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Sub-dimensional localization, also known as directional localization, arises when 2d electrons are subject to a periodic potential and incommensurate magnetic field which cause them to become exponentially localized along one crystal axis, while remaining extended in the orthogonal direction. We establish that sub-dimensional localization is robust to the presence of electronic interactions, which are known to generically destabilize single-particle localization. Consequently we predict that sub-dimensional localization may occur in moire materials, where it is manifest as a total absence of conductivity in the localized (transverse) direction, with finite conductivity in the extended (longitudinal) direction. We find the electrons form longitudinal charge density waves which are incommensurate with the moire potential, allowing them to slide and hence conduct in the longitudinal direction. The conductivity remains prohibited in the transverse direction due an emergent charge dipole moment conservation law., Comment: 7 pages

For modeling the spectra of exoplanets one must know their atmospheric composition. This is necessary because the abundance of molecules, atoms, ions and condensates is needed to construct the total cross-section for the interaction between electro-magnetic radiation and matter. In addition, when solving for the temperature structure of an atmosphere the so-called adiabatic temperature gradient must be known, which describes the pressure-temperature dependence in convectively unstable regions well. Depending on the planetary properties, the composition and adiabatic gradients may be well described by equilibrium chemistry, which means that chemical reactions occur faster than any other relevant processes in the atmosphere, such as mixing. What is more, the equilibrium assumption often serves as a useful starting point for non-equilibrium calculations. Efficient and easy-to-use codes for determining equilibrium abundances are therefore needed. Here we report on our easyCHEM Python package that calculates atmospheric compositions and adiabatic temperature gradients in chemical equilibrium for any user-specified elemental composition., Comment: Submitted for review to the Journal of Open Source Software (JOSS)

We work out the unfolded formulation of the fields in the non-linear realisation of $E_{11}$. Using the connections in this formalism, we propose, at the linearised level, an infinite number of first-order duality relations between the dual fields in $E_{11}$. In this way, we introduce extra fields that do not belong to $E_{11}$ and we investigate their origin. The equations of motion of the fields are obtained by taking derivatives and higher traces of the duality relations., Comment: 89 pages, 9 tables

Apathy impairs the quality of life for older adults and their care providers. While few pharmacological remedies exist, current non-pharmacologic approaches are resource intensive. To address these concerns, this study utilizes a user-centered design (UCD) process to develop and test a set of dyadic activities that provide physical, cognitive, and social stimuli to older adults residing in long-term care (LTC) communities. Within the design, a novel framework that combines socially assistive robots and non-immersive virtual reality (SAR-VR) emphasizing human-robot interaction (HRI) and human-computer interaction (HCI) is utilized with the roles of the robots being coach and entertainer. An interdisciplinary team of engineers, nurses, and physicians collaborated with an advisory panel comprising LTC activity coordinators, staff, and residents to prototype the activities. The study resulted in four virtual activities: three with the humanoid robot, Nao, and one with the animal robot, Aibo. Fourteen participants tested the acceptability of the different components of the system and provided feedback at different stages of development. Participant approval increased significantly over successive iterations of the system highlighting the importance of stakeholder feedback. Five LTC staff members successfully set up the system with minimal help from the researchers, demonstrating the usability of the system for caregivers. Rationale for activity selection, design changes, and both quantitative and qualitative results on the acceptability and usability of the system have been presented. The paper discusses the challenges encountered in developing activities for older adults in LTCs and underscores the necessity of the UCD process to address them.

Measurements of $CP$ observables and the CKM angle $\gamma$ are performed in $B^{\pm} \to D K^*(892)^{\pm}$ decays, where $D$ represents a superposition of $D^0$ and $\overline{D}{}^0$ states, using the LHCb dataset collected during Run 1 (2011-2012) and Run 2 (2015-2018). A comprehensive study of this channel is presented with the $D$ meson reconstructed in two-body final states $K^{\pm}\pi^{\mp}$, $K^+K^-$ and $\pi^+\pi^-$; four-body final states $K^{\pm}\pi^{\mp}\pi^{\pm}\pi^{\mp}$ and $\pi^+\pi^-\pi^+\pi^-$; and three-body final states $K^0_{S} \pi^+\pi^-$ and $K^0_{S} K^+ K^-$. This analysis includes the first observation of the suppressed $B^{\pm} \to [\pi^+K^-]_D K^{*\pm}$ and $B^{\pm} \to [\pi^+K^-\pi^+\pi^-]_D K^{*\pm}$ decays. The combined result gives $\gamma=(63\pm 13)^\circ$., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3180/ (LHCb public pages)

This paper addresses the medical imaging problem of joint detection in the upper limbs, viz. elbow, shoulder, wrist and finger joints. Localization of joints from X-Ray and Computerized Tomography (CT) scans is an essential step for the assessment of various bone-related medical conditions like Osteoarthritis, Rheumatoid Arthritis, and can even be used for automated bone fracture detection. Automated joint localization also detects the corresponding bones and can serve as input to deep learning-based models used for the computerized diagnosis of the aforementioned medical disorders. This in-creases the accuracy of prediction and aids the radiologists with analyzing the scans, which is quite a complex and exhausting task. This paper provides a detailed comparative study between diverse Deep Learning (DL) models - YOLOv3, YOLOv7, EfficientDet and CenterNet in multiple bone joint detections in the upper limbs of the human body. The research analyses the performance of different DL models, mathematically, graphically and visually. These models are trained and tested on a portion of the openly available MURA (musculoskeletal radiographs) dataset. The study found that the best Mean Average Precision (mAP at 0.5:0.95) values of YOLOv3, YOLOv7, EfficientDet and CenterNet are 35.3, 48.3, 46.5 and 45.9 respectively. Besides, it has been found YOLOv7 performed the best for accurately predicting the bounding boxes while YOLOv3 performed the worst in the Visual Analysis test. Code available at https://github.com/Sohambasu07/BoneJointsLocalization

