Search

We report upgraded Giant Metrewave radio telescope and Karl J. Jansky Very Large Array radio observations of a low-mass merging galaxy cluster PSZ2 G181.06+48.47. This exceptional galaxy cluster hosts two megaparsec-scale diffuse sources, symmetrically located with respect to the cluster center and separated by about 2.6 Mpc. We detect these low surface brightness sources in our new high-frequency observations (0.3-2 GHz) and classify them as radio relics associated with merger-driven shock fronts. The southwest relic exhibits an inverted morphology and shows evidence of spectral steepening in the post-shock region, potentially tracing a high Mach number shock ($\sim 4$) under the framework of diffusive shock acceleration. The northeast relic is found to be highly polarized with a 22% average polarization fraction at 1.5 GHz and aligned magnetic field vectors. Its spectral and polarization properties, along with the presence of a nearby tailed galaxy, support re-acceleration scenarios. The merger axis defined by the two relics is tilted by $\sim 45$ degree with respect to the plane of the sky, which implies an unprecedented projected separation of $\sim 3.5$ Mpc. We also detect a possible faint radio halo, suggesting weak turbulence in the central cluster region. We conclude that the faint double relics can be best explained by two outward moving shock waves in which particles are (re-)accelerated and that the cluster is in an evolved merger state. PSZ2 G181.06+48.47 presents a unique opportunity to investigate particle acceleration in low mass systems characterized by large relic separations., Comment: 23 pages, 11 figures, Submitted to ApJ

Relics are diffuse, highly-polarized radio sources that trace merger-driven shocks at the periphery of merging galaxy clusters. The LOFAR survey recently discovered a rare example of double relics in the low-mass cluster PSZ2 G181.06+48.47. Through a detailed exploration of new Chandra and XMM-Newton observations, we reveal that PSZ2 G181.06+48.47 has a lower mass ($M_{500,X}=2.57^{+0.37}_{-0.38}\times10^{14}$ M$_{\odot}$) than previously thought. Despite its cool global temperature of $kT_{500}=3.86^{+0.27}_{-0.29}$ keV, PSZ2 G181.06+48.47 is one of the most disturbed clusters in the Planck sample, with a complex morphological and thermodynamic structure. We discover a set of three discontinuities within <500 kpc of the cluster center, and, from a surface brightness analysis, place $5\sigma$ upper limits of $M_{NE}<1.43$ and $M_{SW}<1.57$ for any shock associated with the relic locations. We also revise established scaling relations for double radio-relics by adding 12 new systems not included in previous work. The PSZ2 G181.06+48.47 relics have the widest separation (scaled for $r_{500}$) of all known double-relic systems. The exceptional distance from the cluster center ($>r_{200}$), indicates the relics may be associated with shocks in the ``run-away" phase. We propose that this late-stage, post-apocenter merger is captured as the two subclusters with a mass ratio of 1.2-1.4 fall back into each other. The outer relic shocks were likely produced at the first core passage, while the inner discontinuities are associated with the second infall., Comment: Submitted to ApJ on Jan 12, 2025. 29 pages, 11 figures. Companion papers discussing the radio properties and the weak-lensing reconstruction will appear on ArXiv in the next days

Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane's proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory., Comment: 94 pages

We introduce a categorical formalization of diffusion, motivated by data science and information dynamics. Central to our construction is the Lawvere Laplacian, an endofunctor on a product category indexed by a graph and enriched in a quantale. This framework permits the systematic study of diffusion processes on network sheaves taking values in a class of enriched categories.

We present new observations of the cosmic ultraviolet background (CUVB) at high Galactic latitudes ($|b| > 40^{\circ}$), made using the Alice UV spectrograph on board the New Horizons spacecraft. These observations were taken at about 57 AU from the Sun, outside much of the foreground emission affecting previous missions, and allowed a new determination of the spectrum of the CUVB between 912 -- 1100~\AA\ and 1400 -- 1800~\AA. We found a linear correlation between the CUVB and the Planck E(B~-~V) with offsets at zero-reddening of $221 \pm 11$ photon units at 1000~\AA\ and $264 \pm 24$ \photu\ at 1500~\AA\ ($4.4 \pm 0.2$ nW m$^{-2}$ sr$^{-1}$ at 1000~\AA\ and $5.3 \pm 0.5$ nW m$^{-2}$ sr$^{-1}$ at 1500~\AA). The former is the first firm detection of the offset in the range 912 -- 1100 \AA\ while the latter result confirms previous results from \galex, showing that there is little emission from the Solar System from 1400 -- 1800 \AA. About half of the offset may be explained by known sources (the integrated light of unresolved galaxies, unresolved stars, emission from ionized gas, and two-photon emission from warm hydrogen in the halo) with the source of the remaining emission as yet unidentified. There is no detectable emission below the Lyman limit with an upper limit of $3.2 \pm 3.0$ photon units., Comment: Accepted in AJ. 19 pages, 16 figures, 8 tables

Algebraic theories with dependency between sorts form the structural core of Martin-L\"of type theory and similar systems. Their denotational semantics are typically studied using categorical techniques; many different categorical structures have been introduced to model them (contextual categories, categories with families, display map categories, etc.) Comparisons of these models are scattered throughout the literature, and a detailed, big-picture analysis of their relationships has been lacking. We aim to provide a clear and comprehensive overview of the relationships between as many such models as possible. Specifically, we take *comprehension categories* as a unifying language and show how almost all established notions of model embed as sub-2-categories (usually full) of the 2-category of comprehension categories., Comment: 24 pages. Presented at APLAS 2024; this version lightly revised and expanded, numbering unchanged

