Miracle-dz/UniFAR-FTUs huggingface dataset:
Telltale signs of metal recycling in the circumgalactic medium of a z ∼ 0.77 galaxy

We present gravitational-arc tomography of the cool-warm enriched circumgalactic medium (CGM) of an isolated galaxy (‘G1’) at z ≈ 0.77. Combining VLT/MUSE adaptive-optics and Magellan/MagE echelle spectroscopy, we obtain partially resolved kinematics of Mg ii in absorption and [O ii] in emission. The unique arc configuration allows us to probe 42 spatially independent arc positions transverse to G1, plus four positions in front of it. The transverse positions cover G1’s minor and major axes at impact parameters of ≈10–30 and ≈60 kpc, respectively. We observe a direct kinematic connection between the cool-warm enriched CGM (traced by Mg ii) and the interstellar medium (traced by [O ii]). This provides strong evidence for the existence of an extended disc that co-rotates with the galaxy out to tens of kiloparsecs. The Mg ii velocity dispersion (σ ≈ 30–100 km s−1, depending on position) is of the same order as the modelled galaxy rotational velocity (vrot ≈ 80 km s−1), providing evidence for the presence of a turbulent and pressure-supported CGM component. We regard the absorption to be modulated by a galactic-scale outflow, as it offers a natural scenario for the observed line-of-sight dispersion and asymmetric profiles observed against both the arcs and the galaxy. An extended enriched co-rotating disc together with the signatures of a galactic outflow, are telltale signs of metal recycling in the z ∼ 1 CGM.
Modulating Regularization Frequency for Efficient Compression-Aware Model Training

While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time. In this paper, we introduce regularization frequency (i.e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method. For various regularization techniques, such as weight decay and dropout, optimizing the regularization strength is crucial to improve generalization in Deep Neural Networks (DNNs). While model compression also demands the right amount of regularization, the regularization strength incurred by model compression has been controlled only by compression ratio. Throughout various experiments, we show that regularization frequency critically affects the regularization strength of model compression. Combining regularization frequency and compression ratio, the amount of weight updates by model compression per mini-batch can be optimized to achieve the best model accuracy. Modulating regularization frequency is implemented by occasional model compression while conventional compression-aware training is usually performed for every mini-batch.
An Exploratory Study of Debugging Episodes

Many studies have long investigated how developers debug, shaping our understanding of debugging and helping motivate the creation of more effective tools. However, less is known about the typical progression of debugging in real world settings. In this study, we focus on characterizing debugging episodes from the moment at which developers first encounter a defect to the moment at which it is resolved. We investigate the typical duration and frequency of debugging episodes and the typical activities which occur. We observed developers by watching professional developers at work in live-streamed programming sessions. Using this data source, we curated 15 sessions in which 11 professional developers worked for 30 hours. We then systematically coded the debugging episodes and activities that occurred within these videos, yielding a dataset of 2137 debugging activities and 1407 programming activities. We found that debugging was frequent, even in programming work, occurring once every eight minutes. Debugging episodes vary greatly in time, with most being less than a few minutes and a few as more than 100 minutes. However, most debugging time is spent in long debugging episodes. We found no single activity that dominated debugging time, and long debugging episodes often involved many diverse activities. Finally, we found that,in terms of the activities developers did, programming and debugging were remarkably similar, particularly in the frequency of editing and browsing code.
Software Engineering for AI-Based Systems: A Survey

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
Observations of near-perfect nonclassical correlation using coherent light

Complementarity theory is the essence of the Copenhagen interpretation. Since the Hanbury Brown and Twiss experiments, the particle nature of photons has been intensively studied for various quantum phenomena such as anticorrelation and Bell inequality violation in terms of two-photon correlation. Regarding the fundamental question on these quantum features, however, no clear answer exists for how to generate such an entanglement photon pair and what causes the maximum correlation between them. Here, we experimentally demonstrate the physics of anticorrelation on a beam splitter using sub-Poisson distributed coherent photons, where a particular photon number is post-selected using a multiphoton resolving coincidence measurement technique. According to Born rule regarding self-interference in an interferometric scheme, a photon does not interact with others, but can interfere by itself. This is the heart of anticorrelation, where a particular phase relation between paired photons is unveiled for anticorrelation, satisfying the complementarity theory of quantum mechanics.
Polynomial Graph Parsing with Non-Structural Reentrancies

