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We present two methods for computing the dynamic structure factor for warm dense hydrogen without invoking either the Born-Oppenheimer approximation or the Chihara decomposition, by employing a wave packet description which resolves the electron dynamics during ion evolution. Firstly, a semiclassical method is discussed which is corrected based on known quantum constraints, and secondly, a direct computation of the density-density response function within the molecular dynamics. The wave packet models are compared to PIMC and DFT-MD for the static and low-frequency behaviour. For the high-frequency behaviour the models recover the expected behaviour in the limits of small and large momentum transfers and show the characteristic flattening of the plasmon dispersion for intermediate momentum transfers due to interactions, in agreement with commonly used models for X-ray Thomson scattering. By modelling the electrons and ions on an equal footing, both the ion and free electron part of the spectrum can now be treated within a single framework where we simultaneously resolve the ion-acoustic and plasmon mode, with a self-consistent description of collisions and screening., Comment: 17 pages, 9 figures

We study the problem of assigning agents to the vertices of a graph such that no pair of neighbors can benefit from swapping assignments -- a property we term neighborhood stability. We further assume that agents' utilities are based solely on their preferences over the assignees of adjacent vertices and that those preferences are binary. Having shown that even this very restricted setting does not guarantee neighborhood stable assignments, we focus on special cases that provide such guarantees. We show that when the graph is a cycle or a path, a neighborhood stable assignment always exists for any preference profile. Furthermore, we give a general condition under which neighborhood stable assignments always exist. For each of these results, we give a polynomial-time algorithm to compute a neighborhood stable assignment.

The density dependence of the symmetry energy in relativistic mean-field models with density dependent couplings is discussed in terms of the possible opening of nucleonic direct Urca processes inside neutron stars, which induce a very rapid cooling of the star. The modification of the parametrization of the isospin channel of two models, DD2 and DDMEX, keeping the same isoscalar properties is considered and the implications are discussed. Within the models discussed it is not possible the onset of nucleonic direct Urca processes in stars with a mass below $\sim1.6-1.8\, M_\odot$. The lowest masses that allow direct Urca processes are associated to a slope of the symmetry energy $\sim60$ MeV and a symmetry energy incompressibility close to zero. It is shown that the parametrization of the isospin channel proposed destroys the correlation between symmetry energy slope and incompressibility previously identified in several works., Comment: 13 pages, 6 figures, 3 tables, submitted to Phys. Rev. C

Cycle scenarios are a significant class of contextuality scenarios, with the Clauser-Horne-Shimony-Holt (CHSH) scenario being a notable example. While binary outcome measurements in these scenarios are well understood, the generalization to arbitrary outcomes remains less explored, except in specific cases. In this work, we employ homotopical methods in the framework of simplicial distributions to characterize all contextual vertices of the non-signaling polytope corresponding to cycle scenarios with arbitrary outcomes. Additionally, our techniques utilize the bundle perspective on contextuality and the decomposition of measurement spaces. This enables us to extend beyond scenarios formed by gluing cycle scenarios and describe contextual extremal simplicial distributions in these generalized contexts., Comment: 19 pages, 5 figures

We study committee voting rules under ranked preferences, which map the voters' preference relations to a subset of the alternatives of predefined size. In this setting, the compatibility between proportional representation and committee monotonicity is a fundamental open problem that has been mentioned in several works. We design a new multi-winner voting rule called the Solid Coalition Refinement (SCR) Rule that simultaneously satisfies committee monotonicity and Dummett's PSC as well as one of its variants called inclusion PSC. This is the first rule known to satisfy both of these properties. Moreover, we show that this is effectively the best that we can hope for as other fairness notions adapted from approval voting such as Rank-JR and Rank-PJR+ are incompatible with committee monotonicity.

We report improved wave function for mesons in QCD potential model approach using multiplicative method for solution of Schr\"{o}dinger equation for the extreme cases of inter-quark separations ($r\rightarrow0$ and $r\rightarrow\infty$). Using the wave function we find the range of the scale factor c of the Cornell potential with reference to the mass scale of B and D sectors heavy-flavoured mesons. With our computed range of c, we also explore the critical inter-quark separation which should govern the choice of parent-child terms in perturbation method out of the linear and Coulombic terms of the Cornell potential.

