To estimate the direction of arrival (DOA) of multiple speakers, subspace-based prototype transfer function matching methods such as multiple signal classification (MUSIC) or relative transfer function (RTF) vector matching are commonly employed. In general, these methods require calibrated microphone arrays, which are characterized by a known array geometry or a set of known prototype transfer functions for several directions. In this paper, we consider a partially calibrated microphone array, composed of a calibrated binaural hearing aid and a (non-calibrated) external microphone at an unknown location with no available set of prototype transfer functions. We propose a procedure for completing sets of prototype transfer functions by exploiting the orthogonality of subspaces, allowing to apply matching-based DOA estimation methods with partially calibrated microphone arrays. For the MUSIC and RTF vector matching methods, experimental results for two speakers in noisy and reverberant environments clearly demonstrate that for all locations of the external microphone DOAs can be estimated more accurately with completed sets of prototype transfer functions than with incomplete sets. \c{opyright}20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Internet of Things (IoT)-based healthcare systems offer significant potential for improving healthcare delivery in humanitarian and resource-constrained environments, providing essential services to underserved populations in remote areas. However, limited network infrastructure in such regions makes reliable communication challenging for traditional IoT systems. This paper presents a real-time chest X-ray decision support system designed for hospitals in remote locations. The proposed system integrates a fine-tuned ResNet50 deep learning model for disease classification with Fast DDS real-time middleware to ensure reliable and low-latency communication between healthcare practitioners and the inference system. Experimental results show that the model achieves an accuracy of 88.61%, precision of 88.76%, and recall of 88.49%. The system attains an average throughput of 3.2 KB/s and an average latency of 65 ms, demonstrating its suitability for deployment in bandwidth-constrained environments. These results highlight the effectiveness of DDS-based middleware in enabling real-time medical decision support for remote healthcare applications.

Diffusion-based purification (DBP) has become a cornerstone defense against adversarial examples (AEs), regarded as robust due to its use of diffusion models (DMs) that project AEs onto the natural data manifold. We refute this core claim, theoretically proving that gradient-based attacks effectively target the DM rather than the classifier, causing DBP's outputs to align with adversarial distributions. This prompts a reassessment of DBP's robustness, attributing it to two critical flaws: incorrect gradients and inappropriate evaluation protocols that test only a single random purification of the AE. We show that with proper accounting for stochasticity and resubmission risk, DBP collapses. To support this, we introduce DiffBreak, the first reliable toolkit for differentiation through DBP, eliminating gradient flaws that previously further inflated robustness estimates. We also analyze the current defense scheme used for DBP where classification relies on a single purification, pinpointing its inherent invalidity. We provide a statistically grounded majority-vote (MV) alternative that aggregates predictions across multiple purified copies, showing partial but meaningful robustness gain. We then propose a novel adaptation of an optimization method against deepfake watermarking, crafting systemic perturbations that defeat DBP even under MV, challenging DBP's viability.

Coronary artery disease (CAD), one of the leading causes of mortality worldwide, necessitates effective risk assessment strategies, with coronary artery calcium (CAC) scoring via computed tomography (CT) being a key method for prevention. Traditional methods, primarily based on UNET architectures implemented on pre-built models, face challenges like the scarcity of annotated CT scans containing CAC and imbalanced datasets, leading to reduced performance in segmentation and scoring tasks. In this study, we address these limitations by introducing DINO-LG, a novel label-guided extension of DINO (self-distillation with no labels) that incorporates targeted augmentation on annotated calcified regions during self-supervised pre-training. Our three-stage pipeline integrates Vision Transformer (ViT-Base/8) feature extraction via DINO-LG trained on 914 CT scans comprising 700 gated and 214 non-gated acquisitions, linear classification to identify calcified slices, and U-NET segmentation for CAC quantification and Agatston scoring. DINO-LG achieved 89% sensitivity and 90% specificity for detecting CAC-containing CT slices, compared to standard DINO's 79% sensitivity and 77% specificity, reducing false-negative and false-positive rates by 49% and 57% respectively. The integrated system achieves 90% accuracy in CAC risk classification on 45 test patients, outperforming standalone U-NET segmentation (76% accuracy) while processing only the relevant subset of CT slices. This targeted approach enhances CAC scoring accuracy by feeding the UNET model with relevant slices, improving diagnostic precision while lowering healthcare costs by minimizing unnecessary tests and treatments.