Graph-based semantic representations are valuable in natural language processing, where it is often simple and effective to represent linguistic concepts as nodes, and relations as edges between them. Several attempts has been made to find a generative device that is sufficiently powerful to represent languages of semantic graphs, while at the same allowing efficient parsing. We add to this line of work by introducing graph extension grammar, which consists of an algebra over graphs together with a regular tree grammar that generates expressions over the operations of the algebra. Due to the design of the operations, these grammars can generate graphs with non-structural reentrancies; a type of node-sharing that is excessively common in formalisms such as abstract meaning representation, but for which existing devices offer little support. We provide a parsing algorithm for graph extension grammars, which is proved to be correct and run in polynomial time.
LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing

We present LEGOEval, an open-source toolkit that enables researchers to easily evaluate dialogue systems in a few lines of code using the online crowdsource platform, Amazon Mechanical Turk. Compared to existing toolkits, LEGOEval features a flexible task design by providing a Python API that maps to commonly used React.js interface components. Researchers can personalize their evaluation procedures easily with our built-in pages as if playing with LEGO blocks. Thus, LEGOEval provides a fast, consistent method for reproducing human evaluation results. Besides the flexible task design, LEGOEval also offers an easy API to review collected data.
QueryInst: Parallelly Supervised Mask Query for Instance Segmentation

Recently, query based object detection frameworks achieve comparable performance with previous state-of-the-art object detectors. However, how to fully leverage such frameworks to perform instance segmentation remains an open problem. In this paper, we present QueryInst, a query based instance segmentation method driven by parallel supervision on dynamic mask heads. The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage. This approach eliminates the explicit multi-stage mask head connection and the proposal distribution inconsistency issues inherent in non-query based multi-stage instance segmentation meth-ods. We conduct extensive experiments on three challenging benchmarks, i.e . , COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in instance segmentation and video instance segmentation (VIS) task. Speciﬁcally, using ResNet-101-FPN backbone, QueryInst obtains 48.1 box AP and 42.8 mask AP on COCO test-dev, which is 2 points higher than HTC in terms of both box AP and mask AP, while runs 2.4 times faster. For video instance segmentation, QueryInst achieves the best performance among all online VIS approaches and strikes a decent speed-accuracy trade-off. Code is available at https://github.com/hustvl/QueryInst .
The diverse nature of small-scale turbulence

The self-similar Richardson cascade admits two logically possible scenarios of small-scale turbulence at high Reynolds numbers. In the first scenario, eddies' population densities vary as a function of eddies' scales. As a result, one or a few eddy types dominate at small scales, and small-scale turbulence lacks diversity. In the second scenario, eddies' population densities are scale-invariant across the inertial range, resulting in small-scale diversity. That is, there are as many types of eddies at the small scales as at the large scales. In this letter, we measure eddies' population densities in three-dimensional isotropic turbulence and determine the nature of small-scale turbulence. The result shows that eddies' population densities are scale-invariant.
TENET: A Framework for Modeling Tensor Dataflow Based on Relation-centric Notation

Accelerating tensor applications on spatial architectures provides high performance and energy-efficiency, but requires accurate performance models for evaluating various dataflow alternatives. Such modeling relies on the notation of tensor dataflow and the formulation of performance metrics. Recent proposed compute-centric and data-centric notations describe the dataflow using imperative directives. However, these two notations are less expressive and thus lead to limited optimization opportunities and inaccurate performance models.In this paper, we propose a framework TENET that models hardware dataflow of tensor applications. We start by introducing a relation-centric notation, which formally describes the hardware dataflow for tensor computation. The relation-centric notation specifies the hardware dataflow, PE interconnection, and data assignment in a uniform manner using relations. The relation-centric notation is more expressive than the compute-centric and data-centric notations by using more sophisticated affine transformations. Another advantage of relation-centric notation is that it inherently supports accurate metrics estimation, including data reuse, bandwidth, latency, and energy. TENET computes each performance metric by counting the relations using integer set structures and operators. Overall, TENET achieves 37.4% and 51.4% latency reduction for CONV and GEMM kernels compared with the state-of-the-art data-centric notation by identifying more sophisticated hardware dataflows.
Foundations of Intelligence in Natural and Artificial Systems: A Workshop Report

In March of 2021, the Santa Fe Institute hosted a workshop as part of its Foundations of Intelligence in Natural and Artificial Systems project. This project seeks to advance the field of artificial intelligence by promoting interdisciplinary research on the nature of intelligence. During the workshop, speakers from diverse disciplines gathered to develop a taxonomy of intelligence, articulating their own understanding of intelligence and how their research has furthered that understanding. In this report, we summarize the insights offered by each speaker and identify the themes that emerged during the talks and subsequent discussions.
The X-SHOOTER Lyman α survey at z = 2 (XLS-z2) I: what makes a galaxy a Lyman α emitter?

