The Beauty of Space-Filling Curves: Understanding the Hilbert CurveTowards Data Scienceon September 7, 2025 at 4:00 pm A quick journey from theory to implementation and application
The post The Beauty of Space-Filling Curves: Understanding the Hilbert Curve appeared first on Towards Data Science.
A quick journey from theory to implementation and application
The post The Beauty of Space-Filling Curves: Understanding the Hilbert Curve appeared first on Towards Data Science. Read More
Hands-On with Agents SDK: Safeguarding Input and Output with GuardrailsTowards Data Scienceon September 6, 2025 at 4:00 pm A practical exploration of how guardrails safeguard multi-agent systems in Python using OpenAI Agents SDK, Streamlit, and Pydantic
The post Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails appeared first on Towards Data Science.
A practical exploration of how guardrails safeguard multi-agent systems in Python using OpenAI Agents SDK, Streamlit, and Pydantic
The post Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails appeared first on Towards Data Science. Read More
UK AI sector growth hits record £2.9B investment AI Newson September 5, 2025 at 3:13 pm A government report has found that surging investment has driven UK AI sector growth to outpace the wider economy by 150 times since 2022. The UK’s AI sector is clearly in the throes of a boom, with revenues shattering previous records to hit £23.9 billion in the last year. The engine room of this growth
The post UK AI sector growth hits record £2.9B investment appeared first on AI News.
A government report has found that surging investment has driven UK AI sector growth to outpace the wider economy by 150 times since 2022. The UK’s AI sector is clearly in the throes of a boom, with revenues shattering previous records to hit £23.9 billion in the last year. The engine room of this growth
The post UK AI sector growth hits record £2.9B investment appeared first on AI News. Read More
Zero-Inflated Data: A Comparison of Regression ModelsTowards Data Scienceon September 5, 2025 at 1:30 pm How to detect it and which model to choose.
The post Zero-Inflated Data: A Comparison of Regression Models appeared first on Towards Data Science.
How to detect it and which model to choose.
The post Zero-Inflated Data: A Comparison of Regression Models appeared first on Towards Data Science. Read More
AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worldscs.AI updates on arXiv.org
AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worldscs.AI updates on arXiv.orgon September 5, 2025 at 4:00 am arXiv:2509.04345v1 Announce Type: cross
Abstract: Speech generation systems can produce remarkably realistic vocalisations that are often indistinguishable from human speech, posing significant authenticity challenges. Although numerous deepfake detection methods have been developed, their effectiveness in real-world environments remains unrealiable due to the domain shift between training and test samples arising from diverse human speech and fast evolving speech synthesis systems. This is not adequately addressed by current datasets, which lack real-world application challenges with diverse and up-to-date audios in both real and deep-fake categories. To fill this gap, we introduce AUDETER (AUdio DEepfake TEst Range), a large-scale, highly diverse deepfake audio dataset for comprehensive evaluation and robust development of generalised models for deepfake audio detection. It consists of over 4,500 hours of synthetic audio generated by 11 recent TTS models and 10 vocoders with a broad range of TTS/vocoder patterns, totalling 3 million audio clips, making it the largest deepfake audio dataset by scale. Through extensive experiments with AUDETER, we reveal that i) state-of-the-art (SOTA) methods trained on existing datasets struggle to generalise to novel deepfake audio samples and suffer from high false positive rates on unseen human voice, underscoring the need for a comprehensive dataset; and ii) these methods trained on AUDETER achieve highly generalised detection performance and significantly reduce detection error rate by 44.1% to 51.6%, achieving an error rate of only 4.17% on diverse cross-domain samples in the popular In-the-Wild dataset, paving the way for training generalist deepfake audio detectors. AUDETER is available on GitHub.
