Search

Exoplanets with short orbit period reside very close to their host stars. They transition very rapidly between different sectors of the circumstellar space environment along their orbit, leading to large variations of the magnetic field in the vicinity of the planet on short timescales. This rapid change of the magnetic flux through the conducting and resistive layer of the planetary upper atmosphere may drive currents that dissipate in the form of Joule Heating. Here, we estimate the amount of Joule Heating dissipation in the upper atmosphere of Trappist-1e, and two hypothetical planets orbiting the Sun in close-in orbits. We find that the rapid orbital motion could drive a significant amount of atmospheric heating and could significantly affect the planetary atmosphere escape rate. Thus, the process should be accounted for when studying the long-term evolution of exoplanetary atmospheres., Comment: 8 pages, 4 figures, accepted to ApJ

Deep Learning has been successfully applied to many application domains, yet its advantages have been slow to emerge for time series forecasting. For example, in the well-known Makridakis (M) Competitions, hybrids of traditional statistical or machine learning techniques have only recently become the top performers. With the recent architectural advances in deep learning being applied to time series forecasting (e.g., encoder-decoders with attention, transformers, and graph neural networks), deep learning has begun to show significant advantages. Still, in the area of pandemic prediction, there remain challenges for deep learning models: the time series is not long enough for effective training, unawareness of accumulated scientific knowledge, and interpretability of the model. To this end, the development of foundation models (large deep learning models with extensive pre-training) allows models to understand patterns and acquire knowledge that can be applied to new related problems before extensive training data becomes available. Furthermore, there is a vast amount of knowledge available that deep learning models can tap into, including Knowledge Graphs and Large Language Models fine-tuned with scientific domain knowledge. There is ongoing research examining how to utilize or inject such knowledge into deep learning models. In this survey, several state-of-the-art modeling techniques are reviewed, and suggestions for further work are provided.

We study reduced products $M=\prod_n M_n/\mathrm{Fin}$ of countable structures in a countable language associated with the Fr\'echet ideal. We prove that such $M$ is $2^{\aleph_0}$-saturated if its theory is stable and not $\aleph_2$-saturated otherwise (regardless of whether the Continuum Hypothesis holds). This implies that $M$ is isomorphic to an ultrapower (associated with an ultrafilter on $\mathbb N$) if its theory is stable, even if the CH fails. We also improve a result of Farah and Shelah and prove that there is a forcing extension in which such reduced product $M$ is isomorphic to an ultrapower if and only if the theory of $M$ is stable. All of these conclusions apply for reduced products associated with $F_\sigma$ ideals or more general layered ideals. We also prove that a reduced product associated with the asymptotic density zero ideal $\mathcal Z_0$, or any other analytic P-ideal that is not $F_\sigma$, is not even $\aleph_1$-saturated if its theory is unstable., Comment: 32 pages

In an era of rapid technological advancement, the rise of Industry 4.0 has prompted industries to pursue innovative improvements in their processes. As we advance towards Industry 5.0, which focuses more on collaboration between humans and intelligent systems, there is a growing requirement for better sensing technologies for healthcare and safety purposes. Consequently, Motion Capture (MoCap) systems have emerged as critical enablers in this technological evolution by providing unmatched precision and versatility in various workplaces, including construction. As the construction workplace requires physically demanding tasks, leading to work-related musculoskeletal disorders (WMSDs) and health issues, the study explores the increasing relevance of MoCap systems within the concept of Industry 4.0 and 5.0. Despite the growing significance, there needs to be more comprehensive research, a scientometric review that quantitatively assesses the role of MoCap systems in construction. Our study combines bibliometric, scientometric, and systematic review approaches to address this gap, analyzing articles sourced from the Scopus database. A total of 52 papers were carefully selected from a pool of 962 papers for a quantitative study using a scientometric approach and a qualitative, indepth examination. Results showed that MoCap systems are employed to improve worker health and safety and reduce occupational hazards.The in-depth study also finds the most tested construction tasks are masonry, lifting, training, and climbing, with a clear preference for markerless systems.

