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We present a study of the detection efficiency for the TESS mission, focusing on the yield of longer-period transiting exoplanets ($P > 25$ days). We created the Transit Investigation and Recoverability Application (TIaRA) pipeline to use real TESS data with injected transits to create sensitivity maps which we combine with occurrence rates derived from Kepler. This allows us to predict longer-period exoplanet yields, which will help design follow-up photometric and spectroscopic programs, such as the NGTS Monotransit Program. For the TESS Year 1 and Year 3 SPOC FFI lightucurves, we find $2271^{+241}_{-138}$ exoplanets should be detectable around AFGKM dwarf host stars. We find $215^{+37}_{-23}$ exoplanets should be detected from single-transit events or "monotransits". An additional $113^{+22}_{-13}$ detections should result from "biennial duotransit" events with one transit in Year 1 and a second in Year 3. We also find that K dwarf stars yield the most detections by TESS per star observed. When comparing our results to the TOI catalogue we find our predictions agree within $1σ$ of the number of discovered systems with periods between 0.78 and 6.25 days and agree to $2σ$ for p

Buddha statues are a part of human culture, especially of the Asia area, and they have been alongside human civilisation for more than 2,000 years. As history goes by, due to wars, natural disasters, and other reasons, the records that show the built years of Buddha statues went missing, which makes it an immense work for historians to estimate the built years. In this paper, we pursue the idea of building a neural network model that automatically estimates the built years of Buddha statues based only on their face images. Our model uses a loss function that consists of three terms: an MSE loss that provides the basis for built year estimation; a KL divergence-based loss that handles the samples with both an exact built year and a possible range of built years (e.g., dynasty or centuries) estimated by historians; finally a regularisation that utilises both labelled and unlabelled samples based on manifold assumption. By combining those three terms in the training process, we show that our method is able to estimate built years for given images with 37.5 years of a mean absolute error on the test set.

With stunning clarity, JWST has revealed the Universe's first billion years. The scientific community is analyzing a wealth of JWST imaging and spectroscopic data from that era, and is in the process of rewriting the astronomy textbooks. Here, 1.5 years into the JWST science mission, we provide a snapshot of the great progress made towards understanding the initial chapters of our cosmic history. We highlight discoveries and breakthroughs, topics and issues that are not yet understood, and questions that will be addressed in the coming years, as JWST continues its revolutionary observations of the Early Universe. While this compendium is written by a small number of authors, invited to ISSI Bern in March 2024 as part of the 2024 ISSI Breakthrough Workshop, we acknowledge the work of a large community that is advancing our collective understanding of the evolution of the Early Universe.

The year 2023 marked a significant surge in the exploration of applying large language model (LLM) chatbots, notably ChatGPT, across various disciplines. We surveyed the applications of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

Background: This invited paper is the result of an invitation to write a retrospective article on a "TSE most influential paper" as part of the journal's 50th anniversary. Objective: To reflect on the progress of software engineering prediction research using the lens of a selected, highly cited research paper and 28 years of hindsight. Methods: The paper examines (i) what was achieved, (ii) what has endured and (iii) what could have been done differently with the benefit of retrospection. Conclusions: While many specifics of software project effort prediction have evolved, key methodological issues remain relevant. The original study emphasised empirical validation with benchmarks, out-of-sample testing and data/tool sharing. Four areas for improvement are identified: (i) stronger commitment to Open Science principles, (ii) focus on effect sizes and confidence intervals, (iii) reporting variability alongside typical results and (iv) more rigorous examination of threats to validity.

We present observations and timing analyses of 68 millisecond pulsars (MSPs) comprising the 15-year data set of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). NANOGrav is a pulsar timing array (PTA) experiment that is sensitive to low-frequency gravitational waves. This is NANOGrav's fifth public data release, including both "narrowband" and "wideband" time-of-arrival (TOA) measurements and corresponding pulsar timing models. We have added 21 MSPs and extended our timing baselines by three years, now spanning nearly 16 years for some of our sources. The data were collected using the Arecibo Observatory, the Green Bank Telescope, and the Very Large Array between frequencies of 327 MHz and 3 GHz, with most sources observed approximately monthly. A number of notable methodological and procedural changes were made compared to our previous data sets. These improve the overall quality of the TOA data set and are part of the transition to new pulsar timing and PTA analysis software packages. For the first time, our data products are accompanied by a full suite of software to reproduce data reduction, analysis, and results. Our timing models include a variety

Thiswork deals with the characterization of the neutron flux in hall B of the CanfrancUnderground Laboratory (LSC) employing the High Efficiency Neutron Spectrometry Array (HENSA). The ultimate goal of this measurement is to set a limit on the corresponding effects of the neutron flux in the background of the ANAIS-112 experiment. The preliminary neutron counting rates of two years of measurement are reported. Various data analysis techniques, including pulse shape discrimination, are discussed. The first results on the spectral reconstruction of the neutron flux are also presented.

