Technical quality: This paper tackles the problem of inference and learning of factored HMMs on large sequences and with large latent dimensionality. The primary contribution of the paper is integrating several existing approaches together to enable large-scale learning of FHMMs without a loss in modeling performance. The technical details of the components of the approach (the bivariate Gaussian copula variation posterior, the recognition network, the SVI learning approach) appear to be technically correct. The experimentation touches on the correct points including the accuracy of the learned models and the scalability of the proposed approach. The accuracy of the learned models is assessed using log likelihood on held-out test data. The experiments show that the model performs similarly to the SMF approach on both simulated and real (the Bach Corals) data.

High-Performance Computing (HPC) is crucial for performing advanced computational tasks, yet their complexity often challenges users, particularly those unfamiliar with HPC-specific commands and workflows. This paper introduces Hypothetical Command Embeddings (HyCE), a novel method that extends Retrieval-Augmented Generation (RAG) by integrating real-time, user-specific HPC data, enhancing accessibility to these systems. HyCE enriches large language models (LLM) with real-time, user-specific HPC information, addressing the limitations of fine-tuned models on such data. We evaluate HyCE using an automated RAG evaluation framework, where the LLM itself creates synthetic questions from the HPC data and serves as a judge, assessing the efficacy of the extended RAG with the evaluation metrics relevant for HPC tasks. Additionally, we tackle essential security concerns, including data privacy and command execution risks, associated with deploying LLMs in HPC environments. This solution provides a scalable and adaptable approach for HPC clusters to leverage LLMs as HPC expert, bridging the gap between users and the complex systems of HPC.

Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on RFSs with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vector-type BP is a special case of set-type BP, where each RFS follows the Bernoulli process. To demonstrate the validity of developed set-type BP, we apply it to the PMB filter for SLAM, which naturally leads to new set-type BP-mapping, SLAM, multi-target tracking, and simultaneous localization and tracking filters. Finally, we explore the relationships between the vector-type BP and the proposed set-type BP PMB-SLAM implementations and show a performance gain of the proposed set-type BP PMB-SLAM filter in comparison with the vector-type BP-SLAM filter.

DB["e"]=5 The study of semantic relationships has revealed a DB["f"]=6 close connection between these relationships and DB["g"]=7 the morphological characteristics of a language. Morphology, as a subfield of linguistics, DB["h"]=8 investigates the internal structure and formation of words. By delving into the relationship between DB["i"]=9 semantic relationships and language morphology, we can gain deeper insights into how the DB["j"]=10 underlying structure of words contributes to the DB["k"]=20 interpretation and comprehension of language. This paper explores the dynamic interplay between DB["l"]=30 semantic relationships and the morphological aspects of different languages, by examining the DB["m"]=40 intricate relationship between language morphology and semantic relationships, valuable DB["n"]=50 insights can be gained regarding how the structure DB["o"]=60 of words influences language comprehension.

Katy stared at the computer monitor in stunned silence. The message displayed was monumental, something that would forever change humanity. A month ago, she was selected as the director of a joint U.S. Cyber and Space Command surveillance team tasked with analyzing Chinese space-based communications satellites utilizing quantum computing codes. The Chinese have made significant advances in quantum computing and artificial intelligence in the past ten years. There were fears in the U.S. national security enterprise that the Chinese would obtain technical dominance.

Have you ever woken up from a dream that made absolutely no sense and wondered what it was all about? A team of researchers claim the dream you've experienced is just a continuation of what is happening in your every day life - with no deeper or hidden meaning. Experts from the Nokia Bell Labs in Cambridge created a Natural Language Processing technique that can automatically analyse dreams and quantify them. According to what sleep scientists call the'continuity hypothesis,' our dreams reflect what we experience in our real lives and the new tool proves the theory. Because our dreams reflect every day life, the authors say it could be possible to build a tool that could help in mental health diagnosis and treatment.

When Gang Xu, a 46-year-old Beijing resident, needs to communicate with his Canadian tenant about rent payments or electricity bills, he opens an app called iFlytek Input in his smartphone and taps an icon that looks like a microphone, and then begins talking. The software turns his Chinese verbal messages into English text messages, and sends them to the Canadian tenant. In China, over 500 million people use iFlytek Input to overcome obstacles in communication such as the one Xu faces. Some also use it to send text messages through voice commands while driving, or to communicate with a speaker of another Chinese dialect. The app was developed by iFlytek, a Chinese AI company that applies deep learning in a range of fields such as speech recognition, natural-language processing, machine translation, and data mining (see "50 Smartest Companies 2017").

Why do 97% of marketing influencers believe the future of digital marketing will involve human marketers working with machine learning-powered automation? Thought leaders in PPC, social and mobile marketing explain in this in-depth survey. Are disciplines such as search engine marketing, social and mobile marketing all trending towards a fully automated world where artificial intelligence (AI) robots take over our jobs? In a survey of top influencers in online marketing with expertise in paid search, social and mobile, 97% of respondents suggested that the future of marketing will actually be smart marketers working hand-in-hand with machine learning-based automation solutions. To help us understand the growing role of machine learning in marketing, we spoke with some of the top influencers in the space, including Michael Brenner (@brennermichael) of Marketing Insider Group, Serena Ehrlich (@serena) of BusinessWire, Adelyn Zhou (@adelynzhou) of TOPBOTS and Chris Messina (@chrismessina), creator of the hashtag - among others.

The difficulty of finding information on the World Wide Web by browsing hypertext documents has led to the development and deployment of various search engines and indexing techniques. However, many information-gathering tasks are better handled by finding a referral to a human expert rather than by simply interacting with online information sources. A personal referral allows a user to judge the quality of the information he or she is receiving as well as to potentially obtain information that is deliberately not made public. The process of finding an expert who is both reliable and likely to respond to the user can be viewed as a search through the network of social relationships between individuals as opposed to a search through the network of hypertext documents. Project is to create models of social networks by data mining the web and develop tools that use the models to assist in locating experts and related information search and evaluation tasks.

WE NEED BETTER STANDARDS FOR AI RESEARCH The state of the art in any science includes the criteria for evaluating research. Like every other aspect of the science, it has to be developed. In my previous message I grumbled about there being insufficient basic research, but one of the reasons for this is the difficulty of evaluating whether a piece of research has made basic progress. It seems that evaluation should be based on the kind of advance the research purports to be. I haven't been able to develop a complete set of criteria, but here are some considerations.