Miracle-dz/UniFAR-FTUs huggingface dataset:
The Subaru HSC Galaxy Clustering with Photometric Redshift. I. Dark Halo Masses versus Baryonic Properties of Galaxies at 0.3 ≤ z ≤ 1.4

We present the clustering properties of low-z (z ≤ 1.4) galaxies selected by the Hyper Suprime-Cam Subaru Strategic Program Wide layer over 145 deg2. The wide-field and multiwavelength observation yields 5,064,770 galaxies at 0.3 ≤ z ≤ 1.4 with photometric redshifts and physical properties. This enables the accurate measurement of angular correlation functions, and the subsequent halo occupation distribution (HOD) analysis allows us to identify the connection between baryonic and dark halo properties. The fraction of less-massive satellite galaxies at z ≲ 1 is found to be almost constant at ∼20%, but it gradually decreases beyond . However, the abundance of satellite galaxies at z > 1 is quite small even for less-massive galaxies due to the rarity of massive centrals at high-z. This decreasing trend is connected to the small satellite fraction of Lyman break galaxies at z > 3. The stellar-to-halo mass ratios at 0.3 ≤ z ≤ 1.4 are almost consistent with the predictions obtained using the latest empirical model; however, we identify small excesses from the theoretical model at the massive end. The pivot halo mass is found to be unchanged at at 0.3 ≤ z ≤ 1.4, and we systematically show that is a universal pivot halo mass up to z ∼ 5 that is derived using only the clustering/HOD analyses. Nevertheless, halo masses with peaked instantaneous baryon conversion efficiencies are much smaller than the pivot halo mass regardless of redshift, and the most efficient stellar-mass assembly is thought to be in progress in dark halos.
Role of fluctuations in the yielding transition of two-dimensional glasses

We numerically study yielding in two-dimensional glasses which are generated with a very wide range of stabilities by swap Monte-Carlo simulations and then slowly deformed at zero temperature. We provide strong numerical evidence that stable glasses yield via a nonequilibrium discontinuous transition in the thermodynamic limit. A critical point separates this brittle yielding from the ductile one observed in less stable glasses. We find that two-dimensional glasses yield similarly to their three-dimensional counterparts but display larger sample-to-sample disorder-induced fluctuations, stronger finite-size effects, and rougher spatial wandering of the observed shear bands. These findings strongly constrain effective theories of yielding.
Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning physical parameters. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding e.g. the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required.
Learning Improvement Heuristics for Solving the Travelling Salesman Problem

Recent studies in using deep learning to solve the Travel-ling Salesman Problem (TSP) focus on construction heuristics, the solution of which may still be far from optimality. To improve solution quality, additional procedures such as sampling or beam search are required. However, they are still based on the same construction policy, which is less effective in reﬁning a solution. In this paper, we propose to directly learn the improvement heuristics for solving TSP based on deep reinforcement learning. We ﬁrst present a reinforcement learning formulation for the improvement heuristic, where the policy guides selection of the next solution. Then, we propose a deep architecture as the policy network based on self-attention. Extensive experiments show that, improvement policies learned by our approach yield better re-sults than state-of-the-art methods, even from random initial solutions. Moreover, the learned policies are more effective than the traditional hand-crafted ones, and robust to different initial solutions with either high or poor quality.
Smart Contract Repair

Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks, which may enable attackers to steal valuable assets of involving parties. There is, therefore, a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated smart contract repair approach that is also gas-aware. Our repair method is search-based and searches among mutations of the buggy contract. Our method also considers the gas usage of the candidate patches by leveraging our novel notion of gas dominance relationship. We have made our smart contract repair tool SCRepair available open-source, for investigation by the wider community.
Leveraging End-to-End Speech Recognition with Neural Architecture Search