Recent work has shown that a person's sympathetic arousal can be estimated from facial videos alone using basic signal processing. This opens up new possibilities in the field of telehealth and stress management, providing a non-invasive method to measure stress only using a regular RGB camera. In this paper, we present SympCam, a new 3D convolutional architecture tailored to the task of remote sympathetic arousal prediction. Our model incorporates a temporal attention module (TAM) to enhance the temporal coherence of our sequential data processing capabilities. The predictions from our method improve accuracy metrics of sympathetic arousal in prior work by 48% to a mean correlation of 0.77. We additionally compare our method with common remote photoplethysmography (rPPG) networks and show that they alone cannot accurately predict sympathetic arousal "out-of-the-box". Furthermore, we show that the sympathetic arousal predicted by our method allows detecting physical stress with a balanced accuracy of 90% - an improvement of 61% compared to the rPPG method commonly used in related work, demonstrating the limitations of using rPPG alone. Finally, we contribute a dataset designed explicitly for the task of remote sympathetic arousal prediction. Our dataset contains synchronized face and hand videos of 20 participants from two cameras synchronized with electrodermal activity (EDA) and photoplethysmography (PPG) measurements. We will make this dataset available to the community and use it to evaluate the methods in this paper. To the best of our knowledge, this is the first dataset available to other researchers designed for remote sympathetic arousal prediction., Comment: Accepted for publication at the IEEE-EMBS International Conference on Biomedical and Health Informatics

Benchmarking of noise that is induced during the implementation of quantum gates is the main concern for practical quantum computers. Several protocols have been proposed that empirically calculate various metrics that quantify the error rates of the quantum gates chosen from a preferred gate set. Unitarity randomized benchmarking (URB) protocol is a method to estimate the coherence of noise induced by the quantum gates which is measured by the metric \textit{unitarity}. In this paper, we for the first time, implement the URB protocol in a quantum simulator with all the parameters and noise model are used from a real quantum device. The direct implementation of the URB protocol in a quantum device is not possible using current technologies, as it requires the preparation of mixed states. To overcome this challenge, we propose a modification of the URB protocol, namely the m-URB protocol, that enables us to practically implement it on any quantum device. We validate our m-URB protocol using two single-qubit noise channels -- (a) depolarising channel and (b) bit-flip channel. We further alter the m-URB protocol, namely, native gate URB or Ng-URB protocol, to study the noise in the native gates into which the quantum circuits are compiled in a quantum computer. Using our Ng-URB protocol, we can also detect the presence of cross-talk errors which are correlated errors caused due to non-local and entangling gates such as CNOT gate. For illustration, we simulate the noise of the native gates taking the noise parameter from two real IBM-Q processors.

Distributed Machine Learning (DML) on resource-constrained edge devices holds immense potential for real-world applications. However, achieving fast convergence in DML in these heterogeneous environments remains a significant challenge. Traditional frameworks like Bulk Synchronous Parallel and Asynchronous Stochastic Parallel rely on frequent, small updates that incur substantial communication overhead and hinder convergence speed. Furthermore, these frameworks often employ static dataset sizes, neglecting the heterogeneity of edge devices and potentially leading to straggler nodes that slow down the entire training process. The straggler nodes, i.e., edge devices that take significantly longer to process their assigned data chunk, hinder the overall training speed. To address these limitations, this paper proposes Hermes, a novel probabilistic framework for efficient DML on edge devices. This framework leverages a dynamic threshold based on recent test loss behavior to identify statistically significant improvements in the model's generalization capability, hence transmitting updates only when major improvements are detected, thereby significantly reducing communication overhead. Additionally, Hermes employs dynamic dataset allocation to optimize resource utilization and prevents performance degradation caused by straggler nodes. Our evaluations on a real-world heterogeneous resource-constrained environment demonstrate that Hermes achieves faster convergence compared to state-of-the-art methods, resulting in a remarkable $13.22$x reduction in training time and a $62.1\%$ decrease in communication overhead., Comment: 11 pages, 19 figures, 3 tables; code: https://github.com/DaSH-Lab-CSIS/Hermes

This study explores the integration of large language models (LLMs) into classic inflation nowcasting frameworks, particularly in light of high inflation volatility periods such as the COVID-19 pandemic. We propose InflaBERT, a BERT-based LLM fine-tuned to predict inflation-related sentiment in news. We use this model to produce NEWS, an index capturing the monthly sentiment of the news regarding inflation. Incorporating our expectation index into the Cleveland Fed's model, which is only based on macroeconomic autoregressive processes, shows a marginal improvement in nowcast accuracy during the pandemic. This highlights the potential of combining sentiment analysis with traditional economic indicators, suggesting further research to refine these methodologies for better real-time inflation monitoring. The source code is available at https://github.com/paultltc/InflaBERT.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

This paper introduces multimodal conformal regression. Traditionally confined to scenarios with solely numerical input features, conformal prediction is now extended to multimodal contexts through our methodology, which harnesses internal features from complex neural network architectures processing images and unstructured text. Our findings highlight the potential for internal neural network features, extracted from convergence points where multimodal information is combined, to be used by conformal prediction to construct prediction intervals (PIs). This capability paves new paths for deploying conformal prediction in domains abundant with multimodal data, enabling a broader range of problems to benefit from guaranteed distribution-free uncertainty quantification., Comment: 20 pages, 34 figures

This paper presents a new look at the neural network (NN) robustness problem, from the point of view of graph theory analysis, specifically graph curvature. Graph curvature (e.g., Ricci curvature) has been used to analyze system dynamics and identify bottlenecks in many domains, including road traffic analysis and internet routing. We define the notion of neural Ricci curvature and use it to identify bottleneck NN edges that are heavily used to ``transport data" to the NN outputs. We provide an evaluation on MNIST that illustrates that such edges indeed occur more frequently for inputs where NNs are less robust. These results will serve as the basis for an alternative method of robust training, by minimizing the number of bottleneck edges.

