We propose a new operator defined between two tensors, the broadcast product. The broadcast product calculates the Hadamard product after duplicating elements to align the shapes of the two tensors. Complex tensor operations in libraries like \texttt{numpy} can be succinctly represented as mathematical expressions using the broadcast product. Finally, we propose a novel tensor decomposition using the broadcast product, highlighting its potential applications in dimensionality reduction.

Reinforcement Learning from Human Feedback aligns the outputs of Large Language Models with human values and preferences. Central to this process is the reward model (RM), which translates human feedback into training signals for optimising LLM behaviour. However, RMs can develop biases by exploiting spurious correlations in their training data, such as favouring outputs based on length or style rather than true quality. These biases can lead to incorrect output rankings, sub-optimal model evaluations, and the amplification of undesirable behaviours in LLMs alignment. This paper addresses the challenge of correcting such biases without additional data and training, introducing the concept of Post-hoc Reward Calibration. We first propose an intuitive approach to estimate the bias term and, thus, remove it to approximate the underlying true reward. We then extend the approach to a more general and robust form with the Locally Weighted Regression. Focusing on the prevalent length bias, we validate our proposed approaches across three experimental settings, demonstrating consistent improvements: (1) a 3.11 average performance gain across 33 reward models on the RewardBench dataset; (2) enhanced alignment of RM rankings with GPT-4 evaluations and human preferences based on the AlpacaEval benchmark; and (3) improved Length-Controlled win rate of the RLHF process in multiple LLM--RM combinations. Our method is computationally efficient and generalisable to other types of bias and RMs, offering a scalable and robust solution for mitigating biases in LLM alignment. Our code and results are available at https://github.com/ZeroYuHuang/Reward-Calibration.

We introduce SeaSplat, a method to enable real-time rendering of underwater scenes leveraging recent advances in 3D radiance fields. Underwater scenes are challenging visual environments, as rendering through a medium such as water introduces both range and color dependent effects on image capture. We constrain 3D Gaussian Splatting (3DGS), a recent advance in radiance fields enabling rapid training and real-time rendering of full 3D scenes, with a physically grounded underwater image formation model. Applying SeaSplat to the real-world scenes from SeaThru-NeRF dataset, a scene collected by an underwater vehicle in the US Virgin Islands, and simulation-degraded real-world scenes, not only do we see increased quantitative performance on rendering novel viewpoints from the scene with the medium present, but are also able to recover the underlying true color of the scene and restore renders to be without the presence of the intervening medium. We show that the underwater image formation helps learn scene structure, with better depth maps, as well as show that our improvements maintain the significant computational improvements afforded by leveraging a 3D Gaussian representation.

This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management. The book focuses on simplifying the complex mathematical concepts behind deep learning, offering intuitive visualizations and practical case studies to help readers understand how neural networks and technologies like Convolutional Neural Networks (CNNs) work. It introduces several classic models and technologies such as Transformers, GPT, ResNet, BERT, and YOLO, highlighting their applications in fields like natural language processing, image recognition, and autonomous driving. The book also emphasizes the importance of pre-trained models and how they can enhance model performance and accuracy, with instructions on how to apply these models in various real-world scenarios. Additionally, it provides an overview of key big data management technologies like SQL and NoSQL databases, as well as distributed computing frameworks such as Apache Hadoop and Spark, explaining their importance in managing and processing vast amounts of data. Ultimately, the book underscores the value of mastering deep learning and big data management skills as critical tools for the future workforce, making it an essential resource for both beginners and experienced professionals.

Abstract-- This paper addresses the challenge of developing a multi-arm quadrupedal robot capable of efficiently harvesting fruit in complex, natural environments. To overcome the inherent limitations of traditional bimanual manipulation, we introduce the first three-arm quadrupedal robot LocoHarv-3 and propose a novel hierarchical tri-manual planning approach, enabling automated fruit harvesting with collision-free trajectories. Our comprehensive semi-autonomous framework integrates teleoperation, supported by LiDAR-based odometry and mapping, with learning-based visual perception for accurate fruit detection and pose estimation. Validation is conducted through a series of controlled indoor experiments using motion capture and extensive field tests in natural settings. Results demonstrate a 90% success rate in in-lab settings with a single attempt, and field trials further verify the system's robustness and efficiency in more challenging real-world environments.

Ontological commitment, i.e., used concepts, relations, and assumptions, are a corner stone of qualitative reasoning (QR) models. The state-of-the-art for processing raw inputs, though, are deep neural networks (DNNs), nowadays often based off from multimodal foundation models. These automatically learn rich representations of concepts and respective reasoning. Unfortunately, the learned qualitative knowledge is opaque, preventing easy inspection, validation, or adaptation against available QR models. So far, it is possible to associate pre-defined concepts with latent representations of DNNs, but extractable relations are mostly limited to semantic similarity. As a next step towards QR for validation and verification of DNNs: Concretely, we propose a method that extracts the learned superclass hierarchy from a multimodal DNN for a given set of leaf concepts. Under the hood we (1) obtain leaf concept embeddings using the DNN's textual input modality; (2) apply hierarchical clustering to them, using that DNNs encode semantic similarities via vector distances; and (3) label the such-obtained parent concepts using search in available ontologies from QR. An initial evaluation study shows that meaningful ontological class hierarchies can be extracted from state-of-the-art foundation models. Furthermore, we demonstrate how to validate and verify a DNN's learned representations against given ontologies. Lastly, we discuss potential future applications in the context of QR.

