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We discuss the impact of ideas of open quantum systems and quantum information to various facets of neutrino and neutral meson oscillations. These oscillations are characterized by a number of quantum correlations, both spatial as well as temporal. For neutrinos, the correlations are shown to be simple functions of the product of neutrino survival and oscillation probabilities. The quantum correlations in the neutral mesons are seen to be non-trivially different from their stable counterparts., Comment: To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh and A R Rao, Springer Nature

In domains such as biomedical, expert insights are crucial for selecting the most informative modalities for artificial intelligence (AI) methodologies. However, using all available modalities poses challenges, particularly in determining the impact of each modality on performance and optimizing their combinations for accurate classification. Traditional approaches resort to manual trial and error methods, lacking systematic frameworks for discerning the most relevant modalities. Moreover, although multi-modal learning enables the integration of information from diverse sources, utilizing all available modalities is often impractical and unnecessary. To address this, we introduce an entropy-based algorithm STORM to solve the modality selection problem for rare event. This algorithm systematically evaluates the information content of individual modalities and their combinations, identifying the most discriminative features essential for rare class classification tasks. Through seizure onset zone detection case study, we demonstrate the efficacy of our algorithm in enhancing classification performance. By selecting useful subset of modalities, our approach paves the way for more efficient AI-driven biomedical analyses, thereby advancing disease diagnosis in clinical settings., Comment: Accepted in IEEE Asilomar Conference on Signals, Systems, and Computers, 2024

Existing Scholarly Question Answering (QA) methods typically target homogeneous data sources, relying solely on either text or Knowledge Graphs (KGs). However, scholarly information often spans heterogeneous sources, necessitating the development of QA systems that integrate information from multiple heterogeneous data sources. To address this challenge, we introduce Hybrid-SQuAD (Hybrid Scholarly Question Answering Dataset), a novel large-scale QA dataset designed to facilitate answering questions incorporating both text and KG facts. The dataset consists of 10.5K question-answer pairs generated by a large language model, leveraging the KGs DBLP and SemOpenAlex alongside corresponding text from Wikipedia. In addition, we propose a RAG-based baseline hybrid QA model, achieving an exact match score of 69.65 on the Hybrid-SQuAD test set.

Let $p(n)$ denote the partition function. In this paper our main goal is to derive an asymptotic expansion up to order $N$ (for any fixed positive integer $N$) along with estimates for error bounds for the shifted quotient of the partition function, namely $p(n+k)/p(n)$ with $k\in \mathbb{N}$, which generalizes a result of Gomez, Males, and Rolen. In order to do so, we derive asymptotic expansions with error bounds for the shifted version $p(n+k)$ and the multiplicative inverse $1/p(n)$, which is of independent interest.

Recovering a physics-driven model, i.e. a governing set of equations of the underlying dynamical systems, from the real-world data has been of recent interest. Most existing methods either operate on simulation data with unrealistically high sampling rates or require explicit measurements of all system variables, which is not amenable in real-world deployments. Moreover, they assume the timestamps of external perturbations to the physical system are known a priori, without uncertainty, implicitly discounting any sensor time-synchronization or human reporting errors. In this paper, we propose a novel liquid time constant neural network (LTC-NN) based architecture to recover underlying model of physical dynamics from real-world data. The automatic differentiation property of LTC-NN nodes overcomes problems associated with low sampling rates, the input dependent time constant in the forward pass of the hidden layer of LTC-NN nodes creates a massive search space of implicit physical dynamics, the physics model solver based data reconstruction loss guides the search for the correct set of implicit dynamics, and the use of the dropout regularization in the dense layer ensures extraction of the sparsest model. Further, to account for the perturbation timing error, we utilize dense layer nodes to search through input shifts that results in the lowest reconstruction loss. Experiments on four benchmark dynamical systems, three with simulation data and one with the real-world data show that the LTC-NN architecture is more accurate in recovering implicit physics model coefficients than the state-of-the-art sparse model recovery approaches. We also introduce four additional case studies (total eight) on real-life medical examples in simulation and with real-world clinical data to show effectiveness of our approach in recovering underlying model in practice., Comment: This paper is published in ECAI 2024, https://ebooks.iospress.nl/volumearticle/69651

