and RoBERTa using sentiment analysis and sarcasm detection datasets. The results show that our method can improve the sentiment analysis datasets by 0.5% and the sarcasm detection datasets by 2.1%.
Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method

In the present work, an innovative two-phase method is presented for parameter tuning in radial basis function artificial neural networks. These kinds of machine learning models find application in many scientific fields in classification problems or in function regression. In the first phase, a technique based on particle swarm optimization is performed to locate a promising interval of values for the network parameters. Particle swarm optimization was used as it is a highly reliable method for global optimization problems, and in addition, it is one of the fastest and most-flexible techniques of its class. In the second phase, the network was trained within the optimal interval using a global optimization technique such as a genetic algorithm. Furthermore, in order to speed up the training of the network and due to the use of a two-stage method, parallel programming techniques were utilized. The new method was applied to a number of famous classification and regression datasets, and the results were more than promising.
Registration of 3D medical images based on unsupervised cooperative cascade of deep networks

In this paper, a deformable registration network (DR-Net) and a multi-scale cascading strategy are designed for the registration of largely deformed 3D medical images. Our DR-Net appears as a U-shaped convolutional neural network with a pyramidal input module (PIM), a light weighted sequential Inception module and an SCAM convolutional attention module. Our multi-scale cooperative cascading strategy integrates the deformation field information within and between sub-networks at different scales to synthesize the cascaded deformation fields. To cooperatively train the cascaded network, not only the output of the final network layer but also the multi-scale outputs from different layers of the decoder in the last cascaded sub-network are used to calculate loss function. As compared with the VoxelMorph and IVTN, the average dice similarity coefficients (Dice) achieved with our DR-Net are 2.4% and 2.5% higher on the Sliver dataset and are 2.5% and 2.4% higher on the LiTS dataset. The average Dice coefficients achieved with our multi-scale cascading strategy of three DR-Nets are 1.6% and 1.9% higher than those of the VM-CR3 and are 1.5% and 1.7% higher than those of the IVTN-CR3 on these two datasets, respectively. These results show that not only our proposed DR-Net itself but also the cascade of them outperform the state-of-the-art methods and their cascades in registration accuracy.
Unpaired virtual histological staining using prior-guided generative adversarial networks

Fibrosis is an inevitable stage in the development of chronic liver disease and has an irreplaceable role in characterizing the degree of progression of chronic liver disease. Histopathological diagnosis is the gold standard for the interpretation of fibrosis parameters. Conventional hematoxylin-eosin (H&E) staining can only reflect the gross structure of the tissue and the distribution of hepatocytes, while Masson trichrome can highlight specific types of collagen fiber structure, thus providing the necessary structural information for fibrosis scoring. However, the expensive costs of time, economy, and patient specimens as well as the non-uniform preparation and staining process make the conversion of existing H&E staining into virtual Masson trichrome staining a solution for fibrosis evaluation. Existing translation approaches fail to extract fiber features accurately enough, and the decoder of staining is unable to converge due to the inconsistent color of physical staining. In this work, we propose a prior-guided generative adversarial network, based on unpaired data for effective Masson trichrome stained image generation from the corresponding H&E stained image. Conducted on a small training set, our method takes full advantage of prior knowledge to set up better constraints on both the encoder and the decoder. Experiments indicate the superior performance of our method that surpasses the previous approaches. For various liver diseases, our results demonstrate a high correlation between the staging of real and virtual stains (ρ=0.82; 95% CI: 0.73-0.89). In addition, our finetuning strategy is able to standardize the staining color and release the memory and computational burden, which can be employed in clinical assessment.