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The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted, including molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.

Speech large language models (speech-LLMs) integrate speech and text-based foundation models to provide a unified framework for handling a wide range of downstream tasks. In this paper, we introduce WHISMA, a speech-LLM tailored for spoken language understanding (SLU) that demonstrates robust performance in various zero-shot settings. WHISMA combines the speech encoder from Whisper with the Llama-3 LLM, and is fine-tuned in a parameter-efficient manner on a comprehensive collection of SLU-related datasets. Our experiments show that WHISMA significantly improves the zero-shot slot filling performance on the SLURP benchmark, achieving a relative gain of 26.6% compared to the current state-of-the-art model. Furthermore, to evaluate WHISMA's generalisation capabilities to unseen domains, we develop a new task-agnostic benchmark named SLU-GLUE. The evaluation results indicate that WHISMA outperforms an existing speech-LLM (Qwen-Audio) with a relative gain of 33.0%., Comment: accepted to SLT 2024

Through the lens of average and peak age-of-information (AoI), this paper takes a fresh look into the uplink medium access solutions for mission-critical (MC) communication coexisting with enhanced mobile broadband (eMBB) service. Considering the stochastic packet arrivals from an MC user, we study three access schemes: orthogonal multiple access (OMA) with eMBB preemption (puncturing), non-orthogonal multiple access (NOMA), and rate-splitting multiple access (RSMA), the latter two both with concurrent eMBB transmissions. Puncturing is found to reduce both average AoI and peak AoI (PAoI) violation probability but at the expense of decreased eMBB user rates and increased signaling complexity. Conversely, NOMA and RSMA offer higher eMBB rates but may lead to MC packet loss and AoI degradation. The paper systematically investigates the conditions under which NOMA or RSMA can closely match the average AoI and PAoI violation performance of puncturing while maintaining data rate gains. Closed-form expressions for average AoI and PAoI violation probability are derived, and conditions on the eMBB and MC channel gain difference with respect to the base station are analyzed. Additionally, optimal power and rate splitting factors in RSMA are determined through an exhaustive search to minimize MC outage probability. Notably, our results indicate that with a small loss in the average AoI and PAoI violation probability the eMBB rate in NOMA and RSMA can be approximately five times higher than that achieved through puncturing., Comment: 14 pages, 9 figures, under review for possible publication in IEEE TVT

Existing multimodal-based human action recognition approaches are either computationally expensive, which limits their applicability in real-time scenarios, or fail to exploit the spatial temporal information of multiple data modalities. In this work, we present an efficient pose-driven attention-guided multimodal network (EPAM-Net) for action recognition in videos. Specifically, we adapted X3D networks for both RGB and pose streams to capture spatio-temporal features from RGB videos and their skeleton sequences. Then skeleton features are utilized to help the visual network stream focusing on key frames and their salient spatial regions using a spatial temporal attention block. Finally, the scores of the two streams of the proposed network are fused for final classification. The experimental results show that our method achieves competitive performance on NTU-D 60 and NTU RGB-D 120 benchmark datasets. Moreover, our model provides a 6.2--9.9x reduction in FLOPs (floating-point operation, in number of multiply-adds) and a 9--9.6x reduction in the number of network parameters. The code will be available at https://github.com/ahmed-nady/Multimodal-Action-Recognition.

Research on dialogue constructiveness assessment focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness and (ii) predicting constructiveness outcomes following dialogues for such use cases. These objectives can be achieved by training either interpretable feature-based models (which often involve costly human annotations) or neural models such as pre-trained language models (which have empirically shown higher task accuracy but lack interpretability). In this paper we propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature-based and neural approaches, while mitigating their downsides. The framework first defines a set of dataset-independent and interpretable linguistic features, which can be extracted by both prompting an LLM and simple heuristics. Such features are then used to train LLM feature-based models. We apply this framework to three datasets of dialogue constructiveness and find that our LLM feature-based models outperform or performs at least as well as standard feature-based models and neural models. We also find that the LLM feature-based model learns more robust prediction rules instead of relying on superficial shortcuts, which often trouble neural models., Comment: Paper accepted by EMNLP 2024

