Abstract

One-class classification (OCC) is a machine learning technique employed in scenarios involving imbalanced data, limited labeled data, or the need to detect anomalies or novel patterns. One-Class Classification by Ensembles of Regression (OCCER) models have shown excellent performance for OCC problems. In this paper, we study whether the combinations of ensemble methods such as random subspace and random projection with OCCER can be used to improve the performance of OCCER. Our main objective is to explore the impact of diversity creation methods, including bagging, random subspace, and random projections, on enhancing the performance of the OCC ensemble method. Additionally, we investigate the feasibility of applying the OCCER ensemble method to a novel low-dimensional space generated by autoencoders, all while maintaining high prediction accuracy. The experimental results over 37 datasets demonstrate that the creation of diverse OCCER models using random subspace and random projection substantially improves the performance of the OCCER. The diversity creation method, Random subspaces, outperforms other diversity creation methods.

Abstract

Autoencoders have been used for finding inter- pretable and disentangled features underlying neural network representations in both image and text domains. While the efficacy and pitfalls of such methods are well-studied in vision, there is a lack of corresponding results, both qualitative and quantitative, for the text domain. We aim to address this gap by training sparse autoencoders (SAEs) on a synthetic testbed of formal languages. Specifically, we train SAEs on the hidden representations of models trained on formal languages (Dyck-2, Expr, and English PCFG) under a wide variety of hyperparameter settings, finding interpretable latents often emerge in the features learned by our SAEs. However, similar to vision, we find performance turns out to be highly sensitive to inductive biases of the training pipeline. Moreover, we show latents correlating to certain features of the input do not always induce a causal impact on model’s computation. We thus ar- gue that causality has to become a central tar- get in SAE training: learning of causal features should be incentivized from the ground- up. Motivated by this, we propose and perform preliminary investigations for an approach that promotes learning of causally relevant features in our formal language setting.

Abstract

In the Himalayan region, traditional buildings made of masonry have existed for centuries and still make up a sizable portion of the building stock. Many of these buildings have proven records of earthquake resilience. At the same time, the primary cause of fatalities from previous earthquakes is the increasing collapse of unreinforced masonry structures. In various Himalayan locations, local seismic culture has evolved over the past few centuries to improve the seismic capacity of stone constructions. These areas use wood or other materials with high tensile strength to reinforce their stone masonry. The post-earthquake reconnaissance surveys clearly demonstrate the superior performance of these timber-reinforced masonry constructions. Nevertheless, there is still a lack of scientific investigation, either through numerical modelling or laboratory-based studies, on the behaviour and mechanism of these structures. Furthermore, there are no explicit criteria for evaluating timber-reinforced masonry in the present Indian seismic code. This study examines several distinct traditional masonry building typologies found in the Himalayas, such as ‘Thathara’, ‘Kath-Kuni’ or ‘Koti-Banal’, ‘Newari’, ‘Dhajji Dewari’, ‘Ikra’ or ‘Assam-Type House’, ‘Bhatar’ or ‘Taq’, and ‘Dry-stone buildings’. For the majority of these typologies, aside from Dry-stone houses, the masonry walls have embedded horizontal timber bands or lacings that enhance box behaviour, thereby preventing premature failure. Through a thorough analysis of timber-reinforced masonry, including architectural details, earthquake-resilient & vulnerable features, and experimental and numerical investigations, the article presents a unique addition to our understanding of seismic performance and how it might best protect the Himalayan region’s traditional heritage. Additionally, the current study contributes to a better understanding of how wood elements give masonry structures greater seismic resilience. This understanding will help with several initiatives: (i) better conservation, preservation, and maintenance of traditional or heritage structures; (ii) seismic codes/standards providing details regarding the assessment of timber-reinforced structures; (iii) supporting and encouraging practitioners to design and build using techniques that have demonstrated superior performance, and (iv) as attempts to lessen the impact of building on our environment increases, provide a way to make safer structures utilizing low-carbon materials.

Abstract

Indian raga music requires a symbolic notation dataset to support music information retrieval (MIR) tasks. With the growing applications of generative artificial intelligence, text-based datasets can be especially valuable, as these models excel when inputted structured symbolic data. A high-level analysis of ragas is difficult from audio data. In this article, we announce our new dataset called InRaMu. Notations of raga-based compositions in Hindustani, Carnatic and Rabindra Sangeet were scraped from the Internet. The compiled dataset consists of 7 sources combined to get 2,295 compositions in 337 ragas. Total number of compositions, with 976,826 individual notes, correspond to a combined playback duration of about 16,280 minutes. Special care was taken during data preprocessing such as filtering of compositions to ensure raga accuracy. This article provides the overview of dataset format, and provides statistical information, along with potential applications of the dataset.

