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We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap encompasses four generations of devices: a single-qubit device that enables a measurement-based qubit benchmarking protocol; a two-qubit device that uses measurement-based braiding to perform single-qubit Clifford operations; an eight-qubit device that can be used to show an improvement of a two-qubit operation when performed on logical qubits rather than directly on physical qubits; and a topological qubit array supporting lattice surgery demonstrations on two logical qubits. Devices that enable this path require a superconductor-semiconductor heterostructure that supports a topological phase, quantum dots and coupling between those quantum dots that can create the appropriate loops for interferometric measurements, and a microwave readout system that can perform fast, low-error single-shot measurements. We describe the key design components of these qubit devices, along with the associated protocols for demonstrations of single-qubit benchmarking, Clifford gate execution, quantum error detection, and quantum error correction, which differ greatly from those in more conventional qubits. Finally, we comment on implications and advantages of this architecture for utility-scale quantum computation., Comment: 11+6 pages, 8+5 figures

Modelling epidemics using contact networks provides a significant improvement over classical compartmental models by explicitly incorporating the network of contacts. However, while network-based models describe disease spread on a given contact structure, their potential for inferring the underlying network from epidemic data remains largely unexplored. In this work, we consider the edge-based compartmental model (EBCM), a compact and analytically tractable framework, and we integrate it within dynamical survival analysis (DSA) to infer key network properties along with parameters of the epidemic itself. Despite correlations between structural and epidemic parameters, our framework demonstrates robustness in accurately inferring contact network properties from synthetic epidemic simulations. Additionally, we apply the framework to real-world outbreaks, namely the 2001 UK foot-and-mouth disease outbreak and the COVID-19 epidemic in Seoul, to estimate both disease parameters and network characteristics. Our results show that our framework achieves good fits to real-world epidemic data and reliable short-term forecasts. These findings highlight the potential of network-based inference approaches to uncover hidden contact structures, providing insights that can inform the design of targeted interventions and public health strategies.

We study two uniform-in-time asymptotic limits for generalized Kuramoto (GK) models. For these GK type models, we first derive the uniform stability estimates with respect to initial data, natural frequency and communication network under a suitable framework, and then as direct applications of this uniform stability estimate, we establish two asymptotic limits which are valid in the whole time interval, namely uniform-in-time continuum and mean-filed limit to the continuum and kinetic GK models, respectively. In the mean-field limit setting (the number of particles tends to infinity), we show global-in-time existence of measure-valued solutions to the corresponding kinetic equation. On the other hand, in a continuum limit setting (the lattice size tends to zero), we show that the lattice GKM solutions converge to a classical solution to the continuum GK model in supremum norm. Two asymptotic limits improve earlier results for the generalized GK type models.

The problem of explaining inconsistency-tolerant reasoning in knowledge bases (KBs) is a prominent topic in Artificial Intelligence (AI). While there is some work on this problem, the explanations provided by existing approaches often lack critical information or fail to be expressive enough for non-binary conflicts. In this paper, we identify structural weaknesses of the state-of-the-art and propose a generic argumentation-based approach to address these problems. This approach is defined for logics involving reasoning with maximal consistent subsets and shows how any such logic can be translated to argumentation. Our work provides dialogue models as dialectic-proof procedures to compute and explain a query answer wrt inconsistency-tolerant semantics. This allows us to construct dialectical proof trees as explanations, which are more expressive and arguably more intuitive than existing explanation formalisms., Comment: 45 pages, 8 gigures, journal

Temporal correlations in the time series observed in various systems have been characterized by the autocorrelation function. Such correlations can be explained by heavy-tailed interevent time distributions as well as by correlations between interevent times. The latter is called higher-order temporal correlations, and they have been captured by the notion of bursts; a burst indicates a set of consecutive events that rapidly occur within a short time period and are separated from other bursts by long time intervals. The number of events in the burst is called a burst size. Some empirical analyses have shown that consecutive burst sizes are correlated with each other. To study the impact of such correlations on the autocorrelation function, we devise a model generating a time series with higher-order temporal correlations by employing the copula method. We successfully derive the analytical solution of the autocorrelation function of the model time series for arbitrary distributions of interevent times and burst sizes when consecutive burst sizes are correlated. For the demonstration of our analysis, we adopt exponential distributions of interevent times and burst sizes to understand how the correlations between consecutive burst sizes affect the decaying behavior of the autocorrelation function., Comment: 9 pages, 2 figures

This study presents the development and testing of a conversational speech system designed for robots to detect speech biomarkers indicative of cognitive impairments in people living with dementia (PLwD). The system integrates a backend Python WebSocket server and a central core module with a large language model (LLM) fine-tuned for dementia to process user input and generate robotic conversation responses in real-time in less than 1.5 seconds. The frontend user interface, a Progressive Web App (PWA), displays information and biomarker score graphs on a smartphone in real-time to human users (PLwD, caregivers, clinicians). Six speech biomarkers based on the existing literature - Altered Grammar, Pragmatic Impairments, Anomia, Disrupted Turn-Taking, Slurred Pronunciation, and Prosody Changes - were developed for the robot conversation system using two datasets, one that included conversations of PLwD with a human clinician (DementiaBank dataset) and one that included conversations of PLwD with a robot (Indiana dataset). We also created a composite speech biomarker that combined all six individual biomarkers into a single score. The speech system's performance was first evaluated on the DementiaBank dataset showing moderate correlation with MMSE scores, with the composite biomarker score outperforming individual biomarkers. Analysis of the Indiana dataset revealed higher and more variable biomarker scores, suggesting potential differences due to study populations (e.g. severity of dementia) and the conversational scenario (human-robot conversations are different from human-human). The findings underscore the need for further research on the impact of conversational scenarios on speech biomarkers and the potential clinical applications of robotic speech systems., Comment: Main paper 28 pages long (pg 2-30), includes 5 figures, 5 tables, 1 Appendix at end