The pursuit of leaderboard rankings in Large Language Models (LLMs) has created a fundamental paradox: models excel at standardized tests while failing to demonstrate genuine language understanding and adaptability. Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum, from basic metrics to complex benchmarks like GLUE and MMLU. These vulnerabilities manifest through benchmark exploitation, dataset contamination, and evaluation bias, creating a false perception of progress in language understanding capabilities. Through extensive review of contemporary evaluation approaches, we identify significant limitations in static benchmark designs, human evaluation protocols, and LLM-as-judge frameworks, all of which compromise the reliability of current performance assessments. As LLM capabilities evolve and existing benchmarks become redundant, we lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks. This requires frameworks that are adapted dynamically, addressing current limitations and providing a more accurate reflection of LLM performance., Comment: 11 pages

In ferrofluids, the vorticity is balanced by the rate of particle rotation, which is known here as critical balance. The universal energy cascade is investigated for a stationary and non-stationary incompressible ferrofluid turbulent system using exact relations. The findings reveal that under moderate magnetic fields, kinetic and total energy cascades occur at identical rates. As the external magnetic field strength increases, the total energy cascade becomes non-stationary and differs from the kinetic one. However, the cascade's universal, scale-independent nature remains unaffected. The suppression of turbulence in ferrofluids is observable at strong magnetic fields., Comment: 14 pages, 17 figures. arXiv admin note: text overlap with arXiv:2405.04139

The aim of this paper is to formalise the task of continual semi-supervised anomaly detection (CSAD), with the aim of highlighting the importance of such a problem formulation which assumes as close to real-world conditions as possible. After an overview of the relevant definitions of continual semi-supervised learning, its components, anomaly detection extension, and the training protocols; the paper introduces a baseline model of a variational autoencoder (VAE) to work with semi-supervised data along with a continual learning method of deep generative replay with outlier rejection. The results show that such a use of extreme value theory (EVT) applied to anomaly detection can provide promising results even in comparison to an upper baseline of joint training. The results explore the effects of how much labelled and unlabelled data is present, of which class, and where it is located in the data stream. Outlier rejection shows promising initial results where it often surpasses a baseline method of Elastic Weight Consolidation (EWC). A baseline for CSAD is put forward along with the specific dataset setups used for reproducability and testability for other practitioners. Future research directions include other CSAD settings and further research into efficient continual hyperparameter tuning.

The Generative Agents framework recently developed by Park et al. has enabled numerous new technical solutions and problem-solving approaches. Academic and industrial interest in generative agents has been explosive as a result of the effectiveness of generative agents toward emulating human behaviour. However, it is necessary to consider the ethical challenges and concerns posed by this technique and its usage. In this position paper, we discuss the extant literature that evaluate the ethical considerations regarding generative agents and similar generative tools, and identify additional concerns of significant importance. We also suggest guidelines and necessary future research on how to mitigate some of the ethical issues and systemic risks associated with generative agents., Comment: Accepted (poster) to Socially Responsible Language Modelling Research (SoLaR) Workshop at NeurIPS 2024

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monochrome image streams showing 19 subjects interacting with 33 diverse rigid objects, multi-modal signals such as eye gaze or scene point clouds, as well as comprehensive ground-truth annotations including 3D poses of objects, hands, and cameras, and 3D models of hands and objects. In addition to simple pick-up/observe/put-down actions, HOT3D contains scenarios resembling typical actions in a kitchen, office, and living room environment. The dataset is recorded by two head-mounted devices from Meta: Project Aria, a research prototype of light-weight AR/AI glasses, and Quest 3, a production VR headset sold in millions of units. Ground-truth poses were obtained by a professional motion-capture system using small optical markers attached to hands and objects. Hand annotations are provided in the UmeTrack and MANO formats and objects are represented by 3D meshes with PBR materials obtained by an in-house scanner. In our experiments, we demonstrate the effectiveness of multi-view egocentric data for three popular tasks: 3D hand tracking, 6DoF object pose estimation, and 3D lifting of unknown in-hand objects. The evaluated multi-view methods, whose benchmarking is uniquely enabled by HOT3D, significantly outperform their single-view counterparts., Comment: arXiv admin note: substantial text overlap with arXiv:2406.09598

