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May 17, 2024 marks the 70th anniversary of Brown v. Board of Education, the landmark Supreme Court decision that ruled segregated schools were "inherently unequal." At the time, North Carolina was one of 17 states that enforced de jure segregation, that is, segregation by law. The state of North Carolina and the school districts within the state have played prominent roles in our nation's history of school desegregation. North Carolina's public school enrollment is increasingly multiracial, and the expansion of school choice means that a growing share of students attends charters and private schools, both of which tend to be more segregated than traditional public schools. On the cusp of this important anniversary, the authors assess where North Carolina schools are now in terms of school desegregation, as segregated schools are systematically linked to unequal educational opportunities and outcomes, while desegregated schools are associated with numerous short-term, long-term, academic, and nonacademic outcomes for individuals and society.

Air-sea exchange processes have been identified as essential for both short- and long-term atmospheric and ocean forecasts. The two phases of the fluid layer covering our planet interact across a vast range of scales that we need to explore to achieve a better understanding of the exchange processes. While satellites provide a distributed large-scale view of the sea surface situation, highly detailed measurements, e.g., from oceanographic towers, are necessarily local. The required intermediate solution (i.e., data that are both accurate and distributed) can be provided by swarms of miniature surface buoys. As size, weight, and cost are reduced, these can be deployed in large numbers to investigate specific processes that are at present only crudely parameterized in our models as a result of scarcity of good measurements. Perhaps the most crucial process is white-capping in stormy conditions, where air-sea exchanges are enhanced by one or two orders of magnitude. Other applications include wave-current interactions, wave-ice interactions, and plunging breakers in the coastal zone. Following a dedicated workshop, we summarize here the main findings and possibilities derived from the different approaches, and summarize the state of the art for a selection of miniature buoys. We list the solutions presented at the workshop, as well as other similar buoys, with their main characteristics and range of application. We describe the various possibilities of practical use and the scientific and engineering problems to be solved. Looking to the future, we also point out where the present technological improvements are leading to.

Integrated access and backhaul (IAB) technology is a flexible solution for network densification. IAB nodes can also be deployed in moving nodes such as buses and trains, i.e., mobile IAB (mIAB). As mIAB nodes can move around the coverage area, the connection between mIAB nodes and their parent macro base stations (BSs), IAB donor, is sometimes required to change in order to keep an acceptable backhaul link, the so called topology adaptation (TA). The change from one IAB donor to another may strongly impact the system load distribution, possibly causing unsatisfactory backhaul service due to the lack of radio resources. Based on this, TA should consider both backhaul link quality and traffic load. In this work, we propose a load balancing algorithm based on TA for IAB networks, and compare it with an approach in which TA is triggered based on reference signal received power (RSRP) only. The results show that our proposed algorithm improves the passengers worst connections throughput in uplink (UL) and, more modestly, also in downlink (DL), without impairing the pedestrian quality of service (QoS) significantly., Comment: Paper submitted to Journal of Communication and Information Systems (JCIS)

This paper describes an online tool for the visualization of medical emergency locations, randomly generated sample paths of medical emergencies, and the animation of ambulance movements under the control of various dispatch methods in response to these emergencies. The tool incorporates statistical models for forecasting emergency locations and call arrival times, the simulation of emergency arrivals and ambulance movement trajectories, and the computation and visualization of performance metrics such as ambulance response time distributions. Data for the Rio de Janeiro Emergency Medical Service are available on the website. A user can upload emergency data for any Emergency Medical Service, and can then use the visualization tool to explore the uploaded data. A user can also use the statistical tools and/or the simulation tool with any of the dispatch methods provided, and can then use the visualization tool to explore the computational output. Future enhancements include the ability of a user to embed additional dispatch algorithms into the simulation; the tool can then be used to visualize the simulation results obtained with the newly embedded algorithms.

Recently, Gheibi, Jorgensen, and Takahashi introduced a new homological invariant called quasi-projective dimension, which is a generalization of projective dimension. They proved that the depth formula holds for two finitely gene\-rated Tor-independent modules over a Noetherian local ring when one of the modules has finite quasi-projective dimension. In this paper, we extend this result by pro\-ving that for finitely generated modules \( M \) and \( N \) over a Noetherian local ring \( R \) with \( \operatorname{qpd}_R M < \infty \), the equality \( \operatorname{depth} N = \operatorname{depth}(\operatorname{Tor}_q^R(M,N)) + \operatorname{qpd}_R M - q \) holds, where \( q := \sup\{ i \geq 0 : \operatorname{Tor}_i^R(M,N) \neq 0 \} \), provided that \( q < \infty \), and either \( q = 0 \) or \( \operatorname{depth}(\operatorname{Tor}_q^R(M,N)) \leq 1 \). This outcome generalizes a celebrated theorem by Auslander and allows us to derive new consequences and applications, for instance, we recover an important theorem of Araya and Yoshino., Comment: 10 pages, comments and suggestions are welcome. In this new version, Corollary 4.4 has been added, which recover a theorem of Araya and Yoshino

High-quality, stable child care and early education (CCEE) can have lasting, positive impacts on children. However, the challenges of recruiting, strengthening, and retaining the CCEE workforce are well documented. CCEE educators typically have low levels of formal education and compensation; limited opportunities for education, training, and professional development; inconsistent working conditions; and high levels of stress and burnout. Additionally, the CCEE sector is well known for high turnover rates, which can strain remaining educators and decrease the quality of care they offer. Turnover can also lead to diminishing returns on an organization's professional development investments. The COVID-19 pandemic has exacerbated these issues. The Building and Sustaining the Child Care and Early Education Workforce (BASE) project conducted an environmental scan and a literature review to identify and document existing knowledge about the CCEE workforce and strategies to strengthen it. One of the main themes identified from this effort was a need for more and better data on the workforce dynamics of CCEE educators. In particular, data are needed on who enters, advances in, stays in, and exits different roles, settings, and types of CCEE care or leaves the field altogether--as well as when, how, and why they do. This information is important for understanding workforce dynamics and informing the development, evaluation, and improvement of strategies that effectively build and sustain a qualified and stable CCEE workforce. For this reason, the BASE project team conducted a data scan to summarize the landscape of existing data sources that may address these gaps and identify areas where future data collection may be most useful. This brief summarizes the findings from the data scan.

