Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Comets: Designing a Visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation

Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post‐flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of Data Comets, an open‐source and web‐based interactive visualization tool for post‐flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in‐the‐wild usage. A free copy of this paper and source code are available at osf.io/h4p7g
Production Economics of Nile Tilapia (Oreochromis niloticus) Pond Culture in El-Fayum Governorate, Egypt

Econometric techniques were used to estimate a production function for tilapia pond culture in El-Fayum Governorate, Egypt, utilizing cross-sectional field data. Explanatory variables were feed, initial stocking weight, and pond size. The function was used to examine returns to scale, estimate the productivity of feed and initial stocking weight, and estimate the profit maximizing demand equations for feed and fingerlings. Diminishing returns to scale seem to exist for tilapia pond culture. Optimal economic stocking and feeding rates, yield, and profits for the whole sample were estimated at prevailing prices. Results indicated that producers were using stocking rates greater than the optimal by 36% to 115% and using insufficient feed by 8% to 22%. Investigation of the effects of changes in tilapia selling price and feed price on optimal stocking and feeding rates, yield, and profits showed that a change in tilapia selling price has greater effect on profits, yield, stocking, and feeding rates than a proportional change in feed price. Profits, feeding, and stocking rates are more sensitive than yield to changes in selling price, while feeding rate is more sensitive than stocking rate, yield, and profits to changes in feed price.
Semileptonic decays 
D→π+π−e+νe
 and 
Ds→π+π−e+νe
 as the probe of constituent quark-antiquark pairs in the light scalar mesons

Decays $D\to\pi^+\pi^-e^+\nu_e$ and $D_s\to\pi^+ \pi^-e^+\nu_e$ serve as probes that check the existence of constituent $q\bar q$ components in the wave functions of scalar mesons decaying into $\pi^+\pi^-$. There exists a great deal of concrete evidence in favor of the exotic four-quark nature of light scalars. At the same time, the further expansion of the area of the $q^2\bar q^2$ model validity for light scalars on ever new processes seems extremely interesting and important. We analyze the BESIII and CLEO data on the decays $D^+\to \pi^+\pi^- e^+\nu_e$ and $D^+_s\to \pi^+\pi^- e^+\nu_e$ and show that the results of these experiments together can be interpreted in favor of the four-quark nature of light scalar mesons $\sigma(500)$ and $f_0(980)$. Our approach can also be applied to the description of other similar decays involving light scalars.
Split Ga vacancies and the colossal anisotropy of positron annihilation spectra in $\boldsymbol{\beta}$-Ga$_2$O$_3$

We report a systematic first principles study on positron annihilation parameters in the $\beta$-Ga$_2$O$_3$ lattice and Ga mono-vacancy defects complemented with direction-dependent experiments of the Doppler broadening of the positron-electron annihilation. We find that both the $\beta$-Ga$_2$O$_3$ lattice and the considered defects exhibit colossal anisotropy in their Doppler broadening signals. This anisotropy is associated with the unusual kind of low symmetry of the $\beta$-Ga$_2$O$_3$ crystal structure that leads to one-dimensional confinement of positrons even in the delocalized state in the lattice. In particular, the split Ga vacancies recently observed by scanning transmission electron microscopy produce unusually anisotropic positron annihilation signals. We show that in experiments, the positron annihilation signals in $\beta$-Ga$_2$O$_3$ samples are dominated by split Ga vacancies.
Deep Learning Convective Flow Using Conditional Generative Adversarial Networks

We developed a general deep learning framework, FluidGAN, that is capable of learning and predicting time-dependent convective flow coupled with energy transport. FluidGAN is thoroughly data-driven with high speed and accuracy and satisfies the physics of fluid without any prior knowledge of underlying fluid and energy transport physics. FluidGAN also learns the coupling between velocity, pressure and temperature fields. Our framework could be used to learn deterministic multiphysics phenomena where the underlying physical model is complex or unknown.
Oxygen consumption and temperature change in the shrimp Palaemon elegans

Oxygen consumption responses to short-term temperature changes were measured in a Mediterranean population of the shrimp Palaemon elegans (Rathke). At all temperatures between 5 and 25°C small shrimp with a body weight of 150 mg showed higher rates of oxygen consumption than larger shrimp. Handling affects metabolic rate; the latter stabilizes only after more than 10 h, especially in small shrimp. Q,o-values decrease with decreasing shrimp weight and with rising temperature. Compared to a cold-acclimated population of the same species (Alcaraz 1974) metabolic rates at 15 and 20 "C are lower in the warm-acclimated Mediterranean population used in this investigation.
Detecting an Odd Restless Markov Arm with a Trembling Hand

