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We present a novel Graph-based debiasing Algorithm for Underreported Data (GRAUD) aiming at an efficient joint estimation of event counts and discovery probabilities across spatial or graphical structures. This innovative method provides a solution to problems seen in fields such as policing data and COVID-19 data analysis. Our approach avoids the need for strong priors typically associated with Bayesian frameworks. By leveraging the graph structures on unknown variables n and p, our method debiases the under-report data and estimates the discovery probability at the same time.

In recent years, deep learning systems have shown a concerning trend toward increased complexity and higher energy consumption. As researchers in this domain and organizers of one of the Detection and Classification of Acoustic Scenes and Events challenges task, we recognize the importance of addressing the environmental impact of data-driven SED systems. In this paper, we propose an analysis focused on SED systems based on the challenge submissions. This includes a comparison across the past two years and a detailed analysis of this year’s SED systems.

We consider the parametric measurement model employed in applications such as line spectral or direction-of-arrival estimation with the goal to estimate the underlying parameter in a gridless manner. We focus on the stochastic maximum likelihood estimation (MLE) framework and overcome the model complexities of the past by reparameterization of the objective and exploiting the sparse Bayesian learning (SBL) approach. SBL is shown to be a correlation-aware method and, for the underlying problem, a grid-based technique for recovering a structured covariance matrix of the measurements.

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components).

Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling (J-DPS) method, which aims to learn optimal selection matrices simultaneously for transmitters, receivers, and Fourier coefficients. This task-based algorithm is realized by introducing a specialized measurement model and integrating a customized Complex Learned FISTA (CL-FISTA) network.

We present MagCIL's approach for the 1st track of the "2nd e-Prevention challenge: Psychotic and Non-Psychotic Relapse Detection using Wearable-Based Digital Phenotyping". First we present our approach for preprocessing and extracting features from the wearable's raw data. We then propose a Transformer model for learning self-supervised representations from augmented features, trained on data from non-relapse days from each of the 9 patients of the challenge. We adopt two unsupervised methods for detecting relapse days as outliers.

This paper introduces CIF-RNNT, a model that incorporates Continuous Integrate-and-Fire into RNN-Transducers (RNNTs) for streaming ASR via acoustic word embeddings (AWEs). CIF can dynamically compress long sequences into shorter ones, while RNNTs can produce multiple symbols given an input vector. We demonstrate that our model can not only streamingly segment acoustic information and produce AWEs, but also recover the represented word using a fixed set of output tokens with a shorter decoding time.

In this paper, we present our approach to the 3D CBCT Challenge 2024, a part of ICASSP SP Grand Challenges 2024. Improvement in Cone Beam Computed Tomography (CBCT) reconstruction has been achieved by integrating Swin Image Restoration (SwinIR) based sinogram and image enhancement modules. The proposed methodology uses Nesterov Accelerated Gradient Descent (NAG) to solve the least squares (NAG-LS) problem in CT image reconstruction.

Presention Slides in ICASSP2024 for IVMSP-L2.4: VT-REID: LEARNING DISCRIMINATIVE VISUAL-TEXT REPRESENTATION FOR POLYP RE-IDENTIFICATION

We introduce GPNN-CAM, a novel method for CNN explanation, that bridges two distinct areas of computer vision:
Image Attribution, which aims to explain a predictor by highlighting image regions it finds important, and Single
Image Generation (SIG), that focuses on learning how to generate variations of a single sample. GPNN-CAM leverages samples generated by Generative