Gaussian process (GP) models have received increasing attention in recent years due to their superb prediction accuracy and modeling flexibility. To address the computational burdens of GP models for large-scale datasets, distributed learning for GPs are often adopted. Current aggregation models for distributed GPs is not time-efficient when incorporating correlations between GP experts. In this work, we propose a novel approach for aggregated prediction in distributed GPs. The technique is suitable for both the exact and sparse variational GPs. The proposed method incorporates correlations among experts, leading to better prediction accuracy with manageable computational requirements. As demonstrated by empirical studies, the proposed approach results in more stable predictions in less time than state-of-the-art consistent aggregation models.

Diffusion models have achieved promising results for Structure-Based Drug Design (SBDD). Nevertheless, high-quality protein subpocket and ligand data are relatively scarce, which hinders the models' generation capabilities. Recently, Direct Preference Optimization (DPO) has emerged as a pivotal tool for aligning generative models with human preferences. In this paper, we propose DecompDPO, a structure-based optimization method aligns diffusion models with pharmaceutical needs using multi-granularity preference pairs. DecompDPO introduces decomposition into the optimization objectives and obtains preference pairs at the molecule or decomposed substructure level based on each objective's decomposability. Additionally, DecompDPO introduces a physics-informed energy term to ensure reasonable molecular conformations in the optimization results. Notably, DecompDPO can be effectively used for two main purposes: (1) fine-tuning pretrained diffusion models for molecule generation across various protein families, and (2) molecular optimization given a specific protein subpocket after generation. Extensive experiments on the CrossDocked2020 benchmark show that DecompDPO significantly improves model performance, achieving up to 95.2% Med. High Affinity and a 36.2% success rate for molecule generation, and 100% Med. High Affinity and a 52.1% success rate for molecular optimization. Code is available at https://github.com/laviaf/DecompDPO.

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain's real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Chung's Lemma is a classical tool for establishing asymptotic convergence rates of (stochastic) optimization methods under strong convexity-type assumptions and appropriate polynomial diminishing step sizes. In this work, we develop a generalized version of Chung's Lemma, which provides a simple non-asymptotic convergence framework for a more general family of step size rules. We demonstrate broad applicability of the proposed generalized lemma by deriving tight non-asymptotic convergence rates for a large variety of stochastic methods. In particular, we obtain partially new non-asymptotic complexity results for stochastic optimization methods, such as Stochastic Gradient Descent (SGD) and Random Reshuffling (RR), under a general $(θ,μ)$-Polyak-Lojasiewicz (PL) condition and for various step sizes strategies, including polynomial, constant, exponential, and cosine step sizes rules. Notably, as a by-product of our analysis, we observe that exponential step sizes exhibit superior adaptivity to both landscape geometry and gradient noise; specifically, they achieve optimal convergence rates without requiring exact knowledge of the underlying landscape or separate parameter selection strategies for noisy and noise-free regimes. Our results demonstrate that the developed variant of Chung's Lemma offers a versatile, systematic, and streamlined approach to establish non-asymptotic convergence rates under general step size rules.

Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social dilemma environments to study the interactions of independent learning agents; however, they tend to ignore the moral heterogeneity that is likely to be present in societies of agents in practice. For example, at different points in time a single learning agent may face opponents who are consequentialist (i.e., focused on maximizing outcomes over time), norm-based (i.e., conforming to specific norms), or virtue-based (i.e., considering a combination of different virtues). The extent to which agents' co-development may be impacted by such moral heterogeneity in populations is not well understood. In this paper, we present a study of the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting. Using an Iterated Prisoner's Dilemma environment with a partner selection mechanism, we investigate the extent to which the prevalence of diverse moral agents in populations affects individual agents' learning behaviors and emergent population-level outcomes. We observe several types of non-trivial interactions between pro-social and anti-social agents, and find that certain types of moral agents are able to steer selfish agents towards more cooperative behavior.

