Miracle-dz/UniFAR-FTUs huggingface dataset:
3D gas-phase elemental abundances across the formation histories of Milky Way-mass galaxies in the FIRE simulations: initial conditions for chemical tagging

We use FIRE-2 simulations to examine 3D variations of gas-phase elemental abundances of [O/H], [Fe/H], and [N/H] in 11 MW and M31-mass galaxies across their formation histories at z ≤ 1.5 ($t_{\rm lookback} \le 9.4 \, \rm {Gyr}$), motivated by characterizing the initial conditions of stars for chemical tagging. Gas within $1 \, \rm {kpc}$ of the disc mid-plane is vertically homogeneous to $\lesssim 0.008 \, \rm {dex}$ at all z ≤ 1.5. We find negative radial gradients (metallicity decreases with galactocentric radius) at all times, which steepen over time from $\approx \! -0.01 \, \rm {dex}\, \rm {kpc}^{-1}$ at z = 1 ($t_{\rm lookback} = 7.8 \, \rm {Gyr}$) to $\approx \! -0.03 \, \rm {dex}\, \rm {kpc}^{-1}$ at z = 0, and which broadly agree with observations of the MW, M31, and nearby MW/M31-mass galaxies. Azimuthal variations at fixed radius are typically $0.14 \, \rm {dex}$ at z = 1, reducing to $0.05 \, \rm {dex}$ at z = 0. Thus, over time radial gradients become steeper while azimuthal variations become weaker (more homogeneous). As a result, azimuthal variations were larger than radial variations at z ≳ 0.8 ($t_{\rm lookback} \gtrsim 6.9 \, \rm {Gyr}$). Furthermore, elemental abundances are measurably homogeneous (to ≲0.05 dex) across a radial range of $\Delta R \approx 3.5 \, \rm {kpc}$ at z ≳ 1 and $\Delta R \approx 1.7 \, \rm {kpc}$ at z = 0. We also measure full distributions of elemental abundances, finding typically negatively skewed normal distributions at z ≳ 1 that evolve to typically Gaussian distributions by z = 0. Our results on gas abundances inform the initial conditions for stars, including the spatial and temporal scales for applying chemical tagging to understand stellar birth in the MW.
Updatable Materialization of Approximate Constraints

Modern big data applications integrate data from various sources. As a result, these datasets may not satisfy perfect constraints, leading to sparse schema information and non-optimal query performance. The existing approach of PatchIndexes enable the definition of approximate constraints and improve query performance by exploiting the materialized constraint information. As real world data warehouse workloads are often not limited to read-only queries, we enhance the PatchIndex structure towards an update-conscious design in this paper. Therefore, we present a sharded bitmap as the underlying data structure which offers efficient update operations, and describe approaches to maintain approximate constraints under updates, avoiding index recomputations and full table scans. In our evaluation, we prove that PatchIndexes provide more lightweight update support than traditional materialization approaches.
Predicting DTPA soil test zinc and associated rice response to applied zinc

Abstract The studies were conducted in field at 168 locations scattered throughout rice (Oryza sativa) growing tract of the Punjab with two plans of zinc (Zn) treatments along with nitrogen (N) and phosphorus (P)/N, P, and potassium (K) during 1991 and 1992. Effect of soil properties fpH, organic matter content (OM), and soil test P] on DTPA soil test Zn and associated rice (var. Basmati‐385) response was investigated. Applied as well as soil test Zn levels signified their importance in paddy yield improvement. However, soil properties (pH, OM, and soil test P) did not affect the rice response. DTPA soil test for Zn estimation was independent of these soil properties. A DTPA soil test Zn critical limit of 1.5 mg Zn/kg was obtained for rice by employing the Cate‐Nelson procedure.
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization

An increasing number of models require the control of the spectral norm of convolutional layers of a neural network. While there is an abundance of methods for estimating and enforcing upper bounds on those during training, they are typically costly in either memory or time. In this work, we introduce a very simple method for spectral normalization of depthwise separable convolutions, which introduces negligible computational and memory overhead. We demonstrate the effectiveness of our method on image classification tasks using standard architectures like MobileNetV2.
What Helped, and what did not? An Evaluation of the Strategies to Improve Continuous Integration