Let $G=(V, E)$ be a graph where $V$ and $E$ are the vertex and edge sets, respectively. For two disjoint subsets $A$ and $B$ of $V$, we say $A$ \textit{dominates} $B$ if every vertex of $B$ is adjacent to at least one vertex of $A$ in $G$. A vertex partition $\pi = \{V_1, V_2, \ldots, V_k\}$ of $G$ is called an \emph{upper domatic partition} of size $k$ if either $V_i$ dominates $V_j$ or $V_j$ dominates $V_i$ or both for all $i, j$, where $1\leq i

Streamwise acceleration of the background flow field is one of various effects occurring when wind turbines operate under non-idealized conditions, such as in complex terrain or in dense wind farms. Thus, studying this effect is essential to improve understanding of aerodynamic performance in these cases. In the present work, a simple model based on momentum theory is derived for the situation of an actuator disc (AD) operating in a background flow field with a constant velocity gradient. Reynolds-averaged Navier-Stokes (RANS) simulations of this scenario are performed, showing that a positive acceleration yields a reduction of induction and vice versa, a negative acceleration leads to an increase of induction. The new model accurately captures this behavior and reduces the prediction error by eighty percent compared to classical momentum theory where the effect of the background flow acceleration is disregarded. Further analysis suggests that the model can be extended to a formulation in which an even higher prediction accuracy can be achieved.

Generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have shown promise in sequential recommendation tasks. However, they face challenges, including posterior collapse and limited representation capacity. The work by Li et al. (2023) introduces a novel approach that leverages diffusion models to address these challenges by representing item embeddings as distributions rather than fixed vectors. This approach allows for a more adaptive reflection of users' diverse interests and various item aspects. During the diffusion phase, the model converts the target item embedding into a Gaussian distribution by adding noise, facilitating the representation of sequential item distributions and the injection of uncertainty. An Approximator then processes this noisy item representation to reconstruct the target item. In the reverse phase, the model utilizes users' past interactions to reverse the noise and finalize the item prediction through a rounding operation. This research introduces enhancements to the DiffuRec architecture, particularly by adding offset noise in the diffusion process to improve robustness and incorporating a cross-attention mechanism in the Approximator to better capture relevant user-item interactions. These contributions led to the development of a new model, DiffuRecSys, which improves performance. Extensive experiments conducted on three public benchmark datasets demonstrate that these modifications enhance item representation, effectively capture diverse user preferences, and outperform existing baselines in sequential recommendation research., Comment: ROEGEN@RecSys'24: The 1st Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommender Systems, Co-located with ACM RecSys in Bari, Italy, October 2024

A fault domain that has been widely studied in black-box conformance testing is the class of finite state machines (FSMs) with at most $k$ extra states. Numerous methods for generating test suites have been proposed that guarantee fault coverage for this class. These test suites grow exponentially in $k$, so one can only run them for small $k$. But the assumption that $k$ is small is not realistic in practice. As a result, completeness for this fault domain has limited practical significance. As an alternative, we propose (much larger) fault domains that capture the assumption that when bugs in an implementation introduce extra states, these states can be reached via a few (at most $k$) transitions from states reachable via a set $A$ of common scenarios. Preliminary evidence suggests these fault domains, which contain FSMs with an exponential number of extra states (in $k$), are of practical use for testing network protocols. We present a sufficient condition for \emph{$k$-$A$-completeness} of test suites with respect to these fault domains, phrased entirely in terms of properties of their testing tree. Our condition implies $k$-$A$-completeness of two prominent test suite generation algorithms, the Wp and HSI methods. Counterexamples show that three other approaches, the H, SPY and SPYH methods, do not always generate $k$-$A$-complete test suites., Comment: 40 pages

We study the connections between ordinary differential equations and optimization algorithms in a non-Euclidean setting. We propose a novel accelerated algorithm for minimising convex functions over a convex constrained set. This algorithm is a natural generalization of Nesterov's accelerated gradient descent method to the non-Euclidean setting and can be interpreted as an additive Runge-Kutta algorithm. The algorithm can also be derived as a numerical discretization of the ODE appearing in Krichene et al. (2015a). We use Lyapunov functions to establish convergence rates for the ODE and show that the discretizations considered achieve acceleration beyond the setting studied in Krichene et al. (2015a). Finally, we discuss how the proposed algorithm connects to various equations and algorithms in the literature., Comment: 25 pages, 5 figures

Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics-based models. Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop forecasting models into Earth-system models (ESMs), capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes. Modeling the Earth system is a much more difficult problem than weather forecasting, not least because the model must represent the alternate (e.g., future) coupled states of the system for which there are no historical observations. Given that the physical principles that enable predictions about the response of the Earth system are often not explicitly coded in these ML-based models, demonstrating the credibility of ML-based ESMs thus requires us to build evidence of their consistency with the physical system. To this end, this paper puts forward five recommendations to enhance comprehensive, standardized, and independent evaluation of ML-based ESMs to strengthen their credibility and promote their wider use.

Body size drives the energetic demands of organisms, constraining trophic interactions between species and playing a significant role in shaping the feasibility of species' populations in a community. On macroevolutionary timescales, these demands feed back to shape the selective landscape driving the evolution of body size and diet. We develop a theoretical framework for a three-level trophic food chain -- typical for terrestrial mammalian ecosystems -- premised on bioenergetic trade-offs to explore mammalian population dynamics. Our results show that interactions between predators, prey, and external subsidies generate instabilities linked to body size extrema, corresponding to observed limits of predator size and diet. These instabilities generate size-dependent constraints on coexistence and highlight a feasibility range for carnivore size between 40 to 110 kg, encompassing the mean body size of terrestrial Cenozoic hypercarnivores. Finally, we show that predator dietary generalization confers a selective advantage to larger carnivores, which then declines at megapredator body sizes, aligning with diet breadth estimates for contemporary and Pleistocene species. Our framework underscores the importance of understanding macroevolutionary constraints through the lens of ecological pressures, where the selective forces shaping and reshaping the dynamics of communities can be explored., Comment: 14 pages, 3 figures, SI Appendices

