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Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. Gliomas, the most common primary brain tumors, arise from the carcinogenesis of glial cells in the brain and spinal cord, with glioblastoma patients having a median survival time of less than 14 months. MRI serves as a non-invasive and effective method for tumor detection, but manual segmentation of brain MRI scans has traditionally been a labor-intensive task for neuroradiologists. Recent advancements in computer-aided design (CAD), machine learning (ML), and deep learning (DL) offer promising solutions for automating this process. This study proposes an automated deep learning model for brain tumor detection and classification using MRI data. The model, incorporating spatial attention, achieved 96.90% accuracy, enhancing the aggregation of contextual information for better pattern recognition. Experimental results demonstrate that the proposed approach outperforms baseline models, highlighting its robustness and potential for advancing automated MRI-based brain tumor analysis.

Quantifying multipartite entanglement poses a significant challenge in quantum information theory, prompting recent advancements in methodologies to assess it. We introduce the notion of \enquote{Volume of Assistance} (VoA), which computes the geometric mean of entanglement of assistance across all potential parties. We demonstrate the feasibility of VoA for three-qubit pure states and certain classes of pure tripartite qudit states. We have extended this measure to four-qubit states and general multipartite scenarios. We have done a comparative analysis to illustrate VoA's distinctiveness from established entanglement measures, notably showing it serves as an upper bound for the much celebrated generalized geometric measure (GGM). Remarkably, VoA excels in distinguishing a broad class of states that elude differentiation by the recently proposed Minimum Pairwise Concurrence (MPC) measure. Finally, VoA is applied to quantify genuine entanglement in the ground states of a three-qubit Heisenberg XY model, which highlights its practical utility in quantum information processing tasks., Comment: 11 pages, 4 figures, latex2e, comments welcome

This review article provides an overview of random matrix theory (RMT) with a focus on its growing impact on the formulation and inference of statistical models and methodologies. Emphasizing applications within high-dimensional statistics, we explore key theoretical results from RMT and their role in addressing challenges associated with high-dimensional data. The discussion highlights how advances in RMT have significantly influenced the development of statistical methods, particularly in areas such as covariance matrix inference, principal component analysis (PCA), signal processing, and changepoint detection, demonstrating the close interplay between theory and practice in modern high-dimensional statistical inference., Comment: 56 pages, 7 figures

Physics-informed neural networks (PINNs) commonly address ill-posed inverse problems by uncovering unknown physics. This study presents a novel unsupervised learning framework that identifies spatial subdomains with specific governing physics. It uses the partition of unity networks (POUs) to divide the space into subdomains, assigning unique nonlinear model parameters to each, which are integrated into the physics model. A vital feature of this method is a physics residual-based loss function that detects variations in physical properties without requiring labeled data. This approach enables the discovery of spatial decompositions and nonlinear parameters in partial differential equations (PDEs), optimizing the solution space by dividing it into subdomains and improving accuracy. Its effectiveness is demonstrated through applications in porous media thermal ablation and ice-sheet modeling, showcasing its potential for tackling real-world physics challenges., Comment: 26 pages

We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a solar cycle. Improvements in Monte Carlo event simulation and better handling of year-to-year differences in data processing significantly reduce systematic uncertainties below the level of statistical fluctuations compared to the previously published results. We confirm the observation of a change in the angular structure of the cosmic-ray anisotropy between 10 TeV and 1 PeV, more specifically in the 100-300 TeV energy range.

We investigate accretion onto an isolated black hole from uniform winds. If the winds are directed towards the black hole, then the accretion process can be well described by the classical Bondi-Hoyle Lyttleton or BHL accretion. If the wind is not directed towards the black hole and flows past it, then a smaller fraction of the flow can be attracted by the black hole, and this type of accretion cannot be described by the classical BHL, and we coin the second kind as the lateral BHL. We show that the classical BHL cannot form an accretion disk, while lateral BHL can form transient accretion disks. To describe the thermodynamics of the flow, we have used a variable adiabatic index equation of state which depends on the temperature of the flow as well as the composition of the gas. We show that the electron-proton gas forms an accretion disk, which disappears and forms a shock cone, only to form the disk again at a later time, while for flows with less protons, the accretion disk, once lost, does not reappear again. Only when the flow is pair-dominated does it form a persistent accretion disk. We also show that a shock cone is less luminous than the accretion disk., Comment: Accepted for publication in ApJ. 15 pages, 12 figures, one table

