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Quantum circuits with local unitaries have emerged as a rich playground for the exploration of many-body quantum dynamics of discrete-time systems. While the intrinsic locality makes them particularly suited to run on current quantum processors, the task of verification at non-trivial scales is complicated for non-integrable systems. Here, we study a special class of maximally chaotic circuits known as dual unitary circuits -- exhibiting unitarity in both space and time -- that are known to have exact analytical solutions for certain correlation functions. With advances in noise learning and the implementation of novel error mitigation methods, we show that a superconducting quantum processor with 91 qubits is able to accurately simulate these correlators. We then probe dynamics beyond exact verification, by perturbing the circuits away from the dual unitary point, and compare our results to classical approximations with tensor networks. These results cement error-mitigated digital quantum simulation on pre-fault-tolerant quantum processors as a trustworthy platform for the exploration and discovery of novel emergent quantum many-body phases., Comment: 7 + 23 pages, 3 + 17 figures, 0 + 2 tables

Scanning tunneling potentiometry (STP) is used to probe the local, current-induced electrochemical potential of carriers in graphene near circular electrostatic barriers in an out-of-plane magnetic field ranging from 0 to 1.4 T. These measurements provide nanometer-resolved information about the local motion of carriers, revealing significant changes in carrier dynamics with increasing field strength. At low magnetic fields the electrochemical potential displays a spiral-like pattern, while at high fields it exhibits distinct changes at particular radii. We show that the observed behavior indicates a transition from diffusive to ballistic transport. Additionally, the sharp changes in the measured potential profile at high fields result from the `spirograph' motion of carriers, which creates a local enhancement of the Hall field one cyclotron diameter away from the semiclassical turning point near the electrostatic barrier., Comment: 18 pages, 6 figures

Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical workflows and diverse populations. This study addresses gaps in medical AI downstream accountability through a case study on age-related macular degeneration (AMD) diagnosis and severity classification. We designed and implemented an AI-assisted diagnostic workflow for AMD, comparing diagnostic performance with and without AI assistance among 24 clinicians from 12 institutions with real patient data sampled from the Age-Related Eye Disease Study (AREDS). Additionally, we demonstrated continual enhancement of an existing AI model by incorporating approximately 40,000 additional medical images (named AREDS2 dataset). The improved model was then systematically evaluated using both AREDS and AREDS2 test sets, as well as an external test set from Singapore. AI assistance markedly enhanced diagnostic accuracy and classification for 23 out of 24 clinicians, with the average F1-score increasing by 20% from 37.71 (Manual) to 45.52 (Manual + AI) (P-value < 0.0001), achieving an improvement of over 50% in some cases. In terms of efficiency, AI assistance reduced diagnostic times for 17 out of the 19 clinicians tracked, with time savings of up to 40%. Furthermore, a model equipped with continual learning showed robust performance across three independent datasets, recording a 29% increase in accuracy, and elevating the F1-score from 42 to 54 in the Singapore population.

Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies implemented and evaluated RAG in downstream domain-specific applications. We developed a RAG pipeline with 70,000 ophthalmology-specific documents that retrieve relevant documents to augment LLMs during inference time. In a case study on long-form consumer health questions, we systematically evaluated the responses including over 500 references of LLMs with and without RAG on 100 questions with 10 healthcare professionals. The evaluation focuses on factuality of evidence, selection and ranking of evidence, attribution of evidence, and answer accuracy and completeness. LLMs without RAG provided 252 references in total. Of which, 45.3% hallucinated, 34.1% consisted of minor errors, and 20.6% were correct. In contrast, LLMs with RAG significantly improved accuracy (54.5% being correct) and reduced error rates (18.8% with minor hallucinations and 26.7% with errors). 62.5% of the top 10 documents retrieved by RAG were selected as the top references in the LLM response, with an average ranking of 4.9. The use of RAG also improved evidence attribution (increasing from 1.85 to 2.49 on a 5-point scale, P<0.001), albeit with slight decreases in accuracy (from 3.52 to 3.23, P=0.03) and completeness (from 3.47 to 3.27, P=0.17). The results demonstrate that LLMs frequently exhibited hallucinated and erroneous evidence in the responses, raising concerns for downstream applications in the medical domain. RAG substantially reduced the proportion of such evidence but encountered challenges.