To estimate the direction of arrival (DOA) of multiple speakers with methods that use prototype transfer functions, frequency-dependent spatial spectra (SPS) are usually constructed. To make the DOA estimation robust, SPS from different frequencies can be combined. According to how the SPS are combined, frequency fusion mechanisms are categorized into narrowband, broadband, or speaker-grouped, where the latter mechanism requires a speaker-wise grouping of frequencies. For a binaural hearing aid setup, in this paper we propose an interaural time difference (ITD)-based speaker-grouped frequency fusion mechanism. By exploiting the DOA dependence of ITDs, frequencies can be grouped according to a common ITD and be used for DOA estimation of the respective speaker. We apply the proposed ITD-based speaker-grouped frequency fusion mechanism for different DOA estimation methods, namely the multiple signal classification, steered response power and a recently published method based on relative transfer function (RTF) vectors. In our experiments, we compare DOA estimation with different fusion mechanisms. For all considered DOA estimation methods, the proposed ITD-based speaker-grouped frequency fusion mechanism results in a higher DOA estimation accuracy compared with the narrowband and broadband fusion mechanisms.

In hearing aid applications, an important objective is to accurately estimate the direction of arrival (DOA) of multiple speakers in noisy and reverberant environments. Recently, we proposed a binaural DOA estimation method, where the DOAs of the speakers are estimated by selecting the directions for which the so-called Hermitian angle spectrum between the estimated relative transfer function (RTF) vector and a database of prototype anechoic RTF vectors is maximized. The RTF vector is estimated using the covariance whitening (CW) method, which requires a computationally complex generalized eigenvalue decomposition. The spatial spectrum is obtained by only considering frequencies where it is likely that one speaker dominates over the other speakers, noise and reverberation. In this contribution, we exploit the availability of an external microphone that is spatially separated from the hearing aid microphones and consider a low-complexity RTF vector estimation method that assumes a low spatial coherence between the undesired components in the external microphone and the hearing aid microphones. Using recordings of two speakers and diffuse-like babble noise in acoustic environments with mild reverberation and low signal-to-noise ratio, simulation results show that the proposed method yields a comparable DOA estimation performance as the CW method at a lower computational complexity.

The growing prevalence of high-resolution displays on edge devices has created a pressing need for efficient high dynamic range (HDR) imaging algorithms. However, most existing HDR methods either struggle to deliver satisfactory visual quality or incur high computational and memory costs, limiting their applicability to high-resolution inputs (typically exceeding 12 megapixels). Furthermore, current HDR dataset collection approaches are often labor-intensive and inefficient. In this work, we explore a novel and practical solution for HDR reconstruction directly from raw sensor data, aiming to enhance both performance and deployability on mobile platforms. Our key insights are threefold: (1) we propose RepUNet, a lightweight and efficient HDR network leveraging structural re-parameterization for fast and robust inference; (2) we design a new computational raw HDR data formation pipeline and construct a new raw HDR dataset, RealRaw-HDR; (3) we design a plug-and-play motion alignment loss to suppress ghosting artifacts under constrained bandwidth conditions effectively. Our model contains fewer than 830K parameters and takes less than 3 ms to process an image of 4K resolution using one RTX 3090 GPU. While being highly efficient, our model also achieves comparable performance to state-of-the-art HDR methods in terms of PSNR, SSIM, and a color difference metric.

There is an emerging need for comparable data for multi-microphone processing, particularly in acoustic sensor networks. However, commonly available databases are often limited in the spatial diversity of the microphones or only allow for particular signal processing tasks. In this paper, we present a database of acoustic impulse responses and recordings for a binaural hearing aid setup, 36 spatially distributed microphones spanning a uniform grid of (5x5) m^2 and 12 source positions. This database can be used for a variety of signal processing tasks, such as (multi-microphone) noise reduction, source localization, and dereverberation, as the measurements were performed using the same setup for three different reverberation conditions (T_60\approx{310, 510, 1300} ms). The usability of the database is demonstrated for a noise reduction task using a minimum variance distortionless response beamformer based on relative transfer functions, exploiting the availability of spatially distributed microphones.

