Search

Dark matter may exist today in the form of ultraheavy composite bound states. Collisions between such dark matter states can release intense bursts of radiation that includes gamma-rays among the final products. Thus, indirect-detection signals of dark matter may include unconventional gamma-ray bursts. Such bursts may have been missed not necessarily because of their low arriving gamma-ray fluxes, but rather their briefness and rareness. We point out that intense bursts whose non-detection thus far are due to the latter can be detected in the near future with existing and planned facilities. In particular, we propose that, with slight experimental adjustments and suitable data analyses, imaging atmospheric Cherenkov telescopes (IACTs) and Pulsed All-sky Near-infrared and Optical Search for Extra-Terrestrial Intelligence (PANOSETI) are promising tools for detecting such rare, brief, but intense bursts. We also show that if we assume these bursts originate from collisions of dark matter states, IACTs and PANOSETI can probe a large dark matter parameter space beyond existing limits. Additionally, we present a concrete model of dark matter that produces bursts potentially detectable in these instruments.

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning., Comment: The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise

We introduce the general-relativistic definition of Lunar Coordinate Time (TCL) based on the IAU 2000 resolutions that provide a framework for relativistic reference systems. From this foundation, we derive a transformation equation that describes the relative rate of TCL with respect to Geocentric Coordinate Time (TCG) for various locations of the clock on lunar surface. This equation serves as the cornerstone for constructing a relativistic TCL--TCG time conversion algorithm. Using this algorithm, we can compute both secular and periodic variations in the rate of an atomic clock placed on the Moon, relative to an identical clock on Earth. The algorithm accounts for various effects, including time dilation caused by the Moon's orbital motion around Earth, gravitational potentials of both Earth and Moon, and direct and indirect time dilation effects due to tidal perturbations caused by the Sun and other major planets of the solar system. Our approach provides exquisite details of the TCL--TCG transformation, achieving a precision of several nanoseconds within the spatial volume dominated by the Earth's gravitational field known as the Hill sphere. This sphere extends from the Earth to a distance of approximately 1.5 million km, substantially encompassing the Moon's orbit. To validate our methodology for lunar coordinate time, we compare it with the mathematical formalism of local inertial frames applied to the Earth-Moon system and confirm their equivalence., Comment: 37 pages, 7 figures, 2 tables, appendix

Foundation models (FMs) have revolutionized computer vision, enabling effective learning across different domains. However, their performance under domain shift is yet underexplored. This paper investigates the zero-shot domain adaptation potential of FMs by comparing different backbone architectures and introducing novel domain-aware components that leverage domain related textual embeddings. We propose domain adaptive normalization, termed as Domino, which explicitly leverages domain embeddings during fine-tuning, thus making the model domain aware. Ultimately, Domino enables more robust computer vision models that can adapt effectively to various unseen domains., Comment: Accepted at ICML 2024 Workshop on Foundation Models in the Wild

Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly., Comment: 8 pages, 9 figures, accepted at 27th International Conference on Information Fusion (FUSION 2024)

Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition compared with the most recent benchmarks, while still being informative when facing out-of-distribution samples and enabling semantic interpretation of latent variables in terms of physical phenomena. The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i.e., events for which we have limited to no training data., Comment: 8 pages, 10 figures, accepted at 26th International Conference on Information Fusion (FUSION 2023)

We report on the results of an image-based search for pulsar candidates toward the Galactic bulge. We used mosaic images from the MeerKAT radio telescope, that were taken as part of a 173 deg**2 survey of the bulge and Galactic center of our Galaxy at L band (856-1712 MHz) in all four Stokes I, Q, U and V. The image root-mean-square noise levels of 12-17 uJy/ba represent a significant increase in sensitivity over past image-based pulsar searches. Our primary search criterion was circular polarization, but we used other criteria including linear polarization, in-band spectral index, compactness, variability and multi-wavelength counterparts to select pulsar candidates. We first demonstrate the efficacy of this technique by searching for polarized emission from known pulsars, and comparing our results with measurements from the literature. Our search resulted in a sample of 75 polarized pulsar candidates. Bright stars or young stellar objects were associated with 28 of these sources, including a small sample of highly polarized dwarf stars with pulsar-like steep spectra. Comparing the properties of this sample with the known pulsars, we identified 30 compelling candidates for pulsation follow-up, including two sources with both strong circular and linear polarization. The remaining 17 sources are either pulsars or stars, but we cannot rule out an extragalactic origin or image artifacts among the brighter, flat spectrum objects., Comment: 28 pages, 10 figures, 6 tables. ApJ, in press

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.

