Miracle-dz/UniFAR-FTUs huggingface dataset:
A Methodology for Assessing the Environmental Effects Induced by ICT Services: Part II: Multiple Services and Companies

Information and communication technologies (ICT) can make existing products and activities more efficient or substitute them altogether and could thus become crucial for the mitigation of climate change. In this context, individual ICT companies, industry organizations and international initiatives have started to estimate the environmental effects of ICT services. Often such assessments rely on crude assumptions and methods, yielding inaccurate or even misleading results. The few existing methodological attempts are too general to provide guidance to practitioners. The starting points of this paper are i) a high-level standard from the European Telecommunication Standardisation Institute (ETSI) and the International Telecommunication Union (ITU), and ii) its suggested enhancements for single service assessment outlined in A Methodology for Assessing the Environmental Effects Induced by ICT Services Part I: Single services (Part I in short). Building on the assessment of single services, the current article identifies and addresses shortcomings of existing methodologies and industry practices with regard to multiple services assessment. For a collection of services, it addresses the goal and scope definition, the so-far ignored aggregation of effects among several services, and the allocation between several companies contributing to one or more services. The article finally brings these considerations together with those of Part I into a workflow for performing such assessments in practice.
Integrated management of powdery mildew of Okra

An experiment was undertaken to manage powdery mildew Erysiphe cichoracearum of Okra by using fungicides and bioagents. Three sprays of difenconazole (17.26% PDI) followed by two sprays of difenconazole in combination with Pseudomonas fluorescens (19.21% PDI) were found effective in managing powdery mildew of okra. The maximum benefit cost ratio was recorded in three sprays of propiconazole which was followed by three sprays of difenconazole. Thus, among the treatment combination Pseudomonas fluorescens and nimbicidin intermixed with propiconazole and difenconazole spray schedule not only reduced the cost of protection but also gave higher benefits.
Sparse GPU Kernels for Deep Learning

Scientific workloads have traditionally exploited high levels of sparsity to accelerate computation and reduce memory requirements. While deep neural networks can be made sparse, achieving practical speedups on GPUs is difficult because these applications have relatively moderate levels of sparsity that are not sufficient for existing sparse kernels to outperform their dense counterparts. In this work, we study sparse matrices from deep learning applications and identify favorable properties that can be exploited to accelerate computation. Based on these insights, we develop high-performance GPU kernels for two sparse matrix operations widely applicable in neural networks: sparse matrix–dense matrix multiplication and sampled dense– dense matrix multiplication. Our kernels reach 27% of single-precision peak on Nvidia V100 GPUs. Using our kernels, we demonstrate sparse Transformer and MobileNet models that achieve $1.2-2.1 \times $ speedups and up to $12.8 \times $ memory savings without sacrificing accuracy.
Stratification Dynamics of Titan’s Lakes via Methane Evaporation

Saturn’s moon Titan is the only extraterrestrial body known to host stable lakes and a hydrological cycle. Titan’s lakes predominantly contain liquid methane, ethane, and nitrogen, with methane evaporation driving its hydrological cycle. Molecular interactions between these three species lead to nonideal behavior that causes Titan’s lakes to behave differently than Earth’s lakes. Here, we numerically investigate how methane evaporation and nonideal interactions affect the physical properties, structure, dynamics, and evolution of shallow lakes on Titan. We find that, under certain temperature regimes, methane-rich mixtures are denser than relatively ethane-rich mixtures. This allows methane evaporation to stratify Titan’s lakes into ethane-rich upper layers and methane-rich lower layers, separated by a strong compositional gradient. At temperatures above 86 K, lakes remain well mixed and unstratified. Between 84 and 86 K, lakes can stratify episodically. Below 84 K, lakes permanently stratify and develop very methane-depleted epilimnia. Despite small seasonal and diurnal deviations (<5 K) from typical surface temperatures, Titan’s rain-filled ephemeral lakes and “phantom lakes” may nevertheless experience significantly larger temperature fluctuations, resulting in polymictic or even meromictic stratification, which may trigger ethane ice precipitation.
Explicit coupling of acoustic and elastic wave propagation in finite difference simulations

We present a mechanism to explicitly couple the finite-difference discretizations of 2D acoustic and isotropic elastic-wave systems that are separated by straight interfaces. Such coupled simulations allow for the application of the elastic model to geological regions that are of special interest for seismic exploration studies (e.g., the areas surrounding salt bodies), with the computationally more tractable acoustic model still being applied in the background regions. Specifically, the acoustic wave system is expressed in terms of velocity and pressure while the elastic wave system is expressed in terms of velocity and stress. Both systems are posed in first-order forms and are discretized on staggered grids. Special variants of the standard finite-difference operators, namely, operators that possess the summation-by-parts property, are used for the approximation of spatial derivatives. Penalty terms, which are also referred to as the simultaneous approximation terms, are designed to weakly impose the elastic-acoustic interface conditions in the finite-difference discretizations and couple the elastic and acoustic wave simulations together. With the presented mechanism, we are able to perform the coupled elastic-acoustic wave simulations stably and accurately. Moreover, it is shown that the energy-conserving property in the continuous systems can be preserved in the discretized systems with carefully designed penalty terms.
How does this interaction affect me? Interpretable attribution for feature interactions

