In the appendix, we start from the proofs of theorem 2.1 and theorem 2.2 in section A. Then, we prove the correctness of proposition 2.2 and proposition 2.3 in section B. After that, the detailed derivation of our proposed loss is provided in section C. At last, the sensitivity of some important Before providing the proofs of the theorems, we restate some important notations first. Here, we also restate some important notations first. Here, we illustrate the detailed derivation of our approximated loss for learning the discriminator. Figure 1(a) demonstrates the effects of the embeddings size (i.e., Figure 1(b) shows the effects of the number of item sample set for learning the discriminator. Figure 1(c) reports the effects of the number of item and context sample set for learning the generator.

Contemporary image restoration and super-resolution techniques effectively harness deep neural networks, markedly outperforming traditional methods. However, astrophotography presents unique challenges for deep learning due to limited training data. This work explores hybrid strategies, such as the Deep Image Prior (DIP) model, which facilitates blind training but is susceptible to overfitting, artifact generation, and instability when handling noisy images. We propose enhancements to the DIP model's baseline performance through several advanced techniques. First, we refine the model to process multiple frames concurrently, employing the Back Projection method and the TVNet model. Next, we adopt a Markov approach incorporating Monte Carlo estimation, Langevin dynamics, and a variational input technique to achieve unbiased estimates with minimal variance and counteract overfitting effectively. Collectively, these modifications reduce the likelihood of noise learning and mitigate loss function fluctuations during training, enhancing result stability. We validated our algorithm across multiple image sets of astronomical and celestial objects, achieving performance that not only mitigates limitations of Lucky Imaging, a classical computer vision technique that remains a standard in astronomical image reconstruction but surpasses the original DIP model, state of the art transformer- and diffusion-based models, underscoring the significance of our improvements.

The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025) is currently in full swing in Philadelphia. So far, delegates have been treated to tutorials, the first few of the invited talks, and an exciting variety of oral and poster presentations. We take a look at what attendees have been getting up to during the opening days of the event. I'll be presenting our #AAAI2025 tutorial tomorrow on "Symbolic Regression: Towards Interpretability and Automated Scientific Discovery"! https://t.co/UcSNYyrkAe If you're attending AAAI-25 and are interested to learn more about symbolic regression and its potential in… pic.twitter.com/yaeCpcPoQI

This week, the Times alleged that OpenAI's engineers inadvertently erased data the paper's team spent more than 150 hours extracting as potential evidence. OpenAI was able to recover much of the data, but the Times' legal team says it's still missing the original file names and folder structure. According to a declaration filed to the court Wednesday by Jennifer B. Maisel, a lawyer for the newspaper, this means the information "cannot be used to determine where the news plaintiffs' copied articles" may have been incorporated into OpenAI's artificial intelligence models. "We disagree with the characterizations made and will file our response soon," OpenAI spokesperson Jason Deutrom told WIRED in a statement. The New York Times declined to comment.

Scientists trained an AI through the eyes of a baby in an effort to teach the tech how humanity develops - amid fears it could destroy us. Researchers at New York University strapped a headcam recorder to Sam when he was just six months old through his second birthday. The footage of 250,000 words and corresponding images was fed to an AI model, which learned how to recognize different objects similar to how Sam did. The AI developed its knowledge in the same way the child did - by observing the environment, listening to nearby people and connecting dots between what was seen and heard. The experiment also determined the connection between visual and linguistic representation in the development of a child.

Log Off Movement CEO Emma Lembke and teacher Matt Miles discuss the impact of artificial intelligence on kids on "The Story." The Irish Times was forced to retract a story it ran that criticized Irish women for using fake tans after it learned the story was allegedly submitted by someone using artificial intelligence to write it. The May 11 op-ed, "Irish women's obsession with fake tan is problematic," argued that women who use fake tans mock people with naturally dark skin. The author of the article was said to be Adriana Acosta-Cortez, a 29-year-old Ecuadorian health worker from the Dublin area. "It was a breach of the trust between the Irish Times and its readers, and we are genuinely sorry," Ruadhán Mac Cormaic said in a statement, according to a report from The Guardian.

Although their presence is not as significant as projected by Sci-Fi movies from the 90s, robots are becoming essential in our daily lives with various applications in various industries and settings. For example, in the healthcare industry, robots are used for surgeries, dispensing medication, and assisting with rehabilitation. In the transportation industry, self-driving cars are beginning to become more widespread. Robots are also used in various other settings, such as agriculture, construction, and even household chores. As technology advances, we can expect more robots to be used in our daily lives.

Artificial intelligence (AI) has enormous potential to improve Air Force pilot training by providing actionable feedback to pilot trainees on the quality of their maneuvers and enabling instructor-less flying familiarization for early-stage trainees in low-cost simulators. Historically, AI challenges consisting of data, problem descriptions, and example code have been critical to fueling AI breakthroughs. The Department of the Air Force-Massachusetts Institute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed such an AI challenge using real-world Air Force flight simulator data. The Maneuver ID challenge assembled thousands of virtual reality simulator flight recordings collected by actual Air Force student pilots at Pilot Training Next (PTN). This dataset has been publicly released at Maneuver-ID.mit.edu and represents the first of its kind public release of USAF flight training data. Using this dataset, we have applied a variety of AI methods to separate "good" vs "bad" simulator data and categorize and characterize maneuvers. These data, algorithms, and software are being released as baselines of model performance for others to build upon to enable the AI ecosystem for flight simulator training.

With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals.

This paper evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neural Networks (CNNs) models and boost the fidelity and performance of the used explainer. CNNs for computer vision have shown exceptional performance in critical industries. However, it is a significant barrier when deploying CNNs due to their complexity and lack of interpretability. Recent studies to explain computer vision models have shifted from extracting low-level features (pixel-based explanations) to mid-or high-level features (concept-based explanations). The current research direction tends to use extracted features in developing approximation algorithms such as linear or decision tree models to interpret an original model. In this work, we modify one of the state-of-the-art concept-based explanations and propose an alternative framework named TreeICE. We design a systematic evaluation based on the requirements of fidelity (approximate models to original model's labels), performance (approximate models to ground-truth labels), and interpretability (meaningful of approximate models to humans). We conduct computational evaluation (for fidelity and performance) and human subject experiments (for interpretability) We find that Tree-ICE outperforms the baseline in interpretability and generates more human readable explanations in the form of a semantic tree structure. This work features how important to have more understandable explanations when interpretability is crucial.