The prevalence of hearing aids is increasing. However, optimizing the amplification processes of hearing aids remains challenging due to the complexity of integrating multiple modular components in traditional methods. To address this challenge, we present NeuroAMP, a novel deep neural network designed for end-to-end, personalized amplification in hearing aids. NeuroAMP leverages both spectral features and the listener's audiogram as inputs, and we investigate four architectures: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Convolutional Recurrent Neural Network (CRNN), and Transformer. We also introduce Denoising NeuroAMP, an extension that integrates noise reduction along with amplification capabilities for improved performance in real-world scenarios. To enhance generalization, a comprehensive data augmentation strategy was employed during training on diverse speech (TIMIT and TMHINT) and music (Cadenza Challenge MUSIC) datasets. Evaluation using the Hearing Aid Speech Perception Index (HASPI), Hearing Aid Speech Quality Index (HASQI), and Hearing Aid Audio Quality Index (HAAQI) demonstrates that the Transformer architecture within NeuroAMP achieves the best performance, with SRCC scores of 0.9927 (HASQI) and 0.9905 (HASPI) on TIMIT, and 0.9738 (HAAQI) on the Cadenza Challenge MUSIC dataset. Notably, our data augmentation strategy maintains high performance on unseen datasets (e.g., VCTK, MUSDB18-HQ). Furthermore, Denoising NeuroAMP outperforms both the conventional NAL-R+WDRC approach and a two-stage baseline on the VoiceBank+DEMAND dataset, achieving a 10% improvement in both HASPI (0.90) and HASQI (0.59) scores. These results highlight the potential of NeuroAMP and Denoising NeuroAMP to deliver notable improvements in personalized hearing aid amplification.

Let $A_n$ be an $n \times n$ deterministic matrix and $\Sigma_n$ be a deterministic non-negative matrix such that $A_n$ and $\Sigma_n$ converge in $*$-moments to operators $a$ and $\Sigma$ respectively in some $W^*$-probability space. We consider the full rank deformed model $A_n + U_n \Sigma_n V_n,$ where $U_n$ and $V_n$ are independent Haar-distributed random unitary matrices. In this paper, we investigate the eigenvalues of $A_n + U_n\Sigma_n V_n$ in two domains that are outside the support of the Brown measure of $a +u \Sigma$. We give a sufficient condition to guarantee that outliers are stable in one domain, and we also prove that there are no outliers in the other domain. When $A_n$ has a bounded rank, the first domain is exactly the one outside the outer boundary of the single ring, and the second domain is the inner disk of the single ring. Our results generalize the results of Benaych-Georges and Rochet (Probab. Theory Relat. Fields, 2016).

With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakness by using the advantage of qubits. In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits. Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process. By utilizing first-order Taylor expansion, we significantly reduce both the computational cost and loss, better than the previous version. Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86\% reduction in the number of iterations while maintaining a lower compression loss, better for high-resolution images. The results confirm that the proposed algorithm provides an efficient and scalable image compression mechanism, making it a promising candidate for future image processing applications.

With a new joint-deconvolution pipeline, we combine the single-dish and interferometric atomic hydrogen (HI) data of M51 observed by the FAST (FEASTS program) and VLA (THINGS). The product data cube has a typical line width of $13\,\text{km}\,\text{s}^{-1}$ and a $2\sigma$ line-of-sight (LOS) sensitivity of HI column density $N_\text{HI}\sim3.2\times10^{18}\,\text{cm}^{-2}$ at a spatial resolution of ${\sim}18''$ (${\sim}0.7\,\text{kpc}$). Among the HI-detected LOSs extending to ${\sim}50\,\text{kpc}$, ${\sim}89\%$ consist of diffuse HI only, which is missed by previous VLA observations. The distribution of dense HI is reproduced by previous hydrodynamical simulations of this system, but the diffuse component is not, likely due to unresolved physics related to the interaction between the circumgalactic and interstellar media. With simple models, we find that these low-$N_\text{HI}$ structures could survive the background ultraviolet photoionization, but are susceptible to the thermal evaporation. We find a positive correlation between LOS velocity dispersion ($\sigma_v$) and $N_\text{HI}$ with a logarithmic index of ${\sim}0.5$. Based on existing turbulent mixing layer (TML) theories and simulations, we propose a scenario of hot gas cooling and accreting onto the disk through a TML, which could reproduce the observed power index of ${\sim}0.5$. We estimate the related cooling and accretion rates to be roughly $1/3$ to $2/3$ of the star-formation rate. A typical column density of diffuse HI (${\sim}10^{19}\,\text{cm}^{-2}$) can be accreted within $300\,\text{Myr}$, the interaction time scale previously estimated for the system. Such a gas accretion channel has been overlooked before, and may be important for gas-rich interacting systems and for high redshift galaxy evolution., Comment: 16 pages of main text, 7 pages of appendices, 27 pages in total; 18 figures, 1 table; accepted for publication in ApJ

In recent years, machine learning algorithms have achieved much success in segmenting lesions across various tissues. There is, however, not one satisfying model that works well on all tissue types universally. In response to this need, we attempt to train a model that 1) works well on all tissue types, and 2) is capable of still performing fast inferences. To this end, we design our architectures, test multiple existing architectures, compare their results, and settle upon SwinUnet. We document our rationales, successes, and failures. Finally, we propose some further directions that we think are worth exploring. codes: https://github.com/KWFredShi/ULS2023NGKD.git