With the growing demand for interpretable deep learning models, this paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique aimed at providing a holistic view of feature importance across Convolutional Neural Networks (CNNs). Traditional gradient-based CAM methods, such as Grad-CAM and Grad-CAM++, primarily use final layer activations to highlight regions of interest, often neglecting critical features derived from intermediate layers. Integrative CAM addresses this limitation by fusing insights across all network layers, leveraging both gradient and activation scores to adaptively weight layer contributions, thus yielding a comprehensive interpretation of the model's internal representation. Our approach includes a novel bias term in the saliency map calculation, a factor frequently omitted in existing CAM techniques, but essential for capturing a more complete feature importance landscape, as modern CNNs rely on both weighted activations and biases to make predictions. Additionally, we generalize the alpha term from Grad-CAM++ to apply to any smooth function, expanding CAM applicability across a wider range of models. Through extensive experiments on diverse and complex datasets, Integrative CAM demonstrates superior fidelity in feature importance mapping, effectively enhancing interpretability for intricate fusion scenarios and complex decision-making tasks. By advancing interpretability methods to capture multi-layered model insights, Integrative CAM provides a valuable tool for fusion-driven applications, promoting the trustworthy and insightful deployment of deep learning models.

Perturbative Quantum Chromodynamics corrections and the colour superconductivity indicate that strongly interacting matter can manifest unique physical behaviours under extreme conditions. Motivated by this notion, we investigate the interior structure and properties of quark stars composed of interacting quark matter, which provides a comprehensive avenue to explore the strong interaction effects, within the framework of $f(Q)$ gravity. A unified equation of state is formulated to describe various phases of quark matter, including up-down quark matter $(2SC)$, strange quark matter $(2SC+s)$, and the Colour-Flavor Locked $(CFL)$ phase. By employing a systematic reparametrisation and rescaling, the number of degrees of freedom in the equation of state is significantly reduced. Utilising the Buchdahl-I metric ansatz and a linear $f(Q)$ functional form, $f(Q)=\alpha_{0}+\alpha_{1}Q$, we derive the exact solutions of the Einstein field equations in presence of the unified interacting quark matter equation of state. For the $2SC$ phase, we examine the properties of quark stars composed of up-down quark matter. For the $(2SC+s)$and $CFL$ phases, we incorporate the effects of a finite strange quark mass $(m_{s}\neq0)$. The Tolman-Oppenheimer-Volkoff equations are numerically solved to determine the maximum mass-radius relations for each phase. Our results indicate that the model satisfies key physical criteria, including causality, energy conditions, and stability requirements, ensuring the viability of the configurations. Furthermore, the predicted radii for certain compact star candidates align well with observational data. The study highlights that quark stars composed of interacting quark matter within the $f(Q)$ gravity framework provide a robust and physically consistent stellar model across all considered phases., Comment: 24 pages, 33 figures

Spontaneous or conversational multilingual speech presents many challenges for state-of-the-art automatic speech recognition (ASR) systems. In this work, we present a new technique AMPS that augments a multilingual multimodal ASR system with paraphrase-based supervision for improved conversational ASR in multiple languages, including Hindi, Marathi, Malayalam, Kannada, and Nyanja. We use paraphrases of the reference transcriptions as additional supervision while training the multimodal ASR model and selectively invoke this paraphrase objective for utterances with poor ASR performance. Using AMPS with a state-of-the-art multimodal model SeamlessM4T, we obtain significant relative reductions in word error rates (WERs) of up to 5%. We present detailed analyses of our system using both objective and human evaluation metrics.

Despite having triggered devastating pandemics in the past, our ability to quantitatively assess the emergence potential of individual strains of animal influenza viruses remains limited. This study introduces Emergenet, a tool to infer a digital twin of sequence evolution to chart how new variants might emerge in the wild. Our predictions based on Emergenets built only using 220,151 Hemagglutinnin (HA) sequences consistently outperform WHO seasonal vaccine recommendations for H1N1/H3N2 subtypes over two decades (average match-improvement: 3.73 AAs, 28.40\%), and are at par with state-of-the-art approaches that use more detailed phenotypic annotations. Finally, our generative models are used to scalably calculate the current odds of emergence of animal strains not yet in human circulation, which strongly correlates with CDC's expert-assessed Influenza Risk Assessment Tool (IRAT) scores (Pearson's $r = 0.721, p = 10^{-4}$). A minimum five orders of magnitude speedup over CDC's assessment (seconds vs months) then enabled us to analyze 6,354 animal strains collected post-2020 to identify 35 strains with high emergence scores ($> 7.7$). The Emergenet framework opens the door to preemptive pandemic mitigation through targeted inoculation of animal hosts before the first human infection., Comment: 35 pages, 15 figures