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

We report the detection of the [O III] auroral line in 42 galaxies within the redshift range of $3 < z < 10$. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMALsurveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in the high-ionization regions of these galaxies range from $T_e$([O III]) = 12,000 to 24,000 K, consistent with temperatures observed in local metal-poor galaxies and previous JWST studies. In 10 galaxies, we also detect the [O II] auroral line, allowing us to determine electron temperatures in the low-ionization regions, which range between $T_e$([O II]) = 10,830 and 20,000 K. The direct-$T_e$-based metallicities of our sample span from 12 + log(O/H) = 7.2 to 8.4, indicating these high-redshift galaxies are relatively metal-poor. By combining our sample with 25 galaxies from the literature, we expand the dataset to a total of 67 galaxies within $3 < z < 10$, effectively more than doubling the previous sample size for direct-$T_e$ based metallicity studies. This larger dataset allow us to derive empirical metallicity calibration relations based exclusively on high-redshift galaxies, using six key line ratios: R3, R2, R23, Ne3O2, O32, and O3N2. Notably, we derive a novel metallicity calibration relation for the first time using high-redshift $T_e$-based metallicities: $\hat{R}$ = 0.18log $R2$ + 0.98log $R3$. This new calibration significantly reduces the scatter in high-redshift galaxies compared to the $\hat{R}$ relation previously calibrated for low-redshift galaxies., Comment: 23 pages, 10 figures, submitted to ApJ

We present new radio observations of the galaxy cluster merger CIZA J0107.7+5408 (CIZA0107), a large, roughly equal mass, post-core passage, dissociative binary system at z = 0.1066. CIZA0107 is an elongated, disturbed system, hosting two subclusters with optical galaxy number density peaks offset from their associated X-ray density peaks and double-peaked diffuse radio structure. We present new 240-470 MHz and 2.0-4.0 GHz Very Large Array observations of CIZA0107. We image the diffuse emission at high resolution, constrain its integrated spectrum, and map the spectral index distribution. We confirm the presence of steep-spectrum ($\alpha$ $\sim$ -1.3) emission on a scale of about 0.5 Mpc in both subclusters. We identify two smaller ultra-steep spectrum ($\alpha$ $<$ -2) regions, superimposed on larger-scale radio emission associated with the southwestern subcluster. At 340 MHz, we detect a radio edge bounding the emission to the south and show that it is coincident with a weak (M $\sim$ 1.2) shock identified in the Chandra image. At 3 GHz, the emission does not show any corresponding edge-like feature, and in fact it extends beyond the shock. We investigate the nature of the emission in CIZA0107 and find that, while the system may host a double halo structure, we cannot rule out a scenario in which the emission arises from two relics projected on the central cluster regions., Comment: 20 pages, 9 figures, 4 tables, ApJ submitted, comments welcome!

We say two spanning trees of a graph are completely independent if their edge sets are disjoint, and for each pair of vertices, the paths between them in each spanning tree do not have any other vertex in common. Pai and Chang constructed two such spanning trees in the hypercube $Q_n$ for sufficiently large $n$, while Kandekar and Mane recently showed there are $3$ pairwise completely independent spanning trees in hypercubes $Q_n$ for sufficiently large $n$. We prove that for each $k$, there exist $k$ completely independent spanning trees in $Q_n$ for sufficiently large $n$. In fact, we show that there are $(\frac{1}{12}+o(1))n$ spanning trees in $Q_n$, each with diameter $(2+o(1))n$. As the minimal diameter of any spanning tree of $Q_n$ is $2n-1$, this diameter is asymptotically optimal. We prove a similar result for the powers $H^n$ of any fixed graph $H$., Comment: 16 pages, 1 figure

Precision radial velocity (RV) spectrographs that use adaptive optics (AO) show promise to advance telescope observing capabilities beyond those of seeing-limited designs. We are building a spectrograph for the Large Binocular Telescope (LBT) named iLocater that uses AO to inject starlight directly into single mode fibers (SMF). iLocater's first acquisition camera system (the `SX' camera), which receives light from one of the 8.4m diameter primary mirrors of the LBT, was initially installed in summer 2019 and has since been used for several commissioning runs. We present results from first-light observations that include on-sky measurements as part of commissioning activities. Imaging measurements of the bright B3IV star 2 Cygni ($V=4.98$) resulted in the direct detection of a candidate companion star at an angular separation of only $\theta = 70$ mas. Follow-up AO measurements using Keck/NIRC2 recover the candidate companion in multiple filters. An $R\approx1500$ miniature spectrograph recently installed at the LBT named ``Lili'' provides spatially resolved spectra of each binary component, indicating similar spectral types and strengthening the case for companionship. Studying the multiplicity of young runaway star systems like 2 Cygni ($36.6 \pm 0.5$ Myr) can help to understand formation mechanisms for stars that exhibit anomalous velocities through the galaxy. This on-sky demonstration illustrates the spatial resolution of the iLocater SX acquisition camera working in tandem with the LBT AO system; it further derisks a number of technical hurdles involved in combining AO with Doppler spectroscopy., Comment: Accepted to AJ

