PCWorld outlines five essential Windows Defender configuration changes to optimize security and performance on new Windows PCs. Key adjustments include disabling redundant system tray icons, turning off unnecessary "no threats found" notifications, and enabling Controlled Folder Access for ransomware protection. Strategic exclusions for trusted files and adjusting Core Isolation settings can improve performance while maintaining robust built-in antivirus protection. Windows Defender is a capable antivirus solution built into Windows itself. Unless you've installed a different antivirus program on your Windows 11 or Windows 10 PC, your PC is using it right now.

ChatGPT's new privacy policy states how the company uses cookies for tracking, to turn free users into paying subscribers. OpenAI is ready to target free users of its services with advertisements around the web, based on what it knows about them. On Thursday, OpenAI sent an email to users laying out major changes to the AI company's privacy policy in the US. "We'll now use cookies to promote OpenAI products and services on other websites," reads the email sent on April 30. "This does not impact your conversations in ChatGPT. Your conversations with ChatGPT are private and are not shared with marketing partners."

Generating chemically valid 3D molecules is hindered by discrete bond topology: small local bond errors can cause global failures (valence violations, disconnections, implausible rings), especially for drug-like molecules with long-range constraints. Many unconditional 3D generators emphasize coordinates and then infer bonds or rely on post-processing, leaving topology feasibility weakly controlled. We propose Hierarchy-Guided Latent Topology Flow (HLTF), a planner-executor model that generates bond graphs with 3D coordinates, using a latent multi-scale plan for global context and a constraint-aware sampler to suppress topology-driven failures. On QM9, HLTF achieves 98.8% atom stability and 92.9% valid-and-unique, improving PoseBusters validity to 94.0% (+0.9 over the strongest reported baseline). On GEOM-DRUGS, HLTF attains 85.5%/85.0% validity/valid-unique-novel without post-processing and 92.2%/91.2% after standardized relaxation, within 0.9 points of the best post-processed baseline. Explicit topology generation also reduces "false-valid" samples that pass RDKit sanitization but fail stricter checks.

We create GTA (a benchmark forGeneral Tool Agents) to evaluate the general tool-use ability ofLLMs inreal-worldscenarios. Who created the dataset (e.g., which team, research group) and on behalf of which entity(e.g.,company,institution,organization)?

We proceed to compute the variances of each estimator. The proof also holds for the non-zero mean case trivially. Causal model details for Section 5.2 In Section 5.2, We include a wide range of machine learning-based causal inference methods to evaluate the performance of causal error estimators. Others configs are kept as default. The others are kept as default.

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The first prominent algorithms in this line of research isADAGRAD [7,22], which uses a per-dimension learning rate based on squared pastgradients.ADAGRADachievedsignificant performance gainsincomparison toSGDwhenthe gradientsaresparse.