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In Hele-Shaw cells, pressure-driven viscous fluid motion between two closely-spaced plates gives rise to a two-dimensional potential flow with zero circulation. Here, we show how the introduction of electromagnetic effects enables the realization of potential flows with circulation. We present canonical Hele-Shaw experiments with circulation prescribed by the electromagnetic configuration, and rationalize the observed flows theoretically. We also draw an analogy between this new class of circulatory potential flows and a class of electrostatic systems.

Previous work has shown that training "helpful-only" LLMs with reinforcement learning on a curriculum of gameable environments can lead models to generalize to egregious specification gaming, such as editing their own reward function or modifying task checklists to appear more successful. We show that gpt-4o, gpt-4o-mini, o1-preview, and o1-mini - frontier models trained to be helpful, harmless, and honest - can engage in specification gaming without training on a curriculum of tasks, purely from in-context iterative reflection (which we call in-context reinforcement learning, "ICRL"). We also show that using ICRL to generate highly-rewarded outputs for expert iteration (compared to the standard expert iteration reinforcement learning algorithm) may increase gpt-4o-mini's propensity to learn specification-gaming policies, generalizing (in very rare cases) to the most egregious strategy where gpt-4o-mini edits its own reward function. Our results point toward the strong ability of in-context reflection to discover rare specification-gaming strategies that models might not exhibit zero-shot or with normal training, highlighting the need for caution when relying on alignment of LLMs in zero-shot settings., Comment: 20 pages, 9 figures

The research builds and evaluates the adversarial potential to introduce copied code or hallucinated AI recommendations for malicious code in popular code repositories. While foundational large language models (LLMs) from OpenAI, Google, and Anthropic guard against both harmful behaviors and toxic strings, previous work on math solutions that embed harmful prompts demonstrate that the guardrails may differ between expert contexts. These loopholes would appear in mixture of expert's models when the context of the question changes and may offer fewer malicious training examples to filter toxic comments or recommended offensive actions. The present work demonstrates that foundational models may refuse to propose destructive actions correctly when prompted overtly but may unfortunately drop their guard when presented with a sudden change of context, like solving a computer programming challenge. We show empirical examples with trojan-hosting repositories like GitHub, NPM, NuGet, and popular content delivery networks (CDN) like jsDelivr which amplify the attack surface. In the LLM's directives to be helpful, example recommendations propose application programming interface (API) endpoints which a determined domain-squatter could acquire and setup attack mobile infrastructure that triggers from the naively copied code. We compare this attack to previous work on context-shifting and contrast the attack surface as a novel version of "living off the land" attacks in the malware literature. In the latter case, foundational language models can hijack otherwise innocent user prompts to recommend actions that violate their owners' safety policies when posed directly without the accompanying coding support request.

In recent experiments, Sawaguchi et al. directly probed the lubrication layer of air beneath a droplet levitating inside a rotating cylindrical drum. For small rotation rates of the drum, the lubrication film beneath the drop adopted a steady shape, while at higher rotation rates, travelling waves propagated along the drop's lower surface with roughly half the wall velocity. We here rationalize the physical origin of these waves. We begin with a simplified model of the lubrication flow beneath the droplet, and examine the linear stability of this base state to perturbations of the Tollmien--Schlichting type. Our developments lead to the Orr-Sommerfeld equation (OSE), whose eigenvalues give the growth rates and phase speeds of the perturbations. By considering wavelengths long relative to the lubrication film thickness, we solve the OSE perturbatively and so deduce the wavelength and phase velocity of the most unstable mode. We find satisfactory agreement between experiment and theory over the parameter regime considered in the laboratory.

