Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Stellar Population and Gas Covering Fraction on the Emergent Lyα Emission of High-redshift Galaxies

We perform joint modeling of the composite rest-frame far-UV and optical spectra of redshift 1.85 ≤ z ≤ 3.49 star-forming galaxies to deduce key properties of the massive stars, ionized interstellar medium (ISM), and neutral ISM, with the aim of investigating the principal factors affecting the production and escape of Lyα photons. Our sample consists of 136 galaxies with deep Keck/LRIS and MOSFIRE spectra covering, respectively, Lyβ through C iii] λλ1907, 1909 and [O ii], [Ne iii], Hβ, [O iii], Hα, [N ii], and [S ii]. Spectral and photoionization modeling indicates that the galaxies are uniformly consistent with stellar population synthesis models that include the effects of stellar binarity. Over the dynamic range of our sample, there is little variation in stellar and nebular abundance with Lyα equivalent width, W λ (Lyα), and only a marginal anticorrelation between age and W λ (Lyα). The inferred range of ionizing spectral shapes is insufficient to solely account for the variation in W λ (Lyα); rather, the covering fraction of optically thick H i appears to be the principal factor modulating the escape of Lyα, with most of the Lyα photons in down-the-barrel observations of galaxies escaping through low column density or ionized channels in the ISM. Our analysis shows that a high star-formation-rate surface density, ΣSFR, particularly when coupled with a low galaxy potential (i.e., low stellar mass), can aid in reducing the covering fraction and ease the escape of Lyα photons. We conclude with a discussion of the implications of our results for the escape of ionizing radiation at high redshift.
Reinforcement Learning Approach to Active Learning for Image Classification

Machine Learning requires large amounts of labelled data to fit a model. Many datasets are already publicly available, nevertheless forcing application possibilities of machine learning to the domains of those public datasets. The ever-growing penetration of machine learning algorithms in new application areas requires solutions for the need of data in those new domains. This thesis works on active learning as one possible solution to reduce the amount of data that needs to be processed by hand, by processing only those datapoints that specifically benefit the training of a strong model for the task. A newly proposed framework from [FLC17] for framing the active learning workflow as a reinforcement learning problem is adapted for image classification and a series of three experiments is conducted. Each experiment is evaluated and potential issues with the approach are outlined. Each following experiment then proposes improvements to the framework and evaluates their impact. After the last experiment, a final conclusion is drawn, unfortunately rejecting this work’s hypothesis and outlining that the proposed framework at the moment is not capable of improving active learning for image classification with a trained reinforcement learning agent.
Galaxy interactions in different environments: An analysis of galaxy pairs from the SDSS

We analyze the galaxy pairs in a volume limited sample ($M_r \leq -21$) from the SDSS to study the effects of galaxy interactions on the star formation rate and colour of galaxies in different environments. We study the star formation rate and colour of the paired galaxies as a function of projected separation and compare the results with their control samples matched in stellar mass, redshift and local density. We find that the major interactions significantly enhance the star formation rate in paired galaxies and turn them bluer with decreasing pair separation within $30$ kpc. The impact of tidal interactions on star formation rate and colour are more significant in the heavier members of the major pairs. The star formation enhancement in major pairs is significantly higher at the low-density environments, where the influence can extend up to $\sim 100$ kpc. Contrarily, the major pairs at high-density environments show suppression in their star formation. Depending on the embedding environments, the major interactions in the intrinsically brighter galaxy pairs can thus enhance or quench star formation. We find that the minor pairs at both low-density and high-density environments are significantly less star-forming and redder than their control galaxies. It indicates that the minor interactions in intrinsically brighter galaxy pairs always suppress the star formation irrespective of their environment. The lighter members in these minor pairs show a greater susceptibility to suppressed star formation. Our results imply that both the major and minor interactions can contribute to the observed bimodality. We conclude that the galaxy evolution is determined by a complex interplay between the galaxy properties, galaxy interactions, and environment.
Reimagining an autonomous vehicle