Modeling late reverberation at interactive speeds is a challenging task when multiple sound sources and listeners are present in the same environment. This is especially problematic when the environment is geometrically complex and/or features uneven energy absorption (e.g. coupled volumes), because in such cases the late reverberation is dependent on the sound sources' and listeners' positions, and therefore must be adapted to their movements in real time. We present a novel approach to the task, named modal decomposition of Acoustic Radiance Transfer (MoD-ART), which can handle highly complex scenarios with efficiency. The approach is based on the geometrical acoustics method of Acoustic Radiance Transfer, from which we extract a set of energy decay modes and their positional relationships with sources and listeners. In this paper, we describe the physical and mathematical meaningfulness of MoD-ART, highlighting its advantages and applicability to different scenarios. Through an analysis of the method's computational complexity, we show that it compares very favourably with ray-tracing. We also present simulation results showing that MoD-ART can capture multiple decay slopes and flutter echoes.

Plasma shape is a significant factor that must be considered for any Fusion Pilot Plant (FPP) as it has significant consequences for plasma stability and core confinement. A new simulator, NSFsim, has been developed based on a historically successful code, DINA, offering tools to simulate both transport and plasma shape. Specifically, NSFsim is a free boundary equilibrium and transport solver and has been configured to match the properties of the DIII-D tokamak. This paper is focused on validating the Grad-Shafranov (GS) solver of NSFsim by analyzing its ability to recreate the plasma shape, the poloidal flux distribution, and the measurements of the simulated diagnostic signals originating from flux loops and magnetic probes in DIII-D. Five different plasma shapes are simulated to show the robustness of NSFsim to different plasma conditions; these shapes are Lower Single Null (LSN), Upper Single Null (USN), Double Null (DN), Inner Wall Limited (IWL), and Negative Triangularity (NT). The NSFsim results are compared against real measured signals, magnetic profile fits from EFIT, and another plasma equilibrium simulator, GSevolve. EFIT reconstructions of shots are readily available at DIII-D, but GSevolve was manually ran by us to provide simulation data to compare against., Comment: 12 pages, 7 figures

Masked Image Modeling (MIM) has emerged as a popular method for Self-Supervised Learning (SSL) of visual representations. However, for high-level perception tasks, MIM-pretrained models offer lower out-of-the-box representation quality than the Joint-Embedding Architectures (JEA) - another prominent SSL paradigm. To understand this performance gap, we analyze the information flow in Vision Transformers (ViT) learned by both approaches. We reveal that whereas JEAs construct their representation on a selected set of relevant image fragments, MIM models aggregate nearly whole image content. Moreover, we demonstrate that MIM-trained ViTs retain valuable information within their patch tokens, which is not effectively captured by the global [cls] token representations. Therefore, selective aggregation of relevant patch tokens, without any fine-tuning, results in consistently higher-quality of MIM representations. To our knowledge, we are the first to highlight the lack of effective representation aggregation as an emergent issue of MIM and propose directions to address it, contributing to future advances in Self-Supervised Learning.

This paper investigates the maximum downlink spectral efficiency of low earth orbit (LEO) constellations. Spectral efficiency, in this context, refers to the sum rate of the entire network per unit spectrum per unit area on the earth's surface. For practicality all links employ single-user codebooks and treat interference as noise. To estimate the maximum achievable spectral efficiency, we propose and analyze a regular configuration, which deploys satellites and ground terminals in hexagonal lattices. Additionally, for wideband networks with arbitrary satellite configurations, we introduce a subband allocation algorithm aimed at maximizing the overall spectral efficiency. Simulation results indicate that the regular configuration is more efficient than random configurations. As the number of randomly placed satellites increases within an area, the subband allocation algorithm achieves a spectral efficiency that approaches the spectral efficiency achieved by the regular configuration. Further improvements are demonstrated by reconfiguring associations so that nearby transmitters avoid pointing to the same area.

Iron oxide (e.g., Fe$_3$O$_4$ or Fe$_2$O$_3$) nanoparticles are promising candidates for a variety of biomedical applications ranging from magnetic hyperthermia therapy to drug delivery and bio-detection, due to their superparamagnetism, non-toxicity, and biodegradability. While particles of small size (below a critical size, ~20 nm) display superparamagnetic behavior at room temperature, these particles tend to penetrate highly sensitive areas of the body such as the Blood-Brain Barrier (BBB), leading to undesired effects. In addition, these particles possess a high probability of retention, which can lead to genotoxicity and biochemical toxicity. Increasing particle size is a means for addressing these problems but also suppresses the superparamagnetism. We have overcome this particle size limit by synthesizing unique polycrystalline iron oxide nanoparticles composed of multiple nanocrystals of 10 to 15 nm size while tuning particle size from 160 to 400 nm. These so-called superparticles preserve superparamagnetic characteristics and exhibit excellent hyperthermia responses. The specific absorption rates (SAR) exceed 250 W/g (HAC = 800 Oe, f = 310 kHz) at a low concentration of 0.5 mg/mL, indicating their capability in cancer treatment with minimum dose. Our study underscores the potential of size-tunable polycrystalline iron oxide superparticles with superparamagnetic properties for advanced biomedical applications and sensing technologies.