Deep neural networks (DNNs) have been demonstrated to outperform many traditional machine learning algorithms in Automatic Speech Recognition (ASR). In this paper, we show that a large improvement in the accuracy of deep speech models can be achieved with effective Neural Architecture Optimization at a very low computational cost. Phone recognition tests with the popular LibriSpeech and TIMIT benchmarks proved this fact by displaying the ability to discover and train novel candidate models within a few hours (less than a day) many times faster than the attention-based seq2seq models. Our method achieves test error of 7% Word Error Rate (WER) on the LibriSpeech corpus and 13% Phone Error Rate (PER) on the TIMIT corpus, on par with state-of-the-art results.
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal ranking model would be a mapping from a document set to a permutation on the set, and should satisfy two critical requirements: (1) it should have the ability to model cross-document interactions so as to capture local context information in a query; (2) it should be permutation-invariant, which means that any permutation of the inputted documents would not change the output ranking. Previous studies on learning-to-rank either design uni-variate scoring functions that score each document separately, and thus failed to model the cross-document interactions; or construct multivariate scoring functions that score documents sequentially, which inevitably sacrifice the permutation invariance requirement. In this paper, we propose a neural learning-to-rank model called SetRank which directly learns a permutation-invariant ranking model defined on document sets of any size. SetRank employs a stack of (induced) multi-head self attention blocks as its key component for learning the embeddings for all of the retrieved documents jointly. The self-attention mechanism not only helps SetRank to capture the local context information from cross-document interactions, but also to learn permutation-equivariant representations for the inputted documents, which therefore achieving a permutation-invariant ranking model. Experimental results on three benchmarks showed that the SetRank significantly outperformed the baselines include the traditional learning-to-rank models and state-of-the-art Neural IR models.
Future Physics Programme of BESIII

There has recently been a dramatic renewal of interest in hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like XYZ states at BESIII and B factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related X(1835) meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons. We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII during the remaining operation period of BEPCII. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.
On Neural Phone Recognition of Mixed-Source ECoG Signals

The emerging field of neural speech recognition (NSR) using electrocorticography has recently attracted remarkable research interest for studying how human brains recognize speech in quiet and noisy surroundings. In this study, we demonstrate the utility of NSR systems to objectively prove the ability of human beings to attend to a single speech source while suppressing the interfering signals in a simulated cocktail party scenario. The experimental results show that the relative degradation of the NSR system performance when tested in a mixed-source scenario is significantly lower than that of automatic speech recognition (ASR). In this paper, we have significantly enhanced the performance of our recently published framework by using manual alignments for initialization instead of the flat start technique. We have also improved the NSR system performance by accounting for the possible transcription mismatch between the acoustic and neural signals.
Constraining the second half of reionization with the Ly β forest

We present an analysis of the evolution of the Lyman-series forest into the epoch of reionization using cosmological radiative transfer simulations in a scenario where reionization ends late. We explore models with different midpoints of reionization and gas temperatures. We find that once the simulations have been calibrated to match the mean flux of the observed Lyman-$\alpha$ forest at $4 6$ will discriminate between different reionization histories. The evolution of the Lyman-$\alpha$ and Lyman-$\gamma$ forests are less promising as a tool for pushing studies of reionization to higher redshifts due to the stronger saturation and foreground contamination, respectively.
Do the TRAPPIST-1 Planets Have Hydrogen-rich Atmospheres?

Recently, transmission spectroscopy in the atmospheres of the TRAPPIST-1 planets revealed flat and featureless absorption spectra, which rule out cloud-free, hydrogen-dominated atmospheres. Earth-sized planets orbiting TRAPPIST-1 likely have either a clear or a cloudy/hazy, hydrogen-poor atmosphere. In this paper, we investigate whether a proposed formation scenario is consistent with expected atmospheric compositions of the TRAPPIST-1 planets. We examine the amount of hydrogen-rich gas that TRAPPIST-1-like planets accreted from the ambient disk until disk dispersal. Since TRAPPIST-1 planets are trapped into a resonant chain, we simulate disk gas accretion onto a migrating TRAPPIST-1-like planet. We find that the amount of accreted hydrogen-rich gas is as small as 10−2 wt% and 0.1 wt% for TRAPPIST-1 b and 1 c, 10−2 wt% for 1 d, 1 wt% for 1 e, a few wt% for 1 f and 1 g and 1 wt% for 1 h, respectively. We also calculate the long-term thermal evolution of TRAPPIST-1-like planets after disk dissipation and estimate the mass loss of their hydrogen-rich atmospheres driven by stellar X-ray and UV irradiation. We find that all the accreted hydrogen-rich atmospheres can be lost via hydrodynamic escape. Therefore, we conclude that TRAPPIST-1 planets should have no primordial hydrogen-rich gases but secondary atmospheres such as a Venus-like one and water vapor, if they currently retain atmospheres.
Graph Neural Networks for Decentralized Multi-Robot Path Planning

Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it is far from obvious what information is crucial to the task at hand, and how and when it must be shared among robots. To side-step these issues and move beyond hand-crafted heuristics, we propose a combined model that automatically synthesizes local communication and decision-making policies for robots navigating in constrained workspaces. Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among robots. We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations. We evaluate our method in simulations by navigating teams of robots to their destinations in 2D cluttered workspaces. We measure the success rates and sum of costs over the planned paths. The results show a performance close to that of our expert algorithm, demonstrating the validity of our approach. In particular, we show our model’s capability to generalize to previously unseen cases (involving larger environments and larger robot teams).
An Approach To Super-Resolution Of Sentinel-2 Images Based On Generative Adversarial Networks

This paper presents a generative adversarial network based super-resolution (SR) approach (which is called as S2GAN) to enhance the spatial resolution of Sentinel-2 spectral bands. The proposed approach consists of two main steps. The first step aims to increase the spatial resolution of the bands with 20m and 60m spatial resolutions by the scaling factors of 2 and 6, respectively. To this end, we introduce a generator network that performs SR on the lower resolution bands with the guidance of the bands associated to 10m spatial resolution by utilizing the convolutional layers with residual connections and a long skip-connection between inputs and outputs. The second step aims to distinguish SR bands from their ground truth bands. This is achieved by the proposed discriminator network, which alternately characterizes the high level features of the two sets of bands and applying binary classification on the extracted features. Then, we formulate the adversarial learning of the generator and discriminator networks as a min-max game. In this learning procedure, the generator aims to produce realistic SR bands as much as possible so that the discriminator incorrectly classifies SR bands. Experimental results obtained on different Sentinel-2 images show the effectiveness of the proposed approach compared to both conventional and deep learning based SR approaches.
Effects of photoevaporation on protoplanetary disc ‘isochrones’

Protoplanetary discs are the site of star and planet formation, and their evolution and consequent dispersal deeply affect the formation of planetary systems. In the standard scenario they evolve on time-scales ∼Myr due to the viscous transport of angular momentum. The analytical self-similar solution for their evolution predicts specific disc isochrones in the accretion rate–disc mass plane. However, photoevaporation by radiation emitted by the central star is likely to dominate the gas disc dispersal of the innermost region, introducing another (shorter) time-scale for this process. In this paper, we include the effect of internal (X and EUV) photoevaporation on the disc evolution, finding numerical solutions for a population of protoplanetary discs. Our models naturally reproduce the expected quick dispersal of the inner region of discs when their accretion rates match the rate of photoevaporative mass loss, in line with previous studies. We find that photoevaporation preferentially removes the lightest discs in the sample. The net result is that, counter-intuitively, photoevaporation increases the average disc mass in the sample, by dispersing the lightest discs. At the same time, photoevaporation also reduces the mass accretion rate by cutting the supply of material from the outer to the inner disc. In a purely viscous framework, this would be interpreted as the result of a longer viscous evolution, leading to an overestimate of the disc age. Our results thus show that photoevaporation is a necessary ingredient to include when interpreting observations of large disc samples with measured mass accretion rates and disc masses. Photoevaporation leaves a characteristic imprint on the shape of the isochrone. Accurate data of the accretion rate–disc mass plane in the low disc mass region therefore give clues on the typical photoevaporation rate.
Awareness of dengue fever among the parents of children coming to the dental outpatient department – A questionnaire study

Aim: The aim of this study is to establish the awareness of dengue fever among the parents of children coming to dental OutPatient department (OP). Objective: The objective was to design and complete a questionnaire to evaluate the awareness of dengue fever among the parents of children coming to dental OP. Background: The World Health Organization declares dengue to be endemic in South Asia. Dengue fever, also known as breakbone fever, is a mosquito-borne tropical disease caused by the dengue virus. Despite the magnitude of problem, no documented evidence exists on the awareness and preventive methods regarding dengue. This study is conducted to establish awareness, knowledge, and prevention regarding dengue fever among the parents of children coming to dental OP. Reason: To increase the awareness and thereby gaining the knowledge about the signs and symptoms of dengue fever for the early diagnosis and treatment. Materials and Methods: A cross-sectional study was performed among the parents of children coming to dental outpatient department. Around 80 parents were randomly selected for the questionnaire survey study. To assess awareness on dengue, the questionnaire was covered about knowledge on the signs and symptoms, mode of transmission, mosquito bite time, preventive measures, and source of their knowledge. Results and Conclusion: The study shows a low prevalence of sufficient knowledge in the population based on overall knowledge score on dengue. The isolated knowledge on signs and symptoms, prevention is adequate, with preventive measures mainly focused toward protection from mosquito bites.
Graph quilting: graphical model selection from partially observed covariances.