I show how to express the question of whether a polyform tiles the plane isohedrally as a Boolean formula that can be tested using a SAT solver. This approach is adaptable to a wide range of polyforms, requires no special-case code for different isohedral tiling types, and integrates seamlessly with existing software for computing Heesch numbers of polyforms., Comment: In Proceedings GASCom 2024, arXiv:2406.14588

We describe a gyroscope that measures rotation based on the effects of the rotation on the polarization of light. Rotation induces a differential phase shift in the propagation of left and right circularly polarized light and this phase shift can be measured in suitably designed interferometric setups. The signal in this setup is independent of the frequency of light, unlike various sources of noise such as vibrations, which cause phase shifts that depend on the frequency. Such vibrations are the practical limit on the sensitivity of conventional Sagnac-style optical interferometers that are typically used as gyroscopes. In the proposed setup, one can potentially mitigate this source of noise by simultaneously using two (or more) sources of light that have different frequencies. The signal in this setup scales with the total storage time of the light. Due to its frequency independence, it is thus most optimal to measure the signal using superconducting RF systems where the high finesse of the available cavities enables considerably longer storage times than is possible in an optical setup., Comment: 15 pages, 1 figure

Several sources of repeating coherent bursts of radio emission with periods of many minutes have now been reported in the literature. These "ultra-long period" (ULP) sources have no clear multi-wavelength counterparts and challenge canonical pulsar emission models, leading to debate regarding their nature. In this work we report the discovery of a bright, highly-polarised burst of radio emission at low Galactic latitude as part of a wide-field survey for transient and variable radio sources. ASKAP\,J175534.9$-$252749.1 does not appear to repeat, with only a single intense two-minute $\sim$200-mJy burst detected from 60~hours of observations. The burst morphology and polarisation properties are comparable to those of classical pulsars but the duration is more than one hundred times longer, analogous to ULPs. Combined with the existing ULP population, this suggests that these sources have a strong Galactic latitude dependence and hints at an unexplored population of transient and variable radio sources in the thin disk of the Milky Way. The resemblance of this burst with both ULPs and pulsars calls for a unified coherent emission model for objects with spin periods from milliseconds to tens of minutes. However, whether or not these are all neutron stars or have the same underlying power source remains open for debate.

In reinforcement learning, specification gaming occurs when AI systems learn undesired behaviors that are highly rewarded due to misspecified training goals. Specification gaming can range from simple behaviors like sycophancy to sophisticated and pernicious behaviors like reward-tampering, where a model directly modifies its own reward mechanism. However, these more pernicious behaviors may be too complex to be discovered via exploration. In this paper, we study whether Large Language Model (LLM) assistants which find easily discovered forms of specification gaming will generalize to perform rarer and more blatant forms, up to and including reward-tampering. We construct a curriculum of increasingly sophisticated gameable environments and find that training on early-curriculum environments leads to more specification gaming on remaining environments. Strikingly, a small but non-negligible proportion of the time, LLM assistants trained on the full curriculum generalize zero-shot to directly rewriting their own reward function. Retraining an LLM not to game early-curriculum environments mitigates, but does not eliminate, reward-tampering in later environments. Moreover, adding harmlessness training to our gameable environments does not prevent reward-tampering. These results demonstrate that LLMs can generalize from common forms of specification gaming to more pernicious reward tampering and that such behavior may be nontrivial to remove., Comment: Make it easier to find samples from the model, and highlight that our operational definition of reward tampering has false positives where the model attempts to complete the task honestly but edits the reward. Add paragraph to conclusion to this effect, and add sentence to figure 1 to this effect