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Those analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions are idealizations that may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks: after fitting our fiducial model to real data, we generate a population of simulated data-set replications, and use them to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients assume extreme values for the real data when compared to the replications. We confirm that the NANOGrav 15 yr data set is consistent with power-law and Hellings--Downs assumptions. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2\ $\sigma$ and 3\ $\sigma$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background as reported by NANOGrav., Comment: 20 pages, 14 Figures

Neutron stars and white dwarfs are both dense remnants of post-main-sequence stars. Pulsars, magnetars and strongly magnetised white dwarfs have all been seen to been observed to exhibit coherent, pulsed radio emission in relation to their rotational period. Recently, a new type of radio long period transient (LPT) has been discovered. The bright radio emission of LPTs resembles that of radio pulsars and magnetars. However, they pulse on timescales (minutes) much longer than previously seen. While minute timescales are common rotation periods for white dwarfs, LPTs are much brighter than the known pulsating white dwarfs, and dipolar radiation from isolated (as opposed to binary) magnetic white dwarfs has yet to be observed. Here, we report the discovery of a new $\sim$421~s LPT, CHIME J0630+25, using the CHIME/FRB and CHIME/Pulsar instruments. We used standard pulsar timing techniques and obtained a phase-coherent timing solution which yielded limits on the inferred magnetic field and characteristic age. CHIME J0630+25 is remarkably nearby ($170 \pm 80$~pc), making it the closest LPT discovered to date., Comment: Submitted

Collective movement inspired by animal groups promises inherited benefits for robot swarms, such as enhanced sensing and efficiency. However, while animals move in groups using only their local senses, robots often obey central control or use direct communication, introducing systemic weaknesses to the swarm. In the hope of addressing such vulnerabilities, developing bio-inspired decentralized swarms has been a major focus in recent decades. Yet, creating robots that move efficiently together using only local sensory information remains an extraordinary challenge. In this work, we present a decentralized, purely vision-based swarm of terrestrial robots. Within this novel framework robots achieve collisionless, polarized motion exclusively through minimal visual interactions, computing everything on board based on their individual camera streams, making central processing or direct communication obsolete. With agent-based simulations, we further show that using this model, even with a strictly limited field of view and within confined spaces, ordered group motion can emerge, while also highlighting key limitations. Our results offer a multitude of practical applications from hybrid societies coordinating collective movement without any common communication protocol, to advanced, decentralized vision-based robot swarms capable of diverse tasks in ever-changing environments.

Algorithmic fairness has emerged as a critical concern in artificial intelligence (AI) research. However, the development of fair AI systems is not an objective process. Fairness is an inherently subjective concept, shaped by the values, experiences, and identities of those involved in research and development. To better understand the norms and values embedded in current fairness research, we conduct a meta-analysis of algorithmic fairness papers from two leading conferences on AI fairness and ethics, AIES and FAccT, covering a final sample of 139 papers over the period from 2018 to 2022. Our investigation reveals two concerning trends: first, a US-centric perspective dominates throughout fairness research; and second, fairness studies exhibit a widespread reliance on binary codifications of human identity (e.g., "Black/White", "male/female"). These findings highlight how current research often overlooks the complexities of identity and lived experiences, ultimately failing to represent diverse global contexts when defining algorithmic bias and fairness. We discuss the limitations of these research design choices and offer recommendations for fostering more inclusive and representative approaches to fairness in AI systems, urging a paradigm shift that embraces nuanced, global understandings of human identity and values.

We define reduced and essential Banach algebras associated to a twisted \'{e}tale (not necessarily Hausdorff) groupoid $(\mathcal{G},\mathcal{L})$ and extend some fundamental results from $C^*$-algebras to this context. For instance, we prove that for topologically free and minimal groupoids every essential Banach algebra is simple, and we give conditions under which reduced algebras are essential (for example Hausdorffness of $\mathcal{G}$ is sufficient). This in particular solves the simplicity problem posed recently by Gardella-Lupini for $L^p$-operator algebras associated to $\mathcal{G}$. In addition, using either the $n$-filling or locally contracting condition we give pure infiniteness criteria for essential simple Banach algebras associated to $(\mathcal{G},\mathcal{L})$. This extends the corresponding $C^*$-algebraic results that were previously known to hold in the untwisted Hausdorff case. The results work nicely, and allow for characterisation of the generalized intersection property, in the realm of $L^P$-operator algebras where $P \subseteq [1,\infty]$ is a non-empty set of parameters. Such algebras cover in particular $L^p$-operator algebras, for $p\in [1,\infty]$, and their Banach $*$-algebra versions. We apply our results to Banach algebra crossed products by twisted partial group actions, Roe-type Banach algebras with coefficients in finite-rank operators on a Banach space, twisted tight $L^P$-operator algebras of inverse semigroups, graph $L^P$-operator algebras, and algebras associated to self-similar group actions on graphs. We also interpret our results in terms of twisted inverse semigroup actions and their crossed products., Comment: Comments welcome