Consider a multi-armed bandit whose arms are independent Markov processes on a common underlying state space. The transition probability matrix of one of the arms (the odd arm) is different from the common transition probability matrix of all the other arms. The goal is to identify the odd arm as quickly as possible while keeping the probability of decision error small. We study the case of restless Markov observations and identify an asymptotic lower bound on the expected stopping time for a decision with vanishing error probability. We then propose a sequential test and show that the asymptotic behaviour of its expected stopping time comes arbitrarily close to that of the lower bound. Prior works dealt with iid arms and rested Markov arms, whereas our work deals with restless Markov arms.
FaR-GAN for One-Shot Face Reenactment

Animating a static face image with target facial expressions and movements is important in the area of image editing and movie production. This face reenactment process is challenging due to the complex geometry and movement of human faces. Previous work usually requires a large set of images from the same person to model the appearance. In this paper, we present a one-shot face reenactment model, FaR-GAN, that takes only one face image of any given source identity and a target expression as input, and then produces a face image of the same source identity but with the target expression. The proposed method makes no assumptions about the source identity, facial expression, head pose, or even image background. We evaluate our method on the VoxCeleb1 dataset and show that our method is able to generate a higher quality face image than the compared methods.
A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas

In an attempt to follow biological information representation and organization principles, the field of neuromorphic engineering is usually approached bottom-up, from the biophysical models to large-scale integration in silico. While ideal as experimentation platforms for cognitive computing and neuroscience, bottom-up neuromorphic processors have yet to demonstrate an efficiency advantage compared to specialized neural network accelerators for real-world problems. Top-down approaches aim at answering this difficulty by (i) starting from the applicative problem and (ii) investigating how to make the associated algorithms hardware-efficient and biologically-plausible. In order to leverage the data sparsity of spike-based neuromorphic retinas for adaptive edge computing and vision applications, we follow a top-down approach and propose SPOON, a 28-nm event-driven CNN (eCNN). It embeds online learning with only 16.8-% power and 11.8-% area overheads with the biologically-plausible direct random target projection (DRTP) algorithm. With an energy per classification of 313nJ at 0.6V and a 0.32-mm2 area for accuracies of 95.3% (on-chip training) and 97.5% (off-chip training) on MNIST, we demonstrate that SPOON reaches the efficiency of conventional machine learning accelerators while embedding on-chip learning and being compatible with event-based sensors, a point that we further emphasize with N-MNIST benchmarking.
Robustly Learning any Clusterable Mixture of Gaussians

We study the efficient learnability of high-dimensional Gaussian mixtures in the outlier-robust setting, where a small constant fraction of the data is adversarially corrupted. We resolve the polynomial learnability of this problem when the components are pairwise separated in total variation distance. Specifically, we provide an algorithm that, for any constant number of components $k$, runs in polynomial time and learns the components of an $\epsilon$-corrupted $k$-mixture within information theoretically near-optimal error of $\tilde{O}(\epsilon)$, under the assumption that the overlap between any pair of components $P_i, P_j$ (i.e., the quantity $1-TV(P_i, P_j)$) is bounded by $\mathrm{poly}(\epsilon)$. Our separation condition is the qualitatively weakest assumption under which accurate clustering of the samples is possible. In particular, it allows for components with arbitrary covariances and for components with identical means, as long as their covariances differ sufficiently. Ours is the first polynomial time algorithm for this problem, even for $k=2$. Our algorithm follows the Sum-of-Squares based proofs to algorithms approach. Our main technical contribution is a new robust identifiability proof of clusters from a Gaussian mixture, which can be captured by the constant-degree Sum of Squares proof system. The key ingredients of this proof are a novel use of SoS-certifiable anti-concentration and a new characterization of pairs of Gaussians with small (dimension-independent) overlap in terms of their parameter distance.
Egg production and egg quality laying hens fed raw or processed sorrel (Hibiscus sabdariffa) seed meal

A study was conducted to evaluate the performance of caged laying hens fed raw, soaked, boiled, sprouted and fermented sorrel seed meal in a semi-arid environment. Six isonitrogenous diets (17% crude protein) were formulated. Diet 1 was based on soyabean meal which was replaced at 50% by processed sorrel seed meal in the other diets. A total of one hundred and eighty (180) Black Harco pullets aged 22 weeks were randomly assigned to the diets with 30 birds per diet each containing 3 replicates of 10 birds. Data were collected on feed consumption, egg production and shell quality. Results showed no dietary effects on feed utilization, egg production, egg weight and shell quality. The cost of feed was reduced with the inclusion of sorrel seed meal in the diet resulting to a lower cost of egg production on these diets compared to the control. No mortality was recorded during the period of the experiment. It was concluded that up to 50% of dietary soyabean meal can be replaced with sorrel seed meal raw or processed without adverse effects on the performance of laying hens. The replacement will reduce the cost of the feed and thus the cost of egg production.
COVID-19 Contact Tracing and Privacy: Studying Opinion and Preferences

