Reviewerly/exHarmony huggingface dataset:
Toward a Total Dataization of Anatomic Pathology: Are You Ready?

*Department of Pathology, McGill University, Montreal, Quebec †Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada The authors declare no conflict of interest. Reprints: Gilbert Bigras, MD, PhD, Cross Cancer Institute, 11560 University Avenue Edmonton, Alberta T6G 1Z2, Canada (e-mail: [email protected]).
The Linear-Gaussian Setting

In this chapter we study the linear-Gaussian setting, where the forward model (·)is linear and both the prior on 𝑢 and the distribution of the observation noise 𝜂 are Gaussian. This setting is highly amenable to analysis and arises frequently in applications. Moreover, as we will see throughout these notes, many methods employed in nonlinear or non-Gaussian settings build on ideas from the linear- Gaussian case by performing linearization or invoking Gaussian approximations.
One-dimensional convolutional neural network for mapping mineral prospectivity: A case study in Changba ore concentration area, Gansu Province

Predictive modeling of mineral prospects is a critical but challenging procedure for delineating undiscovered prospective targets in mineral exploration. In the present study, the one-dimensional convolutional neural network (1D CNN) was utilized for mineral prospectivity mapping (MPM). Local singularity analysis and deposit buffer analysis were used to determine the unfavorable metallogenic regions, and random points were taken as negative samples. Synthetic minority over-sampling technique (SMOTE) was used to generate the training data to enhance the generalization ability of the model. The effects of different hyperparameters on classification performance in 1D CNN were studied and the optimal combination of hyperparameters was determined. The average classification accuracy of this model was 96.2%, and the standard deviation was 2.14%, indicating that the model constructed by this hyperparameter set was robust. On this basis, the geochemical data of 19 elements were used as the input characteristic variable, and the prospecting prospect of lead-zinc deposits in Changba ore concentration area was predicted based on 1D CNN. The results showed that the prospectivity map of lead-zinc deposits generated by the 1D CNN model can effectively link the multivariate geochemical data with the known positions of lead-zinc deposits and greatly increase the precision of the potential exploration areas for lead-zinc deposits.
FAC-fed: Federated adaptation for fairness and concept drift aware stream classification

Abstract Federated learning is an emerging collaborative learning paradigm of Machine learning involving distributed and heterogeneous clients. Enormous collections of continuously arriving heterogeneous data residing on distributed clients require federated adaptation of efficient mining algorithms to enable fair and high-quality predictions with privacy guarantees and minimal response delay. In this context, we propose a federated adaptation that mitigates discrimination embedded in the streaming data while handling concept drifts (FAC-Fed). We present a novel adaptive data augmentation method that mitigates client-side discrimination embedded in the data during optimization, resulting in an optimized and fair centralized server. Extensive experiments on a set of publicly available streaming and static datasets confirm the effectiveness of the proposed method. To the best of our knowledge, this work is the first attempt towards fairness-aware federated adaptation for stream classification, therefore, to prove the superiority of our proposed method over state-of-the-art, we compare the centralized version of our proposed method with three centralized stream classification baseline models (FABBOO, FAHT, CSMOTE). The experimental results show that our method outperforms the current methods in terms of both discrimination mitigation and predictive performance.
Photonic quantum computing with quantum emitters

Quantum emitters enable the deterministic generation of entangled photons, a key resource for photonic quantum computing. We will talk about how these systems can be used to implement fault-tolerant quantum computing, how architectures can be tailored to them, and what are the hardware requirements needed to reach fault-tolerance.
Benchmarking Generations of You Only Look Once Architectures for Detection of Defective and Normal Long Rod Insulators

Abstract Effective infrastructure monitoring is a priority in all technical fields in this century. In high-voltage transmission networks, line inspection is one such task. Fault detection of insulators is crucial, and object detection algorithms can handle this problem. This work presents a comparison of You Only Look Once architectures. The different subtypes of the last three generations (v3, v4, and v5) are compared in terms of losses, precision, recall, and mean average precision on an open-source, augmented dataset of normal and defective insulators from the State Grid Corporation of China. The primary focus of this work is a comprehensive subtype analysis, providing a useful resource for academics and industry professionals involved in insulator detection and surveillance projects. This study aims to enhance the monitoring of insulator health and maintenance for industries relying on power grid stability. YOLOv5 subtypes are found to be the most suitable for this computer vision task, considering their mean average precision, which ranges between 98.1 and 99.0%, and a frame per second rate between 27.1 and 212.8, depending on the architecture size. While their predecessors are faster, they are less accurate. It is also discovered that, for all generations, normal-sized and large architectures generally demonstrate better accuracy. However, small architectures are noted for their significantly faster processing speeds.
A new entanglement measure based dual entropy

