Pharmacovigilance (PV) is essential for drug safety, primarily focusing on adverse event monitoring. Traditionally, accessing safety data required database expertise, limiting broader use. This paper introduces a novel application of Large Language Models (LLMs) to democratize database access for non-technical users. Utilizing OpenAI's GPT-4, we developed a chatbot that generates structured query language (SQL) queries from natural language, bridging the gap between domain knowledge and technical requirements. The proposed application aims for more inclusive and efficient data access, enhancing decision making in drug safety. By providing LLMs with plain language summaries of expert knowledge, our approach significantly improves query accuracy over methods relying solely on database schemas. The application of LLMs in this context not only optimizes PV data analysis, ensuring timely and precise drug safety reporting -- a crucial component in adverse drug reaction monitoring -- but also promotes safer pharmacological practices and informed decision making across various data intensive fields.

Recent advancements in machine learning, networked intelligent systems, and wireless connectivity have paved the way for various innovative applications and use cases across various sectors, including the Internet of Things (IoT), consumer robotics, autonomous transportation, and edge computing. These systems increasingly rely on decentralized learning architectures for processing data where generated, minimizing latency and bandwidth usage while enhancing privacy . However, these benefits come with significant challenges, particularly in terms of ensuring efficient and reliable communication and processing within inherently unstable and diverse network environments. Addressing these challenges requires novel approaches that adapt to the unique demands of decentralized architectures, fostering robust and expandable solutions for real-time data processing and learning. In this work, we consider the problem of communication efficiency in federated learning (FL) [1] and in particular in serverless (fully decentralized) learning settings that operate without a central coordinating server [2-6]. Asynchronous learning, empowering each participant to conduct local training and data transmission at their own pace, is a standard and relevant design choice in decentralized network schemes [7-12]. Asynchronous and decentralized learning have an advantage when used separately from each other, manifesting as adaptability to limited resources and downsized communication overhead. Y et unfortunately, when these two paradigms are combined, their integration poses a greater challenge in achieving a unanimous global consensus, as required for instance in the development of sophisticated navigation algorithms [13]. Decentralized optimization studies in the literature often involve high "synchronization costs" due to the complexity of ensuring consensus.

Deepfake is a generative deep learning algorithm that creates or changes facial features in a very realistic way making it hard to differentiate the real from the fake features It can be used to make movies look better as well as to spread false information by imitating famous people In this paper many different ways to make a Deepfake are explained analyzed and separated categorically Using Deepfake datasets models are trained and tested for reliability through experiments Deepfakes are a type of facial manipulation that allow people to change their entire faces identities attributes and expressions The trends in the available Deepfake datasets are also discussed with a focus on how they have changed Using Deep learning a general Deepfake detection model is made Moreover the problems in making and detecting Deepfakes are also mentioned As a result of this survey it is expected that the development of new Deepfake based imaging tools will speed up in the future This survey gives indepth review of methods for manipulating images of face and various techniques to spot altered face images Four types of facial manipulation are specifically discussed which are attribute manipulation expression swap entire face synthesis and identity swap Across every manipulation category we yield information on manipulation techniques significant benchmarks for technical evaluation of counterfeit detection techniques available public databases and a summary of the outcomes of all such analyses From all of the topics in the survey we focus on the most recent development of Deepfake showing its advances and obstacles in detecting fake images

The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific problems of processing point clouds with deep neural networks, deep learning on point clouds is still in its initial stages. This study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D segmentation. In these approaches benefits, draw backs, and design mechanisms are studied and addressed. This study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets, as well as the most often used pipelines, their advantages and limits, insightful findings and intriguing future research directions.

Text embedding methods have become increasingly popular in both industrial and academic fields due to their critical role in a variety of natural language processing tasks. The significance of universal text embeddings has been further highlighted with the rise of Large Language Models (LLMs) applications such as Retrieval-Augmented Systems (RAGs). While previous models have attempted to be general-purpose, they often struggle to generalize across tasks and domains. However, recent advancements in training data quantity, quality and diversity; synthetic data generation from LLMs as well as using LLMs as backbones encourage great improvements in pursuing universal text embeddings. In this paper, we provide an overview of the recent advances in universal text embedding models with a focus on the top performing text embeddings on Massive Text Embedding Benchmark (MTEB). Through detailed comparison and analysis, we highlight the key contributions and limitations in this area, and propose potentially inspiring future research directions.