The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development. Solving the technology will create social change which parallels the invention of the automobile itself. Today's autonomous driving technology is laudable, though rooted in decisions made a decade ago. We argue that a rethink is required, reconsidering the autonomous vehicle (AV) problem in the light of the body of knowledge that has been gained since the DARPA challenges which seeded the industry. What does AV2.0 look like? We present an alternative vision: a recipe for driving with machine learning, and grand challenges for research in driving.
The Paradox of the Compositionality of Natural Language: A Neural Machine Translation Case Study

Obtaining human-like performance in NLP is often argued to require compositional generalisation. Whether neural networks exhibit this ability is usually studied by training models on highly compositional synthetic data. However, compositionality in natural language is much more complex than the rigid, arithmetic-like version such data adheres to, and artificial compositionality tests thus do not allow us to determine how neural models deal with more realistic forms of compositionality. In this work, we re-instantiate three compositionality tests from the literature and reformulate them for neural machine translation (NMT). Our results highlight that: i) unfavourably, models trained on more data are more compositional; ii) models are sometimes less compositional than expected, but sometimes more, exemplifying that different levels of compositionality are required, and models are not always able to modulate between them correctly; iii) some of the non-compositional behaviours are mistakes, whereas others reflect the natural variation in data. Apart from an empirical study, our work is a call to action: we should rethink the evaluation of compositionality in neural networks and develop benchmarks using real data to evaluate compositionality on natural language, where composing meaning is not as straightforward as doing the math.
Constraining the orbital eccentricity of inspiralling compact binary systems with Advanced LIGO

The detection of ∼ 50 coalescing compact binaries with the Advanced LIGO and Virgo detectors has allowed us to test general relativity, constrain merger rates, and look for evidence of tidal effects, compact object spins, higher waveform modes, and black hole ringdowns. An effect that has not yet been confidently detected is binary eccentricity, which might be present in a small fraction of binaries formed dynamically. Here we discuss general limits on eccentricity that can, in-principle, be placed on all types of compact object binaries by a detector operating at the design sensitivity of Advanced LIGO. Using a post-Newtonian model for gravitational-wave phasing valid in the small eccentricity regime, we assess the relative measurement error for eccentricity for a variety of spinning and non-spinning binaries. Errors and correlations involving the mass and spin parameters are also investigated. We find that decreasing the low frequency limit of a detector’s observational frequency band is one of the key design factors for increasing the odds of measuring binary eccentricity. We also introduce and analytically explore the eccentric chirp mass parameter, which replaces the chirp mass as the key measurable parameter combination in eccentric gravitational waveform models. The eccentric chirp mass parameter explains a degeneracy between the chirp mass and the eccentricity. This degeneracy leads to a bias in the standard chirp mass parameter. We also investigate the systematic parameter bias that arises when eccentric systems are recovered using circular waveform templates. We use both Fisher matrix and Bayesian-inference-based Markov Chain Monte Carlo (MCMC) methods to investigate these parameter estimation issues, and we find good agreement between the two approaches (for both statistical and systematic errors) in the appropriate signal-to-noise ratio regime. This study helps to quantify how effectively one can use eccentricity measurements as a probe of binary formation channels.
37 new validated planets in overlapping K2 campaigns

We analysed 68 candidate planetary systems first identified during Campaigns 5 and 6 (C5 and C6) of the NASA K2 mission. We set out to validate these systems by using a suite of follow-up observations, including adaptive optics, speckle imaging, and reconnaissance spectroscopy. The overlap between C5 with C16 and C18, and C6 with C17, yields light curves with long baselines that allow us to measure the transit ephemeris very precisely, revisit single transit candidates identified in earlier campaigns, and search for additional transiting planets with longer periods not detectable in previous works. Using vespa, we compute false positive probabilities of less than 1 per cent for 37 candidates orbiting 29 unique host stars and hence statistically validate them as planets. These planets have a typical size of 2.2 R⊕ and orbital periods between 1.99 and 52.71 d. We highlight interesting systems including a sub-Neptune with the longest period detected by K2, sub-Saturns around F stars, several multiplanetary systems in a variety of architectures. These results show that a wealth of planetary systems still remains in the K2 data, some of which can be validated using minimal follow-up observations and taking advantage of analyses presented in previous catalogues.
Eliminating unwanted patterns with minimal interference