Spatial misalignments arise from data aggregation or attempts to align misaligned data, leading to information loss. We propose a disaggregation framework that combines the finite element method (FEM) with a first-order Taylor approximation via integrated nested Laplace approximation (INLA). In landslide studies, landslide occurrences are often aggregated into counts based on slope units, reducing spatial detail. Our framework examines point pattern and aggregated count models under four covariate field scenarios: \textit{Raster at Full Resolution (RastFull), Raster Aggregation (RastAgg), Polygon Aggregation (PolyAgg), and Point Values (PointVal)}. The first three involve aggregation, while the latter two have incomplete fields. For these, we estimate the full covariate field using \textit{Value Plugin, Joint Uncertainty, and Uncertainty Plugin} methods, with the latter two accounting for uncertainty propagation and showing superior performance. Even under model misspecification (i.e.\ modelling a nonlinear field as linear), these methods remain more robust. Whenever possible, point pattern observations and full-resolution covariate fields should be prioritized. For incomplete fields, methods incorporating uncertainty propagation are preferred. This framework supports landslide susceptibility and other spatial mapping, integrating seamlessly with R-INLA \ extension packages., Comment: 103 pages

In this work, we provide a comprehensive gradient regularity theory for a broad class of nonlinear kinetic Fokker-Planck equations. We achieve this by establishing precise pointwise estimates in terms of the data in the spirit of nonlinear potential theory, leading to fine gradient regularity results under borderline assumptions on the data. Notably, our gradient estimates are novel already in the absence of forcing terms and even for linear kinetic Fokker-Planck equations in divergence form., Comment: 42 pages

Understanding the behavior of particles in a dispersed phase system via population balances holds fundamental importance in studies of particulate sciences across various fields. Particle behavior, however, is sophisticated as a single particle can undergo internal property changes (e.g., size, cell age, and energy content) through various mechanisms. When confronted with an unknown distributed particulate system, discovering the underlying population balance equation (PBE) entails firstly learning the underlying particulate phenomena followed by the associated phenomenological laws that govern the kinetics and mechanisms of particle transformations in their local conditions. Conventional inverse problem approaches reveal the shape of phenomenological functions for predetermined forms of PBE (e.g., pure breakage/aggregation PBE, etc.). However, these methods can be limited in their ability to uncover the mechanisms which govern uncharacterized particulate systems from data. Leveraging the increasing abundance of data, we devise a data-driven framework based on sparse regression to learn PBEs as linear combinations of an extensive pool of candidate terms. Thus, this approach enables effective and accurate functional identification of PBEs without assuming the structure a priori, hence mitigating any potential loss of details, while minimizing model overfitting and providing a more interpretable representation of particulate systems. We showcase the proficiency of our approach across a wide spectrum of particulate systems, ranging from simple canonical pure breakage and pure aggregation systems to complex systems with multiple particulate processes. Our approach holds the potential to generalize the discovery of PBEs along with their phenomenological laws from data, thus facilitating wider adoption of population balances., Comment: 52 pages, 7 figures, 4 tables

Purpose: To propose and evaluate an accelerated $T_{1\rho}$ quantification method that combines $T_{1\rho}$-weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep learning models for $T_{1\rho}$ mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment. Methods: This retrospective study utilized MRI data from 40 participants (30 OA patients and 10 healthy volunteers). A volume of PD-weighted anatomical FSE images and a volume of $T_{1\rho}$-weighted images acquired at a non-zero spin-lock time were used as input to train deep learning models, including a 2D U-Net and a multi-layer perceptron (MLP). $T_{1\rho}$ maps generated by these models were compared with ground truth maps derived from a traditional non-linear least squares (NLLS) fitting method using four $T_{1\rho}$-weighted images. Evaluation metrics included mean absolute error (MAE), mean absolute percentage error (MAPE), regional error (RE), and regional percentage error (RPE). Results: Deep learning models achieved RPEs below 5% across all evaluated scenarios, outperforming NLLS methods, especially in low signal-to-noise conditions. The best results were obtained using the 2D U-Net, which effectively leveraged spatial information for accurate $T_{1\rho}$ fitting. The proposed method demonstrated compatibility with shorter TSLs, alleviating RF hardware and specific absorption rate (SAR) limitations. Conclusion: The proposed approach enables efficient $T_{1\rho}$ mapping using PD-weighted anatomical images, reducing scan time while maintaining clinical standards. This method has the potential to facilitate the integration of quantitative MRI techniques into routine clinical practice, benefiting OA diagnosis and monitoring., Comment: Submitted to Magnetic Resonance in Medicine

Silicon photonic integrated circuits offer significant improvements in processing bandwidth, power efficiency, and low latency, addressing the needs of future microwave communication systems. Several successful applications have been demonstrated in this field; however, the focus is now shifting toward integrating these applications into single programmable photonic circuits. This approach not only reduces fabrication costs but also makes photonics more accessible for everyday use. This paper presents a scalable silicon-based signal processor with advanced functionalities, including high-speed arbitrary waveform generation,tunable bandwidth filtering and ultrabroadband beamforming. These results highlight improvements in both scale and performance, representing a significant step forward in large-scale, high-performance, multifunctional photonic systems.

The phenomenon of reflection is quite common in digital images, posing significant challenges for various applications such as computer vision, photography, and image processing. Traditional methods for reflection removal often struggle to achieve clean results while maintaining high fidelity and robustness, particularly in real-world scenarios. Over the past few decades, numerous deep learning-based approaches for reflection removal have emerged, yielding impressive results. In this survey, we conduct a comprehensive review of the current literature by focusing on key venues such as ICCV, ECCV, CVPR, NeurIPS, etc., as these conferences and journals have been central to advances in the field. Our review follows a structured paper selection process, and we critically assess both single-stage and two-stage deep learning methods for reflection removal. The contribution of this survey is three-fold: first, we provide a comprehensive summary of the most recent work on single-image reflection removal; second, we outline task hypotheses, current deep learning techniques, publicly available datasets, and relevant evaluation metrics; and third, we identify key challenges and opportunities in deep learning-based reflection removal, highlighting the potential of this rapidly evolving research area.