Understanding the agent's learning process, particularly the factors that contribute to its success or failure post-training, is crucial for comprehending the rationale behind the agent's decision-making process. Prior methods clarify the learning process by creating a structural causal model (SCM) or visually representing the distribution of value functions. Nevertheless, these approaches have constraints as they exclusively function in 2D-environments or with uncomplicated transition dynamics. Understanding the agent's learning process in complicated environments or tasks is more challenging. In this paper, we propose REVEAL-IT, a novel framework for explaining the learning process of an agent in complex environments. Initially, we visualize the policy structure and the agent's learning process for various training tasks. By visualizing these findings, we can understand how much a particular training task or stage affects the agent's performance in test. Then, a GNN-based explainer learns to highlight the most important section of the policy, providing a more clear and robust explanation of the agent's learning process. The experiments demonstrate that explanations derived from this framework can effectively help in the optimization of the training tasks, resulting in improved learning efficiency and final performance., Comment: arXiv admin note: text overlap with arXiv:2307.01452 by other authors

Water pollution has become a global problem. Sources of wastewater majorly include industrial and commercial sectors. To cater to the exponential increase in clean water, efficient technologies are needed to treat wastewater. Several techniques such as redox reactions, membrane filtrations, mechanical processes, chemical treatment and adsorption techniques have been employed. However, their cost and effectiveness is still a major problem. In this study, we employed an effective wastewater treatment technique by synthesizing NiCoMn MOFs using a simple hydrothermal technique and characterized the properties using XRD and SEM for their possible characteristics. XRD analysis confirmed the successful synthesis of NiCoMn MOFs. Sufficient information regarding the surface morphology and topology was given by the SEM analysis which proved a nanoporous structure with high surface area effective for adsorption and oxidative catalysis of contaminants in wastewater. Moreover, a high electrostatic attraction between the MOFs was observed which could attract oppositely charged contaminants. The results showed a high potential for the synthesized NiCoMn MOFs for wastewater treatment applications., Comment: 9 pages, 6 figures

This research presents a comprehensive assessment of pan-sharpening techniques for satellite imagery, focusing on the critical aspects of spectral fidelity and spatial enhancement. Motivated by the need for informed algorithm selection in remote sensing, A novel cascaded and structured evaluation framework has been proposed with a detailed comparative analysis of existing methodologies. The research findings underscore the intricate trade-offs between spectral accuracy of about 88\% with spatial resolution enhancement. The research sheds light on the practical implications of pan-sharpening and emphasizes the significance of both spectral and spatial aspects in remote sensing applications. Various pan-sharpening algorithms were systematically employed to provide a holistic view of their performance, contributing to a deeper understanding of their capabilities and limitations.

Recent strides in multimodal model development have ignited a paradigm shift in the realm of text-to-image generation. Among these advancements, CLIP stands out as a remarkable achievement which is a sophisticated autoencoder adept at encoding both textual and visual information within a unified latent space. This paper delves into a comparative analysis between CLIP and its recent counterpart, CLOOB. To unravel the intricate distinctions within the embedding spaces crafted by these models, we employ topological data analysis. Our approach encompasses a comprehensive examination of the modality gap drivers, the clustering structures existing across both high and low dimensions, and the pivotal role that dimension collapse plays in shaping their respective embedding spaces. Empirical experiments substantiate the implications of our analyses on downstream performance across various contextual scenarios. Through this investigation, we aim to shed light on the nuanced intricacies that underlie the comparative efficacy of CLIP and CLOOB, offering insights into their respective strengths and weaknesses, and providing a foundation for further refinement and advancement in multimodal model research., Comment: 10 pages, 17 figures, 2 tables

Stateless model checking is a fully automatic verification technique for concurrent programs that checks for safety violations by exploring all possible thread schedulings. It becomes effective when coupled with Dynamic Partial Order Reduction (DPOR), which introduces an equivalence on schedulings and reduces the amount of needed exploration. DPOR algorithms that are \emph{optimal} are particularly effective in that they guarantee to explore \emph{exactly} one execution from each equivalence class. Unfortunately, existing sequence-based optimal algorithms may in the worst case consume memory that is exponential in the size of the analyzed program. In this paper, we present Parsimonious-OPtimal (POP) DPOR, an optimal DPOR algorithm for analyzing multi-threaded programs under sequential consistency, whose space consumption is polynomial in the worst case. POP combines several novel algorithmic techniques, including (i) a parsimonious race reversal strategy, which avoids multiple reversals of the same race, (ii) an eager race reversal strategy to avoid storing initial fragments of to-be-explored executions, and (iii) a space-efficient scheme for preventing redundant exploration, which replaces the use of sleep sets. Our implementation in Nidhugg shows that these techniques can significantly speed up the analysis of concurrent programs, and do so with low memory consumption. Comparison to a related optimal DPOR algorithm for a different representation of concurrent executions as graphs shows that POP has comparable worst-case performance for smaller benchmarks and outperforms the other one for larger programs.

