We report the identification of a quasar overdensity in the BOSSJ0210 field, dubbed Cosmic Himalayas, consisting of 11 quasars at $z=2.16-2.20$, the densest overdensity of quasars ($17\sigma$) in the $\sim$10,000 deg$^2$ of the Sloan Digital Sky Survey. We present the spatial distributions of galaxies and quasars and an HI absorption map of the intergalactic medium (IGM). On the map of 465 galaxies selected from the MAMMOTH-Subaru survey, we find two galaxy density peaks that do not fall on the quasar overdensity but instead exist at the northwest and southeast sides, approximately 25 $h^{-1}$ comoving-Mpc apart from the quasar overdensity. With a spatial resolution of 15 $h^{-1}$ comoving Mpc in projection, we produce a three-dimensional HI tomography map by the IGM Ly$\alpha$ forest in the spectra of 23 SDSS/eBOSS quasars behind the quasar overdensity. Surprisingly, the quasar overdensity coincides with neither an absorption peak nor a transmission peak of IGM HI but lies near the border separating opaque and transparent volumes, with the more luminous quasars located in an environment with lesser IGM HI. Hence remarkably, the overdensity region traced by the 11 quasars, albeit all in coherently active states, has no clear coincidence with peaks of galaxies or HI absorption densities. Current physical scenarios with mixtures of HI overdensities and quasar photoionization cannot fully interpret the emergence of Cosmic Himalayas, suggesting this peculiar structure is an excellent laboratory to unveil the interplay between galaxies, quasars, and the IGM.

In this paper, we present an approach to image enhancement with diffusion model in underwater scenes. Our method adapts conditional denoising diffusion probabilistic models to generate the corresponding enhanced images by using the underwater images and the Gaussian noise as the inputs. Additionally, in order to improve the efficiency of the reverse process in the diffusion model, we adopt two different ways. We firstly propose a lightweight transformer-based denoising network, which can effectively promote the time of network forward per iteration. On the other hand, we introduce a skip sampling strategy to reduce the number of iterations. Besides, based on the skip sampling strategy, we propose two different non-uniform sampling methods for the sequence of the time step, namely piecewise sampling and searching with the evolutionary algorithm. Both of them are effective and can further improve performance by using the same steps against the previous uniform sampling. In the end, we conduct a relative evaluation of the widely used underwater enhancement datasets between the recent state-of-the-art methods and the proposed approach. The experimental results prove that our approach can achieve both competitive performance and high efficiency. Our code is available at \href{mailto:this https URL}{\color{blue}{this https URL\_underwater}}.

Hydrogen Lyman-$\alpha$ (Ly$\alpha$) emission has been one of the major observational probes for the high redshift universe, since the first discoveries of high-$z$ Ly$\alpha$ emitting galaxies in the late 1990s. Due to the strong Ly$\alpha$ emission originated by resonant scattering and recombination of the most-abundant element, Ly$\alpha$ observations witness not only HII regions of star formation and AGN but also diffuse HI gas in the circum-galactic medium (CGM) and the inter-galactic medium (IGM). Here we review Ly$\alpha$ sources, and present theoretical interpretations reached to date. We conclude that: 1) A typical Ly$\alpha$ emitter (LAE) at $z\gtrsim 2$ with a $L^*$ Ly$\alpha$ luminosity is a high-$z$ counterpart of a local dwarf galaxy, a compact metal-poor star-forming galaxy (SFG) with an approximate stellar (halo) mass and star-formation rate of $10^{8-9} M_\odot$ ($10^{10-11} M_\odot$) and$1-10 M_\odot$yr$^{-1}$, respectively; 2) High-$z$ SFGs ubiquitously have a diffuse Ly$\alpha$ emitting halo in the CGM extending to the halo virial radius and beyond; 3) Remaining neutral hydrogen at the epoch of reionization makes a strong dimming of Ly$\alpha$ emission for galaxies at $z>6$ that suggest the late reionization history. The next generation large telescope projects will combine Ly$\alpha$ emission data with HI Ly$\alpha$ absorptions and 21cm radio data that map out the majority of hydrogen (HI+HII) gas, uncovering the exchanges of i) matter by outflow/inflow and ii) radiation, relevant to cosmic reionization, between galaxies and the CGM/IGM.