Many information-theoretic quantities have corresponding representations in terms of sets. The prevailing signed measure space for characterising entropy, the $I$-measure of Yeung, is occasionally unable to discern between qualitatively distinct systems. In previous work, we presented a refinement of this signed measure space and demonstrated its capability to represent many quantities, which we called logarithmically decomposable quantities. In the present work we demonstrate that this framework has natural algebraic behaviour which can be expressed in terms of ideals (characterised here as upper-sets), and we show that this behaviour allows us to make various counting arguments and characterise many fixed-parity information quantity expressions. As an application, we give an algebraic proof that the only completely synergistic system of three finite variables $X$, $Y$ and $Z = f(X,Y)$ is the XOR gate., Comment: 12 pages, 2 figures

The J=1$\rightarrow$0 spectral line of carbon monoxide (CO(1-0)) is the canonical tracer of molecular gas. However, CO(2-1) is frequently used in its place, following the assumption that the higher energy line can be used to infer the CO(1-0) luminosity and molecular gas mass. The use of CO(2-1) depends on a knowledge of the ratio between CO(2-1) and CO(1-0) luminosities, r21. Here we present galaxy-integrated r21 measurements for 122 galaxies spanning stellar masses from 10$^9$ to 10$^{11.5}$ M$_\odot$ and star formation rates (SFRs) from 0.08 to 35 M$_\odot$/yr. We find strong trends between r21 and SFR, SFR surface density, star formation efficiency, and distance from the star formation main sequence (SFMS). We show that the assumption of a constant r21 can introduce biases into the molecular gas trends in galaxy population studies and demonstrate how this affects the recovery of important galaxy scaling relations, including the Kennicutt-Schmidt law and the relation between SFMS offset and star formation efficiency. We provide a prescription which accounts for variations in r21 as a function of SFR and can be used to convert between CO(2-1) and CO(1-0) when only one line is available. Our prescription matches variations in r21 for both AMISS and literature samples and can be used to derive more accurate gas masses from CO(2-1) observations., Comment: Submitted to ApJ

The Shannon entropy of a random variable X has much behaviour analogous to a signed measure. Previous work has explored this connection by defining a signed measure on abstract sets, which are taken to represent the information that different random variables contain. This construction is sufficient to derive many measure-theoretical counterparts to information quantities such as the mutual information $I(X; Y) = \mu(\tilde{X} \cap \tilde{Y})$, the joint entropy $H(X,Y) = \mu(\tilde{X} \cup \tilde{Y})$, and the conditional entropy $H(X|Y) = \mu(\tilde{X} \setminus \tilde{Y})$. Here we provide concrete characterisations of these abstract sets and a corresponding signed measure, and in doing so we demonstrate that there exists a much finer decomposition with intuitive properties which we call the logarithmic decomposition (LD). We show that this signed measure space has the useful property that its logarithmic atoms are easily characterised with negative or positive entropy, while also being consistent with Yeung's I-measure. We present the usability of our approach by re-examining the G\'acs-K\"orner common information and the Wyner common information from this new geometric perspective and characterising it in terms of our logarithmic atoms - a property we call logarithmic decomposability. We present possible extensions of this construction to continuous probability distributions before discussing implications for quality-led information theory. Lastly, we apply our new decomposition to examine the Dyadic and Triadic systems of James and Crutchfield and show that, in contrast to the I-measure alone, our decomposition is able to qualitatively distinguish between them., Comment: 25 pages, 10 figures, extended version of a conference paper

