Orthogonal nonnegative matrix factorization (ONMF) has become a standard approach for clustering. As far as we know, most works on ONMF rely on the Frobenius norm to assess the quality of the approximation. This paper presents a new model and algorithm for ONMF that minimizes the Kullback-Leibler (KL) divergence. As opposed to the Frobenius norm which assumes Gaussian noise, the KL divergence is the maximum likelihood estimator for Poisson-distributed data, which can model better sparse vectors of word counts in document data sets and photo counting processes in imaging. We develop an algorithm based on alternating optimization, KL-ONMF, and show that it performs favorably with the Frobenius-norm based ONMF for document classification and hyperspectral image unmixing.

What's that feature called on pixel phones? I forget what Android in general about Android specifics. But yes, there there was like a magic erase option there, too Yeah, I was going to say magic eraser, but that is a that's a clean thing it's something like that too, but It works really well like in terms of highlighting a specific object and removing it there are instances where it's too big and it can't like extrapolate like what should be a background so it looks really messy but sometimes like it just like smooths out a bright ugly object in the background was just like general unfocused stuff and that actually may be better.

Many proposed applications of neural networks in machine learning, cognitive/brain science, and society hinge on the feasibility of inner interpretability via circuit discovery. This calls for empirical and theoretical explorations of viable algorithmic options. Despite advances in the design and testing of heuristics, there are concerns about their scalability and faithfulness at a time when we lack understanding of the complexity properties of the problems they are deployed to solve. To address this, we study circuit discovery with classical and parameterized computational complexity theory: (1) we describe a conceptual scaffolding to reason about circuit finding queries in terms of affordances for description, explanation, prediction and control; (2) we formalize a comprehensive set of queries that capture mechanistic explanation, and propose a formal framework for their analysis; (3) we use it to settle the complexity of many query variants and relaxations of practical interest on multi-layer perceptrons (part of, e.g., transformers). Our findings reveal a challenging complexity landscape. Many queries are intractable (NP-hard, $\Sigma^p_2$-hard), remain fixed-parameter intractable (W[1]-hard) when constraining model/circuit features (e.g., depth), and are inapproximable under additive, multiplicative, and probabilistic approximation schemes. To navigate this landscape, we prove there exist transformations to tackle some of these hard problems (NP- vs. $\Sigma^p_2$-complete) with better-understood heuristics, and prove the tractability (PTIME) or fixed-parameter tractability (FPT) of more modest queries which retain useful affordances. This framework allows us to understand the scope and limits of interpretability queries, explore viable options, and compare their resource demands among existing and future architectures.

In August 2023, Scott Aaronson and I reported the results of testing GPT4 with the Wolfram Alpha and Code Interpreter plug-ins over a collection of 105 original high-school level and college-level science and math problems (Davis and Aaronson, 2023). In September 2024, I tested the recently released model GPT-4o1-preview on the same collection. Overall I found that performance had significantly improved, but was still considerably short of perfect. In particular, problems that involve spatial reasoning are often stumbling blocks. On September 12, OpenAI (2024) released two preliminary versions, "ChatGPT-o1-preview" and "ChatGPT-o1-mini" of a forthcoming product "ChatGPT-o1".

We introduce a novel method for movie genre classification, capitalizing on a diverse set of readily accessible pretrained models. These models extract high-level features related to visual scenery, objects, characters, text, speech, music, and audio effects. To intelligently fuse these pretrained features, we train small classifier models with low time and memory requirements. Employing the transformer model, our approach utilizes all video and audio frames of movie trailers without performing any temporal pooling, efficiently exploiting the correspondence between all elements, as opposed to the fixed and low number of frames typically used by traditional methods. Our approach fuses features originating from different tasks and modalities, with different dimensionalities, different temporal lengths, and complex dependencies as opposed to current approaches. Our method outperforms state-of-the-art movie genre classification models in terms of precision, recall, and mean average precision (mAP). To foster future research, we make the pretrained features for the entire MovieNet dataset, along with our genre classification code and the trained models, publicly available.