Machine learning transparency calls for interpretable explanations of how inputs relate to predictions. Feature attribution is a way to analyze the impact of features on predictions. Feature interactions are the contextual dependence between features that jointly impact predictions. There are a number of methods that extract feature interactions in prediction models; however, the methods that assign attributions to interactions are either uninterpretable, model-specific, or non-axiomatic. We propose an interaction attribution and detection framework called Archipelago which addresses these problems and is also scalable in real-world settings. Our experiments on standard annotation labels indicate our approach provides significantly more interpretable explanations than comparable methods, which is important for analyzing the impact of interactions on predictions. We also provide accompanying visualizations of our approach that give new insights into deep neural networks.
Provably adaptive reinforcement learning in metric spaces

We study reinforcement learning in continuous state and action spaces endowed with a metric. We provide a refined analysis of the algorithm of Sinclair, Banerjee, and Yu (2019) and show that its regret scales with the \emph{zooming dimension} of the instance. This parameter, which originates in the bandit literature, captures the size of the subsets of near optimal actions and is always smaller than the covering dimension used in previous analyses. As such, our results are the first provably adaptive guarantees for reinforcement learning in metric spaces.
A general framework for defining and optimizing robustness

Robustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. Therefore, in this paper, we propose a rigorous and flexible framework for defining different types of robustness that also help to explain the interplay between adversarial robustness and generalization. The different robustness objectives directly lead to an adjustable family of loss functions. For two robustness concepts of particular interest we show effective ways to minimize the corresponding loss functions. One loss is designed to strengthen robustness against adversarial off-manifold attacks, and another to improve generalization under the given data distribution. Empirical results show that we can effectively train under different robustness objectives, obtaining higher robustness scores and better generalization, for the two examples respectively, compared to the state-of-the-art data augmentation and regularization techniques.
Tracking the Footprints of Spin Fluctuations: A MultiMethod, MultiMessenger Study of the Two-Dimensional Hubbard Model

The Hubbard model represents the fundamental model for interacting quantum systems and electronic correlations. Using the two-dimensional half-filled Hubbard model at weak coupling as testing grounds, we perform a comparative study of a comprehensive set of state of the art quantum many-body methods. Upon cooling into its insulating antiferromagnetic ground-state, the model hosts a rich sequence of distinct physical regimes with crossovers between a high-temperature incoherent regime, an intermediate temperature metallic regime and a low-temperature insulating regime with a pseudogap created by antiferromagnetic fluctuations. We assess the ability of each method to properly address these physical regimes and crossovers through the computation of several observables probing both quasiparticle properties and magnetic correlations, with two numerically exact methods (diagrammatic and determinantal quantum Monte Carlo) serving as a benchmark. By combining computational results and analytical insights, we elucidate the nature and role of spin fluctuations in each of these regimes and explain, in particular, how quasiparticles can coexist with increasingly long-range antiferromagnetic correlations in the metallic regime. We also critically discuss whether imaginary time methods are able to capture the non-Fermi liquid singularities of this fully nested system.
Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation

According to the World Health Organization, distracted driving is one of the leading cause of motor accidents and deaths in the world. In our study, we tackle the problem of distracted driving by aiming to build a robust multi-class classifier to detect and identify different forms of driver inattention using the State Farm Distracted Driving Dataset. We utilize combinations of pretrained image classification models, classical data augmentation, OpenCV based image preprocessing and skin segmentation augmentation approaches. Our best performing model combines several augmentation techniques, including skin segmentation, facial blurring, and classical augmentation techniques. This model achieves an approximately 15% increase in F1 score over the baseline, thus showing the promise in these techniques in enhancing the power of neural networks for the task of distracted driver detection.
Attention Mesh: High-fidelity Face Mesh Prediction in Real-time

We present Attention Mesh, a lightweight architecture for 3D face mesh prediction that uses attention to semantically meaningful regions. Our neural network is designed for real-time on-device inference and runs at over 50 FPS on a Pixel 2 phone. Our solution enables applications like AR makeup, eye tracking and AR puppeteering that rely on highly accurate landmarks for eye and lips regions. Our main contribution is a unified network architecture that achieves the same accuracy on facial landmarks as a multi-stage cascaded approach, while being 30 percent faster.
Molecular Modulation of Estrogen-Induced Apoptosis in Long-Term Estrogen-Deprived Breast Cancer Cells