Nanosatellites are proliferating as low-cost dedicated sensing systems with lean development cycles. Kyushu Institute of Technology and collaborators have launched a joint venture for a nanosatellite mission, VERTECS. The primary mission is to elucidate the formation history of stars by observing the optical-wavelength cosmic background radiation. The VERTECS satellite will be equipped with a small-aperture telescope and a high-precision attitude control system to capture the cosmic data for analysis on the ground. However, nanosatellites are limited by their onboard memory resources and downlink speed capabilities. Additionally, due to a limited number of ground stations, the satellite mission will face issues meeting the required data budget for mission success. To alleviate this issue, we propose an on-orbit system to autonomously classify and then compress desirable image data for data downlink prioritization and optimization. The system comprises a prototype Camera Controller Board (CCB) which carries a Raspberry Pi Compute Module 4 which is used for classification and compression. The system uses a lightweight Convolutional Neural Network (CNN) model to classify and determine the desirability of captured image data. The model is designed to be lean and robust to reduce the computational and memory load on the satellite. The model is trained and tested on a novel star field dataset consisting of data captured by the Sloan Digital Sky Survey (SDSS). The dataset is meant to simulate the expected data produced by the 6U satellite. The compression step implements GZip, RICE or HCOMPRESS compression, which are standards for astronomical data. Preliminary testing on the proposed CNN model results in a classification accuracy of about 100\% on the star field dataset, with compression ratios of 3.99, 5.16 and 5.43 for GZip, RICE and HCOMPRESS that were achieved on tested FITS image data., Comment: 14 pages

The CO(1-0) line has been carefully calibrated as a tracer of molecular gas mass. However, recent studies often favor higher J transitions of the CO molecule which are brighter and accessible for redshift ranges where CO(1-0) is not. These lines are not perfect analogues for CO(1-0), owing to their more stringent excitation conditions, and must be calibrated for use as molecular gas tracers. Here we introduce the Arizona Molecular ISM Survey with the SMT (AMISS), a multi-CO line survey of z~0 galaxies conducted to calibrate the CO(2-1) and CO(3-2) lines. The final survey includes CO(2-1) spectra of 176 galaxies and CO(3-2) spectra for a subset of 45. We supplement these with archival CO(1-0) spectra from xCOLD GASS for all sources and additional CO(1-0) observations with the Kitt Peak 12m Telescope. Targets were selected to be representative of the galaxy population in the stellar mass range of $10^9$ to $10^{11.5}$ M$_\odot$. Our project emphasized careful characterization of statistical and systematic uncertainties to enable studies of trends in CO line ratios. We show that optical and CO disk sizes are on average equal, for both the CO(1-0) and CO(2-1) line. We measure the distribution of CO line luminosity ratios, finding medians (16th-84th percentile) of 0.71 (0.51-0.96) for the CO(2-1)-to-CO(1-0) ratio, 0.39 (0.24-0.53) for the CO(3-2)-to-CO(1-0) ratio, and 0.53 (0.41-0.74) for the CO(3-2)-to-CO(2-1) ratio. A companion paper presents our study of CO(2-1)'s applicability as a molecular gas mass tracer and search for trends in the CO(2-1)-to-CO(1-0) ratio. Our catalog of CO line luminosities will be publicly available with the published version of this article., Comment: Resubmitted to ApJ after incorporating reviewer feedback

In this paper, we study the problem of distributed transmission control and video streaming optimization for unmanned aerial vehicles (UAVs) operating in unlicensed spectrum bands. We develop a rigorous cross-layer analysis framework that jointly considers three inter-dependent factors: (i) in-band interference introduced by ground/aerial nodes at the physical (PHY) layer, (ii) limited-size queues with delay-constrained packet arrival at the medium access control (MAC) layer, and (iii) video encoding rate at the application layer. First, we formulate an optimization problem to maximize the throughput by optimizing the fading threshold. To this end, we jointly analyze the queue-related packet loss probabilities (i.e., buffer overflow and time threshold event) as well as the outage probability due to the low signal-to-interference-plus-noise ratio (SINR). We introduce the Distributed Transmission Control (DTC) algorithm that maximizes the throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. Second, we incorporate the video distortion model to develop distributed peak signal-to-noise ratio (PSNR) optimization for video streaming. The formulated optimization incorporates two cross-layer parameters, specifically the fading threshold and video encoding rate. To tackle this problem, we develop the Joint Distributed Video Transmission and Encoder Control (JDVT-EC) algorithm that enhances the PSNR for all nodes by fine-tuning transmission policies and video encoding rates to balance the trade-offs between packet loss and lossy video compression distortions. Through extensive numerical analysis, we thoroughly examine the proposed algorithms and demonstrate that they are able to find the optimal transmission policies and video encoding rates under various scenarios., Comment: Submitted to IEEE Transactions on Communications

