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Mathew Ingram (@mathewi@journa.host)
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InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Somnath Banerjee, Maulindu Sarkar, Punyajoy Saha, Binny Mathew, Animesh Mukherjee
https://arxiv.org/abs/2402.14702

InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Recently, influence functions present an apparatus for achieving explainability for deep neural models by quantifying the perturbation of individual train instances that might impact a test prediction. Our objectives in this paper are twofold. First we incorporate influence functions as a feedback into the model to improve its performance. Second, in a dataset extension exercise, using influence functions to automatically identify data points that have been initially `silver' annotated by some …

Road To Paris
Great Australian Pods Podcast Directory: #GreatAusPods

Road To Paris | Great Australian Pods Podcast Directory
Podcast: Road To Paris Host: Mathew Thompson Category: Sport Sub-Category: Sports News & Commentary First Episode: 2024 For 125 years, the Olympic Games have been the greatest show on earth…

Enhancing Generalization in Audio Deepfake Detection: A Neural Collapse based Sampling and Training Approach
Mohammed Yousif, Jonat John Mathew, Huzaifa Pallan, Agamjeet Singh Padda, Syed Daniyal Shah, Sara Adamski, Madhu Reddiboina, Arjun Pankajakshan
https://arxiv.org/abs/2404.13008

Enhancing Generalization in Audio Deepfake Detection: A Neural Collapse based Sampling and Training Approach
Generalization in audio deepfake detection presents a significant challenge, with models trained on specific datasets often struggling to detect deepfakes generated under varying conditions and unknown algorithms. While collectively training a model using diverse datasets can enhance its generalization ability, it comes with high computational costs. To address this, we propose a neural collapse-based sampling approach applied to pre-trained models trained on distinct datasets to create a new t…

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
Yifeng Tian, Soumendu Bagchi, Liam Myhill, Giacomo Po, Enrique Martinez, Yen Ting Lin, Nithin Mathew, Danny Perez
https://arxiv.org/abs/2403.14015

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach bec…

On Graph Grammars and Games
Jayakrishna Vijayakumar, Lisa Mathew
https://arxiv.org/abs/2403.07607 https://arxiv.org/pdf/2403.07607

On Graph Grammars and Games
Graph grammars form an interesting area of research because of their versatility in modelling diverse situations with graphs as the structures which are to be manipulated. A new class of graph grammars, nc-eNCE Graph Grammars has been introduced recently with an aim of restricting the order of application of graph production rules, thereby generating different graph classes using the same set of rules. On the other hand 2D game design using an algorithmic approach known as procedural content ge…

Sensitive 3mm Imaging of Discrete Sources in the Fields of tSZ-Selected Galaxy Clusters
Simon R. Dicker, Karen Perez Sarmiento, Brian Mason, Tanay Bhandarkar, Mark J. Devlin, Luca Di Mascolo, Saianeesh Haridas, Matt Hilton, Mathew Madhavacheril, Emily Moravec, Tony Mroczkowski, John Orlowski-Scherer, Charles Romero, Craig L. Sarazin, Jonathan Sievers

Sensitive 3mm Imaging of Discrete Sources in the Fields of tSZ-Selected Galaxy Clusters
In this paper we present the results of a blind survey for compact sources in 243 Galaxy clusters that were identified using the thermal Sunyaev-Zeldovich effect (tSZ). The survey was carried out at 90 GHz using MUSTANG2 on the Green Bank telescope and achieved a $5σ$ detection limit of 1 mJy in the center of each cluster. We detected 24 discrete sources. The majority (18) of these correspond to known radio sources, and of these, 5 show signs of significant variability, either with time or in …

Maximal mass of the neutron star with a deconfined quark core
Muhammed Shafeeque, Arun Mathew, Malay K. Nandy
https://arxiv.org/abs/2404.04009 https://

Maximal mass of the neutron star with a deconfined quark core
The nature of equation of state for the matter in the neutron star plays an important role in determining its maximal mass. In addition, it must comply with the condition of causality. Noting that the central density of a maximally massive neutron star is well above the nuclear saturation density, a deconfined quark core in the central region is motivated in this paper. We analyze this scenario by employing the MIT bag model to represent the core region and one of the unified equations of state…

Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms
Jonat John Mathew, Rakin Ahsan, Sae Furukawa, Jagdish Gautham Krishna Kumar, Huzaifa Pallan, Agamjeet Singh Padda, Sara Adamski, Madhu Reddiboina, Arjun Pankajakshan
https://arxiv.org/abs/2403.11778

Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms
Deepfake audio poses a rising threat in communication platforms, necessitating real-time detection for audio stream integrity. Unlike traditional non-real-time approaches, this study assesses the viability of employing static deepfake audio detection models in real-time communication platforms. An executable software is developed for cross-platform compatibility, enabling real-time execution. Two deepfake audio detection models based on Resnet and LCNN architectures are implemented using the AS…

Constraints on Dark Matter-Dark Energy Scattering from ACT DR6 CMB Lensing
Alex Lagu\"e, Fiona McCarthy, Mathew Madhavacheril, J. Colin Hill, Frank J. Qu
https://arxiv.org/abs/2402.08149

Constraints on Dark Matter-Dark Energy Scattering from ACT DR6 CMB Lensing
The predicted present-day amplitude of matter fluctuations based on cosmic microwave background (CMB) anisotropy data has sometimes been found discrepant with more direct measurements of late-time structure. This has motivated many extensions to the standard cosmological model, including kinetic interactions between dark matter and dark energy that introduce a drag force slowing the growth of structure at late times. Exploring this scenario, we develop a model for quasi-linear scales in the mat…