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Generative Adversarial Networks (GANs) are influential machine learning models that have gained prominence across various research fields, including high-energy physics (HEP) simulations. Among them, Conditional GANs (CGANs) offer a unique advantage by conditioning on specific parameters, making them particularly well-suited for meson studies. Given the limited size of the available meson dataset$ - $based on the quark content, quantum numbers, mass, and width$ - $this study, for the first time, applies the CGAN framework to augment the dataset of mesons, while preserving the inherent characteristics of the original data. Using this enhanced dataset, we employ the CGAN model to estimate the mass and width of both ordinary and exotic mesons, based solely on their quark content and quantum numbers. Our CGAN$ - $based predictions highlight the framework's potential as a reliable tool for meson property estimation, providing valuable insights for future research in particle physics., Comment: 27 Pages, 2 Figures and 6 Tables

The proton's internal structure is characterized not only by its charge and magnetic distribution but also by its mechanical and mass properties, which are encoded in the energy-momentum tensor (EMT) of quantum chromodynamics (QCD). These properties provide insights into the spatial distributions of energy, pressure, and shear forces within the proton. Understanding the proton's internal structure, including properties such as its mechanical and mass radii, is essential for unraveling the complex interplay between quarks and gluons that govern its stability and dynamics. In this study, we investigate the gravitational form factors (GFFs) of the proton, particularly the D-term, which encodes key information about the internal stress distribution, pressure, and shear forces within the nucleon. Using a model for skewness-dependent generalized parton distributions (GPDs) constructed from the double-distribution representation, we extract the quark contribution to the $ D(t) $ GFF of the EMT by analyzing available data on Compton form factors (CFFs). We then employ this extracted GFF to explore the mechanical properties of the proton, including its mechanical and mass radii, as well as the internal pressure and shear force distributions. Our results provide new insights into the proton's internal structure and contribute to the broader understanding of nucleon properties., Comment: 23 Pages and 7 Figures

The full width of the light isovector hybrid meson $H_{\mathrm{V}}$ with spin-parities $1^{\mathrm{-+}}$ and content $(\overline{u}gu-\overline{d}gd)/ \sqrt{2}$ is evaluated by considering the decays $H_{\mathrm{V}} \to \rho^{\pm}\pi^{\mp}$, $b_1^{\pm}\pi^{\mp}$, $f_1(1285)\pi$, $f_1(1420)\pi$, $ \eta \pi$, and $\eta^{\prime} \pi$. To calculate the partial widths of these channels, we use QCD three-point sum rule method which is necessary to determine strong couplings at the corresponding hybrid-meson-meson vertices. It turns out that the main contribution to the full width $\Gamma[H_{\mathrm{ V}}]=109.7 \pm 16.0$ of the hybrid meson comes from the processes $H_{ \mathrm{V}} \to \rho^{\pm}\pi^{\mp}$ partial width of which amounts to $ \approx 67~\mathrm{MeV}$. The effects of the decays $H_{\mathrm{V}} \to b_1\pi $ and $H_{\mathrm{V}} \to f_1\pi, f_1^{\prime} \pi$ are also sizeable: Their partial widths are equal to $13~\mathrm{MeV}$ and $20~ \mathrm{MeV}$, respectively. The decays to $\eta \pi$ and $\eta^{\prime} \pi$ mesons are subdominant reactions, nevertheless they form $\approx 9\%$ of the full width $\Gamma[H_{\mathrm{V}}]$. Results obtained in this work may be interesting to unravel the tangle of predictions about $H_{\mathrm{V}}$ existing in the literature, as well as useful in analyses of different resonances., Comment: 10 Pages and 4 Figures

Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study, we present an enhanced ResUNet architecture for automatic brain tumor segmentation, integrating an EfficientNetB0 encoder, a channel attention mechanism, and an Atrous Spatial Pyramid Pooling (ASPP) module. The EfficientNetB0 encoder leverages pre-trained features to improve feature extraction efficiency, while the channel attention mechanism enhances the model's focus on tumor-relevant features. ASPP enables multiscale contextual learning, crucial for handling tumors of varying sizes and shapes. The proposed model was evaluated on two benchmark datasets: TCGA LGG and BraTS 2020. Experimental results demonstrate that our method consistently outperforms the baseline ResUNet and its EfficientNet variant, achieving Dice coefficients of 0.903 and 0.851 and HD95 scores of 9.43 and 3.54 for whole tumor and tumor core regions on the BraTS 2020 dataset, respectively. compared with state-of-the-art methods, our approach shows competitive performance, particularly in whole tumor and tumor core segmentation. These results indicate that combining a powerful encoder with attention mechanisms and ASPP can significantly enhance brain tumor segmentation performance. The proposed approach holds promise for further optimization and application in other medical image segmentation tasks., Comment: 13 pages, 1 figure

Modern high-throughput biological datasets with thousands of perturbations provide the opportunity for large-scale discovery of causal graphs that represent the regulatory interactions between genes. Differentiable causal graphical models have been proposed to infer a gene regulatory network (GRN) from large scale interventional datasets, capturing the causal gene regulatory relationships from genetic perturbations. However, existing models are limited in their expressivity and scalability while failing to address the dynamic nature of biological processes such as cellular differentiation. We propose PerturbODE, a novel framework that incorporates biologically informative neural ordinary differential equations (neural ODEs) to model cell state trajectories under perturbations and derive the causal GRN from the neural ODE's parameters. We demonstrate PerturbODE's efficacy in trajectory prediction and GRN inference across simulated and real over-expression datasets.