In the AdS/CFT correspondence, direct scattering in the bulk may not have a local boundary analog. A nonlocal implementation on the boundary requires $O(1/G_N)$ mutual information. This statement is formalized by the connected wedge theorem, which can be proven using general relativity within AdS$_3$ but also by applying quantum information theory on the boundary, suggesting that the theorem applies to any holographic duality. We examine scattering within the static patch of asymptotically dS$_3$ spacetime, which is conjectured to be described by a quantum theory on the stretched horizon in static patch holography. We prove that causality on the horizon should be induced from null infinities $\mathcal{I}^{\pm}$ to maintain consistency with the theorem. Specifically, signals propagating in the static patch are associated with local operators at $\mathcal{I}^{\pm}$. Our results suggest a novel connection between static patch holography and the dS/CFT correspondence., Comment: 45 pages, 13 figures

Deployed SAE level 4+ Automated Driving Systems (ADS) without a human driver are currently operational ride-hailing fleets on surface streets in the United States. This current use case and future applications of this technology will determine where and when the fleets operate, potentially resulting in a divergence from the distribution of driving of some human benchmark population within a given locality. Existing benchmarks for evaluating ADS performance have only done county-level geographical matching of the ADS and benchmark driving exposure in crash rates. This study presents a novel methodology for constructing dynamic human benchmarks that adjust for spatial and temporal variations in driving distribution between an ADS and the overall human driven fleet. Dynamic benchmarks were generated using human police-reported crash data, human vehicle miles traveled (VMT) data, and over 20 million miles of Waymo's rider-only (RO) operational data accumulated across three US counties. The spatial adjustment revealed significant differences across various severity levels in adjusted crash rates compared to unadjusted benchmarks with these differences ranging from 10% to 47% higher in San Francisco, 12% to 20% higher in Maricopa, and 7% lower to 34% higher in Los Angeles counties. The time-of-day adjustment in San Francisco, limited to this region due to data availability, resulted in adjusted crash rates 2% lower to 16% higher than unadjusted rates, depending on severity level. The findings underscore the importance of adjusting for spatial and temporal confounders in benchmarking analysis, which ultimately contributes to a more equitable benchmark for ADS performance evaluations.

We describe old and prove new results on properties of the Fibonacci Lie algebra in a self-contained exposition. First, we study the growth of this algebra in more details. So, we show that the polynomial behaviour of the growth function in not uniform. We establish bounds on the growth of its universal enveloping algebra. We find bounds on nilpotency indices for elements of the Fibonacci restricted Lie algebra. We prove that the Fibonacci Lie algebra is not PI. Our approach is also based on geometric ideas. The Fibonacci Lie algebra is $\mathbb Z^2$-graded and its homogeneous components belong to a strip. We illustrate the results by computing the initial components and show positions of the homogeneous components in the strip. We also discuss properties and conjectures on related associative algebras and (restricted) Poisson algebras. Second, we prove infiniteness results on homology and Euler characteristic of arbitrary finitely generated graded Lie algebras of subexponential growth. In case of the Fibonacci Lie algebra, we find bounds on the homology groups and the Euler characteristic, and determine respective positions on plane. The computation of the initial part of the Euler characteristic of the Fibonacci Lie algebra shows that its behaviour is really chaotic. Finally, we formulate results and conjectures on homology groups and infinite presentation.

We study mean-field spin glass models with general vector spins and convex covariance function. For those models, it is known that the limit of the free energy can be written as the supremum of a functional, this is the celebrated Parisi formula. In this paper, we observe that the Parisi functional extends into a concave and Lipschitz functional on the set of signed measures. We use this fact and Fenchel-Moreau duality to derive an un-inverted version of the Parisi formula. Namely, we show that the limit of the free energy can be written as the infimum of a functional related to the Parisi functional. This un-inverted formula can be interpreted as a Hopf-like formula for some Hamilton-Jacobi equation in Wasserstein space., Comment: 49 pages

We present VoxelPrompt, an agent-driven vision-language framework that tackles diverse radiological tasks through joint modeling of natural language, image volumes, and analytical metrics. VoxelPrompt is multi-modal and versatile, leveraging the flexibility of language interaction while providing quantitatively grounded image analysis. Given a variable number of 3D medical volumes, such as MRI and CT scans, VoxelPrompt employs a language agent that iteratively predicts executable instructions to solve a task specified by an input prompt. These instructions communicate with a vision network to encode image features and generate volumetric outputs (e.g., segmentations). VoxelPrompt interprets the results of intermediate instructions and plans further actions to compute discrete measures (e.g., tumor growth across a series of scans) and present relevant outputs to the user. We evaluate this framework in a sandbox of diverse neuroimaging tasks, and we show that the single VoxelPrompt model can delineate hundreds of anatomical and pathological features, measure many complex morphological properties, and perform open-language analysis of lesion characteristics. VoxelPrompt carries out these objectives with accuracy similar to that of fine-tuned, single-task models for segmentation and visual question-answering, while facilitating a much larger range of tasks. Therefore, by supporting accurate image processing with language interaction, VoxelPrompt provides comprehensive utility for numerous imaging tasks that traditionally require specialized models to address., Comment: 21 pages, 5 figures, vision-language agent, medical image analysis, neuroimage foundation model