Continuous integration (CI) is a widely used practice in modern software engineering. Unfortunately, it is also an expensive practice - Google and Mozilla estimate their CI systems in millions of dollars. There are a number of techniques and tools designed to or having the potential to save the cost of CI or expand its benefit - reducing time to feedback. However, their benefits in some dimensions may also result in drawbacks in others. They may also be beneficial in other scenarios where they are not designed to help. In this paper, we perform the first exhaustive comparison of techniques to improve CI, evaluating 14 variants of 10 techniques using selection and prioritization strategies on build and test granularity. We evaluate their strengths and weaknesses with 10 different cost and time-tofeedback saving metrics on 100 real-world projects. We analyze the results of all techniques to understand the design decisions that helped different dimensions of benefit. We also synthesized those results to lay out a series of recommendations for the development of future research techniques to advance this area.
End-To-End Audio-Visual Speech Recognition with Conformers

In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and Convolution-augmented transformer (Conformer), that can be trained in an end-to-end manner. In particular, the audio and visual encoders learn to extract features directly from raw pixels and audio waveforms, respectively, which are then fed to conformers and then fusion takes place via a Multi-Layer Perceptron (MLP). The model learns to recognise characters using a combination of CTC and an attention mechanism. We show that end-to-end training, instead of using pre-computed visual features which is common in the literature, the use of a conformer, instead of a recurrent network, and the use of a transformer-based language model, significantly improve the performance of our model. We present results on the largest publicly available datasets for sentence-level speech recognition, Lip Reading Sentences 2 (LRS2) and Lip Reading Sentences 3 (LRS3), respectively. The results show that our proposed models raise the state-of-the-art performance by a large margin in audio-only, visual-only, and audio-visual experiments.
Chlorpyrifos degradation by plant growth-promoting Alcaligenes faecalis bacteria isolated from oil-contaminated soil

Abstract Chlorpyrifos (Chlp), one of the major organophosphates is extensively used in agriculture to control pests. With a half-life of 7–120 d, it may persist for up to one year in soil depending on the soil conditions and affect the soil microflora. Chlp degradation by soil microbes proves to be an effective and environment friendly method to remove it from the soil. This study aims to identify and characterize Chlp degrading soil microbes. Total 173 bacteria isolated from different soil samples were screened for Chlp degradation. Characterization of 10 selected samples showed that all the strains produced excessive exo-polysaccharides (EPSs), which positively correlated with the Chlp degradation under in vitro conditions. Among these 10 bacterial strains, the most potent Chlp degrading bacteria was identified to be Alcaligenes faecalis (NBRI OSS2-5) which paired with the Chlp degrading Pseudomonas in the phylogenetic tree. The strain was shown to possess opd gene reported for Chlp degradation. From the study, it was concluded that EPS production may be a common feature of Chlp degrading microbes which may have some role in the Chlp biodegradation process and the pesticide-contaminated fields may not necessarily be the ideal source of isolating pesticide degrading microbes.
Domain-driven models yield better predictions at lower cost than reservoir computers in Lorenz systems