We present a detailed investigation on the structure of neutron stars, incorporating the presence of hyperons within a relativistic model under the mean-field approximation. Employing coupling constants derived from QCD sum rules, we explore the particle fraction in beta equilibrium and establish the mass-radius relationship for neutron stars with hyperonic matter. Additionally, we compute the stellar Love number ($\mathcal{K}_{2}$) and the tidal deformability parameter ($\varLambda$), providing valuable insights into the dynamical properties of these celestial objects. Through comparison with theoretical predictions and observational data, our results exhibit good agreement, affirming the validity of our approach. These findings contribute significantly to refining the understanding of neutron star physics, particularly in environments containing hyperons, and offer essential constraints on the equation of state governing such extreme astrophysical conditions., Comment: 18 pages, 5 figures, 5 tables

Generative AI has transformed the animation industry. Several models have been developed for image-to-image translation, particularly focusing on converting real-world images into anime through unpaired translation. Scenimefy, a notable approach utilizing contrastive learning, achieves high fidelity anime scene translation by addressing limited paired data through semi-supervised training. However, it faces limitations due to its reliance on paired data from a fine-tuned StyleGAN in the anime domain, often producing low-quality datasets. Additionally, Scenimefy's high parameter architecture presents opportunities for computational optimization. This research introduces NijiGAN, a novel model incorporating Neural Ordinary Differential Equations (NeuralODEs), which offer unique advantages in continuous transformation modeling compared to traditional residual networks. NijiGAN successfully transforms real-world scenes into high fidelity anime visuals using half of Scenimefy's parameters. It employs pseudo-paired data generated through Scenimefy for supervised training, eliminating dependence on low-quality paired data and improving the training process. Our comprehensive evaluation includes ablation studies, qualitative, and quantitative analysis comparing NijiGAN to similar models. The testing results demonstrate that NijiGAN produces higher-quality images compared to AnimeGAN, as evidenced by a Mean Opinion Score (MOS) of 2.192, it surpasses AnimeGAN's MOS of 2.160. Furthermore, our model achieved a Frechet Inception Distance (FID) score of 58.71, outperforming Scenimefy's FID score of 60.32. These results demonstrate that NijiGAN achieves competitive performance against existing state-of-the-arts, especially Scenimefy as the baseline model.

We investigate the kinematically allowed baryon to baryon-meson strong transitions in all the light and heavy sectors. We consider only the ground state particles in the baryonic and mesonic channels. In the case of mesons, only the well-established pseudoscslar and vector nonets are involved. Using all the restrictions and conservation laws, a \texttt{mathematica}/ \texttt{python} code selects the allowed strong channels among many possible transitions. It is obtained that only five decay channels satisfy the required conditions that all the states are in their on-shell modes. Using the strong coupling constants defining these transitions, we estimate the strong width and beaching fraction at each channel, that may help ongoing experimental and theoretical investigations., Comment: 4 Pages and 1 Table

This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-grain sentiments (fear, surprise, joy, sadness, anger, disgust, and neutral), readability, offensiveness, and domain diversity. Each component is systematically weighted through a multi-step Principal Component Analysis (PCA)-based framework, prioritizing features according to their variance contributions across temporal sentiment shifts. This approach dynamically adjusts component importance, enabling CSEI to precisely capture high-sensitivity shifts in public sentiment. The development of CSEI showed statistically significant correlations with its constituent features, underscoring internal consistency and sensitivity to specific sentiment dimensions. CSEI's responsiveness was validated using a dataset of 4,510,178 Reddit posts about COVID-19. The analysis focused on 15 major events, including the WHO's declaration of COVID-19 as a pandemic, the first reported cases of COVID-19 across different countries, national lockdowns, vaccine developments, and crucial public health measures. Cumulative changes in CSEI revealed prominent peaks and valleys aligned with these events, indicating significant patterns in public sentiment across different phases of the pandemic. Pearson correlation analysis further confirmed a statistically significant relationship between CSEI daily fluctuations and these events (p = 0.0428), highlighting the capacity of CSEI to infer and interpret shifts in public sentiment and engagement in response to major events related to COVID-19.

The scalar tetraquarks $T_{b}$ and $T_{c}$ with asymmetric contents $bb \overline{b}\overline{c}$ and $cc \overline{c}\overline{b}$ are explored using the QCD sum rule method. These states are modeled as the diquark-antidiquarks composed of the axial-vector components. The masses and current couplings of $T_{b}$ and $T_{c}$ are calculated using the two-point sum rule approach. The predictions obtained for the masses of these four-quark mesons prove that they are unstable against the strong two-meson fall-apart decays to conventional mesons. In the case of the tetraquark $ T_{b}$ this is the decay $T_{\mathrm{b}}\to \eta _{b}B_{c}^{-}$. The processes $T_{\mathrm{c}}\rightarrow \eta _{c}B_{c}^{+}$ and $J/\psi B_{c}^{\ast +}$ are kinematically allowed decay modes of the tetraquark $ T_{c}$. The widths of corresponding processes are evaluated by employing the QCD three-point sum rule approach which are necessary to estimate strong couplings at the tetraquark-meson-meson vertices of interest. The mass $ m=(15697 \pm 95)~\mathrm{MeV}$ and width $\Gamma[T_b]=(36.0 \pm 10.2)~ \mathrm{MeV}$ of the tetraquark $T_{b}$ as well as the parameters $ \widetilde{m}=(9680 \pm 102)~\mathrm{MeV}$ and $\Gamma[T_c]=(54.7 \pm 9.9)~ \mathrm{MeV}$ in the case of $T_{c}$ provide useful information to search for and interpret new exotic states., Comment: 10 Pages and 5 Figures. arXiv admin note: text overlap with arXiv:2410.22439