We present a systematic search for tidal disruption events (TDEs) using radio data from the Variables and Slow Transients (VAST) Pilot Survey conducted using the Australian Square Kilometre Array Pathfinder (ASKAP). Historically, TDEs have been identified using observations at X-ray, optical, and ultraviolet wavelengths. After discovery, a few dozen TDEs have been shown to have radio counterparts through follow-up observations. With systematic time-domain radio surveys becoming available, we can now identify new TDEs in the radio regime. A population of radio-discovered TDEs has the potential to provide several key insights including an independent constraint on their volumetric rate. We conducted a search to select variable radio sources with a single prominent radio flare and a position consistent within 2$\sigma$ of the nucleus of a known galaxy. While TDEs were the primary target of our search, sources identified in this search may also be consistent with active galactic nuclei exhibiting unusual flux density changes at the timescales probed, uncharacteristically bright supernovae, or a population of gamma-ray bursts. We identify a sample of 12 radio-bright candidate TDEs. The timescales and luminosities range from ~6 to 230 days and ~10$^{38}$ to 10$^{41}$ erg s$^{-1}$, consistent with models of radio emission from TDEs that launch relativistic jets. After calculating the detection efficiency of our search using a Monte Carlo simulation of TDEs, and assuming all 12 sources are jetted TDEs, we derive a volumetric rate for jetted TDEs of 0.80$^{+0.31}_{-0.23}$ Gpc$^{-3}$ yr$^{-1}$, consistent with previous empirically estimated rates., Comment: 37 pages, 37 figures, accepted to ApJ

Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sensors viewing the scene from different perspectives to estimate the target location while also employing multiple modalities for enhanced robustness and accuracy. Recently, such systems have employed end-to-end deep neural models trained on large datasets due to their superior performance and ability to handle data from diverse sensor modalities. However, such neural models are often trained on data collected from a particular set of sensor poses (i.e., locations and orientations). During real-world deployments, slight deviations from these sensor poses can result in extreme inaccuracies. To address this challenge, we introduce FlexLoc, which employs conditional neural networks to inject node perspective information to adapt the localization pipeline. Specifically, a small subset of model weights are derived from node poses at run time, enabling accurate generalization to unseen perspectives with minimal additional overhead. Our evaluations on a multimodal, multiview indoor tracking dataset showcase that FlexLoc improves the localization accuracy by almost 50% in the zero-shot case (no calibration data available) compared to the baselines. The source code of FlexLoc is available at https://github.com/nesl/FlexLoc.

It has been shown that some Lorentz-invariant quantum field theories, such as those with higher-dimensional operators with negative coefficients, lead to superluminality on some classical backgrounds. While superluminality by itself is not logically inconsistent, these theories also predict the formation of closed time-like curves at the classical level, starting from initial conditions without such curves. This leads to the formation of a Cauchy Horizon which prevents a complete description of the time evolution of such systems. Inspired by the chronology protection arguments of General Relativity, we show that quantum mechanical effects from low energy quanta strongly backreact on such configurations, exciting unknown short-distance degrees of freedom and invalidating the classical predictions. Thus, there is no obvious low-energy obstruction to the existence of these operators., Comment: 5 pages, 2 figures

Projective Reed-Muller codes are constructed from the family of projective hypersurfaces of a fixed degree over a finite field $\F_q$. We consider the relationship between projective Reed-Muller codes and their duals. We determine when these codes are self-dual, when they are self-orthogonal, and when they are LCD. We then show that when $q$ is sufficiently large, the dimension of the hull of a projective Reed-Muller code is 1 less than the dimension of the code. We determine the dimension of the hull for a wider range of parameters and describe how this leads to a new proof of a recent result of Ruano and San Jos\'e (2024)., Comment: 20 pages

This paper describes how to propagate wavefields for arbitrary numbers of traditional time steps in a single step, called a superstep. We show how to construct operators that accomplish this task for finite-difference time domain schemes, including temporal first-order schemes in isotropic, anisotropic and elastic media, as well as temporal second-order schemes for acoustic media. This task is achieved by implementing a computational tradeoff differing from traditional single step wavefield propagators by precomputing propagator matrices for each model location for k timesteps (a superstep) and using these propagator matrices to advance the wavefield k time steps at once. This tradeoff separates the physics of the propagator matrix computation from the computer science of wavefield propagation and allows each discipline to provide their optimal modular solutions.