We present the galaxy size-mass ($R_{e}-M_{\ast}$) distributions using a stellar-mass complete sample of $\sim1.5$ million galaxies, covering $\sim100$ deg$^2$, with \log(M_{\ast}/M_{\odot})>10.2~(9.2) over the redshift range $0.2

We present the galaxy size-mass ($R_{e}-M_{\ast}$) distributions using a stellar-mass complete sample of $\sim1.5$ million galaxies, covering $\sim100$ deg$^2$, with \log(M_{\ast}/M_{\odot})>10.2~(9.2) over the redshift range $0.2

The paper presents the third data release of Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), a wide-field multi-band imaging survey with the Subaru 8.2m telescope. HSC-SSP has three survey layers (Wide, Deep, and UltraDeep) with different area coverages and depths, designed to address a wide array of astrophysical questions. This third release from HSC-SSP includes data from 278 nights of observing time and covers about 670 square degrees in all five broad-band filters at the full depth ($\sim26$~mag at $5\sigma$) in the Wide layer. If we include partially observed area, the release covers 1,470 square degrees. The Deep and UltraDeep layers have $\sim80\%$ of the originally planned integration times, and are considered done, as we have slightly changed the observing strategy in order to compensate for various time losses. There are a number of updates in the image processing pipeline. Of particular importance is the change in the sky subtraction algorithm; we subtract the sky on small scales before the detection and measurement stages, which has significantly reduced false detections. Thanks to this and other updates, the overall quality of the processed data has improved since the previous release. However, there are limitations in the data (for example, the pipeline is not optimized for crowded fields), and we encourage the user to check the quality assurance plots as well as a list of known issues before exploiting the data. The data release website is this https URL.

We report the Subaru Hyper Suprime-Cam (HSC) discovery of two Ly$\alpha$ blobs (LABs), dubbed z70-1 and z49-1 at $z=6.965$ and $z=4.888$ respectively, that are Ly$\alpha$ emitters with a bright ($\log L_{\rm Ly\alpha}/{\rm [erg\ s^{-1}]}>43.4$) and spatially-extended Ly$\alpha$ emission, and present the photometric and spectroscopic properties of a total of seven LABs; the two new LABs and five previously-known LABs at $z=5.7-6.6$. The z70-1 LAB shows the extended Ly$\alpha$ emission with a scale length of $1.4\pm 0.2$ kpc, about three times larger than the UV continuum emission, making z70-1 the most distant LAB identified to date. All of the 7 LABs, except z49-1, exhibit no AGN signatures such as X-ray emission, {\sc Nv}$\lambda$1240 emission, or Ly$\alpha$ line broadening, while z49-1 has a strong {\sc Civ}$\lambda$1548 emission line indicating an AGN on the basis of the UV-line ratio diagnostics. We carefully model the point-spread functions of the HSC images, and conduct two-component exponential profile fitting to the extended Ly$\alpha$ emission of the LABs. The Ly$\alpha$ scale lengths of the core (star-forming region) and the halo components are $r_{\rm c}=0.6-1.2$ kpc and $r_{\rm h}=2.0-13.8$ kpc, respectively. The average $r_{\rm h}$ of the LABs falls on the extrapolation of the $r_{\rm h}$-Ly$\alpha$ luminosity relation of the Ly$\alpha$ halos around VLT/MUSE star-forming galaxies at the similar redshifts, suggesting that typical LABs at $z\gtrsim5$ are not special objects, but star-forming galaxies at the bright end.

In this research, we study the change in the performance of machine learning (ML) classifiers when various linguistic preprocessing methods of a dataset were used, with the specific focus on linguistically-backed embeddings in Convolutional Neural Networks (CNN). Moreover, we study the concept of Feature Density and confirm its potential to comparatively predict the performance of ML classifiers, including CNN. The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The dataset was re-annotated by objective experts (psychologists), as the importance of professional annotation in cyberbullying research has been indicated multiple times. The study confirmed the effectiveness of Neural Networks in cyberbullying detection and the correlation between classifier performance and Feature Density while also proposing a new approach of training various linguistically-backed embeddings for Convolutional Neural Networks.