We report a comprehensive Raman study of the phonon behaviour in PdSe$_2$ in the temperature range of 5 K to 300 K. A remarkable change in the Raman spectrum is observed at 120 K: a significant enhancement of the out-of-plane phonon A$^{1}_{g}$ mode, accompanied by a suppression of the in-plane A$^{2}_{g}$ and B$^{2}_{1g}$ modes. This intriguing behavior is attributed to a temperature-dependent resonant excitation effect. The results are supported by density functional theory (DFT) calculations, which demonstrate that the electron-phonon coupling for the phonon modes varies and is strongly associated with the relevant electronic states. Furthermore, nonlinear frequency shifts are identified in all modes, indicating the decay of an optical phonon into multiple acoustic phonons. The study of Raman emission reported here, complemented by linear optical spectroscopy, reveals an unexpected scenario for the vibrational properties of PdSe$_2$ that holds substantial promise for future applications in PdSe$_2$-based optoelectronics.

This paper introduces the Global-Local Image Perceptual Score (GLIPS), an image metric designed to assess the photorealistic image quality of AI-generated images with a high degree of alignment to human visual perception. Traditional metrics such as FID and KID scores do not align closely with human evaluations. The proposed metric incorporates advanced transformer-based attention mechanisms to assess local similarity and Maximum Mean Discrepancy (MMD) to evaluate global distributional similarity. To evaluate the performance of GLIPS, we conducted a human study on photorealistic image quality. Comprehensive tests across various generative models demonstrate that GLIPS consistently outperforms existing metrics like FID, SSIM, and MS-SSIM in terms of correlation with human scores. Additionally, we introduce the Interpolative Binning Scale (IBS), a refined scaling method that enhances the interpretability of metric scores by aligning them more closely with human evaluative standards. The proposed metric and scaling approach not only provides more reliable assessments of AI-generated images but also suggest pathways for future enhancements in image generation technologies., Comment: 10 pages, 3 figures. Submitted to IEEE Transactions on Human-Machine Systems

Phase change materials (PCMs) have gained a tremendous interest as a means to actively tune nanophotonic devices through the large optical modulation produced by their amorphous to crystalline reversible transition. Recently, materials such as Sb$_2$S$_3$ emerged as particularly promising low loss PCMs, with both large refractive index modulations and transparency in the visible and NIR. Controlling the local and reversible phase transition in this material is of major importance for future applications, and an appealing method to do so is to exploit pulsed lasers. Yet, the physics and limits involved in the optical switching of Sb$_2$S$_3$ are not yet well understood. Here, we investigate the reversible laser-induced phase transition of Sb$_2$S$_3$, focusing specifically on the mechanisms that drive the optically induced amorphization, with multi-physics considerations including the optical and thermal properties of the PCM and its environment. We theoretically and experimentally determine the laser energy threshold for reversibly changing the phase of the PCM, not only between fully amorphous and crystalline states but also between partially recrystallized states. We then reveal the non-negligible impact of the material's polycrystallinity and anisotropy on the power thresholds for optical switching. Finally, we address the challenges related to laser amorphization of thick Sb$_2$S$_3$ layers, as well as strategies to overcome them. These results enable a qualitative and quantitative understanding of the physics behind the optically-induced reversible change of phase in Sb$_2$S$_3$ layers.

Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for diagnostic and interventional procedures. However, using it effectively requires extensive training due to the intricate nature of image acquisition and interpretation. To enhance the efficiency of novice sonographers and reduce variability in scan acquisitions, we propose a novel ultrasound (US) navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (GCRL). We augment the previous framework using a novel contrastive patient batching method (CPB) and a data-augmented contrastive loss, both of which we demonstrate are essential to ensure generalization to anatomical variations across patients. The proposed framework enables navigation to both standard diagnostic as well as intricate interventional views with a single model. Our method was developed with a large dataset of 789 patients and obtained an average error of 6.56 mm in position and 9.36 degrees in angle on a testing dataset of 140 patients, which is competitive or superior to models trained on individual views. Furthermore, we quantitatively validate our method's ability to navigate to interventional views such as the Left Atrial Appendage (LAA) view used in LAA closure. Our approach holds promise in providing valuable guidance during transesophageal ultrasound examinations, contributing to the advancement of skill acquisition for cardiac ultrasound practitioners., Comment: 11 pages, 3 figures

This paper outlines potential actions and roles for intermediaries to facilitate the development and implementation of truly equitable education-to-career pathways systems. Specifically, it could guide the broader field of intermediary organizations and efforts to engage in policy and advocacy by outlining how intermediaries can influence the design and implementation of policy solutions to address systemic inequity. Recognizing that intermediaries range widely in size, capacity, and purpose, we offer suggestions that organizations can apply wherever they sit in the policy space. The resource also highlights a set of Building Equitable Pathways (BEP) intermediaries and their work in policy and advocacy.

Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit quantization. Thereby, a maximum a posteriori (MAP) estimate of the most responsible mixture component is inferred for a quantized received signal, which is subsequently utilized in the EM algorithm as side information. Numerical results demonstrate the significant performance improvement of our proposed approach over both a simplistic Gaussian prior and current state-of-the-art channel estimators. Furthermore, the proposed estimation framework exhibits adaptability to higher resolution systems and alternative generative priors.

Using heterodyne transient grating spectroscopy, we observe a significant enhancement of exciton diffusion within a monolayer WSe$_2$ stacked on top of graphene. We further demonstrate that the diffusion dynamics can be optically tuned on the ultrafast time scale (i.e. a few picoseconds) by altering the photoexcited charge carrier density in graphene. The results reveal that, on a time scale of a few picoseconds, the effective diffusion constant in the WSe$_2$/graphene heterostructure is approximately 40 cm$^2 $/s, representing a substantial improvement over the 2 cm$^2 $/s typical for an isolated monolayer of WSe$_2$. The enhanced diffusion can be understood in terms of a transient screening of impurities, charge traps, and defect states in WSe$_2$ by photoexcited charge carriers in graphene. Furthermore, we observe that the diffusion within WSe$_2$ is affected by interlayer interactions, like charge transfer, exhibiting different dynamic states depending on the incident excitation fluence. These findings underscore the dynamic nature of screening and diffusion processes in heterostructures of 2D semiconductors and graphene and provide valuable insights for future applications of these systems in ultrafast optoelectronic devices.

This study presents an agent-based model (ABM) developed to simulate the resilience of a community to hurricane-induced infrastructure disruptions, focusing on the interdependencies between electric power and transportation networks. In this ABM approach, agents represent the components of a system, where interactions within a system shape intra-dependency of a system and interactions among systems shape interdependencies. To study household resilience subject to a hurricane, a library of agents has been created including electric power network, transportation network, wind/flooding hazards, and household agents. The ABM is applied over the household and infrastructure data from a community (Zip code 33147) in Miami-Dade County, Florida. Interdependencies between the two networks are modeled in two ways, (i) representing the role of transportation in fuel delivery to power plants and restoration teams' access, (ii) impact of power outage on transportation network components. Restoring traffic signals quickly is crucial as their outage can slow down traffic and increase the chance of crashes. We simulate three restoration strategies: component based, distance based, and traffic lights based restoration. The model is validated against Hurricane Irma data, showing consistent behavior with varying hazard intensities. Scenario analyses explore the impact of restoration strategies, road accessibility, and wind speed intensities on power restoration. Results demonstrate that a traffic lights based restoration strategy efficiently prioritizes signal recovery without delaying household power restoration time. Restoration of power services will be faster if restoration teams do not need to wait due to inaccessible roads and fuel transportation to power plants is not delayed.

We demonstrate a bipartition technique using a super-lattice architecture to access correlations between alternating planes of a mesoscopic array of spin-3 chromium atoms trapped in a 3D optical lattice. Using this method, we observe that out-of-equilibrium dynamics driven by long-range dipolar interactions lead to spin anti-correlations between the two spatially separated subsystems. Our bipartite measurements reveal a subtle interplay between the anisotropy of the 3D dipolar interactions and that of the lattice structure, without requiring single-site addressing. We compare our results to theoretical predictions based on a truncated cumulant expansion and a new cluster semi-classical method that we use to investigate correlations at the microscopic scale. Comparison with a high-temperature analytical model reveals quantum thermalization at a high negative spin temperature., Comment: Main article: 5 pages, 3 Figures Supp Mat: 4 pages, 3 Figures