Pulsar timing arrays (PTAs) use an array of millisecond pulsars to search for gravitational waves in the nanohertz regime in pulse time of arrival data. This paper presents rigorous tests of PTA methods, examining their consistency across the relevant parameter space. We discuss updates to the 15-year isotropic gravitational-wave background analyses and their corresponding code representations. Descriptions of the internal structure of the flagship algorithms Enterprise and PTMCMCSampler are given to facilitate understanding of the PTA likelihood structure, how models are built, and what methods are currently used in sampling the high-dimensional PTA parameter space. We introduce a novel version of the PTA likelihood that uses a two-step marginalization procedure that performs much faster in gravitational wave searches, reducing the required resources facilitating the computation of Bayes factors via thermodynamic integration and sampling a large number of realizations for computing Bayesian false-alarm probabilities. We perform stringent tests of consistency and correctness of the Bayesian and frequentist analysis methods. For the Bayesian analysis, we test prior recovery, simulat

Pulsar timing arrays (PTAs) are galactic-scale gravitational wave detectors. Each individual arm, composed of a millisecond pulsar, a radio telescope, and a kiloparsecs-long path, differs in its properties but, in aggregate, can be used to extract low-frequency gravitational wave (GW) signals. We present a noise and sensitivity analysis to accompany the NANOGrav 15-year data release and associated papers, along with an in-depth introduction to PTA noise models. As a first step in our analysis, we characterize each individual pulsar data set with three types of white noise parameters and two red noise parameters. These parameters, along with the timing model and, particularly, a piecewise-constant model for the time-variable dispersion measure, determine the sensitivity curve over the low-frequency GW band we are searching. We tabulate information for all of the pulsars in this data release and present some representative sensitivity curves. We then combine the individual pulsar sensitivities using a signal-to-noise-ratio statistic to calculate the global sensitivity of the PTA to a stochastic background of GWs, obtaining a minimum noise characteristic strain of $7\times 10^{-15}$ a

The NANOGrav 15-year data set shows evidence for the presence of a low-frequency gravitational-wave background (GWB). While many physical processes can source such low-frequency gravitational waves, here we analyze the signal as coming from a population of supermassive black hole (SMBH) binaries distributed throughout the Universe. We show that astrophysically motivated models of SMBH binary populations are able to reproduce both the amplitude and shape of the observed low-frequency gravitational-wave spectrum. While multiple model variations are able to reproduce the GWB spectrum at our current measurement precision, our results highlight the importance of accurately modeling binary evolution for producing realistic GWB spectra. Additionally, while reasonable parameters are able to reproduce the 15-year observations, the implied GWB amplitude necessitates either a large number of parameters to be at the edges of expected values, or a small number of parameters to be notably different from standard expectations. While we are not yet able to definitively establish the origin of the inferred GWB signal, the consistency of the signal with astrophysical expectations offers a tantalizin

In bibliometrics studies, a common challenge is how to deal with incorrect or incomplete data. However, given a large volume of data, there often exists certain relationships between the data items that can allow us to recover missing data items and correct erroneous data. In this paper, we study a particular problem of this sort - estimating the missing year information associated with publications (and hence authors' years of active publication). We first propose a simple algorithm that only makes use of the "direct" information, such as paper citation/reference relationships or paper-author relationships. The result of this simple algorithm is used as a benchmark for comparison. Our goal is to develop algorithms that increase both the coverage (the percentage of missing year papers recovered) and accuracy (mean absolute error of the estimated year to the real year). We propose some advanced algorithms that extend inference by information propagation. For each algorithm, we propose three versions according to the given academic social network type: a) Homogeneous (only contains paper citation links), b) Bipartite (only contains paper-author relations), and, c) Heterogeneous (both

We use the small scales of the Dark Energy Survey (DES) Year-3 cosmic shear measurements, which are excluded from the DES Year-3 cosmological analysis, to constrain the baryonic feedback. To model the baryonic feedback, we adopt a baryonic correction model and use the numerical package \texttt{Baccoemu} to accelerate the evaluation of the baryonic nonlinear matter power spectrum. We design our analysis pipeline to focus on the constraints of the baryonic suppression effects, utilizing the implication given by a principal component analysis on the Fisher forecasts. Our constraint on the baryonic effects can then be used to better model and ameliorate the effects of baryons in producing cosmological constraints from the next generation large-scale structure surveys. We detect the baryonic suppression on the cosmic shear measurements with a $\sim 2 σ$ significance. The characteristic halo mass for which half of the gas is ejected by baryonic feedback is constrained to be $M_c > 10^{13.2} h^{-1} M_{\odot}$ (95\% C.L.). The best-fit baryonic suppression is $\sim 5\%$ at $k=1.0 {\rm Mpc}\ h^{-1}$ and $\sim 15\%$ at $k=5.0 {\rm Mpc} \ h^{-1}$. Our findings are robust with respect to th

