Antibody binding site prediction plays a pivotal role in computational immunology and therapeutic antibody design. Existing sequence or structure methods rely on single-view features and fail to identify antibody-specific binding sites on the antigens. In this paper, we propose \textbf{CAME-AB}, a novel Cross-modality Attention framework with a Mixture-of-Experts (MoE) backbone for robust antibody binding site prediction. CAME-AB integrates five biologically grounded modalities, including raw amino acid encodings, BLOSUM substitution profiles, pretrained language model embeddings, structure-aware features, and GCN-refined biochemical graphs, into a unified multimodal representation. To enhance adaptive cross-modal reasoning, we propose an \emph{adaptive modality fusion} module that learns to dynamically weight each modality based on its global relevance and input-specific contribution. A Transformer encoder combined with an MoE module further promotes feature specialization and capacity expansion. We additionally incorporate a supervised contrastive learning objective to explicitly shape the latent space geometry, encouraging intra-class compactness and inter-class separability. To improve optimization stability and generalization, we apply stochastic weight averaging during training. Extensive experiments on benchmark antibody-antigen datasets demonstrate that CAME-AB consistently outperforms strong baselines on multiple metrics, including Precision, Recall, F1-score, AUC-ROC, and MCC. Ablation studies further validate the effectiveness of each architectural component and the benefit of multimodal feature integration. The model implementation details and the codes are available on https://anonymous.4open.science/r/CAME-AB-C525

When you purchase through links in our articles, we may earn a small commission. Anthropic's Claude AI chatbot can now create and edit Office files Claude AI is now more of an active collaborator, says Anthropic. According to an announcement post, Anthropic has launched a new feature in Claude that allows you to create and edit files directly in the AI's chat--including Word documents, Excel spreadsheets, PowerPoint presentations, and PDFs. Previously, only basic file support was offered. Through a private computing environment, Claude can now write code and run programs to generate files and analyses.

CFOs are experimenting with AI use cases to free up capacity for business-critical work. Generative AI has the potential to transform the finance function. By taking on some of the more mundane tasks that can occupy a lot of time, generative AI tools can help free up capacity for more high-value strategic work. For chief financial officers, this could mean spending more time and energy on proactively advising the business on financial strategy as organizations around the world continue to weather ongoing geopolitical and financial uncertainty. CFOs can use large language models (LLMs) and generative AI tools to support everyday tasks like generating quarterly reports, communicating with investors, and formulating strategic summaries, says Andrew W. Lo, Charles E. and Susan T. Harris professor and director of the Laboratory for Financial Engineering at the MIT Sloan School of Management. "LLMs can't replace the CFO by any means, but they can take a lot of the drudgery out of the role by providing first drafts of documents that summarize key issues and outline strategic priorities."

The Download: Trump's impact on science, and meet our climate and energy honorees How Trump's policies are affecting early-career scientists--in their own words Every year MIT Technology Review celebrates accomplished young scientists, entrepreneurs, and inventors from around the world in our Innovators Under 35 list. We've just published the 2025 edition . This year, though, the context is different: The US scientific community is under attack. Since Donald Trump took office in January, his administration has fired top government scientists, targeted universities and academia, and made substantial funding cuts to the country's science and technology infrastructure. We asked our six most recent cohorts about both positive and negative impacts of the administration's new policies. Their responses provide a glimpse into the complexities of building labs, companies, and careers in today's political climate.

AI models are trained on vast swathes of data from every corner of the internet, by humans. AI models are trained on vast swathes of data from every corner of the internet, by humans. How thousands of'overworked, underpaid' humans train Google's AI to seem smart In the spring of 2024, when Rachael Sawyer, a technical writer from Texas, received a LinkedIn message from a recruiter hiring for a vague title of writing analyst, she assumed it would be similar to her previous gigs of content creation. On her first day a week later, however, her expectations went bust. Instead of writing words herself, Sawyer's job was to rate and moderate the content created by artificial intelligence. The job initially involved a mix of parsing through meeting notes and chats summarized by Google's Gemini, and, in some cases, reviewing short films made by the AI.

Discover ways to save at TopResume, including their free review service and 4-week Career Services Platform trial. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. AI is making it harder to find a job. AI-driven Application Tracking Systems (ATS) can dump your resume before a recruiter has ever seen it, even if you have all of your qualifications clearly spelled out.

Neural operators are promising surrogates for dynamical systems but when trained with standard L2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15x while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114x wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics. See our project page: https://vivekoommen.github.io/Gen4Turb/

This paper introduces a new approach to generating sample paths of unknown stochastic differential equations (SDEs) using diffusion models, a class of generative AI models commonly employed in image and video applications. Unlike the traditional Monte Carlo methods for simulating SDEs, which require explicit specifications of the drift and diffusion coefficients, our method takes a model-free, data-driven approach. Given a finite set of sample paths from an SDE, we utilize conditional diffusion models to generate new, synthetic paths of the same SDE. To demonstrate the effectiveness of our approach, we conduct a simulation experiment to compare our method with alternative benchmark ones including neural SDEs. Furthermore, in an empirical study we leverage these synthetically generated sample paths to enhance the performance of reinforcement learning algorithms for continuous-time mean-variance portfolio selection, hinting promising applications of diffusion models in financial analysis and decision-making.

Integrative analysis of multi-institutional Electronic Health Record (EHR) data enhances the reliability and generalizability of translational research by leveraging larger, more diverse patient cohorts and incorporating multiple data modalities. However, harmonizing EHR data across institutions poses major challenges due to data heterogeneity, semantic differences, and privacy concerns. To address these challenges, we introduce $\textit{PEHRT}$, a standardized pipeline for efficient EHR data harmonization consisting of two core modules: (1) data pre-processing and (2) representation learning. PEHRT maps EHR data to standard coding systems and uses advanced machine learning to generate research-ready datasets without requiring individual-level data sharing. Our pipeline is also data model agnostic and designed for streamlined execution across institutions based on our extensive real-world experience. We provide a complete suite of open source software, accompanied by a user-friendly tutorial, and demonstrate the utility of PEHRT in a variety of tasks using data from diverse healthcare systems.

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models.