Data encoding remains a fundamental bottleneck in quantum machine learning, where amplitude encoding of high-dimensional classical vectors into quantum states incurs exponential cost. In this work, we propose a pre-trained tensor-train (TT) encoding network (Pre-TT-Encoder) that significantly reduces the computational complexity of amplitude encoding while preserving essential data structure. The Pre-TT-Encoder exploits low-rank TT decompositions learned from classical data, enabling polynomial-time state preparation in the number of qubits and TT-ranks. We provide a theoretical analysis of the encoding complexity and establish fidelity bounds that quantify the trade-off between TT-rank and approximation error. Empirical evaluations on classical (MNIST) and quantum-native (semiconductor quantum dot) datasets demonstrate that our approach achieves substantial gains in encoding efficiency over direct amplitude encoding and PCA-based dimensionality reduction, while maintaining competitive performance in downstream variational quantum circuit classification tasks. The proposed method highlights the role of tensor networks as scalable intermediaries between classical data and quantum processors.

Although recent speech processing technologies have achieved significant improvements in objective metrics, there still remains a gap in human perceptual quality. This paper proposes Diffiner, a novel solution that utilizes the powerful generative capability of diffusion models' prior distributions to address this fundamental issue. Diffiner leverages the probabilistic generative framework of diffusion models and learns natural prior distributions of clean speech to convert outputs from existing speech processing systems into perceptually natural high-quality audio. In contrast to conventional deterministic approaches, our method simultaneously analyzes both the original degraded speech and the pre-processed speech to accurately identify unnatural artifacts introduced during processing. Then, through the iterative sampling process of the diffusion model, these degraded portions are replaced with perceptually natural and high-quality speech segments. Experimental results indicate that Diffiner can recover a clearer harmonic structure of speech, which is shown to result in improved perceptual quality w.r.t. several metrics as well as in a human listening test. This highlights Diffiner's efficacy as a versatile post-processor for enhancing existing speech processing pipelines.

Recently, a relative transfer function (RTF)-vector-based method has been proposed to estimate the direction of arrival (DOA) of a target speaker for a binaural hearing aid setup, assuming the availability of external microphones. This method exploits the external microphones to estimate the RTF vector corresponding to the binaural hearing aid and constructs a one-dimensional spatial spectrum by comparing the estimated RTF vector against a database of anechoic prototype RTF vectors for several directions. In this paper we assume the availability of a calibrated array of external microphones, which is characterized by a second database of anechoic prototype RTF vectors. We propose a method, where the external microphones are not only exploited to estimate the RTF vector corresponding to the binaural hearing aid but also assist in estimating the DOA of the target speaker. Based on the estimated RTF vector for all microphones and both prototype databases, a two-dimensional spatial spectrum is constructed from which the DOA is estimated. Experimental results for a reverberant environment with diffuse-like noise show that assisted DOA estimation outperforms DOA estimation where the prototype database characterizing the array of external microphones is not used.

This article presents an artificial intelligence (AI) architecture intended to simulate the iterative updating of the human working memory system. It features several interconnected neural networks designed to emulate the specialized modules of the cerebral cortex. These are structured hierarchically and integrated into a global workspace. They are capable of temporarily maintaining high-level representational patterns akin to the psychological items maintained in working memory. This maintenance is made possible by persistent neural activity in the form of two modalities: sustained neural firing (resulting in a focus of attention) and synaptic potentiation (resulting in a short-term store). Representations held in persistent activity are recursively replaced resulting in incremental changes to the content of the working memory system. As this content gradually evolves, successive processing states overlap and are continuous with one another. The present article will explore how this architecture can lead to iterative shift in the distribution of coactive representations, ultimately leading to mental continuity between processing states, and thus to human-like thought and cognition. Taken together, these components outline a biologically motivated route toward synthetic consciousness or artificial sentience and subjectivity.

Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a logically consistent way in inference and prediction. By Bayes rule, the external information (prior distribution) and the internal information (training data likelihood) are combined coherently, and the posterior distribution and the posterior predictive (marginal) distribution obtained by Bayes rule summarize the total information needed in the inference and prediction, respectively. In this paper, we study the Bayesian framework of the Tensor Network from two perspective. First, we introduce the prior distribution to the weights in the Tensor Network and predict the labels of the new observations by the posterior predictive (marginal) distribution. Since the intractability of the parameter integral in the normalization constant computation, we approximate the posterior predictive distribution by Laplace approximation and obtain the out-product approximation of the hessian matrix of the posterior distribution of the Tensor Network model. Second, to estimate the parameters of the stationary mode, we propose a stable initialization trick to accelerate the inference process by which the Tensor Network can converge to the stationary path more efficiently and stably with gradient descent method. We verify our work on the MNIST, Phishing Website and Breast Cancer data set. We study the Bayesian properties of the Bayesian Tensor Network by visualizing the parameters of the model and the decision boundaries in the two dimensional synthetic data set. For a application purpose, our work can reduce the overfitting and improve the performance of normal Tensor Network model.