The problem of robust quickest change detection (QCD) in non-stationary processes under a multi-stream setting is studied. In classical QCD theory, optimal solutions are developed to detect a sudden change in the distribution of stationary data. Most studies have focused on single-stream data. In non-stationary processes, the data distribution both before and after change varies with time and is not precisely known. The multi-dimension data even complicates such issues. It is shown that if the non-stationary family for each dimension or stream has a least favorable law (LFL) or distribution in a well-defined sense, then the algorithm designed using the LFLs is robust optimal. The notion of LFL defined in this work differs from the classical definitions due to the dependence of the post-change model on the change point. Examples of multi-stream non-stationary processes encountered in public health monitoring and aviation applications are provided. Our robust algorithm is applied to simulated and real data to show its effectiveness.

The field of computational imaging has witnessed a promising paradigm shift with the emergence of untrained neural networks, offering novel solutions to inverse computational imaging problems. While existing techniques have demonstrated impressive results, they often operate either in the high-data regime, leveraging Generative Adversarial Networks (GANs) as image priors, or through untrained iterative reconstruction in a data-agnostic manner. This paper delves into lensless image reconstruction, a subset of computational imaging that replaces traditional lenses with computation, enabling the development of ultra-thin and lightweight imaging systems. To the best of our knowledge, we are the first to leverage implicit neural representations for lensless image deblurring, achieving reconstructions without the requirement of prior training. We perform prior-embedded untrained iterative optimization to enhance reconstruction performance and speed up convergence, effectively bridging the gap between the no-data and high-data regimes. Through a thorough comparative analysis encompassing various untrained and low-shot methods, including under-parameterized non-convolutional methods and domain-restricted low-shot methods, we showcase the superior performance of our approach by a significant margin.

Quantum speed limit (QSL) defines the theoretical upper bound on how fast a quantum system can evolve between states. It imposes a fundamental constraint on the rate of quantum information processing. For a relativistic spin-up electron in a uniform magnetic field, QSL increased with the magnetic field strength till around $10^{15}$ Gauss, before saturating at a saturated QSL (SQSL) of 0.2407c, where c is the speed of light. We show that by using variable magnetic fields, it is possible to surpass this limit, achieving SQSL upto 0.4-0.6c. To attain this quantum phenomenon, we solve the evolution equation of relativistic electron in spatially varying magnetic fields and find that the energies of various electron states become non-degenerate as opposed to the constant magnetic field case. This redistribution of energy is the key ingredient to accomplish higher QSL and, thus, a high information processing speed. We further explore how QSL can serve as a bridge between relativistic and non-relativistic quantum dynamics, providing insights via the Bremermann-Bekenstein bound, a quantity which constrains the maximal rate of information production. We also propose a practical experimental setup to realize these advancements. These results hold immense potential for propelling fields of quantum computation, thermodynamics and metrology., Comment: To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh and A R Rao, Springer Nature

We investigate the quantum speed limit (QSL) during the time evolution of neutrino-antineutrino system under the influence of the gravitational field of a spinning primordial black hole (PBH). We derive an analytical expression for the four-vector gravitational potential in the underlying Hermitian Dirac Hamiltonian using the Boyer-Lindquist (BL) coordinates. This gravitational potential leads to an axial vector term in the Dirac equation in curved spacetime, contributing to the effective mass matrix of the neutrino-antineutrino systems. Our findings indicate that the gravitational field, expressed in BL coordinates, significantly influences the transition probabilities in two-flavor oscillations of the neutrino-antineutrino system. We then apply the expression for transition probabilities between states to analyze the Bures angle, which quantifies the closeness between the initial and final states of the time-evolved flavor state. We use this concept to probe the QSL for the time evolution of the initial flavor neutrino state., Comment: 16 pages, 12 figures, Accepted for publication in the Proceedings of the 17th Marcel Grossman Meeting (MG17), Pescara, Italy, 7-12 July 2024. Based on the talk presented in the parallel session "Unveiling neutrino secrets through cosmology: current status and future developments (NU2)"