Tensor networks and quantum computation are two of the most powerful tools for the simulation of quantum many-body systems. Rather than viewing them as competing approaches, here we consider how these two methods can work in tandem. We introduce a novel algorithm that combines tensor networks and quantum computation to produce results that are more accurate than what could be achieved by either method used in isolation. Our algorithm is based on multiproduct formulas (MPF) - a technique that linearly combines Trotter product formulas to reduce algorithmic error. Our algorithm uses a quantum computer to calculate the expectation values and tensor networks to calculate the coefficients used in the linear combination. We present a detailed error analysis of the algorithm and demonstrate the full workflow on a one-dimensional quantum simulation problem on $50$ qubits using two IBM quantum computers: $ibm\_torino$ and $ibm\_kyiv$.

We report on the effects of cosmic ray interactions with the Kinetic Inductance Detector (KID) based focal plane array for the Terahertz Intensity Mapper (TIM). TIM is a NASA-funded balloon-borne experiment designed to probe the peak of the star formation in the Universe. It employs two spectroscopic bands, each equipped with a focal plane of four $\sim\,$900-pixel, KID-based array chips. Measurements of an 864-pixel TIM array shows 791 resonators in a 0.5$\,$GHz bandwidth. We discuss challenges with resonator calibration caused by this high multiplexing density. We robustly identify the physical positions of 788 (99.6$\,$%) detectors using a custom LED-based identification scheme. Using this information we show that cosmic ray events occur at a rate of 2.1$\,\mathrm{events/min/cm^2}$ in our array. 66$\,$% of the events affect a single pixel, and another 33$\,$% affect $<\,$5 KIDs per event spread over a 0.66$\,\mathrm{cm^2}$ region (2 pixel pitches in radius). We observe a total cosmic ray dead fraction of 0.0011$\,$%, and predict that the maximum possible in-flight dead fraction is $\sim\,$0.165$\,$%, which demonstrates our design will be robust against these high-energy events., Comment: 14 pages, 5 figures. Accepted for the publication in Journal of Low Temperature Physics (2024)

We present results from a pilot study, using a laser-produced plasma, to identify new lines in the 350 to 1000 nm spectral region for the r-process element gold (Au), of relevance to studies of neutron star mergers. This was achieved via optical-IR spectroscopy of a laser-produced Au plasma, with an Au target of high purity (99.95 %) and a low vacuum pressure to remove any air contamination from the experimental spectra. Our data were recorded with a spectrometer of 750 mm focal length and 1200 lines mm-1 grating, yielding a resolution of 0.04 nm. We find 54 lines not previously identified and which are not due to the impurities (principally copper (Cu) and silver (Ag)) in our Au sample. Of these 54 lines, we provisionally match 21 strong transitions to theoretical results from collisional-radiative models that include energy levels derived from atomic structure calculations up to the 6s level. Some of the remaining 33 unidentified lines in our spectra are also strong and may be due to transitions involving energy levels which are higher-lying than those in our plasma models. Nevertheless, our experiments demonstrate that laser-produced plasmas are well suited to the identification of transitions in r-process elements, with the method applicable to spectra ranging from UV to IR wavelengths.

Let $R$ be a ring. The unitary addition Cayley graph of $R$, denoted $\mathcal{U}(R)$, is the graph with vertex $R$, and two distinct vertices $x$ and $y$ are adjacent if and only if $x+y$ is a unit. We determine a formula for the clique number and chromatic number of such graphs when $R$ is a finite commutative ring. This includes the special case when $R$ is $\mathbb{Z}_n$, the integers modulo $n$, where these parameters had been found under the assumption that $n$ is even, or $n$ is a power of an odd prime. Additionally, we study the achromatic number of $\mathcal{U}( \mathbb{Z}_n )$ in the case that $n$ is the product of two primes. We prove that the achromatic number of $\mathcal{U} ( \mathbb{Z}_{3q})$ is equal to $\frac{3q+1}{2}$ when $q > 3$ is a prime. We also prove a lower bound that applies when $n = pq$ where $p$ and $q$ are distinct odd primes.

