Reviewerly/exHarmony huggingface dataset:
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification

Multiple Instance Learning (MIL) and transformers are increasingly popular in histopathology Whole Slide Image (WSI) classification. However, unlike human pathologists who selectively observe specific regions of histopathology tissues under different magnifications, most methods do not incorporate multiple resolutions of the WSIs, hierarchically and attentively, thereby leading to a loss of focus on the WSIs and information from other resolutions. To resolve this issue, we propose a Hierarchical Attention-Guided Multiple Instance Learning framework to fully exploit the WSIs. This framework can dynamically and attentively discover the discriminative regions across multiple resolutions of the WSIs. Within this framework, an Integrated Attention Transformer is proposed to further enhance the performance of the transformer and obtain a more holistic WSI (bag) representation. This transformer consists of multiple Integrated Attention Modules, which is the combination of a transformer layer and an aggregation module that produces a bag representation based on every instance representation in that bag. The experimental results show that our method achieved state-of-the-art performances on multiple datasets, including Camelyon16, TCGA-RCC, TCGA-NSCLC, and an in-house IMGC dataset. The code is available at https://github.com/BearCleverProud/HAG-MIL.
Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning

Abstract Images depicting dark skin tones are significantly underrepresented in the educational materials used to teach primary care physicians and dermatologists to recognize skin diseases. This could contribute to disparities in skin disease diagnosis across different racial groups. Previously, domain experts have manually assessed textbooks to estimate the diversity in skin images. Manual assessment does not scale to many educational materials and introduces human errors. To automate this process, we present the Skin Tone Analysis for Representation in EDucational materials (STAR-ED) framework, which assesses skin tone representation in medical education materials using machine learning. Given a document (e.g., a textbook in .pdf), STAR-ED applies content parsing to extract text, images, and table entities in a structured format. Next, it identifies images containing skin, segments the skin-containing portions of those images, and estimates the skin tone using machine learning. STAR-ED was developed using the Fitzpatrick17k dataset. We then externally tested STAR-ED on four commonly used medical textbooks. Results show strong performance in detecting skin images (0.96 ± 0.02 AUROC and 0.90 ± 0.06 F 1 score) and classifying skin tones (0.87 ± 0.01 AUROC and 0.91 ± 0.00 F 1 score). STAR-ED quantifies the imbalanced representation of skin tones in four medical textbooks: brown and black skin tones (Fitzpatrick V-VI) images constitute only 10.5% of all skin images. We envision this technology as a tool for medical educators, publishers, and practitioners to assess skin tone diversity in their educational materials.
Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net

Existing salient object detection (SOD) methods mainly rely on U-shaped convolution neural networks (CNNs) with skip connections to combine the global contexts and local spatial details that are crucial for locating salient objects and refining object details, respectively. Despite great successes, the ability of CNNs in learning global contexts is limited. Recently, the vision transformer has achieved revolutionary progress in computer vision owing to its powerful modeling of global dependencies. However, directly applying the transformer to SOD is suboptimal because the transformer lacks the ability to learn local spatial representations. To this end, this paper explores the combination of transformers and CNNs to learn both global and local representations for SOD. We propose a transformer-based Asymmetric Bilateral U-Net (ABiU-Net). The asymmetric bilateral encoder has a transformer path and a lightweight CNN path, where the two paths communicate at each encoder stage to learn complementary global contexts and local spatial details, respectively. The asymmetric bilateral decoder also consists of two paths to process features from the transformer and CNN encoder paths, with communication at each decoder stage for decoding coarse salient object locations and fine-grained object details, respectively. Such communication between the two encoder/decoder paths enables AbiU-Net to learn complementary global and local representations, taking advantage of the natural merits of transformers and CNNs, respectively. Hence, ABiU-Net provides a new perspective for transformer-based SOD. Extensive experiments demonstrate that ABiU-Net performs favorably against previous state-of-the-art SOD methods. The code is available at https://github.com/yuqiuyuqiu/ABiU-Net.
Complete Characterization of Quantum Correlations by Randomized Measurements