Quantum entropy is an important measure for describing the uncertainty of a quantum state, more uncertainty in subsystems for pure states implies stronger quantum entanglement between subsystems. Our goal in this work is to quantify bipartite entanglement and quantum entangled networks using quantum dual entropy. We first propose a type of dual entropy from Shannon entropy. We define $$S^{t}$$ -entropy entanglement based on von Neumann entropy and its complementary dual. This implies an analytic formula for two-qubit systems. We show that the monogamy properties of the $$S^{t}$$ -entropy entanglement and the entanglement of formation (EOF) are inequivalent with $$S^{t}$$ -entropy entanglement for quantum entangled networks. We finally prove a new type of entanglement polygon inequality in terms of $$S^{t}$$ -entropy entanglement for quantum entangled networks. These results show new features of multipartite entanglement in quantum information processing.
SBlocksim: A sharding-based tool for simulating consensus efficiency

With more people joining blockchain, traditional methods for reaching consensus are becoming slower.The inefficiencies of blockchain transactions could hamper rapid progress.The existing simulators ignore sharding strategy, which is a popular solution to improve consensus efficiency in blockchain.In this perspective, we propose a blockchain simulator called SBlocksim with a sharding strategy.SBlocksim can flexibly combine to simulate different types of blockchains through modularization.The main goal of SBlocksim is to allow researchers to study the relationship between efficiency and algorithms, structures, and hyperparameters in blockchain.Possible further development of SBlocksimis is also briefly discussed.
Combined Path and Motion Planning for Workspace Restricted Mobile Manipulators in Planetary Exploration

A highly restricted workspace of the robotic arm may hinder to perform safely any autonomous mobile manipulation task with planetary exploration rovers. To ensure mission safety as well as high efficiency, a coupled path and motion planner for mobile manipulation is presented in this work. First, a Fast Marching Method based path planner generates a safe trajectory to reach the goal vicinity, avoiding obstacles and non-traversable areas in the scenario. The path planner is able to control the final rover orientation to ensure that the goal is finally reachable by the arm. Second, a 3D Fast Marching Method based motion planner generates the arm joints motion profile, by creating a 3D tunnel-like cost volume surrounding the already computed rover base trajectory. This tunnel makes use of an offline-computed safe workspace of the manipulator, thus ensuring that no self-collision will occur during the planned motion. The presented algorithm has been tested with multiple simulation experiments, benchmarked with an off-the-shelf motion planning library, and validated in a field test campaign with the rover <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SherpaTT</i> of DFKI Robotics Innovation Center. The tests consisted in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SherpaTT</i> approaching an interesting area on the scenario and performing a mobile manipulation sample scanning operation. These experiments have demonstrated that the proposed motion planner increases efficiency as well as ensures mission safety. This is thanks to, on the one hand, a coordinated base-arm movement that results in maximized efficiency in time terms, and, on the other hand, considering the manipulator workspace offline in the mobile manipulation motion planner to guarantee self-collision avoidance.
Identification of Malicious Calls with Temporal Filtering

In mobile communication, private user information such as phone numbers are likely to be stole by attackers. By exploiting this information, attackers can make malicious harassment calls to bring troubles to mobile customers, leading to significant impacts on the society. In this paper, we construct a temporal filtering method to address this problem. This method analyzes the 24-h behaviors of phone numbers. According to the analysis results, it uses neural networks to simulate the behaviors of malicious calls and that of various normal calls, including express delivery, takeaway delivery and so on. With such a model, it accurately identifies malicious calls. Our experiments indicate that compared to existing methods, our approach significantly improves the accuracy and coverage respectively.
Improved Short-term Dense Bottleneck network for efficient scene analysis

Visual scene understanding mainly depends on pixel-wise classification obtained from a deep convolutional neural network. However, existing semantic segmentation models often face difficulties in real-time applications due to their large network architecture. Although there are real-time semantic segmentation models available, their shallow backbone can degrade the performance considerably. This paper introduces SDBNetV2, a lightweight semantic segmentation model designed to improve real-time performance without increasing computational costs. A key contribution is a novel Short-term Dense Bottleneck (SDB) module in the encoder, which provides varied field-of-views to capture different geometrical objects in a complex scene. Additionally, we propose dense feature refinement and improved semantic aggregation modules at the decoder end to enhance contextualization and object localization. We evaluate the proposed model’s performance on several indoor and outdoor datasets in structured and unstructured environments. The results show that SDBNetV2 achieves superior segmentation performance over other real-time models with less than 2 million parameters.
Mapping of Sustainability Impacts in the Performance Assessment of Distributed Teams Software Development