In pursuit of participatory budgeting (PB) outcomes with broader fairness guarantees, we initiate the study of lotteries over discrete PB outcomes. As the projects have heterogeneous costs, the amount spent may not be equal ex ante and ex post. To address this, we develop a technique to bound the amount by which the ex-post spend differs from the ex-ante spend -- the property is termed budget balanced up to one project (BB1). With respect to fairness, we take a best-of-both-worlds perspective, seeking outcomes that are both ex-ante and ex-post fair. Towards this goal, we initiate a study of ex-ante fairness properties in PB, including Individual Fair Share (IFS), Unanimous Fair Share (UFS) and their stronger variants, as well as Group Fair Share (GFS). We show several incompatibility results between these ex-ante fairness notions and existing ex-post concepts based on justified representation. One of our main contributions is a randomized algorithm which simultaneously satisfies ex-ante Strong UFS, ex-post full justified representation (FJR) and ex-post BB1 for PB with binary utilities., Comment: Appears in the 38th AAAI Conference on Artificial Intelligence (AAAI), 2024

We study the homotopy theory of diagrams of chain complexes over a field indexed by a finite poset, and show that it can be completely described in terms of appropriate diagrams of graded vector spaces.

Regulatory efforts to protect against algorithmic bias have taken on increased urgency with rapid advances in large language models (LLMs), which are machine learning models that can achieve performance rivaling human experts on a wide array of tasks. A key theme of these initiatives is algorithmic "auditing," but current regulations -- as well as the scientific literature -- provide little guidance on how to conduct these assessments. Here we propose and investigate one approach for auditing algorithms: correspondence experiments, a widely applied tool for detecting bias in human judgements. In the employment context, correspondence experiments aim to measure the extent to which race and gender impact decisions by experimentally manipulating elements of submitted application materials that suggest an applicant's demographic traits, such as their listed name. We apply this method to audit candidate assessments produced by several state-of-the-art LLMs, using a novel corpus of applications to K-12 teaching positions in a large public school district. We find evidence of moderate race and gender disparities, a pattern largely robust to varying the types of application material input to the models, as well as the framing of the task to the LLMs. We conclude by discussing some important limitations of correspondence experiments for auditing algorithms.

The effects of different forms of weak measurements on the nature of the measurement induced phase transition are theoretically studied in hybrid random quantum circuits of qubits. We use a combination of entanglement measures, ancilla purification dynamics, and a transfer matrix approach to compute the critical exponents, the effective central charge, and the multifractal spectrum of the measurement induced transitions. We compare weak measurements with an infinite number of discrete outcomes to a protocol with only a pair of outcomes and find that to within our numerical accuracy the universal critical properties are unaffected by the weak measurement protocols and are consistent with the universality class found for strong projective measurements., Comment: 12 + 2 pages, 6 + 3 figures

We investigate the impact of monoatomic vacancies in 2D materials on the performance of magnetic tunnel junction (MTJ) devices using first-principles calculations within Density Functional Theory (DFT). Specifically, we analyze the influence on hexagonal boron nitride (hBN) with various layer configurations, uncovering distinct transmission probability patterns. Transmission calculations were conducted using the Landauer-B\"uttiker formula employing the Non-Equilibrium Green's Function (NEGF) method. In the Ni/hBN(V$_B$)-hBN/Ni system, a significant reduction in transmission probability was observed compared to non-vacancy configurations. However, when two hBN vacancies were considered, creating the Ni/hBN(V$_B$)-hBN(V$_B$)/Ni MTJ system, a new transmission channel mediated by vacancy localized states emerged. The introduction of a monoatomic boron vacancy in the middle hBN layer of the Ni/3hBN/Ni system revealed nuanced effects on the transmission probability, highlighting alterations in the spin minority and majority channels. Additionally, we explore the monoatomic vacancy in the graphene layer in the Ni/hBN-Gr-hBN/Ni MTJ, uncovering a unique transmission channel influenced by the proximity effect. Our findings suggest that the creation of monoatomic vacancies on the insulator barrier of 2D materials induces distinctive characteristics shaped by the interaction between the surface state of the electrode and the localized state of the monoatomic vacancy layer in the MTJ system.

