Neural operator surrogates for time-dependent partial differential equations (PDEs) conventionally employ autoregressive prediction schemes, which accumulate error over long rollouts and require uniform temporal discretization. We introduce the Continuous Flow Operator (CFO), a framework that learns continuous-time PDE dynamics without the computational burden of standard continuous approaches, e.g., neural ODE. The key insight is repurposing flow matching to directly learn the right-hand side of PDEs without backpropagating through ODE solvers. CFO fits temporal splines to trajectory data, using finite-difference estimates of time derivatives at knots to construct probability paths whose velocities closely approximate the true PDE dynamics. A neural operator is then trained via flow matching to predict these analytic velocity fields. This approach is inherently time-resolution invariant: training accepts trajectories sampled on arbitrary, non-uniform time grids while inference queries solutions at any temporal resolution through ODE integration. Across four benchmarks (Lorenz, 1D Burgers, 2D diffusion-reaction, 2D shallow water), CFO demonstrates superior long-horizon stability and remarkable data efficiency. CFO trained on only 25% of irregularly subsampled time points outperforms autoregressive baselines trained on complete data, with relative error reductions up to 87%. Despite requiring numerical integration at inference, CFO achieves competitive efficiency, outperforming autoregressive baselines using only 50% of their function evaluations, while uniquely enabling reverse-time inference and arbitrary temporal querying.

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 hippocampus, a critical brain structure involved in memory processing and various neurodegenerative and psychiatric disorders, comprises three key subregions: the dentate gyrus (DG), Cornu Ammonis 1 (CA1), and Cornu Ammonis 3 (CA3). Accurate segmentation of these subregions from histol ogical tissue images is essential for advancing our understanding of disease mechanisms, developmental dynamics, and therapeutic interventions. However, no existing methods address the automated segmentation of hippocampal subregions from tissue images, pa rticularly from immunohistochemistry (IHC) images. To bridge this gap, we introduce a novel set of four comprehensive murine hippocampal IHC datasets featuring distinct staining modalities: cFos, NeuN, and multiplexed stains combining cFos, NeuN, and eithe r ΔFosB or GAD 67, capturing structural, neuronal activity, and plasticity associated information. Additionally, we propose ROIsGAN, a region - guided U - Net - based generative adversarial network tailored for hippocampal subregion segmentation. By leveraging ad versarial learning, ROIsGAN enhances boundary delineation and structural detail refinement through a novel region guided discriminator loss combining Dice and binary cross - entropy loss. Evaluated across DG, CA1, and CA3 subregions, ROIsGAN consistently out performs conventional segmentation models, achieving performance gains ranging from 1 - 10% in Dice score and up to 11% in Intersection over Union (IoU), particularly under challenging staining conditions. Our work establishes foundational datasets and metho ds for automated hippocampal segmentation, enabling scalable, high - precision analysis of tissue images in neuroscience research. I. INTRODUCTION The hippocampus is one of the most extensively studied areas in the brain because of its significant functional role in memory processing, its remarkable plasticity, and its involvement in This paper is submitted for review on May 13, 2025. Sayed Mehedi Azim is with the Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 18103, USA (e - mail: sayedmehedi.azim@rutgers.edu).

This article explores the dynamic influence of computational entities based on multi-agent systems theory (SMA) combined with large language models (LLM), which are characterized by their ability to simulate complex human interactions, as a possibility to revolutionize human user interaction from the use of specialized artificial agents to support everything from operational organizational processes to strategic decision making based on applied knowledge and human orchestration. Previous investigations reveal that there are limitations, particularly in the autonomous approach of artificial agents, especially when dealing with new challenges and pragmatic tasks such as inducing logical reasoning and problem solving. It is also considered that traditional techniques, such as the stimulation of chains of thoughts, require explicit human guidance. In our approach we employ agents developed from large language models (LLM), each with distinct prototyping that considers behavioral elements, driven by strategies that stimulate the generation of knowledge based on the use case proposed in the scenario (role-play) business, using a discussion approach between agents (guided conversation). We demonstrate the potential of developing agents useful for organizational strategies, based on multi-agent system theories (SMA) and innovative uses based on large language models (LLM based), offering a differentiated and adaptable experiment to different applications, complexities, domains, and capabilities from LLM. Keywords: Multi-Agent Systems (SMA), Artificial Intelligence (AI), Large Language Models (LLM), Artificial Agents

Datarails, the FP&A platform for Excel users, has launched FP&A Genius, "the ChatGPT" for the CFO's Office giving companies unprecedented instant insights about budgets, forecasts, variance, and spend. FP&A Genius provides generative AI answers based on complete and consolidated finance data from across a company. The chat function allows executives to quickly answer questions such as'How did revenue compare 2022 vs 2021?', 'What will happen to the bottom line if inflation is 2x the rate today?' and'How will results be impacted if revenue grows by 10% next year?' Recommended AI: The Future of AI Is Here. Now Let's Make It Ethical The AI feature benefits from the release of FinanceOS also released by Datarails today.

There's been a lot of discussion on the use of artificial intelligence and the future of work. Will human creativity be usurped by bots? How will A.I. be incorporated into the finance function? These are just some of the questions organizations will face. I asked Sayan Chakraborty, copresident at Workday (sponsor of CFO Daily), who also leads the product and technology organization, for his perspective on a balance between tech and human capabilities.

In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a neural network (NN)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse carrier frequency offset (CFO) estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. We further show that PRONTO is able to achieve the same performance in new environments without the need to re-train the CNNs. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.

Top uses for AI in the financial services sector include'next-best action' systems; portfolio optimization; and fraud detection. The recent attention showered on ChatGPT, the natural- language processing tool developed by Microsoft-backed OpenAI that can reportedly mimic advanced conversations and writing tasks, was likely not lost on CFOs. Financial executives are increasingly aware that they must strike a delicate balance between supporting C-suite enthusiasm for AI initiatives while conserving scarce resources. AI's long-term potential, however, is leading executives to prioritize it, according to a study released by tech firm Nvidia this month. The percentage of financial services executives surveyed who said their executive leadership teams value and believe in AI has more than doubled to 64% from 36% of those polled last year, the Santa Clara, Calif.-based Nvidia reported.

The latest Deloitte UK CFO survey, for the third quarter of 2022, demonstrates just how badly external economic factors are impacting businesses. Some 91% of CFOs expect operating margins to decline over the next 12 months, and cost reduction and cash control are now their top priorities. Faced with these challenges, finance departments need to shift away from dealing with basic accountancy tasks, and instead become more strategic, identifying ways to add value to the business to ensure survival through the economic crisis. One of the best ways to achieve this is with technology that helps CFOs not only predict, forecast and plan, but also apply automation to reduce manual finance tasks. Through its enterprise performance management (EPM) and analytics technology, Oracle offers capabilities around planning, budgeting, forecasting and reporting, as well as risk management and automation.

Passionate tech innovator, speaker and obsessed with hyperautomation and providing IDP solutions to customers. With artificial intelligence (AI) surrounding and affecting most of us in many aspects of our daily lives, it raises a question: How will it change leadership for businesses around the world? Gartner, Inc. forecasts that the worldwide AI software market will reach $62 billion this year and that "the top five use case categories for AI software spending in 2022 will be knowledge management, virtual assistants, autonomous vehicles, digital workplace and crowdsourced data." PwC reports that by 2030, AI will potentially contribute $15.7 trillion to the global economy. The colossal potential for AI technology will sweep across the world and change the needs and requirements of businesses and employees.