This paper presents the second data release of the Hyper Suprime-Cam Subaru Strategic Program, a wide-field optical imaging survey on the 8.2 meter Subaru Telescope. The release includes data from 174 nights of observation through January 2018. The Wide layer data cover about 300 deg^2 in all five broadband filters (grizy) to the nominal survey exposure (10min in gr and 20min in izy). Partially observed areas are also included in the release; about 1100 deg^2 is observed in at least one filter and one exposure. The median seeing in the i-band is 0.6 arcsec, demonstrating the superb image quality of the survey. The Deep (26 deg^2) and UltraDeep (4 deg^2) data are jointly processed and the UltraDeep-COSMOS field reaches an unprecedented depth of i~28 at 5 sigma for point sources. In addition to the broad-bands, narrow-band data are also available in the Deep and UltraDeep fields. This release includes a major update to the processing pipeline, including improved sky subtraction, PSF modeling, object detection, and artifact rejection. The overall data quality has been improved, but this release is not without problems; there is a persistent deblender problem as well as new issues with masks around bright stars. The user is encouraged to review the issue list before utilizing the data for scientific explorations. All the image products as well as catalog products are available for download. The catalogs are also loaded to a database, which provides an easy interface for users to retrieve data for objects of interest. In addition to these main data products, detailed galaxy shape measurements withheld from the Public Data Release 1 (PDR1) are now available to the community. The shape catalog is drawn from the S16A internal release, which has a larger area than PDR1 (160 deg^2). All products are available at the data release site, this https URL

We present deep Subaru/FOCAS spectra for two extreme emission line galaxies (EELGs) at $z\sim 1$ with strong {\sc[Oiii]}$\lambda$5007 emission lines, exhibiting equivalent widths (EWs) of $2905^{+946}_{-578}$ \AA\ and $2000^{+188}_{-159}$ \AA, comparable to those of EELGs at high redshifts that are now routinely identified with JWST spectroscopy. Adding a similarly large {\sc [Oiii]} EW ($2508^{+1487}_{-689}$ \AA) EELG found at $z\sim 2$ in the JWST CEERS survey to our sample, we explore for the physical origins of the large {\sc [Oiii]} EWs of these three galaxies with the Subaru spectra and various public data including JWST/NIRSpec, NIRCam, and MIRI data. While there are no clear signatures of AGN identified by the optical line diagnostics, we find that two out of two galaxies covered by the MIRI data show strong near-infrared excess in the spectral energy distributions (SEDs) indicating obscured AGN. Because none of the three galaxies show clear broad H$\beta$ lines, the upper limits on the flux ratios of broad-H$\beta$ to {\sc [Oiii]} lines are small, $\lesssim 0.15$ that are comparable with Seyfert $1.8-2.0$ galaxies. We conduct \texttt{Cloudy} modeling with the stellar and AGN incident spectra, allowing a wide range of parameters including metallicities and ionization parameters. We find that the large {\sc [Oiii]} EWs are not self-consistently reproduced by the spectra of stars or unobscured AGN, but obscured AGN that efficiently produces O$^{++}$ ionizing photons with weak nuclear and stellar continua that are consistent with the SED shapes.

We have initiated a new survey for local extremely metal-poor galaxies (EMPGs) with Subaru/Hyper Suprime-Cam (HSC) large-area (~500 deg^2) optical images reaching a 5 sigma limit of ~26 magnitude, about 100 times deeper than the Sloan Digital Sky Survey (SDSS). To select Z/Z_sun<0.1 EMPGs from ~40 million sources detected in the Subaru images, we first develop a machine-learning (ML) classifier based on a deep neural network algorithm with a training data set consisting of optical photometry of galaxy, star, and QSO models. We test our ML classifier with SDSS objects having spectroscopic metallicity measurements, and confirm that our ML classifier accomplishes 86%-completeness and 46%-purity EMPG classifications with photometric data. Applying our ML classifier to the photometric data of the Subaru sources as well as faint SDSS objects with no spectroscopic data, we obtain 27 and 86 EMPG candidates from the Subaru and SDSS photometric data, respectively. We conduct optical follow-up spectroscopy for 10 out of our EMPG candidates with Magellan/LDSS-3+MagE, Keck/DEIMOS, and Subaru/FOCAS, and find that the 10 EMPG candidates are star-forming galaxies at z=0.007-0.03 with large H_beta equivalent widths of 104-265 A, stellar masses of log(M*/M_sun)=5.0-7.1, and high specific star-formation rates of ~300 Gyr^{-1}, which are similar to those of early galaxies at z>6 reported recently. We spectroscopically confirm that 3 out of 10 candidates are truly EMPGs with Z/Z_sun<0.1, one of which is HSC J1631+4426, the most metal-poor galaxy with Z/Z_sun=0.016 reported ever.

