Background: Tissue Microarrays (TMAs) significantly increase analytical efficiency in histopathology and large-scale epidemiologic studies by allowing multiple tissue cores to be scanned on a single slide. The individual cores can be digitally extracted and then linked to metadata for analysis in a process known as de-arraying. However, TMAs often contain core misalignments and artifacts due to assembly errors, which can adversely affect the reliability of the extracted cores during the de-arraying process. Moreover, conventional approaches for TMA de-arraying rely on desktop solutions.Therefore, a robust yet flexible de-arraying method is crucial to account for these inaccuracies and ensure effective downstream analyses. Results: We developed TMA-Grid, an in-browser, zero-footprint, interactive web application for TMA de-arraying. This web application integrates a convolutional neural network for precise tissue segmentation and a grid estimation algorithm to match each identified core to its expected location. The application emphasizes interactivity, allowing users to easily adjust segmentation and gridding results. Operating entirely in the web-browser, TMA-Grid eliminates the need for downloads or installations and ensures data privacy. Adhering to FAIR principles (Findable, Accessible, Interoperable, and Reusable), the application and its components are designed for seamless integration into TMA research workflows. Conclusions: TMA-Grid provides a robust, user-friendly solution for TMA dearraying on the web. As an open, freely accessible platform, it lays the foundation for collaborative analyses of TMAs and similar histopathology imaging data. Availability: Web application: https://episphere.github.io/tma-grid Code: https://github.com/episphere/tma-grid Tutorial: https://youtu.be/miajqyw4BVk

This paper utilizes statistical data from various open datasets in Calgary to to uncover patterns and insights for community crimes, disorders, and traffic incidents. Community attributes like demographics, housing, and pet registration were collected and analyzed through geospatial visualization and correlation analysis. Strongly correlated features were identified using the chi-square test, and predictive models were built using association rule mining and machine learning algorithms. The findings suggest that crime rates are closely linked to factors such as population density, while pet registration has a smaller impact. This study offers valuable insights for city managers to enhance community safety strategies.

Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the lack of interpretability and interactivity for clinicians. To address these challenges, we propose the medical decision transformer (MeDT), a novel and versatile framework based on the goal-conditioned reinforcement learning paradigm for sepsis treatment recommendation. MeDT uses the decision transformer architecture to learn a policy for drug dosage recommendation. During offline training, MeDT utilizes collected treatment trajectories to predict administered treatments for each time step, incorporating known treatment outcomes, target acuity scores, past treatment decisions, and current and past medical states. This analysis enables MeDT to capture complex dependencies among a patient's medical history, treatment decisions, outcomes, and short-term effects on stability. Our proposed conditioning uses acuity scores to address sparse reward issues and to facilitate clinician-model interactions, enhancing decision-making. Following training, MeDT can generate tailored treatment recommendations by conditioning on the desired positive outcome (survival) and user-specified short-term stability improvements. We carry out rigorous experiments on data from the MIMIC-III dataset and use off-policy evaluation to demonstrate that MeDT recommends interventions that outperform or are competitive with existing offline reinforcement learning methods while enabling a more interpretable, personalized and clinician-directed approach.

Medical imaging technologies have undergone extensive development, enabling non-invasive visualization of clinical information. The traditional review of medical images by clinicians remains subjective, time-consuming, and prone to human error. With the recent availability of medical imaging data, quantification have become important goals in the field. Radiomics, a methodology aimed at extracting quantitative information from imaging data, has emerged as a promising approach to uncover hidden biological information and support decision-making in clinical practice. This paper presents a review of the radiomic pipeline from the clinical neuroimaging perspective, providing a detailed overview of each step with practical advice. It discusses the application of handcrafted and deep radiomics in neuroimaging, stratified by neurological diagnosis. Although radiomics shows great potential for increasing diagnostic precision and improving treatment quality in neurology, several limitations hinder its clinical implementation. Addressing these challenges requires collaborative efforts, advancements in image harmonization methods, and the establishment of reproducible and standardized pipelines with transparent reporting. By overcoming these obstacles, radiomics can significantly impact clinical neurology and enhance patient care.

-- Drug sample comparison is a process used by the French National Police to identify drug distribution networks. The current approach is based on a manual comparison done by forensic experts. In this article, we present our approach to acquire, formalise, and specify expert knowledge to improve the current process. We use an ontology coupled with logical rules to model the underlying knowledge. The different steps of our approach are designed to be reused in other application domains. The results obtained are explainable making them usable by experts in different fields. The fight against drug trafficking has been one of the French government's priorities since the end of 2019 and has led to the creation of the National Stup plan. This plan comprises 55 measures, including the use of new indicators to understand consumer habits and dealers' methods. The work described in this article is part of this plan and aims to support scientific experts in the decision-making process for narcotic profiling. As part of the fight against drug trafficking, several arrests may be made, often accompanied by seizures. Forensic experts perform several analyses on samples from a seizure. They aim to correlate different samples from different seizures to identify trafficking networks best. To do so, experts use sample matching to pair samples according to their characteristics. Paired samples constitute an ensemble called a batch. The sample characteristics used are represented by different data, namely: macroscopic data (e.g., sample dimension, drug logos), qualitative data (e.g., list of active substances), quantitative data (e.g., dosage of substances) or non-confidential seizure data (e.g., date, place of seizure). In France, such data is stored in the national STUPS database.

