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Reconstruction of the point spread function (PSF) plays an important role in many areas of astronomy, including photometry, astrometry, galaxy morphology, and shear measurement. The atmospheric and instrumental effects are the two main contributors to the PSF, both of which may exhibit complex spatial features. Current PSF reconstruction schemes typically rely on individual exposures, and its ability of reproducing the complicated features of the PSF distribution is therefore limited by the number of stars. Interestingly, in conventional methods, after stacking the model residuals of the PSF ellipticities and (relative) sizes from a large number of exposures, one can often observe some stable and nontrivial spatial patterns on the entire focal plane, which could be quite detrimental to, e.g., weak lensing measurements. These PSF residual patterns are caused by instrumental effects as they consistently appear in different exposures. Taking this as an advantage, we propose a multi-layer PSF reconstruction method to remove such PSF residuals, the second and third layers of which make use of all available exposures together. We test our method on the i-band data of the second release of Hyper Suprime-Cam. Our method successfully eliminates most of the PSF residuals. Using the Fourier\_Quad shear measurement method, we further test the performance of the resulting PSF fields on shear recovery using the field distortion effect. The PSF residuals have strong correlations with the shear residuals, and our new multi-layer PSF reconstruction method can remove most of such systematic errors related to PSF, leading to much smaller shear biases.

The combined matrix is a very useful concept for many applications. Almost strictly sign regular (ASSR) matrices form an important structured class of matrices with two possible zero patterns, which are either type-I staircase or type-II staircase. We prove that, under an irreducibility condition, the pattern of zero and nonzero entries of an ASSR matrix is preserved by the corresponding combined matrix. Without the irreducibility condition, it is proved that type-I and type-II staircases are still preserved. Illustrative numerical examples are included., Comment: 8 pages

Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large scale galaxy surveys, in which image qualities are crucial for the success of galaxy shape measurements. In this work we present two automatic methods for detecting anomalous images in astronomical datasets. The anomalous features can be divided into two types: one is associated with the source images, and the other appears on the background. Our first method, called the Entropy Method, utilizes the randomness of the orientation distribution of the source shapes and the background gradients to quantify the likelihood of an exposure being anomalous. Our second method involves training a neural network (autoencoder) to detect anomalies. We evaluate the effectiveness of the Entropy Method on the CFHTLenS and DECaLS DR3 data. In CFHTLenS, with 1171 exposures, the Entropy Method outperforms human inspection by detecting 12 of the 13 anomalous exposures found during human inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the Entropy method detects a significant number of anomalous exposures while keeping a low false positive rate. We find that although the neural network performs relatively well in detecting source anomalies, its current performance is not as good as the Entropy Method.

Malaria can be prevented, diagnosed, and treated; however, every year, there are more than 200 million cases and 200.000 preventable deaths. Malaria remains a pressing public health concern in low- and middle-income countries, especially in sub-Saharan Africa. We describe how by means of mobile health applications, machine-learning-based adaptive interventions can strengthen malaria surveillance and treatment adherence, increase testing, measure provider skills and quality of care, improve public health by supporting front-line workers and patients (e.g., by capacity building and encouraging behavioral changes, like using bed nets), reduce test stockouts in pharmacies and clinics and informing public health for policy intervention., Comment: Accepted for ICLR 2023 Workshop on Machine Learning and Global Health

Based on independent shear measurements using the DECaLS/DR8 imaging data, we measure the weak lensing signals around isolated central galaxies (ICGs) from SDSS/DR7 at $z\sim0.1$. The projected stellar mass density profiles of surrounding satellite galaxies are further deduced, using photometric sources from the Hyper Suprime-Cam (HSC) survey (pDR3). The signals of ICGs $+$ their extended stellar halos are taken from Wang et al.(2021). All measurements are compared with predictions by the Illustris-TNG300-1 simulation. We find, overall, a good agreement between observation and TNG300. In particular, a correction to the stellar mass of massive observed ICGs is applied based on the calibration of He et al.(2013), which brings a much better agreement with TNG300 predicted lensing signals at $\log_{10}M_\ast/M_\odot>11.1$. In real observation, red ICGs are hosted by more massive dark matter halos, have more satellites and more extended stellar halos than blue ICGs at fixed stellar mass. However, in TNG300 there are more satellites around blue ICGs at fixed stellar mass, and the outer stellar halos of red and blue ICGs are similar. The stellar halos of TNG galaxies are more extended compared with real observed galaxies, especially for blue ICGs with $\log_{10}M_\ast/M_\odot>10.8$. We find the same trend for TNG100 galaxies and for true halo central galaxies. The tensions between TNG and real galaxies might indicate that satellite disruptions are stronger in TNG. In both TNG300 and observation, satellites approximately trace the underlying dark matter distribution beyond $0.1R_{200}$, but the fraction of total stellar mass in TNG300 does not show the same radial distribution as real galaxies., Comment: accepted by ApJ, comments welcome