We calculate the valence generalized parton distribution functions (GPDs) of pion at zero skewness applying a theoretical approach in which the valence GPDs are related to valence quark distribution functions, directly. To this end, we use the results of modified chiral quark model ($\chi QM$) for the valence quark distributions of pion obtained in our previous work. We also determine the electromagnetic and gravitational form factors of pion and compare the results of our theoretical model for valence GPDs and form factors of pion with the results of some other models and available experimental data., Comment: 8 Pages and 10 Figures

We present new parton distribution functions (PDFs) at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) in perturbative QCD, derived from a comprehensive global QCD analysis of high-precision data sets from combined HERA deep-inelastic scattering (DIS), the Tevatron, and the Large Hadron Collider (LHC). To improve constraints on quark flavor separation, we incorporate Drell-Yan pair production data, which provides critical sensitivity to the quark distributions. In addition, we include the latest W and Z boson production data from the CDF, D0, ATLAS, and CMS collaborations, further refining both quark and gluon distributions. Our nominal global QCD fit integrates these datasets and examines the resulting impact on the PDFs and their associated uncertainties. Uncertainties in the PDFs are quantified using the Hessian method, ensuring robust error estimates. Furthermore, we explore the sensitivity of the strong coupling constant, $\alpha_s(M_Z^2)$, and proton PDFs in light of the projected measurements from the Electron-Ion Collider (EIC), where improvements in precision are expected. The analysis also investigates the effects of inclusive jet and dijet production data, which provide enhanced constraints on the gluon PDF and $\alpha_s(M_Z^2)$., Comment: 41 pages, 23 figures

Upon absorbing a photon, the ionized electron sails through the target force field in attoseconds to reach free space. This navigation probes details of the potential landscape that get imprinted into the phase of the ionization amplitude. The Eisenbud-Wigner-Smith (EWS) time delay, the energy derivative of this phase, provides the navigation time relative to the time of the electron's ``free'' exit. This time is influenced by the diffraction of the electron from the potential landscape, offering structural and dynamical information about interactions. If the potential has an intrinsic symmetry, a regular pattern in the time delay, including subpatterns of delays and advances, may occur from the diffraction process. The recent synthesis of a polyhedral fluorocarbon instigates the current study of photoionization from a cubic molecule. Our simulation of the EWS delay unravels rich diffraction motifs within $\pm$100 attoseconds in both energy and angular distributions. Averaging over the Euler angles from the laboratory to the molecular frame and over the photoelectron azimuthal direction indicates that the pattern should be discernible in ultrafast chronoscopy. The study benchmarks diffraction in molecular photoionization as a fundamental process which can be experimentally accessed through ultrafast time delay.

The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn-Sham (KS) eigenvalues and eigenfunctions, obtained from a Density Functional Theory (DFT) calculation are used as a starting point to build the Green's function G and the screened Coulomb interaction W, yielding the one-shot G0W0 selfenergy if no further update of these quantities are made. Multiple implementations exist for both the DFT and the subsequent G0W0 calculation, leading to possible differences in quasiparticle energies. In the present work, the G0W0 quasiparticle energies for states close to the band gap are calculated for six crystalline solids, using four different codes: Abinit, exciting, FHI-aims, and GPAW. This comparison helps to assess the impact of basis-set types (planewaves versus localized orbitals) and the treatment of core and valence electrons (all-electron full potentials versus pseudopotentials). The impact of unoccupied states as well as the algorithms for solving the quasiparticle equation are also briefly discussed. For the KS-DFT band gaps, we observe good agreement between all codes, with differences not exceeding 0.1 eV, while the G0W0 results deviate on the order of 0.1-0.3 eV. Between all-electron codes (FHI-aims and exciting), the agreement is better than 15 meV for KS-DFT and, with one exception, about 0.1 eV for G0W0 band gaps., Comment: 14 pages, 2 figures

Precise breast cancer classification on histopathological images has the potential to greatly improve the diagnosis and patient outcome in oncology. The data imbalance problem largely stems from the inherent imbalance within medical image datasets, where certain tumor subtypes may appear much less frequently. This constitutes a considerable limitation in biased model predictions that can overlook critical but rare classes. In this work, we adopted EfficientNet, a state-of-the-art convolutional neural network (CNN) model that balances high accuracy with computational cost efficiency. To address data imbalance, we introduce an intensive data augmentation pipeline and cost-sensitive learning, improving representation and ensuring that the model does not overly favor majority classes. This approach provides the ability to learn effectively from rare tumor types, improving its robustness. Additionally, we fine-tuned the model using transfer learning, where weights in the beginning trained on a binary classification task were adopted to multi-class classification, improving the capability to detect complex patterns within the BreakHis dataset. Our results underscore significant improvements in the binary classification performance, achieving an exceptional recall increase for benign cases from 0.92 to 0.95, alongside an accuracy enhancement from 97.35 % to 98.23%. Our approach improved the performance of multi-class tasks from 91.27% with regular augmentation to 94.54% with intensive augmentation, reaching 95.04% with transfer learning. This framework demonstrated substantial gains in precision in the minority classes, such as Mucinous carcinoma and Papillary carcinoma, while maintaining high recall consistently across these critical subtypes, as further confirmed by confusion matrix analysis., Comment: 19 pages, 7 figures

Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these challenges, we introduce Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes to accelerate building ML for health applications. The models cover various modalities and domains, including radiology (X-rays and computed tomography), histopathology, dermatological imaging, and audio. These models provide domain specific embeddings that facilitate AI development with less labeled data, shorter training times, and reduced computational costs compared to traditional approaches. In addition, we utilize a common interface and style across these models, and prioritize usability to enable developers to integrate HAI-DEF efficiently. We present model evaluations across various tasks and conclude with a discussion of their application and evaluation, covering the importance of ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting. This technical report will be updated over time as more modalities and features are added., Comment: 16 pages, 8 figures