The quantum speed limits (QSLs) determine the minimal amount of time required for a quantum system to evolve from an initial to a final state. We investigate QSLs for the unitary evolution of the neutrino-antineutrino system in the presence of a gravitational field. It is known that the transition probabilities between neutrino and antineutrino in the framework of one and two flavors depend on the strength of the gravitational field. The behavior of the QSL time in the two-flavor system indicates fast flavor transitions as the gravitational field strength increases. Subsequently, we observe quick suppression of entanglement by exploring the speed limit for entanglement entropy of two-flavor oscillations in the neutrino-antineutrino system in the proximity of a spinning primordial black hole., Comment: 12 pages, 5 figures, To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh & A R Rao, Springer Nature

Traditional archival practices for describing electronic theses and dissertations (ETDs) rely on broad, high-level metadata schemes that fail to capture the depth, complexity, and interdisciplinary nature of these long scholarly works. The lack of detailed, chapter-level content descriptions impedes researchers' ability to locate specific sections or themes, thereby reducing discoverability and overall accessibility. By providing chapter-level metadata information, we improve the effectiveness of ETDs as research resources. This makes it easier for scholars to navigate them efficiently and extract valuable insights. The absence of such metadata further obstructs interdisciplinary research by obscuring connections across fields, hindering new academic discoveries and collaboration. In this paper, we propose a machine learning and AI-driven solution to automatically categorize ETD chapters. This solution is intended to improve discoverability and promote understanding of chapters. Our approach enriches traditional archival practices by providing context-rich descriptions that facilitate targeted navigation and improved access. We aim to support interdisciplinary research and make ETDs more accessible. By providing chapter-level classification labels and using them to index in our developed prototype system, we make content in ETD chapters more discoverable and usable for a diverse range of scholarly needs. Implementing this AI-enhanced approach allows archives to serve researchers better, enabling efficient access to relevant information and supporting deeper engagement with ETDs. This will increase the impact of ETDs as research tools, foster interdisciplinary exploration, and reinforce the role of archives in scholarly communication within the data-intensive academic landscape.

We present for the first time the second-order corrections of pseudo-scalar($A$) Higgs decay to three parton to higher orders in the dimensional regulator. We compute the one and two-loop amplitudes for processes, $A\to ggg$ and $A\to q\bar{q}g$ in the effective theory framework. With suitable crossing of the external momenta, these calculations are well-suited for predicting the differential distribution of pseudo-scalar Higgs in association with a jet at hadron colliders, up to next-to-next-to-leading order (NNLO) in the strong coupling constant. These results expanded to higher orders in dimensional regulator will contribute to the full three loop cross section. We implement the finite pieces of the amplitudes in a numerical code which can be used with any Monte Carlo phase space generator., Comment: 11 pages, 7 figures and 1 table

The Virginia Tech University Libraries (VTUL) Digital Library Platform (DLP) hosts digital collections that offer our users access to a wide variety of documents of historical and cultural importance. These collections are not only of academic importance but also provide our users with a glance at local historical events. Our DLP contains collections comprising digital objects featuring complex layouts, faded imagery, and hard-to-read handwritten text, which makes providing online access to these materials challenging. To address these issues, we integrate AI into our DLP workflow and convert the text in the digital objects into a machine-readable format. To enhance the user experience with our historical collections, we use custom AI agents for handwriting recognition, text extraction, and large language models (LLMs) for summarization. This poster highlights three collections focusing on handwritten letters, newspapers, and digitized topographic maps. We discuss the challenges with each collection and detail our approaches to address them. Our proposed methods aim to enhance the user experience by making the contents in these collections easier to search and navigate.

As research institutions increasingly commit to supporting the United Nations' Sustainable Development Goals (SDGs), there is a pressing need to accurately assess their research output against these goals. Current approaches, primarily reliant on keyword-based Boolean search queries, conflate incidental keyword matches with genuine contributions, reducing retrieval precision and complicating benchmarking efforts. This study investigates the application of autoregressive Large Language Models (LLMs) as evaluation agents to identify relevant scholarly contributions to SDG targets in scholarly publications. Using a dataset of academic abstracts retrieved via SDG-specific keyword queries, we demonstrate that small, locally-hosted LLMs can differentiate semantically relevant contributions to SDG targets from documents retrieved due to incidental keyword matches, addressing the limitations of traditional methods. By leveraging the contextual understanding of LLMs, this approach provides a scalable framework for improving SDG-related research metrics and informing institutional reporting.