The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively conducting multi-institutional EHR studies is the data heterogeneity across systems with numerous codes that either do not exist or represent different clinical concepts across institutions. The need for data privacy further limits the feasibility of including multi-institutional patient-level data required to study similarities and differences across patient subgroups. To address these challenges, we developed the GAME algorithm. Tested and validated across 7 institutions and 2 languages, GAME integrates data in several levels: (1) at the institutional level with knowledge graphs to establish relationships between codes and existing knowledge sources, providing the medical context for standard codes and their relationship to each other; (2) between institutions, leveraging language models to determine the relationships between institution-specific codes with established standard codes; and (3) quantifying the strength of the relationships between codes using a graph attention network. Jointly trained embeddings are created using transfer and federated learning to preserve data privacy. In this study, we demonstrate the applicability of GAME in selecting relevant features as inputs for AI-driven algorithms in a range of conditions, e.g., heart failure, rheumatoid arthritis. We then highlight the application of GAME harmonized multi-institutional EHR data in a study of Alzheimer's disease outcomes and suicide risk among patients with mental health disorders, without sharing patient-level data outside individual institutions.

We present a matter-space framework characterizing particles and establish its compatibility with electromagnetism. In this approach, matter, such as photons, is considered to reside in a three-dimensional matter space, with the electromagnetic fields observed in four-dimensional spacetime interpreted as projections from this space. By imposing gauge symmetry through constraint equations, we derive the relationship between the vector field $A_a$ and the antisymmetric tensor $F_{ab}$, forming part of Maxwell's equations. The remaining Maxwell equation is obtained through the action principle in relativistic fluid dynamics. Notably, we demonstrate that this imposition of the gauge symmetry and constraints develop the dynamics. This framework offers a fresh perspective on particle-field interactions and deepens the theoretical foundation of relativistic fluid dynamics., Comment: minor corrections, 7 pages, 1 figure

Traditional methods for visualizing dynamic human expressions, particularly in medical training, often rely on flat-screen displays or static mannequins, which have proven inefficient for realistic simulation. In response, we propose a platform that leverages a 3D interactive facial avatar capable of displaying non-verbal feedback, including pain signals. This avatar is projected onto a stereoscopic, view-dependent 3D display, offering a more immersive and realistic simulated patient experience for pain assessment practice. However, there is no existing solution that dynamically predicts and projects interactive 3D facial avatars in real-time. To overcome this, we emphasize the need for a 3D display projection system that can project the facial avatar holographically, allowing users to interact with the avatar from any viewpoint. By incorporating 3D Gaussian Splatting (3DGS) and real-time view-dependent calibration, we significantly improve the training environment for accurate pain recognition and assessment.

We present the optical discovery and multiwavelength follow-up observations of AT2024kmq, a likely tidal disruption event (TDE) associated with a supermassive ($M_{\rm BH}\sim 10^{8} M_\odot$) black hole in a massive galaxy at $z=0.192$. The optical light curve of AT2024kmq exhibits two distinct peaks: an early fast (timescale 1 d) and luminous ($M\approx-20$ mag) red peak, then a slower (timescale 1 month) blue peak with a higher optical luminosity ($M\approx-22$ mag) and featureless optical spectra. The second component is similar to the spectroscopic class of "featureless TDEs" in the literature, and during this second component we detect highly variable, luminous ($L_X\approx 10^{44}$ erg s$^{-1}$), and hard ($f_\nu \propto \nu^{-1.5}$) X-ray emission. Luminous ($10^{29} $erg s$^{-1}$ Hz$^{-1}$ at 10 GHz) but unchanging radio emission likely arises from an underlying active galactic nucleus. The luminosity, timescale, and color of the early red optical peak can be explained by synchrotron emission, or alternatively by thermal emission from material at a large radius ($R\approx\mathrm{few}\times10^{15}$ cm). Possible physical origins for this early red component include an off-axis relativistic jet, and shocks from self-intersecting debris leading to the formation of the accretion disk. Late-time radio observations will help distinguish between the two possibilities., Comment: 23 pages, 7 figures, 6 tables. Submitted to journal on 11 Feb 2025. Comments welcome

We explore the decay rates of optical modes in asymmetric microcavities with mixed phase space across a wide range of wavelengths that extend deep into the semiclassical, i.e., short-wavelength limit. Implementing an efficient numerical method, we computed 1000000 eigenmodes and discovered that certain decay rates form sequential separate branches with increasing wavenumber that eventually merge into smooth curves. The analysis of the localization properties and Husimi distributions reveals that each branch corresponds to a periodic orbit in the closed classical system. Our findings show that these decay rates gradually resolve the structure of the islands in mixed phase space as we approach the short-wavelength limit. We present an effective semiclassical model incorporating wavenumber-dependent localization, Fresnel reflection, and the Goos-Haenchen shift and demonstrate that these effects are crucial in accounting for the observed branches of decay rate curves.

Using density functional theory calculations we investigated possible Weyl semimetal (WSM) phases in antimony arsenide ordered alloys Sb1-xAsx (x=0, 1/6, 1/3, 1/2, 2/3, 5/6, 1). We find WSM phases for all As compositions of Sb1-xAsx with broken inversion symmetry, in contrast to Bi1-xSbx where only compositions x=1/2 and 5/6 were predicted to exhibit WSM phases. The WSM phases in Sb1-xAsx are characterized by the presence of 12 Weyl points, located within 55 meV from the Fermi level in the case of x=1/2. The robust spin-orbit coupling strength and Berry curvature in these alloys produce large spin-Hall conductivity in the range of 176-602 (hbar/e)(S/cm), comparable to that in the BiSb alloys. Finally, Sb0.5As0.5 is predicted to be almost lattice-matched to GaAs(111), with the Fermi level within the gap of the semiconductor, facilitating growth and characterization, and thus, offering promising integration with conventional semiconductors.