Recent advances in computing algorithms and hardware have rekindled interest in developing high-accuracy, low-cost surrogate models for simulating physical systems. The idea is to replace expensive numerical integration of complex coupled partial differential equations at fine time scales performed on supercomputers, with machine-learned surrogates that efficiently and accurately forecast future system states using data sampled from the underlying system. One particularly popular technique being explored within the weather and climate modelling community is the echo state network (ESN), an attractive alternative to other well-known deep learning architectures. Using the classical Lorenz 63 system, and the three tier multi-scale Lorenz 96 system (Thornes T, Duben P, Palmer T. 2017 Q. J. R. Meteorol. Soc. 143, 897–908. (doi:10.1002/qj.2974)) as benchmarks, we realize that previously studied state-of-the-art ESNs operate in two distinct regimes, corresponding to low and high spectral radius (LSR/HSR) for the sparse, randomly generated, reservoir recurrence matrix. Using knowledge of the mathematical structure of the Lorenz systems along with systematic ablation and hyperparameter sensitivity analyses, we show that state-of-the-art LSR-ESNs reduce to a polynomial regression model which we call Domain-Driven Regularized Regression (D2R2). Interestingly, D2R2 is a generalization of the well-known SINDy algorithm (Brunton SL, Proctor JL, Kutz JN. 2016 Proc. Natl Acad. Sci. USA 113, 3932–3937. (doi:10.1073/pnas.1517384113)). We also show experimentally that LSR-ESNs (Chattopadhyay A, Hassanzadeh P, Subramanian D. 2019 (http://arxiv.org/abs/1906.08829)) outperform HSR ESNs (Pathak J, Hunt B, Girvan M, Lu Z, Ott E. 2018 Phys. Rev. Lett. 120, 024102. (doi:10.1103/PhysRevLett.120.024102)) while D2R2 dominates both approaches. A significant goal in constructing surrogates is to cope with barriers to scaling in weather prediction and simulation of dynamical systems that are imposed by time and energy consumption in supercomputers. Inexact computing has emerged as a novel approach to helping with scaling. In this paper, we evaluate the performance of three models (LSR-ESN, HSR-ESN and D2R2) by varying the precision or word size of the computation as our inexactness-controlling parameter. For precisions of 64, 32 and 16 bits, we show that, surprisingly, the least expensive D2R2 method yields the most robust results and the greatest savings compared to ESNs. Specifically, D2R2 achieves 68 × in computational savings, with an additional 2 × if precision reductions are also employed, outperforming ESN variants by a large margin. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Expanded Boundaries: Art and Non-human Essence as Playful Inspirations for Children's Prosthetics

There is an important gap in Assistive Technology related to the multiple capabilities of exploratory creativity, as with multi-aesthetic and inter-mediatic surfaces, especially in the design and development of child prosthetics. This in-progress research explores creative interactions and combinations of art and digital fabrication to design playful prosthetics for children's upper limbs. In order to do so, we are using feline animal life as inspiration and as a ludic premise to engagement through playfulness. With this artistic exploration in the scope of tangible and embodied interfaces, we seek to contribute to improve and incorporate engagement and fantasy through playful interfaces, ultimately transforming a perceived difference into empowerment of a child's self-esteem, awareness and imagination.
An Interactive Garment for Orchestra Conducting: IoT-enabled Textile & Machine Learning to Direct Musical Performance

We present an overview and initial results from a project bringing together orchestra conducting, e-textile material studies, costume tailoring, low power computing and machine learning (ML). We describe a wearable interactive system comprising of textile sensors embedded into a suit, low-power transmission and gesture recognition using creative computing tools. We introduce first observations made during the semi-participatory approach, which placed the conductor’s movements and personal performative expressiveness at the centre for technical and conceptual development. The project is a two-month collaboration between the Verworner-Krause Kammerorchester (VKKO), technical and design researchers, currently still running. Preliminary analyses of the data recorded while the conductor is wearing the prototype demonstrate that the developed system can be used to robustly decode a large number of conducting and performative movements. In particular the user interface of the ML system is designed such that the training of the algorithms can be intuitively controlled by the conductor, in sync with the MIDI clock.
Phytoextraction Potential of Indian Mustard at Various Levels of Zinc Exposure

ABSTRACT Indian mustard (Brassica juncea Czern.) has the potential to extract zinc (Zn) and other metals from contaminated soils, but the potential to accumulate metals at different levels of exposure is not well documented. The objectives of this research were to assess plant growth and Zn accumulation for different metal-accumulating accessions of Indian mustard grown with various Zn concentrations. In the experiment, three accessions of Indian mustard (426308, 182921, and 211000) were supplied with 12 levels of Zn (ranging from 0.0 to 7.0 mg L−1) for three weeks in solution culture. Accession 426308 had a greater capacity for dry-mass accumulation than the others, but differences among accessions lessened as the concentration of Zn in solution increased. Accessions did not differ in Zn concentrations in shoots, but accession 426308 had a greater potential to accumulate Zn than the other accessions. Elevating the Zn supply in solutions had a limited effect on increasing the total Zn accumulation of shoots. Plants suffered Zn-induced iron (Fe) deficiency if the Zn concentration in solution exceeded 2.0 mg Zn L−1. The level of Zn tolerance of Indian mustard accessions was: 211000 > 182921 > 426308. Maximum Zn accumulation in shoots was approximately 5.0 mg Zn per plant. The phytoextraction potential of Indian mustard may be limited under Zn-contaminated conditions by nutrient disorders and toxic effects of Zn that suppress growth.
Flowcuits: Crafting Tangible and Interactive Electrical Components with Liquid Metal Circuits