Because spin-orbit coupling (SOC) is invisible in the band structure when inversion symmetry exists, whether spins are trivially degenerate or strongly coupled to momentum due to SOC is presumed to make little difference in transport measurements, such as magnetoresistance and quantum oscillations. In this work, however, we show that hidden Rashba SOC in a centrosymmetric two-dimensional material can lead to the quantum Hall effect with only even-integer plateaus, unlike a spinless electron gas. Here, two Rashba layers that are degenerate but with opposite SOC due to inversion symmetry, hybridize with each other and create two doubly-degenerate bands with hidden spin texture. Correspondingly, two branches of Landau levels interact, resulting in significant suppression of spin splitting due to the balancing of intralayer SOC and interlayer hybridization. Furthermore, we show that breaking inversion symmetry restores the ordinary quantum Hall fluid by introducing spin-split Fermi surfaces. Our theory can apply to centrosymmetric materials with strong SOC, as demonstrated in a recent experiment on the two-dimensional semiconductor Bi$_2$O$_2$Se., Comment: 11 pages + appendices

The fibula-free flap (FFF) is a valuable reconstructive technique in maxillofacial surgery; however, the assessment of osteotomy accuracy remains challenging. We devised two novel methodologies to compare planned and postoperative osteotomies in FFF reconstructions that minimized user input but would still generalize to other operations involving the analysis of osteotomies. Our approaches leverage basic mathematics to derive both quantitative and qualitative insights about the relationship of the postoperative osteotomy to the planned model. We have coined our methods 'analysis by a shared reference angle' and 'Euler angle analysis'. In addition to describing our algorithm and the clinical utility, we present a thorough validation of both methods. Code is available at https://github.com/monkeygobah/osteoplane.

Nonlinear Hall effect (NHE) can originate from the quantum metric mechanism in antiferromagnetic topological materials with PT symmetry, which has been experimentally observed in MnBi2Te4. In this work, we propose that breaking PT symmetry via external electric fields can lead to a dramatic enhancement of NHE, thus allowing for an electric control of NHE. Microscopically, this is because breaking PT symmetry can lift spin degeneracy of a Kramers' pair, giving rise to additional contributions within one Kramers' pair of bands. We demonstrate this enhancement through a model Hamiltonian that describes an antiferromagnetic topological insulator sandwich structure.

We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent that a single all-encompassing definition may not exist. Thus, we employ a different strategy and aim to "sandwich" the concept of reconstruction attacks by addressing two complementing questions: (i) What conditions guarantee that a given system is protected against such attacks? (ii) Under what circumstances does a given attack clearly indicate that a system is not protected? More specifically, * We introduce a new definitional paradigm -- Narcissus Resiliency -- to formulate a security definition for protection against reconstruction attacks. This paradigm has a self-referential nature that enables it to circumvent shortcomings of previously studied notions of security. Furthermore, as a side-effect, we demonstrate that Narcissus resiliency captures as special cases multiple well-studied concepts including differential privacy and other security notions of one-way functions and encryption schemes. * We formulate a link between reconstruction attacks and Kolmogorov complexity. This allows us to put forward a criterion for evaluating when such attacks are convincingly successful.

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.

We show that driving optical phonons above a threshold fluence induces spatiotemporal orders, where material properties oscillate at an incommensurate wavevector $q_0$ in space and at half the drive frequency in time. The order is robust against temperature on timescales much larger than the lifetime of the excited modes and can be accompanied by a static $2q_0$ modulation. We make predictions for time-resolved diffraction and provide estimates for candidate materials. Our results show the possibility of using THz waves in solids to realize tunable incommensurate order on the nanoscale., Comment: 5 pages, 4 figures + supplementary information

During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star-black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (FAR < 1 per year) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg$^2$ within the skymaps of the high-significance GW events, reaching a median depth of g~20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag/day can peak at a magnitude brighter than -17.5 mag., Comment: submitted

Motivated by the quantum description of gauge theories, we study the cosmological effects of relaxing the Hamiltonian and momentum constraints in general relativity and Gauss' law in electromagnetism. We show that the unconstrained theories have new source terms that mimic a pressureless dust and a charge density that only follows geodesics. The source terms may be the simplest explanation for dark matter and generically predict a charged component. We comment that discovery of such terms would rule out inflation and be a direct probe of the initial conditions of the universe., Comment: 6 pages