Artificial synthesis of DNA molecules is an essential part of the study of biological mechanisms. The design of a synthetic DNA molecule usually involves many objectives. One of the important objectives is to eliminate short sequence patterns that correspond to binding sites of restriction enzymes or transcription factors. While many design tools address this problem, no adequate formal solution exists for the pattern elimination problem. In this work, we present a formal description of the elimination problem and suggest efficient algorithms that eliminate unwanted patterns and allow optimization of other objectives with minimal interference to the desired DNA functionality. Our approach is flexible, efficient, and straightforward, and therefore can be easily incorporated in existing DNA design tools, making them considerably more powerful.
Automatic Gaze Analysis: A Survey of Deep Learning Based Approaches

Eye gaze analysis is an important research problem in the field of Computer Vision and Human-Computer Interaction. Even with notable progress in the last 10 years, automatic gaze analysis still remains challenging due to the uniqueness of eye appearance, eye-head interplay, occlusion, image quality, and illumination conditions. There are several open questions, including what are the important cues to interpret gaze direction in an unconstrained environment without prior knowledge and how to encode them in real-time. We review the progress across a range of gaze analysis tasks and applications to elucidate these fundamental questions, identify effective methods in gaze analysis, and provide possible future directions. We analyze recent gaze estimation and segmentation methods, especially in the unsupervised and weakly supervised domain, based on their advantages and reported evaluation metrics. Our analysis shows that the development of a robust and generic gaze analysis method still needs to address real-world challenges such as unconstrained setup and learning with less supervision. We conclude by discussing future research directions for designing a real-world gaze analysis system that can propagate to other domains including Computer Vision, Augmented Reality (AR), Virtual Reality (VR), and Human Computer Interaction (HCI).
Challenges and Opportunities of Future Rural Wireless Communications

Broadband access is key to ensuring robust economic development and improving quality of life. Unfortunately, the communication infrastructure deployed in rural areas throughout the world lags behind its urban counterparts due to low population density and economics. This article examines the motivations and challenges of providing broadband access over vast rural regions, with an emphasis on the wireless aspect in view of its irreplaceable role in closing the digital gap. Applications and opportunities for future rural wireless communications are discussed for a variety of areas, including residential welfare, digital agriculture, and transportation. This article also comprehensively investigates current and emerging wireless technologies that could facilitate rural deployment. Although there is no simple solution, there is an urgent need for researchers to work on coverage, cost, and reliability of rural wireless access.
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds

This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points. By analyzing the step of weighted least squares surface fitting, we find that it is hard to determine the polynomial order of the fitting surface and the fitting surface is sensitive to outliers. To address these problems, we propose a simple yet effective solution that adds an additional offset prediction to improve the quality of normal estimation. Furthermore, in order to take advantage of points from different neighborhood sizes, a novel Cascaded Scale Aggregation layer is proposed to help the network predict more accurate point-wise offsets and weights. Extensive experiments demonstrate that AdaFit achieves state-of-the-art performance on both the synthetic PCPNet dataset and the real-word SceneNN dataset. The code is publicly available at https://github.com/Runsong123/AdaFit.
Weakly Supervised Medical Image Segmentation

In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). The results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios.
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm

Recently, pre-trained language models such as BERT have been applied to document ranking for information retrieval, which first pre-train a general language model on an unlabeled large corpus and then conduct ranking-specific fine-tuning on expert-labeled relevance datasets. Ideally, an IR system would model relevance from a user-system dualism: the user's view and the system's view. User's view judges the relevance based on the activities of"real users"while the system's view focuses on the relevance signals from the system side, e.g., from the experts or algorithms, etc. Inspired by the user-system relevance views and the success of pre-trained language models, in this paper we propose a novel ranking framework called Pre-Rank that takes both user's view and system's view into consideration, under the pre-training and fine-tuning paradigm. Specifically, to model the user's view of relevance, Pre-Rank pre-trains the initial query-document representations based on large-scale user activities data such as the click log. To model the system's view of relevance, Pre-Rank further fine-tunes the model on expert-labeled relevance data. More importantly, the pre-trained representations, are fine-tuned together with handcrafted learning-to-rank features under a wide and deep network architecture. In this way, Pre-Rank can model the relevance by incorporating the relevant knowledge and signals from both real search users and the IR experts. To verify the effectiveness of Pre-Rank, we showed two implementations by using BERT and SetRank as the underlying ranking model, respectively. Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.
A shape optimization problem constrained with the Stokes equations to address maximization of vortices