Tracking geographic entities from historical maps, such as buildings, offers valuable insights into cultural heritage, urbanization patterns, environmental changes, and various historical research endeavors. However, linking these entities across diverse maps remains a persistent challenge for researchers. Traditionally, this has been addressed through a two-step process: detecting entities within individual maps and then associating them via a heuristic-based post-processing step. In this paper, we propose a novel approach that combines segmentation and association of geographic entities in historical maps using video instance segmentation (VIS). This method significantly streamlines geographic entity alignment and enhances automation. However, acquiring high-quality, video-format training data for VIS models is prohibitively expensive, especially for historical maps that often contain hundreds or thousands of geographic entities. To mitigate this challenge, we explore self-supervised learning (SSL) techniques to enhance VIS performance on historical maps. We evaluate the performance of VIS models under different pretraining configurations and introduce a novel method for generating synthetic videos from unlabeled historical map images for pretraining. Our proposed self-supervised VIS method substantially reduces the need for manual annotation. Experimental results demonstrate the superiority of the proposed self-supervised VIS approach, achieving a 24.9\% improvement in AP and a 0.23 increase in F1 score compared to the model trained from scratch.

The $\sim$5 Myr PDS 70 is the only known system with protoplanets residing in the cavity of the circumstellar disk from which they formed, ideal for studying exoplanet formation and evolution within its natal environment. Here we report the first spin constraint and C/O measurement of PDS 70b from Keck/KPIC high-resolution spectroscopy. We detected CO (3.8 $\sigma$) and H$_2$O (3.5 $\sigma$) molecules in the PDS 70b atmosphere via cross-correlation, with a combined CO and H$_2$O template detection significance of 4.2 $\sigma$. Our forward model fits, using BT-Settl model grids, provide an upper limit for the spin-rate of PDS 70b ($<$29 km s$^{-1}$). The atmospheric retrievals constrain the PDS 70b C/O ratio to ${0.28}^{+0.20}_{-0.12}$ ($<$0.63 under 95$\%$ confidence level) and a metallicity [C/H] of ${-0.2}^{+0.8}_{-0.5}$ dex, consistent with that of its host star. The following scenarios can explain our measured C/O of PDS 70b in contrast with that of the gas-rich outer disk (for which C/O $\gtrsim$ 1). First, the bulk composition of PDS 70b might be dominated by dust+ice aggregates rather than disk gas. Another possible explanation is that the disk became carbon-enriched $\textit{after}$ PDS 70b was formed, as predicted in models of disk chemical evolution and as observed in both very low mass star and older disk systems with $\textit{JWST}$/MIRI. Because PDS 70b continues to accrete and its chemical evolution is not yet complete, more sophisticated modeling of the planet and the disk, and higher quality observations of PDS 70b (and possibly PDS 70c), are necessary to validate these scenarios., Comment: Accepted to ApJ Letters; 13 pages, 3 figures

Identifying predictive features from high-dimensional datasets is a major task in biomedical research. However, it is difficult to determine the robustness of selected features. Here, we investigate the performance of randomly chosen features, what we term "random feature baselines" (RFBs), in the context of disease risk prediction from blood plasma proteomics data in the UK Biobank. We examine two published case studies predicting diagnosis of (1) dementia and (2) hip fracture. RFBs perform similarly to published proteins of interest (using the same number, randomly chosen). We then measure the performance of RFBs for all 607 disease outcomes in the UK Biobank, with various numbers of randomly chosen features, as well as all proteins in the dataset. 114/607 outcomes showed a higher mean AUROC when choosing 5 random features than using all proteins, and the absolute difference in mean AUC was 0.075. 163 outcomes showed a higher mean AUROC when choosing 1000 random features than using all proteins, and the absolute difference in mean AUC was 0.03. Incorporating RFBs should become part of ML practice when feature selection or target discovery is a goal., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 5 pages

The estimation of room impulse responses (RIRs) between static loudspeaker and microphone locations can be done using a number of well-established measurement and inference procedures. While these procedures assume a time-invariant acoustic system, time variations need to be considered for the case of spatially dynamic scenarios where loudspeakers and microphones are subject to movement. If the RIR is modeled using image sources, then movement implies that the distance to each image source varies over time, making the estimation of the spatially dynamic RIR particularly challenging. In this paper, we propose a procedure to estimate the early part of the spatially dynamic RIR between a stationary source and a microphone moving on a linear trajectory at constant velocity. The procedure is built upon a state-space model, where the state to be estimated represents the early RIR, the observation corresponds to a microphone recording in a spatially dynamic scenario, and time-varying distances to the image sources are incorporated into the state transition matrix obtained from static RIRs at the start and end point of the trajectory. The performance of the proposed approach is evaluated against state-of-the-art RIR interpolation and state-space estimation methods using simulations, demonstrating the potential of the proposed state-space model., Comment: 30 pages, 13 figures

