Clinical trials are central to medical progress because they help improve understanding of human health and the healthcare system. They play a key role in discovering new ways to detect, prevent, or treat diseases, and it is essential that clinical trials include participants with appropriate and diverse medical backgrounds. In this paper, we propose a system that leverages Natural Language Processing (NLP) and Large Language Models (LLMs) to automate multi-label clinical text eligibility classification and summarization. The system combines feature extraction methods such as word embeddings (Word2Vec) and named entity recognition to identify relevant medical concepts, along with traditional vectorization techniques such as count vectorization and TF-IDF (Term Frequency-Inverse Document Frequency). We further explore weighted TF-IDF word embeddings that integrate both count-based and embedding-based strengths to capture term importance effectively. Multi-label classification using Random Forest and SVM models is applied to categorize documents based on eligibility criteria. Summarization techniques including TextRank, Luhn, and GPT-3 are evaluated to concisely summarize eligibility requirements. Evaluation with ROUGE scores demonstrates the effectiveness of the proposed methods. This system shows potential for automating clinical trial eligibility assessment using data-driven approaches, thereby improving research efficiency.

Large Language Models (LLMs) have rapidly become integral to numerous applications in critical domains where reliability is paramount. Despite significant advances in safety frameworks and guardrails, current protective measures exhibit crucial vulnerabilities, particularly in multilingual contexts. Existing safety systems remain susceptible to adversarial attacks in low-resource languages and through code-switching techniques, primarily due to their English-centric design. Furthermore, the development of effective multilingual guardrails is constrained by the scarcity of diverse cross-lingual training data. Even recent solutions like Llama Guard-3, while offering multilingual support, lack transparency in their decision-making processes. We address these challenges by introducing X-Guard agent, a transparent multilingual safety agent designed to provide content moderation across diverse linguistic contexts. X-Guard effectively defends against both conventional low-resource language attacks and sophisticated code-switching attacks. Our approach includes: curating and enhancing multiple open-source safety datasets with explicit evaluation rationales; employing a jury of judges methodology to mitigate individual judge LLM provider biases; creating a comprehensive multilingual safety dataset spanning 132 languages with 5 million data points; and developing a two-stage architecture combining a custom-finetuned mBART-50 translation module with an evaluation X-Guard 3B model trained through supervised finetuning and GRPO training. Our empirical evaluations demonstrate X-Guard's effectiveness in detecting unsafe content across multiple languages while maintaining transparency throughout the safety evaluation process. Our work represents a significant advancement in creating robust, transparent, and linguistically inclusive safety systems for LLMs and its integrated systems.

The Otago Exercise Program (OEP) represents a crucial rehabilitation initiative tailored for older adults, aimed at enhancing balance and strength. Despite previous efforts utilizing wearable sensors for OEP recognition, existing studies have exhibited limitations in terms of accuracy and robustness. This study addresses these limitations by employing a single waist-mounted Inertial Measurement Unit (IMU) to recognize OEP exercises among community-dwelling older adults in their daily lives. A cohort of 36 older adults participated in laboratory settings, supplemented by an additional 7 older adults recruited for at-home assessments. The study proposes a Dual-Scale Multi-Stage Temporal Convolutional Network (DS-MS-TCN) designed for two-level sequence-to-sequence classification, incorporating them in one loss function. In the first stage, the model focuses on recognizing each repetition of the exercises (micro labels). Subsequent stages extend the recognition to encompass the complete range of exercises (macro labels). The DS-MS-TCN model surpasses existing state-of-the-art deep learning models, achieving f1-scores exceeding 80% and Intersection over Union (IoU) f1-scores surpassing 60% for all four exercises evaluated. Notably, the model outperforms the prior study utilizing the sliding window technique, eliminating the need for post-processing stages and window size tuning. To our knowledge, we are the first to present a novel perspective on enhancing Human Activity Recognition (HAR) systems through the recognition of each repetition of activities.

Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. We use deformed BERT and sentence attention overcome the limitations of BERT. Our experiment result is highest performance compared to other deep learning methods.

This work follows the approach of multi - label classification for non - intrusive load monitoring (NILM) . We modify the popu lar sparse representation based classification (SRC) approach (developed for single label classification) to solve multi - label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state - of - the - art t echniques with small volume of training data . N non - intrusive load monitoring (NILM) the technical goal is to estimate the power consumption of different appliances given the aggregate smart - meter readings [1] . The broader social objective is to feedback this information to the household so that they can reduce power consumption and thereby save energy.

However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting. Applying machine learning model onto the predication of epileptic seizure co uld help us obtain a better result and there have been plenty of scientists who have been doing such works so that there are sufficient medical data provided for researchers to do training of machine learning models. In our research, we applied traditional machine learning algorithms, such as Linear SVM, Logistic Regression, KNN (K Nearest Neighbors), and Neural Networks, like CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), and LSTM (Long Short - Term Memory), for prediction. The emphasi s of our research is to compare the AUC (Area Under the Curve) and accuracy of various models. The research result indicates that machine learning has made epileptic seizure prediction an achievable reality.

With the rise of libraries such as Tensorflow 2.0, PyTorch and Fastai, implementing deep learning has become accessible to so many more people and it helps to understand the fundamentals behind deep neural networks. Hopefully this article will be of help people to people on the path of learning about deep neural networks. Back when I first learnt about neural nets and implemented my first, they were always represented as individual artificial neurons, essentially nodes with individually weighted inputs, a summed output and an activation function. When first returning into learning about deep neural networks, the concept of how this equated to matrix multiplication didn't appear obvious. Also, linked to this is why Graphics Processing Units (GPUs) and their spin-offs have helped advance deep learning results so much.

High-dimensional data acquired from biological experiments such as nextgeneration sequencingare subject to a number of confounding effects. These effects include both technical effects, such as variation across batches from instrument noiseor sample processing ("batch effects"), or institution-specific differences insample acquisition and physical handling ("institutional variability"), as well as biological effects arising from true but irrelevant differences in the biology of each sample, such as age biases in diseases. Prior work has used linear methods toadjust for such batch effects. Here, we apply contrastive metric learning by a nonlinear triplet network to optimize the ability to distinguish biologically distinct sample classes in the presence of irrelevant technical and biological variation. Usingwhole-genome cell-free DNA data from 817 patients, we demonstrate that our approach, METric learning for Confounder Control (METCC), is able to match or exceed the classification performance achieved using a best-in-class linear method(HCP) or no normalization. Critically, results from METCC appear less confounded by irrelevant technical variables like institution and batch than those from other methods even without access to high quality metadata information requiredby many existing techniques; offering hope for improved generalization.