Logical errors in programming don't raise compiler alerts, making them hard to detect. These silent errors can disrupt a program's function or cause run-time issues. Their correction requires deep insight into the program's logic, highlighting the importance of automated detection and repair. In this paper, we introduce LecPrompt to localize and repair logical errors, an prompt-based approach that harnesses the capabilities of CodeBERT, a transformer-based large language model trained on code. First, LecPrompt leverages a large language model to calculate perplexity and log probability metrics, pinpointing logical errors at both token and line levels. Through statistical analysis, it identifies tokens and lines that deviate significantly from the expected patterns recognized by large language models, marking them as potential error sources. Second, by framing the logical error correction challenge as a Masked Language Modeling (MLM) task, LecPrompt employs CodeBERT to autoregressively repair the identified error tokens. Finally, the soft-prompt method provides a novel solution in low-cost scenarios, ensuring that the model can be fine-tuned to the specific nuances of the logical error correction task without incurring high computational costs. To evaluate LecPrompt's performance, we created a method to introduce logical errors into correct code and applying this on QuixBugs to produce the QuixBugs-LE dataset. Our evaluations on the QuixBugs-LE dataset for both Python and Java highlight the impressive capabilities of our method, LecPrompt. For Python, LecPrompt achieves a noteworthy 74.58% top-1 token-level repair accuracy and 27.4% program-level repair accuracy. In Java, LecPrompt delivers a 69.23\% top-1 token-level repair accuracy and 24.7% full program-level repair accuracy.

As Large Language Models (LLMs) become increasingly sophisticated, they raise significant security concerns, including the creation of fake news and academic misuse. Most detectors for identifying model-generated text are limited by their reliance on variance in perplexity and burstiness, and they require substantial computational resources. In this paper, we proposed a watermarking method embedding a specific watermark into the text during its generation by LLMs, based on a pre-defined signal pattern. This technique not only ensures the watermark's invisibility to humans but also maintains the quality and grammatical integrity of model-generated text. We utilize LLMs and Fast Fourier Transform (FFT) for token probability computation and detection of the signal watermark. The unique application of signal processing principles within the realm of text generation by LLMs allows for subtle yet effective embedding of watermarks, which do not compromise the quality or coherence of the generated text. Our method has been empirically validated across multiple LLMs, consistently maintaining high detection accuracy, even with variations in temperature settings during text generation. In the experiment of distinguishing between human-written and watermarked text, our method achieved an AUROC score of 0.97, significantly outperforming existing methods like GPTZero, which scored 0.64. The watermark's resilience to various attacking scenarios further confirms its robustness, addressing significant challenges in model-generated text authentication.

Recent work has initiated the study of dense graph processing using graph sketching methods, which drastically reduce space costs by lossily compressing information about the input graph. In this paper, we explore the strange and surprising performance landscape of sketching algorithms. We highlight both their surprising advantages for processing dense graphs that were previously prohibitively expensive to study, as well as the current limitations of the technique. Most notably, we show how sketching can avoid bottlenecks that limit conventional graph processing methods. Single-machine streaming graph processing systems are typically bottlenecked by CPU performance, and distributed graph processing systems are typically bottlenecked by network latency. We present Landscape, a distributed graph-stream processing system that uses linear sketching to distribute the CPU work of computing graph properties to distributed workers with no need for worker-to-worker communication. As a result, it overcomes the CPU and network bottlenecks that limit other systems. In fact, for the connected components problem, Landscape achieves a stream ingestion rate one-fourth that of maximum sustained RAM bandwidth, and is four times faster than random access RAM bandwidth. Additionally, we prove that for any sequence of graph updates and queries Landscape consumes at most a constant factor more network bandwidth than is required to receive the input stream. We show that this system can ingest up to 332 million stream updates per second on a graph with $2^{17}$ vertices. We show that it scales well with more distributed compute power: given a cluster of 40 distributed worker machines, it can ingest updates 35 times as fast as with 1 distributed worker machine. Landscape uses heuristics to reduce its query latency by up to four orders of magnitude over the prior state of the art.

Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explore a common scenario in omics, where statistically independent cross-sectional samples are available at a few time points, and the goal is to infer the underlying diffusion process that generated the data. Existing inference approaches often simplify or ignore noise intrinsic to the system, compromising accuracy for the sake of optimization ease. We circumvent this compromise by inferring the phase-space probability flow that shares the same time-dependent marginal distributions as the underlying stochastic process. Our approach, probability flow inference (PFI), disentangles force from intrinsic stochasticity while retaining the algorithmic ease of ODE inference. Analytically, we prove that for Ornstein-Uhlenbeck processes the regularized PFI formalism yields a unique solution in the limit of well-sampled distributions. In practical applications, we show that PFI enables accurate parameter and force estimation in high-dimensional stochastic reaction networks, and that it allows inference of cell differentiation dynamics with molecular noise, outperforming state-of-the-art approaches.

We propose a duality between the complex Liouville string and a two-matrix integral. The complex Liouville string is defined by coupling two Liouville theories with complex central charges $c = 13 \pm i \lambda$ on the worldsheet. The matrix integral is characterized by its spectral curve which allows us to compute the perturbative string amplitudes recursively via topological recursion. This duality constitutes a controllable instance of holographic duality. The leverage on the theory is provided by the rich analytic structure of the string amplitudes that we discussed in arXiv:2409.18759 and allows us to perform numerous tests on the duality., Comment: 52 pages + appendices

We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood estimation, specifically morphing-aware estimation and derivative learning. Technically, we introduce a new and more efficient smearing algorithm, illustrate how uncertainties can be approximated through repulsive ensembles, and show how equivariant networks can improve likelihood estimation. After illustrating these aspects for a toy model, we target di-boson production at the LHC and find that our improvements significantly increase numerical control and stability., Comment: 25 pages, 13 figures

Program errors can occur in any type of programming, and can manifest in a variety of ways, such as unexpected output, crashes, or performance issues. And program error diagnosis can often be too abstract or technical for developers to understand, especially for beginners. The goal of this paper is to present a novel machine-learning approach for Multi-task Program Error Repair and Explanatory Diagnosis (mPRED). A pre-trained language model is used to encode the source code, and a downstream model is specifically designed to identify and repair errors. Programs and test cases will be augmented and optimized from several perspectives. Additionally, our approach incorporates a "chain of thoughts" method, which enables the models to produce intermediate reasoning explanations before providing the final correction. To aid in visualizing and analyzing the program structure, we use a graph neural network for program structure visualization. Overall, our approach offers a promising approach for repairing program errors across different programming languages and providing helpful explanations to programmers.