Airborne transmission has been recognized as an important route of transmission for SARS-CoV-2, the virus responsible for the COVID-19 pandemic. While coughing and sneezing are spectacular sources of production of infected aerosols with far-reaching airflows, the prevalence of asymptomatic transmissions highlighted the importance of social activities. Gathering around a table, a common scenario of human interactions, not only fixes a typical distance between static interlocutors, but influences airborne transmission, by serving both as a flow diverter and a surface for droplet deposition. Here, we use high-fidelity large-eddy simulations to characterize short-range airborne transmission when two people face each other at a typical table. We show that compared to the natural distance travelled by free buoyant puffs and jets, the distance between the table and the emission constitutes a new length scale that modifies downward exhaled flows, common during nose breathing, speech, and laughter. When the table is close to the emitter, its main effect is to restrict the forward spread of emitted particles. However, if the distance between individuals is too short, particles reaching the recipient become more concentrated, rising transmission risks. Additionally, simulations of forceful exhalations, like laughter, demonstrate that the table acts as a filter that collects medium-sized particles that would have remained in the free jet otherwise, but can in that case be involved in the fomite transmission route. The table introduces a cut-off size for particles that depends on the inertia of the exhaled material, thereby modifying the size distribution of particles suspended in the air., Comment: 27 pages, 9 figures. Submitted to PNAS

As Internet of Things (IoT) technology advances, end devices like sensors and smartphones are progressively equipped with AI models tailored to their local memory and computational constraints. Local inference reduces communication costs and latency; however, these smaller models typically underperform compared to more sophisticated models deployed on edge servers or in the cloud. Cooperative Inference Systems (CISs) address this performance trade-off by enabling smaller devices to offload part of their inference tasks to more capable devices. These systems often deploy hierarchical models that share numerous parameters, exemplified by Deep Neural Networks (DNNs) that utilize strategies like early exits or ordered dropout. In such instances, Federated Learning (FL) may be employed to jointly train the models within a CIS. Yet, traditional training methods have overlooked the operational dynamics of CISs during inference, particularly the potential high heterogeneity in serving rates across clients. To address this gap, we propose a novel FL approach designed explicitly for use in CISs that accounts for these variations in serving rates. Our framework not only offers rigorous theoretical guarantees, but also surpasses state-of-the-art (SOTA) training algorithms for CISs, especially in scenarios where inference request rates or data availability are uneven among clients.

PINT is a pure-Python framework for high-precision pulsar timing developed on top of widely used and well-tested Python libraries, supporting both interactive and programmatic data analysis workflows. We present a new frequentist framework within PINT to characterize the single-pulsar noise processes present in pulsar timing datasets. This framework enables the parameter estimation for both uncorrelated and correlated noise processes as well as the model comparison between different timing and noise models in a computationally inexpensive way. We demonstrate the efficacy of the new framework by applying it to simulated datasets as well as a real dataset of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature., Comment: Accepted for publication in ApJ

Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability. To investigate this claim rigorously, we commission Grade School Math 1000 (GSM1k). GSM1k is designed to mirror the style and complexity of the established GSM8k benchmark, the gold standard for measuring elementary mathematical reasoning. We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more. When evaluating leading open- and closed-source LLMs on GSM1k, we observe accuracy drops of up to 8%, with several families of models showing evidence of systematic overfitting across almost all model sizes. Further analysis suggests a positive relationship (Spearman's r^2 = 0.36) between a model's probability of generating an example from GSM8k and its performance gap between GSM8k and GSM1k, suggesting that some models may have partially memorized GSM8k. Nevertheless, many models, especially those on the frontier, show minimal signs of overfitting, and all models broadly demonstrate generalization to novel math problems guaranteed to not be in their training data., Comment: 2024 NeurIPS Camera Ready (Datasets and Benchmarks Track)

Competitive Influence Maximization (CIM) involves entities competing to maximize influence in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in CIM rely on simplistic binary opinion models (i.e., an opinion is represented by either 0 or 1) and often overlook the complexity of users' behavioral characteristics and their prior knowledge. We propose a novel DRL-based framework that enhances CIM analysis by integrating Subjective Logic (SL) to accommodate uncertain opinions, users' behaviors, and their preferences. This approach targets the mitigation of false information by effectively propagating true information. By modeling two competitive agents, one spreading true information and the other spreading false information, we capture the strategic interplay essential to CIM. Our framework utilizes an uncertainty-based opinion model (UOM) to assess the impact on information quality in OSNs, emphasizing the importance of user behavior alongside network topology in selecting influential seed nodes. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, achieving faster and more influential results (i.e., outperforming over 20%) under realistic network conditions. Moreover, our method shows robust performance in partially observable networks, effectively doubling the performance when users are predisposed to disbelieve true information., Comment: 8 pages, 3 figures, submitted to ASONAM 2024