This paper addresses the problem of consistently estimating a continuous-time (CT) diffusively coupled network (DCN) to identify physical components in a physical network. We develop a three-step frequency-domain identification method for linear CT DCNs that allows to accurately recover all the physical component values of the network while exploiting the particular symmetric structure in a DCN model. This method uses the estimated noise covariance as a non-parametric noise model to minimize variance of the parameter estimates, obviating the need to select a parametric noise model. Moreover, this method is extended to subnetworks identification, which enables identifying the local dynamics in DCNs on the basis of partial measurements. The method is illustrated with an application from In-Circuit Testing of printed circuit boards. Experimental results highlight the method's ability to consistently estimate component values in a complex network with only a single excitation., Comment: 12 pages, 6 figures, extended version of paper submitted to European Control Conference, 2025, Thessaloniki, Greece

The design and control of atomic-scale spin structures constitute major challenges for spin-based quantum technology platforms, including quantum dots, color centers, and molecular spins. Here, we showcase a strategy for designing the quantum properties of molecular spin qubits by combining tip-assisted on-surface assembly with electron spin resonance scanning tunneling microscopy (ESR-STM): We fabricate magnetic dimer complexes that consist of an iron phthalocyanine (FePc) molecule and an organometallic half-sandwich complex formed by the FePc ligand and an attached iron atom, Fe(C6H6). The total complex forms a mixed-spin (1/2,1) quantum ferrimagnet with a well-separated correlated ground state doublet, which we utilize for coherent control. As a result of the correlation, the quantum ferrimagnet shows an improved spin lifetime (>1.5 ${\mu}$s) as it is partially protected against inelastic electron scattering. Lastly, the ferrimagnet units also enable intermolecular coupling, that can be used to realize both ferromagnetic or antiferromagnetic structures. Thus, quantum ferrimagnets provide a versatile platform to improve coherent control in general and to study complex magnetic interactions., Comment: 17 pages, 4 figures

We construct a mean-field model that describes the nonlinear dynamics of a spin-polarized electron gas interacting with fixed, positively-charged ions possessing a magnetic moment that evolves in time. The mobile electrons are modeled by a four-component distribution function in the two-dimensional phase space $(x,v)$, obeying a Vlasov-Poisson set of equations. The ions are modeled by a Landau-Lifshitz equation for their spin density, which contains ion-ion and electron-ion magnetic exchange terms. We perform a linear response study of the coupled Vlasov-Poisson-Landau-Lifshitz (VPLL) equations for the case of a Maxwell-Boltzmann equilibrium, focussing in particular on the spin dispersion relation. Condition of stability or instability for the spin modes are identified, which depend essentially on the electron spin polarization rate $\eta$ and the electron-ion magnetic coupling constant $K$. We also develop an Eulerian grid-based computational code for the fully nonlinear VPLL equations, based on the geometric Hamiltonian method first developed in [N. Crouseilles et al. Journal of Plasma Physics, 89(2):905890215, 2023]. This technique allows us to achieve great accuracy for the conserved quantities, such as the modulus of the ion spin vector and the total energy. Numerical tests in the linear regime are in accordance with the estimations of the linear response theory. For two-stream equilibria, we study the interplay of instabilities occurring in both the charge and the spin sectors. The set of parameters used in the simulations, with densities close to those of solids ($\approx 10^{29} \rm m^{-3}$) and temperatures of the order of 10 eV, may be relevant to the warm dense matter regime appearing in some inertial fusion experiments.

The world is evolving and so is the vocabulary used to discuss topics in speech. Analysing political speech data from more than 30 years requires the use of flexible topic models to uncover the latent topics and their change in prevalence over time as well as the change in the vocabulary of the topics. We propose the temporal Poisson factorisation (TPF) model as an extension to the Poisson factorisation model to model sparse count data matrices obtained based on the bag-of-words assumption from text documents with time stamps. We discuss and empirically compare different model specifications for the time-varying latent variables consisting either of a flexible auto-regressive structure of order one or a random walk. Estimation is based on variational inference where we consider a combination of coordinate ascent updates with automatic differentiation using batching of documents. Suitable variational families are proposed to ease inference. We compare results obtained using independent univariate variational distributions for the time-varying latent variables to those obtained with a multivariate variant. We discuss in detail the results of the TPF model when analysing speeches from 18 sessions in the U.S. Senate (1981-2016)., Comment: main paper: 19 pages (2 single figures, 3 double figures, 3 tables) appendix: 9 pages (3 quadruple figures, 1 table) references: 3 pages

Graphs are an intuitive way to represent relationships between variables in fields such as finance and neuroscience. However, these graphs often need to be inferred from data. In this paper, we propose a novel framework to infer a latent graph by treating the observed multidimensional data as graph-referenced stationary signals. Specifically, we introduce the graphical autoregressive model (GAR), where the inverse covariance matrix of the observed signals is expressed as a second-order polynomial of the normalized graph Laplacian of the latent graph. The GAR model extends the autoregressive model from time series analysis to general undirected graphs, offering a new approach to graph inference. To estimate the latent graph, we develop a three-step procedure based on penalized maximum likelihood, supported by theoretical analysis and numerical experiments. Simulation studies and an application to S&P 500 stock price data show that the GAR model can outperform Gaussian graphical models when it fits the observed data well. Our results suggest that the GAR model offers a promising new direction for inferring latent graphs across diverse applications. Codes and example scripts are available at https://github.com/jed-harwood/SGM .

This paper investigates properties of concatenated polar codes and their potential applications. We start with reviewing previous work on stopping set analysis for conventional polar codes, which we extend in this paper to concatenated architectures. Specifically, we present a stopping set analysis for the factor graph of concatenated polar codes, deriving an upper bound on the size of the minimum stopping set. To achieve this bound, we propose new bounds on the size of the minimum stopping set for conventional polar code factor graphs. The tightness of these proposed bounds is investigated empirically and analytically. We show that, in some special cases, the exact value of the minimum stopping set can be determined with a time complexity of $O(N)$, where $N$ is the codeword length. The stopping set analysis motivates a novel construction method for concatenated polar codes. This method is used to design outer polar codes for two previously proposed concatenated polar code architectures: augmented polar codes and local-global polar codes. Simulation results demonstrate the advantage of the proposed codes over previously proposed constructions based on density evolution (DE)., Comment: Submitted to IEEE Transactions on Communications. arXiv admin note: substantial text overlap with arXiv:2402.04486