One of the key issues contributing to inefficiency in Puskesmas is the time-consuming nature of doctor-patient interactions. Doctors need to conduct thorough consultations, which include diagnosing the patient's condition, providing treatment advice, and transcribing detailed notes into medical records. In regions with diverse linguistic backgrounds, doctors often have to ask clarifying questions, further prolonging the process. While diagnosing is essential, transcription and summarization can often be automated using AI to improve time efficiency and help doctors enhance care quality and enable early diagnosis and intervention. This paper proposes a solution using a localized large language model (LLM) to transcribe, translate, and summarize doctor-patient conversations. We utilize the Whisper model for transcription and GPT-3 to summarize them into the ePuskemas medical records format. This system is implemented as an add-on to an existing web browser extension, allowing doctors to fill out patient forms while talking. By leveraging this solution for real-time transcription, translation, and summarization, doctors can improve the turnaround time for patient care while enhancing the quality of records, which become more detailed and insightful for future visits. This innovation addresses challenges like overcrowded facilities and the administrative burden on healthcare providers in Indonesia. We believe this solution will help doctors save time, provide better care, and produce more accurate medical records, representing a significant step toward modernizing healthcare and ensuring patients receive timely, high-quality care, even in resource-constrained settings.

Volunteer Geographic Information (VGI), with its rich variety, large volume, rapid updates, and diverse sources, has become a critical source of geospatial data. However, VGI data from platforms like OSM exhibit significant quality heterogeneity across different data types, particularly with urban building data. To address this, we propose a multi-source geographic data transformation solution, utilizing accessible and complete VGI data to assist in generating urban building footprint data. We also employ a multimodal data generation framework to improve accuracy. First, we introduce a pipeline for constructing an 'image-text-metadata-building footprint' dataset, primarily based on road network data and supplemented by other multimodal data. We then present ControlCity, a geographic data transformation method based on a multimodal diffusion model. This method first uses a pre-trained text-to-image model to align text, metadata, and building footprint data. An improved ControlNet further integrates road network and land-use imagery, producing refined building footprint data. Experiments across 22 global cities demonstrate that ControlCity successfully simulates real urban building patterns, achieving state-of-the-art performance. Specifically, our method achieves an average FID score of 50.94, reducing error by 71.01% compared to leading methods, and a MIoU score of 0.36, an improvement of 38.46%. Additionally, our model excels in tasks like urban morphology transfer, zero-shot city generation, and spatial data completeness assessment. In the zero-shot city task, our method accurately predicts and generates similar urban structures, demonstrating strong generalization. This study confirms the effectiveness of our approach in generating urban building footprint data and capturing complex city characteristics.

We introduce Mixture-of-Depths (MoD) for Convolutional Neural Networks (CNNs), a novel approach that enhances the computational efficiency of CNNs by selectively processing channels based on their relevance to the current prediction. This method optimizes computational resources by dynamically selecting key channels in feature maps for focused processing within the convolutional blocks (Conv-Blocks), while skipping less relevant channels. Unlike conditional computation methods that require dynamic computation graphs, CNN MoD uses a static computation graph with fixed tensor sizes which improve hardware efficiency. It speeds up the training and inference processes without the need for customized CUDA kernels, unique loss functions, or finetuning. CNN MoD either matches the performance of traditional CNNs with reduced inference times, GMACs, and parameters, or exceeds their performance while maintaining similar inference times, GMACs, and parameters. For example, on ImageNet, ResNet86-MoD exceeds the performance of the standard ResNet50 by 0.45% with a 6% speedup on CPU and 5% on GPU. Moreover, ResNet75-MoD achieves the same performance as ResNet50 with a 25% speedup on CPU and 15% on GPU.

Hawrami, a dialect of Kurdish, is classified as an endangered language as it suffers from the scarcity of data and the gradual loss of its speakers. Natural Language Processing projects can be used to partially compensate for data availability for endangered languages/dialects through a variety of approaches, such as machine translation, language model building, and corpora development. Similarly, NLP projects such as text classification are in language documentation. Several text classification studies have been conducted for Kurdish, but they were mainly dedicated to two particular dialects: Sorani (Central Kurdish) and Kurmanji (Northern Kurdish). In this paper, we introduce various text classification models using a dataset of 6,854 articles in Hawrami labeled into 15 categories by two native speakers. We use K-nearest Neighbor (KNN), Linear Support Vector Machine (Linear SVM), Logistic Regression (LR), and Decision Tree (DT) to evaluate how well those methods perform the classification task. The results indicate that the Linear SVM achieves a 96% of accuracy and outperforms the other approaches.