We investigate the problem of conditional dependence graph estimation when several pairs of nodes have no joint observation. For these pairs even the simplest metric of covariability, the sample covariance, is unavailable. This problem arises, for instance, in calcium imaging recordings where the activities of a large population of neurons are typically observed by recording from smaller subsets of cells at once, and several pairs of cells are never recorded simultaneously. With no additional assumption, the unavailability of parts of the covariance matrix translates into the unidentifiability of the precision matrix that, in the Gaussian graphical model setting, specifies the graph. Recovering a conditional dependence graph in such settings is fundamentally an extremely hard challenge, because it requires to infer conditional dependences between network nodes with no empirical evidence of their covariability. We call this challenge the "graph quilting problem". We demonstrate that, under mild conditions, it is possible to correctly identify not only the edges connecting the observed pairs of nodes, but also a superset of those connecting the variables that are never observed jointly. We propose an $\ell_1$ regularized graph estimator based on a partially observed sample covariance matrix and establish its rates of convergence in high-dimensions. We finally present a simulation study and the analysis of calcium imaging data of ten thousand neurons in mouse visual cortex.
Vulval Cancer: When should I Stop Resecting? Identifying the Factors that Predict Recurrence

Abstract Context: Vulval cancer surgery has become more conservative and it is important to understand whether resection margins alone influence recurrence rates or whether other prognostic factors should be considered when planning treatment. Aims: The aim of this study is to define factors that predict vulval cancer recurrence, enabling development of a recurrence prediction model. Settings and Design: This was a Aretrospective descriptive analysis of new vulval squamous cell carcinoma cases in a gynecological oncology center (January 1, 2007 to December 31, 2013). Subjects and Methods: Analysis of tumor characteristics and treatments. Patient outcomes were recorded, identifying recurrences, and subsequent interventions. Statistical Analysis Used: Univariable and multivariable logistic regression tools applied to determine recurrence risk factors. Results: Seventy patients underwent primary vulval surgery. Bilateral groin node dissection was performed in 26/70 (37.1%) cases and unilateral groin node dissection in 9/70 (12.9%) cases. 57/70 (82%) cases had a negative vulval resection margin, with 67% <8-mm margin. 18/70 (26%) patients underwent adjuvant treatment. Overall recurrence rate of 21/70 (30%): 14/70 locally and 7/70 at the groin. Median survival was 84.2 months and median disease-free interval was 19.1 months. Factors that were statistically significant in predicting recurrence were positive groin histology, lymphovascular space invasion (LVSI), and disease stage. Conclusions: We reported a reduction in the size of tumor-free margins at primary excision. The recurrence rate of 30% is within the previously reported range, suggesting that factors aside from resection margin (LVSI, stage, and groin node involvement) are also important in predicting recurrence. These factors should be incorporated into a prediction model when planning adjuvant treatment.
A Novel 3D-Printed Multi-Drive System for Synchronous Electrophysiological Recording in Multiple Brain Regions

Extracellular electrophysiology has been widely applied in neural network studies. Local field potentials and single-unit activities can be recorded with high-density electrodes, which facilitate the decoding of neural codes. However, the chronic multi-regional recording is still a challenging task for achieving high placement accuracy and long-term stability. Here, we present a novel electrode design with low-cost 3D-printed parts and custom printed circuits boards. This new design could facilitate precise electrode placement in multiple brain regions simultaneously and reduce the working time for surgical procedures as well. In this paper, the design and fabrication of the 3D printed multi-channel microdrive are explained in detail. We also show the result of high-quality electrophysiological recordings in eight pain-related areas from rats and the electrode placement accuracy. This novel 3D-printed multi-drive system could achieve synchronous electrophysiological recording in multiple brain regions and facilitate future neural network research.