We study an optimization problem that aims to determine the shape of an obstacle that is submerged in a fluid governed by the Stokes equations. The mentioned flow takes place in a channel, which motivated the imposition of a Poiseuille-like input function on one end and a do-nothing boundary condition on the other. The maximization of the vorticity is addressed by the \begin{document}$ L^2 $\end{document}-norm of the curl and the det-grad measure of the fluid. We impose a Tikhonov regularization in the form of a perimeter functional and a volume constraint to address the possibility of topological change. Having been able to establish the existence of an optimal shape, the first order necessary condition was formulated by utilizing the so-called rearrangement method. Finally, numerical examples are presented by utilizing a finite element method on the governing states, and a gradient descent method for the deformation of the domain. On the said gradient descent method, we use two approaches to address the volume constraint: one is by utilizing the augmented Lagrangian method; and the other one is by utilizing a class of divergence-free deformation fields.
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review

With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.
A Model of Multi-dimensional Targeting

The advancements in targeting technology have allowed firms to engage in more precise targeting based on several distinct aspects of customer preferences. Exposed to more targeted ads, consumers are becoming increasingly aware of being targeted and respond accordingly. In this paper, we provide a theoretical analysis of multi-dimensional targeting in which consumers are aware that ads are targeted and, therefore, draw inferences about their expected utility from the advertised product based on whether or not they are targeted. By comparing single-dimensional and multi-dimensional targeting, we show that the firm can be worse off under multi-dimensional targeting. When this result holds, the firm may want to adopt a single-dimensional targeting strategy. However, we show that the firm cannot credibly commit to such a strategy once given access to multiple dimensions of customer data. We further find that the firm's commitment problem for how to utilize customer data for targeting can be mitigated by a higher advertising cost, and thus the firm's profit can counter-intuitively increase in the advertising cost. We also show that the firm can use pricing as a commitment device and thus recover some of, but not entirely offset, the pitfalls of multi-dimensional targeting. We provide implications of our results to the recently observed practice of more transparency in online targeted advertising.
Characterization of Gentiana rigescens by Ultraviolet–Visible and Infrared Spectroscopies with Chemometrics

ABSTRACT Gentiana rigescens is a medicinal plant for treatment of rheumatism, convulsions, and jaundice. The present paper reports the potential for chemometric characterization of G. rigescens using ultraviolet–visible and infrared spectroscopies, individually and together. Low- and mid-level data fusions were considered in this study. Partial least square discriminant analysis and support vector machine were used to analyze single and fused spectral data, respectively. The data fusion strategy improved the classification capacity. In particular, the mid-level data fusion provided the best results by selecting important variance from the data matrixes, with 100% correct classification of test samples by partial least square discriminant analysis and support vector machine characterization. The results demonstrated that the combined use of ultraviolet–visible and infrared spectroscopies is more suitable for the discrimination of G. rigescens than the individual methods.
Nutritional Management in a Patient with Citrullinemia Type 1

For patients with citrullinemia type 1, nutritional management is essential to prevent the occurrence of complications associated with hyperammonemia. This report describes a patient who had been receiving nutrition intervention for more than 3 years. A newborn diagnosed with hyperammonemia due to citrullinemia visited Ajou University Hospital and was referred to the nutrition team. After receiving acute treatment, the infant was regularly fed with specialized formula. A protein-restricted diet is recommended for maintaining normal development and achieving long-term survival. Through continuous provision of nutritional intervention, the child showed normal growth and development, and the energy-protein supply was maintained appropriately. This case clearly shows the importance of medical nutrition therapy for patients with citrullinemia.