The dominant part in the mental calculation of the day of the week for any given date is to determine the year share, that is, the contribution of the two-digit year part of the date. This paper describes a number of year share computation methods, some well-known and some new. The "Parity Minus 3" method, in particular, is a new alternative to the popular "Odd+11" method. The paper categorizes the methods of year share computation, and presents simpler proofs of their correctness than usually provided. Keywords and phrases: day of the week, calendar algorithms, doomsday method, first Sunday algorithm, mental arithmetic, year share

We report multiple lines of evidence for a stochastic signal that is correlated among 67 pulsars from the 15-year pulsar-timing data set collected by the North American Nanohertz Observatory for Gravitational Waves. The correlations follow the Hellings-Downs pattern expected for a stochastic gravitational-wave background. The presence of such a gravitational-wave background with a power-law-spectrum is favored over a model with only independent pulsar noises with a Bayes factor in excess of $10^{14}$, and this same model is favored over an uncorrelated common power-law-spectrum model with Bayes factors of 200-1000, depending on spectral modeling choices. We have built a statistical background distribution for these latter Bayes factors using a method that removes inter-pulsar correlations from our data set, finding $p = 10^{-3}$ (approx. $3σ$) for the observed Bayes factors in the null no-correlation scenario. A frequentist test statistic built directly as a weighted sum of inter-pulsar correlations yields $p = 5 \times 10^{-5} - 1.9 \times 10^{-4}$ (approx. $3.5 - 4σ$). Assuming a fiducial $f^{-2/3}$ characteristic-strain spectrum, as appropriate for an ensemble of binary supermas

The WMAP satellite has completed 3 years of observations of the cosmic microwave background radiation. The 3-year data products include several sets of full sky maps of the Stokes I, Q and U parameters in 5 frequency bands, spanning 23 to 94 GHz, and supporting items, such as beam window functions and noise covariance matrices. The processing used to produce the current sky maps and supporting products represents a significant advancement over the first year analysis, and is described herein. Improvements to the pointing reconstruction, radiometer gain modeling, window function determination and radiometer spectral noise parametrization are presented. A detailed description of the updated data processing that produces maximum likelihood sky map estimates is presented, along with the methods used to produce reduced resolution maps and corresponding noise covariance matrices. Finally two methods used to evaluate the noise of the full resolution sky maps are presented along with several representative year-to-year null tests, demonstrating that sky maps produced from data from different observational epochs are consistent.

In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled "Gathering Strength, Gathering Storms," answers a set of 14 questions probing critical areas of AI development addressing the major risks and dangers of AI, its effects on society, its public perception and the future of the field. The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects. The questions were developed by the AI100 Standing Committee, chaired by Peter Stone of the University of Texas at Austin, consisting of a group of AI leaders with expertise in computer science, sociology, ethics, economics, and other disciplines.

We present new full-sky temperature and polarization maps in five frequency bands from 23 to 94 GHz, based on data from the first five years of the WMAP sky survey. The five-year maps incorporate several improvements in data processing made possible by the additional years of data and by a more complete analysis of the instrument calibration and in-flight beam response. We present several new tests for systematic errors in the polarization data and conclude that Ka band data (33 GHz) is suitable for use in cosmological analysis, after foreground cleaning. This significantly reduces the overall polarization uncertainty. With the 5 year WMAP data, we detect no convincing deviations from the minimal 6-parameter LCDM model: a flat universe dominated by a cosmological constant, with adiabatic and nearly scale-invariant Gaussian fluctuations. Using WMAP data combined with measurements of Type Ia supernovae and Baryon Acoustic Oscillations, we find (68% CL uncertainties): Omega_bh^2 = 0.02267 \pm 0.00059, Omega_ch^2 = 0.1131 \pm 0.0034, Omega_Lambda = 0.726 \pm 0.015, n_s = 0.960 \pm 0.013, tau = 0.084 \pm 0.016, and Delta_R^2 = (2.445 \pm 0.096) x 10^-9. From these we derive: sigma_8 = 0

The 15-year pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pul

A combined analysis of Cosmic Microwave Background (CMB) and Lyman-a forest data allows to constrain the matter power spectrum from small scales of about 1 Mpc/h all the way to the horizon scale. The long lever arm and complementarity provided by such an analysis has previously led to a significant tightening of the constraints on the shape and the amplitude of the power spectrum of primordial density fluctuations. We present here a combined analysis of the WMAP three year results with Lyman-a forest data. The amplitude of the matter power spectrum sigma_8 and the spectral index ns inferred from the joint analysis with high resolution Lyman-a forest data and low resolution Lyman-a forest data as analyzed by Viel & Haehnelt (2006) are consistent with the new WMAP results to within 1 sigma. The joint analysis with the mainly low resolution data as analysed by McDonald et al. (2005) suggests a value of sigma_8 which is ~ 2 sigma higher than that inferred from the WMAP three year data alone. The joint analysis of the three year WMAP and the Lyman-a forest data also does not favour a running of the spectral index. The best fit values for a combined analysis of the three year WMAP da