Reconfiguration problems involve determining whether two given configurations can be transformed into each other under specific rules. The Token Sliding problem asks whether, given two different set of tokens on vertices of a graph $G$, we can transform one into the other by sliding tokens step-by-step along edges of $G$ such that each resulting set of tokens forms an independent set in $G$. Recently, Ito et al. [MFCS 2022] introduced a directed variant of this problem. They showed that for general oriented graphs (i.e., graphs where no pair of vertices can have directed edges in both directions), the problem remains $\mathsf{PSPACE}$-complete, and is solvable in polynomial time on oriented trees. In this paper, we further investigate the Token Sliding problem on various oriented graph classes. We show that the problem remains $\mathsf{PSPACE}$-complete for oriented planar graphs, split graphs, bipartite graphs and bounded treewidth graphs. Additionally, we present polynomial-time algorithms for solving the problem on oriented cycles and cographs.

The influence of the presence of cosmic fluid on the magnetosonic waves and modulation instabilities in the interstellar medium of spiral galaxies is investigated. The fluid model is developed by modifying the pressure equation in such dissipative rotating magnetoplasmas incorporating thermal ionized gas and cosmic rays. Applying the normal mode analysis, a modified dispersion relation is derived to study linear magnetosonic wave modes and their instabilities. The cosmic rays influence the wave damping by accelerating the damping rate. The standard reductive perturbation method is employed in the fluid model leading to a Korteweg de Vries Burgers (KdVB) equation in the small-amplitude limit. Several nonlinear wave shapes are assessed by solving the KdVB equation, analytically and numerically. The cosmic ray diffusivity and magnetic resistivity are responsible for the generation of shock waves. The modulational instability (MI) and the rogue wave solutions of the magnetosonic waves are studied by deriving a nonlinear Schrodinger (NLS) equation from the obtained KdVB equation under the assumption that the cosmic ray diffusion and magnetic resistivity are weak and the carrier wave frequency is considerably lower than the wave frequency. The influence of various plasma parameters on the growth rate of MI is examined. The modification of the pressure term due to cosmic fluid reduces the MI growth in the interstellar medium. In addition, a quantitative analysis of the characteristics of rogue wave solutions is presented. Our investigation's applicability to the interstellar medium of spiral galaxies is traced out., Comment: 14 pages, 10 figures. Submitted to the Physics of Fluids

Automatic Speech Recognition (ASR) systems in the clinical domain face significant challenges, notably the need to recognise specialised medical vocabulary accurately and meet stringent precision requirements. We introduce United-MedASR, a novel architecture that addresses these challenges by integrating synthetic data generation, precision ASR fine-tuning, and advanced semantic enhancement techniques. United-MedASR constructs a specialised medical vocabulary by synthesising data from authoritative sources such as ICD-10 (International Classification of Diseases, 10th Revision), MIMS (Monthly Index of Medical Specialties), and FDA databases. This enriched vocabulary helps finetune the Whisper ASR model to better cater to clinical needs. To enhance processing speed, we incorporate Faster Whisper, ensuring streamlined and high-speed ASR performance. Additionally, we employ a customised BART-based semantic enhancer to handle intricate medical terminology, thereby increasing accuracy efficiently. Our layered approach establishes new benchmarks in ASR performance, achieving a Word Error Rate (WER) of 0.985% on LibriSpeech test-clean, 0.26% on Europarl-ASR EN Guest-test, and demonstrating robust performance on Tedlium (0.29% WER) and FLEURS (0.336% WER). Furthermore, we present an adaptable architecture that can be replicated across different domains, making it a versatile solution for domain-specific ASR systems., Comment: 15 pages