We investigate the potential for generating an AMPS firewall from the replica wormhole topology, which describes the post-Page time spacetime in the evaporation process of a black hole. Our analysis reveals that this topology gives rise to a Dirac delta force experienced by infalling particles at the event horizon, consistent with the AMPS firewall hypothesis. This force, proportional to the particle's total energy squared, is a direct consequence of the inherent Dirac delta in the Ricci scalar of the replica wormhole. We provide a regularization scheme for this force, which is crucial for obtaining physically meaningful results. Notably, our approach suggests that considering the replica wormhole resolves both the information paradox and the AMPS argument regarding the monogamy of entanglement simultaneously, yielding a more coherent framework., Comment: 7 pages, 2 figures, published in Physics Letters B

Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades, advancements in functional Magnetic Resonance Imaging and machine learning have greatly improved our ability to map visual stimuli to brain activity, especially in the visual cortex. Concurrently, research has expanded into decoding more complex processes like language and memory across the whole brain, utilizing techniques to handle greater variability and improve signal accuracy. We argue that "seeing" involves more than just mapping visual stimuli onto the visual cortex; it engages the entire brain, as various emotions and cognitive states can emerge from observing different scenes. In this paper, we develop algorithms to enhance our understanding of visual processes by incorporating whole-brain activation maps while individuals are exposed to visual stimuli. We utilize large-scale fMRI encoders and Image generative models pre-trained on large public datasets, which are then fine-tuned through Image-fMRI contrastive learning. Our models hence can decode visual experience across the entire cerebral cortex, surpassing the traditional confines of the visual cortex. We first compare our method with state-of-the-art approaches to decoding visual processing and show improved predictive semantic accuracy by 43%. A network ablation analysis suggests that beyond the visual cortex, the default mode network contributes most to decoding stimuli, in line with the proposed role of this network in sense-making and semantic processing. Additionally, we implemented zero-shot imagination decoding on an extra validation dataset, achieving a p-value of 0.0206 for mapping the reconstructed images and ground-truth text stimuli, which substantiates the model's capability to capture semantic meanings across various scenarios.

Spectroscopic parameters and decays of the exotic tensor meson $T$ with content $bc \overline{b}\overline{c}$ are explored in the context of the diquark-antidiquark model. We treat it as a state built of axial-vector diquark $b^{T}C\gamma _{\mu }c$ and antidiquark $\overline{b}\gamma _{\nu }C \overline{c}^{T}$, where $C$ is the charge conjugation matrix. The mass $m$ and current coupling $\Lambda$ of this tetraquark are extracted from two-point sum rules. Our result for $m=(12.70 \pm 0.09)~\mathrm{GeV}$ proves that $T$ is unstable against strong dissociations to two-meson final states. Its dominant decay channels are processes $T \to J/\psi \Upsilon $, $ \eta_{b}\eta _{c}$, and $B_{c}^{(\ast) +}B_{c}^{(\ast) -}$. Kinematically allowed transformations of $T$ include also decays $T\rightarrow D^{(\ast )+}D^{(\ast )-}$ and $D^{(\ast )0}\overline{D}^{(\ast )0}$, which are generated by $b\overline{b}$ annihilation inside of $T$. The full width of $ T $ is estimated by considering all of these channels. Their partial widths are calculated by invoking methods of three-point sum rule approach which are required to evaluate strong couplings at corresponding tetraquark-meson-meson vertices. Our predictions for the mass and width $ \Gamma_{T}=(117.4 \pm 15.9)~ \mathrm{MeV} $ of the tensor state $T$ provide useful information for experimental studies of fully heavy four-quark exotic structures., Comment: 14 Pages and 4 Figures

We conduct an investigation on the spin $\frac{3}{2}\rightarrow \frac{1}{2}$ semileptonic weak transition of single heavy baryons for the exclusive decay $\Sigma_{b}^{*0}\,\rightarrow\, \Sigma_{c}^{+}\,\ell\,\bar{\nu}_{\ell}$ in three possible lepton channels within the three point QCD sum rule method. We compute the responsible form factors of this semileptonic decay by incorporating both perturbative and non-perturbative contributions of the operator product expansion series up to mass dimension six. Having acquired the form factors, the decay widths of the processes in all lepton channels are determined. Our findings as well as possible future experimental information can be employed in order to check the SM predictions and explore the possibility of new physics in heavy baryonic decay channels., Comment: 22 Pages, 5 Figures and 7 Tables

Force/torque sensing is an important modality for robotic manipulation, but commodity solutions, generally developed with other applications in mind, do not generally fit the needs of robot hands. This paper introduces a novel method for six-axis force/torque sensing, using LEDs to sense the displacement between two plates connected by a transparent elastomer. Our method allows for finger-size packaging with no amplification electronics, low cost manufacturing, and easy integration into a complete hand. On test forces between 0-2 N, our prototype sensor exhibits a mean error between 0.05 and 0.07 N across the three force directions, suggesting future applicability to fine manipulation tasks.