Terrestrial exoplanets around M- and K-type stars are important targets for atmospheric characterisation. Such planets are likely tidally locked with the order of spin-orbit resonances (SORs) depending on eccentricity. We explore the impact of SORs on 3D atmospheric dynamics and chemistry, employing a 3D coupled Climate-Chemistry Model to simulate Proxima Centauri b in 1:1 and 3:2 SOR. For a 1:1 SOR, Proxima Centauri b is in the Rhines rotator circulation regime with dominant zonal gradients (global mean surface temperature 229 K). An eccentric 3:2 SOR warms Proxima Centauri b to 262 K with gradients in the meridional direction. We show how a complex interplay between stellar radiation, orbit, atmospheric circulation, and (photo)chemistry determines the 3D ozone distribution. Spatial variations in ozone column densities align with the temperature distribution and are driven by stratospheric circulation mechanisms. Proxima Centauri b in a 3:2 SOR demonstrates additional atmospheric variability, including daytime-nighttime cycles in water vapour of ${+}$55% to ${-}$34% and ozone ($\pm5.2$%) column densities and periastron-apoastron water vapour cycles of ${+}$17% to ${-}$10%. Synthetic emission spectra for the spectral range of the Large Interferometer For Exoplanets fluctuate by up to 36 ppm with orbital phase angle for a 1:1 SOR due to 3D spatial and temporal asymmetries. The homogeneous atmosphere for the 3:2 SOR results in relatively constant emission spectra and provides an observational discriminant from the 1:1 SOR. Our work emphasizes the importance of understanding the 3D nature of exoplanet atmospheres and associated spectral variations to determine habitability and interpret atmospheric spectra., Comment: 31 pages, 19 figures, revised and re-submitted to The Planetary Science Journal

Videos are often used to learn or extract the necessary information to complete tasks in ways different than what text and static imagery alone can provide. However, many existing agent benchmarks neglect long-context video understanding, instead focusing on text or static image inputs. To bridge this gap, we introduce VideoWebArena (VideoWA), a benchmark for evaluating the capabilities of long-context multimodal agents for video understanding. VideoWA consists of 2,021 web agent tasks based on manually crafted video tutorials, which total almost four hours of content. For our benchmark, we define a taxonomy of long-context video-based agent tasks with two main areas of focus: skill retention and factual retention. While skill retention tasks evaluate whether an agent can use a given human demonstration to complete a task efficiently, the factual retention task evaluates whether an agent can retrieve instruction-relevant information from a video to complete a task. We find that the best model achieves 13.3% success on factual retention tasks and 45.8% on factual retention QA pairs, far below human performance at 73.9% and 79.3%, respectively. On skill retention tasks, long-context models perform worse with tutorials than without, exhibiting a 5% performance decrease in WebArena tasks and a 10.3% decrease in VisualWebArena tasks. Our work highlights the need to improve the agentic abilities of long-context multimodal models and provides a testbed for future development with long-context video agents.

This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs). By assessing how atypical a prediction is within the context of its nearest neighbours' target distribution, Target Strangeness can surpass the current state-of-the-art performance. This novel difficulty estimator is evaluated against others in the context of several conformal regression experiments., Comment: 8 pages, 2 figures

Pricing algorithms have demonstrated the capability to learn tacit collusion that is largely unaddressed by current regulations. Their increasing use in markets, including oligopolistic industries with a history of collusion, calls for closer examination by competition authorities. In this paper, we extend the study of tacit collusion in learning algorithms from basic pricing games to more complex markets characterized by perishable goods with fixed supply and sell-by dates, such as airline tickets, perishables, and hotel rooms. We formalize collusion within this framework and introduce a metric based on price levels under both the competitive (Nash) equilibrium and collusive (monopolistic) optimum. Since no analytical expressions for these price levels exist, we propose an efficient computational approach to derive them. Through experiments, we demonstrate that deep reinforcement learning agents can learn to collude in this more complex domain. Additionally, we analyze the underlying mechanisms and structures of the collusive strategies these agents adopt., Comment: 37 pages, 25 figures. Under review

We continue the study of the Erd\H{o}s-Dushnik-Miller theorem (A graph with an uncountable set of vertices has either an infinite independent set or an uncountable clique) in set theory without the axiom of choice. We show that there are three inequivalent versions of this theorem and we give some results about the positions of these versions in the deductive hierarchy of weak choice principles.

This paper presents the first measurement of $\psi{(2S)}$ and $\chi_{c1}(3872)$ meson production within fully reconstructed jets. Each quarkonium state (tag) is reconstructed via its decay to the $J/\psi$($\rightarrow\mu^+\mu^-$)$\pi^+\pi^-$ final state in the forward region using proton-proton collision data collected by the LHCb experiment at the center-of-mass-energy of $13 \text{TeV}$ in 2016, corresponding to an integrated luminosity of $1.64 \text{fb}^{-1}$. The fragmentation function, presented as the ratio of the quarkonium-tag transverse momentum to the full jet transverse momentum ($p_{\mathrm{T}}(\text{tag})/p_{\mathrm{T}}(\text{jet})$), is measured differentially in $p_{\mathrm{T}}(\text{jet})$ and $p_{\mathrm{T}}(\text{tag})$ bins. The distributions are separated into promptly produced quarkonia from proton-proton collisions and quarkonia produced from displaced $b$-hadron decays. While the displaced quarkonia fragmentation functions are in general well described by parton-shower predictions, the prompt quarkonium distributions differ significantly from fixed-order non-relativistic QCD (NRQCD) predictions followed by a QCD parton shower., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1618/ (LHCb public pages)