We present the first results from the X-SHOOTER Lyman α survey at z = 2 (XLS-z2). XLS-z2 is a deep spectroscopic survey of 35 Lyman α emitters (LAEs) utilizing ≈90 h of exposure time with Very Large Telescope/X-SHOOTER and covers rest-frame Ly α to H α emission with R ≈ 4000. We present the sample selection, the observations, and the data reduction. Systemic redshifts are measured from rest-frame optical lines for 33/35 sources. In the stacked spectrum, our LAEs are characterized by an interstellar medium with little dust, a low metallicity, and a high ionization state. The ionizing sources are young hot stars that power strong emission lines in the optical and high-ionization lines in the ultraviolet (UV). The LAEs exhibit clumpy UV morphologies and have outflowing kinematics with blueshifted Si ii absorption, a broad [O iii] component, and a red-skewed Ly α line. Typically, 30 per cent of the Ly α photons escape, of which one quarter on the blue side of the systemic velocity. A fraction of Ly α photons escape directly at the systemic suggesting clear channels enabling an ≈10 per cent escape of ionizing photons, consistent with an inference based on Mg ii. A combination of a low effective H i column density, a low dust content, and young starburst determines whether a star-forming galaxy is observed as an LAE. The first is possibly related to outflows and/or a fortunate viewing angle, while we find that the latter two in LAEs are typical for their stellar mass of 109 M⊙.
BasicBlocker: ISA Redesign to Make Spectre-Immune CPUs Faster

Recent research has revealed an ever-growing class of microarchitectural attacks that exploit speculative execution, a standard feature in modern processors. Proposed and deployed countermeasures involve a variety of compiler updates, firmware updates, and hardware updates. None of the deployed countermeasures have convincing security arguments, and many of them have already been broken. The obvious way to simplify the analysis of speculative-execution attacks is to eliminate speculative execution. This is normally dismissed as being unacceptably expensive, but the underlying cost analyses consider only software written for current instruction-set architectures, so they do not rule out the possibility of a new instruction-set architecture providing acceptable performance without speculative execution. A new ISA requires compiler and hardware updates, but these are happening in any case. This paper introduces BasicBlocker, a generic ISA modification that works for all common ISAs and that allows non-speculative CPUs to obtain most of the performance benefit that would have been provided by speculative execution. To demonstrate the feasibility of BasicBlocker, this paper defines a variant of the RISC-V ISA called BBRISC-V and provides a thorough evaluation on both a 5-stage in-order soft core and a superscalar out-of-order processor using an associated compiler and a variety of benchmarks.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce and news feed sites). However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos). Developing an optimal advertising algorithm in recommendations faces immense challenges because interpolating ads improperly or too frequently may decrease user experience, while interpolating fewer ads will reduce the advertising revenue. Thus, in this paper, we propose a novel advertising strategy for the rec/ads trade-off. To be specific, we develop an RL-based framework that can continuously update its advertising strategies and maximize reward in the long run. Given a recommendation list, we design a novel Deep Q-network architecture that can determine three internally related tasks jointly, i.e., (i) whether to interpolate an ad or not in the recommendation list, and if yes, (ii) the optimal ad and (iii) the optimal location to interpolate. The experimental results based on real-world data demonstrate the effectiveness of the proposed framework.
A Novel mHealth Monitoring System during Cycling in Elite Athletes

