To understand the growing phenomena of new vocabulary on nationwide online social media, we analyzed monthly word count time series extracted from approximately 1 billion Japanese blog articles from 2007 to 2019. In particular, we first introduced the extended logistic equation by adding one parameter to the original equation and showed that the model can consistently reproduce various patterns of actual growth curves, such as the logistic function, linear growth, and finite-time divergence. Second, by analyzing the model parameters, we found that the typical growth pattern is not only a logistic function, which often appears in various complex systems, but also a nontrivial growth curve that starts with an exponential function and asymptotically approaches a power function without a steady state. Furthermore, we observed a connection between the functional form of growth and the peak-out. Finally, we showed that the proposed model and statistical properties are also valid for Google Trends data (English, French, Spanish, and Japanese), which is a time series of the nationwide popularity of search queries.

The Internet of Things is transforming our society, providing new services that improve the quality of life and resource management. These applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power, capable of collecting and storing data from heterogeneous sources in real-time. To avoid network saturation and high delays, new architectures such as fog computing are emerging to bring computing infrastructure closer to data sources. Additionally, new data centers are needed to provide real-time Big Data and data analytics capabilities at the edge of the network, where energy efficiency needs to be considered to ensure a sustainable and effective deployment in areas of human activity. In this research, we present an IoT model based on the principles of Model-Based Systems Engineering defined using the Discrete Event System Specification formalism. The provided mathematical formalism covers the description of the entire architecture, from IoT devices to the processing units in edge data centers. Our work includes the location-awareness of user equipment, network, and computing infrastructures to optimize federated resource management in terms of delay and power consumption. We present an effective framework to assist the dimensioning and the dynamic operation of IoT data stream analytics applications, demonstrating our contributions through a driving assistance use case based on real traces and data.

The Aerospace Corporation President and CEO Steve Isakowitz said he anticipates the future of space exploration and defense will include AI-controlled satellites and permanent living on the surface of the Moon and Mars. Speaking with Fox News Digital at the Milken Global Conference on May 4, Isakowitz noted that NASA has been using artificial intelligence (AI) for many years in Mars rovers because of the time it takes to communicate back and forth with Earth. The rover needed to know where to go and how to do so safely to combat the delay. Today, with the expansion in capabilities of AI and smaller, more affordable computer chips, advanced AI tech can now be packed into the satellites orbiting Earth. "I do think we're entering an age where we're going to have hyper-intelligence satellites, satellites that will not just be dumb cameras that are looking at the Earth and just filming everything, but you could tell it what to look for. So, don't just take pictures of the Pacific Ocean. Look for these kinds of tankers or look for these kinds of ships or look for these kind of warships or these kind of airplanes where you actually have the satellite. Know what it's looking at that has the intelligence to know if it doesn't feel well," Isakowitz said.

In an unassuming office on a quiet, mostly residential street in Mountain View, California -- located eight minutes from Google's sprawling headquarters -- a couple of ex-Googlers and their team of 50 are trying to build a search engine they hope will someday rival their former employer's. The company, Neeva, was started in 2020 by Sridhar Ramaswamy, who ran Google's $162 billion advertising arm before stepping down in 2018, and Vivek Raghunathan, a former Google vice president who worked on monetizing YouTube and other parts of the company. For a few years, the startup, which has raised over $77 million from some of Silicon Valley's top investors, focused on differentiating itself from Google by shunning invasive advertising and allowing power users to pay for extra features. Then, around the end of last year, the team at Neeva watched as a chatbot called ChatGPT created by the San Francisco–based startup OpenAI went viral. ChatGPT's ability to divine answers to nearly every question with an eerily humanlike sentience made it an instant hit, unleashing a modern AI wave. Suddenly, people around the world were talking about replacing Google search with ChatGPT. After all, if a chatbot could instantly answer any question for you, why would you need a search engine that simply spat out a bunch of links for you to trawl through?

Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the significance of weakly-supervised information and fail to preserve the feature properties of multiple views, thus resulting in unsatisfactory clustering performance. To address these issues, in this paper, we propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimizes three kinds of losses during networks finetuning, including multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss. Specifically, a KL divergence based multi-view clustering loss is imposed on the common representation of multi-view data to perform heterogeneous feature optimization, multi-view weighting and clustering prediction simultaneously. Then, we innovatively propose to integrate pairwise constraints into the process of multi-view clustering by enforcing the learned multi-view representation of must-link samples (cannot-link samples) to be similar (dissimilar), such that the formed clustering architecture can be more credible. Moreover, unlike existing rivals that only preserve the encoders for each heterogeneous branch during networks finetuning, we further propose to tune the intact autoencoders frame that contains both encoders and decoders. In this way, the issue of serious corruption of view-specific and view-shared feature space could be alleviated, making the whole training procedure more stable. Through comprehensive experiments on eight popular image datasets, we demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors.

Editor's note: What follows is exclusively adapted from a longer essay that was published as a part of the Ronald Reagan Institute's Essay Series on Presidential Principles and Beliefs which is premised on the conviction that President Reagan's words and ideas hold important lessons for today. You can find more about the essay series here. At the height of the Cold War, President Ronald Reagan had the foresight to call upon the nation to support the Strategic Defense Initiative, later known as the "Star Wars" defense system, to protect the United States from a potential USSR missile attack. Due to fierce Democrat opposition, our nation never fully built out our missile defense capability and doubled down on nuclear deterrence. We have relied upon our adversaries' fear that our nuclear weapons could destroy them to deter their use of nuclear weapons against us or our allies.

International maritime crime is becoming increasingly sophisticated, often associated with wider criminal networks. Detecting maritime threats by means of fusing data purely related to physical movement (i.e., those generated by physical sensors, or hard data) is not sufficient. This has led to research and development efforts aimed at combining hard data with other types of data (especially human-generated or soft data). Existing work often assumes that input soft data is available in a structured format, or is focused on extracting certain relevant entities or concepts to accompany or annotate hard data. Much less attention has been given to extracting the rich knowledge about the situations of interest implicitly embedded in the large amount of soft data existing in unstructured formats (such as intelligence reports and news articles). In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i.e., in the form of probabilistic knowledge graphs). This will increase the accuracy of and confidence in, the extracted knowledge and facilitate subsequent reasoning and learning. To this end, we propose Maritime DeepDive, an initial prototype for the automated construction of probabilistic knowledge graphs from natural language data for the maritime domain. In this paper, we report on the current implementation of Maritime DeepDive, together with preliminary results on extracting probabilistic events from maritime piracy incidents. This pipeline was evaluated on a manually crafted gold standard, yielding promising results.

Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word's visual makeup as a series of glyphs. To quantify this effect, we conduct a series of experiments comparing character-aware vs. character-blind text encoders. In the text-only domain, we find that character-aware models provide large gains on a novel spelling task (WikiSpell). Applying our learnings to the visual domain, we train a suite of image generation models, and show that character-aware variants outperform their character-blind counterparts across a range of novel text rendering tasks (our DrawText benchmark). Our models set a much higher state-of-the-art on visual spelling, with 30+ point accuracy gains over competitors on rare words, despite training on far fewer examples.

No technology since the atomic bomb has inspired the apocalyptic imagination like artificial intelligence. Ever since ChatGPT began exhibiting glints of logical reasoning in November, the internet has been awash in doomsday scenarios. Many are self-consciously fanciful--they're meant to jar us into envisioning how badly things could go wrong if an emerging intelligence comes to understand the world, and its own goals, even a little differently from how its human creators do. One scenario, however, requires less imagination, because the first steps toward it are arguably already being taken--the gradual integration of AI into the most destructive technologies we possess today. Check out more from this issue and find your next story to read. The world's major military powers have begun a race to wire AI into warfare.

The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating and editing images. Handling generic images requires a diverse underlying generative model, hence the latest works utilize diffusion models, which were shown to surpass GANs in terms of diversity. One major drawback of diffusion models, however, is their relatively slow inference time. In this paper, we present an accelerated solution to the task of local text-driven editing of generic images, where the desired edits are confined to a user-provided mask. Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space. We first convert the LDM into a local image editor by incorporating Blended Diffusion into it. Next we propose an optimization-based solution for the inherent inability of this LDM to accurately reconstruct images. Finally, we address the scenario of performing local edits using thin masks. We evaluate our method against the available baselines both qualitatively and quantitatively and demonstrate that in addition to being faster, our method achieves better precision than the baselines while mitigating some of their artifacts.