We provide explicit constant-depth local unitary circuits that realize general anyon permutations in Kitaev's quantum double models. This construction can be naturally understood through a correspondence between anyon permutation symmetries of two-dimensional topological orders and self-dualities in one-dimensional systems, where local gates implement self-duality transformations on the boundaries of microscopic regions. From this holographic perspective, general anyon permutations in the $D(G)$ quantum double correspond to compositions of three classes of one-dimensional self-dualities, including gauging of certain subgroups of $G$, stacking with $G$ symmetry-protected topological phases, and outer automorphisms of the group $G$. We construct circuits realizing the first class by employing self-dual unitary gauging maps, and present transversal circuits for the latter two classes.

The proper equilibrium, introduced by Myerson (1978), is a classic refinement of the Nash equilibrium that has been referred to as the "mother of all refinements." For normal-form games, computing a proper equilibrium is known to be PPAD-complete for two-player games and FIXP$_a$-complete for games with at least three players. However, the complexity beyond normal-form games -- in particular, for extensive-form games (EFGs) -- was a long-standing open problem first highlighted by Miltersen and Sørensen (SODA '08). In this paper, we resolve this problem by establishing PPAD- and FIXP$_a$-membership (and hence completeness) of normal-form proper equilibria in two-player and multi-player EFGs respectively. Our main ingredient is a technique for computing a perturbed (proper) best response that can be computed efficiently in EFGs. This is despite the fact that, as we show, computing a best response using the classic perturbation of Kohlberg and Mertens based on the permutahedron is #P-hard even in Bayesian games. In stark contrast, we show that computing a proper equilibrium in polytope games is NP-hard. This marks the first natural class in which the complexity of computing equilibrium refinements does not collapse to that of Nash equilibria, and the first problem in which equilibrium computation in polytope games is strictly harder -- unless there is a collapse in the complexity hierarchy -- relative to extensive-form games.

Motivated by the recent discovery of superconductivity in the bilayer nickelate La$_3$Ni$_2$O$_7$ (LNO) under pressure, we study a mixed-dimensional (mixD) bilayer $t_\parallel$-$J_\parallel$-$J_\perp$ model, which has been proposed as an effective low-energy description of LNO. Using neural quantum states (NQS), and in particular Gutzwiller-projected Hidden Fermion Pfaffian State, we access the ground-state properties on large lattices up to $8\times 8\times 2$ sites. We show that this model exhibits superconductivity across a wide range of dopings and couplings, and analyze the pairing behavior in detail. We identify a crossover from tightly bound, Bose-Einstein-condensed interlayer pairs at strong interlayer exchange to more spatially extended Bardeen-Cooper-Schrieffer-like pairs as the interlayer exchange is decreased. Furthermore, upon tuning the intralayer exchange, we observe a sharp transition from interlayer $s$-wave pairing to intralayer $d$-wave pairing, consistent with a first-order change in the pairing symmetry. We verify that our simulations are accurate by comparing with matrix product state simulations on coupled ladders. Our results represent the first simulation of a fermionic multi-orbital system with NQS, and provide the first evidence for superconductivity in two-dimensonal bilayers using high-precision numerics. These findings provide insight into superconductivity in bilayer nickelates and cold atom quantum simulation platforms.

We introduce a novel observable associated to Abelian monopole currents defined in the Maximal Abelian Projection of SU(3) Yang-Mills theory that captures the topology of the current loop. This observable, referred to as the $\textit{simplicity}$, is defined as the ratio of the zeroth over the first Betti number of the current graph for a given field configuration. A numerical study of the expectation value of the simplicity performed in the framework of Lattice Gauge Theories enables us to determine the deconfinement temperature to a higher degree of accuracy than that reached by conventional methods at a comparable computational effort. Our results suggest that Abelian current loops are strongly correlated with the degrees of freedoms of the theory that determine confinement. Our investigation opens new perspectives for the definition of an order parameter for deconfinement in Quantum Chromodynamics able to expose the potentially rich phase structure of the theory.