A language can have different varieties. These varieties can affect the performance of natural language processing (NLP) models, including large language models (LLMs), which are often trained on data from widely spoken varieties. This paper introduces a novel and cost-effective approach to benchmark model performance across language varieties. We argue that international online review platforms, such as Booking.com, can serve as effective data sources for constructing datasets that capture comments in different language varieties from similar real-world scenarios, like reviews for the same hotel with the same rating using the same language (e.g., Mandarin Chinese) but different language varieties (e.g., Taiwan Mandarin, Mainland Mandarin). To prove this concept, we constructed a contextually aligned dataset comprising reviews in Taiwan Mandarin and Mainland Mandarin and tested six LLMs in a sentiment analysis task. Our results show that LLMs consistently underperform in Taiwan Mandarin., Comment: Accepted by 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), theme track

$\rm{RuO_2}$ stands as a quintessential rutile-type compound under ambient conditions, with its structural exploration under pressure bearing significant implications for both phase transition investigations and Earth science. Nonetheless, the precise phase transition sequence remains a debate. In this study, we disclose the emergence of the $Pbca$ phase alongside the enduring metallic character of $\rm{RuO_2}$ under megabar pressure. Employing state-of-the-art synchrotron X-ray diffraction, our observations delineate a phase transition trajectory progressing through rutile, $\rm{CaCl_2}$, and ultimately $Pbca$ phases. Notably, the $Pbca$ phase manifests immediately just after the rutile-$\rm{CaCl_2}$ transition, confining a narrow pressure regime for the pure $\rm{CaCl_2}$-type phase. Within the pressure range of 15.5 to 35.0 GPa, a coexistence of the $\rm{CaCl_2}$-type and $Pbca$ phases is observed, transforming to a sole presence of the $Pbca$ phase beyond 35.0 GPa. Electrical transport measurements conducted on both single crystal and powder samples confirm the enduring metallic conductivity of $\rm{RuO_2}$, persisting up to at least $\sim$120 GPa, albeit exhibiting a diminished conductivity at ultrahigh pressures due to a reduction in electronic density of states at the Fermi level. This study furnishes compelling evidence for the presence of the $Pbca$ phase across a broad pressure range, diverging from the previously widely acknowledged $Pa\bar{3}$ phase, thereby offering crucial insights into phase transition phenomena in other metal dioxides and advancing our comprehension of electronic behaviors within 4d and 5d electron systems., Comment: 7 pages, 5 figures

The maximum entropy principle is foundational for statistical analyses of complex dynamics. This principle has been challenged by the findings of a previous work [Phys. Rev. X 7, 031034 (2017)], where it was argued that a quantum system driven in time by a certain aperiodic sequence without any explicit symmetries, dubbed the Thue-Morse drive, gives rise to emergent nonergodic steady states which are underpinned by effective conserved quantities. Here, we resolve this apparent tension. We rigorously prove that the Thue-Morse drive achieves a very strong notion of quantum ergodicity in the long-time limit: the time evolution of any initial state uniformly visits every corner of its Hilbert space. On the other hand, we find the dynamics also approximates a Floquet drive for arbitrarily long albeit finite periods of time with no characteristic timescale, resulting in a scale-free ergodic dynamics we call critically slow complete Hilbert-space ergodicity (CS-CHSE). Furthermore, numerical studies reveal that CS-CHSE is not specific to the Thue-Morse drive and is in fact exhibited by many other aperiodic drives derived from morphic sequences, i.e., words derived from repeatedly applying substitution rules on basic characters. Our work presents a new class of dynamics in time-dependent quantum systems where full ergodicity is eventually attained, but only after astronomically long times., Comment: 6 pages, 3 figures (main) + 9 pages, 1 figure (supplemental)

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Intense electrostatic fields, such as those able to break bonds and cause field-ion emission, can fundamentally alter the behaviour of atoms at and on the surface. Using density functional theory (DFT) calculations on the Li (110) surface under high electrostatic fields, we identify a critical field strength at which surface atoms occupying a kink site become thermodynamically unstable against adatom formation. This mechanism leads to the formation of a highly concentrated two-dimensional (2D) adatom gas on the surface. Moreover, the applied field reverses the stability of preferred adsorption sites, enabling barrierless diffusion of lithium atoms even well below the threshold required for field evaporation. The here identified mechanisms offer a unified explanation for experimental observations in atom probe tomography and for understanding high electric field phenomena in systems such as battery interfaces and electrochemical environments.

Topological superconductors are essential elements of the periodic table of topological quantum matter. However, the relevant odd-parity spin-triplet superconductors are rare. We report high-resolution measurements of the complex electrical impedance of YbRh$_2$Si$_2$ down to 0.4 mK, that reveal the presence of several superconducting states, suppressed differently by magnetic field, both Pauli-limited and beyond the Pauli limit. Superconductivity is abruptly switched off at the critical field of the primary antiferromagnetic order. The onset of electro-nuclear spin density wave order enhances the superconductivity, which we account for by the simultaneous formation of a spin-triplet pair density wave. Together these observations provide compelling evidence for odd-parity superconductivity, and its underpinning by antiferromagnetism, and allow us to identify the topological helical state., Comment: 24 pages, 4 main and 15 supplementary figures, 1 supplementary table

Twisted van der Waals heterostructures provide a platform for studying a wide range of electron correlation phenomena, including unconventional superconductivity and correlated insulating states. However, fabricating such devices is challenging due to the difficulty in achieving and maintaining homogeneous twist-angles. Here, we present a fabrication method to preserve the twist-angle with minimal deformation. We fabricated marginally twisted double-bilayer graphene (mTDBG) stacks and directly imaged the resulting triangular superlattice periodicity via scattering-type scanning near-field optical microscopy (s-SNOM). This technique enabled us to monitor twist-angle deformation at each fabrication step, paving the way for more reliable device fabrication and facilitating the exploration of twist-angle-dependent physics., Comment: 15 pages, 4 figures

