Fox News medical contributor Marc Siegel joined'Fox News @ Night' to discuss the latest on a New York shark attack, the FDA's approval of a pill for postpartum depression and an Indiana mother who died from drinking too much water. About one in every eight women experiences symptoms of postpartum depression, also known as perinatal mood and anxiety disorders (PMADs), per CDC data -- and with a national shortage of mental health providers, many may find it difficult to get care. Researchers at the University of Texas are looking to bridge that gap by using artificial intelligence, according to a report from Kris 6 News in Texas. In partnership with the nonprofit organization Postpartum Support International, the researchers are testing a new AI chatbot that will be available to women through a free app. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Large Language Models (LLMs) are transforming the landscape of mobile intelligence. Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM. Though recent efforts have addressed the network issue induced by the vast model size, they have not practically mitigated vital challenges concerning integration with mobile devices, such as significant memory consumption and sluggish model convergence. In response to these challenges, this work introduces FwdLLM, an innovative FL protocol designed to enhance the FedLLM efficiency. The key idea of FwdLLM to employ backpropagation (BP)-free training methods, requiring devices only to execute ``perturbed inferences''. Consequently, FwdLLM delivers way better memory efficiency and time efficiency (expedited by mobile NPUs and an expanded array of participant devices). FwdLLM centers around three key designs: (1) it combines BP-free training with parameter-efficient training methods, an essential way to scale the approach to the LLM era; (2) it systematically and adaptively allocates computational loads across devices, striking a careful balance between convergence speed and accuracy; (3) it discriminatively samples perturbed predictions that are more valuable to model convergence. Comprehensive experiments with five LLMs and three NLP tasks illustrate FwdLLM's significant advantages over conventional methods, including up to three orders of magnitude faster convergence and a 14.6x reduction in memory footprint. Uniquely, FwdLLM paves the way for federated learning of billion-parameter LLMs such as LLaMA on COTS mobile devices -- a feat previously unattained.

In this paper, we introduce the DiffuseStyleGesture+, our solution for the Generation and Evaluation of Non-verbal Behavior for Embodied Agents (GENEA) Challenge 2023, which aims to foster the development of realistic, automated systems for generating conversational gestures. Participants are provided with a pre-processed dataset and their systems are evaluated through crowdsourced scoring. Our proposed model, DiffuseStyleGesture+, leverages a diffusion model to generate gestures automatically. It incorporates a variety of modalities, including audio, text, speaker ID, and seed gestures. These diverse modalities are mapped to a hidden space and processed by a modified diffusion model to produce the corresponding gesture for a given speech input. Upon evaluation, the DiffuseStyleGesture+ demonstrated performance on par with the top-tier models in the challenge, showing no significant differences with those models in human-likeness, appropriateness for the interlocutor, and achieving competitive performance with the best model on appropriateness for agent speech. This indicates that our model is competitive and effective in generating realistic and appropriate gestures for given speech. The code, pre-trained models, and demos are available at https://github.com/YoungSeng/DiffuseStyleGesture/tree/DiffuseStyleGesturePlus/BEAT-TWH-main.

Plant diseases are major causes of production losses and may have a significant impact on the agricultural sector. Detecting pests as early as possible can help increase crop yields and production efficiency. Several robotic monitoring systems have been developed allowing to collect data and provide a greater understanding of environmental processes. An agricultural robot can enable accurate timely detection of pests, by traversing the field autonomously and monitoring the entire cropped area within a field. However, in many cases it is impossible to sample all plants due to resource limitations. In this thesis, the development and evaluation of several sampling algorithms are presented to address the challenge of an agriculture-monitoring ground robot designed to locate insects in an agricultural field, where complete sampling of all the plants is infeasible. Two situations were investigated in simulation models that were specially developed as part of this thesis: where no a-priori information on the insects is available and where prior information on the insects distributions within the field is known. For the first situation, seven algorithms were tested, each utilizing an approach to sample the field without prior knowledge of it. For the second situation, we present the development and evaluation of a dynamic sampling algorithm which utilizes real-time information to prioritize sampling at suspected points, locate hot spots and adapt sampling plans accordingly. The algorithm's performance was compared to two existing algorithms using Tetranychidae insect data from previous research. Analyses revealed that the dynamic algorithm outperformed the others.

Memory is identified as a crucial human faculty that allows for the retention of visual and linguistic information within the hippocampus and neurons in the brain, which can subsequently be retrieved to address real-world challenges that arise through a lifetime of learning. The resolution of complex AI tasks through the application of acquired knowledge represents a stride toward the realization of artificial general intelligence. However, despite the prevalence of Large Language Models (LLMs) like GPT-3.5 and GPT-4 \cite{brown2020language, leiter2023chatgpt, zaitsu2023distinguishing, OpenAI2023GPT4TR} , which have displayed remarkable capabilities in language comprehension, generation, interaction, and reasoning, they are inhibited by constraints on context length that preclude the processing of extensive, continually evolving knowledge bases. This paper proposes that LLMs could be augmented through the selective integration of knowledge from external repositories, and in doing so, introduces a novel methodology for External Reasoning, exemplified by ChatPDF. Central to this approach is the establishment of a tiered policy for \textbf{External Reasoning based on Multiple LLM Interchange Assistance} in \cref{fig:overall}, where the level of support rendered is modulated across entry, intermediate, and advanced tiers based on the complexity of the query, with adjustments made in response to human feedback. A comprehensive evaluation of this methodology is conducted using multiple LLMs and the results indicate state-of-the-art performance in \cref{comparison} , surpassing existing solutions including ChatPDF.com. Moreover, the paper emphasizes that this approach is more efficient compared to the direct processing of full text by LLMs. The source code is publicly available at: \url{https://github.com/AkideLiu/ANLP}.