The flooding extent area in a river valley is related to river gauge observations. The higher the water elevation, the larger the flooding area. Due to synthetic aperture radar\textquoteright s (SAR) capabilities to penetrate through clouds, radar images have been commonly used to estimate flooding extent area with various methods, from simple thresholding to deep learning models. In this study, we propose a physics-guided neural network for flooding area detection. Our approach takes as input data the Sentinel 1 time-series images and the water elevations in the river assigned to each image. We apply the Pearson correlation coefficient between the predicted sum of water extent areas and the local water level observations of river water elevations as the loss function. The effectiveness of our method is evaluated in five different study areas by comparing the predicted water maps with reference water maps obtained from digital terrain models and optical satellite images. The highest Intersection over Union (IoU) score achieved by our models was 0.89 for the water class and 0.96 for the non-water class. Additionally, we compared the results with other unsupervised methods. The proposed neural network provided a higher IoU than the other methods, especially for SAR images registered during low water elevation in the river.

UniGlyph is a constructed language (conlang) designed to create a universal transliteration system using a script derived from seven-segment characters. The goal of UniGlyph is to facilitate cross-language communication by offering a flexible and consistent script that can represent a wide range of phonetic sounds. This paper explores the design of UniGlyph, detailing its script structure, phonetic mapping, and transliteration rules. The system addresses imperfections in the International Phonetic Alphabet (IPA) and traditional character sets by providing a compact, versatile method to represent phonetic diversity across languages. With pitch and length markers, UniGlyph ensures accurate phonetic representation while maintaining a small character set. Applications of UniGlyph include artificial intelligence integrations, such as natural language processing and multilingual speech recognition, enhancing communication across different languages. Future expansions are discussed, including the addition of animal phonetic sounds, where unique scripts are assigned to different species, broadening the scope of UniGlyph beyond human communication. This study presents the challenges and solutions in developing such a universal script, demonstrating the potential of UniGlyph to bridge linguistic gaps in cross-language communication, educational phonetics, and AI-driven applications.

Recent approaches to zero-shot commonsense reasoning have enabled Pre-trained Language Models (PLMs) to learn a broad range of commonsense knowledge without being tailored to specific situations. However, they often suffer from human reporting bias inherent in textual commonsense knowledge, leading to discrepancies in understanding between PLMs and humans. In this work, we aim to bridge this gap by introducing an additional information channel to PLMs. We propose Imagine (Machine Imagination-based Reasoning), a novel zero-shot commonsense reasoning framework designed to complement textual inputs with visual signals derived from machine-generated images. To achieve this, we enhance PLMs with imagination capabilities by incorporating an image generator into the reasoning process. To guide PLMs in effectively leveraging machine imagination, we create a synthetic pre-training dataset that simulates visual question-answering. Our extensive experiments on diverse reasoning benchmarks and analysis show that Imagine outperforms existing methods by a large margin, highlighting the strength of machine imagination in mitigating reporting bias and enhancing generalization capabilities.

Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research community. Nevertheless, although the data is accessible to any community member interested in exploration, there exists a formidable learning curve and no standardized process for accessing, pre-processing, and leveraging the data for subsequent tasks. In this study, we democratize this data by presenting a flexible and efficient end to end pipeline for working with the Dynamic World dataset, a cutting-edge near-real-time land use/land cover (LULC) dataset. This includes a pre-processing and representation framework which tackles noise removal, efficient extraction of large amounts of data, and re-representation of LULC data in a format well suited for several downstream tasks. To demonstrate the power of our pipeline, we use it to extract data for an urbanization prediction problem and build a suite of machine learning models with excellent performance. This task is easily generalizable to the prediction of any type of land cover and our pipeline is also compatible with a series of other downstream tasks.

Accurate bicycling volume estimation is crucial for making informed decisions about future investments in bicycling infrastructure. Traditional link-level volume estimation models are effective for motorised traffic but face significant challenges when applied to the bicycling context because of sparse data and the intricate nature of bicycling mobility patterns. To the best of our knowledge, we present the first study to utilize a Graph Convolutional Network (GCN) architecture to model link-level bicycling volumes. We estimate the Annual Average Daily Bicycle (AADB) counts across the City of Melbourne, Australia using Strava Metro bicycling count data. To evaluate the effectiveness of the GCN model, we benchmark it against traditional machine learning models, such as linear regression, support vector machines, and random forest. Our results show that the GCN model performs better than these traditional models in predicting AADB counts, demonstrating its ability to capture the spatial dependencies inherent in bicycle traffic data. We further investigate how varying levels of data sparsity affect performance of the GCN architecture. The GCN architecture performs well and better up to 80% sparsity level, but its limitations become apparent as the data sparsity increases further, emphasizing the need for further research on handling extreme data sparsity in bicycling volume estimation. Our findings offer valuable insights for city planners aiming to improve bicycling infrastructure and promote sustainable transportation.