The Simons Observatory is a ground-based telescope array located at an elevation of 5200 meters, in the Atacama Desert in Chile, designed to measure the temperature and polarization of the cosmic microwave background. It comprises four telescopes: three 0.42-meter small aperture telescopes (SATs), focused on searching for primordial gravitational waves, and one 6-meter large aperture telescope, focused on studying small-scale perturbations. Each of the SATs will field over 12,000 TES bolometers, with two SATs sensitive to both 90 and 150GHz frequency bands (SAT-MF1, and SAT-MF2), while the third SAT is sensitive to 220 and 280GHz frequency bands. Prior to its deployment in 2023, the optical properties of SAT-MF1 were characterized in the laboratory. We report on measurements of near-field beam maps acquired using a thermal source along with measurements using a holographic method that enables characterization of the amplitude and phase of the beam response, yielding an estimate of the far-field radiation pattern received by SAT-MF1. We find that the near-field half-width-half-maximum (HWHM) requirements are met across the focal plane array for the 90GHz frequency band, and through most of the focal plane array for the 150GHz frequency band. The mean of the bandpass averaged HWHM of the edge-detector focal plane modules match the simulated HWHM to 10.4%, with the discrepancy caused by fringing in the simulation. The measured beam profiles match simulations to within 2dB from the beam center to at least the -10dB level. Holography estimates of the far-field 90GHz beams match the full-width-half-maximum from simulation within 1%, and the beam profiles deviate by less than 2dB inside the central lobe. The success of the holography and thermal beam map experiments confirmed the optical performance was sufficient to meet the science requirements. On-site observations are currently underway., Comment: 16 pages, 10 figures, Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

A near-minimal instance of optical cooling is experimentally presented wherein the internal-state entropy of a single atom is reduced more than twofold by illuminating it with broadband, incoherent light. Since the rate of optical pumping by a thermal state increases monotonically with its temperature, the cooling power in this scenario increases with higher thermal occupation, an example of a phenomenon known as cooling by heating. In contrast to optical pumping by coherent, narrow-band laser light, here we perform the same task with fiber-coupled, broadband sunlight, the brightest laboratory-accessible source of continuous blackbody radiation., Comment: 4 pages

Linked micromaps were originally developed to display geographically indexed statistics in an intuitive way by connecting them to a sequence of small maps. The approach integrates several visualization design principles, such as small multiples, discrete color indexing, and ordering. Linked micromaps allow for other types of data displays that are connected to and conditional on geographic areas. Initial applications of micromaps used data from the National Cancer Institute and the Environmental Protection Agency. In this paper, we will show how linked micromaps can be used to better understand and explore relationships and distributions of statistics linked to US states and Washington, DC. We will compare linked micromaps with other popular data displays of geographic data, such as bubble maps, choropleth maps, and bar charts. We will illustrate how linked micromaps can be used for evidence-based decision-making using data from the Bureau of Labor Statistics, the Census Bureau, and the Economic Research Service. The presentations, R scripts, and the data sets used in this article are available here: https://github.com/wlmcensus/Joint-Statistical-Meetings-Presentation-2024. The work discussed in this article was presented at the Joint Statistical Meetings (JSM) 2024 and the American Association for Public Opinion Research (AAPOR) 2024 Annual Conference., Comment: 18 pages, 13 figures, JSM 2024 Conference Proceedings

Fun acts as a catalyst for learning by enhancing motivation, active engagement and knowledge retention. As social robots gain traction as educational tools, understanding how their unique affordances can be leveraged to cultivate fun becomes crucial. This research investigates the concept of fun in educational games involving social robots to support the design of REMind:a robot-mediated role-play game aimed at encouraging bystander intervention against peer bullying among children. To incorporate fun elements into design of REMind, we conducted a user-centered Research through Design (RtD) study with focus groups of children to gain a deeper understanding of their perceptions of fun. We analyzed children's ideas by using Framework Analysis and leveraging LeBlanc's Taxonomy of Game Pleasures and identified 28 elements of fun that can be incorporated into robot-mediated games. We present our observations, discuss their impact on REMind's design, and offer recommendations for designing fun educational games using social robots., Comment: This is a pre-print of a manuscript that was accepted to International Conference on Social Robotics 2024 (ICSR'24 + AI), 2024, which was held in Odense, Denmark

Hierarchical sorting is a fundamental task for programmable matter, inspired by the spontaneous formation of interfaces and membranes in nature. The task entails particles of different types, present in fixed densities, sorting into corresponding regions of a space that are themselves organized. By analyzing the Gibbs distribution of a general fixed-magnetization model of equilibrium statistical mechanics, we prove that particles moving stochastically according to local affinities solve the hierarchical sorting task. The analysis of fixed-magnetization models is notoriously difficult, and approaches that have led to recent breakthroughs in sampling the low-temperature regime only work in the variable-magnetization setting by default. To overcome this barrier, we introduce a new approach for comparing the partition functions of fixed- and variable-magnetization models. The core technique identifies a special class of configurations that contribute comparably to the two partition functions, which then serves as a bridge between the fixed- and variable-magnetization settings. Our main result is an estimate of the Gibbs distribution that unifies existing and new results for models at fixed magnetization, including the Ising, Potts, and Blume--Capel models, and leads to stochastic distributed algorithms for hierarchical sorting and other self-organizing tasks, like compression and separation.