Studying transient phenomena, such as individual pulses from pulsars, has garnered considerable attention in the era of astronomical big data. Of specific interest to this study are Rotating Radio Transients (RRATs), nulling, and intermittent pulsars. This study introduces a new algorithm named LuNfit, tailored to correct the selection biases originating from the telescope and detection pipelines. Ultimately LuNfit estimates the intrinsic luminosity distribution and nulling fraction of the single pulses emitted by pulsars. LuNfit relies on Bayesian nested sampling so that the parameter space can be fully explored. Bayesian nested sampling also provides the additional benefit of simplifying model comparisons through the Bayes ratio. The robustness of LuNfit is shown through simulations and applying LuNfit onto pulsars with known nulling fractions. LuNfit is then applied to three RRATs, J0012+5431, J1538+1523, and J2355+1523, extracting their intrinsic luminosity distribution and burst rates. We find that their nulling fraction is 0.4(2), 0.749(5) and 0.995(2) respectively. We further find that a log-normal distribution likely describes the single pulse luminosity distribution of J0012+5431 and J1538+1523, while the Bayes ratio for J2355+1523 slightly favors an exponential distribution. We show the conventional method of correcting selection effects by "scaling up" the missed fraction of radio transients can be unreliable when the mean luminosity of the source is faint relative to the telescope sensitivity. Finally, we discuss the limitations of the current implementation of LuNfit while also delving into potential enhancements that would enable LuNfit to be applied to sources with complex pulse morphologies., Comment: Accepted by ApJ

We extend the theory of Fourier--Stieltjes algebras to the category of twisted actions by \'etale groupoids on arbitrary C*-bundles, generalizing theories constructed previously by B\'{e}dos and Conti for twisted group actions on unital C*-algebras, and by Renault and others for groupoid C*-algebras, in each case motivated by the classical theory of Fourier--Stieltjes algebras of discrete groups. To this end we develop a toolbox including, among other things, a theory of multiplier C*-correspondences, multiplier C*-correspondence bundles, Busby--Smith twisted groupoid actions, and the associated crossed products, equivariant representations and Fell's absorption theorems. For a fixed \'etale groupoid $G$ a Fourier--Stieltjes multiplier is a family of maps acting on fibers, arising from an equivariant representation. It corresponds to a certain fiber-preserving strict completely bounded map between twisted full (or reduced) crossed products. We establish a KSGNS-type dilation result which shows that the correspondence above restricts to a bijection between positive-definite multipliers and a particular class of completely positive maps. Further we introduce a subclass of Fourier multipliers, that enjoys a natural absorption property with respect to Fourier--Stieltjes multipliers and gives rise to `reduced to full' multiplier maps on crossed products. Finally we provide several applications of the theory developed, for example to the approximation properties, such as weak containment or nuclearity, of the crossed products and actions in question, and discuss outstanding open problems., Comment: 76 pages

Pulsar timing arrays (PTAs) are designed to detect low-frequency gravitational waves (GWs). GWs induce achromatic signals in PTA data, meaning that the timing delays do not depend on radio-frequency. However, pulse arrival times are also affected by radio-frequency dependent "chromatic" noise from sources such as dispersion measure (DM) and scattering delay variations. Furthermore, the characterization of GW signals may be influenced by the choice of chromatic noise model for each pulsar. To better understand this effect, we assess if and how different chromatic noise models affect achromatic noise properties in each pulsar. The models we compare include existing DM models used by NANOGrav and noise models used for the European PTA Data Release 2 (EPTA DR2). We perform this comparison using a subsample of six pulsars from the NANOGrav 15 yr data set, selecting the same six pulsars as from the EPTA DR2 six-pulsar dataset. We find that the choice of chromatic noise model noticeably affects the achromatic noise properties of several pulsars. This is most dramatic for PSR J1713+0747, where the amplitude of its achromatic red noise lowers from $\log_{10}A_{\text{RN}} = -14.1^{+0.1}_{-0.1}$ to $-14.7^{+0.3}_{-0.5}$, and the spectral index broadens from $\gamma_{\text{RN}} = 2.6^{+0.5}_{-0.4}$ to $\gamma_{\text{RN}} = 3.5^{+1.2}_{-0.9}$. We also compare each pulsar's noise properties with those inferred from the EPTA DR2, using the same models. From the discrepancies, we identify potential areas where the noise models could be improved. These results highlight the potential for custom chromatic noise models to improve PTA sensitivity to GWs.

Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context. Given the success of contemporary learning algorithms, we argue that the bottleneck in artificial intelligence (AI) progress is shifting from data assimilation to novel data generation. We bring together evidence showing that natural intelligence emerges at multiple scales in networks of interacting agents via collective living, social relationships and major evolutionary transitions, which contribute to novel data generation through mechanisms such as population pressures, arms races, Machiavellian selection, social learning and cumulative culture. Many breakthroughs in AI exploit some of these processes, from multi-agent structures enabling algorithms to master complex games like Capture-The-Flag and StarCraft II, to strategic communication in Diplomacy and the shaping of AI data streams by other AIs. Moving beyond a solipsistic view of agency to integrate these mechanisms suggests a path to human-like compounding innovation through ongoing novel data generation., Comment: 17 pages, 2 figures, 1 box

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficulties with verbalising pedagogical intuitions into gen AI prompts and the lack of good evaluation practices, reinforced by the challenges in defining excellent pedagogy. Here we present our work collaborating with learners and educators to translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks, spanning quantitative, qualitative, automatic and human evaluations; and to develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini, introducing LearnLM-Tutor. Our evaluations show that LearnLM-Tutor is consistently preferred over a prompt tuned Gemini by educators and learners on a number of pedagogical dimensions. We hope that this work can serve as a first step towards developing a comprehensive educational evaluation framework, and that this can enable rapid progress within the AI and EdTech communities towards maximising the positive impact of gen AI in education.

Pulsar Timing Array (PTA) collaborations recently reported evidence for the presence of a gravitational wave background (GWB) in their datasets. The main candidate that is expected to produce such a GWB is the population of supermassive black hole binaries (SMBHB). Some analyses showed that the recovered signal may exhibit time-dependent properties, i.e. non-stationarity. In this paper, we propose an approximated non-stationary Gaussian process (GP) model obtained from the perturbation of stationary processes. The presented method is applied to the second data release of the European pulsar timing array to search for non-stationary features in the GWB. We analyzed the data in different time slices and showed that the inferred properties of the GWB evolve with time. We find no evidence for such non-stationary behavior and the Bayes factor in favor of the latter is $\mathcal{B}^{NS}_{S} = 1.5$. We argue that the evolution of the GWB properties most likely comes from the \mf{improvement of the observation cadence} with time and \mf{better} characterization of the noise of individual pulsars. Such non-stationary GWB could also be produced by the leakage of non-stationary features in the noise of individual pulsars or by the presence of an eccentric single source., Comment: 14 pages, 14 figures

The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays through excursions from, and breaks in, the expected $f_{\mathrm{GW}}^{-2/3}$ power-law of the GWB strain spectrum. To do this, we create a semi-analytic SMBHB population model, fit to NANOGrav's 15 yr GWB amplitude, and with 1,000 realizations we study the populations' characteristic strain and residual spectra. Comparing our models to the NANOGrav 15 yr spectrum, we find two interesting excursions from the power-law. The first, at $2 \; \mathrm{nHz}$, is below our GWB realizations with $p$-value significance $p = 0.05$ to $0.06$ ($\approx 1.8 \sigma - 1.9 \sigma$). The second, at $16 \; \mathrm{nHz}$, is above our GWB realizations with $p = 0.04$ to $0.15$ ($\approx 1.4 \sigma - 2.1 \sigma$). We explore the properties of a loud SMBHB which could cause such an excursion. Our simulations also show that the expected number of SMBHBs decreases by three orders of magnitude, from $\sim 10^6$ to $\sim 10^3$, between $2\; \mathrm{nHz}$ and $20 \; \mathrm{nHz}$. This causes a break in the strain spectrum as the stochasticity of the background breaks down at $26^{+28}_{-19} \; \mathrm{nHz}$, consistent with predictions pre-dating GWB measurements. The diminished GWB signal from SMBHBs at frequencies above the $26$~nHz break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early universe., Comment: 10 pages, 8 figures, 1 appendix, submitted to ApJ

