A U.S. judge has ordered South Korean game developer Krafton to reinstate the head of one of its video game studios after ruling that he had been improperly removed as part of a takeover plan hatched by ChatGPT. WILMINGTON, DELAWARE - A Delaware judge on Monday ordered that South Korean game developer Krafton reinstate the head of one of its video game studios, ruling he had been improperly removed as part of a takeover plan hatched by ChatGPT. Krafton CEO Changhan Kim had largely followed the advice of artificial intelligence tool ChatGPT during a $250 million dispute with the leaders of the Subnautica game maker Unknown Worlds Entertainment, which Krafton had acquired, according to the ruling by Vice Chancellor Lori Will of the Court of Chancery in Delaware. Businesses and governments are scrambling for new ways to use AI, and the technology has been blamed for mass layoffs, fears of autonomous weapons and concerns about civil rights. Companies caught in takeover-related legal battles often spend millions of dollars on teams of attorneys and advisers from top-flight Wall Street firms. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

If taking a closer look at the MedDRA classification on the system organ level on its website, we can find a claim of "System Organ Classes (SOCs) which are groupings by aetiology (e.g. However, as claimed in the original paper, "It should be noted that we did not perform any preprocessing of our datasets, such as Tab. These datasets appear in MoleculeNet as well. As mentioned in the introduction in the main paper, there are also issues with inconsistent representations and undefined stereochemistry. We list an example for each in Figure 1 and Figure 1.

Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, W elQrate .

Nico (right) and Matteo Mucchetti pose with their homemade dark ride vehicle. We may earn revenue from the products available on this page and participate in affiliate programs. When 12-year-old Matteo Mucchetti mapped out an amusement-style attraction that he wanted to create in his family's basement and then showed it to his older brother Nico, the high-school sophomore was immediately sold. "This is amazing," said Nico. "Let's make it!" Matteo had sketched on paper a top-down view of the multi-room space in Bear, Delaware, where they live.

In this work, we mainly focus onthe notions ofgroup fairness.

Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning. Studies of the internal representations of Large Language Models (LLMs) support their ability to parse text when predicting the next word, while representing semantic notions independently of surface form. Yet, which data statistics make these feats possible, and how much data is required, remain largely unknown. Probabilistic context-free grammars (PCFGs) provide a tractable testbed for studying these questions. However, prior work has focused either on the post-hoc characterization of the parsing-like algorithms used by trained networks; or on the learnability of PCFGs with fixed syntax, where parsing is unnecessary. Here, we (i) introduce a tunable class of PCFGs in which both the degree of ambiguity and the correlation structure across scales can be controlled; (ii) provide a learning mechanism -- an inference algorithm inspired by the structure of deep convolutional networks -- that links learnability and sample complexity to specific language statistics; and (iii) validate our predictions empirically across deep convolutional and transformer-based architectures. Overall, we propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.

EEG recordings contain rich information about neural activity but are subject to artifacts, noise, and superficial differences due to sensors, amplifiers, and filtering. Independent component analysis and automatic labeling of independent components (ICs) enable artifact removal in EEG pipelines. Convolutional Monge Mapping Normalization (CMMN) is a recent tool used to achieve spectral conformity of EEG signals, which was shown to improve deep neural network approaches for sleep staging. Here we propose a novel extension of the CMMN method with two alternative approaches to computing the source reference spectrum the target signals are mapped to: (1) channel-averaged and $l_1$-normalized barycenter, and (2) a subject-to-subject mapping that finds the source subject with the closest spectrum to the target subject. Notably, our extension yields space-time separable filters that can be used to map between datasets with different numbers of EEG channels. We apply these filters in an IC classification task, and show significant improvement in recognizing brain versus non-brain ICs. Clinical relevance - EEG recordings are used in the diagnosis and monitoring of multiple neuropathologies, including epilepsy and psychosis. While EEG analysis can benefit from automating artifact removal through independent component analysis and labeling, differences in recording equipment and context (the presence of noise from electrical wiring and other devices) may impact the performance of machine learning models, but these differences can be minimized by appropriate spectral normalization through filtering.

Bipedal walking is one of the most important hallmarks of human that robots have been trying to mimic for many decades. Although previous control methodologies have achieved robot walking on some terrains, there is a need for a framework allowing stable and robust locomotion over a wide range of compliant surfaces. This work proposes a novel biomechanics-inspired controller that adjusts the stiffness of the legs in support for robust and dynamic bipedal locomotion over compliant terrains. First, the 3D Dual-SLIP model is extended to support for the first time locomotion over compliant surfaces with variable stiffness and damping parameters. Then, the proposed controller is compared to a Linear-Quadratic Regulator (LQR) controller, in terms of robustness on stepping on soft terrain. The LQR controller is shown to be robust only up to a moderate ground stiffness level of 174 kN/m, while it fails in lower stiffness levels. On the contrary, the proposed controller can produce stable gait in stiffness levels as low as 30 kN/m, which results in a vertical ground penetration of the leg that is deeper than 10% of its rest length. The proposed framework could advance the field of bipedal walking, by generating stable walking trajectories for a wide range of compliant terrains useful for the control of bipeds and humanoids, as well as by improving controllers for prosthetic devices with tunable stiffness.

Natural disasters remain a major challenge for Bangladesh, so real-time monitoring and quick response systems are essential. In this study, we present BanglaMM-Disaster, an end-to-end deep learning-based multimodal framework for disaster classification in Bangla, using both textual and visual data from social media. We constructed a new dataset of 5,037 Bangla social media posts, each consisting of a caption and a corresponding image, annotated into one of nine disaster-related categories. The proposed model integrates transformer-based text encoders, including BanglaBERT, mBERT, and XLM-RoBERTa, with CNN backbones such as ResNet50, DenseNet169, and MobileNetV2, to process the two modalities. Using early fusion, the best model achieves 83.76% accuracy. This surpasses the best text-only baseline by 3.84% and the image-only baseline by 16.91%. Our analysis also shows reduced misclassification across all classes, with noticeable improvements for ambiguous examples. This work fills a key gap in Bangla multimodal disaster analysis and demonstrates the benefits of combining multiple data types for real-time disaster response in low-resource settings.

Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) is a cornerstone in biomolecular analysis, offering precise identification of pathogens through unique mass spectral signatures. Yet, its reliance on labor-intensive sample preparation and multi-shot spectral averaging restricts its use to laboratory settings, rendering it impractical for real-time environmental monitoring. These limitations are especially pronounced in emerging aerosol MALDI-MS systems, where autonomous sampling generates noisy spectra for unknown aerosol analytes, requiring single-shot detection for effective analysis. Addressing these challenges, we propose the Mass Spectral Dictionary-Guided Transformer (MS-DGFormer): a data-driven framework that redefines spectral analysis by directly processing raw, minimally prepared mass spectral data. MS-DGFormer leverages a transformer architecture, designed to capture the long-range dependencies inherent in these time-series spectra. To enhance feature extraction, we introduce a novel dictionary encoder that integrates denoised spectral information derived from Singular Value Decomposition (SVD), enabling the model to discern critical biomolecular patterns from single-shot spectra with robust performance. This innovation provides a system to achieve superior pathogen identification from aerosol samples, facilitating autonomous, real-time analysis in field conditions. By eliminating the need for extensive preprocessing, our method unlocks the potential for portable, deployable MALDI-MS platforms, revolutionizing environmental pathogen detection and rapid response to biological threats.