The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. We showcase the power of LLMs in handling unstructured conversational data through four case studies: (1) analyzing mental health discussions on Reddit, (2) developing a personalized chatbot for cognitive engagement in seniors, (3) summarizing medical conversation datasets, and (4) designing an AI-powered patient engagement system. These case studies demonstrate how LLMs can effectively extract insights and summarizations from unstructured dialogues and engage patients in guided, goal-oriented conversations. Leveraging LLMs for conversational analysis and generation opens new doors for many patient-centered outcomes research opportunities. However, integrating LLMs into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance. We discuss best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings. Realizing the full potential of LLMs in digital health will require close collaboration between the AI and healthcare professionals communities to address technical challenges and ensure these powerful tools' safety, efficacy, and equity.

Artificial Intelligence (AI) can potentially transform the industry, enhancing the production process and minimizing manual, repetitive tasks. Accordingly, the synergy between high-performance computing and powerful mathematical models enables the application of sophisticated data analysis procedures like Machine Learning. However, challenges exist regarding effective, efficient, and flexible processing to generate valuable knowledge. Consequently, this work comprehensively describes industrial challenges where AI can be exploited, focusing on the dairy industry. The conclusions presented can help researchers apply novel approaches for cattle monitoring and farmers by proposing advanced technological solutions to their needs.

Open Domain Question Answering (ODQA) within natural language processing involves building systems that answer factual questions using large-scale knowledge corpora. Recent advances stem from the confluence of several factors, such as large-scale training datasets, deep learning techniques, and the rise of large language models. High-quality datasets are used to train models on realistic scenarios and enable the evaluation of the system on potentially unseen data. Standardized metrics facilitate comparisons between different ODQA systems, allowing researchers to objectively track advancements in the field. Our study presents a thorough examination of the current landscape of ODQA benchmarking by reviewing 52 datasets and 20 evaluation techniques across textual and multimodal modalities. We introduce a novel taxonomy for ODQA datasets that incorporates both the modality and difficulty of the question types. Additionally, we present a structured organization of ODQA evaluation metrics along with a critical analysis of their inherent trade-offs. Our study aims to empower researchers by providing a framework for the robust evaluation of modern question-answering systems. We conclude by identifying the current challenges and outlining promising avenues for future research and development.

Large language models (LLMs) have significantly advanced various natural language processing tasks, but deploying them remains computationally expensive. Knowledge distillation (KD) is a promising solution, enabling the transfer of capabilities from larger teacher LLMs to more compact student models. Particularly, sequence-level KD, which distills rationale-based reasoning processes instead of merely final outcomes, shows great potential in enhancing students' reasoning capabilities. However, current methods struggle with sequence level KD under long-tailed data distributions, adversely affecting generalization on sparsely represented domains. We introduce the Multi-Stage Balanced Distillation (BalDistill) framework, which iteratively balances training data within a fixed computational budget. By dynamically selecting representative head domain examples and synthesizing tail domain examples, BalDistill achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models.

The critical inquiry pervading the realm of Philosophy, and perhaps extending its influence across all Humanities disciplines, revolves around the intricacies of morality and normativity. Surprisingly, in recent years, this thematic thread has woven its way into an unexpected domain, one not conventionally associated with pondering "what ought to be": the field of artificial intelligence (AI) research. Central to morality and AI, we find "alignment", a problem related to the challenges of expressing human goals and values in a manner that artificial systems can follow without leading to unwanted adversarial effects. More explicitly and with our current paradigm of AI development in mind, we can think of alignment as teaching human values to non-anthropomorphic entities trained through opaque, gradient-based learning techniques. This work addresses alignment as a technical-philosophical problem that requires solid philosophical foundations and practical implementations that bring normative theory to AI system development. To accomplish this, we propose two sets of necessary and sufficient conditions that, we argue, should be considered in any alignment process. While necessary conditions serve as metaphysical and metaethical roots that pertain to the permissibility of alignment, sufficient conditions establish a blueprint for aligning AI systems under a learning-based paradigm. After laying such foundations, we present implementations of this approach by using state-of-the-art techniques and methods for aligning general-purpose language systems. We call this framework Dynamic Normativity. Its central thesis is that any alignment process under a learning paradigm that cannot fulfill its necessary and sufficient conditions will fail in producing aligned systems.