We investigate the weak semileptonic decay of the $ \Omega^{+}_{ccb} \rightarrow \Xi^{++}_{cc} ~{\ell}\bar\nu_{\ell}$, where a triply heavy baryon with spin 1/2 decays into the observed doubly heavy baryon with spin 1/2, using QCD sum rule method in all lepton channels. We compute the six relevant vector and axial vector form factors entering the low energy matrix elements in full theory. The invariant form factors are building blocks, using the fit faction of which in terms of $ q^2 $ in whole physical region, we calculate the exclusive widths in three lepton channels. Our predictions may help the present and future experiments in the course of their search for doubly and triply heavy baryons., Comment: 23 Pages, 8 Figures and 4 Tables

Mass and width of a hidden charm-bottom axial-vector structure $T$ containing $bc \overline{b}\overline{c}$ quarks are calculated in QCD sum rule framework. It is treated as a diquark-antidiquark state built of scalar diquark and axial-vector antidiquark components. The mass of $T$ is computed using the two-point sum rule method. The width of this particle is evaluated by considering eight decay modes: The decays to $\eta _{b}J/\psi $, $\eta _{c}\Upsilon (1S)$, $B_{c}^{-}B_{c}^{\ast +}$, and $B_{c}^{+}B_{c}^{\ast -}$ are dissociation processes, in which all initial quarks are distributed between the final-state particles. Four decays to $DD$ mesons with appropriate charges and spin-parities are channels generated due to the annihilation of $b$ and $\overline{b}$ quarks from $T$. Partial widths for all of these processes are obtained by employing the three-point sum rule approach necessary to find the strong couplings at relevant tetraquark-meson-meson vertices. Our results for the mass $m=(12715\pm 90)~\mathrm{MeV}$ and width $\Gamma[T] =(104.2 \pm 11.2)~ \mathrm{MeV}$ of the tetraquark $T$, as well as its numerous decay channels explored in this article are useful for ongoing and future experimental investigations of fully heavy resonances., Comment: 10 Pages and 4 Figures

Simulated patient systems play a crucial role in modern medical education and research, providing safe, integrative learning environments and enabling clinical decision-making simulations. Large Language Models (LLM) could advance simulated patient systems by replicating medical conditions and patient-doctor interactions with high fidelity and low cost. However, ensuring the effectiveness and trustworthiness of these systems remains a challenge, as they require a large, diverse, and precise patient knowledgebase, along with a robust and stable knowledge diffusion to users. Here, we developed AIPatient, an advanced simulated patient system with AIPatient Knowledge Graph (AIPatient KG) as the input and the Reasoning Retrieval-Augmented Generation (Reasoning RAG) agentic workflow as the generation backbone. AIPatient KG samples data from Electronic Health Records (EHRs) in the Medical Information Mart for Intensive Care (MIMIC)-III database, producing a clinically diverse and relevant cohort of 1,495 patients with high knowledgebase validity (F1 0.89). Reasoning RAG leverages six LLM powered agents spanning tasks including retrieval, KG query generation, abstraction, checker, rewrite, and summarization. This agentic framework reaches an overall accuracy of 94.15% in EHR-based medical Question Answering (QA), outperforming benchmarks that use either no agent or only partial agent integration. Our system also presents high readability (median Flesch Reading Ease 77.23; median Flesch Kincaid Grade 5.6), robustness (ANOVA F-value 0.6126, p>0.1), and stability (ANOVA F-value 0.782, p>0.1). The promising performance of the AIPatient system highlights its potential to support a wide range of applications, including medical education, model evaluation, and system integration., Comment: 42 pages, 6 figures, 7 tables

In this paper, we propose a framework to enhance the robustness of the neural models by mitigating the effects of process-induced and aging-related variations of analog computing components on the accuracy of the analog neural networks. We model these variations as the noise affecting the precision of the activations and introduce a denoising block inserted between selected layers of a pre-trained model. We demonstrate that training the denoising block significantly increases the model's robustness against various noise levels. To minimize the overhead associated with adding these blocks, we present an exploration algorithm to identify optimal insertion points for the denoising blocks. Additionally, we propose a specialized architecture to efficiently execute the denoising blocks, which can be integrated into mixed-signal accelerators. We evaluate the effectiveness of our approach using Deep Neural Network (DNN) models trained on the ImageNet and CIFAR-10 datasets. The results show that on average, by accepting 2.03% parameter count overhead, the accuracy drop due to the variations reduces from 31.7% to 1.15%.

In this paper, we propose a network architecture where two types of aerial infrastructures together with a ground station provide connectivity to a remote area. A high altitude platform station (HAPS) is equipped with reconfigurable intelligent surface (RIS), so-called HAPS-RIS, to be exploited to assist the unmanned aerial vehicle (UAV)-based wireless networks. A key challenge in such networks is the restricted number of UAVs, which limits full coverage and leaves some users unsupported. To tackle this issue, we propose a hierarchical bilevel optimization framework including a leader and a follower problem. The users served by HAPS-RIS are in a zone called HAPS-RIS zone and the users served by the UAVs are in another zone called UAV zone. In the leader problem, the goal is to establish the zone boundary that maximizes the number of users covered by HAPS-RIS while ensuring that users in this zone meet their rate requirements. This is achieved through an algorithm that integrates RIS clustering, subcarrier allocation, and zone determination. The follower problem focuses on minimizing the number of UAVs required, ensuring that the rate requirements of the users in the UAV zone are met. This is addressed using an algorithm that employs k-means clustering and subcarrier allocation. Our study reveals that increasing the number of RIS elements significantly decreases the number of required UAVs.

Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computational power means that it is possible to run many simulations, yet setting up simulation runs remaining a time-consuming and error-prone process that involves many manual steps. duqtools is an open-source workflow tool written in Python for that addresses this bottleneck by automating the set up of new simulations. This enables uncertainty quantification and large scale validation of fusion energy modelling simulations. In this work, we demonstrate how duqtools can be used to set up and launch 2000 different simulations of plasma experiments to validate aspects of the JINTRAC modelling suite. With this large-scale validation we identified issues in preserving data consistency in model initialization of the current ($I(p)$) distribution. Furthermore, we used duqtools for sensitivity analysis on the QLKNN-jetexp-15D surrogate model to verify its correctness in multiple regimes., Comment: Now with clarifications requested by reviewers

Via numerical simulations and analytical calculations, depletion forces are studied in mixtures of small and big particles that interact via soft repulsive potentials. While big particles are spherical, small particles are nonspherical with shapes that vary gradually, from squares to rods via intermediate shapes. The mixtures are studied for a wide range of densities and temperature. Depletion forces and their resulting potentials depend on the interplay of shape, temperature and density, an argument that is elaborated qualitatively and quantitatively. While in some thermodynamic conditions, depletion potentials of distinct shapes are distinguishable, in different conditions, they are very similar. Finally, I propose novel computational models and experiments for further investigation of the effect of morphology on phase separation in and out of thermal equilibrium.

We study the semileptonic decay of the doubly heavy baryon $ \Xi^{++}_{cc} $ into the singly heavy baryon $ \Xi^+_{c}$ within the three-point QCD sum rule approach in two possible lepton channels. Our analysis includes perturbative as well as nonperturbative condensation contributions up to dimension 5. We evaluate the form factors of this semileptonic decay entering the amplitude described by the vector and axial vector transition currents. The fit functions of the form factors with respect to the transferred momentum squared are utilized to predict the decay widths and branching ratios of the $ \Xi^{++}_{cc}\rightarrow \Xi^+_{c} \bar{\ell}\nu_{\ell}$ channels. We compare our findings with other predictions in the literature. Our outcomes can be useful for experimental groups in their search for the weak decays of doubly heavy baryons and may be checked via future experiments such as LHCb., Comment: 17 Pages, 6 Figures and 4 Tables

Graph Neural Networks (GNNs) excel in handling graph-structured data but often underperform in link prediction tasks compared to classical methods, mainly due to the limitations of the commonly used Message Passing GNNs (MPNNs). Notably, their ability to distinguish non-isomorphic graphs is limited by the 1-dimensional Weisfeiler-Lehman test. Our study presents a novel method to enhance the expressivity of GNNs by embedding induced subgraphs into the graph Laplacian matrix's eigenbasis. We introduce a Learnable Lanczos algorithm with Linear Constraints (LLwLC), proposing two novel subgraph extraction strategies: encoding vertex-deleted subgraphs and applying Neumann eigenvalue constraints. For the former, we conjecture that LLwLC establishes a universal approximator, offering efficient time complexity. The latter focuses on link representations enabling differentiation between $k$-regular graphs and node automorphism, a vital aspect for link prediction tasks. Our approach results in an extremely lightweight architecture, reducing the need for extensive training datasets. Empirically, our method improves performance in challenging link prediction tasks across benchmark datasets, establishing its practical utility and supporting our theoretical findings. Notably, LLwLC achieves 20x and 10x speedup by only requiring 5% and 10% data from the PubMed and OGBL-Vessel datasets while comparing to the state-of-the-art.

The proton-radius puzzle refers to the discrepancy observed in measurements of the proton's charge radius when using different methods. This inconsistency has prompted extensive research and debate within the physics community, as it challenges the understanding of quantum electrodynamics and the fundamental properties of protons. In the present study, we determine the charge and magnetic radii of the proton and neutron through a global analysis of the generalized parton distributions (GPDs) at zero skewness for the first time. Our results challenge the measurements or analyses in which the nucleon's radii are extracted considering just a particular experiment or observable, or using only data points covering particular kinematic regions. We emphasize that simultaneous analysis of all available experimental data related to the radii of the nucleons would be preferable to determine their exact values. The final results obtained from our analysis by this way are: $ r_{pE} = 0.8558 \pm 0.0135~\textrm{fm} $, $ r_{pM} = 0.8268 \pm 0.0533~\textrm{fm} $, $ \left = -0.1181 \pm 0.0270~\textrm{fm}^2 $, and $ r_{nM} = 0.8367 \pm 0.0845~\textrm{fm} $., Comment: 8 Pages, 4 Figures and 2 Tables

This research introduces an innovative method for adaptive traffic signal control (ATSC) through the utilization of multi-objective deep reinforcement learning (DRL) techniques. The proposed approach aims to enhance control strategies at intersections while simultaneously addressing safety, efficiency, and decarbonization objectives. Traditional ATSC methods typically prioritize traffic efficiency and often struggle to adapt to real-time dynamic traffic conditions. To address these challenges, the study suggests a DRL-based ATSC algorithm that incorporates the Dueling Double Deep Q Network (D3QN) framework. The performance of this algorithm is assessed using a simulated intersection in Changsha, China. Notably, the proposed ATSC algorithm surpasses both traditional ATSC and ATSC algorithms focused solely on efficiency optimization by achieving over a 16% reduction in traffic conflicts and a 4% decrease in carbon emissions. Regarding traffic efficiency, waiting time is reduced by 18% compared to traditional ATSC, albeit showing a slight increase (0.64%) compared to the DRL-based ATSC algorithm integrating the D3QN framework. This marginal increase suggests a trade-off between efficiency and other objectives like safety and decarbonization. Additionally, the proposed approach demonstrates superior performance, particularly in scenarios with high traffic demand, across all three objectives. These findings contribute to advancing traffic control systems by offering a practical and effective solution for optimizing signal control strategies in real-world traffic situations.