Supervised learning has become a cornerstone of modern machine learning, yet a comprehensive theory explaining its effectiveness remains elusive. Empirical phenomena, such as neural analogy-making and the linear representation hypothesis, suggest that supervised models can learn interpretable factors of variation in a linear fashion. Recent advances in self-supervised learning, particularly nonlinear Independent Component Analysis, have shown that these methods can recover latent structures by inverting the data generating process. We extend these identifiability results to parametric instance discrimination, then show how insights transfer to the ubiquitous setting of supervised learning with cross-entropy minimization. We prove that even in standard classification tasks, models learn representations of ground-truth factors of variation up to a linear transformation. We corroborate our theoretical contribution with a series of empirical studies. First, using simulated data matching our theoretical assumptions, we demonstrate successful disentanglement of latent factors. Second, we show that on DisLib, a widely-used disentanglement benchmark, simple classification tasks recover latent structures up to linear transformations. Finally, we reveal that models trained on ImageNet encode representations that permit linear decoding of proxy factors of variation. Together, our theoretical findings and experiments offer a compelling explanation for recent observations of linear representations, such as superposition in neural networks. This work takes a significant step toward a cohesive theory that accounts for the unreasonable effectiveness of supervised deep learning.

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

Recently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement in 3D scene reconstruction, attracting considerable attention due to its ability to recover high-fidelity details while maintaining low complexity. Despite the promising results achieved by 3DGS, its rendering performance is constrained by its dependence on costly non-commutative alpha-blending operations. These operations mandate complex view dependent sorting operations that introduce computational overhead, especially on the resource-constrained platforms such as mobile phones. In this paper, we propose Weighted Sum Rendering, which approximates alpha blending with weighted sums, thereby removing the need for sorting. This simplifies implementation, delivers superior performance, and eliminates the "popping" artifacts caused by sorting. Experimental results show that optimizing a generalized Gaussian splatting formulation to the new differentiable rendering yields competitive image quality. The method was implemented and tested in a mobile device GPU, achieving on average $1.23\times$ faster rendering.

We investigate the different transport mechanisms that can occur in atomically precise advanced-manufacturing (APAM) pn junction devices at cryogenic and room temperatures. We first elucidate the potential cause of the anomalous behaviors observed in the forward-bias response of these devices in recent cryogenic temperature measurements, which deviates from the theoretical response of a silicon Esaki diode. Specifically, the suppression of the tunneling current at low bias and the appearance of regular diode current at lower biases than theoretically expected for silicon. We find that the latter can be attributed to modifications in the electronic band structure within the $\delta$-layer regions, leading to band-gap narrowing induced by the high density of dopants. We also find that a combination of two sets of band-to-band tunneling (BTBT) parameters can qualitatively approximate the shape of the tunneling current at low bias. This can arise from band quantization and realignment due to the strong potential confinement in APAM-doped layers. Finally, we extend our analyses to room temperature operation, and we predict that trap-assisted tunneling (TAT) may become significant, leading to a complex superposition of BTBT and TAT transport mechanisms in the electrical measurements.

Large Language Models (LLMs) are widely deployed in real-world applications, yet little is known about their training dynamics at the token level. Evaluation typically relies on aggregated training loss, measured at the batch level, which overlooks subtle per-token biases arising from (i) varying token-level dynamics and (ii) structural biases introduced by hyperparameters. While weight decay is commonly used to stabilize training, we reveal that it silently introduces performance biases detectable only at the token level. In fact, we empirically show across different dataset sizes, model architectures and sizes ranging from 270M to 3B parameters that as weight decay increases, low-frequency tokens are disproportionately depreciated. This is particularly concerning, as these neglected low-frequency tokens represent the vast majority of the token distribution in most languages, calling for novel regularization techniques that ensure fairness across all available tokens.

Low-Rank Adaptation (LoRA) is the bread and butter of Large Language Model (LLM) finetuning. LoRA learns an additive low-rank perturbation, $AB$, of a pretrained matrix parameter $W$ to align the model to a new task or dataset with $W+AB$. We identify three core limitations to LoRA for finetuning--a setting that employs limited amount of data and training steps. First, LoRA employs Dropout to prevent overfitting. We prove that Dropout is only suitable for long training episodes but fails to converge to a reliable regularizer for short training episodes. Second, LoRA's initialization of $B$ at $0$ creates a slow training dynamic between $A$ and $B$. That dynamic is also exacerbated by Dropout that further slows the escape from $0$ for $B$ which is particularly harmful for short training episodes. Third, the scaling factor multiplying each LoRA additive perturbation creates ``short-sighted'' interactions between the LoRA modules of different layers. Motivated by principled analysis of those limitations, we find an elegant solution: a Dropout-free, scaling-free, LoRA with Adaptive Learning rate--coined ALLoRA. By scaling the per sample and per parameter gradients with a coefficient inversely proportional to parameters' $\ell_2$ norm, ALLoRA alleviates those three limitations. As a by-product, ALLoRA removes two hyper-parameters from LoRA: the scaling factor and the dropout rate. Empirical results show that ALLoRA admits better accuracy than LoRA on various settings, including against recent LoRA variants such as Weight-Decomposed Low-Rank Adaptation (DoRA). Ablation studies show our solution is the optimal in a family of weight-dependent / output-dependent approaches on various LLMs including the latest Llama3.