The complex scaling method (CSM) is a useful similarity transformation of the Schr\"odinger equation, in which bound-state spectra are not changed but continuum spectra are separated into resonant and non-resonant continuum ones. Because the asymptotic wave functions of the separated resonant states are regularized by the CSM, many-body resonances can be obtained by solving an eigenvalue problem with the $L^2$ basis functions. Applying this method to a system consisting of a core and valence nucleons, we investigate many-body resonant states in weakly bound nuclei very far from the stability lines. Non-resonant continuum states are also obtained with the discretized eigenvalues on the rotated branch cuts. Using these complex eigenvalues and eigenstates in CSM, we construct the extended completeness relations and Green's functions to calculate strength functions and breakup cross sections. Various kinds of theoretical calculations and comparisons with experimental data are presented.

The primordial He abundance $Y_\mathrm{P}$ is a powerful probe of cosmology. Currently, $Y_\mathrm{P}$ is best determined by observations of metal-poor galaxies, while there are only a few known local extremely metal-poor (&lt;0.1 Z_\odot) galaxies (EMPGs) having reliable He/H measurements with HeI$\lambda$10830 near-infrared (NIR) emission. Here we present deep Subaru NIR spectroscopy for 10 EMPGs. Combining the existing optical data, He/H values of 5 out of the 10 EMPGs are reliably derived by the Markov chain Monte Carlo algorithm. Adding the existing 3 EMPGs and 51 moderately metal-poor ($0.1-0.4 Z_\odot$) galaxies with reliable He/H estimates, we obtain $Y_\mathrm{P}=0.2370^{+0.0034}_{-0.0033}$ by linear regression in the $\mathrm{(He/H)}-\mathrm{(O/H)}$ plane, where we increase the number of EMPGs from 3 to 8 anchoring He/H of the most metal-poor gas in galaxies. Although our $Y_\mathrm{P}$ measurement and previous measurements are consistent, our result is slightly ($\sim 1\sigma$) smaller due to our EMPGs. With our $Y_\mathrm{P}$ and the existing primordial deuterium $D_\mathrm{P}$ measurement, we constrain the effective number of neutrino species $N_\mathrm{eff}$ and the baryon-to-photon ratio $\eta$ showing $\gtrsim 1-2\sigma$ tensions with the Standard Model and Planck Collaboration et al. (2020). Motivated by the tensions, we allow the degeneracy parameter of electron-neutrino $\xi_e$ to vary as well as $N_\mathrm{eff}$ and $\eta$. We obtain $\xi_e = 0.05^{+0.03}_{-0.02}$, $N_\mathrm{eff}=3.11^{+0.34}_{-0.31}$, and $\eta\times10^{10}=6.08^{+0.06}_{-0.06}$ from the $Y_\mathrm{P}$ and $D_\mathrm{P}$ measurements with a prior of $\eta$ taken from Planck Collaboration et al. (2020). Our constraints suggest a lepton asymmetry and allow for a high value of $N_\mathrm{eff}$ within the $1\sigma$ level, which could mitigate the Hubble tension.

Prostate cancer, the second most prevalent male malignancy, requires advanced diagnostic tools. We propose an explainable AI system combining BERT (for textual clinical notes) and Random Forest (for numerical lab data) through a novel multimodal fusion strategy, achieving superior classification performance on PLCO-NIH dataset (98% accuracy, 99% AUC). While multimodal fusion is established, our work demonstrates that a simple yet interpretable BERT+RF pipeline delivers clinically significant improvements - particularly for intermediate cancer stages (Class 2/3 recall: 0.900 combined vs 0.824 numerical/0.725 textual). SHAP analysis provides transparent feature importance rankings, while ablation studies prove textual features' complementary value. This accessible approach offers hospitals a balance of high performance (F1=89%), computational efficiency, and clinical interpretability - addressing critical needs in prostate cancer diagnostics.