Purpose: To harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color-maps for representing T1 and T2 maps. Methods: Perceptually linearized color-maps were chosen to have similar color settings as those proposed by Griswold et al. in 2018. A Delphi process, polling the opinion of a panel of 81 experts, was used to generate consensus on the suitability of these maps. Results: Consensus was reached on the suitability of the logarithm-processed Lipari color-map for T1 and the logarithm-processed Navia color-map for T2. There was consensus on color bars being mandatory and on the use of a specific value indicating invalidity. There was no consensus on whether the ranges should be fixed per anatomy. Conclusion: The authors recommend the use of the logarithm-processed Lipari color map for displaying quantitative T1 maps and R1 maps; likewise, the authors recommend the logarithm-processed Navia color-map for displaying T2, T2*, R2 and R2* maps., Comment: 22 pages; embedded are 5 figures and 5 tables; contact the first author for supplementary material. Submitted to Magnetic Resonance in Medicine

Quantum computing platforms are subject to contradictory engineering requirements: qubits must be protected from mutual interactions when idling ('doing nothing'), and strongly interacting when in operation. If idling qubits are not sufficiently protected, information can 'leak' into neighbouring qubits, become non-locally distributed, and ultimately inaccessible. Candidate solutions to this dilemma include patterning-enhanced many-body localization, dynamical decoupling, and active error correction. However, no information-theoretic protocol exists to actually quantify this information loss due to internal dynamics in a similar way to e.g. SPAM errors or dephasing times. In this work, we develop a scalable, flexible, device non-specific protocol for quantifying this bitwise idle information loss based on the exploitation of tools from quantum information theory. We implement this protocol in over 3500 experiments carried out across 4 months (Dec 2023 - Mar 2024) on IBM's entire Falcon 5.11 series of processors. After accounting for other sources of error, and extrapolating results via a scaling analysis in shot count to zero shot noise, we detect idle information leakage to a high degree of statistical significance. This work thus provides a firm quantitative foundation from which the protection-operation dilemma can be investigated and ultimately resolved., Comment: 11 pages, 5 figures

Behaviour change lies at the heart of many observable collective phenomena such as the transmission and control of infectious diseases, adoption of public health policies, and migration of animals to new habitats. Representing the process of individual behaviour change in computer simulations of these phenomena remains an open challenge. Often, computational models use phenomenological implementations with limited support from behavioural data. Without a strong connection to observable quantities, such models have limited utility for simulating observed and counterfactual scenarios of emergent phenomena because they cannot be validated or calibrated. Here, we present a simple stochastic individual-based model of reversal learning that captures fundamental properties of individual behaviour change, namely, the capacity to learn based on accumulated reward signals, and the transient persistence of learned behaviour after rewards are removed or altered. The model has only two parameters, and we use approximate Bayesian computation to demonstrate that they are fully identifiable from empirical reversal learning time series data. Finally, we demonstrate how the model can be extended to account for the increased complexity of behavioural dynamics over longer time scales involving fluctuating stimuli. This work is a step towards the development and evaluation of fully identifiable individual-level behaviour change models that can function as validated submodels for complex simulations of collective behaviour change., Comment: 23 pages, 5 figures

The tetragonal heavy-fermion superconductor CeRh$_2$As$_2$ ($T_{\rm c}=0.3$ K) exhibits an exceptionally high critical field of 14 T for $\textbf{B} \parallel \textbf{c}$. It undergoes a field-driven first-order phase transition between superconducting (SC) states, potentially transitioning from spin-singlet to spin-triplet superconductivity. To elucidate the underlying pairing mechanism, we probe spin fluctuations in CeRh$_2$As$_2$ using neutron scattering. We find dynamic $(\pi,\pi)$ antiferromagnetic spin correlations with an anisotropic quasi-two-dimensional correlation volume. Our data place an upper limit of 0.31 $\mu_{\rm B}$ on the staggered magnetization of corresponding N\'{e}el orders at $T=0.08$ K. Density functional theory (DFT) calculations, treating Ce $4f$ electrons as core states, show that the AFM wave vector connects significant areas of the Fermi surface. Our findings show the dominant excitations in CeRh$_2$As$_2$ for $\hbar\omega< 1.2$~meV are magnetic and indicate superconductivity in CeRh$_2$As$_2$ is mediated by AFM spin fluctuations associated with a proximate quantum critical point., Comment: 7+6 pages, 4+4 figures