We present Flowcuits, a DIY fabrication method to prototype tangible, interactive and functional electrical components by manipulating liquid metals. The prototypes afford both physical and visual interactions to demonstrate the inner working mechanics of fundamental electronic elements, which enables tangible and playful learning. The fabrication process follows simple imprinting and sealing of fluidic circuits with a 3D-printed stamp on an accessible moldable-substrates such as ‘Blu Tack’. Utilizing conductive gallium indium liquid metal, we demonstrated interactive and re-configurable electronic components such as switches, variable resistors, variable capacitors, logic gates and pressure sensors. In this paper, we present the design analogy of Flowcuits, DIY fabrication approach including a parametric 3D stamp design toolkit and results from a technical evaluation. The stamps are printed with a low-cost 3D printer and all the materials are inexpensive and reusable, enabling Flowcuits to be easily used without any advanced lab facilities.
Risk Perception and PTSD Symptoms of Medical Staff Combating Against COVID-19: A PLS Structural Equation Model

Medical staff were battling against coronavirus disease 2019 (COVID-19) at the expense of their physical and mental health, particularly at risk for posttraumatic stress disorder (PTSD). In this case, intervening PTSD of medical staff and preparing them for future outbreaks are important. Previous studies showed that perceived stress was related to the development of PTSD. Hence, in this study, the association between risk perception of medical staff and PTSD symptoms in COVID-19 and the potential links were explored. Three hundred four medical staff's exposure to COVID-19 patients, risk perception for working during COVID-19, PTSD symptoms, anxiety, and sleep quality were measured. Mediation analysis tested the indirect effects of anxiety and sleep quality on the relationship between risk perceptions and PTSD symptoms; 27.6% of participants were deemed as having probable PTSD diagnosis. Mediation analysis showed a significant chain-mediating effect of anxiety and sleep quality on the relationships between risk perceptions and PTSD symptoms; higher risk perceptions were related to increased anxiety, worsened sleep quality, and severe PTSD symptoms. Conclusively, medical staff have a high prevalence of PTSD symptoms after 3 months of COVID-19. Their PTSD symptoms were associated with the perceived risk level through the potential links with anxiety and sleep quality. Therefore, risk perception could be critical for our medical staff's responses to public health emergencies. It could be plausible to intervene in the perceived stress to alleviate aroused anxiety and improve sleep quality and thereby deter the development of PTSD.
KINEIN: A Making Guide on Indefinitely Deploying a Kinetic Display as a Research Product

Large-scale kinetic displays have been present for decades, both as mesmerising art installations and as information displays. In this paper we present KINEIN, a kinetic display whose large-scale and indefinite deployment time distinguish it from the prototypical level of HCI research artefacts. It consists of 625 “tiles” or “pixels” mounted on a 25 x 25 matrix and operates through mechanical movement. Instead of a motor rotating each tile individually, KINEIN has a vertical column system that moves tiles sequentially. Situated at the entrance of a learning environment, KINEIN is not only a playful artefact for children to physically interact with, but also the first encounter for the school's visitors. We make use of the pictorial format to illustrate and contribute our Research-through-Design (RtD) process as well as its implications regarding large physical scale and indefinite deployment.
In vitro Assessment of the DNA Damage Response in Dental Mesenchymal Stromal Cells Following Low Dose X-ray Exposure