This article introduces a novel, low-cost technique for hiding data in commercially available resistive-RAM (ReRAM) chips. The data is kept hidden in ReRAM cells by manipulating its analog physical properties through switching ($\textit{set/reset}$) operations. This hidden data, later, is retrieved by sensing the changes in cells' physical properties (i.e., $\textit{set/reset}$ time of the memory cells). The proposed system-level hiding technique does not affect the normal memory operations and does not require any hardware modifications. Furthermore, the proposed hiding approach is robust against temperature variations and the aging of the devices through normal read/write operation. The silicon results show that our proposed data hiding technique is acceptably fast with ${\sim}0.4bit/min$ of encoding and ${\sim}15.625bits/s$ of retrieval rates, and the hidden message is unrecoverable without the knowledge of the secret key, which is used to enhance the security of hidden information., Comment: arXiv admin note: text overlap with arXiv:2204.02104

The response of neurons is highly sensitive to the stimulus. The stimulus can be associated with a direct injection in vitro experimentation (e.g., time dependent and independent inputs); or post-synaptic potentials resulting from the interaction of many neurons. A typical incoming stimulus resembles a noise which in principle can be described as a random variable. In computational neuroscience, the noise has been extensively studied for different setups. In this study, we investigate the effect of noisy inputs in a minimal network of two identical leaky integrate-and-fire (LIF) neurons interacting with finite pulses. In particular, we consider a Gaussian white noise as a standard function for stochastic modelling of neurons, while taking into account the pulse width as an elementary component for the signal transmission. By exploring the role of noise and finite pulses, the two neurons show a synchronous spiking behaviour characterized by fluctuations in the interspike intervals. Above some critical values the synchronous regime collapses onto asynchronous dynamics. The abrupt change in such dynamics is accompanied by a hysteresis, i.e., the coexistence of synchronous and asynchronous firing behaviour., Comment: 11 pages, 8 figures

The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the charged particle passing through from pixel-cluster properties. This detector technology immediately improves the situation for offline tracking, but any major improvements in physics reach are unrealized since they are dominated by lowest-level hardware trigger acceptance. We will demonstrate track angle and hit position prediction, including errors, using a mixture density network within a single layer of silicon as well as the progress towards and status of implementing the neural network in hardware on both FPGAs and ASICs., Comment: 6 pages, 3 figures, submitted to Neural Information Processing Systems 2023 (NeurIPS)

FRB 20220912A is a repeating Fast Radio Burst (FRB) that was discovered in Fall 2022 and remained highly active for several months. We report the detection of 35 FRBs from 541 hours of follow-up observations of this source using the recently refurbished Allen Telescope Array, covering 1344 MHz of bandwidth primarily centered at 1572 MHz. All 35 FRBs were detected in the lower half of the band with non-detections in the upper half and covered fluences from 4-431 Jy-ms (median$=$48.27 Jy-ms). We find consistency with previous repeater studies for a range of spectrotemporal features including: bursts with downward frequency drifting over time; a positive correlation between bandwidth and center frequency; and a decrease in sub-burst duration over time. We report an apparent decrease in the center frequency of observed bursts over the 2 months of the observing campaign (corresponding to a drop of $6.21\pm 0.76$ MHz per day). We predict a cut-off fluence for FRB 20220912A of $F_\textrm{max}\lesssim 10^4$ Jy-ms, for this source to be consistent with the all-sky rate, and find that FRB 20220912A significantly contributed to the all-sky FRB rate at a level of a few percent for fluences of $\sim$100 Jy-ms. Finally, we investigate characteristic timescales and sub-burst periodicities and find a) a median inter-subburst timescale of 5.82$\pm$1.16 ms in the multi-component bursts and b) no evidence of strict periodicity even in the most evenly-spaced multi-component burst in the sample. Our results demonstrate the importance of wideband observations of FRBs, and provide an important set of observational parameters against which to compare FRB progenitor and emission mechanism models., Comment: 17 pages, 8 figures, 2 tables, accepted to MNRAS

Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behavior detection (also referred to in this paper as anomalies). Most existing ML-based detectors rely on (fully) supervised ML methods, which require substantial labeled data. However, ground truth labels are not always available in the real world, and labeling large amounts of data is tedious. Thus, there is a need to explore unsupervised or semi-supervised methods to make the anomaly detection process more feasible and efficient. To fill this research gap, this study analyzes large-scale real-world data revealing several abnormal driving behaviors (e.g., sudden acceleration, rapid lane-changing) and develops a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method using partly labeled data to accurately detect the identified abnormal driving behaviors. Moreover, previous ML-based approaches predominantly utilized basic vehicle motion features (such as velocity and acceleration) to label and detect abnormal driving behaviors, while this study seeks to introduce Surrogate Measures of Safety (SMoS) as input features for ML models to improve the detection performance. Results from extensive experiments demonstrate the effectiveness of the proposed semi-supervised ML model with the introduced SMoS serving as important features. The proposed semi-supervised ML method outperforms other baseline semi-supervised or unsupervised methods regarding various metrics, e.g., delivering the best accuracy at 99.58% and the best F-1 measure at 0.9913. The ablation study further highlights the significance of SMoS for advancing the detection performance of abnormal driving behaviors., Comment: 24 pages, 10 figures, accepted by the 103rd Transportation Research Board (TRB) Annual Meeting, under third round review by Transportation Research Record: Journal of the Transportation Research Board

The burgeoning navigation services using digital maps provide great convenience to drivers. Nevertheless, the presence of anomalies in lane rendering map images occasionally introduces potential hazards, as such anomalies can be misleading to human drivers and consequently contribute to unsafe driving conditions. In response to this concern and to accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into a classification problem and proposes a four-phase pipeline consisting of data pre-processing, self-supervised pre-training with the masked image modeling (MiM) method, customized fine-tuning using cross-entropy based loss with label smoothing, and post-processing to tackle it leveraging state-of-the-art deep learning techniques, especially those involving Transformer models. Various experiments verify the effectiveness of the proposed pipeline. Results indicate that the proposed pipeline exhibits superior performance in lane rendering image anomaly detection, and notably, the self-supervised pre-training with MiM can greatly enhance the detection accuracy while significantly reducing the total training time. For instance, employing the Swin Transformer with Uniform Masking as self-supervised pretraining (Swin-Trans-UM) yielded a heightened accuracy at 94.77% and an improved Area Under The Curve (AUC) score of 0.9743 compared with the pure Swin Transformer without pre-training (Swin-Trans) with an accuracy of 94.01% and an AUC of 0.9498. The fine-tuning epochs were dramatically reduced to 41 from the original 280. In conclusion, the proposed pipeline, with its incorporation of self-supervised pre-training using MiM and other advanced deep learning techniques, emerges as a robust solution for enhancing the accuracy and efficiency of lane rendering image anomaly detection in digital navigation systems., Comment: 22 pages, 6 figures, accepted by the 103rd Transportation Research Board (TRB) Annual Meeting, under review by Transportation Research Record: Journal of the Transportation Research Board

AT 2019azh is a H+He tidal disruption event (TDE) with one of the most extensive ultraviolet and optical data sets available to date. We present our photometric and spectroscopic observations of this event starting several weeks before and out to approximately two years after the g-band peak brightness and combine them with public photometric data. This extensive data set robustly reveals a change in the light-curve slope and a possible bump in the rising light curve of a TDE for the first time, which may indicate more than one dominant emission mechanism contributing to the pre-peak light curve. Indeed, we find that the MOSFiT-derived parameters of AT 2019azh, which assume reprocessed accretion as the sole source of emission, are not entirely self-consistent. We further confirm the relation seen in previous TDEs whereby the redder emission peaks later than the bluer emission. The post-peak bolometric light curve of AT 2019azh is better described by an exponential decline than by the canonical t^{-5/3} (and in fact any) power-law decline. We find a possible mid-infrared excess around the peak optical luminosity, but cannot determine its origin. In addition, we provide the earliest measurements of the Halpha emission-line evolution and find no significant time delay between the peak of the V-band light curve and that of the Halpha luminosity. These results can be used to constrain future models of TDE line formation and emission mechanisms in general. More pre-peak 1-2 days cadence observations of TDEs are required to determine whether the characteristics observed here are common among TDEs. More importantly, detailed emission models are needed to fully exploit such observations for understanding the emission physics of TDEs., Comment: Submitted to ApJ

Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of uncertainty have largely been limited to applications of ad-hoc solutions that do not consistently improve reliability. In this work, we consider stochastic neural networks and design a tailor-made multimodal data-driven (M2D2) prior distribution over network parameters. We use simple and scalable Gaussian mean-field variational inference to train a Bayesian neural network using the M2D2 prior. We train and evaluate the proposed approach using clinical time-series data in MIMIC-IV and corresponding chest X-ray images in MIMIC-CXR for the classification of acute care conditions. Our empirical results show that the proposed method produces a more reliable predictive model compared to deterministic and Bayesian neural network baselines., Comment: Published in ML4H 2023 Findings Track Collection