Two-dimensional heterostructures have been crucial in advancing optoelectronic devices utilizing van der Waals materials. Semiconducting transition metal dichalcogenide monolayers, known for their unique optical properties, offer extensive possibilities for light-emitting devices. Recently, a memory-driven optical device, termed a Mem-emitter, was proposed using these monolayers atop dielectric substrates. The successful realization of such devices heavily depends on selecting the optimal substrate. Here, we report a pronounced memory effect in a MoSe$_2$/clinochlore device, evidenced by electric hysteresis in the intensity and energy of MoSe$_2$ monolayer emissions. This demonstrates both population-driven and transition-rate-driven Mem-emitter abilities. Our theoretical approach correlates these memory effects with internal state variables of the substrate, emphasizing that clinochlore layered structure is crucial for a robust and rich memory response. This work introduces a novel two-dimensional device with promising applications in memory functionalities, highlighting the importance of alternative insulators in fabricating van der Waals heterostructures.

Cookie paywalls allow visitors to access the content of a website only after making a choice between paying a fee (paying option) or accepting tracking (cookie option). The practice has been studied in previous research in regard to its prevalence and legal standing, but the effects of the clients' device and geographic location remain unexplored. To address these questions, this study explores the effects of three factors: 1) the clients' browser, 2) the device type (desktop or mobile), and 3) the geographic location on the presence and behavior of cookie paywalls and the handling of users' data. Using an automatic crawler on our dataset composed of 804 websites that present a cookie paywall, we observed that the presence of a cookie paywall was most affected by the geographic location of the user. We further showed that both the behavior of a cookie paywall and the processing of user data are impacted by all three factors, but no patterns of significance could be found. Finally, an additional type of paywall was discovered to be used on approximately 11% of the studied websites, coined the "double paywall", which consists of a cookie paywall complemented by another paywall once tracking is accepted., Comment: Submitted to ICISSP 2025

This paper has taken into advantage the relationship between Fuzzy Relation Equations (FRE) and Concept Lattices in order to introduce a procedure to reduce a FRE, without losing information. Specifically, attribute reduction theory in property-oriented and object-oriented concept lattices has been considered in order to present a mechanism for detecting redundant equations. As a first consequence, the computation of the whole solution set of a solvable FRE is reduced. Moreover, we will also introduce a novel method for computing approximate solutions of unsolvable FRE related to a (real) dataset with uncertainty/imprecision data.

We develop a framework for studying the enhanced dissipation of passive scalars advected by shear flows based on analyzing the particle trajectories of the stochastic differential equation associated with the governing drift-diffusion equation. We consider both shear flows on $\mathbb{T}^2$ and radially symmetric shears on $\mathbb{R}^2$ or the unit disk. Using our probabilistic approach, we are able to recover the well-known enhanced dissipation timescale for smooth shear flows on $\mathbb{T}^2$ with finite-order vanishing critical points [1, 5, 34, 36] and a generalized version of the results for radially symmetric shear flows from [42]. We also obtain results for shear flows with singularities and critical points where the derivative vanishes to infinite order. The proofs are all based on using Girsanov's theorem to reduce enhanced dissipation to a quantitative control problem that can be solved by leveraging the shearing across streamlines. Our method also has the feature that it is local in space, which allows us also to obtain estimates on the precise decay rate of solutions along each streamline in terms of the local shear profile., Comment: 42 pages

Novel view synthesis (NVS) enables to generate new images of a scene or convert a set of 2D images into a comprehensive 3D model. In the context of Space Domain Awareness, since space is becoming increasingly congested, NVS can accurately map space objects and debris, improving the safety and efficiency of space operations. Similarly, in Rendezvous and Proximity Operations missions, 3D models can provide details about a target object's shape, size, and orientation, allowing for better planning and prediction of the target's behavior. In this work, we explore the generalization abilities of these reconstruction techniques, aiming to avoid the necessity of retraining for each new scene, by presenting a novel approach to 3D spacecraft reconstruction from single-view images, DreamSat, by fine-tuning the Zero123 XL, a state-of-the-art single-view reconstruction model, on a high-quality dataset of 190 high-quality spacecraft models and integrating it into the DreamGaussian framework. We demonstrate consistent improvements in reconstruction quality across multiple metrics, including Contrastive Language-Image Pretraining (CLIP) score (+0.33%), Peak Signal-to-Noise Ratio (PSNR) (+2.53%), Structural Similarity Index (SSIM) (+2.38%), and Learned Perceptual Image Patch Similarity (LPIPS) (+0.16%) on a test set of 30 previously unseen spacecraft images. Our method addresses the lack of domain-specific 3D reconstruction tools in the space industry by leveraging state-of-the-art diffusion models and 3D Gaussian splatting techniques. This approach maintains the efficiency of the DreamGaussian framework while enhancing the accuracy and detail of spacecraft reconstructions. The code for this work can be accessed on GitHub (https://github.com/ARCLab-MIT/space-nvs)., Comment: Presented at the 75th International Astronautical Congress, October 2024, Milan, Italy

The possibility of observing correlation-driven charge migration has been a driving force behind theoretical and experimental developments in the field of attosecond molecular science since its inception. Despite significant accomplishments, the unambiguous experimental observation of this quantum beating remains elusive. In this work, we present a method to selectively trigger such dynamics using molecules predicted to exhibit long-lived electron coherence. We show that these dynamics can be selectively triggered using infrared multi-photon ionization and probed using the spacial resolution of X-ray free-electron laser, proposing a promising experimental scheme to study these pivotal dynamics. Additionally, we demonstrate that real-time time-dependent density-functional theory can describe correlation-driven charge migration resulting from a hole mixing structure involving the HOMO of a molecule.