Abstract

Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models—through increased size, instruction-tuning, and multimodality—has led to better representational alignment with neural data. Recently, a new class of instruction-tuned multimodal LLMs (MLLMs) have emerged, showing remarkable zero-shot capabilities in open-ended multimodal vision tasks. However, it is unknown whether MLLMs, when prompted with natural instructions, lead to better brain alignment and effectively capture instruction-specific representations. To address this, we first investigate the brain alignment, i.e., measuring the degree of predictivity of neural visual activity using text output response embeddings from MLLMs as participants engage in watching natural scenes. Experiments with 10 different instructions (like image captioning, visual question answering, etc.) show that MLLMs exhibit significantly better brain alignment than vision-only models and perform comparably to non-instruction-tuned multimodal models like CLIP. We also find that while these MLLMs are effective at generating high-quality responses suitable to the task-specific instructions, not all instructions are relevant for brain alignment. Further, by varying instructions, we make the MLLMs encode instruction-specific visual concepts related to the input image. This analysis shows that MLLMs effectively capture count-related and recognition-related concepts, demonstrating strong alignment with brain activity. Notably, the majority of the explained variance of the brain encoding models is shared between MLLM embeddings of image captioning and other instructions. These results indicate that enhancing MLLMs' ability to capture more task-specific information could allow for better differentiation between various types of instructions, and hence improve their precision in predicting brain responses.

Abstract

Community detection is the problem of identifying natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this paper, we design efficient community detection algorithms in the batch dynamic setting. First, we present our parallel Dynamic Frontier approach. Given a batch update of edge deletions or insertions, this approach incrementally identifies an approximate set of affected vertices in the graph with minimal overhead. We apply this approach to both Louvain, a high quality, and Label Propagation Algorithm (LPA), a fast static community detection algorithm. Our approach achieves a mean speedup of 7.3× and 6.7×, when applied to Louvain and LPA respectively, compared to our parallel and optimized implementation of Δ-screening, a recently proposed state-of-the-art approach. Finally, we show how to combine Louvain and LPA with the Dynamic Frontier approach to arrive at a hybrid algorithm. This algorithm produces high-quality communities while being 14.6× faster than state-of-the-art, and identifying communities with the same quality score.

Abstract

Automated segmentation of medical image volumes promises to reduce costly medical experts’ time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. This paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72×. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64×. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

Abstract

This paper explores the concept of solving implicature in Natural Language Processing (NLP), highlighting its significance in understanding indirect communication. Drawing on foundational theories by Austin, Searle, and Grice, we discuss how implicature extends beyond literal language to convey nuanced meanings. We review existing datasets, including the Pragmatic Understanding Benchmark (PUB), that assess models’ capabilities in recognizing and interpreting implicatures. Despite recent advances in large language models (LLMs), challenges remain in effectively processing implicature due to limitations in training data and the complexities of contextual interpretation. We propose future directions for research, including the enhancement of datasets and the integration of pragmatic reasoning tasks, to improve LLMs’ understanding of implicature and facilitate better human-computer interaction.

Abstract

This research utilises the capabilities of Google Earth Engine, a cloud-based computing platform, to conduct a comprehensive spatiotemporal assessment of the Coringa mangrove. Situated within the Coringa Wildlife Sanctuary in Andhra Pradesh, India, this mangrove – the country's second largest – is evaluated across various timeframes: 1999, 2002, 2007, 2013, 2017, and 2022. ML algorithms – Random Forest and Support Vector Machine with an RBF kernel – are employed. These algorithms analyze band composites derived from Landsat-7 ETM+ (1999, 2002, 2007), Landsat-8 (2013, 2017), and Sentinel-2 MSI (2022) satellite images. This analysis incorporates key spectral indices and utilises two spectral index thresholds for mangrove classification: one derived from established literature that identifies common thresholds at which mangroves typically occur (standard threshold), and the other is a customised threshold obtained from individual spectral index maps, tailored specifically to delineate mangrove areas in the study area (customized threshold). The results show that RF, particularly with the CT, outperforms all other methods, including RF with the ST and SVM with both thresholds, in terms of training and testing accuracy. These findings affirm the effectiveness of RF with the CT approach in accurately differentiating mangrove areas, emphasizing the critical role of threshold selection in enhancing the accuracy and competence of classification methods for mapping mangrove ecosystems.

Abstract

The significant improvements in point cloud representation learning have increased its applicability in many real-life applications, resulting in the need for lightweight, better-performing models. One widely proposed efficient method is knowledge distillation, where a lightweight model uses knowledge from large models. Very few works exist on distilling the knowledge for point clouds. Most of the work focuses on cross-modal-based approaches that make the method expensive to train. This paper proposes PointKAD, an adversarial knowledge distillation framework for point cloud-based tasks. PointKAD includes adversarial feature distillation and response distillation with the help of discriminators to extract and distill the representation of feature maps and logits. We conduct extensive experimental studies on both synthetic (ModelNet40) and real (ScanObjectNN) datasets to show that PointKAD achieves state-of-the-art results compared to the existing knowledge distillation methods for point cloud classification. Additionally, we present results on the part segmentation task, highlighting the efficacy of the PointKAD framework. Our experiments further reveal that PointKAD is capable of transferring knowledge across different tasks and datasets, showcasing its versatility. Furthermore, we demonstrate that PointKAD can be applied to a cross-modal training setup, achieving competitive performance with cross-modal-based point cloud methods for classification.