The combination of technological advances with the evolution of sustainable development has enabled software companies to take advantage of the work model of distributed teams. This model provides competitive advantages to organizations, allowing them to hire people worldwide, eliminating physical availability in the office and also optimizing working time. Based on this scenario, it is possible to identify that the sustainability aspects are aligned with the performance of the distributed teams. Thus, the aim of this study is to map out what are the sustainable factors that impact or interfere in the performance of distributed software development teams. To this end, the filtering and analysis of a Bibliographic Portfolio related to the theme was carried out, using as a research instrument the Proknow-C (Knowledge Development Process - Constructivist) methodology, which deals with the systematization of the process of search and selection of articles. After obtaining the portfolio, the bibliometric analysis of the articles and their references was used. In this process, the sustainable aspects were mapping, categorizing them into environmental, social and economic factors related to the perspectives of the distributed teams. This research focuses on the vision of environmental aspects, directing at points related to the environment and resources made available by it; social centralized in human needs, such as quality of life; economics related to the performance of operations, such as resource productivity, efficiency, cost, quality, time and others. Scientific contributions expand in demonstrating how much sustainable development demands integrated actions with a positive future vision in relation to the performance assessment of distributed teams in the scope of software development.
Recognition of Radiation Sources for Satellite Communication Based on LSTM

Recognition of radiation sources remains a higher priority mission for the battlefield intelligence support and even the electronic countermeasure. Recent space networking and the ubiquitous military encrypted satellite communication (satcom) makes the acquisition of measurement and signature intelligence (MASINT) for satcom individuals quite a necessary, which does not depend on the decryption of 0/1 bit stream or priori knowledge of communication messages. This work is dedicated to the recognition of the radiation sources for satcom by virtue of specific phase modulation signals to enhance the MASINT acquisition. A novel classification for satellite communication terminals based on Long Short-Term Memory (LSTM) deep learning method is proposed to explore the underlying features of transmission signals. By utilizing directly the normalized sampling data and designing two hidden layers of multi-memory nodes network, one well-suited deep architecture for nearly real time temporal sequence processing is finally figured out. The impact of arrangement and segmentation of the dataset on the model training is also investigated. Finally, the experimental results have demonstrated the encouraging performance of proposed method.
Multimodal Intent Recognition Based on Contrastive Learning

Multimodal intent recognition plays a critical role in service science as it enables the achievement of more intelligent and personalized services. Recently, multimodal intent recognition has become a hot topic, and some research has been carried out on this topic. However, most existing research methods focus on exploring intra-modal or inter-modal interactions, which ignore the relationship between multimodal samples. To address this issue, we propose an approach for Multimodal Intent Recognition Based on Contrastive Learning (MIRCL). Firstly, the original samples and augmented samples are separately fed into crossmodal Transformer modules for multimodal feature fusion. The resulting fused multimodal features are then used for contrastive learning. Meanwhile, the fused multimodal features of the original samples are passed into a multimodal information bottleneck (MIB) module for intent recognition. Finally, the contrastive learning task and intent recognition task are jointly trained to optimize the whole model. We carried out comprehensive validation tests based on the publically available dataset MIntRec to prove the superiority of our proposed method.
Text Analytics Applications to Support Decision Making and Performance Management – A Systematic Literature Review

An ever-growing volume of data is generated by people and companies daily. This data is a valuable asset for organizations given its potential of providing useful information regarding operational processes, consumer habits, and customers’ preferences. The performance management and decision-making processes are important organizational activities that can benefit from good quality information extracted from large-scale data generated, for instance, by customers’ reviews. The literature already presents several applications of different data types to support operations management; however, there is much potential to be explored. Therefore, a promising approach is the use of text analytics techniques, which enable extracting useful information from textual data (e.g. customers’ reviews, reports, comments on social media) to support performance management decision-making. Given these points, the objective of this study was to investigate scientific literature to identify and analyze applications of text analytics techniques to support performance management and improve decision-making within organizations. To do so, a systematic literature review (SLR) was conducted. This SLR comprised articles published in scientific journals and conferences from 2010 to 2022, available in English on the Web of Science, Scopus, and IEEE Xplore Digital Library databases. The analyses of the selected articles demonstrated the potential of text analytics applications to support performance analysis and/or improve decision-making in a variety of contexts, such as telecommunication, railways service, and academic institutions. Furthermore, the most applied text analytics techniques are sentiment analysis and topic modelling. Finally, it is worth mentioning that, as few articles remained in the content analysis phase, it may be argued that there is still a small number of applications of text analytics techniques in organizational processes. This fact, associated with an increasing volume of data generated by customers in online reviews and social media, and the great analytical potential of these techniques, points out to a promising growth of this research field and motivates future developments.
Multiscale Context Deep Hashing for Remote Sensing Image Retrieval