In this paper, we extend the class of admissible functions for the trace formula of the second order in the self-adjoint, unitary, and contraction cases for a perturbation in the Hilbert-Schmidt class $\mathcal{S}^2(\mathcal{H})$ by assuming a certain factorization of the divided difference $f^{[2]}$. This class is the natural one to ensure that the second order Taylor remainder is a trace class operator. It encompasses all the classes of functions for which the trace formula was previously known. Secondly, for a Schatten $\mathcal{S}^p$-perturbation, $1

We describe a numerical method for simulating stochastic fluid dynamics near a critical point in the Ising universality class. This theory is known as model H, and is expected to govern the non-equilibrium dynamics of Quantum Chromodynamics (QCD) near a possible critical endpoint of the phase transition between a hadron liquid and the quark-gluon plasma. The numerical algorithm is based on a Metropolis scheme, and automatically ensures that the distribution function of the hydrodynamic variables in equilibrium is independent of the transport coefficients and only governed by the microscopic free energy. We verify dynamic scaling near the critical point of a two and three-dimensional fluid and extract the associated critical exponent $z$. We find $z\simeq 3$ in three dimensions, and $z\simeq 2$ for a two-dimensional fluid. In a finite system, we observe a crossover between the mean field value $z=4$ and the true critical exponent $z\simeq 3$ ($z \simeq 2$ in $d=2$). This crossover is governed by the values of the correlation length and the renormalized shear viscosity., Comment: 36 pages, 11 figures

Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often called as NeuralPDEs, derived from first principle physics. Therefore, there is a widespread assumption in the community that NeuralPDEs are more trustworthy and generalizable than black box models. However, like any SciML model, differentiable programming relies predominantly on high-quality PDE simulations as "ground truth" for training. However, mathematics dictates that these are only discrete numerical approximations of the true physics. Therefore, we ask: Are NeuralPDEs and differentiable programming models trained on PDE simulations as physically interpretable as we think? In this work, we rigorously attempt to answer these questions, using established ideas from numerical analysis, experiments, and analysis of model Jacobians. Our study shows that NeuralPDEs learn the artifacts in the simulation training data arising from the discretized Taylor Series truncation error of the spatial derivatives. Additionally, NeuralPDE models are systematically biased, and their generalization capability is likely enabled by a fortuitous interplay of numerical dissipation and truncation error in the training dataset and NeuralPDE, which seldom happens in practical applications. This bias manifests aggressively even in relatively accessible 1-D equations, raising concerns about the veracity of differentiable programming on complex, high-dimensional, real-world PDEs, and in dataset integrity of foundation models. Further, we observe that the initial condition constrains the truncation error in initial-value problems in PDEs, thereby exerting limitations to extrapolation. Finally, we demonstrate that an eigenanalysis of model weights can indicate a priori if the model will be inaccurate for out-of-distribution testing.

The majority of atomic nuclei have deformed shapes and nearly all these shapes are symmetric with respect to reflection. There are only a few reflection asymmetric pear-shaped nuclei that have been found in actinide and lanthanide regions, which have static octupole deformation. These nuclei possess an intrinsic electric dipole moment due to the shift between the center of charge and the center of mass. This manifests in the enhancement of the electric dipole transition rates. In this article, we report on the measurement of the lifetimes of the high spin levels of the two alternate parity bands in $^{100}$Ru through the Doppler Shift Attenuation Method. The estimated electric dipole transition rates have been compared with the calculated transition rates using the triaxial projected shell model without octupole deformation, and are found to be an order of magnitude enhanced. Thus, the observation of seven inter-leaved electric dipole transitions with enhanced rates establish $^{100}$Ru as possibly the first octupole deformed nucleus reported in the A $\approx$ 100 mass region.