This research introduces an innovative security enhancement approach, employing advanced image analysis and soft computing. The focus is on an intelligent surveillance system that detects unauthorized individuals in restricted areas by analyzing attire. Traditional security measures face challenges in monitoring unauthorized access. Leveraging YOLOv8, an advanced object detection algorithm, our system identifies authorized personnel based on their attire in CCTV footage. The methodology involves training the YOLOv8 model on a comprehensive dataset of uniform patterns, ensuring precise recognition in specific regions. Soft computing techniques enhance adaptability to dynamic environments and varying lighting conditions. This research contributes to image analysis and soft computing, providing a sophisticated security solution. Emphasizing uniform-based anomaly detection, it establishes a foundation for robust security systems in restricted areas. The outcomes highlight the potential of YOLOv8-based surveillance in ensuring safety in sensitive locations., Comment: 9 pages, 6 figures

In this study we explore the astrophysical implications of pressure anisotropy on the physical characteristics of millisecond pulsars within the framework of $f(Q)$ gravity. Starting off with the field equations for anisotropic matter distributions, we adopt the physically salient Durgapal-Fuloria ansatz together with a well-motivated anisotropic factor for the interior matter distribution. This leads to a nonlinear second order differential equation which is integrated to give the complete gravitational and thermodynamical properties of the stellar object. The resulting model is subjected to rigorous tests to ensure that it qualifies as a physically viable compact object within the $f(Q)$-gravity framework. We study in detail two factors, one being non-metricity (gravitational interaction) and the other the pressure anisotropy (matter variation) and their contributions to the mass, radius and stability of the star. Our analyses indicate that our models are well-behaved, singularity-free and can account for the existence of a wide range of observed pulsars with masses ranging from 2.08 to 2.67 $M_{\odot}$, with the upper value being in the so-called {\em mass gap} regime observed in gravitational events such as GW190814. A novel finding of this work is two-fold: an increase in the non-metricity factor (fixed anisotropy) leads to a spectrum of pulsars with radii ranging from a minimum of 10 km whereas a variation in anisotropy (fixed non-metricity) restricts the radii to a narrow window between ($12 - 13.2$) km for a 450 Hz pulsar. In addition, contributions from anisotropy outweigh the impact of nonmetricity in predicting the masses of pulsars with the ratio, $\left(\frac{M_\Delta}{M_Q}\right)_{max} \approx 2.43 $ for low mass pulsars., Comment: 6 figures and 2 tables (Submssion for JCAP)

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed for predicting the dielectric permittivity (as a moisture proxy) of the bottom soil layer and the top biomass layer from radar signals. Specifically, the use of a combination of a higher and a lower frequency radar compared to a single frequency in predicting the permittivity of both the soil and the overlaying layer was investigated in this study. The results show that each layer was best characterized via one of the frequencies. However, for the simultaneous prediction of both layers permittivity, the most consistent results were achieved by inversion of data from a combination of both frequencies, showing better correlation with in situ permittivity and reduced prediction errors., Comment: There are 5 pages, 5 figures and 1 table. This study has been accepted at 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

We propose a novel, to the best of our knowledge, approach to superresolution optical imaging by combining quantum optics and near-field optics. Our concept involves the utilization of single-photon quantum emitters to generate a stand-alone evanescent wave. We demonstrate that the quantum interference effects of single-photon emitters, in conjunction with their near-field, result in a higher resolution of subwavelength structures than systems that are only quantum enhanced or only near-field enhanced. We believe that nano-sized emitters could be employed to accomplish the goals of this research, taking into account the current progress in nanophotonics and quantum optics technology., Comment: 13 pages, 7 figures, Research article

Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited receptive fields and overcoming gradient instability in deeper models. Despite their success, transformer-based models require substantial resources for training, including data, memory, and computational power, which may restrict their applicability for end users with limited resources. In particular, existing transformer-based 3D image registration architectures face three critical gaps that challenge their efficiency and effectiveness. Firstly, while mitigating the quadratic complexity of full attention by focusing on local regions, window-based attention mechanisms often fail to adequately integrate local and global information. Secondly, feature similarities across attention heads that were recently found in multi-head attention architectures indicate a significant computational redundancy, suggesting that the capacity of the network could be better utilized to enhance performance. Lastly, the granularity of tokenization, a key factor in registration accuracy, presents a trade-off; smaller tokens improve detail capture at the cost of higher computational complexity, increased memory demands, and a risk of overfitting. Here, we propose EfficientMorph, a transformer-based architecture for unsupervised 3D image registration. It optimizes the balance between local and global attention through a plane-based attention mechanism, reduces computational redundancy via cascaded group attention, and captures fine details without compromising computational efficiency, thanks to a Hi-Res tokenization strategy complemented by merging operations. Notably, EfficientMorph sets a new benchmark for performance on the OASIS dataset with 16-27x fewer parameters.