Digital twins (DTs) are high-fidelity virtual models of physical systems. This paper details a novel two-stage optimization method for real-time parameterization of photovoltaic digital twins (PVDTs) using field measurements. Initially, the method estimates equivalent irradiance from PV power, voltage, and current data, eliminating the need for direct irradiance sensors. This is crucial for tuning the DT's parameters to actual environmental conditions, thereby improving power prediction accuracy. The second stage focuses on refining these parameters by minimizing discrepancies between measured and predicted outputs. This optimization utilizes the estimated equivalent irradiance as a model input, maintaining synchronization with real-world conditions. Parameter updates are event-trigger, launched when deviations exceed predefined thresholds. This strategy optimizes prediction accuracy and manages communication loads efficiently. Validated with extensive data from a PV farm, this approach outperforms existing methodologies in predictive accuracy and operational efficiency, significantly improving the performance DTs in real-time grid operations., Comment: 11 pages, 12 figures, 4 tables

The performance of quantum key distribution (QKD) heavily depends on the finite statistics of its security proof. For multiple protocols and proof techniques, the central statistical task is a random sampling problem, which is customarily addressed by invoking suitable tail bounds on the hypergeometric distribution. In this work, we introduce an alternative solution that exploits a link between random sampling with and without replacement. Despite its simplicity, it notably boosts the achievable secret key rate, particularly in the regime of small data block sizes critical for satellite QKD and other envisioned QKD applications. Moreover, as a by-product of the proposed tool, tight Neyman constructions are derived for the average of independent Bernoulli variables. Bounds of this kind naturally fit in finite-key security proofs of decoy-state QKD schemes, further sharpening the finite statistics compared to previous approaches., Comment: 15 pages, 4 figures

We present a proof-of-concept of a model comparison approach for analyzing spatio-temporal observations of interacting populations. Our model variants are a collection of structurally similar Bayesian networks. Their distinct Noisy-Or conditional probability distributions describe interactions within the population, with each distribution corresponding to a specific mechanism of interaction. To determine which distributions most accurately represent the underlying mechanisms, we examine the accuracy of each Bayesian network with respect to observational data. We implement such a system for observations of bacterial populations engaged in conjugation, a type of horizontal gene transfer that allows microbes to share genetic material with nearby cells through physical contact. Evaluating cell-specific factors that affect conjugation is generally difficult because of the stochastic nature of the process. Our approach provides a new method for gaining insight into this process. We compare eight model variations for each of three experimental trials and rank them using two different metrics, Comment: 17 pages, 4 figures, 3 tables

Neutron scattering characterization of materials allows for the study of entanglement and microscopic structure, but is inefficient to simulate classically for comparison to theoretical models and predictions. However, quantum processors, notably analog quantum simulators, have the potential to offer an unprecedented, efficient method of Hamiltonian simulation by evolving a state in real time to compute phase transitions, dynamical properties, and entanglement witnesses. Here, we present a method for simulating neutron scattering on QuEra's Aquila processor by measuring the dynamic structure factor (DSF) for the prototypical example of the critical transverse field Ising chain, and propose a method for error mitigation. We provide numerical simulations and experimental results for the performance of the procedure on the hardware, up to a chain of length $L=25$. Additionally, the DSF result is used to compute the quantum Fisher information (QFI) density, where we confirm bipartite entanglement in the system experimentally., Comment: 13 pages, 10 figures

How to elect the representatives in legislative bodies is a question that every modern democracy has to answer. This design task has to consider various elements so as to fulfill the citizens' expectations and contribute to the maintenance of a healthy democracy. The notion of proportionality, in that the support of a given idea in the house should be nearly proportional to its support in the general public, lies at the core of this design task. In the last decades, demographic aspects beyond political support have been incorporated by requiring that they are also fairly represented in the body, giving rise to a multidimensional version of the apportionment problem. In this work, we provide an axiomatic justification for a recently proposed notion of multidimensional proportionality and extend it to encompass two relevant constraints often used in electoral systems: a threshold on the number of votes that a list needs in order to be eligible and the election of the most-voted candidate in each district. We then build upon these results to design methods based on multidimensional proportionality. We use the Chilean Constitutional Convention election (May 15-16, 2021) results as a testing ground -- where the dimensions are given by political lists, districts, and genders -- and compare the apportionment obtained under each method according to three criteria: proportionality, representativeness, and voting power. While local and global methods exhibit a natural trade-off between local and global proportionality, including the election of most-voted candidates on top of methods based on 3-dimensional proportionality allows us to incorporate both notions while ensuring higher levels of representativeness and a balanced voting power.

First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, significantly improving sample efficiency in robot control compared to standard model-free reinforcement learning. However, FoPG algorithms can exhibit poor learning dynamics in contact-rich tasks like locomotion. Previous approaches address this issue by alleviating contact dynamics via algorithmic or simulation innovations. In contrast, we propose guiding the policy search by learning a residual over a simple baseline policy. For quadruped locomotion, we find that the role of residual policy learning in FoPG-based training (FoPG RPL) is primarily to improve asymptotic rewards, compared to improving sample efficiency for model-free RL. Additionally, we provide insights on applying FoPG's to pixel-based local navigation, training a point-mass robot to convergence within seconds. Finally, we showcase the versatility of FoPG RPL by using it to train locomotion and perceptive navigation end-to-end on a quadruped in minutes.