We present the broadband spectral and timing properties of the atoll source 4U 1702-429 using two observations of AstroSat with the second one having simultaneous NICER data. For both observations, the spectra can be represented by a Comptonizing medium with a black body seed photon source which can be identified with the surface of the neutron star. A disk emission along with a distant reflection is also required for both spectra. For the first observation, the coronal temperature ($\sim 7$ keV) is smaller than the second ($\sim 13$ keV), and the disk is truncated at a larger radius, $\sim 150$ km, compared to the second, $\sim 25$ km, for an assumed distance of 7 kpc. A kHz QPO at $\sim 800$ Hz is detected in the first and is absent in the second observation. Modeling the energy-dependent r.m.s and time lag of the kHz QPO reveals a corona size of $\leq$ 30 km. A similar model can explain the energy dependence of the broadband noise at $\sim 10$ Hz for the second observation. The results suggest that kHz QPOs are associated with a compact corona surrounding the neutron star and may occur when the disk is truncated at large distances. We emphasize the need for more wide-band observations of the source to confirm these results., Comment: 12 Pages, 7 Figures, Accepted for publication in The Astrophysical Journal

We explore the distribution in space and time of a single-photon excitation shared by a network of dipole-dipole interacting atoms that are also coupled to a common photonic field mode. Time-averaged distributions reveal partial trapping of the excitation near the initially excited atom. This trapping is associated with resonances of the excitation at crossing points of the photon-dressed energy eigenvalues of the network. The predicted photon-induced many-atom trapped excitation (PIMATE) is sensitive to atomic position disorder which broadens the excitation resonances and transforms them to avoided crossings. PIMATE is shown to allow highly effective and accurate sensing of multi-atom networks and their disorder.

We investigate the task of deterministically condensing randomness from Online Non-Oblivious Symbol Fixing (oNOSF) sources, a natural model for which extraction is impossible [AORSV, EUROCRYPT'20]. A $(g,\ell)$-oNOSF source is a sequence of $\ell$ blocks where at least $g$ of the blocks are good (independent and have some min-entropy) and the remaining bad blocks are controlled by an online adversary where each bad block can be arbitrarily correlated with any block that appears before it. The existence of condensers was studied in [CGR, FOCS'24]. They proved condensing impossibility results for various values of $g, \ell$ and showed the existence of condensers matching the impossibility results in the case when $n$ is extremely large compared to $\ell$. In this work, we make significant progress on proving the existence of condensers with strong parameters in almost all parameter regimes, even when $n$ is a large enough constant and $\ell$ is growing. This almost resolves the question of the existence of condensers for oNOSF sources, except when $n$ is a small constant. We construct the first explicit condensers for oNOSF sources, achieve parameters that match the existential results of [CGR, FOCS'24], and obtain an improved construction for transforming low-entropy oNOSF sources into uniform ones. We find applications of our results to collective coin flipping and sampling, well-studied problems in fault-tolerant distributed computing. We use our condensers to provide simple protocols for these problems. To understand the case of small $n$, we focus on $n=1$ which corresponds to online non-oblivious bit-fixing (oNOBF) sources. We initiate a study of a new, natural notion of influence of Boolean functions which we call online influence. We establish tight bounds on the total online influence of Boolean functions, implying extraction lower bounds., Comment: 36 pages

Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capturing intricate relationships within money laundering networks. However, the effectiveness of AML solutions is hindered by data silos within financial institutions, limiting collaboration and overall efficacy. This research presents a novel privacy-preserving approach for collaborative AML machine learning, facilitating secure data sharing across institutions and borders while preserving privacy and regulatory compliance. Leveraging Fully Homomorphic Encryption (FHE), computations are directly performed on encrypted data, ensuring the confidentiality of financial data. Notably, FHE over the Torus (TFHE) was integrated with graph-based machine learning using Zama Concrete ML. The research contributes two key privacy-preserving pipelines. First, the development of a privacy-preserving Graph Neural Network (GNN) pipeline was explored. Optimization techniques like quantization and pruning were used to render the GNN FHE-compatible. Second, a privacy-preserving graph-based XGBoost pipeline leveraging Graph Feature Preprocessor (GFP) was successfully developed. Experiments demonstrated strong predictive performance, with the XGBoost model consistently achieving over 99% accuracy, F1-score, precision, and recall on the balanced AML dataset in both unencrypted and FHE-encrypted inference settings. On the imbalanced dataset, the incorporation of graph-based features improved the F1-score by 8%. The research highlights the need to balance the trade-off between privacy and computational efficiency., Comment: 14th International Conference on Security, Privacy, and Applied Cryptographic Engineering (SPACE) 2024