arXiv:2509.04345v1 Announce Type: cross
Abstract: Speech generation systems can produce remarkably realistic vocalisations that are often indistinguishable from human speech, posing significant authenticity challenges. Although numerous deepfake detection methods have been developed, their effectiveness in real-world environments remains unrealiable due to the domain shift between training and test samples arising from diverse human speech and fast evolving speech synthesis systems. This is not adequately addressed by current datasets, which lack real-world application challenges with diverse and up-to-date audios in both real and deep-fake categories. To fill this gap, we introduce AUDETER (AUdio DEepfake TEst Range), a large-scale, highly diverse deepfake audio dataset for comprehensive evaluation and robust development of generalised models for deepfake audio detection. It consists of over 4,500 hours of synthetic audio generated by 11 recent TTS models and 10 vocoders with a broad range of TTS/vocoder patterns, totalling 3 million audio clips, making it the largest deepfake audio dataset by scale. Through extensive experiments with AUDETER, we reveal that i) state-of-the-art (SOTA) methods trained on existing datasets struggle to generalise to novel deepfake audio samples and suffer from high false positive rates on unseen human voice, underscoring the need for a comprehensive dataset; and ii) these methods trained on AUDETER achieve highly generalised detection performance and significantly reduce detection error rate by 44.1% to 51.6%, achieving an error rate of only 4.17% on diverse cross-domain samples in the popular In-the-Wild dataset, paving the way for training generalist deepfake audio detectors. AUDETER is available on GitHub. Read More
Tool Masking: The Layer MCP ForgotTowards Data Scienceon September 5, 2025 at 12:00 pm Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and errors, boost speed and reliability. Start prompt engineering your tools
The post Tool Masking: The Layer MCP Forgot appeared first on Towards Data Science.
Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and errors, boost speed and reliability. Start prompt engineering your tools
The post Tool Masking: The Layer MCP Forgot appeared first on Towards Data Science. Read More
Keypoint-based Diffusion for Robotic Motion Planning on the NICOL Robotcs.AI updates on arXiv.orgon September 5, 2025 at 4:00 am arXiv:2509.04076v1 Announce Type: cross
Abstract: We propose a novel diffusion-based action model for robotic motion planning. Commonly, established numerical planning approaches are used to solve general motion planning problems, but have significant runtime requirements. By leveraging the power of deep learning, we are able to achieve good results in a much smaller runtime by learning from a dataset generated by these planners. While our initial model uses point cloud embeddings in the input to predict keypoint-based joint sequences in its output, we observed in our ablation study that it remained challenging to condition the network on the point cloud embeddings. We identified some biases in our dataset and refined it, which improved the model’s performance. Our model, even without the use of the point cloud encodings, outperforms numerical models by an order of magnitude regarding the runtime, while reaching a success rate of up to 90% of collision free solutions on the test set.
arXiv:2509.04076v1 Announce Type: cross
Abstract: We propose a novel diffusion-based action model for robotic motion planning. Commonly, established numerical planning approaches are used to solve general motion planning problems, but have significant runtime requirements. By leveraging the power of deep learning, we are able to achieve good results in a much smaller runtime by learning from a dataset generated by these planners. While our initial model uses point cloud embeddings in the input to predict keypoint-based joint sequences in its output, we observed in our ablation study that it remained challenging to condition the network on the point cloud embeddings. We identified some biases in our dataset and refined it, which improved the model’s performance. Our model, even without the use of the point cloud encodings, outperforms numerical models by an order of magnitude regarding the runtime, while reaching a success rate of up to 90% of collision free solutions on the test set. Read More
MTQA:Matrix of Thought for Enhanced Reasoning in Complex Question Answeringcs.AI updates on arXiv.orgon September 5, 2025 at 4:00 am arXiv:2509.03918v1 Announce Type: cross
Abstract: Complex Question Answering (QA) is a fundamental and challenging task in NLP. While large language models (LLMs) exhibit impressive performance in QA, they suffer from significant performance degradation when facing complex and abstract QA tasks due to insufficient reasoning capabilities. Works such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT) aim to enhance LLMs’ reasoning abilities, but they face issues such as in-layer redundancy in tree structures and single paths in chain structures. Although some studies utilize Retrieval-Augmented Generation (RAG) methods to assist LLMs in reasoning, the challenge of effectively utilizing large amounts of information involving multiple entities and hops remains critical. To address this, we propose the Matrix of Thought (MoT), a novel and efficient LLM thought structure. MoT explores the problem in both horizontal and vertical dimensions through the “column-cell communication” mechanism, enabling LLMs to actively engage in multi-strategy and deep-level thinking, reducing redundancy within the column cells and enhancing reasoning capabilities. Furthermore, we develop a fact-correction mechanism by constructing knowledge units from retrieved knowledge graph triples and raw text to enhance the initial knowledge for LLM reasoning and correct erroneous answers. This leads to the development of an efficient and accurate QA framework (MTQA). Experimental results show that our framework outperforms state-of-the-art methods on four widely-used datasets in terms of F1 and EM scores, with reasoning time only 14.4% of the baseline methods, demonstrating both its efficiency and accuracy. The code for this framework is available at https://github.com/lyfiter/mtqa.