In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users (SUs) to opportunistically utilize detected "spectrum holes". Our overall framework consists of three main stages. Firstly, in the model training stage, we explore dataset generation in a multi-cell environment and training a machine learning (ML) model using the federated learning (FL) architecture. Unlike the existing studies on FL for wireless that presume datasets are readily available for training, we propose a novel architecture that directly integrates wireless dataset generation, which involves capturing I/Q samples from over-the-air signals in a multi-cell environment, into the FL training process. Secondly, in the collaborative spectrum inference stage, we propose a collaborative spectrum fusion strategy that is compatible with the unmanned aircraft system traffic management (UTM) ecosystem. Finally, in the spectrum scheduling stage, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users. To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base-station~(BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary users channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices., Comment: This is a preprint version submitted to IEEE Transactions on Machine learning in Communications and Networking. arXiv admin note: text overlap with arXiv:2308.05036

Due to advances in observation and imaging technologies, modern astronomical satellites generate large volumes of data. This necessitates efficient onboard data processing and high-speed data downlink. Reflecting this trend is the VERTECS 6U Astronomical Nanosatellite. Designed for the observation of Extragalactic Background Light (EBL), this mission is expected to generate a substantial amount of image data, particularly within the confines of CubeSat capabilities. This paper introduces the VERTECS Camera Control Board (CCB), an open-source payload interface board leveraging Commercial Off-The-Shelf (COTS) components, with a Raspberry Pi Compute Module 4 at its core. The VERTECS CCB hardware and software have been designed from the ground up to serve as the sole interface between the VERTECS bus system and astronomical imaging payload, while providing compute capability not usually seen in nanosatellites of this class. Responsible for mission data processing, it will facilitate high-speed data transfer from the imaging payload via gigabit Ethernet, while also providing a high-bitrate serial connection to the payload X-band transmitter for mission data downlink. Additional interfaces for secondary payloads are provided via USB-C and standard 15-pin camera connectors. The Raspberry Pi embedded within the VERTECS CCB operates on a standard Linux distribution, streamlining the software development process. Beyond addressing the current mission's payload control and data handling requirements, the CCB sets the stage for future missions with heightened data demands. Furthermore, it supports the adoption of machine learning and other compute-intensive applications in orbit. This paper delves into the development of the VERTECS CCB, offering insights into the design and validation of this next-generation payload interface, to ensure that it can survive the rigors of space flight., Comment: 10 pages, to be presented at SPIE Software and Cyberinfrastructure for Astronomy VIII

We introduce a Monte Carlo method for computing derivatives of the solution to a partial differential equation (PDE) with respect to problem parameters (such as domain geometry or boundary conditions). Derivatives can be evaluated at arbitrary points, without performing a global solve or constructing a volumetric grid or mesh. The method is hence well suited to inverse problems with complex geometry, such as PDE-constrained shape optimization. Like other walk on spheres (WoS) algorithms, our method is trivial to parallelize, and is agnostic to boundary representation (meshes, splines, implicit surfaces, etc.), supporting large topological changes. We focus in particular on screened Poisson equations, which model diverse problems from scientific and geometric computing. As in differentiable rendering, we jointly estimate derivatives with respect to all parameters -- hence, cost does not grow significantly with parameter count. In practice, even noisy derivative estimates exhibit fast, stable convergence for stochastic gradient-based optimization, as we show through examples from thermal design, shape from diffusion, and computer graphics., Comment: 18 pages, includes demo video of results. Project page https://imaging.cs.cmu.edu/differential_walk_on_spheres

