The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vita...
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Emotion analysis has been attracting researchers’ attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recogni...
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Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression s...
In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the pro...
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In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.
Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these gui...
Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum me...
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Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and ma...
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Background: Algorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. However, their accuracy and reliability in older adults after hip...
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Background: Algorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. However, their accuracy and reliability in older adults after hip fracture, who often walk slowly for short durations, is underexplored. Objective: This study examined DMO accuracy and reliability in a hip fracture cohort considering walking bout (WB) duration, physical function, days since surgery, and walking aid use. Methods: In total, 19 community-dwelling participants were real-world monitored for 2.5 hours using a lower back wearable device and a reference system combining inertial modules, distance sensors, and pressure insoles. A total of 6 DMO estimates from 164 WBs from 58% (11/19) of the participants (aged 71-90 years;assessed 32-390 days after surgery;Short Physical Performance Battery [SPPB] scores of 3-12;gait speed range 0.39-1.34 m/s) were assessed against the reference system at the WB and participant level. We stratified by WB duration (all WBs, WBs of >10 seconds, WBs of 10-30 seconds, and WBs of >30 seconds) and lower versus higher SPPB scores and observed whether days since surgery and walking aid use affected DMO accuracy and reliability. Results: Across WBs, walking speed and distance ranged from 0.25 to 1.29 m/s and from 1.7 to 436.5 m, respectively. Estimation of walking speed, cadence, stride duration, number of steps, and distance stratified by WB duration showed intraclass correlation coefficients (ICCs) ranging from 0.50 to 0.99 and mean relative errors (MREs) from –6.9% to 12.8%. Stride length estimation showed poor reliability, with ICCs ranging from 0.30 to 0.49 and MREs from 6.1% to 13.2%. Walking speed and distance ICCs in the higher–SPPB score group ranged from 0.85 to 0.99, and MREs ranged from –10.1% to –1.7%. In the lower–SPPB score group, walking speed and distance ICCs ranged from 0.17 to 0.99, and MREs ranged from 13.5% to 32.6%. There was no discernible effect of time since s
The spatial organization of “immune hubs” in tumor microenvironments has gained recognition across histologies and tissues. Bone marrow offers a valuable platform for investigating immune hubs in the context of acut...
The spatial organization of “immune hubs” in tumor microenvironments has gained recognition across histologies and tissues. Bone marrow offers a valuable platform for investigating immune hubs in the context of acute myeloid leukemia (AML) following hematopoietic stem cell transplant (HSCT). This setting may provide fresh insights into mechanisms of relapse and maintenance of remission after donor lymphocyte infusion (DLI). While it is generally understood that DLI exerts its effects through a graft-versus-leukemia (GVL) response, the specific cellular players and spatial organization driving GVL remain unidentified. Addressing this question can inform the rational design of cancer cellular therapies. A major challenge for spatial transcriptomic assays interrogating bone marrow samples is RNA degradation due to decalcification in addition to that caused by formalin fixation and paraffin embedding (FFPE). Furthermore, existing spatial platforms lack single-cell resolution and their throughput is generally limited to a small number of samples. To address these constraints, Singular Genomics is developing the G4XTM, an in-situ next-generation sequencing (NGS) platform that enables rapid sample throughput (up to 20-fold greater than existing platforms), and enables combined readouts of transcriptomics, proteomics, and fluorescent H&E staining in the same FFPE section. We used a pre-release version of the G4X to analyze bone marrow biopsies of patients with relapsed AML before and after treatment with DLI. We profiled 24 specimens (6 responder [R] pre-DLI, 7 R post-DLI, 5 non-responder [NR] pre-DLI, 6 NR post-DLI), aiming to elucidate the spatial relationships of immune cellular networks in the marrow microenvironment. Our targeted transcript panel included 153 immune- and marrow-related genes defining specific T cell subsets and other immune cell lineages, as well as 8 protein markers (CD3, CD4, CD8, CD45RA, HLA-DR, CD34, KI67, ATPase). We filtered out cells with transc
Music industry is evolving rapidly over the past decades due to the revolution of digital recordings and distributions. The industry is flooded with innumerable music albums, and production houses are always looking f...
Music industry is evolving rapidly over the past decades due to the revolution of digital recordings and distributions. The industry is flooded with innumerable music albums, and production houses are always looking for innovative strategies to present the albums to the listeners. For instance, they rely on recommendation-based systems to get attention from the listeners. Amidst the clouds of genres and tags to differentiate and organize the digital media, listeners have their own music taste and preference. By considering new-age music genre, musician networks are constructed using the data from AllMusic and *** websites. Complex network structural properties such as nodes centralities, degree distribution, and community structure of the musician networks were investigated. The results show distinct differences in terms of defining new-age music and the main styles of new-age music. Furthermore, the musicians collaboration network reveals interesting collaboration trend in the new-age music industry. These observations may provide useful information and ideas in the establishment and improvement of music recommendation websites in near future.
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