In enzymatic recycling or biodegradation of semi-crystalline plastic waste, crystalline spherulites embedded into an amorphous matrix hinder and slow down depolymerisation. When the enzymatic depolymerisation temperature exceeds the glass transition temperature, these spherulites tend to grow. The depolymerisation process is thus controlled by a competition between erosion of the amorphous matrix from the particle surface and the growth of recalcitrant spherulites within the particle bulk and at its surface. We present a geometric model that captures this competition, together with an algorithm to solve the equations numerically. Our algorithm introduces a new extension of Voronoi/Delaunay tessellation in space. We extract the parameters for the model from experimental data on the enzymatic depolymerization by hydrolase LCC-ICCG of PET bottle flakes and textile waste, in order to make a prediction of the observed degradation yield as a function of time. Both the final yield and the degradation kinetics are correctly predicted. Most importantly, the model clarifies how and to which extent nucleating agents, impurities, additives, and/or rapid crystal growth present in the waste can undermine pretreatment efforts aiming to initiate depolymerisation from a material with a low initial crystallinity.

In this paper we apply the canonical spin wave theory developed for itinerant Kondo lattice magnets in the strong coupling regime to Kondo ferromagnets, and address two general questions pertaining to their magnetic excitations. First, we compute corrections to the strong coupling (i.e., double-exchange) spin wave dispersion of itinerant ferromagnets. We show that the spin wave dispersion beyond the strong coupling limit can be mapped to the spin wave dispersion of a Heisenberg ferromagnet with farther neighbor exchange couplings, and discuss how this affects instabilities towards antiferromagnetism. Second, we examine the effect of including electronic spin-orbit coupling in the spin wave theory of Kondo ferromagnets. Including spin-orbit coupling is natural and straightforward in the formulation of the canonical spin wave expansion. Our key result is to demonstrate that the linear spin wave Hamiltonian of the itinerant Kondo ferromagnet can be mapped to the spin wave Hamiltonian of a Heisenberg ferromagnet with easy-axis Ising anisotropy and antisymmetric Dzyaloshinskii-Moriya exchange interaction. We show that in the case of the Kane-Mele honeycomb lattice Kondo ferromagnet this leads to topological magnon bands, and discuss the implications of this result for itinerant ferromagnets more broadly.

Type Ia supernovae (SNe Ia) are thermonuclear runaways in certain white dwarfs in binary systems. They have been extensively studied, yet their progenitor and explosion mechanisms remain poorly understood. We study a large sample of SNe Ia comparing the narrow interstellar absorption features in their spectra with various photometric and spectroscopic supernova properties, as well as with environmental characteristics. We find that the sodium absorption is significantly stronger in younger, more star-forming and more centrally located SNe Ia, as expected. However, we also show that there is a strong dependence on intrinsic properties that is independent of the environment. In fact, we find strong evidence for two environmental SN Ia populations, an old and a young one, with the young population showing significantly different distributions of sodium strength when divided according to the Si II ejecta velocity, nebular velocity, extinction, E(B-V), and reddening curve, RV. Performing a clustering of the SNe Ia, we recover an old population of SNe with low extinction and normal ejecta velocity, while we confirm that the young population can be subdivided into a group of highly-extincted, high-velocity SNe Ia with much stronger blueshifted sodium absorption, and another of low-extincted, normal-velocity objects with little sodium absorption. We interpret this relation of intervening material with intrinsic properties as evidence for the young SN Ia population, occurring in young and star-forming environments, to have asymmetric radiation that interacts with nearby material, and whose observables depend thus on the viewing angle. Finally, we show that the cosmological mass-step is consistent with these populations.