We present a set of 1194 Type-1 Rosseland-mean opacity tables for four different metallicity mixtures. These new Los Alamos OPLIB atomic radiative opacity tables are an order of magnitude larger in number than any previous opacity table release, and span regimes where previous opacity tables have not existed. For example, the new set of opacity tables expands the metallicity range to $Z$\,=\,10$^{-6}$ to $Z$\,=\,0.2 which allows improved accuracy of opacities at low and high metallicity, increases the table density in the metallicity range $Z$\,=\,10$^{-4}$ to $Z$\,=\,0.1 to enhance the accuracy of opacities drawn from interpolations across neighboring metallicities, and adds entries for hydrogen mass fractions between $X$\,=\,0 and $X$\,=\,0.1 including $X$\,=\,$10^{-2}, 10^{-3}, 10^{-4}, 10^{-5}, 10^{-6}$ that can improve stellar models of hydrogen deficient stars. We implement these new OPLIB radiative opacity tables in \MESA, and find that calibrated solar models agree broadly with previously published helioseismic and solar neutrino results. We find differences between using the new 1194 OPLIB opacity tables and the 126 OPAL opacity tables range from $\approx$\,20--80\% across individual chemical mixtures, up to $\approx$\,8\% and $\approx$\,15\% at the bottom and top of the solar convection zone respectively, and $\approx$\,7\% in the solar core. We also find differences between standard solar models using different opacity table sources that are on par with altering the initial abundance mixture. We conclude that this new, open-access set of OPLIB opacity tables does not solve the solar modeling problem, and suggest the investigation of physical mechanisms other than the atomic radiative opacity., Comment: 28 pages, 12 (13) figures. Accepted for Publication in ApJ

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort that contains 160 hematoxylin and eosin whole-slide images of primary melanomas (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep-learning method to allow for classification, at the slide level, of nevi and melanomas. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors., Comment: 5 figures, NeurIPS 2022 Workshop

Some time ago, it has been suggested that gravitons can acquire mass in the process of spontaneous symmetry breaking of diffeomorphisms through the condensation of scalar fields [Chamseddine and Mukhanov, JHEP, 2010]. Taking this possibility into account, in the present paper, first we show how the graviton mass intricately reshapes the gravitational potential akin to a Yukawa-like potential at large distances. Notably, this long-range force modifies the Newton's law in large distances and might explain the phenomena of dark matter. The most important finding in the present paper is the derivation of a modified Newtons law of gravity by modifying the Verlindes entropic force relation due to the graviton contribution. The graviton contribution to the entropy basically measures the correlation of graviton and matter fields which then reproduces the Bekenstein-Hawking entropy at the horizon. This result shows the dual description of gravity: in the language of quantum information and entropy the gravity can be viewed as an entropic force, however in terms of particles and fields, it can be viewed as a longe range force. Further we have recovered the corrected Einstein field equations as well as the $\Lambda$CDM where dark matter emerges as an apparent effect., Comment: Accepted by Annals of Physics

It is well known that the cosmological constant term in the Einstein field equations can be interpreted as a stress tensor for dark energy. This stress tensor is formally analogous to an elastic constitutive equation in continuum mechanics. As a result, the cosmological constant leads to a "shear modulus" and "bulk modulus" affecting all gravitational fields in the universe. The form of the constitutive equation is also analogous to the London constitutive equation for a superconductor. Treating dark energy as a type of superconducting medium for gravitational waves leads to a Yukawa-like gravitational potential and a massive graviton within standard General Relativity. We discuss a number of resulting phenomenological aspects such as a screening length scale that can also be used to describe the effects generally attributed to dark matter. In addition, we find a gravitational wave plasma frequency, index of refraction, and impedance. The expansion of the universe is interpreted as a Meissner-like effect as dark energy causes an outward "expulsion" of space-time similar to a superconductor expelling a magnetic field. The fundamental cause of these effects is interpreted as a type of spontaneous symmetry breaking of a scalar field. There is an associated chemical potential, critical temperature, and an Unruh-Hawking effect associated with the formulation., Comment: Accepted for publication in JCAP

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning model-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates the original EfficientNet, but with an added Attention Mechanism: EfficientNet-AM. Also, unlike the traditional/past pipelines, which relied on a pre-processing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different backbones nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline still outperforms all approaches presented by other teams in last year's instance of the same challenge, at least based on the validation subset of the competition dataset.