In fast-paced, ever-changing environments, dynamic Motion Planning for Multi-Agent Systems in the presence of obstacles is a universal and unsolved problem. Be it from path planning around obstacles to the movement of robotic arms, or in planning navigation of robot teams in settings such as Robosoccer, dynamic motion planning is needed to avoid collisions while reaching the targeted destination when multiple agents occupy the same area. In continuous domains where the world changes quickly, existing classical Motion Planning algorithms such as RRT* and A* become computationally expensive to rerun at every time step. Many variations of classical and well-formulated non-learning path-planning methods have been proposed to solve this universal problem but fall short due to their limitations of speed, smoothness, optimally, etc. Deep Learning models overcome their challenges due to their ability to adapt to varying environments based on past experience. However, current learning motion planning models use discretized environments, do not account for heterogeneous agents or replanning, and build up to improve the classical motion planners' efficiency, leading to issues with scalability. To prevent collisions between heterogenous team members and collision to obstacles while trying to reach the target location, we present a learning-based dynamic navigation model and show our model working on a simple environment in the concept of a simple Robosoccer Game.

Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360{\deg} unbounded scenes. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. Our approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. We also introduce 360-USID, the first comprehensive dataset for 360{\deg} unbounded scene inpainting with ground truth. Extensive experiments demonstrate that AuraFusion360 significantly outperforms existing methods, achieving superior perceptual quality while maintaining geometric accuracy across dramatic viewpoint changes. See our project page for video results and the dataset at https://kkennethwu.github.io/aurafusion360/., Comment: Project page: https://kkennethwu.github.io/aurafusion360/

Kramers escape from a metastable state in the presence of both thermal and quantum fluctuations under strong damping is treated as a thermally activated process in a quantum modified semiclassical potential. Dirac's time-dependent variational method together with the Jackiw-Kerman function is employed to derive the semiclassical potential., Comment: 4 pages, no figures

We develop a model predictive control (MPC) policy for station-keeping (SK) on a Near-Rectilinear Halo Orbit (NRHO). The proposed policy achieves full-state tracking of a reference NRHO via a two-maneuver control horizon placed one revolution apart. Our method abides by the typical mission requirement that at most one maneuver is used for SK during each NRHO revolution. Simultaneously, the policy has sufficient controllability for full-state tracking, making it immune to phase deviation issues in the along-track direction of the reference NRHO, a common drawback of existing SK methods with a single maneuver per revolution. We report numerical simulations with a navigation filter to demonstrate the MPC's performance with output feedback. Our approach successfully maintains the spacecraft's motion in the vicinity of the reference in both space and phase, with tighter tracking than state-of-the-art SK methods and comparable delta-V performance., Comment: 8 pages, 6 figures

A smell in software source code denotes an indication of suboptimal design and implementation decisions, potentially hindering the code understanding and, in turn, raising the likelihood of being prone to changes and faults. Identifying these code issues at an early stage in the software development process can mitigate these problems and enhance the overall quality of the software. Current research primarily focuses on the utilization of deep learning-based models to investigate the contextual information concealed within source code instructions to detect code smells, with limited attention given to the importance of structural and design-related features. This paper proposes a novel approach to code smell detection, constructing a deep learning architecture that places importance on the fusion of structural features and statistical semantics derived from pre-trained models for programming languages. We further provide a thorough analysis of how different source code embedding models affect the detection performance with respect to different code smell types. Using four widely-used code smells from well-designed datasets, our empirical study shows that incorporating design-related features significantly improves detection accuracy, outperforming state-of-the-art methods on the MLCQ dataset with with improvements ranging from 5.98% to 28.26%, depending on the type of code smell., Comment: This paper has been peer reviewed and accepted for publication with the Journal of Systems and Software

Jailbreak attacks exploit specific prompts to bypass LLM safeguards, causing the LLM to generate harmful, inappropriate, and misaligned content. Current jailbreaking methods rely heavily on carefully designed system prompts and numerous queries to achieve a single successful attack, which is costly and impractical for large-scale red-teaming. To address this challenge, we propose to distill the knowledge of an ensemble of SOTA attackers into a single open-source model, called Knowledge-Distilled Attacker (KDA), which is finetuned to automatically generate coherent and diverse attack prompts without the need for meticulous system prompt engineering. Compared to existing attackers, KDA achieves higher attack success rates and greater cost-time efficiency when targeting multiple SOTA open-source and commercial black-box LLMs. Furthermore, we conducted a quantitative diversity analysis of prompts generated by baseline methods and KDA, identifying diverse and ensemble attacks as key factors behind KDA's effectiveness and efficiency.

Low-rank adaptation (LoRA) has emerged as a powerful method for fine-tuning large-scale foundation models. Despite its popularity, the theoretical understanding of LoRA has remained limited. This paper presents a theoretical analysis of LoRA by examining its connection to the Mixture of Experts models. Under this framework, we show that simple reparameterizations of the LoRA matrices can notably accelerate the low-rank matrix estimation process. In particular, we prove that reparameterization can reduce the data needed to achieve a desired estimation error from an exponential to a polynomial scale. Motivated by this insight, we propose Reparameterized Low-rank Adaptation (RepLoRA), which incorporates lightweight MLPs to reparameterize the LoRA matrices. Extensive experiments across multiple domains demonstrate that RepLoRA consistently outperforms vanilla LoRA. Notably, with limited data, RepLoRA surpasses LoRA by a margin of up to 40.0% and achieves LoRA's performance with only 30.0% of the training data, highlighting both the theoretical and empirical robustness of our PEFT method.