Josephson junction spectroscopy is a powerful local microwave spectroscopy technique that has promising potential as a diagnostic tool to probe the microscopic origins of noise in superconducting qubits. We present advancements toward realizing Josephson junction spectroscopy in a scanning geometry, where the Josephson junction is formed between a superconducting sample and a high capacitance superconducting STM tip. Data from planar Nb-based Josephson junction devices first demonstrate the benefits of including a high capacitance shunt across the junction, which decreases linewidth and improves performance at elevated temperatures. We show how an equivalent circuit can be implemented by utilizing a planarized STM tip with local prominences, which are fabricated via electron beam lithography and reactive ion etching, followed by coating with a superconducting layer. Differential conductance measurements on a superconducting NbN surface demonstrate the ability of these high capacitance tips to decrease both thermal noise and P(E)-broadening in comparison to typical wire tips., Comment: 8 pages, 7 figures

This study introduces a novel metric, Plan Aperture Modulation (PAM), developed to quantify the modulation of radiotherapy treatment plans. PAM aims to provide a clear geometric interpretation, addressing the limitations of previous complexity metrics and facilitating its integration into treatment planning systems (TPSs) and clinical workflows. The PAM metric was defined as the average fraction of the target area outside the beam aperture, weighted over all control points in a treatment plan. The metric was evaluated in VMAT plans for two sites: prostate with lymph nodes and lung SBRT. Plans with varying complexities were generated using the Eclipse TPS, and PAM was compared to established metrics, including Plan Modulation (PM), Modulation Complexity Score (MCS), and monitor units per Gray (MU/Gy). The relationship between PAM and the Modulation Factor (MF), which quantifies the increase in MUs due to plan modulation, was also investigated. PAM provided a more intuitive assessment of plan modulation compared to the other metrics, and was validated across different delivery systems, such as C-arm linacs and Halcyon systems. The metric outperformed the previous metrics, indicated a zero modulation for Dynamic Conformal Arc plans, and was independent of confounding variables, such as treatment technique, beam energy, delivery system, and patient anatomy. Derived equations enabled the calculation of MF based on PAM, allowing for a robust quantification of plan modulation. PAM is a robust and intuitive metric for quantifying modulation in radiotherapy plans. It overcomes the limitations of previous metrics and can be readily implemented in TPSs to control plan modulation during optimization and for reporting. PAM is a promising tool for improving treatment planning workflows and for comparing and benchmarking radiotherapy plans in multi-institutional studies, clinical trials, and audits.

As the number of user equipments increases in fifth generation (5G) and beyond, it is desired to densify the cellular network with auxiliary nodes assisting the base stations. Examples of these nodes are integrated access and backhaul (IAB) nodes, network-controlled repeaters (NCRs) and reconfigurable intelligent surfaces (RISs). In this context, this work presents a system level overview of these three nodes. Moreover, this work evaluates through simulations the impact of network planning aiming at enhancing the performance of a network used to cover an outdoor sport event. We show that, in the considered scenario, in general, IAB nodes provide an improved signal to interference-plus-noise ratio and throughput, compared to NCRs and RISs. However, there are situations where NCR outperforms IAB due to higher level of interference caused by the latter. Finally, we show that the deployment of these nodes in unmanned aerial vehicles (UAVs) also achieves performance gains due to their aerial mobility. However, UAV constraints related to aerial deployment may prevent these nodes from reaching results as good as the ones achieved by their stationary deployment., Comment: Paper submitted to IEEE Systems Journal

Semiconductor quantum dot arrays are a promising platform to perform spin-based error-corrected quantum computation with large numbers of qubits. However, due to the diverging number of possible charge configurations combined with the limited sensitivity of large-footprint charge sensors, achieving single-spin occupancy in each dot in a growing quantum dot array is exceedingly complex. Therefore, to scale-up a spin-based architecture we must change how individual charges are readout and controlled. Here, we demonstrate single-spin occupancy of each dot in a foundry-fabricated array by combining two methods. 1/ Loading a finite number of electrons into the quantum dot array; simplifying electrostatic tuning by isolating the array from the reservoirs. 2/ Deploying multiplex gate-based reflectometry to dispersively probe charge tunneling and spin states without charge sensors or reservoirs. Our isolated arrays probed by embedded multiplex readout can be readily electrostatically tuned. They are thus a viable, scalable approach for spin-based quantum architectures., Comment: 15 pages 5 figures

We show that the color restriction map $\mathrm{Op}^h({\mathcal{SC}}_m,{\mathcal{SC}}_n)\to \mathrm{Op}^h({\mathcal E}_{m-1},{\mathcal E}_{n-1})$ from the derived mapping space of Swiss cheese operads to that of little discs operads, is a weak homotopy equivalence. We explain how this may help in the study of disc concordance embedding spaces., Comment: 7 pages

Large language models (LLMs) have demonstrated remarkable capabilities, but they also pose risks related to the generation of toxic or harmful content. This work introduces Precision Knowledge Editing (PKE), an advanced technique that builds upon existing knowledge editing methods to more effectively identify and modify toxic parameter regions within LLMs. By leveraging neuron weight tracking and activation pathway tracing, PKE achieves finer granularity in toxic content management compared to previous methods like Detoxifying Instance Neuron Modification (DINM). Our experiments demonstrate that PKE significantly reduces the attack success rate (ASR) across various models, including Llama2-7b and Llama-3-8b-instruct, while maintaining overall model performance. Additionally, we also compared the performance of some closed-source models (gpt-4-0613 and Claude 3 Sonnet) in our experiments, and found that models adjusted using our method far outperformed the closed-source models in terms of safety. This research contributes to the ongoing efforts to make LLMs safer and more reliable for real-world applications.