Snyder's quantum space-time which is Lorentz invariant is investigated. It is found that the quanta of space-time have a positive mass that is interpreted as a positive real mass gap of space-time. This mass gap is related to the minimal length of measurement which is provided by Snyder's algebra. Several reasons to consider the space-time quanta as a 24-cell are discussed. Geometric reasons include its self-duality property and its 24 vertices that may represent the standard model of elementary particles. The 24-cell symmetry group is the Weyl/Coxeter group of the $F_4$ group which was found recently to generate the gauge group of the standard model. It is found that 24-cell may provide a geometric interpretation of the mass generation, Avogadro number, color confinement, and the flatness of the observable universe. The phenomenology and consistency with measurements is discussed., Comment: 14 pages, two figures, revtex4, Published in Fortschritte der Physik - Progress of Physics

Anti-Money Laundering (AML) is a crucial task in ensuring the integrity of financial systems. One keychallenge in AML is identifying high-risk groups based on their behavior. Unsupervised learning, particularly clustering, is a promising solution for this task. However, the use of hundreds of features todescribe behavior results in a highdimensional dataset that negatively impacts clustering performance.In this paper, we investigate the effectiveness of combining clustering method agglomerative hierarchicalclustering with four dimensionality reduction techniques -Independent Component Analysis (ICA), andKernel Principal Component Analysis (KPCA), Singular Value Decomposition (SVD), Locality Preserving Projections (LPP)- to overcome the issue of high-dimensionality in AML data and improve clusteringresults. This study aims to provide insights into the most effective way of reducing the dimensionality ofAML data and enhance the accuracy of clustering-based AML systems. The experimental results demonstrate that KPCA outperforms other dimension reduction techniques when combined with agglomerativehierarchical clustering. This superiority is observed in the majority of situations, as confirmed by threedistinct validation indices.

Motivated by the potential connection between metric-affine gravity and linear Generalized Uncertainty Principle (GUP) in the phase space, we develop a covariant form of linear GUP and an associated modified Poincar\'e algebra, which exhibits distinctive behavior, nearing nullity at the minimal length scale proposed by linear GUP. We use 3-torus geometry to visually represent linear GUP within a covariant framework. The 3-torus area provides an exact geometric representation of Bekenstein's universal bound. We depart from Bousso's approach, which adapts Bekenstein's bound by substituting the Schwarzschild radius ($r_s$) with the radius ($R$) of the smallest sphere enclosing the physical system, thereby basing the covariant entropy bound on the sphere's area. Instead, our revised covariant entropy bound is described by the area of a 3-torus, determined by both the inner radius $r_s$ and outer radius $R$ where $r_s\leq R $ due to gravitational stability. This approach results in a more precise geometric representation of Bekenstein's bound, notably for larger systems where Bousso's bound is typically much larger than Bekensetin's universal bound. Furthermore, we derive an equation that turns the standard uncertainty inequality into an equation when considering the contribution of the 3-torus covariant entropy bound, suggesting a new avenue of quantum gravity., Comment: 10 pages, 1 figure, to appear in CQG

When a star undergoes core collapse, a vast amount of energy is released in a ~10 s long burst of neutrinos of all species. Inverse beta decay in the star's hydrogen envelope causes an electromagnetic cascade which ultimately results in a flare of gamma rays - an "echo" of the neutrino burst - at the characteristic energy of 0.511 MeV. We study the phenomenology and detectability of this flare. Its luminosity curve is characterized by a fast, seconds-long, rise and an equally fast decline, with a minute- or hour-long plateau in between. For a near-Earth star (distance D<1 kpc) the echo will be observable at near future gamma ray telescopes with an effective area of 10^3 cm^2 or larger. Its observation will inform us on the envelope size and composition. In conjunction with the direct detection of the neutrino burst, it will also give information on the neutrino emission away from the line of sight and will enable tests of neutrino propagation effects between the stellar surface and Earth., Comment: 10 pages, 5 figures and 1 table. Description of the microphysics has been expanded; minor edits were made for clarity. This version matches the one to be published in the Astrophysical Journal

The emerging mission-critical Internet of Things (IoT) play a vital role in remote healthcare, haptic interaction, and industrial automation, where timely delivery of status updates is crucial. The Age of Information (AoI) is an effective metric to capture and evaluate information freshness at the destination. A system design based solely on the optimization of the average AoI might not be adequate to capture the requirements of mission-critical applications, since averaging eliminates the effects of extreme events. In this paper, we introduce a Deep Reinforcement Learning (DRL)-based algorithm to improve AoI in mission-critical IoT applications. The objective is to minimize an AoI-based metric consisting of the weighted sum of the average AoI and the probability of exceeding an AoI threshold. We utilize the actor-critic method to train the algorithm to achieve optimized scheduling policy to solve the formulated problem. The performance of our proposed method is evaluated in a simulated setup and the results show a significant improvement in terms of the average AoI and the AoI violation probability compared to the related-work.