We present 20,567 Ly$\alpha$ emitters (LAEs) at $z=2.2-7.3$ that are photometrically identified by the SILVERRUSH program in a large survey area up to 25 deg$^2$ with deep images of five broadband filters (grizy) and seven narrowband filters targeting Ly$\alpha$ lines at $z=2.2$, $3.3$, $4.9$, $5.7$, $6.6$, $7.0$, and $7.3$ taken by the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) and the Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS) survey. We select secure $>5\sigma$ sources showing narrowband color excesses via Ly$\alpha$ break screening, taking into account the spatial inhomogeneity of limiting magnitudes. After removing spurious sources by careful masking and visual inspection of coadded and multi-epoch images obtained over the 7 yr of the surveys, we construct LAE samples consisting of 6995, 4641, 726, 6124, 2058, 18, and 5 LAEs at $z=2.2$, 3.3, 4.9, 5.7, 6.6, 7.0, and 7.3, respectively, although the $z=7.3$ candidates are tentative. Our LAE catalogs contain 241 spectroscopically confirmed LAEs at the expected redshifts from previous work. We demonstrate that the number counts of our LAEs are consistent with previous studies with similar LAE selection criteria. The LAE catalogs will be made public on our project webpage with detailed descriptions of the content and ancillary information about the masks and limiting magnitudes.

In recent years, neural models learned through self-supervised pretraining on large scale multilingual text or speech data have exhibited promising results for underresourced languages, especially when a relatively large amount of data from related language(s) is available. While the technology has a potential for facilitating tasks carried out in language documentation projects, such as speech transcription, pretraining a multilingual model from scratch for every new language would be highly impractical. We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language, focusing on actual fieldwork data from a critically endangered tongue: Ainu. Specifically, we (i) examine the feasibility of leveraging data from similar languages also in fine-tuning; (ii) verify whether the model's performance can be improved by further pretraining on target language data. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language and leads to considerable reduction in error rates. Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.

We present the Prime Focus Spectrograph (PFS) Galaxy Evolution pillar of the 360-night PFS Subaru Strategic Program. This 130-night program will capitalize on the wide wavelength coverage and massive multiplexing capabilities of PFS to study the evolution of typical galaxies from cosmic dawn to the present. From Lyman alpha emitters at z~7 to probe reionization, drop-outs at z~3 to map the inter-galactic medium in absorption, and a continuum-selected sample at z~1.5, we will chart the physics of galaxy evolution within the evolving cosmic web. This article is dedicated to the memory of Olivier Le Fevre, who was an early advocate for the construction of PFS, and a key early member of the Galaxy Evolution Working Group.

This paper provides a construction method of the nearest graph Laplacian to a matrix identified from measurement data of graph Laplacian dynamics that include biochemical systems, synchronization systems, and multi-agent systems. We consider the case where the network structure, i.e., the connection relationship of edges of a given graph, is known. A problem of finding the nearest graph Laplacian is formulated as a convex optimization problem. Thus, our problem can be solved using interior point methods. However, the complexity of each iteration by interior point methods is $O(n^6)$, where $n$ is the number of nodes of the network. That is, if $n$ is large, interior point methods cannot solve our problem within a practical time. To resolve this issue, we propose a simple and efficient algorithm with the calculation complexity $O(n^2)$. Simulation experiments demonstrate that our method is useful to perform data-driven modeling of graph Laplacian dynamics.

We present new measurements of rest-UV luminosity functions and angular correlation functions from 4,100,221 galaxies at z~2-7 identified in the Subaru/Hyper Suprime-Cam survey and CFHT Large-Area U-band Survey. The obtained luminosity functions at z~4-7 cover a very wide UV luminosity range of ~0.002-2000L*uv combined with previous studies, revealing that the dropout luminosity function is a superposition of the AGN luminosity function dominant at Muv<-24 mag and the galaxy luminosity function dominant at Muv>-22 mag, consistent with galaxy fractions based on 1037 spectroscopically-identified sources. Galaxy luminosity functions estimated from the spectroscopic galaxy fractions show the bright end excess beyond the Schechter function at >2sigma levels, which is possibly made by inefficient mass quenching, low dust obscuration, and/or hidden AGN activity. By analyzing the correlation functions at z~2-6 with halo occupation distribution models, we find a weak redshift evolution (within 0.3 dex) of the ratio of the star formation rate (SFR) to the dark matter accretion rate, SFR/(dMh/dt), indicating the almost constant star formation efficiency at z~2-6, as suggested by our earlier work at z~4-7. Meanwhile, the ratio gradually increases with decreasing redshift at z<5 within 0.3 dex, which quantitatively reproduces the redshift evolution of the cosmic SFR density, suggesting that the evolution is primarily driven by the increase of the halo number density due to the structure formation, and the decrease of the accretion rate due to the cosmic expansion. Extrapolating this calculation to higher redshifts assuming the constant efficiency suggests a rapid decrease of the SFR density at z>10 with $\propto10^{-0.5(1+z)}$, which will be directly tested with JWST.