arXiv:2509.03918v1 Announce Type: cross
Abstract: Complex Question Answering (QA) is a fundamental and challenging task in NLP. While large language models (LLMs) exhibit impressive performance in QA, they suffer from significant performance degradation when facing complex and abstract QA tasks due to insufficient reasoning capabilities. Works such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT) aim to enhance LLMs’ reasoning abilities, but they face issues such as in-layer redundancy in tree structures and single paths in chain structures. Although some studies utilize Retrieval-Augmented Generation (RAG) methods to assist LLMs in reasoning, the challenge of effectively utilizing large amounts of information involving multiple entities and hops remains critical. To address this, we propose the Matrix of Thought (MoT), a novel and efficient LLM thought structure. MoT explores the problem in both horizontal and vertical dimensions through the “column-cell communication” mechanism, enabling LLMs to actively engage in multi-strategy and deep-level thinking, reducing redundancy within the column cells and enhancing reasoning capabilities. Furthermore, we develop a fact-correction mechanism by constructing knowledge units from retrieved knowledge graph triples and raw text to enhance the initial knowledge for LLM reasoning and correct erroneous answers. This leads to the development of an efficient and accurate QA framework (MTQA). Experimental results show that our framework outperforms state-of-the-art methods on four widely-used datasets in terms of F1 and EM scores, with reasoning time only 14.4% of the baseline methods, demonstrating both its efficiency and accuracy. The code for this framework is available at https://github.com/lyfiter/mtqa. Read More
Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Supportcs.AI updates on arXiv.orgon September 5, 2025 at 4:00 am arXiv:2509.03741v1 Announce Type: cross
Abstract: Eye-tracking offers rich insights into student cognition and engagement, but remains underutilized in classroom-facing educational technology due to challenges in data interpretation and accessibility. In this paper, we present the iterative design and evaluation of a gaze-based learning analytics dashboard for English Language Arts (ELA), developed through five studies involving teachers and students. Guided by user-centered design and data storytelling principles, we explored how gaze data can support reflection, formative assessment, and instructional decision-making. Our findings demonstrate that gaze analytics can be approachable and pedagogically valuable when supported by familiar visualizations, layered explanations, and narrative scaffolds. We further show how a conversational agent, powered by a large language model (LLM), can lower cognitive barriers to interpreting gaze data by enabling natural language interactions with multimodal learning analytics. We conclude with design implications for future EdTech systems that aim to integrate novel data modalities in classroom contexts.
arXiv:2509.03741v1 Announce Type: cross
Abstract: Eye-tracking offers rich insights into student cognition and engagement, but remains underutilized in classroom-facing educational technology due to challenges in data interpretation and accessibility. In this paper, we present the iterative design and evaluation of a gaze-based learning analytics dashboard for English Language Arts (ELA), developed through five studies involving teachers and students. Guided by user-centered design and data storytelling principles, we explored how gaze data can support reflection, formative assessment, and instructional decision-making. Our findings demonstrate that gaze analytics can be approachable and pedagogically valuable when supported by familiar visualizations, layered explanations, and narrative scaffolds. We further show how a conversational agent, powered by a large language model (LLM), can lower cognitive barriers to interpreting gaze data by enabling natural language interactions with multimodal learning analytics. We conclude with design implications for future EdTech systems that aim to integrate novel data modalities in classroom contexts. Read More
Boosting Your Anomaly Detection With LLMsTowards Data Scienceon September 4, 2025 at 1:00 pm The 7 emerging application patterns you should know
The post Boosting Your Anomaly Detection With LLMs appeared first on Towards Data Science.
The 7 emerging application patterns you should know
The post Boosting Your Anomaly Detection With LLMs appeared first on Towards Data Science. Read More