Search

Ejection fraction (EF) of the left ventricle (LV) is considered as one of the most important measurements for diagnosing acute heart failure and can be estimated during cardiac ultrasound acquisition. While recent successes in deep learning research successfully estimate EF values, the proposed models often lack an explanation for the prediction. However, providing clear and intuitive explanations for clinical measurement predictions would increase the trust of cardiologists in these models. In this paper, we explore predicting EF measurements with Natural Language Explanation (NLE). We propose a model that in a single forward pass combines estimation of the LV contour over multiple frames, together with a set of modules and routines for computing various motion and shape attributes that are associated with ejection fraction. It then feeds the attributes into a large language model to generate text that helps to explain the network's outcome in a human-like manner. We provide experimental evaluation of our explanatory output, as well as EF prediction, and show that our model can provide EF comparable to state-of-the-art together with meaningful and accurate natural language explanation to the prediction. The project page can be found at https://github.com/guybenyosef/EchoNarrator ., Comment: accepted for MICCAI 2024

This study will investigate the user experience while interacting with highly photorealistic virtual job interviewer avatars in Virtual Reality (VR), Augmented Reality (AR), and on a 2D screen. Having a precise speech recognition mechanism, our virtual character performs a mock-up software engineering job interview to adequately immerse the user in a life-like scenario. To evaluate the efficiency of our system, we measure factors such as the provoked level of anxiety, social presence, self-esteem, and intrinsic motivation. This research is a work in progress with a prospective within-subject user study including approximately 40 participants. All users will engage with three job interview conditions (VR, AR, and desktop) and provide their feedback. Additionally, users' bio-physical responses will be collected using a biosensor to measure the level of anxiety during the job interview., Comment: 2-page ISMAR poster paper

Various industries have widely adopted Virtual Reality (VR) and Augmented Reality (AR) technologies to enhance productivity and user experiences. However, their integration introduces significant security challenges. This systematic literature review focuses on identifying devices used in AR and VR technologies and specifies the associated vulnerabilities, particularly during the reconnaissance phase and vulnerability assessment, which are critical steps in penetration testing. Following Kitchenham and Charters' guidelines, we systematically selected and analyzed primary studies. The reconnaissance phase involves gathering detailed information about AR and VR systems to identify potential attack vectors. In the vulnerability assessment phase, these vectors are analyzed to pinpoint weaknesses that malicious actors could exploit. Our findings reveal that AR and VR devices, such as headsets (e.g., HTC Vive, Oculus Quest), development platforms (e.g., Unity Framework, Google Cardboard SDK), and applications (e.g., Bigscreen VR, VRChat), are susceptible to various attacks, including remote code execution, cross-site scripting (XSS), eavesdropping, and man-in-the-room attacks. Specifically, the Bigscreen VR application exhibited severe vulnerabilities like remote code execution (RCE) via the 'Application.OpenURL' API, XSS in user inputs, and botnet propagation. Similarly, the Oculus Quest demonstrated susceptibility to side-channel attacks and ransomware. This paper provides a detailed overview of specific device vulnerabilities and emphasizes the importance of the initial steps in penetration testing to identify security weaknesses in AR and VR systems. By highlighting these vulnerabilities, we aim to assist researchers in exploring and mitigating these security challenges, ensuring the safe deployment and use of AR and VR technologies across various sectors.

In speaker anonymization, speech recordings are modified in a way that the identity of the speaker remains hidden. While this technology could help to protect the privacy of individuals around the globe, current research restricts this by focusing almost exclusively on English data. In this study, we extend a state-of-the-art anonymization system to nine languages by transforming language-dependent components to their multilingual counterparts. Experiments testing the robustness of the anonymized speech against privacy attacks and speech deterioration show an overall success of this system for all languages. The results suggest that speaker embeddings trained on English data can be applied across languages, and that the anonymization performance for a language is mainly affected by the quality of the speech synthesis component used for it., Comment: accepted at Interspeech 2024

