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

Inverse of an invertible convolution is an important operation that comes up in Normalizing Flows, Image Deblurring, etc. The naive algorithm for backpropagation of this operation using Gaussian elimination has running time where is the number of pixels in the image. We give a fast parallel backpropagation algorithm with running time for a square image and provide a GPU implementation of the same. Inverse Convolutions are usually used in Normalizing Flows in the sampling pass, making them slow. We propose to use Inverse Convolutions in the forward (image to latent vector) pass of the Normalizing flow. Since the sampling pass is the inverse of the forward pass, it will use convolutions only, resulting in efficient sampling times. We use our parallel backpropagation algorithm for optimizing the inverse convolution layer resulting in fast training times also. We implement this approach in various Normalizing Flow backbones, resulting in our Inverse-Flow models. We benchmark Inverse-Flow on standard datasets and show significantly improved sampling times with similar bits per dimension compared to previous models.

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

Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks. Recognising their importance, there has been extensive research focused on explaining GNN predictions, aiming to enhance their interpretability and trustworthiness. However, GNNs and their explainers face a notable challenge: graphs are primarily designed to model pair-wise relationships between nodes, which can make it tough to capture higher-order, multi-node interactions. This characteristic can pose difficulties for existing explainers in fully representing multi-node relationships. To address this gap, we present Framework For Higher-Order Representations In Graph Explanations (FORGE), a framework that enables graph explainers to capture such interactions by incorporating higher-order structures, resulting in more accurate and faithful explanations. Extensive evaluation shows that on average real-world datasets from the GraphXAI benchmark and synthetic datasets across various graph explainers, FORGE improves average explanation accuracy by 1.9x and 2.25x, respectively. We perform ablation studies to confirm the importance of higher-order relations in improving explanations, while our scalability analysis demonstrates FORGE's efficacy on large graphs.

Abstract

Developing Virtual Reality (VR) software products with discrete and incremental requirements is a challenging task for VR practitioners. A domain expert’s assistance plays a key role in VR product completeness, as most VR requirements are abstract or under-specified during the early stages. Slight changes to the requirements can significantly impact the overall flow of the VR software development process. In this paper, we introduce VReqST, a tool for VR requirement analysts to specify requirements for building effective VR software products. It is developed based on a Role-based model template for specifying virtual environments, custom behaviors, VR-specific algorithms, user-flows, action responses, timeline of events, etc. The tool has been developed after several interactions with VR practitioners from industry & academia. We share our insights on the effectiveness of this tool in practice.

Abstract

One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option setting is not previously explored. In this paper, we propose NCwR, a novel approach to node classification in Graph Neural Networks (GNNs) with an integrated reject option, which allows the model to abstain from making predictions when uncertainty is high. We propose both cost-based and coverage-based methods for classification with abstention in node classification setting using GNNs. We perform experiments using our method on three standard citation network datasets Cora, Citeseer and Pubmed and compare with relevant baselines. We also model the Legal judgment prediction problem on ILDC dataset as a node classification problem where nodes represent legal cases and edges represent citations. We further interpret the model by analyzing the cases that the model abstains from predicting by visualizing which part of the input features influenced this decision.

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 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.