Consider the motion of a thin layer of electrically conducting fluid, between two closely spaced parallel plates, in a classical Hele-Shaw geometry. Furthermore, let the system be immersed in a uniform external magnetic field (normal to the plates) and let electrical current be driven between conducting probes immersed in the fluid layer. In the present paper, we analyse the ensuing fluid flow at low Hartmann numbers. We first elucidate the mechanism of flow generation both physically and mathematically. We proceed by presenting mathematical solutions for a class of canonical multiply-connected geometries, in terms of the prime function developed by Crowdy (2020). Notably, those solutions can be written explicitly as series, and are thus exact, in doubly-connected geometries. Note that in higher connectivities, the prime function must be evaluated numerically. We then demonstrate how recently developed fast numerical methods may be applied to accurately determine the flow-field in arbitrary geometries when exact solutions are inaccessible.

The widespread adoption of voice-activated systems has modified routine human-machine interaction but has also introduced new vulnerabilities. This paper investigates the susceptibility of automatic speech recognition (ASR) algorithms in these systems to interference from near-ultrasonic noise. Building upon prior research that demonstrated the ability of near-ultrasonic frequencies (16 kHz - 22 kHz) to exploit the inherent properties of microelectromechanical systems (MEMS) microphones, our study explores alternative privacy enforcement means using this interference phenomenon. We expose a critical vulnerability in the most common microphones used in modern voice-activated devices, which inadvertently demodulate near-ultrasonic frequencies into the audible spectrum, disrupting the ASR process. Through a systematic analysis of the impact of near-ultrasonic noise on various ASR systems, we demonstrate that this vulnerability is consistent across different devices and under varying conditions, such as broadcast distance and specific phoneme structures. Our findings highlight the need to develop robust countermeasures to protect voice-activated systems from malicious exploitation of this vulnerability. Furthermore, we explore the potential applications of this phenomenon in enhancing privacy by disrupting unauthorized audio recording or eavesdropping. This research underscores the importance of a comprehensive approach to securing voice-activated systems, combining technological innovation, responsible development practices, and informed policy decisions to ensure the privacy and security of users in an increasingly connected world.

This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) Software Foundation's (HSF) Analysis Facilities forum, established in March 2022, the Analysis Ecosystems II workshop, that took place in May 2022, and the WLCG/HSF pre-CHEP workshop, that took place in May 2023. The paper attempts to cover all the aspects of an analysis facility.

Lienhard (2019) reported that the shape factor of the interior of a simply-connected region ($\Omega$) is equal to that of its exterior ($\mathbb{R}^2\backslash\Omega$) under the same boundary conditions. In that study, numerical examples supported the claim in particular cases; for example, it was shown that for certain boundary conditions on circles and squares, the conjecture holds. In the present paper, we show that the conjecture is not generally true, unless some additional condition is met. We proceed by elucidating why the conjecture does in fact hold in all of the examples analysed by Lienhard. We thus deduce a simple criterion which, when satisfied, ensures the equality of interior and exterior shape factors in general. Our criterion notably relies on a beautiful and little-known symmetry method due to Hersch (1982) which we introduce in a tutorial manner.

Researchers and developers increasingly rely on toxicity scoring to moderate generative language model outputs, in settings such as customer service, information retrieval, and content generation. However, toxicity scoring may render pertinent information inaccessible, rigidify or "value-lock" cultural norms, and prevent language reclamation processes, particularly for marginalized people. In this work, we extend the concept of algorithmic recourse to generative language models: we provide users a novel mechanism to achieve their desired prediction by dynamically setting thresholds for toxicity filtering. Users thereby exercise increased agency relative to interactions with the baseline system. A pilot study ($n = 30$) supports the potential of our proposed recourse mechanism, indicating improvements in usability compared to fixed-threshold toxicity-filtering of model outputs. Future work should explore the intersection of toxicity scoring, model controllability, user agency, and language reclamation processes -- particularly with regard to the bias that many communities encounter when interacting with generative language models., Comment: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA 2024)