Privacy is a human right. It ensures that individuals are free to engage in discussions, participate in groups, and form relationships online or offline without fear of their data being inappropriately harvested, analyzed, or otherwise used to harm them. Preserving privacy has emerged as a critical factor in research, particularly in the computational social science (CSS), artificial intelligence (AI) and data science domains, given their reliance on individuals' data for novel insights. The increasing use of advanced computational models stands to exacerbate privacy concerns because, if inappropriately used, they can quickly infringe privacy rights and lead to adverse effects for individuals -- especially vulnerable groups -- and society. We have already witnessed a host of privacy issues emerge with the advent of large language models (LLMs), such as ChatGPT, which further demonstrate the importance of embedding privacy from the start. This article contributes to the field by discussing the role of privacy and the issues that researchers working in CSS, AI, data science and related domains are likely to face. It then presents several key considerations for researchers to ensure participant privacy is best preserved in their research design, data collection and use, analysis, and dissemination of research results., Comment: International Association for the Advancement of Artificial Intelligence (AAAI) Conference on Web and Social Media (ICWSM) Workshops (Disrupt, Ally, Resist, Embrace (DARE) Workshop), 2024

Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike 'fixed' automotive radar, commercially available spinning radar systems typically do not produce radial velocities due to the lack of repeated measurements in the same direction and the fundamental hardware setup. To make these radial velocities observable, we modified the firmware of a commercial spinning radar to use triangular frequency modulation. In this paper, we develop a novel way to use this modulation to extract radial Doppler velocity measurements from consecutive azimuths of a radar intensity scan, without any data association. We show that these noisy, error-prone measurements contain enough information to provide good ego-velocity estimates, and incorporate these estimates into different modern odometry pipelines. We extensively evaluate the pipelines on over 110 km of driving data in progressively more geometrically challenging autonomous-driving environments. We show that Doppler velocity measurements improve odometry in well-defined geometric conditions and enable it to continue functioning even in severely geometrically degenerate environments, such as long tunnels., Comment: 8 pages, 7 figures, 2 tables, submitted to Robotics and Automation Letters (RA-L)

We consider a random growth model based on the IDLA protocol with sourcesin a hyperplane of $Z^d$ . We provide a stabilization result and a shape theoremgeneralizing [7] in any dimension by introducing new techniques leading to a roughglobal upper bound.

In this technical report, we compare treating an IMU as an input to a motion model against treating it as a measurement of the state in a continuous-time state estimation framework. Treating IMU measurements as inputs to a motion model and then preintegrating these measurements has almost become a de-facto standard in many robotics applications. However, this approach has a few shortcomings. First, it conflates the IMU measurement noise with the underlying process noise. Second, it is unclear how the state will be propagated in the case of IMU measurement dropout. Third, it does not lend itself well to dealing with multiple high-rate sensors such as a lidar and an IMU or multiple IMUs. In this work, we methodically compare the performance of these two approaches on a 1D simulation and show that they perform identically, assuming that each method's hyperparameters have been tuned on a training set. We show how to preintegrate heterogeneous factors using Gaussian process interpolation. We also provide results for our continuous-time lidar-inertial odometry in simulation and on the Newer College Dataset. Code for our lidar-inertial odometry can be found at: https://github.com/utiasASRL/steam_icp, Comment: Submitted to Robotica March 9th, 2024

FeSi is a curious example of a $d$-electron system that manifests many of the same phenomena associated with $f$-electron Kondo insulators, including conducting surface states with potentially non-trivial topology. Here we investigate the magnetization and magnetotransport of these surface states and how a 2D ferromagnetic state at the surface of FeSi influences the surface conductivity. We confirm the 2D ferromagnetism via a systematic study of magnetization on groups of filtered fragments with increasing surface area-to-volume ratios, identifying characteristic temperatures and magnetic fields associated with the ordered state. The paramagnetic to ferromagnetic transition appears broadened, suggesting disorder, which allows spin fluctuations to manifest up to at least 9 T at 2 K. This highlights the need to understand the relation between the disorder of the 2D ferromagnetism and the surface conductivity in FeSi., Comment: 8 pages, 5 figures