In this work, we investigate the topological properties of knotted defects in smectic liquid crystals. Our story begins with screw dislocations, whose radial surface structure can be smoothly accommodated on $S^3$ for fibred knots by using the corresponding knot fibration. To understand how a smectic texture may take on a screw dislocation in the shape of a knot without a fibration, we study first knotted edge defects. Unlike screw defects, knotted edge dislocations force singular points in the system for any non-trivial knot. We provide a lower bound on the number of such point defects required for a given edge dislocation knot and draw an analogy between the point defect structure of knotted edge dislocations and that of focal conic domains. By showing that edge dislocations, too, are sensitive to knot fibredness, we reinterpret the so-called Morse-Novikov points required for non-fibred screw dislocation knots as analogous smectic defects. Our methods are then applied to $+1/2$ and negative-charge disclinations in the smectic phase, furthering the analogy between knotted smectic defects and focal conic domains and uncovering an intricate relationship between point and line defects in smectic liquid crystals. The connection between smectic defects and knot theory not only unravels the uniquely topological knotting of smectic defects but also provides a mathematical and experimental playground for modern questions in knot and Morse-Novikov theory.

Accurate anomaly detection is critical in vision-based infrastructure inspection, where it helps prevent costly failures and enhances safety. Self-Supervised Learning (SSL) offers a promising approach by learning robust representations from unlabeled data. However, its application in anomaly detection remains underexplored. This paper addresses this gap by providing a comprehensive evaluation of SSL methods for real-world anomaly detection, focusing on sewer infrastructure. Using the Sewer-ML dataset, we evaluate lightweight models such as ViT-Tiny and ResNet-18 across SSL frameworks, including BYOL, Barlow Twins, SimCLR, DINO, and MAE, under varying class imbalance levels. Through 250 experiments, we rigorously assess the performance of these SSL methods to ensure a robust and comprehensive evaluation. Our findings highlight the superiority of joint-embedding methods like SimCLR and Barlow Twins over reconstruction-based approaches such as MAE, which struggle to maintain performance under class imbalance. Furthermore, we find that the SSL model choice is more critical than the backbone architecture. Additionally, we emphasize the need for better label-free assessments of SSL representations, as current methods like RankMe fail to adequately evaluate representation quality, making cross-validation without labels infeasible. Despite the remaining performance gap between SSL and supervised models, these findings highlight the potential of SSL to enhance anomaly detection, paving the way for further research in this underexplored area of SSL applications.

3D reconstruction in endoscopic sinus surgery (ESS) demands exceptional accuracy, with the mean error and standard deviation necessitating within the range of a single CT slice (0.625 mm), as the critical structures in the nasal cavity are situated within submillimeter distances from surgical instruments. This poses a formidable challenge when using conventional monocular endoscopes. Depth estimation is crucial for 3D reconstruction, yet existing depth estimation methodologies either suffer from inherent accuracy limitations or, in the case of learning-based approaches, perform poorly when applied to ESS despite succeeding on their original datasets. In this study, we present a novel, highly generalizable method that combines Neural Radiance Fields (NeRF) and stereo depth estimation for 3D reconstruction that can derive metric monocular depth. Our approach begins with an initial NeRF reconstruction yielding a coarse 3D scene, the subsequent creation of binocular pairs within coarse 3D scene, and generation of depth maps through stereo vision, These depth maps are used to supervise subsequent NeRF iteration, progressively refining NeRF and binocular depth, the refinement process continues until the depth maps converged. This recursive process generates high-accuracy depth maps from monocular endoscopic video. Evaluation in synthetic endoscopy shows a depth accuracy of 0.125 $\pm$ 0.443 mm, well within the 0.625 mm threshold. Further clinical experiments with real endoscopic data demonstrate a mean distance to CT mesh of 0.269 mm, representing the highest accuracy among monocular 3D reconstruction methods in ESS.

The Gemini Planet Imager (GPI) is a high contrast imaging instrument that aims to detect and characterize extrasolar planets. GPI is being upgraded to GPI 2.0, with several subsystems receiving a re-design to improve its contrast. To enable observations on fainter targets and increase performance on brighter ones, one of the upgrades is to the adaptive optics system. The current Shack-Hartmann wavefront sensor (WFS) is being replaced by a pyramid WFS with an low-noise electron multiplying CCD (EMCCD). EMCCDs are detectors capable of counting single photon events at high speed and high sensitivity. In this work, we characterize the performance of the HN\"u 240 EMCCD from N\"uv\"u Cameras, which was custom-built for GPI 2.0. Through our performance evaluation we found that the operating mode of the camera had to be changed from inverted-mode (IMO) to non-inverted mode (NIMO) in order to improve charge diffusion features found in the detector's images. Here, we characterize the EMCCD's noise contributors (readout noise, clock-induced charges, dark current) and linearity tests (EM gain, exposure time) before and after the switch to NIMO., Comment: Proceeding of the SPIE Astronomical Telescopes+Instrumentation. 14 pages, 15 figures

This paper evaluates the performance of the one-shot Semi-Persistent Scheduling (SPS) mechanism in Cellular Vehicle-to-Everything (C-V2X) networks under Denial-of-Service (DoS) smart attack scenarios. The study focuses on the impact of these attacks on key performance metrics, including Packet Delivery Ratio (PDR), Inter-Packet Gap (IPG), and Age of Information (AoI). Through extensive Monte Carlo simulations, we demonstrate that the one-shot mechanism significantly enhances network resilience by mitigating the adverse effects of smart DoS attacks. The findings reveal that while the one-shot mechanism improves the PDR and reduces the IPG and AoI tail values, its effectiveness diminishes slightly in high-density vehicular environments. Nevertheless, the one-shot mechanism proves to be a robust solution for maintaining the stability and reliability of C-V2X communications under adversarial conditions.