We analyze measurements of the thermal Sunyaev-Zeldovich (tSZ) effect arising in the circumgalactic medium (CGM) of $L^*$ galaxies, reported by Bregman et al. 2022 and Das et al. 2023. In our analysis we use the Faerman et al. 2017 and Faerman et al. 2020 CGM models, a new power-law model (PLM), and the TNG100 simulation. For a given $M_{\rm vir}$, our PLM has four parameters; the fraction, $f_{\rm hCGM}$, of the halo baryon mass in hot CGM gas, the ratio, $\phi_T$, of the actual gas temperature at the virial radius to the virial temperature, and the power-law indicies, $a_{P,{\rm th}}$ and $a_n$ for the thermal electron pressure and the hydrogen nucleon density. The B+22 Compton-$y$ profile implies steep electron pressure slopes ($a_{P,{\rm th}}\simeq 2$). For isothermal conditions the temperature is at least $1.1\times 10^6$ K, with a hot CGM gas mass of up to $3.5\times 10^{11}$ M$_\odot$ for a virial mass of $2.75\times 10^{12}$ M$_\odot$. However, if isothermal the gas must be expanding out of the halos. An isentropic equation of state is favored for which hydrostatic equilibrium is possible. The B+22 and D+23 results are consistent with each other and with recent (0.5-2 keV) CGM X-ray observations by Zhang et al. 2024 of Milky Way mass systems. For $M_{\rm vir}\simeq 3\times 10^{12}$ M$_\odot$, the scaled Compton pressure integrals, $E(z)^{-2/3}Y_{500}/M_{\rm vir,12}^{5/3}$, lie in the narrow range, $2.5\times 10^{-4}$ to $5.0\times 10^{-4}$ kpc$^2$, for all three sets of observations. TNG100 underpredicts the tSZ parameters by factors $\sim 0.5$ dex for the $L^*$ galaxies, suggesting that the feedback strengths and CGM gas losses are overestimated in the simulated halos at these mass scales., Comment: Accepted for publication in the Astrophysical Journal. 29 pages, 17 figures

Diffuse Correlation Spectroscopy (DCS) allows the label-free investigation of microvascular dynamics deep within living tissue. However, common implementations of DCS are currently limited to measurement depths of $\sim 1-1.5cm$, which can limit the accuracy of cerebral hemodynamics measurement. Here we present massively parallelized DCS (pDCS) using novel single photon avalanche detector (SPAD) arrays with up to 500x500 individual channels. The new SPAD array technology can boost the signal-to-noise ratio by a factor of up to 500 compared to single-pixel DCS, or by more than 15-fold compared to the most recent state-of-the-art pDCS demonstrations. Our results demonstrate the first in vivo use of this massively parallelized DCS system to measure cerebral blood flow changes at $\sim 2cm$ depth in human adults. We compared different modes of operation and applied a dual detection strategy, where a secondary SPAD array is used to simultaneously assess the superficial blood flow as a built-in reference measurement. While the blood flow in the superficial scalp tissue showed no significant change during cognitive activation, the deep pDCS measurement showed a statistically significant increase in the derived blood flow index of 8-12% when compared to the control rest state.

Multi-machine empirical scaling predicts an extremely narrow heat exhaust layer in future high magnetic field tokamaks, producing high power densities that require mitigation. In the experiments presented, the width of this exhaust layer is nearly doubled using actuators to increase turbulent transport in the plasma edge. This is achieved in low collisionality, high confinement edge pedestals with their gradients limited by turbulent transport instead of large-scale, coherent instabilities. The exhaust heat flux profile width and divertor leg diffusive spreading both double as a high frequency band of turbulent fluctuations propagating in the electron diamagnetic direction doubles in amplitude. The results are quantitatively reproduced in electromagnetic XGC particle-in-cell simulations which show the heat flux carried by electrons emerges to broaden the heat flux profile, directly supported by Langmuir probe measurements., Comment: 8 pages, 11 figures

This investigation reveals a novel exploit derived from PNG image file formats, specifically their alpha transparency layer, and its potential to fool multiple AI vision systems. Our method uses this alpha layer as a clandestine channel invisible to human observers but fully actionable by AI image processors. The scope tested for the vulnerability spans representative vision systems from Apple, Microsoft, Google, Salesforce, Nvidia, and Facebook, highlighting the attack's potential breadth. This vulnerability challenges the security protocols of existing and fielded vision systems, from medical imaging to autonomous driving technologies. Our experiments demonstrate that the affected systems, which rely on convolutional neural networks or the latest multimodal language models, cannot quickly mitigate these vulnerabilities through simple patches or updates. Instead, they require retraining and architectural changes, indicating a persistent hole in multimodal technologies without some future adversarial hardening against such vision-language exploits.