Pre-trained language models (PLMs) are fundamental for natural language processing applications. Most existing PLMs are not tailored to the noisy user-generated text on social media, and the pre-training does not factor in the valuable social engagement logs available in a social network. We present TwHIN-BERT, a multilingual language model productionized at Twitter, trained on in-domain data from the popular social network. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision, but also with a social objective based on the rich social engagements within a Twitter heterogeneous information network (TwHIN). Our model is trained on 7 billion tweets covering over 100 distinct languages, providing a valuable representation to model short, noisy, user-generated text. We evaluate our model on various multilingual social recommendation and semantic understanding tasks and demonstrate significant metric improvement over established pre-trained language models. We open-source TwHIN-BERT and our curated hashtag prediction and social engagement benchmark datasets to the research community.

Former New Jersey Gov. Chris Christie tore into GOP presidential candidate Vivek Ramaswamy one day after the first primary debate in Milwaukee, arguing the entrepreneur's answers showed he has "absolutely no idea what he's talking about." Christie and Ramaswamy sparred over several issues during the two-hour debate from the United States' role in funding the war in Ukraine to supporting former President Donald Trump if he's convicted. Trump praised Ramaswamy's debate performance on his social media site Truth Social. Ramaswamy also praised Trump on stage as the "best president of the 21st century." "Well, I'm stunned that as I was talking about Donald Trump and all the ways that he's let down our party and our country, that he [Trump] didn't mention me as a winner of the debate last night," Christie said Thursday on "Your World."

The advent of Large Language Models (LLM) has revolutionized the field of natural language processing, enabling significant progress in various applications. One key area of interest is the construction of Knowledge Bases (KB) using these powerful models. Knowledge bases serve as repositories of structured information, facilitating information retrieval and inference tasks. Our paper proposes LLM2KB, a system for constructing knowledge bases using large language models, with a focus on the Llama 2 architecture and the Wikipedia dataset. We perform parameter efficient instruction tuning for Llama-2-13b-chat and StableBeluga-13B by training small injection models that have only 0.05 % of the parameters of the base models using the Low-Rank Adaptation (LoRA) technique. These injection models have been trained with prompts that are engineered to utilize Wikipedia page contexts of subject entities fetched using a Dense Passage Retrieval (DPR) algorithm, to answer relevant object entities for a given subject entity and relation. Our best performing model achieved an average F1 score of 0.6185 across 21 relations in the LM-KBC challenge held at the ISWC 2023 conference.

Then, randomly selected image patches are masked and both masked and unmasked image patches are In this paper, we introduce Masked Feature Modelling fed into ViTs. The objective of the pretraining procedure (MFM), a novel approach for the unsupervised pre-training involves recovering the original visual tokens based on the of a Graph Attention Network (GAT) block. MFM utilizes masked image patches. This pretraining does not require a pretrained Visual Tokenizer to reconstruct masked features any ground-truth annotations for the employed image corpus. of objects within a video, leveraging the MiniKinetics Subsequently, the pretrained vision encoder can be dataset. We then incorporate the pre-trained GAT block into deployed and fine-tuned for various downstream tasks by a state-of-the-art bottom-up supervised video-event recognition appending lightweight task-specific layers. Although many architecture, ViGAT, to improve the model's starting studies in the image domain have experimented with this point and overall accuracy. Experimental evaluations on masking approach, masking has not yet been thoroughly the YLI-MED dataset demonstrate the effectiveness of MFM explored in the realm of video event recognition or videorelated in improving event recognition performance.

Recognizing and comprehending human actions and gestures is a crucial perception requirement for robots to interact with humans and carry out tasks in diverse domains, including service robotics, healthcare, and manufacturing. Event cameras, with their ability to capture fast-moving objects at a high temporal resolution, offer new opportunities compared to standard action recognition in RGB videos. However, previous research on event camera action recognition has primarily focused on sensor-specific network architectures and image encoding, which may not be suitable for new sensors and limit the use of recent advancements in transformer-based architectures. In this study, we employ a computationally efficient model, namely the video transformer network (VTN), which initially acquires spatial embeddings per event-frame and then utilizes a temporal self-attention mechanism. In order to better adopt the VTN for the sparse and fine-grained nature of event data, we design Event-Contrastive Loss ($\mathcal{L}_{EC}$) and event-specific augmentations. Proposed $\mathcal{L}_{EC}$ promotes learning fine-grained spatial cues in the spatial backbone of VTN by contrasting temporally misaligned frames. We evaluate our method on real-world action recognition of N-EPIC Kitchens dataset, and achieve state-of-the-art results on both protocols - testing in seen kitchen (\textbf{74.9\%} accuracy) and testing in unseen kitchens (\textbf{42.43\% and 46.66\% Accuracy}). Our approach also takes less computation time compared to competitive prior approaches, which demonstrates the potential of our framework \textit{EventTransAct} for real-world applications of event-camera based action recognition. Project Page: \url{https://tristandb8.github.io/EventTransAct_webpage/}