There is growing interest in technology-enabled contact tracing, the process of identifying potentially infected COVID-19 patients by notifying all recent contacts of an infected person. Governments, technology companies, and research groups alike recognize the potential for smartphones, IoT devices, and wearable technology to automatically track "close contacts" and identify prior contacts in the event of an individual's positive test. However, there is currently significant public discussion about the tensions between effective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of a sequence of online surveys focused on contact tracing and privacy, each with 100 participants. Our first surveys were on April 1 and 3, and we report primarily on those first two surveys, though we present initial findings from later survey dates as well. Our results present the diversity of public opinion and can inform the public discussion on whether and how to leverage technology to reduce the spread of COVID-19. We are continuing to conduct longitudinal measurements, and will update this report over time; citations to this version of the report should reference Report Version 1.0, May 8, 2020.
Constraining the growth rate of structure with phase correlations

We show that correlations between the phases of the galaxy density field in redshift space provide additional information about the growth rate of large-scale structure that is complementary to the power-spectrum multipoles. In particular, we consider the multipoles of the line correlation function (LCF), which correlates phases between three collinear points, and use the Fisher forecasting method to show that the LCF multipoles can break the degeneracy between the measurement of the growth rate of structure f and the amplitude of perturbations σ8 that is present in the power-spectrum multipoles at large scales. This leads to an improvement in the measurement of f and σ8 by up to 220 per cent for $k_{\rm max} = 0.15 \, h\, \mathrm{Mpc}^{-1}$ and up to 50 per cent for $k_{\rm max} = 0.30 \, h\, \mathrm{Mpc}^{-1}$ at redshift z = 0.25, with respect to power-spectrum measurements alone for the upcoming generation of galaxy surveys like DESI and Euclid. The average improvements in the constraints on f and σ8 for $k_{\rm max} = 0.15 \, h\, \mathrm{Mpc}^{-1}$ are ∼90 per cent for the DESI BGS sample with mean redshift $\overline{z}=0.25$, ∼40 per cent for the DESI ELG sample with $\overline{z}=1.25$, and ∼40 per cent for the Euclid Hα galaxies with $\overline{z}=1.3$. For $k_{\rm max} = 0.30 \, h\, \mathrm{Mpc}^{-1}$, the average improvements are ∼40 per cent for the DESI BGS sample and ∼20 per cent for both the DESI ELG and Euclid Hα galaxies.
Geometric considerations around the Littlewood conjecture

In this paper we adopt a geometric point of view regarding a famous conjecture due to Littlewood in diophantine approximation of real numbers. Following the spirit of the geometric theory of continued fractions, we give a sufficient condition for the conjecture to hold. An advantage of our method is that it is effective, in the sense that we know how to construct the eventual solution.
On the Global Convergence Rates of Softmax Policy Gradient Methods

We make three contributions toward better understanding policy gradient methods in the tabular setting. First, we show that with the true gradient, policy gradient with a softmax parametrization converges at a $O(1/t)$ rate, with constants depending on the problem and initialization. This result significantly expands the recent asymptotic convergence results. The analysis relies on two findings: that the softmax policy gradient satisfies a Łojasiewicz inequality, and the minimum probability of an optimal action during optimization can be bounded in terms of its initial value. Second, we analyze entropy regularized policy gradient and show that it enjoys a significantly faster linear convergence rate $O(e^{-t})$ toward softmax optimal policy. This result resolves an open question in the recent literature. Finally, combining the above two results and additional new $\Omega(1/t)$ lower bound results, we explain how entropy regularization improves policy optimization, even with the true gradient, from the perspective of convergence rate. The separation of rates is further explained using the notion of non-uniform Łojasiewicz degree. These results provide a theoretical understanding of the impact of entropy and corroborate existing empirical studies.
Two-dimensional graphene–HfS2 van der Waals heterostructure as electrode material for alkali-ion batteries