In this work we focus on the effect of the QCD coupling parameter ($\alpha_c$) on various physical properties of compact objects admitting the MIT bag model EoS in the framework of the Tolman IV potential in the presence of nonzero strange quark mass ($m_s$). The internal matter consisting of the deconfined phase of the $3$-flavour quark, is overall charge neutral due to the presence of electrons and is assumed to strongly interact. Interestingly, the coupling parameter $\alpha_c$ has an upper limit due to thermodynamic consistency, and affects the stability in terms of energy per baryon ($E_B$). Our model is suitable for stars with masses of approximately $\leq 2.00~M_{\odot}$. The predicted radius from our model is in agreement with the estimated values of the radius from the observations. Various stability checks of our model are carried out and it is found that the model is stable within the parameter space used here., Comment: 32 pages, 29 figures

We consider a chain of divalent atoms with two electronic orbitals per atom, whose length is periodically oscillating with time in response to an external oscillating pressure source. We introduce both inter-orbital and intra-orbital hopping between neighboring atoms which as a consequence of the oscillating lattice separation, are also periodically oscillating in time. Within the tight binding approximation and in the limit of very small inter-orbital hopping, we get two bands separated by an indirect band gap which itself oscillates due to the oscillating chain length. Under suitable choice of hopping parameters (about which it oscillates) and orbital energies, we show that there can be a periodic metal-insulator transition in the half-filled system. If the frequency of the metal-insulator transition resonates with an externally applied alternating electric field, it can give rise to the phenomena of half-wave rectification wherein in one half of the cycle the system is metallic and conducting, where as in the other half of the cycle it is insulating and non-conducting., Comment: 4 pages, 3 figures

In this article, we explore the properties of hybrid star composed of deconfined quarks and dark energy considering finite value of mass of strange quark ($m_s\neq0$). We have studied the various properties of such stars assuming a linear relation between dark energy pressure and density as $p^{de}=\omega\rho^{de}$, where $-1<\omega<-\frac{1}{3}$, within the framework of Finch-Skea ansatz of $g_{rr}$ component of line element by varying the dark energy coupling parameter ($\beta$). In this model, $\frac{\beta}{1+\beta}$ represents the percentage of dark energy. Following the relation $\rho^{de}=\beta\rho^Q$, we have noted some restriction on the coupling parameter $\beta$ as $0<\beta<-\frac{1}{3\omega}$. It is interesting to note that with the change of percentage composition of dark energy, there is a prominent change of phase within in such stars. Solving TOV equations, the maximum mass attainable in this model is $\approx2~M_{\odot}$ and radius $11.37~km$. Both mass and radius decrease with the increase of $m_s$ and $\beta$ for constant $\omega$. On the other hand, maximum mass increases with the decrease of $\omega$. Various stability conditions along with causality and energy conditions are studied and found to be in agreement with the conditions of a viable stellar model. We have predicted the radii of recently observed compact stars and lighter component of the GW event $170817$ and it is interesting to note that the predicted radius of the model is close to the estimated value of the radius from observations., Comment: 25 pages, 20 figures

Simulating quantum circuits is a computationally intensive task that relies heavily on tensor products and matrix multiplications, which can be inefficient. Recent advancements, eliminate the need for tensor products and matrix multiplications, offering significant improvements in efficiency and parallelization. Extending these optimizations, we adopt a block-simulation methodology applicable to qubit-qudit hybrid systems. This method interprets the statevector as a collection of blocks and applies gates without computing the entire circuit unitary. Our method, a spiritual successor of the simulator QuDiet \cite{Chatterjee_2023}, utilizes this block-simulation method, thereby gaining major improvements over the simulation methods used by its predecessor. We exhibit that the proposed method is approximately 10$\times$ to 1000$\times$ faster than the state-of-the-art simulator for simulating multi-level quantum systems with various benchmark circuits., Comment: 12 pages, 10 figures

Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences. However, contemporary QoS prediction methods frequently encounter data sparsity and cold-start issues, which hinder accurate QoS predictions and limit the ability to capture diverse user preferences. Additionally, these methods often assume QoS data reliability, neglecting potential credibility issues such as outliers and the presence of greysheep users and services with atypical invocation patterns. Furthermore, traditional approaches fail to leverage diverse features, including domain-specific knowledge and complex higher-order patterns, essential for accurate QoS predictions. In this paper, we introduce a real-time, trust-aware framework for temporal QoS prediction to address the aforementioned challenges, featuring an end-to-end deep architecture called the Hypergraph Convoluted Transformer Network (HCTN). HCTN combines a hypergraph structure with graph convolution over hyper-edges to effectively address high-sparsity issues by capturing complex, high-order correlations. Complementing this, the transformer network utilizes multi-head attention along with parallel 1D convolutional layers and fully connected dense blocks to capture both fine-grained and coarse-grained dynamic patterns. Additionally, our approach includes a sparsity-resilient solution for detecting greysheep users and services, incorporating their unique characteristics to improve prediction accuracy. Trained with a robust loss function resistant to outliers, HCTN demonstrated state-of-the-art performance on the large-scale WSDREAM-2 datasets for response time and throughput., Comment: 16 pages, 12 figures

In recent decades, event studies have emerged as a central methodology in health and social research for evaluating the causal effects of staggered interventions. In this paper, we analyze event studies from experimental design principles for observational studies, with a focus on information borrowing across measurements. We develop robust weighting estimators that increasingly use more information across units and time periods, justified by increasingly stronger assumptions on the treatment assignment and potential outcomes mechanisms. As a particular case of this approach, we offer a novel decomposition of the classical dynamic two-way fixed effects (TWFE) regression estimator for event studies. Our decomposition is expressed in closed form and reveals in finite samples the hypothetical experiment that TWFE regression adjustments approximate. This decomposition offers insights into how standard regression estimators borrow information across different units and times, clarifying and supplementing the notion of forbidden comparison noted in the literature. The proposed approach enables the generalization of treatment effect estimates to a target population and offers new diagnostics for event studies, including covariate balance, sign reversal, effective sample size, and the contribution of each observation to the analysis. We also provide visualization tools for event studies and illustrate them in a case study of the impact of divorce reforms on female suicide.

We investigate black hole-star binaries formed in $N$-body simulations of massive, dense star clusters. We simulate 32 clusters with varying initial masses ($10^{4}~\rm M_{\odot}$ to $10^{6}~\rm M_{\odot}$), densities ($1200~\rm M_{\odot}~pc^{-3}$ to $10^{5}~\rm M_{\odot}~pc^{-3}$), and metallicities $(Z = 0.01,~0.001,~0.0001)$. Our results reveal that star clusters produce a diverse range of BH-star binaries, with dynamical interactions leading to extreme systems characterised by large orbital separations and high black hole masses. Of the ejected BH-main sequence (BH-MS) binaries, $20\%$ form dynamically, while the rest originate from the primordial binary population initially present in the cluster. Ejected BH-MS binaries that are dynamically formed have more massive black holes, lower-mass stellar companions, and over half are in a hierarchical triple system. All unbound BH-giant star (BH-GS) binaries were ejected as BH-MS binaries and evolved into the BH-GS phase outside the cluster. Due to their lower-mass companions, most dynamically formed binaries do not evolve into BH-GS systems within a Hubble time. Consequently, only 2 of the 35 ejected BH-GS binaries are dynamically formed. We explore the formation pathways of Gaia-like systems, identifying two Gaia BH1-like binaries that formed through dynamical interactions, and two Gaia BH2-like systems with a primordial origin. We did not find any system resembling Gaia BH3, which may however be attributed to the limited sample size of our simulations.

Matter falling onto black holes, {also called} accretion discs, emit intense high-energy radiation. Accretion discs during {hard to hard intermediate} spectral states also emit bipolar outflows. Radiation drag was supposed to impose the upper limit on the terminal speed. It was later shown that a radiation field around an advective accretion disc imposes no upper limit on speed, about a few hundred of Schwarzschild radius from the disc surface. We {study radiatively driven electron-proton and electron-positron jets, for gemeotrically thick and slim transonic discs} by using numerical simulation. We show that pair-dominated jets can reach ultra-relativistic speeds by radiation driving. We also discuss at what limits radiative acceleration may fail., Comment: 10 pages, 6 figures. Refereed conference procedings of International Symposium on Recent Developments i Relativistic Astrophysics (ISRA 2023), held in SRM University Sikkim

Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather/climate models. An important open question is whether AI models can extrapolate from weaker weather events present in the training set to stronger, unseen weather extremes. To test this, we train independent versions of the AI model FourCastNet on the 1979-2015 ERA5 dataset with all data, or with Category 3-5 tropical cyclones (TCs) removed, either globally or only over the North Atlantic or Western Pacific basin. We then test these versions of FourCastNet on 2018-2023 Category 5 TCs (gray swans). All versions yield similar accuracy for global weather, but the one trained without Category 3-5 TCs cannot accurately forecast Category 5 TCs, indicating that these models cannot extrapolate from weaker storms. The versions trained without Category 3-5 TCs in one basin show some skill forecasting Category 5 TCs in that basin, suggesting that FourCastNet can generalize across tropical basins. This is encouraging and surprising because regional information is implicitly encoded in inputs. No version satisfies gradient-wind balance, implying that enforcing such physical constraints may not improve generalizability to gray swans. Given that current state-of-the-art AI weather/climate models have similar learning strategies, we expect our findings to apply to other models and extreme events. Our work demonstrates that novel learning strategies are needed for AI weather/climate models to provide early warning or estimated statistics for the rarest, most impactful weather extremes.

Automatic speech recognition (ASR) for low-resource languages remains a challenge due to the scarcity of labeled training data. Parameter-efficient fine-tuning and text-only adaptation are two popular methods that have been used to address such low-resource settings. In this work, we investigate how these techniques can be effectively combined using a multilingual multimodal model like SeamlessM4T. Multimodal models are able to leverage unlabeled text via text-only adaptation with further parameter-efficient ASR fine-tuning, thus boosting ASR performance. We also show cross-lingual transfer from a high-resource language, achieving up to a relative 17% WER reduction over a baseline in a zero-shot setting without any labeled speech.

Time series forecasts are often influenced by exogenous contextual features in addition to their corresponding history. For example, in financial settings, it is hard to accurately predict a stock price without considering public sentiments and policy decisions in the form of news articles, tweets, etc. Though this is common knowledge, the current state-of-the-art (SOTA) forecasting models fail to incorporate such contextual information, owing to its heterogeneity and multimodal nature. To address this, we introduce ContextFormer, a novel plug-and-play method to surgically integrate multimodal contextual information into existing pre-trained forecasting models. ContextFormer effectively distills forecast-specific information from rich multimodal contexts, including categorical, continuous, time-varying, and even textual information, to significantly enhance the performance of existing base forecasters. ContextFormer outperforms SOTA forecasting models by up to 30% on a range of real-world datasets spanning energy, traffic, environmental, and financial domains.

Movie posters are not just decorative; they are meticulously designed to capture the essence of a movie, such as its genre, storyline, and tone/vibe. For decades, movie posters have graced cinema walls, billboards, and now our digital screens as a form of digital posters. Movie genre classification plays a pivotal role in film marketing, audience engagement, and recommendation systems. Previous explorations into movie genre classification have been mostly examined in plot summaries, subtitles, trailers and movie scenes. Movie posters provide a pre-release tantalizing glimpse into a film's key aspects, which can ignite public interest. In this paper, we presented the framework that exploits movie posters from a visual and textual perspective to address the multilabel movie genre classification problem. Firstly, we extracted text from movie posters using an OCR and retrieved the relevant embedding. Next, we introduce a cross-attention-based fusion module to allocate attention weights to visual and textual embedding. In validating our framework, we utilized 13882 posters sourced from the Internet Movie Database (IMDb). The outcomes of the experiments indicate that our model exhibited promising performance and outperformed even some prominent contemporary architectures.

Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel framework that addresses these challenges by leveraging the recently proposed multi-step penalty (MP) optimization technique. Our approach extends the applicability of MP optimization to a wide range of deep learning architectures, including Fourier Neural Operators and UNETs. By introducing penalized local discontinuities in the forecast trajectory, we effectively handle the non-convexity of loss landscapes commonly encountered in training neural networks for chaotic systems. We demonstrate the effectiveness of our method through its application to two challenging use-cases: the prediction of flow velocity evolution in two-dimensional turbulence and ocean dynamics using reanalysis data. Our results highlight the potential of this approach for accurate and stable long-term prediction of chaotic dynamics, paving the way for new advancements in data-driven modeling of complex natural phenomena., Comment: 7 pages, 5 Figures, Submitted to CASML2024

Scalar Non-Standard Interactions (SNSI) in neutrinos can arise when a scalar mediator couples to both neutrinos and standard model fermions. This beyond the Standard Model (BSM) scenario is particularly interesting as the SNSI contribution appears as a density-dependent perturbation to the neutrino mass, rather than appearing as a matter-induced potential, and the neutrino oscillation probabilities uniquely depend on the absolute neutrino masses. In this work, we show the complex dependence of the SNSI contributions on the neutrino masses and discuss how the mass of the lightest neutrino would regulate any possible SNSI contribution in both mass ordering scenarios. We derive the analytic expressions for neutrino oscillation probabilities, employing the Cayley-Hamilton theorem, in the presence of diagonal elements of SNSI. The expressions are compact and shows explicit dependence on matter effects and the absolute neutrino masses. The analytic expressions calculated here allow us to obtain the dependence of the SNSI contribution on mass terms of the form $m_1 + m_2$, $m_2 - m_1$, $m_1c_{12}^2 + m_2s_{12}^2,$ $ m_1s_{12}^2 + m_2c_{12}^2$, and $m_3$. We then explore the non-trivial impact of neutrino mass ordering on the SNSI contribution. The dependence of the SNSI contribution on the 3$\nu$ parameters is then thoroughly explored using our analytic expressions., Comment: 40 pages, 11 figures

We present a minimal cosmological solution to the hierarchy problem. Our model consists of a light pseudoscalar and an extra Higgs doublet in addition to the field content of the Standard Model. We consider a landscape of vacua with varying values of the electroweak vacuum expectation value (VEV). The vacuum energy in our model peaks in a region of the landscape where the electroweak VEV is non-zero and much smaller than the cutoff. During inflation, due to exponential expansion, such regions of the landscape with maximal vacuum energy, dominate the universe in volume, thus explaining the smallness of the electroweak scale with respect to the cutoff. The pseudoscalar potential in our model is that of a completely generic pseudogoldstone boson$-$not requiring the clockwork mechanism$-$and its field value never exceeds its decay constant or the Planck scale. Our mechanism is robust to the variation of other model parameters in the landscape along with the electroweak VEV. It also predicts a precise and falsifiable relationship between the masses and couplings of the different Higgs boson mass-eigenstates. Moreover, the pseudoscalar in our model can account for the observed dark matter relic density., Comment: 28 pages, 4 figures

The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, and therefore, it would be beneficial to authors if title generation can be automated. In this paper, we fine-tune pre-trained language models to generate titles of papers from their abstracts. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate paper titles. The performance of the models is measured with ROUGE, METEOR, MoverScore, BERTScore and SciBERTScore metrics. We find that fine-tuned PEGASUS-large outperforms the other models, including fine-tuned LLaMA-3-8B and GPT-3.5-turbo, across most metrics. We also demonstrate that ChatGPT can generate creative titles for papers. Our observations suggest that AI-generated paper titles are generally accurate and appropriate.

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results., Comment: 24 pages, 8 figures, 6 tables, Scientia Horticulturae

In his seminal work in 1923, Neyman studied the variance estimation problem for the difference-in-means estimator of the average treatment effect in completely randomized experiments. He proposed a variance estimator that is conservative in general and unbiased when treatment effects are homogeneous. While widely used under complete randomization, there is no unique or natural way to extend this estimator to more complex designs. To this end, we show that Neyman's estimator can be alternatively derived in two ways, leading to two novel variance estimation approaches: the imputation approach and the contrast approach. While both approaches recover Neyman's estimator under complete randomization, they yield fundamentally different variance estimators for more general designs. In the imputation approach, the variance is expressed as a function of observed and missing potential outcomes and then estimated by imputing the missing potential outcomes, akin to Fisherian inference. In the contrast approach, the variance is expressed as a function of several unobservable contrasts of potential outcomes and then estimated by exchanging each unobservable contrast with an observable contrast. Unlike the imputation approach, the contrast approach does not require separately estimating the missing potential outcome for each unit. We examine the theoretical properties of both approaches, showing that for a large class of designs, each produces conservative variance estimators that are unbiased in finite samples or asymptotically under homogeneous treatment effects.