The fact that quantum mechanics predicts stronger correlations than classical physics is an essential cornerstone of quantum information processing. Indeed, these quantum correlations are a valuable resource for various tasks, such as quantum key distribution or quantum teleportation, but characterizing these correlations in an experimental setting is a formidable task, especially in scenarios where no shared reference frames are available. By definition, quantum correlations are reference-frame independent, i.e., invariant under local transformations; this physically motivated invariance implies, however, a dedicated mathematical structure and, therefore, constitutes a roadblock for an efficient analysis of these correlations in experiments. Here we provide a method to directly measure any locally invariant property of quantum states using locally randomized measurements, and we present a detailed toolbox to analyze these correlations for two quantum bits. We implement these methods experimentally using pairs of entangled photons, characterizing their usefulness for quantum teleportation and their potential to display quantum nonlocality in its simplest form. Our results can be applied to various quantum computing platforms, allowing simple analysis of correlations between arbitrary distant qubits in the architecture.
Recommending Solution Paths for Solving Optimization Problems with Quantum Computing

Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths in an automated way. This introduces a novel abstraction layer that is required to make quantum-computing-assisted solution techniques accessible to end users without requiring a deeper knowledge of quantum technologies. State-of-the-art hybrid algorithms, encoding and decomposition techniques can be integrated in a modular manner and evaluated using problem-specific performance metrics. Equally, tools for the graphical analysis of variational quantum algorithms are developed. Classical, fault tolerant quantum and quantum-inspired methods can be included as well to ensure a fair comparison resulting in useful solution paths. We demonstrate and validate our approach on a selected set of options and illustrate its application on the capacitated vehicle routing problem (CVRP). We also identify crucial requirements and the major design challenges for the proposed abstraction layer within a quantum-assisted solution workflow for optimization problems.
Effects of Aging on Successful Object Encoding: Enhanced Semantic Representations Compensate for Impaired Visual Representations

Although episodic memory and visual processing decline substantially with healthy aging, semantic knowledge is generally spared. There is evidence that older adults' spared semantic knowledge can support episodic memory. Here, we used functional magnetic resonance imaging (fMRI) combined with representational similarity analyses (RSAs) to examine how novel visual and preexisting semantic representations at encoding predict subjective memory vividness at retrieval. Eighteen young and seventeen older adults (female and male participants) encoded images of objects during fMRI scanning and recalled these images while rating the vividness of their memories. After scanning, participants discriminated between studied images and similar lures. RSA based on a deep convolutional neural network and normative concept feature data were used to link patterns of neural activity during encoding to visual and semantic representations. Relative to young adults, the specificity of activation patterns for visual features was reduced in older adults, consistent with dedifferentiation. However, the specificity of activation patterns for semantic features was enhanced in older adults, consistent with hyperdifferentiation. Despite dedifferentiation, visual representations in early visual cortex (EVC) predicted high memory vividness in both age groups. In contrast, semantic representations in lingual gyrus (LG) and fusiform gyrus (FG) were associated with high memory vividness only in the older adults. Intriguingly, data suggests that older adults with lower specificity of visual representations in combination with higher specificity of semantic representations tended to rate their memories as more vivid. Our findings suggest that memory vividness in aging relies more on semantic representations over anterior regions, potentially compensating for age-related dedifferentiation of visual information in posterior regions. SIGNIFICANCE STATEMENT Normal aging is associated with impaired memory for events while semantic knowledge might even improve. We investigated the effects of aging on the specificity of visual and semantic information in the brain when viewing common objects and how this information enables subsequent memory vividness for these objects. Using functional magnetic resonance imaging (fMRI) combined with modeling of the stimuli we found that visual information was represented with less specificity in older than young adults while still supporting memory vividness. In contrast semantic information supported memory vividness only in older adults and especially in those individuals that had the lowest specificity of visual information. These findings provide evidence for a spared semantic memory system increasingly recruited to compensate for degraded visual representations in older age.
Personalizable AI platform for universal access to research and diagnosis in digital pathology