Altermagnets are a newly discovered class of magnetic phases that combine the spin polarization behavior of ferromagnetic band structures with the vanishing net magnetization characteristic of antiferromagnets. Initially proposed for collinear magnets, the concept has since been extended to include certain non-collinear structures. A recent development in Landau theory for collinear altermagnets incorporates spin-space symmetries, providing a robust framework for identifying this class of materials. Here we expand on that theory to identify altermagnetic multipolar order parameters in non-collinear chiral materials. We demonstrate that the interplay between non-collinear altermagnetism and chirality allows for spatially odd multipole components, leading to non-trivial spin textures on Fermi surfaces and unexpected transport phenomena, even in the absence of SOC. This makes such chiral altermagnets fundamentally different from the well-known SOC-driven Rashba-Edelstein and spin Hall effects used for 2D spintronics. Choosing the chiral topological magnetic material Mn$_3$IrSi as a case study, we apply toy models and first-principles calculations to predict experimental signatures, such as large spin-Hall and Edelstein effects, that have not been previously observed in altermagnets. These findings pave the way for a new realm of spintronics applications based on spin-transport properties of chiral altermagnets., Comment: 22 pages, 3 figures

With the great progress of 3D sensing and acquisition technology, the volume of point cloud data has grown dramatically, which urges the development of efficient point cloud compression methods. In this paper, we focus on the task of learned lossy point cloud attribute compression (PCAC). We propose an efficient attention-based method for lossy compression of point cloud attributes leveraging on an autoencoder architecture. Specifically, at the encoding side, we conduct multiple downsampling to best exploit the local attribute patterns, in which effective External Cross Attention (ECA) is devised to hierarchically aggregate features by intergrating attributes and geometry contexts. At the decoding side, the attributes of the point cloud are progressively reconstructed based on the multi-scale representation and the zero-padding upsampling tactic. To the best of our knowledge, this is the first approach to introduce attention mechanism to point-based lossy PCAC task. We verify the compression efficiency of our model on various sequences, including human body frames, sparse objects, and large-scale point cloud scenes. Experiments show that our method achieves an average improvement of 1.15 dB and 2.13 dB in BD-PSNR of Y channel and YUV channel, respectively, when comparing with the state-of-the-art point-based method Deep-PCAC. Codes of this paper are available at https://github.com/I2-Multimedia-Lab/Att2CPC.

II Zw 40 is a starburst dwarf and merger product, and holds a radio-infrared supernebula excited by thousands of embedded OB stars. We present here observations of three aspects of the supernebula: maps of the K and KU radio continuum that trace dense ionized gas with spatial resolution $\sim0.1^{\prime\prime}$, a spectral data cube of the [S IV]$10.5\mu$m emission line that measures the kinematics of the ionized gas with velocity resolution $4.5$ km s$^{-1}$, and an ALMA spectral cube of the CO(3-2) line that probes the dense warm molecular gas with spatial and velocity resolution comparable to the ionized gas. The observations suggest that the supernebula is the overlap,collision or merger of two star clusters, each associated with an elongated molecular cloud. We accordingly modelled the supernebula with simulations of colliding clusters. The model that best agrees with the data is a grazing collision that has distorted the gas and stars to create the distinctive structures observed. These models may have wide applicability in the cluster-rich regions of young starbursts., Comment: Accepted for publication at MNRAS

Centaurus X-3 is a persistent high-mass X-ray binary with the long-term light curve from the source exhibiting orbit-to-orbit intensity variations with no apparent superorbital periodicity. We used $\sim$13.5 years of MAXI/GSC data to study the long-term behaviour of X-ray absorption caused by the stellar wind from the companion star and any absorbing structures present in the binary. We used orbital-phase-resolved spectroscopy to study the variation in the photoelectric absorption along the line of sight of the source for both the intensity-averaged data and intensity-resolved data after dividing all the data binned with orbital period into three intensity levels. We find an asymmetric variation in the photoelectric absorption along the line of sight across an orbit of the source. The orbital-phase-resolved spectra show a clear increase in photoelectric absorption after $\phi_\text{orb}\sim$ 0.5, which deviates from a spherically symmetric stellar wind model. The flux of Cen X-3 shows significant variation between consecutive orbits. An intensity-resolved spectral analysis of the source was performed, followed by an intensity-resolved and orbital-phase-resolved spectral analysis, which showed that at the medium and high intensity levels, the orbital-phase-resolved photoelectric absorption is slightly asymmetric with respect to mid-phase ($\phi_\text{orb}=$ 0.5). The asymmetry is very pronounced at the lowest intensity level and cannot be explained by a spherically symmetric wind from the companion star. The differences in the orbital phase-dependence of absorption for different intensity levels suggest that the presence of an accretion wake, photoionization wake, or tidal stream is more prominent at a lower intensity level for Centaurus X-3 than at a higher intensity level., Comment: Accepted for publication in Astronomy and Astrophysics

Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical images, driving advances in medical image analysis, disease diagnosis, and treatment planning. This chapter explores various deep generative models for 3D medical image synthesis, with a focus on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Models (DDMs). We discuss the fundamental principles, recent advances, as well as strengths and weaknesses of these models and examine their applications in clinically relevant problems, including unconditional and conditional generation tasks like image-to-image translation and image reconstruction. We additionally review commonly used evaluation metrics for assessing image fidelity, diversity, utility, and privacy and provide an overview of current challenges in the field.

Reinforcement learning (RL) has been successfully applied to solve the problem of finding obstacle-free paths for autonomous agents operating in stochastic and uncertain environments. However, when the underlying stochastic dynamics of the environment experiences drastic distribution shifts, the optimal policy obtained in the trained environment may be sub-optimal or may entirely fail in helping find goal-reaching paths for the agent. Approaches like domain randomization and robust RL can provide robust policies, but typically assume minor (bounded) distribution shifts. For substantial distribution shifts, retraining (either with a warm-start policy or from scratch) is an alternative approach. In this paper, we develop a novel approach called {\em Evolutionary Robust Policy Optimization} (ERPO), an adaptive re-training algorithm inspired by evolutionary game theory (EGT). ERPO learns an optimal policy for the shifted environment iteratively using a temperature parameter that controls the trade off between exploration and adherence to the old optimal policy. The policy update itself is an instantiation of the replicator dynamics used in EGT. We show that under fairly common sparsity assumptions on rewards in such environments, ERPO converges to the optimal policy in the shifted environment. We empirically demonstrate that for path finding tasks in a number of environments, ERPO outperforms several popular RL and deep RL algorithms (PPO, A3C, DQN) in many scenarios and popular environments. This includes scenarios where the RL algorithms are allowed to train from scratch in the new environment, when they are retrained on the new environment, or when they are used in conjunction with domain randomization. ERPO shows faster policy adaptation, higher average rewards, and reduced computational costs in policy adaptation., Comment: Pubblished in ECAI 2024