This paper reports world averages of measurements of $b$-hadron, $c$-hadron, and $\tau$-lepton properties obtained by the Heavy Flavour Averaging Group using results available before October 2023. In rare cases, significant results obtained several months later are also used. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters, \CP~violation parameters, parameters of semileptonic decays, and Cabibbo-Kobayashi-Maskawa matrix elements., Comment: 523 pages. arXiv admin note: substantial text overlap with arXiv:2206.07501, arXiv:1909.12524; text overlap with arXiv:1612.07233, arXiv:1412.7515

This paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized M\"{o}bius transformation to define the regression curve. By mapping the real axis to the circle, the model effectively captures the relationship between linear and angular components. A key innovation is the introduction of an area-based loss function, inspired by the geometry of a curved torus, for efficient parameter estimation. The semi-parametric nature of the model eliminates the need for specific distributional assumptions about the angular error, enhancing its versatility. Extensive simulation studies, incorporating von Mises and wrapped Cauchy distributions, highlight the robustness of the framework. The model's practical utility is demonstrated through real-world data analysis of Bitcoin and Ethereum, showcasing its ability to derive meaningful insights from complex data structures.

Decayless kink oscillations, characterized by their lack of decay in amplitude, have been detected in coronal loops of varying scales in active regions, quiet Sun and coronal holes. Short-period (< 50 s) decayless oscillations have been detected in short loops (< 50 Mm) within active regions. Nevertheless, long-period decayless oscillations in these loops remain relatively unexplored and crucial for understanding the wave modes and excitation mechanisms of decayless oscillations. We present the statistical analysis of decayless oscillations from two active regions observed by the Extreme Ultraviolet Imager (EUI) onboard Solar Orbiter. The average loop length and period of the detected oscillations are 19 Mm and 151 seconds, respectively. We find 82 long-period and 23 short-period oscillations in these loops. We do not obtain a significant correlation between loop length and period. We discuss the possibility of different wave modes in short loops, although standing waves can not be excluded from possible wave modes. Furthermore, a different branch exists for active region short loops in the loop length vs period relation, similar to decayless waves in short loops in quiet Sun and coronal holes. The magnetic fields derived from MHD seismology, based on standing kink modes, show lower values for multiple oscillations compared to previous estimates for long loops in active regions. Additionally, the comparison of period distributions in short loops across different coronal regions indicates that different excitation mechanisms may trigger short-period kink oscillations in active regions compared to the quiet Sun and coronal holes., Comment: 17 Pages, 12 Figures, Accepted for publication in the APJ

AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our framework incorporates ReXGradient, the largest test dataset consisting of 10,000 studies, and three public datasets (MIMIC-CXR, IU-Xray, CheXpert Plus) for report generation assessment. ReXrank employs 8 evaluation metrics and separately assesses models capable of generating only findings sections and those providing both findings and impressions sections. By providing this standardized evaluation framework, ReXrank enables meaningful comparisons of model performance and offers crucial insights into their robustness across diverse clinical settings. Beyond its current focus on chest X-rays, ReXrank's framework sets the stage for comprehensive evaluation of automated reporting across the full spectrum of medical imaging.

The ground-based measurement of gravitational waves (GW) from merging binary black holes (BBH) uniquely allows determination of spins of stellar-remnant black holes (BH), thereby offering insights into their formation mechanisms. The observed population of BBH mergers exhibits two intriguing peculiarities related to BH spins, namely, a positively biased distribution of effective spin parameter, $\chi_{\rm eff}$, and an apparent anti-correlation between merger mass ratio, $q$, and $\chi_{\rm eff}$. Here we investigate the potential mechanisms for such observed properties, in BBH mergers via isolated binary evolution. We synthesize BBH mergers with the fast binary evolution code BSE. The role of various physical assumptions is explored, including tidal spin-up, compact remnant mass, and mass transfer physics. We compare the properties of WR-BH binaries that formed through stable mass transfer (SMT) and common envelope evolution (CE). We find that both the asymmetry in $\chi_{\rm eff}$ distribution and the $\chi_{\rm eff}-q$ anti-correlation are natural outcomes of isolated-binary BBH formation with the standard assumptions on mass transfer physics. The anti-correlation is particularly pronounced for SMT-channel BBH mergers that experience a mass-ratio-reversal, i.e., those where the second-born BH is the more massive member. The anti-correlation arises from the dependence of orbital shrinking during mass transfer and the Roche lobe size on the system's mass ratio. This characteristic $\chi_{\rm eff}-q$ trend diminishes with increasing metallicity and when the isolated-binary BBH merger population is mixed with a significant contribution of dynamically formed BBH mergers. Our results demonstrate that isolated massive binary evolution via the SMT sub-channel can reproduce the observable BBH merger population, with characteristic signatures in mass, mass ratio, and spin distributions.[abr.], Comment: 25 pages, 18 figures, 3 appendices. Submitted to Astronomy and Astrophysics. Comments are welcome

