Given the destructive impacts of tropical cyclones, it is critical to have a reliable system for cyclone intensity detection. Various techniques are available for this purpose, each with differing levels of accuracy. In this paper, we introduce two ensemble-based models based on AlexNet architecture to estimate tropical cyclone intensity using visible satellite images. The first model, trained on the entire dataset, is called the global AlexNet model. The second model is a distributed version of AlexNet in which multiple AlexNets are trained separately on subsets of the training data categorized according to the Saffir-Simpson wind speed scale prescribed by the meterologists. We evaluated the performance of both models against a deep learning benchmark model called \textit{Deepti} using a publicly available cyclone image dataset. Results indicate that both the global model (with a root mean square error (RMSE) of 9.03 knots) and the distributed model (with a RMSE of 9.3 knots) outperform the benchmark model (with a RMSE of 13.62 knots). We provide a thorough discussion of our solution approach, including an explanantion of the AlexNet's performance using gradient class activation maps (grad-CAM). Our proposed solution strategy allows future experimentation with various deep learning models in both single and multi-channel settings.

We present an empirical study of groundedness in long-form question answering (LFQA) by retrieval-augmented large language models (LLMs). In particular, we evaluate whether every generated sentence is grounded in the retrieved documents or the model's pre-training data. Across 3 datasets and 4 model families, our findings reveal that a significant fraction of generated sentences are consistently ungrounded, even when those sentences contain correct ground-truth answers. Additionally, we examine the impacts of factors such as model size, decoding strategy, and instruction tuning on groundedness. Our results show that while larger models tend to ground their outputs more effectively, a significant portion of correct answers remains compromised by hallucinations. This study provides novel insights into the groundedness challenges in LFQA and underscores the necessity for more robust mechanisms in LLMs to mitigate the generation of ungrounded content.

Shin-Etsu Chemical said Tuesday that it will build a new semiconductor materials plant in the city of Isesaki, Gunma Prefecture, at a cost of some 83 billion. The plant, slated to be completed by 2026, will make photoresists, including extreme ultraviolet resists used for state-of-the-art chips for generative artificial intelligence systems, and other semiconductor-related materials. The investment includes the cost to buy a 150,000-square-meter site for the factory. It will be the Japanese company's first new domestic production base since its plant in the city of Kamisu, Ibaraki Prefecture, was built in 1970. The Isesaki plant will also carry out research and development in the future. Currently, the company makes photoresists and related products at its plants in the prefectures of Niigata and Fukui, both along the Sea of Japan, and in Taiwan.

The intricate nature of time series data analysis benefits greatly from the distinct advantages offered by time and frequency domain representations. While the time domain is superior in representing local dependencies, particularly in non-periodic series, the frequency domain excels in capturing global dependencies, making it ideal for series with evident periodic patterns. To capitalize on both of these strengths, we propose ATFNet, an innovative framework that combines a time domain module and a frequency domain module to concurrently capture local and global dependencies in time series data. Specifically, we introduce Dominant Harmonic Series Energy Weighting, a novel mechanism for dynamically adjusting the weights between the two modules based on the periodicity of the input time series. In the frequency domain module, we enhance the traditional Discrete Fourier Transform (DFT) with our Extended DFT, designed to address the challenge of discrete frequency misalignment. Additionally, our Complex-valued Spectrum Attention mechanism offers a novel approach to discern the intricate relationships between different frequency combinations. Extensive experiments across multiple real-world datasets demonstrate that our ATFNet framework outperforms current state-of-the-art methods in long-term time series forecasting.

Mark your calendars: Tesla CEO Elon Musk suggested this afternoon that his electric automaker is going all-in on autonomous vehicle tech--and that Tesla's robotaxi will be unveiled on August 8. The announcement, posted by Musk on X Friday afternoon, capped off a weird day of reports and counter-reports that sent Tesla's stock on a roller-coaster ride, slipping down 6 points on the day before recovering in after-hours trading. Earlier in the day, Reuters reported that Tesla had canceled long-gestating plans to develop an affordable electric vehicle for the masses. The "next generation" vehicle is widely thought to be key to the electric automaker's survival, especially as competition heats up in the EV space. Instead, the news agency reported, Tesla would focus on building a robotaxi, which would use much of the same hardware as the low-cost vehicle.