Poor electrical conductivity and large volume expansion during repeated charge and discharge is what has characterized many battery electrode materials in current use. This has led to 2D materials, specifically multi-layered 2D systems, being considered as alternatives. Among these 2D multi-layered systems are the graphene-based van der Waals heterostructures with transition metal di-chalcogenides (TMDCs) as one of the layers. Thus in this study, the graphene–hafnium disulphide (Gr–HfS2) system, has been investigated as a prototype Gr–TMDC system for application as a battery electrode. Density functional theory calculations indicate that Gr–HfS2 van der Waals heterostructure formation is energetically favoured. In order to probe its battery electrode application capability, Li, Na and K intercalants were introduced between the layers of the heterostructure. Li and K were found to be good intercalants as they had low diffusion barriers as well as a positive open circuit voltage. A comparison of bilayer graphene and bilayer HfS2 indicates that Gr–HfS2 is a favourable battery electrode system.
snapshot: connections between internal and surface properties of massive stars

We introduce SNAPSHOT, a technique to systematically compute stellar structure models in hydrostatic and thermal equilibrium based on 3 structural properties - core mass $M_{\rm core}$, envelope mass $M_{\rm env}$ and core composition. This approach allows us to connect these properties of stellar interiors to the luminosity and effective temperature $T_{\rm eff}$ in a more systematic way than with stellar evolution models. For MS models, we derive an analytical relationship between $M_{\rm core}$, $M_{\rm total}$ and central H abundance that can be used in rapid stellar evolution algorithms. Core-He burning models with $M_{\rm core}/M_{\rm total}$ from 0.2 to 0.8 have convective envelopes, low $T_{\rm eff}$ and will appear as red supergiants. For a given $M_{\rm core}$, they exhibit a small variation in luminosity (0.02 dex) and $T_{\rm eff}$ ($\sim 400\,\mathrm{K}$) over a wide range of $M_{\rm env}$ ($\sim 2 - 20\,\mathrm{M}_{\odot}$). This means that it is not possible to derive red supergiant masses from luminosities and $T_{\rm eff}$ alone. We derive the following relationship between $M_{\rm core}$ and the total luminosity of a red supergiant during core He burning: $\log M_{\rm core} \simeq 0.44 \log L/L_{\odot} - 1.38$. At $M_{\rm core}$/$M_{\rm total} \approx 0.2$, our models exhibit a bi-stability and jump from a RSG to a BSG structure. Our models with $M_{\rm core}/M_{\rm total} > 0.8$, which correspond to stripped stars produced by mass loss or binary interaction, show that $T_{\rm eff}$ has a strong dependence on $M_{\rm env}$, $M_{\rm core}$ and the core composition. We find the mass of one of these stripped stars in a binary system, HD 45166, to be less than its dynamical mass. When a large observational sample of stripped stars becomes available, our results can be used to constrain their masses and the physics of binary interaction.
Oil yield components and oil quality of high stearic-high oleic sunflower genotypes as affected by intercepted solar radiation during grain filling

Abstract. High stearic-high oleic sunflower oil presents high thermal stability. This oil is an alternative to the hydrogenation process which produces trans fatty acids. The effect of intercepted solar radiation (ISR) per plant during grain filling on oil yield components and oil fatty acid composition was investigated in three sunflower high stearic-high oleic genotypes. Three field experiments were conducted and treatments to modify ISR per plant were applied during grain filling: shading, defoliating and thinning plants. Increasing ISR per plant linearly increased grain number per capitulum, weight per grain and in some cases palmitic and stearic acid percentages. In the hybrid, grain oil percentage and oleic acid concentration increased with a decreasing rate, reaching a maximum value at high levels of ISR per plant. Linoleic acid percentage decreased with a decreasing rate, reaching a minimum value at high levels of ISR per plant. Oil yield components presented heterosis. This information contributes to explain the effects of environment on yield and oil quality in high stearic-high oleic genotypes and could be used to design management practices that optimise these traits.
INFOTABS: Inference on Tables as Semi-structured Data

In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them. We argue that such data can prove as a testing ground for understanding how we reason about information. To study this, we introduce a new dataset called INFOTABS, comprising of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. Our analysis shows that the semi-structured, multi-domain and heterogeneous nature of the premises admits complex, multi-faceted reasoning. Experiments reveal that, while human annotators agree on the relationships between a table-hypothesis pair, several standard modeling strategies are unsuccessful at the task, suggesting that reasoning about tables can pose a difficult modeling challenge.
Accelerating Large-Scale Inference with Anisotropic Vector Quantization

Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases. Traditional approaches to quantization aim to minimize the reconstruction error of the database points. Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions. Under natural statistical assumptions, we show that quantization with these loss functions leads to a new variant of vector quantization that more greatly penalizes the parallel component of a datapoint's residual relative to its orthogonal component. The proposed approach achieves state-of-the-art results on the public benchmarks available at \url{this http URL}.