We thank the reviewers for their thoughtful comments and insights. We are also pleased that reviewers liked the figures and interactive tool provided in the supp. Code and dataset release: Since our submission, we have indeed released all code and the Icons-8 dataset. Readability: We thank R1 for the suggestions, and will revise the specified parts to increase readability. We regret that the description of our baseline (L192-197) is short due to space limitations.

Professional development (PD) serves as the cornerstone for teacher tutors to grasp content knowledge. However, providing equitable and timely PD opportunities for teachers poses significant challenges. To address this issue, we introduce I-VIP (Intelligent Virtual Interactive Program), an intelligent tutoring platform for teacher professional development, driven by large language models (LLMs) and supported by multi-agent frameworks. This platform offers a user-friendly conversational interface and allows users to employ a variety of interactive tools to facilitate question answering, knowledge comprehension, and reflective summarization while engaging in dialogue. To underpin the functionality of this platform, including knowledge expectation analysis, response scoring and classification, and feedback generation, the multi-agent frameworks are leveraged to enhance the accuracy of judgments and mitigate the issue of missing key points.

Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims), a no-code web tool that allows users to perform clustering and similarity search over defined regions of interest using Google Earth Engine as a backend. Sims is designed to complement existing modeling tools by focusing on feature exploration rather than model creation. We demonstrate the utility of Sims through a case study analyzing simulated maize yield data in Rwanda, where we evaluate how different combinations of soil, weather, and agronomic features affect the clustering of yield response zones. Sims is open source and available at https://github.com/microsoft/Sims

If you were asked to envisage an actor, a neurotic person might spring to mind, while the thought of an salesperson may conjure up someone who is chatty and extraverted. While some consider these lazy stereotypes, a comprehensive new study suggests that such common assumptions are actually true. Using data from 68,540 people, researchers have identified the personality traits that typify more than 260 job roles. They found that actors, journalists, town planners, authors and graphic designers are among those that tend to be more neurotic. Meanwhile, PR managers, marketers, psychologists, dental assistants and film directors are generally more extraverted. 'People often have stereotypes about the personality traits typical of different jobs, and it turns out that many of these intuitions are quite accurate,' said study author Dr René Mõttus at the University of Edinburgh.

Climate change not only threatens agricultural producers but also strains financial institutions. These important food system actors include government entities tasked with both insuring grower livelihoods and supporting response to continued global warming. We use an artificial neural network to predict future maize yields in the U.S. Corn Belt, finding alarming changes to institutional risk exposure within the Federal Crop Insurance Program. Specifically, our machine learning method anticipates more frequent and more severe yield losses that would result in the annual probability of Yield Protection (YP) claims to more than double at mid-century relative to simulations without continued climate change. Furthermore, our dual finding of relatively unchanged average yields paired with decreasing yield stability reveals targeted opportunities to adjust coverage formulas to include variability. This important structural shift may help regulators support grower adaptation to continued climate change by recognizing the value of risk-reducing strategies such as regenerative agriculture. Altogether, paired with open source interactive tools for deeper investigation, our risk profile simulations fill an actionable gap in current understanding, bridging granular historic yield estimation and climate-informed prediction of future insurer-relevant loss.

Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing code representations can further enable human experts to gain an intuitive insight into the code. Unfortunately, as of today, there is no universal tool that can simultaneously visualise different types of code representations. In this paper, we introduce a tool, CodeLens, which provides a visual interaction environment that supports various representation methods and helps developers understand and explore them. CodeLens is designed to support multiple programming languages, such as Java, Python, and JavaScript, and four types of code representations, including sequence of tokens, abstract syntax tree (AST), data flow graph (DFG), and control flow graph (CFG). By using CodeLens, developers can quickly visualize the specific code representation and also obtain the represented inputs for models of code. The Web-based interface of CodeLens is available at http://www.codelens.org. The demonstration video can be found at http://www.codelens.org/demo.

Imagine that you wish to classify data consisting of tens of thousands of ex(cid:173) amples residing in a twenty thousand dimensional space. We describe the Parallel Prob(cid:173) lems Server (PPServer) and MATLAB*P. In tandem they allow users of networked computers to work transparently on large data sets from within Matlab. This work is motivated by the desire to bring the many benefits of scientific computing algorithms and computational power to machine learning researchers. We demonstrate the usefulness of the system on a number of tasks.

The idea of a robot taking your job may sound like the plot from the latest episode of Black Mirror. But experts predict it could soon become a reality for many people in the future. Researchers from the Ecole Polytechnique Fédérale de Lausanne recently developed an interactive tool that reveals which jobs are most and least likely to be taken by robots. Their findings suggest that meat packers, cleaners and builders face the highest risk of being replaced by machines, while teachers, lawyers and physicists are safe for now. So how safe is your job?

Deep Learning (DL) techniques have changed the field of computer vision significantly during the last decade, providing state-of-the-art solutions for classical tasks (e.g., object detection and image classification) and opening the doors for solving challenging new problems, such as image-to-image translation and visual question answering (VQA). The success and popularization of DL in computer vision and related areas (e.g., medical image analysis) has been fostered, in great part, by the availability of rich tools, apps and frameworks in the Python and MATLAB ecosystems. In this blog post, I will show how your team can use both MATLAB and Python effectively and provide an easy-to-follow recipe that you should allow you to leverage "the best of both worlds" when building computer vision solutions using deep learning. Python is a programming language created by Guido van Rossum in the early 1990s. It has been adopted by many data scientists and machine/deep learning researchers thanks to popular packages (e.g., scikit-learn) and frameworks (e.g., Keras, TensorFlow, PyTorch).