Stem cells contained within the dental mesenchymal stromal cell (MSC) population are crucial for tissue homeostasis. Assuring their genomic stability is therefore essential. Exposure of stem cells to ionizing radiation (IR) is potentially detrimental for normal tissue homeostasis. Although it has been established that exposure to high doses of ionizing radiation (IR) has severe adverse effects on MSCs, knowledge about the impact of low doses of IR is lacking. Here we investigated the effect of low doses of X-irradiation with medical imaging beam settings (<0.1 Gray; 900 mGray per hour), in vitro, on pediatric dental mesenchymal stromal cells containing dental pulp stem cells from deciduous teeth, dental follicle progenitor cells and stem cells from the apical papilla. DNA double strand break (DSB) formation and repair kinetics were monitored by immunocytochemistry of γH2AX and 53BP1 as well as cell cycle progression by flow cytometry and cellular senescence by senescence-associated β-galactosidase assay and ELISA. Increased DNA DSB repair foci, after exposure to low doses of X-rays, were measured as early as 30 min post-irradiation. The number of DSBs returned to baseline levels 24 h after irradiation. Cell cycle analysis revealed marginal effects of IR on cell cycle progression, although a slight G2/M phase arrest was seen in dental pulp stromal cells from deciduous teeth 72 h after irradiation. Despite this cell cycle arrest, no radiation-induced senescence was observed. In conclusion, low X-ray IR doses (< 0.1 Gray; 900 mGray per hour), were able to induce significant increases in the number of DNA DSBs repair foci, but cell cycle progression seems to be minimally affected. This highlights the need for more detailed and extensive studies on the effects of exposure to low IR doses on different mesenchymal stromal cells.
Using the power of pixels in the physical world: Bridging digital visual tools to control the world of physical computing

Mapping values is part of the craftmanship of tangible interaction design. It is easy to imagine but hard to put in words and even harder to code. This swatch presents ways that might allow us to move faster from idea to experience. Through direct manipulation the experience becomes some sort of ‘clay’ that we can experience and manipulate simultaneously. This is done by using pixels to control the physical world and allows the use of software in the graphical domain to be used in the physical computing domain.
A Distributed Reactivity Model for Sorption by Soils and Sediments. 9. General Isotherm Nonlinearity and Applicability of the Dual Reactive Domain Model

Sorption isotherms for a hydrophobic solute probe, phenanthrene, were measured experimentally for 27 different soils and sediments. The linear and Freundlich isotherm models and the Dual Reactive Domain Model (DRDM) were used to fit the resulting data. The results reveal for all soils and sediments studied that (i) the Freundlich model and the DRDM fit the data well, whereas a linear model fails to do so; (ii) values of the organic carbon-normalized distribution coefficient, KOC, calculated from individual isotherm points for a specific sorbent−solute system vary significantly with the aqueous-phase solute concentration, Ce; and (iii) all commonly used correlations of KOC with octanol−water partitioning coefficients and solute solubility limits significantly underestimate KOC for Ce values smaller than approximately one-tenth of aqueous-phase solute solubility, CS. The sorption behaviors of all of the soils and sediments studied are thus inconsistent with the simple concept of linear phase partitioning. T.
Microbiomics of irrigation with xylitol or Lactococcus lactis in chronic rhinosinusitis

Topical sinonasal rinse therapies may alter the local microbiome and improve disease control in chronic rhinosinusitis (CRS). The objective of this study was to examine microbiome changes in post‐surgical CRS patients when rinsing with commercially available products containing xylitol or Lactococcus lactis.
DNA Methylation Analysis to Unravel Altered Genetic Pathways Underlying Early Onset and Late Onset Neonatal Sepsis. A Pilot Study

Background: Neonatal sepsis is a systemic condition widely affecting preterm infants and characterized by pro-inflammatory and anti-inflammatory responses. However, its pathophysiology is not yet fully understood. Epigenetics regulates the immune system, and its alteration leads to the impaired immune response underlying sepsis. DNA methylation may contribute to sepsis-induced immunosuppression which, if persistent, will cause long-term adverse effects in neonates. Objective: To analyze the methylome of preterm infants in order to determine whether there are DNA methylation marks that may shed light on the pathophysiology of neonatal sepsis. Design: Prospective observational cohort study performed in the neonatal intensive care unit (NICU) of a tertiary care center. Patients: Eligible infants were premature ≤32 weeks admitted to the NICU with clinical suspicion of sepsis. The methylome analysis was performed in DNA from blood using Infinium Human Methylation EPIC microarrays to uncover methylation marks. Results: Methylation differential analysis revealed an alteration of methylation levels in genomic regions involved in inflammatory pathways which participate in both the innate and the adaptive immune response. Moreover, differences between early and late onset sepsis as compared to normal controls were assessed. Conclusions: DNA methylation marks can serve as a biomarker for neonatal sepsis and even contribute to differentiating between early and late onset sepsis.