Background: Cycling is a very demanding physical activity that may create various health disorders during an athlete’s career. Recently, smart mobile and wearable technologies have been used to monitor physiological responses and possible disturbances during physical activity. Thus, the application of mHealth methods in sports poses a challenge today. This study used a novel mobile-Health method to monitor athletes’ physiological responses and to detect health disorders early during cycling in elite athletes. Methods: Sixteen high-level cyclists participated in this study, which included a series of measurements in the laboratory; health and performance assessments; and then application in the field of mHealth monitoring in two training seasons, at the beginning of their training period and in the race season. A field monitoring test took place during 30 min of uphill cycling with the participant’s heart rate at the ventilatory threshold. During monitoring periods, heart rate, oxygen saturation, respiratory rate, and electrocardiogram were monitored via the mHealth system. Moreover, the SpO2 was estimated continuously, and the symptoms during effort were reported. Results: A significant correlation was found between the symptoms reported by the athletes in the two field tests and the findings recorded with the application of the mHealth monitoring method. However, from the pre-participation screening in the laboratory and from the spiroergometric tests, no abnormal findings were detected that were to blame for the appearance of the symptoms. Conclusions: The application of mHealth monitoring during competitive cycling is a very useful method for the early recording of cardiac and other health disorders of athletes, whose untimely evaluation could lead to unforeseen events.
The non-sagittal knee moment vector identifies ‘at risk’ individuals that the knee abduction moment alone does not

ABSTRACT Multi-planar forces and moments are known to injure the anterior cruciate ligament (ACL). In ACL injury risk studies, however, the uni-planar frontal plane external knee abduction moment is frequently studied in isolation. This study aimed to determine if the frontal plane knee moment (KM-Y) could classify all individuals crossing a risk threshold compared to those classified by a multi-planar non-sagittal knee moment vector (KM-YZ). Recreationally active females completed three sports tasks—drop vertical jumps, single-leg drop vertical jumps and planned sidesteps. Peak knee abduction moments and peak non-sagittal resultant knee moments were obtained for each task, and a risk threshold of the sample mean plus 1.6 standard deviations was used for classification. A sensitivity analysis of the threshold from 1–2 standard deviations was also conducted. KM-Y did not identify all participants who crossed the risk threshold as the non-sagittal moment identified unique individuals. This result was consistent across tasks and threshold sensitivities. Analysing the peak uni-planar knee abduction moment alone is therefore likely overly reductionist, as this study demonstrates that a KM-YZ threshold identifies ‘at risk’ individuals that a KM-Y threshold does not. Multi-planar moment metrics such as KM-YZ may help facilitate the development of screening protocols across multiple tasks.
Quantitative EEG (QEEG) Analysis of Emotional Interaction between Abusers and Victims in Intimate Partner Violence: A Pilot Study

Background: The perpetrators of intimate partner violence (IPV) and their victims have different emotional states. Abusers typically have problems associated with low self-esteem, low self-awareness, violence, anger, and communication, whereas victims experience mental distress and physical pain. The emotions surrounding IPV for both abuser and victim are key influences on their behavior and their relationship. Methods: The objective of this pilot study was to examine emotional and psychological interactions between IPV abusers and victims using quantified electroencephalogram (QEEG). Two abuser–victim case couples and one non-abusive control couple were recruited from the Mental Image Recovery Program for domestic violence victims in Seoul, South Korea, from 7–30 June 2017. Data collection and analysis were conducted using BrainMaster and NeuroGuide. The emotional pattern characteristics between abuser and victim were examined and compared to those of the non-abusive couple. Results: Emotional states and reaction patterns were different for the non-abusive and IPV couples. Strong delta, theta, and beta waves in the right frontal and left prefrontal lobes were observed in IPV case subjects. This indicated emotional conflict, anger, and a communication block or impaired communication between abuser and victim. Conclusions: Our study findings suggest brainwave control training via neurofeedback could be a possible therapy in managing emotional and communication problems related to IPV.
The Impact of Age on Response to Infection in Drosophila

This review outlines the known cellular pathways and mechanisms involved in Drosophila age-dependent immunity to pathogenic microorganisms such as bacteria and fungi. We discuss the implication of host signaling pathways such as the Toll, Immune Deficiency (IMD), Janus kinase signal transducer and activator of transcription (JAK/STAT), and Insulin/Insulin Growth Factor/Target of Rapamycin (IIS/TOR) on immune function with aging. Additionally, we review the effects that factors such as sexual dimorphism, environmental stress, and cellular physiology exert on age-dependent immunity in Drosophila. We discuss potential tradeoffs between heightened immune function and longevity in the absence of infection, and we provide detailed tables outlining the various assays and pathogens used in the cited studies, as well as the age, sex, and strains of Drosophila used. We also discuss the overlapping effects these pathways and mechanisms have on one another. We highlight the great utility of Drosophila as a model organism and the importance of a greater focus on age-dependent antiviral immunity for future studies.