The LLaMA-Adapter has recently emerged as an efficient fine-tuning technique for LLaMA models, leveraging zero-initialized attention to stabilize training and enhance performance. However, despite its empirical success, the theoretical foundations of zero-initialized attention remain largely unexplored. In this paper, we provide a rigorous theoretical analysis, establishing a connection between zero-initialized attention and mixture-of-expert models. We prove that both linear and non-linear prompts, along with gating functions, can be optimally estimated, with non-linear prompts offering greater flexibility for future applications. Empirically, we validate our findings on the open LLM benchmarks, demonstrating that non-linear prompts outperform linear ones. Notably, even with limited training data, both prompt types consistently surpass vanilla attention, highlighting the robustness and adaptability of zero-initialized attention., Comment: 43 pages, 5 tables, 6 figures

Multi-person motion capture over sparse angular observations is a challenging problem under interference from both self- and mutual-occlusions. Existing works produce accurate 2D joint detection, however, when these are triangulated and lifted into 3D, available solutions all struggle in selecting the most accurate candidates and associating them to the correct joint type and target identity. As such, in order to fully utilize all accurate 2D joint location information, we propose to independently triangulate between all same-typed 2D joints from all camera views regardless of their target ID, forming the Joint Cloud. Joint Cloud consist of both valid joints lifted from the same joint type and target ID, as well as falsely constructed ones that are from different 2D sources. These redundant and inaccurate candidates are processed over the proposed Joint Cloud Selection and Aggregation Transformer (JCSAT) involving three cascaded encoders which deeply explore the trajectile, skeletal structural, and view-dependent correlations among all 3D point candidates in the cross-embedding space. An Optimal Token Attention Path (OTAP) module is proposed which subsequently selects and aggregates informative features from these redundant observations for the final prediction of human motion. To demonstrate the effectiveness of JCSAT, we build and publish a new multi-person motion capture dataset BUMocap-X with complex interactions and severe occlusions. Comprehensive experiments over the newly presented as well as benchmark datasets validate the effectiveness of the proposed framework, which outperforms all existing state-of-the-art methods, especially under challenging occlusion scenarios., Comment: Accepted by IEEE Transactions on Visualization and Computer Graphics

Altermagnetism is a newly identified phase of magnetism distinct from ferromagnetism and antiferromagnetism. RuO$_2$ has been considered a prototypical metallic altermagnet with a critical temperature higher than room temperature. Previous interpretations of the unusual magnetic properties of RuO$_2$ relied on the theoretical prediction that local moments on two Ru sublattices, which are connected by four-fold rotational symmetry, are quite significant (approximately 1 $\mu_B$), leading to long-range antiferromagnetic order. However, accumulated experimental data suggest that local moments on Ru in RuO$_2$ are vanishingly small, indicating that the bulk material is likely non-magnetic. This observation is consistent with the delocalized nature of the 4$d$ electrons of Ru and the strong screening effect in the metallic state. In this work, we show that despite the non-magnetic bulk, the RuO$_2$(110) surface exhibits spontaneous magnetization. We attribute this effect to the breaking of local symmetry, which results in electronic redistribution and magnetic moment enhancement. The emergence of surface magnetism gives rise to interesting spectroscopic phenomena, including spin-polarized surface states, spin-polarized scanning probe microscopy images, and potentially spin-dependent transport effects. These findings highlight the important role of surface magnetic structures in the otherwise non-magnetic bulk RuO$_2$., Comment: 18 pages, 11 figures

Backdoor Malware are installed by an attacker on the victim's server(s) for authorized access. A customized backdoor is weaponized to execute unauthorized system, database and application commands to access the user credentials and confidential digital assets. Recently, we discovered and analyzed a targeted persistent module backdoor in Web Server in an online business company that was undetectable by their deployed Anti-Virus software for a year. This led us to carry out research to detect this specific type of persistent module backdoor installed in Web servers. Other than typical Malware static analysis, we carry out analysis with binary similarity, strings, and command obfuscation over the backdoor, resulting in the Target Attack Backdoor Malware Analysis Matrix (TABMAX) for organizations to detect this sophisticated target attack backdoor instead of a general one which can be detected by Anti-Virus detectors. Our findings show that backdoor malware can be designed with different APIs, commands, strings, and query language on top of preferred libraries used by typical Malware., Comment: 12 pages, 8 figures, 2 tables, DFRWS

One of the major goals of incident response is to help an organization or a system owner to quickly identify and halt the attacks to minimize the damages (and financial loss) to the system being attacked. Typical incident responses rely very much on the log information captured by the system during the attacks and if needed, may need to isolate the victim from the network to avoid further destructive attacks. However, there are real cases that there are insufficient log records/information for the incident response team to identify the attacks and their origins while the attacked system cannot be stopped due to service requirements (zero downtime online systems) such as online gaming sites. Typical incident response procedures and industrial standards do not provide an adequate solution to address this scenario. In this paper, being motivated by a real case, we propose a solution, called "Attack-Driven Incident Response and Defense System (ADIRDS)" to tackle this problem. ADIRDS is an online monitoring system to run with the real system. By modeling the real system as a graph, critical nodes/assets of the system are closely monitored. Instead of relying on the original logging system, evidence will be collected from the attack technique perspectives. To migrate the risks, realistic honeypots with very similar business context as the real system are deployed to trap the attackers. We successfully apply this system to a real case. Based on our experiments, we verify that our new approach of designing the realistic honeypots is effective, 38 unique attacker's IP addresses were captured. We also compare the performance of our realistic honey with both low and high interactive honeypots proposed in the literature, the results found that our proposed honeypot can successfully cheat the attackers to attack our honeypot, which verifies that our honeypot is more effective., Comment: 18 pages, 3 figures, 4 tables

The contact issue for two-dimensional (2D) materials-based field-effect transistors (FETs) has drawn enormous attention in recent years. Although ohmic behavior is achieved at room temperature, the drain current of 2DFETs shifts from ohmic to non-ohmic behavior at cryogenic temperatures. In this work, we demonstrate that the shift is attributed to the asymmetric current reduction at the metal-semiconductor contact at low temperature. Under low drain bias, carriers tunnel from the source to the channel but diffuse to the drain side due to the channel-to-drain barrier, resulting in the current suppression. By studying the property of ohmic metal-semiconductor contact at different temperatures, we analyzed the mechanisms behind this phenomenon and the dependence on metal-to-semiconductor barrier height. The work opens the semiconductor physics of 2D material contact at cryogenic temperature and the importance of contact metal selection in the development of 2DFET at cryogenic temperature., Comment: 13 pages, 5 figures

This research presents an exploration and study of the recipe generation task by fine-tuning various very small language models, with a focus on developing robust evaluation metrics and comparing across different language models the open-ended task of recipe generation. This study presents extensive experiments with multiple model architectures, ranging from T5-small (Raffel et al., 2023) and SmolLM-135M(Allal et al., 2024) to Phi-2 (Research, 2023), implementing both traditional NLP metrics and custom domain-specific evaluation metrics. Our novel evaluation framework incorporates recipe-specific metrics for assessing content quality and introduces approaches to allergen substitution. The results indicate that, while larger models generally perform better on standard metrics, the relationship between model size and recipe quality is more nuanced when considering domain-specific metrics. SmolLM-360M and SmolLM-1.7B demonstrate comparable performance despite their size difference before and after fine-tuning, while fine-tuning Phi-2 shows notable limitations in recipe generation despite its larger parameter count. The comprehensive evaluation framework and allergen substitution systems provide valuable insights for future work in recipe generation and broader NLG tasks that require domain expertise and safety considerations., Comment: 18 pages, 10 figures,14 tables

Ransomware impact different organizations for years, it causes huge monetary, reputation loss and operation impact. Other than typical data encryption by ransomware, attackers can request ransom from the victim organizations via data extortion, otherwise, attackers will publish stolen data publicly in their ransomware dashboard forum and data-sharing platforms. However, there is no clear and proven published incident response strategy to satisfy different business priorities and objectives under ransomware attack in detail. In this paper, we quote one of our representative front-line ransomware incident response experiences for Company X. Organization and incident responder can reference our established model strategy and implement proactive threat intelligence-based incident response architecture if one is under ransomware attack, which helps to respond the incident more effectively and speedy., Comment: 10 pages, 1 figure, 2 tables, case study

Algorithmic fairness has conventionally adopted a perspective of racial color-blindness (i.e., difference unaware treatment). We contend that in a range of important settings, group difference awareness matters. For example, differentiating between groups may be necessary in legal contexts (e.g., the U.S. compulsory draft applies to men but not women) and harm assessments (e.g., calling a girl a terrorist may be less harmful than calling a Muslim person one). In our work we first introduce an important distinction between descriptive (fact-based), normative (value-based), and correlation (association-based) benchmarks. This distinction is significant because each category requires distinct interpretation and mitigation tailored to its specific characteristics. Then, we present a benchmark suite composed of eight different scenarios for a total of 16k questions that enables us to assess difference awareness. Finally, we show results across ten models that demonstrate difference awareness is a distinct dimension of fairness where existing bias mitigation strategies may backfire.

Dual active galactic nuclei (AGNs) with comparable masses are commonly witnessed among the major merged galaxies with interaction remnants. Considering almost every massive galaxy is associated with multiple dwarf satellites around it, minor mergers involving galaxies with disproportional stellar masses should be much more common than major mergers, which would naturally lead to black hole (BH) pairs with significantly different masses. However, dual AGNs generated by minor mergers involving one or two dwarf galaxies are exceptionally rare and understudied. Moreover, good estimates of the masses of both BHs are not yet available to test this idea. Here we report the evidence of a dual AGN candidate with mass ratio $\sim$7:1 located in an undisturbed disk galaxy. We identify the central BH with mass of $9.4 \times 10^6M_\odot$ from its radio emission as well as AGN-driven galactic-scale biconical outflows. The off-centered BH generates obvious broad and narrow emission-line regions, which gives us a robust estimation of a $1.3 \times 10^6M_\odot$ BH mass. We explore alternative scenarios for explaining the observational features of this system, including the complex gas kinematics triggered by central AGN activity and dust attenuation of the broad-line region of the central BH, finding that they failed to fully account for the kinematics of both the redshifted off-centered broad and narrow emission-line components., Comment: Published in ApJL on 30 January 2025; 14 pages, 7 figures, 1 table

We use the IllustrisTNG cosmological hydrodynamical simulation to study the rotation curves of galaxies in the local universe. To do that, we first select the galaxies with 9.4 $<$ $\log{(M_\mathrm{star}/M_\odot)}$ $<$ 11.5 to make a sample comparable to that of SDSS/MaNGA observations. We then construct the two-dimensional line-of-sight velocity map and conduct the fit to determine the rotational velocity and the slope of the rotation curve in the outer region ($R_\mathrm{t}

Originally motivated by the (fold-)transversal related initialisation of logical surface code $|Y\rangle$ states from [arXiv:1603.02286, arXiv:2302.07395, arXiv:2302.12292], which was then explicitly extended to the fold-transversal $S$ gate implementation in [arXiv:2412.01391] for the rotated surface code, we employ ZX-calculus and Pauli web to understand the $|Y\rangle=S|+\rangle$ state transversal initialisation scheme.

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress., Comment: 14 pages, 5 figures, 3 tables, preprint