While proportionality is frequently named as a desirable property of voting rules, its interpretation in multiwinner voting differs significantly from that in apportionment. We aim to bridge these two distinct notions of proportionality by introducing the concept of allocation proportionality, founded upon the framework of party elections, where each candidate in a multiwinner election is assigned to a party. A voting rule is allocation proportional if each party's share of elected candidates equals that party's aggregate score. Recognizing that no committee scoring rule can universally satisfy allocation proportionality in practice, we introduce a new measure of allocation proportionality degree and discuss how it relates to other quantitative measures of proportionality. This measure allows us to compare OWA-based committee scoring rules according to how much they diverge from the ideal of allocation proportionality. We present experimental results for several common rules: SNTV, $k$-Borda, Chamberlin-Courant, Harmonic Borda, Proportional $k$-Approval Voting, and Bloc Voting.

Mixing by hydrodynamical instabilities plays a key role in cloud-wind interactions, causing cloud destruction in the adiabatic limit and facilitating cloud survival with efficient radiative cooling. However, the rate of mixing in numerical simulations is sensitive to the smallest resolved scale, and the relationship between resolution and cloud evolution is under-explored. Using a set of cloud-crushing simulations, we investigate the effects of numerical resolution on cloud survival and acceleration. Modeling both adiabatic and radiative cases, in a subsonic and supersonic wind, we find that cloud survival and velocity does depend on the numerical resolution, however, no single resolution requirement can be applied to all scenarios. In the radiative subsonic case, we find that mass growth and acceleration appear converged at only 4 cells per cloud radius. Conversely, in the supersonic regime, we see a clear dependence of cloud destruction and velocity on resolution that is not converged even at 48 cells per cloud radius, implying that accurately capturing cloud destruction may require higher resolution than capturing growth. We also present a simple model illustrating how ram pressure accelerates cool clouds at early times before mixing kicks in as an acceleration mechanism.

As one of the most fundamental problems in graph processing, the Single-Source Shortest Path (SSSP) problem plays a critical role in numerous application scenarios. However, existing GPU-based solutions remain inefficient, as they typically rely on a single, fixed queue design that incurs severe synchronization overhead, high memory latency, and poor adaptivity to diverse inputs. To address these inefficiencies, we propose MultiLevelMultiQueue (MLMQ), a novel data structure that distributes multiple queues across the GPU's multi-level parallelism and memory hierarchy. To realize MLMQ, we introduce a cache-like collaboration mechanism for efficient inter-queue coordination, and develop a modular queue design based on unified Read and Write primitives. Within this framework, we expand the optimization space by designing a set of GPU-friendly queues, composing them across multiple levels, and further providing an input-adaptive MLMQ configuration scheme. Our MLMQ design achieves average speedups of 1.87x to 17.13x over state-of-the-art implementations. Our code is open-sourced at https://github.com/Leo9660/MLMQ.git.

We use measurements of the intergalactic medium (IGM) temperature from the Lyman-$α$ forest to place new limits on models in which long-lived dark sector (DS) particles, with lifetimes longer than $10^{16}$ s, deposit energy into the IGM through their decays. Such DS decays into Standard Model (SM) states can modify the late-time thermal history of the IGM, making Lyman-$α$ data a sensitive probe of hidden sectors with cosmologically long lifetimes. Our analysis demonstrates that constraints from late-time IGM heating offer a complementary window to those from the Cosmic Microwave Background (CMB), in constraining dark sector parameter space. We further revisit limits on such decaying DS models from Planck's measurements of the optical depth to reionization and provide updates relevant for DS lifetimes longer than $10^{14}$ s. The model-independent constraints on the DS parameter space we derive in this work can be reinterpreted for a wide range of decaying hidden-sector scenarios, including evaporating primordial black holes and SM-coupled dark photons.

In this paper, we consider a generalized polyharmonic eigenvalue problem of the form $A(u)= λh(u)$ in a bounded smooth domain with Dirichlet boundary conditions in the setting of higher-order Orlicz-Sobolev spaces. Here, $A$ is a very general operator depending on $u$ and arbitrary higher-order derivatives of $u$, whose growth is governed by an Orlicz function, and $h$ is a lower order term. Combining the theories of pseudomonotone operators with complementary systems, we prove that this eigenvalue problem has an infinite number of eigenfunctions and that the corresponding sequence of eigenvalues tends to infinity. We point out that the $Δ_2$-condition is not assumed for the involved Orlicz functions. Finally, we prove a first regularity result for eigenfunctions by following a De Giorgi's iteration scheme.