Faking a political endorsement in Taiwan ahead of its crucial January election, sharing memes to amplify outrage over Japan's disposal of nuclear wastewater, and spreading conspiracy theories that claim the U.S. government was behind Hawaii's wildfire and Kentucky's train derailment last year. These are just some of the ways that China's influence operations have ramped up their use of artificial intelligence to sow disinformation and stoke discord worldwide over the last seven months, according to a new report released Friday by Microsoft Threat Intelligence. Microsoft has observed notable trends from state-backed actors, the report said, "that demonstrate not only doubling down on familiar targets, but also attempts to use more sophisticated influence techniques to achieve their goals." In particular, Chinese influence actors "experimented with new media" and "continued to refine AI-generated or AI-enhanced content." Among the operations highlighted in the report was a "a notable uptick in content featuring Taiwanese political figures ahead of the January 13 presidential and legislative elections."

Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web. In light of the challenge, we develop AutoWebGLM, a GPT-4-outperforming automated web navigation agent built upon ChatGLM3-6B. Inspired by human browsing patterns, we design an HTML simplification algorithm to represent webpages, preserving vital information succinctly. We employ a hybrid human-AI method to build web browsing data for curriculum training. Then, we bootstrap the model by reinforcement learning and rejection sampling to further facilitate webpage comprehension, browser operations, and efficient task decomposition by itself. For testing, we establish a bilingual benchmark -- AutoWebBench -- for real-world web browsing tasks. We evaluate AutoWebGLM across diverse web navigation benchmarks, revealing its improvements but also underlying challenges to tackle real environments. Related code, model, and data will be released at \url{https://github.com/THUDM/AutoWebGLM}.

Coolant leaks, space debris collisions and unplanned engine thrusts are just some of the unexpected challenges astronauts aboard the International Space Station must overcome. NASA is investigating an object that a Florida resident says came from space and plummeted into his home last month. Alejandro Otero said a piece of equipment from the International Space Station hit his Naples home and posted photos on X in response to an astronomer who was tracking where and when the equipment entered Earth's atmosphere. Otero was on vacation but said the object caused significant damage and nearly stuck his son, local outlet WINK News first reported. "My son was home when the piece tore through the roof with a loud crash that could be heard on our security cameras as well," Otero told Fox News.

Misogyny is often expressed through figurative language. Some neutral words can assume a negative connotation when functioning as pejorative epithets. Disambiguating the meaning of such terms might help the detection of misogyny. In order to address such task, we present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level. We evaluate the impact of injecting information about disambiguated words into a model targeting misogyny detection. In particular, we explore two different approaches for injection: concatenation of pejorative information and substitution of ambiguous words with univocal terms. Our experimental results, both on our corpus and on two popular benchmarks on Italian tweets, show that both approaches lead to a major classification improvement, indicating that word sense disambiguation is a promising preliminary step for misogyny detection. Furthermore, we investigate LLMs' understanding of pejorative epithets by means of contextual word embeddings analysis and prompting.

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing approaches relying on smoothness assumptions of temporal differences and simple convex optimization techniques have inherent limitations. To address these challenges, we propose a novel approach that incorporates a learning module to enhance the accuracy of the downstream task. To this end, we introduce the Gegenbauer-based graph convolutional (GegenConv) operator, which is a generalization of the conventional Chebyshev graph convolution by leveraging the theory of Gegenbauer polynomials. By deviating from traditional convex problems, we expand the complexity of the model and offer a more accurate solution for recovering time-varying graph signals. Building upon GegenConv, we design the Gegenbauer-based time Graph Neural Network (GegenGNN) architecture, which adopts an encoder-decoder structure. Likewise, our approach also utilizes a dedicated loss function that incorporates a mean squared error component alongside Sobolev smoothness regularization. This combination enables GegenGNN to capture both the fidelity to ground truth and the underlying smoothness properties of the signals, enhancing the reconstruction performance. We conduct extensive experiments on real datasets to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that GegenGNN outperforms state-of-the-art methods, showcasing its superior capability in recovering time-varying graph signals.