Estrogen receptor (ER)-positive breast cancer cell lines have been instrumental in modeling breast cancer and providing an opportunity to document the development and evolution of acquired anti-hormone resistance. Mode ls of long-term estrogen-deprived breast cancer cells are utilized in the laboratory to mimi c clinical aromatase inhibitor-resistant breast cancer and serve as a tool to discover new therapeu tic strategies. The MCF-7:5C and MCF-7:2A subclones were generated through long-term estrogen deprivation of ER-positive MCF-7 cells, and represent anti-hormone resistant breast cancer. MCF-7:5C cells paradoxically undergo estrogen-induced apoptosis within seven days of est rogen (estradiol, E 2) treatment; MCF-7:2A cells also experience E 2-induced apoptosis but evade dramatic cell death un il approximately 14 days of treatment. Our data suggest that MCF-7:2A c ells employ stronger antioxidant defense mechanisms than do MCF-7:5C cells, and that oxidati ve stress is ultimately required for MCF7:2A cells to die in response to E 2 treatment. Tumor necrosis factor (TNF) family memb r activation also correlates with E 2-induced apoptosis in MCF-7:2A cells; up-regulation of TNFα occurs simultaneously with oxidative stress activat ion. Additionally, increased insulin-like growth factor receptor beta (IGF-1R β) confers a mechanism of growth and anti-apoptotic advantage in MCF-7:2A cells.
Morphological and molecular identification of economically important Tortricidae (Lepidoptera) on deciduous fruit tree crops in South Africa

Four tortricid species are of major economic importance on deciduous fruit tree crops in South Africa. They are codling moth, Cydia pomonella, oriental fruit moth, Grapholita molesta, false codling moth, Thaumatotibia leucotreta, and carnation worm, Epichoristodes acerbella. Pest management and phytosanitary practices are often hampered by the inability to distinguish between the immature stages of these four species. To address this need, the final instar larvae and pupae of E. acerbella are described and illustrated. The morphology of these immature stages of E. acerbella is compared with those of the aforementioned tortricid species. Keys are included to distinguish between the four species. In addition, as an alternative method of identification of any life stage, a means of discriminating between the four species, based on analysis of nucleotide sequences of the mitochondrial cytochrome oxidase I gene, is described.
Regression Networks for Meta-Learning Few-Shot Classification

We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction of data generally contains richer information than magnitude. Next to this, state-of-the-art few-shot metric methods that compare distances with aggregated class representations, have shown superior performance. Combining these two insights, we propose to meta-learn classification of embedded points by regressing the closest approximation in every class subspace while using the regression error as a distance metric. Similarly to recent approaches for few-shot learning, regression networks reflect a simple inductive bias that is beneficial in this limited-data regime and they achieve excellent results, especially when more aggregate class representations can be formed with multiple shots.
MICROBIOTIC FORMATION OF SILICATE MINERALS IN THE WEATHERING ENVIRONMENT OF A PYROCLASTIC DEPOSIT

Bacterial mineralization in weathered pyroclastic deposits of the Kaimondake volcanic ash (4040 ybp) and the Koya pyroclastic flow (6400 ybp) was investigated to evaluate the impacts of bacteria on mineral formation, and to characterize the microbiogenic minerals in the weathering environment. The mineralogy of abiogenic weathering products was also investigated for comparison with the microbiogenic products, and mineral saturation indices were calculated for porewaters using the PHREEQC computer code. The results indicated that these weathered pyroclastic deposits contain 10 ­ 10 cells/g, consisting of spherical to rod-shaped bacteria. Associated abiogenic allophane had an Al/Si ratio ranging from 1.01 to 2.13. The bacterial cell surfaces were completely or partially covered by poorly-ordered silicate minerals, which could be divided into two groups based on their chemical and morphological characteristics. Group I was characterized by well developed fibrous to smectite-like flaky habits with variable Al, Si and Fe, corresponding to compositions between proto-imogolite allophane and chamosite. These Al-Si-Fe minerals were the most abundant and major microbiogenic products in both lithologies. Group II exhibited poorly-developed aggregates of allophane-like granular materials composed mainly of Al and Si with minor Fe. Geochemical calculations revealed that the porewaters were saturated with respect to allophane and other crystalline clay minerals such as halloysite, kaolinite, montmorillonite and nontronite. These microbiogenic minerals may be formed as the earliest phase of these clay minerals after interaction of the bacterial cell surfaces with dissolved cations mainly Si, Al and Fe, in the porewaters. Key Words—Allophane, Bacteria, Bacterial Mineralizati on, Cell Surface, Pyroclastic Deposit, Silicate Mineral, Weathering.
Constraining early dark energy with large-scale structure