Treatment plans often involve multiple neurodevelopmental condition marked by difficulties in social sessions with different therapists, and the absence of a standardized interaction, communication impediments, and repetitive behaviors. This fragmented approach continue to pose significant challenges due to the variability in can impede effective communication and coordination among symptomatology and the necessity for multidisciplinary care healthcare providers, adversely affecting the quality of care. This paper investigates the potential of Artificial Furthermore, parents and caregivers may find it challenging to access Intelligence (AI) to augment the capabilities of healthcare and manage the extensive records necessary for consistent treatment, professionals and caregivers in managing ASD. We have developed further complicating the overall management of ASD. a sophisticated algorithm designed to analyze facial and bodily Artificial Intelligence (AI) presents a promising solution to the expressions during daily activities of both autistic and non-autistic complexities involved in diagnosing and treating Autism Spectrum children, leading to the development of a powerful deep learningbased Disorder (ASD) [6]. AI-powered tools have the potential to autism detection system. Our study demonstrated that AI standardize the diagnostic process by analyzing extensive datasets to models, specifically the Xception and ResNet50V2 architectures, uncover patterns and correlations that might be overlooked by human achieved high accuracy in diagnosing Autism Spectrum Disorder evaluators.

The multi-state constraint Kalman filter (MSCKF) has been proven to be more efficient than graph optimization for visual-based odometry while with similar accuracy. However, it has not yet been properly considered and studied for LiDAR-based odometry. In this paper, we propose a novel tightly coupled LiDAR-inertial odometry based on the MSCKF framework, named MSC-LIO. An efficient LiDAR same-plane-point (LSPP) tracking method, without explicit feature extraction, is present for frame-to-frame data associations. The tracked LSPPs are employed to build an LSPP measurement model, which constructs a multi-state constraint. Besides, we propose an effective point-velocity-based LiDAR-IMU time-delay (LITD) estimation method, which is derived from the proposed LSPP tracking method. Extensive experiments were conducted on both public and private datasets. The results demonstrate that the proposed MSC-LIO yields higher accuracy and efficiency than the state-of-the-art methods. The ablation experiment results indicate that the data-association efficiency is improved by nearly 3 times using the LSPP tracking method. Besides, the proposed LITD estimation method can effectively and accurately estimate the LITD.

Recent years have witnessed a trend that large language model (LLM) based text-to-speech (TTS) emerges into the mainstream due to their high naturalness and zero-shot capacity. In this paradigm, speech signals are discretized into token sequences, which are modeled by an LLM with text as prompts and reconstructed by a token-based vocoder to waveforms. Obviously, speech tokens play a critical role in LLM-based TTS models. Current speech tokens are learned in an unsupervised manner, which lacks explicit semantic information and alignment to the text. In this paper, we propose to represent speech with supervised semantic tokens, which are derived from a multilingual speech recognition model by inserting vector quantization into the encoder. Based on the tokens, we further propose a scalable zero-shot TTS synthesizer, CosyVoice, which consists of an LLM for text-to-token generation and a conditional flow matching model for token-to-speech synthesis. Experimental results show that supervised semantic tokens significantly outperform existing unsupervised tokens in terms of content consistency and speaker similarity for zero-shot voice cloning. Moreover, we find that utilizing large-scale data further improves the synthesis performance, indicating the scalable capacity of CosyVoice. To the best of our knowledge, this is the first attempt to involve supervised speech tokens into TTS models.

Keyword spotting (KWS) is one of the speech recognition tasks most sensitive to the quality of the feature representation. However, the research on KWS has traditionally focused on new model topologies, putting little emphasis on other aspects like feature extraction. This paper investigates the use of the multitaper technique to create improved features for KWS. The experimental study is carried out for different test scenarios, windows and parameters, datasets, and neural networks commonly used in embedded KWS applications. Experiment results confirm the advantages of using the proposed improved features.

With the advancements in human-robot interaction (HRI), robots are now capable of operating in close proximity and engaging in physical interactions with humans (pHRI). Likewise, contact-based pHRI is becoming increasingly common as robots are equipped with a range of sensors to perceive human motions. Despite the presence of surveys exploring various aspects of HRI and pHRI, there is presently a gap in comprehensive studies that collect, organize and relate developments across all aspects of contact-based pHRI. It has become challenging to gain a comprehensive understanding of the current state of the field, thoroughly analyze the aspects that have been covered, and identify areas needing further attention. Hence, the present survey. While it includes key developments in pHRI, a particular focus is placed on contact-based interaction, which has numerous applications in industrial, rehabilitation and medical robotics. Across the literature, a common denominator is the importance to establish a safe, compliant and human intention-oriented interaction. This endeavour encompasses aspects of perception, planning and control, and how they work together to enhance safety and reliability. Notably, the survey highlights the application of data-driven techniques: backed by a growing body of literature demonstrating their effectiveness, approaches like reinforcement learning and learning from demonstration have become key to improving robot perception and decision-making within complex and uncertain pHRI scenarios. As the field is yet in its early stage, these observations may help guide future developments and steer research towards the responsible integration of physically interactive robots into workplaces, public spaces, and elements of private life.