On 24 September 2023, the NASA OSIRIS-REx mission dropped a capsule to Earth containing approximately 120 g of pristine carbonaceous regolith from Bennu. We describe the delivery and initial allocation of this asteroid sample and introduce its bulk physical, chemical, and mineralogical properties from early analyses. The regolith is very dark overall, with higher-reflectance inclusions and particles interspersed. Particle sizes range from sub-micron dust to a stone about 3.5 cm long. Millimeter-scale and larger stones typically have hummocky or angular morphologies. A subset of the stones appears mottled by brighter material that occurs as veins and crusts. Hummocky stones have the lowest densities and mottled stones have the highest. Remote sensing of the surface of Bennu detected hydrated phyllosilicates, magnetite, organic compounds, carbonates, and scarce anhydrous silicates, all of which the sample confirms. We also find sulfides, presolar grains, and, less expectedly, Na-rich phosphates, as well as other trace phases. The sample composition and mineralogy indicate substantial aqueous alteration and resemble those of Ryugu and the most chemically primitive, low-petrologic-type carbonaceous chondrites. Nevertheless, we find distinct hydrogen, nitrogen, and oxygen isotopic compositions, and some of the material we analyzed is enriched in fluid-mobile elements. Our findings underscore the value of sample return, especially for low-density material that may not readily survive atmospheric entry, and lay the groundwork for more comprehensive analyses., Comment: 73 pages, 22 figures

Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. This scenario necessitates the use of composite class labels. In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL). By placing a grouped Dirichlet distribution on the class probabilities, we treat predictions of a neural network as parameters of hyper-subjective opinions and learn the network that collects both single and composite evidence leading to these hyper-opinions by a deterministic DNN from data. We introduce a new uncertainty type called vagueness originally designed for hyper-opinions in SL to quantify composite classification uncertainty for DNNs. Our results demonstrate that HENN outperforms its state-of-the-art counterparts based on four image datasets. The code and datasets are available at: https://github.com/Hugo101/HyperEvidentialNN., Comment: In Proceedings of The Twelfth International Conference on Learning Representations, ICLR 2024

This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called "The Transformation Risk-Benefit Model of Artificial Intelligence" to address the increasing fears and levels of AI risk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefits outweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model., Comment: 22 pages, 2 figures, 3 tables

We present the Sydney Radio Star Catalogue, a new catalogue of stars detected at megahertz to gigahertz radio frequencies. It consists of 839 unique stars with 3,405 radio detections, more than doubling the previously known number of radio stars. We have included stars from large area searches for radio stars found using circular polarisation searches, cross-matching, variability searches, and proper motion searches as well as presenting hundreds of newly detected stars from our search of Australian SKA Pathfinder observations. The focus of this first version of the catalogue is on objects detected in surveys using SKA precursor instruments; however we will expand this scope in future versions. The 839 objects in the Sydney Radio Star Catalogue are distributed across the whole sky and range from ultracool dwarfs to Wolf-Rayet stars. We find that the radio luminosities of cool dwarfs are lower than the radio luminosities of more evolved sub-giant and giant stars. We use X-ray detections of 530 radio stars by the eROSITA soft X-ray instrument onboard the SRG spacecraft to show that almost all of the radio stars in the catalogue are over-luminous in the radio, indicating that the majority of stars at these radio frequencies are coherent radio emitters. The Sydney Radio Star Catalogue can be found in Vizier or at https://radiostars.org., Comment: 21 pages, 8 Figures, 7 Tables, Submitted to PASA

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 $26~\mathrm{nHz}$ break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early universe., Comment: 13 pages, 8 figures, 2 appendices, in press at ApJ

We present the formalism and implementation of quasi-particle self-consistent GW (qsGW) and eigenvalue only quasi-particle self-consistent GW (evGW) adapted to standard quantum chemistry packages. Our implementation is benchmarked against high-level quantum chemistry computations (coupled-cluster theory) and experimental results using a representative set of molecules. Furthermore, we compare the qsGW approach for five molecules relevant for organic photovoltaics to self-consistent GW results (scGW) and analyze the effects of the self-consistency on the ground state density by comparing calculated dipole moments to their experimental values. We show that qsGW makes a significant improvement over conventional G0W0 and that partially self-consistent flavors (in particular evGW) can be excellent alternatives.