Blind and low-vision (BLV) people face many challenges when venturing into public environments, often wishing it were easier to get help from people nearby. Ironically, while many sighted individuals are willing to help, such interactions are infrequent. Asking for help is socially awkward for BLV people, and sighted people lack experience in helping BLV people. Through a mixed-ability research-through-design process, we explore four diverse approaches toward how assistive technology can serve as help supporters that collaborate with both BLV and sighted parties throughout the help process. These approaches span two phases: the connection phase (finding someone to help) and the collaboration phase (facilitating help after finding someone). Our findings from a 20-participant mixed-ability study reveal how help supporters can best facilitate connection, which types of information they should present during both phases, and more. We discuss design implications for future approaches to support face-to-face help., Comment: To Appear In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) Association for Computing Machinery, New York, NY, USA. 24 pages

We present an extensive, in-depth analysis of the eye tracking capabilities of the Meta Quest Pro virtual reality headset using a dataset of eye movement recordings collected from 78 participants. In addition to presenting classical signal quality metrics--spatial accuracy, spatial precision and linearity--in ideal settings, we also study the impact of background luminance and headset slippage on device performance. We additionally present a user-centered analysis of eye tracking signal quality, where we highlight the potential differences in user experience as a function of device performance. This work contributes to a growing understanding of eye tracking signal quality in virtual reality headsets, where the performance of applications such as gaze-based interaction, foveated rendering, and social gaze are directly dependent on the quality of eye tracking signal., Comment: 14 pages

Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern many problems ranging from weather and climate prediction to turbulence simulations. Recent advances have seen machine learning (ML) increasingly applied to model these subgrid processes, resulting in the development of hybrid physics-ML models through the integration with numerical solvers. In this work, we introduce a novel framework for the joint estimation of physical parameters and machine learning parameterizations with uncertainty quantification. Our framework incorporates online training and efficient Bayesian inference within a high-dimensional parameter space, facilitated by differentiable programming. This proof of concept underscores the substantial potential of differentiable programming in synergistically combining machine learning with differential equations, thereby enhancing the capabilities of hybrid physics-ML modeling., Comment: Accepted at ICLR 2024 Workshop on AI4Differential Equations in Science

Deep optical emission-line images are presented for nine known plus three new Galactic supernova remnants (SNRs), all but one having at least one angular dimension greater than one degree. Wide-field images taken in H$\alpha$ and [O III] $\lambda$5007 reveal many new and surprising remnant structures including large remnant shock extensions and `breakout' features not seen in published optical or radio data. These images represent over 12,000 individual images totaling more than 1000 hours of exposure time taken over the last two years mainly using small aperture telescopes which detected fainter nebular line emissions than published emission-line images. During the course of this imaging program, we discovered three new SNRs, namely G107.5-5.1 (the Nereides Nebula), G209.9-8.2, and G210.5+1.3, two of which have diameters >1.5 degrees. Besides offering greater structural detail on the nine already known SNRs, a key finding of this study is the importance of [O III] emission-line imaging for mapping the complete shock emissions of Galactic SNRs., Comment: 53 pages, 40 figures, 3 tables. Accepted for publication in the Astrophysical Journal Supplement Series

Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD-SIGNet, an end-to-end trainable and auto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch-and-extrusion from an input point cloud. Our model learns visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular, a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine-grained details of the sketches. Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next-step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings, namely, full design history recovery and conditional auto-completion from point clouds.

Language models (LMs) are statistical models trained to assign probability to human-generated text. As such, it is reasonable to question whether they approximate linguistic variability exhibited by humans well. This form of statistical assessment is difficult to perform at the passage level, for it requires acceptability judgements (i.e., human evaluation) or a robust automated proxy (which is non-trivial). At the word level, however, given some context, samples from an LM can be assessed via exact matching against a prerecorded dataset of alternative single-word continuations of the available context. We exploit this fact and evaluate the LM's ability to reproduce variability that humans (in particular, a population of English speakers) exhibit in the 'next word prediction' task. This can be seen as assessing a form of calibration, which, in the context of text classification, Baan et al. (2022) termed calibration to human uncertainty. We assess GPT2, BLOOM and ChatGPT and find that they exhibit fairly low calibration to human uncertainty. We also verify the failure of expected calibration error (ECE) to reflect this, and as such, advise the community against relying on it in this setting., Comment: 22 pages, EACL 2024