We performed near-daily observations on the binary pulsars PSR J0218+4232, PSR J1518+4904 and PSR J2023+2853 with the Canadian Hydrogen Intensity Mapping Experiment (CHIME). For the first time, we detected the Shapiro time delay in all three pulsar-binary systems, using only 2--4 years of CHIME/Pulsar timing data. We measured the pulsar masses to be $1.49^{+0.23}_{-0.20}$ M$_\odot$, $1.470^{+0.030}_{-0.034}$ M$_\odot$ and $1.50^{+0.49}_{-0.38}$ M$_\odot$ respectively. The companion mass to PSR J0218+4232 was found to be $0.179^{+0.018}_{-0.016}$ M$_\odot$. We constrained the mass of the neutron-star companion of PSR J1518+4904 to be $1.248^{+0.035}_{-0.029}$ M$_\odot$, using the observed apsidal motion as a constraint on mass estimation. The binary companion to PSR J2023+2853 was found to have a mass of $0.93^{+0.17}_{-0.14}$ M$_\odot$; in the context of the near-circular orbit, this mass estimate suggests that the companion to PSR J2023+2853 is likely a high-mass white dwarf. By comparing the timing model obtained for PSR J0218+4232 with previous observations, we found a significant change in the observed orbital period of the system of $\dot{P_{\rm b}} = 0.14(2) \times 10^{-12}$; we determined that this variation arises from ``Shklovskii acceleration" due to relative motion of the binary system, and used this measurement to estimate a distance of $d=(6.7 \pm 1.0)$ kpc to PSR J0218+4232. This work demonstrates the capability of high-cadence observations, enabled by the CHIME/Pulsar system, to detect and refine general-relativistic effects of binary pulsars over short observing timescales., Comment: 14 pages, 4 figures, accepted for publication in ApJ

This paper investigates a novel algorithmic vulnerability when imperceptible image layers confound multiple vision models into arbitrary label assignments and captions. We explore image preprocessing methods to introduce stealth transparency, which triggers AI misinterpretation of what the human eye perceives. The research compiles a broad attack surface to investigate the consequences ranging from traditional watermarking, steganography, and background-foreground miscues. We demonstrate dataset poisoning using the attack to mislabel a collection of grayscale landscapes and logos using either a single attack layer or randomly selected poisoning classes. For example, a military tank to the human eye is a mislabeled bridge to object classifiers based on convolutional networks (YOLO, etc.) and vision transformers (ViT, GPT-Vision, etc.). A notable attack limitation stems from its dependency on the background (hidden) layer in grayscale as a rough match to the transparent foreground image that the human eye perceives. This dependency limits the practical success rate without manual tuning and exposes the hidden layers when placed on the opposite display theme (e.g., light background, light transparent foreground visible, works best against a light theme image viewer or browser). The stealth transparency confounds established vision systems, including evading facial recognition and surveillance, digital watermarking, content filtering, dataset curating, automotive and drone autonomy, forensic evidence tampering, and retail product misclassifying. This method stands in contrast to traditional adversarial attacks that typically focus on modifying pixel values in ways that are either slightly perceptible or entirely imperceptible for both humans and machines.

Human participants play a central role in the development of modern artificial intelligence (AI) technology, in psychological science, and in user research. Recent advances in generative AI have attracted growing interest to the possibility of replacing human participants in these domains with AI surrogates. We survey several such "substitution proposals" to better understand the arguments for and against substituting human participants with modern generative AI. Our scoping review indicates that the recent wave of these proposals is motivated by goals such as reducing the costs of research and development work and increasing the diversity of collected data. However, these proposals ignore and ultimately conflict with foundational values of work with human participants: representation, inclusion, and understanding. This paper critically examines the principles and goals underlying human participation to help chart out paths for future work that truly centers and empowers participants., Comment: Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2024)