Despite the impressive text-to-image (T2I) synthesis capabilities of diffusion models, they often struggle to understand compositional relationships between objects and attributes, especially in complex settings. Existing solutions have tackled these challenges by optimizing the cross-attention mechanism or learning from the caption pairs with minimal semantic changes. However, can we generate high-quality complex contrastive images that diffusion models can directly discriminate based on visual representations? In this work, we leverage large-language models (LLMs) to compose realistic, complex scenarios and harness Visual-Question Answering (VQA) systems alongside diffusion models to automatically curate a contrastive dataset, ConPair, consisting of 15k pairs of high-quality contrastive images. These pairs feature minimal visual discrepancies and cover a wide range of attribute categories, especially complex and natural scenarios. To learn effectively from these error cases, i.e., hard negative images, we propose EvoGen, a new multi-stage curriculum for contrastive learning of diffusion models. Through extensive experiments across a wide range of compositional scenarios, we showcase the effectiveness of our proposed framework on compositional T2I benchmarks.

Large Language Models (LLMs) have demonstrated impressive abilities in symbol processing through in-context learning (ICL). This success flies in the face of decades of predictions that artificial neural networks cannot master abstract symbol manipulation. We seek to understand the mechanisms that can enable robust symbol processing in transformer networks, illuminating both the unanticipated success, and the significant limitations, of transformers in symbol processing. Borrowing insights from symbolic AI on the power of Production System architectures, we develop a high-level language, PSL, that allows us to write symbolic programs to do complex, abstract symbol processing, and create compilers that precisely implement PSL programs in transformer networks which are, by construction, 100% mechanistically interpretable. We demonstrate that PSL is Turing Universal, so the work can inform the understanding of transformer ICL in general. The type of transformer architecture that we compile from PSL programs suggests a number of paths for enhancing transformers' capabilities at symbol processing. (Note: The first section of the paper gives an extended synopsis of the entire paper.), Comment: 101 pages (including 30 pages of Appendices), 18 figures

An important task in the study of fast radio bursts (FRBs) remains the automatic classification of repeating and non-repeating sources based on their morphological properties. We propose a statistical model that considers a modified logistic regression to classify FRB sources. The classical logistic regression model is modified to accommodate the small proportion of repeaters in the data, a feature that is likely due to the sampling procedure and duration and is not a characteristic of the population of FRB sources. The weighted logistic regression hinges on the choice of a tuning parameter that represents the true proportion $\tau$ of repeating FRB sources in the entire population. The proposed method has a sound statistical foundation, direct interpretability, and operates with only 5 parameters, enabling quicker retraining with added data. Using the CHIME/FRB Collaboration sample of repeating and non-repeating FRBs and numerical experiments, we achieve a classification accuracy for repeaters of nearly 75\% or higher when $\tau$ is set in the range of $50$ to $60$\%. This implies a tentative high proportion of repeaters, which is surprising, but is also in agreement with recent estimates of $\tau$ that are obtained using other methods., Comment: 16 pages, 7 figures. Submitted to ApJ

Fragmentation and evolution for the molecular shells of the compact HII regions are less explored compared to their evolved counterparts. We map nine compact HII regions with a typical diameter of 0.4 pc that are surrounded by molecular shells traced by CCH. Several to a dozen dense gas fragments probed by H13CO+ are embedded in these molecular shells. These gas fragments, strongly affected by the HII region, have a higher surface density, mass, and turbulence than those outside the shells but within the same pc-scale natal clump. These features suggest that the shells swept up by the early HII regions can enhance the formation of massive dense structures that may host the birth of higher-mass stars. We examine the formation of fragments and find that fragmentation of the swept-up shell is unlikely to occur in these early HII regions, by comparing the expected time scale of shell fragmentation with the age of HII region. We propose that the appearance of gas fragments in these shells is probably the result of sweeping up pre-existing fragments into the molecular shell that has not yet fragmented. Taken together, this work provides a basis for understanding the interplay of star-forming sites with an intricate environment containing ionization feedback such as those observed in starburst regions., Comment: Accepted by MNRAS, 24 pages, 14 figures

Drop deformation in shear flow is determined up to second order theory in Ca while considering kinetic effects on surfactants distributions in steady state. Surfactants inside the drop are adsorbed faster than those on the surface leading to an increase in total surfactants concentration on the semi-minor axis of the ellipsoidal droplet. New expressions for Taylor deformation and angle deviation are proposed involving kinetics effects. Theoretical calculations lead to a possibility of lesser deformation than the usual limit condition and a minor correction for the deviation angle.

Creativity is the ability to produce novel, useful, and surprising ideas, and has been widely studied as a crucial aspect of human cognition. Machine creativity on the other hand has been a long-standing challenge. With the rise of advanced generative AI, there has been renewed interest and debate regarding AI's creative capabilities. Therefore, it is imperative to revisit the state of creativity in AI and identify key progresses and remaining challenges. In this work, we survey leading works studying the creative capabilities of AI systems, focusing on creative problem-solving, linguistic, artistic, and scientific creativity. Our review suggests that while the latest AI models are largely capable of producing linguistically and artistically creative outputs such as poems, images, and musical pieces, they struggle with tasks that require creative problem-solving, abstract thinking and compositionality and their generations suffer from a lack of diversity, originality, long-range incoherence and hallucinations. We also discuss key questions concerning copyright and authorship issues with generative models. Furthermore, we highlight the need for a comprehensive evaluation of creativity that is process-driven and considers several dimensions of creativity. Finally, we propose future research directions to improve the creativity of AI outputs, drawing inspiration from cognitive science and psychology., Comment: 44 pages