With the rise of increasingly powerful and user-facing NLP systems, there is growing interest in assessing whether they have a good representation of uncertainty by evaluating the quality of their predictive distribution over outcomes. We identify two main perspectives that drive starkly different evaluation protocols. The first treats predictive probability as an indication of model confidence; the second as an indication of human label variation. We discuss their merits and limitations, and take the position that both are crucial for trustworthy and fair NLP systems, but that exploiting a single predictive distribution is limiting. We recommend tools and highlight exciting directions towards models with disentangled representations of uncertainty about predictions and uncertainty about human labels., Comment: EACL 2024 main

The recent emergence of ubiquitous, multi-platform eye tracking has raised user privacy concerns over re-identification across platforms, where a person is re-identified across multiple eye tracking-enabled platforms using personally identifying information that is implicitly expressed through their eye movement. We present an empirical investigation quantifying a modern eye movement biometric model's ability to link subject identities across three different eye tracking devices using eye movement signals from each device. We show that a state-of-the art eye movement biometrics model demonstrates above-chance levels of biometric performance (34.99% equal error rate, 15% rank-1 identification rate) when linking user identities across one pair of devices, but not for the other. Considering these findings, we also discuss the impact that eye tracking signal quality has on the model's ability to meaningfully associate a subject's identity between two substantially different eye tracking devices. Our investigation advances a fundamental understanding of the privacy risks for identity linkage across platforms by employing both quantitative and qualitative measures of biometric performance, including a visualization of the model's ability to distinguish genuine and imposter authentication attempts across platforms., Comment: 11 pages, 4 figures. This paper is set to appear in the 2023 proceedings of the IEEE International Joint Conference on Biometrics (IEEE 2023) as an accepted paper

The development of intrusion detection systems (IDS) for the in-vehicle Controller Area Network (CAN) bus is one of the main efforts being taken to secure the in-vehicle network against various cyberattacks, which have the potential to cause vehicles to malfunction and result in dangerous accidents. These CAN IDS are evaluated in disparate experimental conditions that vary in terms of the workload used, the features used, the metrics reported, etc., which makes direct comparison difficult. Therefore, there have been several benchmarking frameworks and comparative studies designed to evaluate CAN IDS in similar experimental conditions to understand their relative performance and facilitate the selection of the best CAN IDS for implementation in automotive networks. This work provides a comprehensive survey of CAN IDS benchmarking frameworks and comparative studies in the current literature. A CAN IDS evaluation design space is also proposed in this work, which draws from the wider CAN IDS literature. This is not only expected to serve as a guide for designing CAN IDS evaluation experiments but is also used for categorizing current benchmarking efforts. The surveyed works have been discussed on the basis of the five aspects in the design space-namely IDS type, attack model, evaluation type, workload generation, and evaluation metrics-and recommendations for future work have been identified., Comment: Under Review at Journal of Computer Security

Ultrasound is well-established as an imaging modality for diagnostic and interventional purposes. However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies. Automating the image acquisition task could improve acquisition reproducibility and quality but training such an algorithm requires large amounts of navigation data, not saved in routine examinations. Thus, we propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions, such that this pipeline can later be used by learning algorithms for navigation. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized volumetric data representation and GPU-accelerated Monte Carlo path tracing to generate view-dependent and patient-specific ultrasound images. We extensively validate the correctness of our pipeline with a phantom experiment, where structures' sizes, contrast and speckle noise properties are assessed. Furthermore, we demonstrate its usability to train neural networks for navigation in an echocardiography view classification experiment by generating synthetic images from more than 1000 patients. Networks pre-trained with our simulations achieve significantly superior performance in settings where large real datasets are not available, especially for under-represented classes. The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated., Comment: 24 pages, 10 figures, 5 tables

Digital pixel sensor (DPS) has evolved as a pivotal component in modern imaging systems and has the potential to revolutionize various fields such as medical imaging, astronomy, surveillance, IoT devices, etc. Compared to analog pixel sensors, the DPS offers high speed and good image quality. However, the introduced intrinsic complexity within each pixel, primarily attributed to the accommodation of the ADC circuit, engenders a substantial increase in the pixel pitch. Unfortunately, such a pronounced escalation in pixel pitch drastically undermines the feasibility of achieving high-density integration, which is an obstacle that significantly narrows down the field of potential applications. Nonetheless, designing compact conversion circuits along with strategic integration of 3D architectural paradigms can be a potential remedy to the prevailing situation. This review article presents a comprehensive overview of the vast area of DPS technology. The operating principles, advantages, and challenges of different types of DPS circuits have been analyzed. We categorize the schemes into several categories based on ADC operation. A comparative study based on different performance metrics has also been showcased for a well-rounded understanding.

To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline.