Search

Axion-like particles (ALPs) arise from well-motivated extensions to the Standard Model and could account for dark matter. ALP dark matter would manifest as a nearly monochromatic field oscillating at an (as of yet) unknown frequency. The frequency depends on the ALP mass, which could plausibly range from $10^{-22}$ eV/$c^2$ to $10$ eV/$c^2$. We report on a direct search for ALP dark matter through the ALP-nucleon interaction by interfering the signals of two atomic K-Rb-$^3$He comagnetometers, with one situated in Mainz, Germany, and the other in Krak\'ow, Poland. We use the ALP dark matter's spatiotemporal coherence properties assuming the standard halo model of dark matter in the Milky Way to improve the sensitivity and exclude spurious candidates. The search extends over nine orders of magnitude in ALP mass. In this range, no significant evidence of an ALP signal is found. We thus place new upper limits on the ALP-neutron and ALP-proton couplings of $g_{aNN}<10^{-5}$ GeV$^{-1}$ and $g_{aPP}<5 \times 10^{-4}$ GeV$^{-1}$ at a mass of $10^{-22}$ eV/$c^2$ and extending to a mass of $10^{-15}$ eV/$c^2$ where the upper limits reach below $g_{aNN}<10^{-9}$ GeV$^{-1}$ and $g_{aPP}<10^{-7}$ GeV$^{-1}$, respectively. For both neutron and proton couplings, this work is an improvement of up to four orders of magnitude compared to previous laboratory constraints.

In this paper, we calculate a basic strategy for several variations of a simplified version of Blackjack. In short, for these variants the player has only the two options of hit or stand, and they may only make either decision once. Other minor variations including rule modifications, changes in number of decks, alternate payout structures, and different given information about dealer's hand are also considered. An interesting theoretical result regarding the asymptotic behavior of the basic strategy and overall expectation as the number of decks increase is also proved.

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance., Comment: Resubmitted to ApJ. 22 pages, 16 figures, for more information about the combined pre-supernova alert system, see https://www.lowbg.org/presnalarm/

Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automated clinical diagnostic tool design strategies relying on radiology reads and supervised learning, entail the cumbersome requirement of high quality annotated training data. To address this challenge, self-supervised pre-training has proven to outperform supervised pre-training in numerous downstream vision tasks, representing a significant breakthrough in the field. However, medical imaging pre-training significantly differs from pre-training with natural images (e.g., ImageNet) due to unique attributes of clinical images. In this context, we introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm that leverages a student teacher framework for learning diverse concepts and hence effective representation of the CXR data. Hence, expanding beyond merely modeling a single primary label within an image, instead, effectively harnessing the information from all the concepts inherent in the CXR. The pre-trained model is subsequently fine-tuned to address diverse domain-specific tasks. Our proposed paradigm consistently demonstrates robust performance across multiple downstream tasks on multiple datasets, highlighting the success and generalizability of the pre-training strategy. To establish the efficacy of our methods we analyze both the power of learned representations and the speed of convergence (SoC) of our models. For diverse data and tasks, DiCoM is able to achieve in most cases better results compared to other state-of-the-art pre-training strategies. This when combined with the higher SoC and generalization capabilities positions DiCoM to be established as a foundation model for CXRs, a widely used imaging modality.

Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\sim$12.7% and $\sim$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce., Comment: 5 pages, 3 figures, 2 tables. This paper has been accepted at IEEE ISBI 2024

Powered by the increasing predictive capabilities of machine learning algorithms, artificial intelligence (AI) systems have begun to be used to overrule human mistakes in many settings. We provide the first field evidence this AI oversight carries psychological costs that can impact human decision-making. We investigate one of the highest visibility settings in which AI oversight has occurred: the Hawk-Eye review of umpires in top tennis tournaments. We find that umpires lowered their overall mistake rate after the introduction of Hawk-Eye review, in line with rational inattention given psychological costs of being overruled by AI. We also find that umpires increased the rate at which they called balls in, which produced a shift from making Type II errors (calling a ball out when in) to Type I errors (calling a ball in when out). We structurally estimate the psychological costs of being overruled by AI using a model of rational inattentive umpires, and our results suggest that because of these costs, umpires cared twice as much about Type II errors under AI oversight.

Adaptive Systems (ASs) are capable to monitor their behavior and make adjustments when quality goals are not achieved through the MAPE-K, a widely recognized reference model that offers abstractions for designing ASs. By making these abstractions evident in the system structure, numerous benefits emerge, particularly in terms of enhancing the architecture's maintenance and comprehensibility. However, it is observed that many existing ASs are not designed in accordance with MAPE-K, causing these abstractions to remain hidden in their architecture. To address this issue, Architectural Conformance Checking (ACC) emerges as a valuable technique for verifying whether the current architecture (CA) of a system adheres to the rules prescribed by the planned architecture (PA) or a reference model, such as MAPE-K. In this paper, we present REMEDY, a domain-specific approach that encompasses the specification of the planned adaptive architecture based on the MAPE-K reference model, the recovery of the current adaptive architecture, the conformance checking process, and architecture visualizations. Furthermore, our approach is specifically tailored for ASs, incorporating well-known rules from the MAPE-K model. The evaluation of the REMEDY DSL involves a comparison with a general-purpose DSL, and the results demonstrate improvements in productivity. REMEDY facilitates the identification and correction of architectural non-conformance issues, thereby enhancing the overall quality of adaptive systems., Comment: Submitted to JSERD

We present a mean field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyse the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units $f< f_t \leq 1/2$, even for a finite system. In finite systems, when $f > f_t$ the first order transition is replaced by a pseudo critical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behaviour of a continuous phase transition order parameter. However, in the thermodynamic limit, we found that $f_t\to 1/2$ and the activity for $f\geq f_t$ becomes negligible for any value of $T>0$, while the first order transition for $f

The origin of thick disks and their evolutionary connection with thin disks are still a matter of debate. We provide new insights into this topic by connecting the stellar populations of thick disks at redshift $z=0$ with their past formation and growth, in 24 Milky Way-mass galaxies from the AURIGA zoom-in cosmological simulations. We projected each galaxy edge on, and decomposed it morphologically into two disk components, in order to define geometrically the thin and the thick disks as usually done in observations. We produced age, metallicity and [Mg/Fe] edge-on maps. We quantified the impact of satellite mergers by mapping the distribution of ex-situ stars. Thick disks are on average $\sim 3$~Gyr older, $\sim 0.25$~dex more metal poor and $\sim 0.06$~dex more [Mg/Fe]-enhanced than thin disks. Their average ages range from $\sim 6$ to $\sim 9$~Gyr, metallicities from $\sim -0.15$ to $\sim 0.1$~dex, and [Mg/Fe] from $\sim 0.12$ to $\sim 0.16$~dex. These properties are the result of an early initial in-situ formation, followed by a later growth driven by the combination of direct accretion of stars, some in-situ star formation fueled by mergers, and dynamical heating of stars. The balance between these processes varies from galaxy to galaxy. Mergers play a key role in the mass assembly of thick disks, contributing an average accreted mass fraction of $\sim 22$\% in the analyzed thick-disk dominated regions. In two galaxies, about half of the geometric thick-disk mass was directly accreted. While primordial thick disks form at high redshift in all galaxies, young metal-rich thin disks, with much lower [Mg/Fe] abundances, start to form later but at different times (higher or lower redshift) depending on the galaxy. We conclude that thick disks result from the interplay of external processes with the internal evolution of the galaxy., Comment: 57 pages, 43 figures, 3 tables; Published in Astronomy & Astrophysics

The emergent abilities of Large Language Models (LLMs), which power tools like ChatGPT and Bard, have produced both excitement and worry about how AI will impact academic writing. In response to rising concerns about AI use, authors of academic publications may decide to voluntarily disclose any AI tools they use to revise their manuscripts, and journals and conferences could begin mandating disclosure and/or turn to using detection services, as many teachers have done with student writing in class settings. Given these looming possibilities, we investigate whether academics view it as necessary to report AI use in manuscript preparation and how detectors react to the use of AI in academic writing.

The merger history of a galaxy is thought to be one of the major factors determining its internal dynamics, with galaxies having undergone different types or mergers (e.g. dry, minor or major mergers) predicted to show different dynamical properties. We study the instantaneous orbital distribution of galaxies in the Eagle simulation, colouring the orbits of the stellar particles by their stellar age, in order to understand whether stars form in particular orbits (e.g. in a thin or thick disc). We first show that Eagle reproduces well the observed stellar mass fractions in different stellar orbital families as a function of stellar mass and spin parameter at z = 0. We find that the youngest stars reside in a thin disc component that can extend to the very inner regions of galaxies, and that older stars have warmer orbits, with the oldest ones showing orbits consistent with both hot and counter-rotating classifications, which is consistent with the trend found in the Milky-Way and other disc galaxies. We also show that counter-rotating orbits trace galaxy mergers - in particular dry mergers, and that in the absence of mergers, counter-rotating orbits can also be born from highly misaligned gas accretion that leads to star formation., Comment: Submitted to MNRAS, 21 pages

The extend of tool wear significantly affects blanking processes and has a decisive impact on product quality and productivity. For this reason, numerous scientists have addressed their research to wear monitoring systems in order to identify or even predict critical wear at an early stage. Existing approaches are mainly based on indirect monitoring using time series, which are used to detect critical wear states via thresholds or machine learning models. Nevertheless, differentiation between types of wear phenomena affecting the tool during blanking as well as quantification of worn surfaces is still limited in practice. While time series data provides partial insights into wear occurrence and evolution, direct monitoring techniques utilizing image data offer a more comprehensive perspective and increased robustness when dealing with varying process parameters. However, acquiring and processing this data in real-time is challenging. In particular, high dynamics combined with increasing strokes rates as well as the high dimensionality of image data have so far prevented the development of direct image-based monitoring systems. For this reason, this paper demonstrates how high-resolution images of tools at 600 spm can be captured and subsequently processed using semantic segmentation deep learning algorithms, more precisely Fully Convolutional Networks (FCN). 125,000 images of the tool are taken from successive strokes, and microscope images are captured to investigate the worn surfaces. Based on findings from the microscope images, selected images are labeled pixel by pixel according to their wear condition and used to train a FCN (U-Net).

We propose and demonstrate a general method to calibrate the frequency-dependent response of self-compensating noble-gas-alkali-metal comagnetometers to arbitrary spin perturbations. This includes magnetic and nonmagnetic perturbations like rotations and exotic spin interactions. The method is based on a fit of the magnetic field response to an analytical model. The frequency-dependent response of the comagnetometer to arbitrary spin perturbations can be inferred using the fit parameters. We demonstrate the effectiveness of this method by comparing the inferred rotation response to an experimental measurement of the rotation response. Our results show that experiments relying on zero-frequency calibration of the comagnetometer response can over- or under-estimate the comagnetometer sensitivity by orders of magnitude over a wide frequency range. Moreover, this discrepancy accumulates over time as operational parameters tend to drift during comagnetometer operation. The demonstrated calibration protocol enables accurate prediction and control of comagnetometer sensitivity to, for example, ultralight bosonic dark-matter fields coupling to electron or nuclear spins as well as accurate monitoring and control of the relevant system parameters.

Tuberculosis (TB) is still recognized as one of the leading causes of death worldwide. Recent advances in deep learning (DL) have shown to enhance radiologists' ability to interpret chest X-ray (CXR) images accurately and with fewer errors, leading to a better diagnosis of this disease. However, little work has been done to develop models capable of diagnosing TB that offer good performance while being efficient, fast and computationally inexpensive. In this work, we propose LightTBNet, a novel lightweight, fast and efficient deep convolutional network specially customized to detect TB from CXR images. Using a total of 800 frontal CXR images from two publicly available datasets, our solution yielded an accuracy, F1 and area under the ROC curve (AUC) of 0.906, 0.907 and 0.961, respectively, on an independent test subset. The proposed model demonstrates outstanding performance while delivering a rapid prediction, with minimal computational and memory requirements, making it highly suitable for deployment in handheld devices that can be used in low-resource areas with high TB prevalence. Code publicly available at https://github.com/dani-capellan/LightTBNet., Comment: 5 pages, 3 figures, 3 tables. This paper has been accepted at ISBI 2023

Markoff mod-$p$ graphs are conjectured to be connected for all primes $p$. In this paper, we use results of Chen and Bourgain, Gamburd, and Sarnak to confirm the conjecture for all $p > 3.448\cdot10^{392}$. We also provide a method that quickly verifies connectivity for many primes below this bound. In our study of Markoff mod-$p$ graphs we introduce the notion of \emph{maximal divisors} of a number. We prove sharp asymptotic and explicit upper bounds on the number of maximal divisors, which ultimately improves the Markoff graph $p$-bound by roughly 140 orders of magnitude as compared with an approach using all divisors.

Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not trivial: cosmological galaxy simulations are the only tools we have for calibration. We study the robustness of a simulation-based inference of the ex-situ stellar mass fraction of nearby galaxies to different observables -- integrated and spatially-resolved -- and to different galaxy formation models -- IllustrisTNG and EAGLE -- with Machine Learning. We find that at fixed simulation, the fraction of accreted stars can be inferred with very high accuracy, with an error $\sim5$ per cent (10 per cent) from 2D integral-field spectroscopic maps (integrated quantities) throughout the considered stellar mass range. A bias (> 5 per cent) and an increase in scatter by a factor of 2 are introduced when testing with a different simulation, revealing a lack of generalization to distinct galaxy-formation models. Interestingly, upon using only stellar mass and kinematics maps in the central galactic regions for training, we find that this bias is removed and the ex-situ stellar mass fraction can be recovered in both simulations with < 15 per cent scatter, independently of the training set's origin. This opens up the door to a potential robust inference of the accretion histories of galaxies from existing Integral Field Unit surveys, such as MaNGA, covering a similar field of view (FOV) and containing spatially-resolved spectra for tens of thousands of nearby galaxies., Comment: 23 pages, 15 figures. Accepted for publication in MNRAS

In this paper, we give a definition for the Bartnik mass of a domain whose extensions are asymptotically hyperbolic manifolds. With this definition, we show that asymptotically hyperbolic admissible extensions of a domain that achieve the Bartnik mass must admit a static potential. Given a non-static admissible extension of a domain, we are able to construct a one-parameter family of metrics that are close to the original metric, have smaller mass, share the same bound on the scalar curvature, and contain the domain isometrically.

Tuberculosis (TB) is still considered a leading cause of death and a substantial threat to global child health. Both TB infection and disease are curable using antibiotics. However, most children who die of TB are never diagnosed or treated. In clinical practice, experienced physicians assess TB by examining chest X-rays (CXR). Pediatric CXR has specific challenges compared to adult CXR, which makes TB diagnosis in children more difficult. Computer-aided diagnosis systems supported by Artificial Intelligence have shown performance comparable to experienced radiologist TB readings, which could ease mass TB screening and reduce clinical burden. We propose a multi-view deep learning-based solution which, by following a proposed template, aims to automatically regionalize and extract lung and mediastinal regions of interest from pediatric CXR images where key TB findings may be present. Experimental results have shown accurate region extraction, which can be used for further analysis to confirm TB finding presence and severity assessment. Code publicly available at https://github.com/dani-capellan/pTB_LungRegionExtractor., Comment: This work has been accepted at the SPIE Medical Imaging 2023, Image Processing conference

Recent integral-field spectroscopy observations have revealed that thick- and thin-disk star-formation histories are regulated by the interplay of internal and external processes. We analyze stellar-population properties of 24 spiral galaxies from the AURIGA zoom-in cosmological simulations, to offer a more in-depth interpretation of observable properties. We present edge-on maps of stellar age, metallicity and [Mg/Fe] abundance, and we extract the star-formation and chemical-evolution histories of thin and thick disks. Both show signs of the interplay between internal chemical enrichment and gas and star accretion. Thick disks show particularly complex stellar populations, including an in-situ component, formed from both slowly enriched and accreted more pristine gas, and an additional significant fraction of ex-situ stars., Comment: 3 pages, one figure, Proceedings IAU Symposium S373

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins., Comment: preprint

Athletics are a quintessential and universal expression of humanity. From French monks who in the 12th century invented jeu de paume, the precursor to modern lawn tennis, back to the K'iche' people who played the Maya Ballgame as a form of religious expression over three thousand years ago, humans have sought to train their minds and bodies to excel in sporting contests. Advances in robotics are opening up the possibility of robots in sports. Yet, key challenges remain, as most prior works in robotics for sports are limited to pristine sensing environments, do not require significant force generation, or are on miniaturized scales unsuited for joint human-robot play. In this paper, we propose the first open-source, autonomous robot for playing regulation wheelchair tennis. We demonstrate the performance of our full-stack system in executing ground strokes and evaluate each of the system's hardware and software components. The goal of this paper is to (1) inspire more research in human-scale robot athletics and (2) establish the first baseline for a reproducible wheelchair tennis robot for regulation singles play. Our paper contributes to the science of systems design and poses a set of key challenges for the robotics community to address in striving towards robots that can match human capabilities in sports., Comment: 8 pages, accepted at RA-L, will also be presented at IROS 2023

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, current LfD frameworks are not capable of fast adaptation to heterogeneous human demonstrations nor the large-scale deployment in ubiquitous robotics applications. In this paper, we propose a novel LfD framework, Fast Lifelong Adaptive Inverse Reinforcement learning (FLAIR). Our approach (1) leverages learned strategies to construct policy mixtures for fast adaptation to new demonstrations, allowing for quick end-user personalization, (2) distills common knowledge across demonstrations, achieving accurate task inference; and (3) expands its model only when needed in lifelong deployments, maintaining a concise set of prototypical strategies that can approximate all behaviors via policy mixtures. We empirically validate that FLAIR achieves adaptability (i.e., the robot adapts to heterogeneous, user-specific task preferences), efficiency (i.e., the robot achieves sample-efficient adaptation), and scalability (i.e., the model grows sublinearly with the number of demonstrations while maintaining high performance). FLAIR surpasses benchmarks across three control tasks with an average 57% improvement in policy returns and an average 78% fewer episodes required for demonstration modeling using policy mixtures. Finally, we demonstrate the success of FLAIR in a table tennis task and find users rate FLAIR as having higher task (p<.05) and personalization (p<.05) performance.