Interacting or nonlinear lattices can host emergent particle-like modes, such as Bogoliubov quasiparticles, whose band topology and other properties are potentially highly tunable. Despite originating in the study of superconducting materials, Bogoliubov quasiparticles can also occur in synthetic metamaterials. Here, we implement a nonlinear driven-dissipative circuit whose fluctutations are Bogoliubov modes possessing nontrivial non-Hermitian band topology. We show experimentally that the system exhibits a switchable non-Hermitian skin effect (NHSE), which abruptly appears when the on-site driving voltage amplitude exceeds a threshold. In contrast to earlier realizations of the NHSE and related phenomena in circuit models, the switchable NHSE in our system occurs in Bogoliubov modes, which are strongly affected by how the system is driven. Moreover, unlike other experimental platforms hosting non-Hermitian Bogoliubov modes, our system does not contain unconventional asymmetric hopping nonlinearities, only a local Kerr-type nonlinearity.

Variational empirical Bayes (VEB) methods provide a practically attractive approach to fitting large, sparse, multiple regression models. These methods usually use coordinate ascent to optimize the variational objective function, an approach known as coordinate ascent variational inference (CAVI). Here we propose alternative optimization approaches based on gradient-based (quasi-Newton) methods, which we call gradient-based variational inference (GradVI). GradVI exploits a recent result from Kim et. al. [arXiv:2208.10910] which writes the VEB regression objective function as a penalized regression. Unfortunately the penalty function is not available in closed form, and we present and compare two approaches to dealing with this problem. In simple situations where CAVI performs well, we show that GradVI produces similar predictive performance, and GradVI converges in fewer iterations when the predictors are highly correlated. Furthermore, unlike CAVI, the key computations in GradVI are simple matrix-vector products, and so GradVI is much faster than CAVI in settings where the design matrix admits fast matrix-vector products (e.g., as we show here, trendfiltering applications) and lends itself to parallelized implementations in ways that CAVI does not. GradVI is also very flexible, and could exploit automatic differentiation to easily implement different prior families. Our methods are implemented in an open-source Python software, GradVI (available from https://github.com/stephenslab/gradvi ).

We propose a Bayesian neural network-based continual learning algorithm using Variational Inference, aiming to overcome several drawbacks of existing methods. Specifically, in continual learning scenarios, storing network parameters at each step to retain knowledge poses challenges. This is compounded by the crucial need to mitigate catastrophic forgetting, particularly given the limited access to past datasets, which complicates maintaining correspondence between network parameters and datasets across all sessions. Current methods using Variational Inference with KL divergence risk catastrophic forgetting during uncertain node updates and coupled disruptions in certain nodes. To address these challenges, we propose the following strategies. To reduce the storage of the dense layer parameters, we propose a parameter distribution learning method that significantly reduces the storage requirements. In the continual learning framework employing variational inference, our study introduces a regularization term that specifically targets the dynamics and population of the mean and variance of the parameters. This term aims to retain the benefits of KL divergence while addressing related challenges. To ensure proper correspondence between network parameters and the data, our method introduces an importance-weighted Evidence Lower Bound term to capture data and parameter correlations. This enables storage of common and distinctive parameter hyperspace bases. The proposed method partitions the parameter space into common and distinctive subspaces, with conditions for effective backward and forward knowledge transfer, elucidating the network-parameter dataset correspondence. The experimental results demonstrate the effectiveness of our method across diverse datasets and various combinations of sequential datasets, yielding superior performance compared to existing approaches., Comment: at ICVGIP 2024