Background and motivation: Digital pathology has been evolving over the last years, proposing significant workflow advantages that have fostered its adoption in professional environments. Patient clinical and image data are readily available in remote data banks that can be consumed efficiently over standard communication technologies. The appearance of new imaging techniques and advanced artificial intelligence algorithms have significantly reduced the burden on medical professionals by speeding up the screening process. Despite these advancements, the usage of digital pathology in professional environments has been slowed down by poor interoperability between services resulting from a lack of standard interfaces and integrative solutions. This work addresses this issue by proposing a cloud-based digital pathology platform built on standard and open interfaces. Methods: The work proposes and describes a vendor-neutral platform that provides interfaces for managing digital slides, and medical reports, and integrating digital image analysis services compatible with existing standards. The solution integrates the open-source plugin-based Dicoogle PACS for interoperability and extensibility, which grants the proposed solution great feature customization. Results: The solution was developed in collaboration with iPATH research project partners, including the validation by medical pathologists. The result is a pure Web collaborative framework that supports both research and production environments. A total of 566 digital slides from different pathologies were successfully uploaded to the platform. Using the integration interfaces, a mitosis detection algorithm was successfully installed into the platform, and it was trained with 2400 annotations collected from breast carcinoma images. Conclusion: Interoperability is a key factor when discussing digital pathology solutions, as it facilitates their integration into existing institutions' information systems. Moreover, it improves data sharing and integration of third-party services such as image analysis services, which have become relevant in today's digital pathology workflow. The proposed solution fully embraces the DICOM standard for digital pathology, presenting an interoperable cloud-based solution that provides great feature customization thanks to its extensible architecture.
A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models

Keyphrase Extraction (KE) is a critical component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the document. Typically, a keyphrase extraction system can significantly accelerate the speed of information retrieval and help people get first-hand information from a long document quickly and accurately. Specifically, keyphrases are capable of providing semantic metadata characterizing documents and producing an overview of the content of a document. In this paper, we introduce keyphrase extraction, present a review of the recent studies based on pre-trained language models, offer interesting insights on the different approaches, highlight open issues, and give a comparative experimental study of popular supervised as well as unsupervised techniques on several datasets. To encourage more instantiations, we release the related files mentioned in this paper.
Metaverse and Skill Set: A Conceptual Investigation

The metaverse is a virtual world where people can communicate, collaborate, and transact in a shared, immersive environment. It is an amalgamation of virtual reality, augmented reality, and artificial intelligence technologies and has the potential to revolutionize the way people work, play, and socialize. As the metaverse grows in popularity, it is becoming increasingly important to understand the skills needed to successfully navigate and utilize the new technology. The metaverse will require a variety of technical skills, including those related to computer programming, networking, and digital asset management. Additionally, individuals will need to understand how to use 3D modeling and animation software, as well as the various virtual reality and augmented reality tools. It is also important to be familiar with virtual and augmented reality platforms, such as Microsoft's HoloLens and Google's Daydream. In addition to technical skills, the metaverse will demand a variety of interpersonal skills. These include the ability to effectively communicate with others in the virtual world, as well as the ability to negotiate and resolve conflicts. Furthermore, individuals will need to be able to collaborate and create content in the metaverse, as well as demonstrate leadership and teamwork. Finally, individuals will need to possess business acumen and marketing expertise to be successful in the metaverse. This includes the ability to identify monetization opportunities and create strategies for generating revenue. Additionally, individuals will need to understand how to use social media and other online tools to promote their products and services. The metaverse is an exciting new technology that promises to revolutionize the way people interact and transact with one another. To be successful in the metaverse, individuals will need to possess both technical and interpersonal skills, as well as business acumen and marketing expertise. With the right combination of skills, they will be able to capitalize on the opportunities that the metaverse presents.
Exploring Factors Influencing Organizational Adoption of Artificial Intelligence (AI) in Corporate Social Responsibility (CSR) Initiatives

Background: Globalization has resulted in social, economic, political, commercial, and technological integration. A social problem needs a global collaborative view to find a solution. Wide-ranging partnerships are essential to achieve developmental goals, with public and private partners pooling their resources and competencies. The private sector contributes by engaging in corporate social responsibility (CSR) initiatives. These initiatives can significantly impact by leveraging emerging technologies such as Artificial Intelligence (AI). While many support AI, some believe that AI is a threat to humanity. With mixed attitudes towards AI, its adoption in CSR is somewhat limited. This research leverages the Technology-Organization-Environment (TOE) framework to explore factors influencing AI adoption intention from an organizational perspective. Method: The factors were identified from a thorough literature review and mapped with Carroll's CSR framework. The theorized model was tested via a sample response of 124 Indian firms. Results: The findings of this research share insight into the influence of the nine technological, organizational, and environmental factors and dives deeper through the post-hoc analysis of the variations due to the size of the firm, public or private orientation, and industry sector. Conclusions: Along with the contributions to literature and theory, this research study has several significant contributions to firms, AI products, service companies, AI strategists, and application developers.
The Effect of Digital Transformation on Corruption: A Global Analysis