M-type stars are the most common stars in the universe. They are ideal hosts for the search of exoplanets in the habitable zone (HZ), as their small size and low temperature make the HZ much closer in than their solar twins. Harboring very deep convective layers, they also usually exhibit very intense magnetic fields. Understanding their environment, in particular their coronal and wind properties, is thus very important, as they might be very different from what is observed in the solar system. The mass loss rate of M-type stars is poorly known observationally, and recent attempts to estimate it for some of them (TRAPPIST-1, Proxima Cen) can vary by an order of magnitude. In this work, we revisit the stellar wind properties of M-dwarfs in the light of the latest estimates of $\dot{M}$ through Lyman-$\alpha$ absorption at the astropause and slingshot prominences. We outline a modeling strategy to estimate the mass loss rate, radiative loss and wind speed, with uncertainties, based on an Alfv\'en wave driven stellar wind model. We find that it is very likely that several TRAPPIST-1 planets lie within the Alfv\'en surface, which imply that these planets experience star-planet magnetic interactions (SPMI). We also find that SPMI between Proxima Cen b and its host star could be the reason of recently observed radio emissions., Comment: 15 pages, 9 figures, accepted for publication in ApJ

We consider the task of data-driven identification of dynamical systems, specifically for systems whose behavior at large frequencies is non-standard, as encoded by a non-trivial relative degree of the transfer function or, alternatively, a non-trivial index of a corresponding realization as a descriptor system. We develop novel surrogate modeling strategies that allow state-of-the-art rational approximation algorithms (e.g., AAA and vector fitting) to better handle data coming from such systems with non-trivial relative degree. Our contribution is twofold. On one hand, we describe a strategy to build rational surrogate models with prescribed relative degree, with the objective of mirroring the high-frequency behavior of the high-fidelity problem, when known. The surrogate model's desired degree is achieved through constraints on its barycentric coefficients, rather than through ad-hoc modifications of the rational form. On the other hand, we present a degree-identification routine that allows one to estimate the unknown relative degree of a system from low-frequency data. By identifying the degree of the system that generated the data, we can build a surrogate model that, in addition to matching the data well (at low frequencies), has enhanced extrapolation capabilities (at high frequencies). We showcase the effectiveness and robustness of the newly proposed method through a suite of numerical tests., Comment: 20 pages, 5 figures

Detecting or placing upper limits on spacetime variations of fundamental constants requires quantifying every potential source of uncertainty. We continue our previous study into the impact of continuum variations on measurements of the fine structure constant, here in the context of quasar absorption systems. An automated (hence objective and reproducible) continuum modelling method is reported in an accompanying paper. We apply the method to the $z_{abs}=1.7975$ absorption system towards the quasar PHL957. Multiple continuum fits are generated, and for each, we derive independent models of the system, each giving its own measurement of the fine structure constant $\alpha$. This process isolates and quantifies the error contribution associated with continuum placement uncertainty. This source of uncertainty, ignored in many previous measurements, arises in two ways: (i) slight local continuum tilt uncertainty generates small line shifts, and (ii) different continuum estimates produce slightly different kinematic structures in the absorption system model. Taking continuum placement uncertainty into account, the new PHL957 measurement we obtain is $\Delta\alpha/\alpha= -0.53^{+5.45}_{-5.51} \times 10^{-6}$. This measurement assumes terrestrial magnesium isotopic abundances. Recommendations are provided for future $\alpha$ measurements. Finally, we also note the potential importance of the effects identified here for future redshift drift experiments., Comment: 10 pages, 3 figures. Submitted to Open Journal of Astrophysics

We show that if a Hamilton-Jacobi equation admits a differentiable solution whose gradient is Lipschitz, then this solution is the unique semi-concave weak solution. Our result does not rely on any convexity (nor concavity) assumptions on the initial condition or the nonlinearity, and can therefore be utilized in contexts where the viscosity solution admits no standard variational representation., Comment: 13 pages

We propose a sequential Monte Carlo algorithm for parameter learning when the studied model exhibits random discontinuous jumps in behaviour. To facilitate the learning of high dimensional parameter sets, such as those associated to neural networks, we adopt the emerging framework of differentiable particle filtering, wherein parameters are trained by gradient descent. We design a new differentiable interacting multiple model particle filter to be capable of learning the individual behavioural regimes and the model which controls the jumping simultaneously. In contrast to previous approaches, our algorithm allows control of the computational effort assigned per regime whilst using the probability of being in a given regime to guide sampling. Furthermore, we develop a new gradient estimator that has a lower variance than established approaches and remains fast to compute, for which we prove consistency. We establish new theoretical results of the presented algorithms and demonstrate superior numerical performance compared to the previous state-of-the-art algorithms.

Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. For example, people with limited mobility can benefit from robots helping with daily tasks, or construction workers can have robots perform precision monitoring tasks on-site. However, manually steering a robot while in motion requires significant concentration from the operator, especially in tight or crowded spaces. This reduces walking speed, and the constant need for vigilance increases fatigue and, thus, the risk of accidents. This work presents a virtual leash with which a robot can naturally follow an operator. We use a sensor fusion based on a custom-built RF transponder, RGB cameras, and a LiDAR. In addition, we customize a local avoidance planner for legged platforms, which enables us to navigate dynamic and narrow environments. We successfully validate on the ANYmal platform the robustness and performance of our entire pipeline in real-world experiments.

Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role in MFBO. In this work, we provide guidelines and recommendations to decide when to use MFBO in experimental settings. We investigate MFBO methods applied to molecules and materials problems. First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function. We use our implementation and guidelines to benchmark three real discovery problems and compare them against their single-fidelity counterparts. Our results may help guide future efforts to implement MFBO as a routine tool in the chemical sciences.

In formal verification, runtime monitoring consists of observing the execution of a system in order to decide as quickly as possible whether or not it satisfies a given property. We consider monitoring in a distributed setting, for properties given as reachability timed automata. In such a setting, the system is made of several components, each equipped with its own local clock and monitor. The monitors observe events occurring on their associated component, and receive timestamped events from other monitors through FIFO channels. Since clocks are local, they cannot be perfectly synchronized, resulting in imprecise timestamps. Consequently, they must be seen as intervals, leading monitors to consider possible reorderings of events. In this context, each monitor aims to provide, as early as possible, a verdict on the property it is monitoring, based on its potentially incomplete and imprecise knowledge of the current execution. In this paper, we propose an on-line monitoring algorithm for timed properties, robust to time imprecision and partial information from distant components. We first identify the date at which a monitor can safely compute a verdict based on received events. We then propose a monitoring algorithm that updates this date when new information arrives, maintains the current set of states in which the property can reside, and updates its verdict accordingly.