The $r$-colour Ramsey number $R_r(k)$ is the minimum $n \in \mathbb{N}$ such that every $r$-colouring of the edges of the complete graph $K_n$ on $n$ vertices contains a monochromatic copy of $K_k$. We prove, for each fixed $r \geqslant 2$, that $$R_r(k) \leqslant e^{-\delta k} r^{rk}$$ for some constant $\delta = \delta(r) > 0$ and all sufficiently large $k \in \mathbb{N}$. For each $r \geqslant 3$, this is the first exponential improvement over the upper bound of Erd\H{o}s and Szekeres from 1935. In the case $r = 2$, it gives a different (and significantly shorter) proof of a recent result of Campos, Griffiths, Morris and Sahasrabudhe., Comment: 17 pages

CG (Computer Graphics) is a popular field of CS (Computer Science), but many students find this topic difficult due to it requiring a large number of skills, such as mathematics, programming, geometric reasoning, and creativity. Over the past few years, researchers have investigated ways to harness the power of GenAI (Generative Artificial Intelligence) to improve teaching. In CS, much of the research has focused on introductory computing. A recent study evaluating the performance of an LLM (Large Language Model), GPT-4 (text-only), on CG questions, indicated poor performance and reliance on detailed descriptions of image content, which often required considerable insight from the user to return reasonable results. So far, no studies have investigated the abilities of LMMs (Large Multimodal Models), or multimodal LLMs, to solve CG questions and how these abilities can be used to improve teaching. In this study, we construct two datasets of CG questions requiring varying degrees of visual perception skills and geometric reasoning skills, and evaluate the current state-of-the-art LMM, GPT-4o, on the two datasets. We find that although GPT-4o exhibits great potential in solving questions with visual information independently, major limitations still exist to the accuracy and quality of the generated results. We propose several novel approaches for CG educators to incorporate GenAI into CG teaching despite these limitations. We hope that our guidelines further encourage learning and engagement in CG classrooms., Comment: 8 pages, 8 figures, 1 table, to be published in SIGGRAPH Asia 2024 Educator's Forum

Stroke-based Rendering (SBR) aims to decompose an input image into a sequence of parameterized strokes, which can be rendered into a painting that resembles the input image. Recently, Neural Painting methods that utilize deep learning and reinforcement learning models to predict the stroke sequences have been developed, but suffer from longer inference time or unstable training. To address these issues, we propose AttentionPainter, an efficient and adaptive model for single-step neural painting. First, we propose a novel scalable stroke predictor, which predicts a large number of stroke parameters within a single forward process, instead of the iterative prediction of previous Reinforcement Learning or auto-regressive methods, which makes AttentionPainter faster than previous neural painting methods. To further increase the training efficiency, we propose a Fast Stroke Stacking algorithm, which brings 13 times acceleration for training. Moreover, we propose Stroke-density Loss, which encourages the model to use small strokes for detailed information, to help improve the reconstruction quality. Finally, we propose a new stroke diffusion model for both conditional and unconditional stroke-based generation, which denoises in the stroke parameter space and facilitates stroke-based inpainting and editing applications helpful for human artists design. Extensive experiments show that AttentionPainter outperforms the state-of-the-art neural painting methods.

This study applies Bayesian models to predict hotel booking cancellations, a key challenge affecting resource allocation, revenue, and customer satisfaction in the hospitality industry. Using a Kaggle dataset with 36,285 observations and 17 features, Bayesian Logistic Regression and Beta-Binomial models were implemented. The logistic model, applied to 12 features and 5,000 randomly selected observations, outperformed the Beta-Binomial model in predictive accuracy. Key predictors included the number of adults, children, stay duration, lead time, car parking space, room type, and special requests. Model evaluation using Leave-One-Out Cross-Validation (LOO-CV) confirmed strong alignment between observed and predicted outcomes, demonstrating the model's robustness. Special requests and parking availability were found to be the strongest predictors of cancellation. This Bayesian approach provides a valuable tool for improving booking management and operational efficiency in the hotel industry.

Background: The Cox model and its extensions assuming proportional hazards is widely used to estimate vaccine efficacy (VE). In the typical situation that VE wanes over time, the VE estimates are not only sensitive to study duration and timing of vaccine delivery in relation to disease seasonality but also biased in the presence of sample attrition. Furthermore, estimates of vaccine impact such as number of cases averted (NCA) are sensitive to background disease incidence and timing of vaccine delivery. Comparison of the estimates between trials with different features can be misleading. Methods: We propose estimation of VE as a function of time in the Cox model framework, using the area under the VE curve as a summary measure of VE, and extension of the method to estimate vaccine impact. We use simulations and re-analysis of a RTS,S/AS01 malaria vaccine trial dataset to demonstrate their properties and applications. Results: Simulation under scenarios with different trial duration, magnitude of sample attrition and timing of vaccine delivery, all assuming vaccine protection wanes over time, demonstrated the problems of conventional methods assuming proportional hazard, robustness and unbiasedness of the proposed methods, and comparability of the proposed estimates of vaccine efficacy and impact across trials with different features. Furthermore, the proposed NCA estimators are informative in determining the optimal vaccine delivery strategy in regions with highly seasonal disease transmission. Conclusions: The proposed method based on estimation of vaccine efficacy trajectory provides a robust, unbiased, and flexible approach to evaluate vaccine effects., Comment: 3667 words, 2 figures, 4 tables, 3 supplemental materials

Total variation integer optimal control problems admit solutions and necessary optimality conditions via geometric variational analysis. In spite of the existence of said solutions, algorithms which solve the discretized objective suffer from high numerical cost associated with the combinatorial nature of integer programming. Hence, such methods are often limited to small- and medium-sized problems. We propose a globally convergent, coordinate descent-inspired algorithm that allows tractable subproblem solutions restricted to a partition of the domain. Our decomposition method solves relatively small trust-region subproblems that modify the control variable on a subdomain only. Given sufficient subdomain overlap, we prove that first-order optimality is equivalent to first-order optimality per subdomain. We additionally show that sufficient decrease is achieved on a single subdomain by way of a trust region subproblem solver. We utilize this to prove convergence of our algorithm, which operates via greedy patch selection. Finally, our method demonstrates the capacity to accelerate large PDE-constrained integer control problems.