An axion-like field comprising $\sim 10\%$ of the energy density of the universe near matter-radiation equality is a candidate to resolve the Hubble tension; this is the "early dark energy" (EDE) model. However, as shown in Hill et al. (2020), the model fails to simultaneously resolve the Hubble tension and maintain a good fit to both cosmic microwave background (CMB) and large-scale structure (LSS) data. Here, we use redshift-space galaxy clustering data to sharpen constraints on the EDE model. We perform the first EDE analysis using the full-shape power spectrum likelihood from the Baryon Oscillation Spectroscopic Survey (BOSS), based on the effective field theory (EFT) of LSS. The inclusion of this likelihood in the EDE analysis yields a $25\%$ tighter error bar on $H_0$ compared to primary CMB data alone, yielding $H_0 = 68.54^{+0.52}_{-0.95}$ km/s/Mpc ($68\%$ CL). In addition, we constrain the maximum fractional energy density contribution of the EDE to $f_{\rm EDE} < 0.072$ ($95\%$ CL). We explicitly demonstrate that the EFT BOSS likelihood yields much stronger constraints on EDE than the standard BOSS likelihood. Including further information from photometric LSS surveys,the constraints narrow by an additional $20\%$, yielding $H_0 = 68.73^{+0.42}_{-0.69}$ km/s/Mpc ($68\%$ CL) and $f_{\rm EDE}<0.053$ ($95\%$ CL). These bounds are obtained without including local-universe $H_0$ data, which is in strong tension with the CMB and LSS, even in the EDE model. We also refute claims that MCMC analyses of EDE that omit SH0ES from the combined dataset yield misleading posteriors. Finally, we demonstrate that upcoming Euclid/DESI-like spectroscopic galaxy surveys can greatly improve the EDE constraints. We conclude that current data preclude the EDE model as a resolution of the Hubble tension, and that future LSS surveys can close the remaining parameter space of this model.
An improved spin-down rate for the proposed white dwarf pulsar AR scorpii

We analyze rapid-cadence, multiwavelength photometry of AR Scorpii from three observatories, covering five observing seasons. We measure the arrival times of the system's beat pulses and use them to compute an updated ephemeris. The white dwarf spin-down rate is estimated with an uncertainty of only 4%. These results confirm, beyond any doubt, that the white dwarf's spin period is increasing at the rate consistent with by that of Stiller et al. (2018). We study the evolution of the beat pulse's color index across the orbit. The color of the primary pulse maxima varies significantly across the orbit, with the peaks being bluer after superior conjunction than in the first half of the orbit. Specifically, at orbital phase 0.5, the color index of the primary pulse shows a very sharp discontinuity towards bluer indices. This supports the Potter & Buckley (2018b) synchrotron emission model where the two emitting poles differ significantly in color. However, no corresponding jump in the color of the secondary pulses is seen. Furthermore, our analysis reveals that the arrival times of the pulses can differ by as much as 6s in simultaneous $u$ and $r$ photometry, depending on the binary orbital phase. If left uncorrected, this wavelength-dependent timing offset could lead to erroneous measurements of the spin-period derivative, particularly with heterogeneous datasets.
The Large Magellanic Cloud stellar content with SMASH

The Large Magellanic Cloud (LMC) is the closest and most studied example of an irregular galaxy. Among its principal defining morphological features, its off-centred bar and single spiral arm stand out, defining a whole family of galaxies known as the Magellanic spirals (Sm). These structures are thought to be triggered by tidal interactions and possibly maintained via gas accretion. However, it is still unknown whether they are long-lived stable structures. In this work, by combining photometry that reaches down to the oldest main sequence turn-off in the colour-magnitude diagrams (CMD, up to a distance of ∼4.4 kpc from the LMC centre) from the SMASH survey and CMD fitting techniques, we find compelling evidence supporting the long-term stability of the LMC spiral arm, dating the origin of this structure to more than 2 Gyr ago. The evidence suggests that the close encounter between the LMC and the Small Magellanic Cloud (SMC) that produced the gaseous Magellanic Stream and its Leading Arm also triggered the formation of the LMC’s spiral arm. Given the mass difference between the Clouds and the notable consequences of this interaction, we can speculate that this should have been one of their closest encounters. These results set important constraints on the timing of LMC-SMC collisions, as well as on the physics behind star formation induced by tidal encounters.
Deterministic chaos and fractal entropy scaling in Floquet conformal field theories

In this paper, we study 2d Floquet conformal field theory, where the external periodic driving is described by iterated logistic or tent maps. These maps are known to be typical examples of dynamical systems exhibiting the order-chaos transition, and we show that, as a result of such driving, the entanglement entropy scaling develops fractal features when the corresponding dynamical system approaches the chaotic regime. For the driving set by the logistic map, fractal contribution to the scaling dominates, making entanglement entropy highly oscillating function of the subsystem size.