Helmholtz stereopsis is one the versatile techniques for 3D geometry reconstruction from 2D images of objects with unknown and arbitrary reflectance surfaces. HS eliminates the need for surface reflectance, a challenging parameter to measure, based on the Helmholtz reciprocity principle. Its Bayesian formulation using maximum a posteriori (MAP) probability approach has significantly improved reconstruction accuracy of HS method. This framework enables the inclusion of smoothness priors which enforces observations and neighborhood information in the formulation. We used Markov Random Fields (MRF) which is a powerful tool to integrate diverse prior contextual information and solved the MAP-MRF using belief propagation algorithm. We propose a new smoothness function utilizing the normal field integration method for refined depth estimation within the Bayesian framework. Utilizing three pairs of images with different viewpoints, our approach demonstrates superior depth label accuracy compared to conventional Bayesian methods. Experimental results indicate that our proposed method yields a better depth map with reduced RMS error, showcasing its efficacy in improving depth estimation within Helmholtz stereopsis., Comment: 19 pages

Coupling the fields may lead to the emergence of new phenomena. In the realm of classical fields and nonlinear systems, extensive research has been conducted on their solitary and soliton solutions. In the conducted studies, typically two $\phi^4$ systems, or two sine-Gordon systems, have been coupled. The sine-Gordon system exhibits diverse solutions, all well-behaved, with its soliton solutions fully understood. On the other hand, the $\phi^4$ system, which is significant in field theory, has solitary solutions, but these solutions are not solitonic. For example, from a pair of kink and antikink, we cannot construct a bound state; or that after a collision, these two solutions do not revert to their initial status and become disrupted. In this study, we couple a $\phi^4$ system with a sine-Gordon system to impart stability from the sine-Gordon system to the $\phi^4$ system. We have demonstrated that for a coupled $\phi^4$ and sine-Gordon system, this expectation is somewhat met., Comment: 12 pages, 7 figurs

We predict a near-threshold ("zero energy") peak in multi-photon ionization for a dynamical regime where the photon frequency is large compared to the binding energy of the electron. The peak position does not depend on the laser frequency, but on the binding energy and the pulse duration. The effect originates from the fact that bound-continuum dipole transitions are stronger than continuum-continuum ones. To clearly observe this zero-energy photoelectric effect, the spectral width of the laser pulse should be comparable to the binding energy of the ionized orbital, and the second ionization potential should be larger than the photon energy. This suggests negative ions as ideal candidates for corresponding experiments., Comment: 5 pages, 5 figures, 1 table

Parameters of the heavy four-quark scalar meson $T_{\mathrm{bc\overline{b} \overline{c}}}$ with content $bc \overline{b}\overline{c}$ are calculated by means of the sum rule method. This structure is considered as a diquark-antidiquark state built of scalar diquark and antidiquark components. The mass and current coupling of $T_{\mathrm{bc\overline{b} \overline{c}}}$ are evaluated in the context of the two-point sum rule approach. The full width of this tetraquark is estimated by taking into account two types of its possible strong decay channels. First class includes dissociation of $T_{\mathrm{bc\overline{b}\overline{c}}} $ to mesons $\eta_c\eta_{b}$, $B_{c}^{+}B_{c}^{-}$, $B_{c}^{\ast +}B_{c}^{\ast -}$ and $B_{c}^{+}(1^3P_{0})B_{c}^{\ast-}$. Another type of processes are generated by annihilations $\overline{b}b \to \overline{q}q$ of constituent $ b$-quarks which produces the final-state charmed meson pairs $D^{+}D^{-}$, $ D^{0} \overline{D}^{0}$, $D^{*+}D^{*-}$, and $D^{*0}\overline{D}^{*0}$. Partial width all of these decays are found using the three-point sum rule method which is required to calculate strong couplings at corresponding meson-meson-tetraquark vertices. Predictions obtained for the mass $m=(12697 \pm 90)~\mathrm{MeV}$ and width $\Gamma[T_{\mathrm{bc\overline{b}\overline{c} }}]=(142.4 \pm 16.9)~ \mathrm{MeV}$ of this state are compared with alternative results, and are useful for further experimental investigations of fully heavy resonances., Comment: 11 Pages, 4 Figures and 1 Table

The deployment of Vision Transformers (ViTs) on hardware platforms, specially Field-Programmable Gate Arrays (FPGAs), presents many challenges, which are mainly due to the substantial computational and power requirements of their non-linear functions, notably layer normalization, softmax, and Gaussian Error Linear Unit (GELU). These critical functions pose significant obstacles to efficient hardware implementation due to their complex mathematical operations and the inherent resource count and architectural limitations of FPGAs. PEANO-ViT offers a novel approach to streamlining the implementation of the layer normalization layer by introducing a division-free technique that simultaneously approximates the division and square root function. Additionally, PEANO-ViT provides a multi-scale division strategy to eliminate division operations in the softmax layer, aided by a Pade-based approximation for the exponential function. Finally, PEANO-ViT introduces a piece-wise linear approximation for the GELU function, carefully designed to bypass the computationally intensive operations associated with GELU. In our comprehensive evaluations, PEANO-ViT exhibits minimal accuracy degradation (<= 0.5% for DeiT-B) while significantly enhancing power efficiency, achieving improvements of 1.91x, 1.39x, 8.01x for layer normalization, softmax, and GELU, respectively. This improvement is achieved through substantial reductions in DSP, LUT, and register counts for these non-linear operations. Consequently, PEANO-ViT enables efficient deployment of Vision Transformers on resource- and power-constrained FPGA platforms.