Based on a large group/cluster catalog recently constructed from the DESI Legacy Imaging Surveys DR9 using an extended halo-based group finder, we measure and model the group-galaxy weak lensing signals for groups/clusters in a few redshift bins within redshift range $0.1 \leqslant z<0.6$. Here, the background shear signals are obtained based on the DECaLS survey shape catalog derived with the \textsc{Fourier\_Quad} method. We divide the lens samples into 5 equispaced redshift bins and 7 mass bins, which allow us to probe the redshift and mass dependence of the lensing signals and hence the resulting halo properties. In addition to these sample selections, we have also checked the signals around different group centers, e.g., brightest central galaxy (BCG), luminosity weighted center and number weighted center. We use a lensing model that includes off-centering to describe the lensing signals we measure for all mass and redshift bins. The results demonstrate that our model predictions for the halo masses, bias and concentrations are stable and self-consistent among different samples for different group centers. Taking advantage of the very large and complete sample of groups/clusters, as well as the reliable estimation of their halo masses, we provide measurements of the cumulative halo mass functions up to redshift $z=0.6$, with a mass precision at $0.03\sim0.09$ dex., Comment: revised version submitted to ApJ

We use weak lensing observations to make the first measurement of the characteristic depletion radius, one of the three radii that characterize the region where matter is being depleted by growing haloes. The lenses are taken from the halo catalog produced by the extended halo-based group/cluster finder applied to DESI Legacy Imaging Surveys DR9, while the sources are extracted from the DECaLS DR8 imaging data with the Fourier_Quad pipeline. We study halo masses $12 < \log ( M_{\rm grp} ~[{\rm M_{\odot}}/h] ) \leq 15.3$ within redshifts $0.2 \leq z \leq 0.3$. The virial and splashback radii are also measured and used to test the original findings on the depletion region. When binning haloes by mass, we find consistency between most of our measurements and predictions from the CosmicGrowth simulation, with exceptions to the lowest mass bins. The characteristic depletion radius is found to be roughly $2.5$ times the virial radius and $1.7 - 3$ times the splashback radius, in line with an approximately universal outer density profile, and the average enclosed density within the characteristic depletion radius is found to be roughly $29$ times the mean matter density of the Universe in our sample. When binning haloes by both mass and a proxy for halo concentration, we do not detect a significant variation of the depletion radius with concentration, on which the simulation prediction is also sensitive to the choice of concentration proxy. We also confirm that the measured splashback radius varies with concentration differently from simulation predictions., Comment: 17 pages, 13 figures, accepted: MNRAS May 03, 2022

Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore, this paper tackles the domain of computationally efficient algorithms for NLP tasks. In particular, it investigates distributed representations of n-gram statistics of texts. The representations are formed using hyperdimensional computing enabled embedding. These representations then serve as features, which are used as input to standard classifiers. We investigate the applicability of the embedding on one large and three small standard datasets for classification tasks using nine classifiers. The embedding achieved on par F1 scores while decreasing the time and memory requirements by several times compared to the conventional n-gram statistics, e.g., for one of the classifiers on a small dataset, the memory reduction was 6.18 times; while train and test speed-ups were 4.62 and 3.84 times, respectively. For many classifiers on the large dataset, memory reduction was ca. 100 times and train and test speed-ups were over 100 times. Importantly, the usage of distributed representations formed via hyperdimensional computing allows dissecting strict dependency between the dimensionality of the representation and n-gram size, thus, opening a room for tradeoffs., Comment: 9 pages, 1 figure, 6 tables

In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks. Intent Classification on a small dataset is a challenging task for data-hungry state-of-the-art Deep Learning based systems. Semantic Hashing is an attempt to overcome such a challenge and learn robust text classification. Current word embedding based are dependent on vocabularies. One of the major drawbacks of such methods is out-of-vocabulary terms, especially when having small training datasets and using a wider vocabulary. This is the case in Intent Classification for chatbots, where typically small datasets are extracted from internet communication. Two problems arise by the use of internet communication. First, such datasets miss a lot of terms in the vocabulary to use word embeddings efficiently. Second, users frequently make spelling errors. Typically, the models for intent classification are not trained with spelling errors and it is difficult to think about ways in which users will make mistakes. Models depending on a word vocabulary will always face such issues. An ideal classifier should handle spelling errors inherently. With Semantic Hashing, we overcome these challenges and achieve state-of-the-art results on three datasets: AskUbuntu, Chatbot, and Web Application. Our benchmarks are available online: https://github.com/kumar-shridhar/Know-Your-Intent, Comment: Accepted at IJCNN 2019 (Oral Presentation)

The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.