In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack sufficient data for traditional TTS development. By leveraging a novel integration of massively multilingual pretraining and meta learning to approximate language representations, our approach enables zero-shot speech synthesis in languages without any available data. We validate our system's performance through objective measures and human evaluation across a diverse linguistic landscape. By releasing our code and models publicly, we aim to empower communities with limited linguistic resources and foster further innovation in the field of speech technology., Comment: accepted at Interspeech 2024

The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and evaluation scripts, as well as baseline anonymization systems and a list of training resources formed on the basis of the participants' requests. Participants apply their developed anonymization systems, run evaluation scripts and submit evaluation results and anonymized speech data to the organizers. Results will be presented at a workshop held in conjunction with Interspeech 2024 to which all participants are invited to present their challenge systems and to submit additional workshop papers., Comment: 19 pages, https://www.voiceprivacychallenge.org/. arXiv admin note: substantial text overlap with arXiv:2203.12468

To facilitate diagnosis on cardiac ultrasound (US), clinical practice has established several standard views of the heart, which serve as reference points for diagnostic measurements and define viewports from which images are acquired. Automatic view recognition involves grouping those images into classes of standard views. Although deep learning techniques have been successful in achieving this, they still struggle with fully verifying the suitability of an image for specific measurements due to factors like the correct location, pose, and potential occlusions of cardiac structures. Our approach goes beyond view classification and incorporates a 3D mesh reconstruction of the heart that enables several more downstream tasks, like segmentation and pose estimation. In this work, we explore learning 3D heart meshes via graph convolutions, using similar techniques to learn 3D meshes in natural images, such as human pose estimation. As the availability of fully annotated 3D images is limited, we generate synthetic US images from 3D meshes by training an adversarial denoising diffusion model. Experiments were conducted on synthetic and clinical cases for view recognition and structure detection. The approach yielded good performance on synthetic images and, despite being exclusively trained on synthetic data, it already showed potential when applied to clinical images. With this proof-of-concept, we aim to demonstrate the benefits of graphs to improve cardiac view recognition that can ultimately lead to better efficiency in cardiac diagnosis., Comment: Presented at ASMUS - MICCAI conference 2023, Vancouver

High-precision mass measurements of exotic $^{95-97}$Ag isotopes close to the $N = Z$ line have been conducted with the JYFLTRAP double Penning trap mass spectrometer, with the silver ions produced using the recently commissioned inductively-heated hot cavity catcher laser ion source at the Ion Guide Isotope Separator On-Line facility. The atomic mass of $^{95}$Ag was directly determined for the first time. In addition, the atomic masses of $\beta$-decaying 2$^+$ and 8$^+$ states in $^{96}$Ag have been identified and measured for the first time, and the precision of the $^{97}$Ag mass has been improved. The newly measured masses, with a precision of $\approx$ 1 keV/c$^2$, have been used to investigate the $N =$ 50 neutron shell closure confirming it to be robust. Empirical shell-gap and pairing energies determined with the new ground-state mass data are compared with the state-of-the-art \textit{ab initio} calculations with various chiral effective field theory Hamiltonians. The precise determination of the excitation energy of the $^{96m}$Ag isomer in particular serves as a benchmark for \textit{ab initio} predictions of nuclear properties beyond the ground state, specifically for odd-odd nuclei situated in proximity to the proton dripline below $^{100}$Sn. In addition, density functional theory (DFT) calculations and configuration-interaction shell-model (CISM) calculations are compared with the experimental results. All theoretical approaches face challenges to reproduce the trend of nuclear ground-state properties in the silver isotopic chain across the $N =$50 neutron shell and toward the proton drip-line., Comment: 9 pages, 3 figures

We provide a solution to the v-representability problem for a non-relativistic quantum many-particle system on a ring domain in terms of Sobolev spaces and their duals. Any one-particle density that is square-integrable, has a square-integrable weak derivative, and is gapped away from zero can be realized from the solution of a many-particle Schr\"odinger equation, with or without interactions, by choosing a corresponding external potential. This potential can contain a distributional contribution but still gives rise to a self-adjoint Hamiltonian. Importantly, this allows for a well-defined Kohn-Sham procedure but, on the other hand, invalidates the usual proof of the Hohenberg-Kohn theorem.