Low-rank adaptation (LoRA) has become the default approach to fine-tune large language models (LLMs) due to its significant reduction in trainable parameters. However, trainable parameter demand for LoRA increases with increasing model embedding dimensions, leading to high compute costs. Additionally, its backward updates require storing high-dimensional intermediate activations and optimizer states, demanding high peak GPU memory. In this paper, we introduce large model fine-tuning via spectrally decomposed low-dimensional adaptation (LaMDA), a novel approach to fine-tuning large language models, which leverages low-dimensional adaptation to achieve significant reductions in trainable parameters and peak GPU memory footprint. LaMDA freezes a first projection matrix (PMA) in the adaptation path while introducing a low-dimensional trainable square matrix, resulting in substantial reductions in trainable parameters and peak GPU memory usage. LaMDA gradually freezes a second projection matrix (PMB) during the early fine-tuning stages, reducing the compute cost associated with weight updates to enhance parameter efficiency further. We also present an enhancement, LaMDA++, incorporating a ``lite-weight" adaptive rank allocation for the LoRA path via normalized spectrum analysis of pre-trained model weights. We evaluate LaMDA/LaMDA++ across various tasks, including natural language understanding with the GLUE benchmark, text summarization, natural language generation, and complex reasoning on different LLMs. Results show that LaMDA matches or surpasses the performance of existing alternatives while requiring up to 17.7x fewer parameter updates and up to 1.32x lower peak GPU memory usage during fine-tuning. Code will be publicly available.

The pseudoscalar and vector four-quark states $bb\overline{c}\overline{c}$ are studied in the context of the QCD sum rule method. We model $T_{\mathrm{ \ \ PS}} $ and $T_{\mathrm{V}}$ as structures built of diquarks $ b^{T}C\gamma_{5}b$, $\overline{c}C\overline{c}^{T}$ and $b^{T}C\gamma _{5}b$ , $\overline{c}C\gamma_{\mu}\gamma_{5}\overline{c}^{T}$, respectively, with $ C$ being the charge conjugation matrix. The spectroscopic parameters of the tetraquarks $T_{\mathrm{PS}}$ and $T_{\mathrm{V}}$, i.e., their masses and current couplings are calculated using QCD two-point sum rule method. We evaluate the full widths of $T_{\mathrm{PS}}$ and $T_{\mathrm{V}}$ by taking into account their kinematically allowed decay channels. In the case of the pseudoscalar particle they are processes $T_{\mathrm{PS}} \to B_{c}^{-}B_{c}^{\ast -}$, $B_{c}^{-}B_{c}^{-}(1^{3}P_{0})$ and $B_{c}^{\ast -}B_{c}^{-}(1^{1}P_{1})$. The vector state $T_{\mathrm{V}}$ can dissociate to meson pairs $2 B_{c}^{-}$, $2 B_{c}^{\ast -}$ and $ B_{c}^{-}B_{c}^{-}(1^{1}P_{1})$. Partial widths of these decays are determined by the strong couplings at relevant tetraquark-meson-meson vertices, which evaluated in the context of the three-point sum rule approach. Predictions obtained for the mass and full width of the pseudoscalar $m =(13.092\pm 0.095)~\mathrm{GeV}$, $\Gamma _{\mathrm{PS} }=(63.7\pm 13.0)~\mathrm{MeV}$ and vector $\widetilde{m} =(13.15\pm 0.10)~ \mathrm{GeV}$, $\Gamma_{\mathrm{V}}=(53.5\pm 10.3)~\mathrm{MeV}$ tetraquarks can be useful for analyses of different four-quark resonances., Comment: 14 Pages, 6 Figures and 2 Tables

Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable. However, the majority of current AI approaches address only a narrowly defined set of tasks, often circumscribed within a particular domain. To bridge this gap, we introduce Tx-LLM, a generalist large language model (LLM) fine-tuned from PaLM-2 which encodes knowledge about diverse therapeutic modalities. Tx-LLM is trained using a collection of 709 datasets that target 66 tasks spanning various stages of the drug discovery pipeline. Using a single set of weights, Tx-LLM simultaneously processes a wide variety of chemical or biological entities(small molecules, proteins, nucleic acids, cell lines, diseases) interleaved with free-text, allowing it to predict a broad range of associated properties, achieving competitive with state-of-the-art (SOTA) performance on 43 out of 66 tasks and exceeding SOTA on 22. Among these, Tx-LLM is particularly powerful and exceeds best-in-class performance on average for tasks combining molecular SMILES representations with text such as cell line names or disease names, likely due to context learned during pretraining. We observe evidence of positive transfer between tasks with diverse drug types (e.g.,tasks involving small molecules and tasks involving proteins), and we study the impact of model size, domain finetuning, and prompting strategies on performance. We believe Tx-LLM represents an important step towards LLMs encoding biochemical knowledge and could have a future role as an end-to-end tool across the drug discovery development pipeline.

Vision-Language Models (VLMs) have recently seen significant advancements through integrating with Large Language Models (LLMs). The VLMs, which process image and text modalities simultaneously, have demonstrated the ability to learn and understand the interaction between images and texts across various multi-modal tasks. Reverse designing, which could be defined as a complex vision-language task, aims to predict the edits and their parameters, given a source image, an edited version, and an optional high-level textual edit description. This task requires VLMs to comprehend the interplay between the source image, the edited version, and the optional textual context simultaneously, going beyond traditional vision-language tasks. In this paper, we extend and fine-tune MiniGPT-4 for the reverse designing task. Our experiments demonstrate the extensibility of off-the-shelf VLMs, specifically MiniGPT-4, for more complex tasks such as reverse designing. Code is available at this \href{https://github.com/VahidAz/MiniGPT-Reverse-Designing}, Comment: 8 pages, 7 figures