As highlighted in the National Spectrum Strategy, Dynamic Spectrum Access (DSA) is key for enabling 6G networks to meet the increasing demand for spectrum from various, heterogeneous emerging applications. In this paper, we consider heterogeneous wireless networks with multiple 6G base stations (BS) and a limited number of frequency bands available for transmission. Each BS is associated with a geographical location, a coverage area, and a bandwidth requirement. We assume that clients/UEs are within the corresponding BS's coverage area. To avoid interference, we impose that BSs with overlapping coverage areas must use different frequency bands. We address the challenging problem of efficiently allocating contiguous frequency bands to BSs while avoiding interference. Specifically, we define performance metrics that capture the feasibility of the frequency allocation task, the number of BSs that can be allocated within the limited frequency bands, and the amount of resources utilized by the network. Then, we consider five different DSA algorithms that prioritize BSs based on different features - one of these algorithms is known in the graph theory literature as Welsh-Powell graph colouring algorithm - and compare their performance using extensive simulations. Our results show that DSA algorithms that attempt to maximize the chances of obtaining a feasible frequency allocation - which have been widely studied in the literature - tend to under-perform in all other metrics.

Observations of the 21 cm signal face significant challenges due to bright astrophysical foregrounds that are several orders of magnitude higher than the brightness of the hydrogen line, along with various systematics. Successful 21 cm experiments require accurate calibration and foreground mitigation. Errors introduced during the calibration process such as systematics, can disrupt the intrinsic frequency smoothness of the foregrounds, leading to power leakage into the Epoch of Reionisation (EoR) window. Therefore, it is essential to develop strategies to effectively address these challenges. In this work, we adopt a stringent approach to identify and address suspected systematics, including malfunctioning antennas, frequency channels corrupted by radio frequency interference (RFI), and other dominant effects. We implement a statistical framework that utilises various data products from the data processing pipeline to derive specific criteria and filters. These criteria and filters are applied at intermediate stages to mitigate systematic propagation from the early stages of data processing. Our analysis focuses on observations from the Murchison Widefield Array (MWA) Phase I configuration. Out of the observations processed by the pipeline, our approach selects 18%, totalling 58 hours, that exhibit fewer systematic effects. The successful selection of observations with reduced systematic dominance enhances our confidence in achieving 21 cm measurements., Comment: 18 pages, 17 figures, 2 tables (accepted for publication in PASA)

X-ray diffraction gratings play an essential role in high-resolution spectroscopy of astrophysical phenomena. We present some scientific highlights from the X-ray grating spectrometers (XGS) on board of the Chandra and XMM/Newton missions, XGS optical design, and the basic physics of grating diffraction geometry and efficiency. We review design, fabrication, and performance of the currently orbiting transmission and reflection grating elements, followed by descriptions of the state-of-the art of more advanced grating technologies that promise orders-of-magnitude improvements in XGS performance, especially in combination with advanced X-ray telescope mirrors. A few key science questions that require new grating technology are posed, and powerful future mission concepts and recent and approved missions are presented., Comment: 40 pages, 12 figures. To be submitted to Springer for publication in the ISSI Scientific Reports series

This work presents the INBD network proposed by Gillert et al. in CVPR-2023 and studies its application for delineating tree rings in RGB images of Pinus taeda cross sections captured by a smartphone (UruDendro dataset), which are images with different characteristics from the ones used to train the method. The INBD network operates in two stages: first, it segments the background, pith, and ring boundaries. In the second stage, the image is transformed into polar coordinates, and ring boundaries are iteratively segmented from the pith to the bark. Both stages are based on the U-Net architecture. The method achieves an F-Score of 77.5, a mAR of 0.540, and an ARAND of 0.205 on the evaluation set. The code for the experiments is available at https://github.com/hmarichal93/mlbrief_inbd., Comment: Submitted to IPOL ad an MLBriefs paper

Local interactions of uncoordinated individuals produce the collective behaviors of many biological systems, inspiring much of the current research in programmable matter. A striking example is the spontaneous assembly of fire ants into "bridges" comprising their own bodies to traverse obstacles and reach sources of food. Experiments and simulations suggest that, remarkably, these ants always form one bridge -- instead of multiple, competing bridges -- despite a lack of central coordination. We argue that the reliable formation of a single bridge does not require sophistication on behalf of the individuals by provably reproducing this behavior in a self-organizing particle system. We show that the formation of a single bridge by the particles is a statistical inevitability of their preferences to move in a particular direction, such as toward a food source, and their preference for more neighbors. Two parameters, $\eta$ and $\beta$, reflect the strengths of these preferences and determine the Gibbs stationary measure of the corresponding particle system's Markov chain dynamics. We show that a single bridge almost certainly forms when $\eta$ and $\beta$ are sufficiently large. Our proof introduces an auxiliary Markov chain, called an "occupancy chain," that captures only the significant, global changes to the system. Through the occupancy chain, we abstract away information about the motion of individual particles, but we gain a more direct means of analyzing their collective behavior. Such abstractions provide a promising new direction for understanding many other systems of programmable matter.