We relate the theory of moduli spaces $\overline{\mathcal{M}}_{0,\mathcal{A}}$ of stable weighted curves of genus $0$ to the equivariant topology of complex Grassmann manifolds $G_{n,2}$, with the canonical action of the compact torus $T^n$. We prove that all spaces $\overline{\mathcal{M}}_{0,\mathcal{A}}$ can be isomorphically or up to birational morphisms embedded in $G_{n,2}/T^n$ . The crucial role for proving this result play the chamber decomposition of the hypersimplex $\Delta _{n,2}$ which corresponds to $(\mathbb{C} ^{\ast})^{n}$-stratification of $G_{n,2}$ and the spaces of parameters over the chambers, which are subspaces in $G_{n,2}/T^n$. We show that the points of these moduli spaces $\overline{\mathcal{M}}_{0, \mathcal{A}}$ have the geometric realization as the points of the spaces of parameters over the chambers. We single out the characteristic categories among such moduli spaces. The morphisms in these categories correspond to the natural projections between the universal space of parameters and the spaces of parameters over the chambers. As a corollary, we obtain the realization of the orbit space $G_{n,2}/T^n$ as an universal object for the introduced categories. As one of our main results we describe the structure of the canonical projection from the Deligne-Mumford compactification to the Losev-Manin compactification of $\mathcal{M}_{0,n}$, using the embedding of $\mathcal{M}_{0, n}\subset \bar{L}_{0, n, 2}$ in $(\mathbb{C} P^{1})^{N}$, $N=\binom{n-2}{2}$, the action of the algebraic torus $(\mathbb{C} ^{\ast})^{n-3}$ on $(\mathbb{C} P^{1})^{N}$ for which $\bar{L}_{0, n, 2}$ is invariant, and the realization of the Losev-Manin compactification as the corresponding permutohedral toric variety., Comment: 43 pages

We consider the family of piecewise linear maps $$F_{a,b}(x,y)=\left(|x| - y + a, x - |y| + b\right),$$ where $(a,b)\in \mathbb{R}^2$. This family belongs to a wider one that has deserved some interest in the recent years as it provides a framework for generalized Lozi-type maps. Among our results, we prove that for $a\ge 0$ all the orbits are eventually periodic and moreover that there are at most three different periodic behaviors formed by at most seven points. For $a<0$ we prove that for each $b\in\mathbb{R}$ there exists a compact graph $\Gamma,$ which is invariant under the map $F$, such that for each $(x,y)\in \mathbb{R}^2$ there exists $n\in\mathbb{N}$ (that may depend on $x$) such that $F_{a,b}^n(x,y)\in \Gamma.$ We give explicitly all these invariant graphs and we characterize the dynamics of the map restricted to the corresponding graph for all $(a,b)\in\mathbb{R}^2$ obtaining, among other results, a full characterization of when $F_{a,b}|_{\Gamma}$ has positive or zero entropy., Comment: 111 pages, 61 figures

Searches for variations of fundamental constants require a comprehensive understanding of measurement errors. This paper examines a source of error that is usually overlooked: the impact of continuum placement error. We investigate the problem using a high resolution, high signal to noise spectrum of the white dwarf G191$-$B2B. Narrow photospheric absorption lines allow us to search for new physics in the presence of a gravitational field approximately $10^4$ times that on Earth. Modelling photospheric lines requires knowing the underlying spectral continuum level. We describe the development of a fully automated, objective, and reproducible continuum estimation method. Measurements of the fine structure constant are produced using several continuum models. The results show that continuum placement variations result in small systematic shifts in the centroids of narrow photospheric absorption lines which impact significantly on fine structure constant measurements. This effect should therefore be included in the error budgets of future measurements. Our results suggest that continuum placement variations should be investigated in other contexts, including fine structure constant measurements in stars other than white dwarfs. The analysis presented here is based on NiV absorption lines in the photosphere of G191$-$B2B. Curiously, the inferred measurement of the fine structure constant obtained in this paper using NiV (the least negative of our measurements is $\Delta\alpha/\alpha = -1.462 \pm 1.121 \times 10^{-5}$) is inconsistent with the most recent previous G191$-$B2B photospheric measurement using FeV ($\Delta\alpha/\alpha = 6.36 \pm 0.35_{stat} \pm 1.84_{sys} \times 10^{-5}$). Given both measurements are derived from the same spectrum, we presume (but in this work are unable to check) that this 3.2$\sigma$ difference results from unknown laboratory wavelength systematics., Comment: Main paper comprises 11 pages, 4 figures, 1 table. 41 pages in total including appendices. Submitted to Open Journal of Astrophysics. arXiv admin note: substantial text overlap with arXiv:2212.00434

Robot soccer, in its full complexity, poses an unsolved research challenge. Current solutions heavily rely on engineered heuristic strategies, which lack robustness and adaptability. Deep reinforcement learning has gained significant traction in various complex robotics tasks such as locomotion, manipulation, and competitive games (e.g., AlphaZero, OpenAI Five), making it a promising solution to the robot soccer problem. This paper introduces MARLadona. A decentralized multi-agent reinforcement learning (MARL) training pipeline capable of producing agents with sophisticated team play behavior, bridging the shortcomings of heuristic methods. Further, we created an open-source multi-agent soccer environment based on Isaac Gym. Utilizing our MARL framework and a modified a global entity encoder as our core architecture, our approach achieves a 66.8% win rate against HELIOS agent, which employs a state-of-the-art heuristic strategy. Furthermore, we provided an in-depth analysis of the policy behavior and interpreted the agent's intention using the critic network.