With the advancement of remote sensing satellites and sensor technology, the quantity and diversity of remote sensing imagery has exhibited a sustained trend of growth. Remote sensing image retrieval has gained significant attention in the realm of remote sensing. Hashing methods have been widely applied in remote sensing image retrieval due to their high computational efficiency, low storage cost and effective performance. However, existing remote sensing image retrieval methods often struggle to accurately capture the intricate information of remote sensing images. They often lack high attention to key features. The neglect of multi-scale and saliency information in remote sensing images can result in feature loss and difficulties in maintaining the balance of hash codes. In response to the issues, we introduce a multi-scale context deep hashing network (MSCDH). Firstly, we can obtain finer-grained multi-scale features and achieve a larger receptive field by incorporating the proposed multi-scale residual blocks. Then, the proposed multi-context attention modules can increase the perceptual field and suppress interference from irrelevant information by aggregating contextual information along channels and spatial dimensions. The experimental results on the UCMerced dataset and WHU-RS dataset demonstrate that the proposed method achieves state-of-the-art retrieval performance.
Identifying and quantifying carbonate minerals in quartz-illite-muscovite-dominated reservoir rocks with SWIR and LWIR spectroscopy

Secondary carbonate minerals may influence the porosity of rocks and, therefore, the quality of hydrocarbon and geothermal reservoirs. These carbonate minerals can be identified using Shortwave Infrared (SWIR) spectroscopy by their typical absorption features near 2.35 μm. However, their detection in quartz-illite-muscovite dominated reservoir rock is hampered by the overlap of diagnostic features of carbonate minerals with the absorption features of illite-muscovite minerals. In this study we used Longwave Infrared (LWIR) spectra to overcome this problem. We investigated the 6.50 and 11.30 μm carbonate features in the LWIR as well as the 2.50 μm feature at the longer wavelength margin of the SWIR to determine whether these features provide a better discrimination of carbonates in illite-muscovite-bearing rocks. We also investigated the possibility to use these features to quantify the carbonate content. We tested this on laboratory SWIR and LWIR spectra of 22 drill core samples from a Dutch reservoir sandstone of Permian age that contain variable amounts of illite-muscovite and carbonate minerals. Spectral parameters, such as the wavelength position and depth of absorption features (or reflectance peaks), were compared with (semi-) quantitative analyses of the mineral composition acquired with QEMSCAN. Our results show that the ratio of band depths of the carbonate features at 2.44 and 2.50 μm are correlated with the relative carbonate content (CIM) with Spearman correlation coefficient (ρ) of -0.87. The best predictor for the presence and content of carbonate minerals is the 6.50 μm feature, with a correlation between the peak height and the absolute total carbonate content of 0.93. Therefore, the 6.50 μm peak height can be best used for the quantification of carbonates in the quartz-illite-muscovite dominated rocks we have investigated. The results of this study are useful for porosity studies of quartz-illite-muscovite rich reservoir rock where carbonate minerals impact the rock porosity.
A Survey of Image Super-Resolution Reconstruction Based on Deep Learning

The Image super-resolution (SR) is a very important research issue in the area of computer vision and image processing. Over the past few years, image SR research has made great strides in utilizing deep learning techniques. Super-resolution image reconstruction (SRIR) refers to reconstructing high-resolution images from low-resolution images. Deep learning methods are increasingly being used in the field of image SR reconstruction. Since the deep network model can autonomously learn the mapping relationship between low-resolution images and high-resolution images, it has shown better performance in this field than traditional methods. Therefore, the method based on deep learning has become the mainstream in the field of image SR reconstruction. An extensive discussion is organized around the exploration of existing models of deep super-resolution networks in terms of reconstruction methods, structural compositions and loss functions. By comparing similarities and differences between different models, analyze the advantages and disadvantages of different methods for constructing applicable models and application scenarios. At the same time, the results of reconstructing different network models on the test dataset are compared, the future directions for research in this field are forward-looking.