Background: This study investigates the effect of digital transformation on corruption at country level. Very few studies have empirically investigated this phenomenon. The model presented lays the relationships between socio-technical and socio-political factors and their impact on technological factors, which in turn, influence corruption. Methods: Secondary data from 139 countries collected by credible international organizations are used for the empirical analyses. The sample of 139 countries, used in this study, makes the results more robust and generalizable than those published in previous studies. PLS-SEM and multi-group analysis were used to test the hypotheses presented in the research model. Results: The results of the analysis show that digital transformation can significantly lower the corruption. The empirical analysis also demonstrates that socio-technical and socio-political factors are important in supporting the technological factors in reducing corruption. Multi-group analysis based on the GNI categorization of the World Bank revealed fine results for nations of different economic level. The significance of the relationships varies based on the development level of the country. Conclusions: This study contributes to the growing empirical base of literature on digital transformation and corruption by empirically assessing new relationships that have not been explored in the extant literature using a relatively larger sample size. The results suggest that technology plays a critical role in reducing the corruption in any country. Factors such as infrastructure, e-participation, education/human capital, laws relating to ICT, and Importance of ICT to government are found to be vital for fighting corruption.
parzer: Parse Messy Geographic Coordinates

parzer is a new package for handling messy geographic coordinates.The first version is now on CRAN, with binaries coming soon hopefully (seenote about installation below). The package recently completed rOpenScireview.&lt;strong&gt;parzer motivation&lt;/strong&gt; The idea for this package started with a tweet from Noam Ross(https://twitter.com/noamross/status/1070733367522590721) about 15 months ago.
Rationalization for explainable NLP: a survey

Recent advances in deep learning have improved the performance of many Natural Language Processing (NLP) tasks such as translation, question-answering, and text classification. However, this improvement comes at the expense of model explainability. Black-box models make it difficult to understand the internals of a system and the process it takes to arrive at an output. Numerical (LIME, Shapley) and visualization (saliency heatmap) explainability techniques are helpful; however, they are insufficient because they require specialized knowledge. These factors led rationalization to emerge as a more accessible explainable technique in NLP. Rationalization justifies a model's output by providing a natural language explanation (rationale). Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users. Since rationalization is a relatively new field, it is disorganized. As the first survey, rationalization literature in NLP from 2007 to 2022 is analyzed. This survey presents available methods, explainable evaluations, code, and datasets used across various NLP tasks that use rationalization. Further, a new subfield in Explainable AI (XAI), namely, Rational AI (RAI), is introduced to advance the current state of rationalization. A discussion on observed insights, challenges, and future directions is provided to point to promising research opportunities.
Rule-Based Out-of-Distribution Detection

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity.
Trustworthiness of Children Stories Generated by Large Language Models

Large Language Models (LLMs) have shown a tremendous capacity for generating literary text. However, their effectiveness in generating children’s stories has yet to be thoroughly examined. In this study, we evaluate the trustworthiness of children’s stories generated by LLMs using various measures, and we compare and contrast our results with both old and new children’s stories to better assess their significance. Our findings suggest that LLMs still struggle to generate children’s stories at the level of quality and nuance found in actual stories.
Are Large Language Models All You Need for Task-Oriented Dialogue?

Instruction-finetuned large language models (LLMs) gained a huge popularity recently, thanks to their ability to interact with users through conversation. In this work, we aim to evaluate their ability to complete multi-turn tasks and interact with external databases in the context of established task-oriented dialogue benchmarks. We show that in explicit belief state tracking, LLMs underperform compared to specialized task-specific models. Nevertheless, they show some ability to guide the dialogue to a successful ending through their generated responses if they are provided with correct slot values. Furthermore, this ability improves with few-shot in-domain examples.