Miracle-dz/UniFAR-FTUs huggingface dataset:
Salinity Rain Impact Model (RIM) for SMAP

When oceanic rainfall occurs, it creates a vertical salinity profile that is fresher at the surface. This freshwater lens is mixed downward by turbulent diffusion, dissipating over a few hours until the upper layer (1–5 m depth) becomes well mixed. Thus, there will be a transient bias between the in situ bulk salinity and the satellite-measured sea surface salinity (SSS) (representative of the first centimeter of the ocean depth). Based on measurements of Aquarius (AQ) SSS under rainy conditions, a model called rain impact model (RIM) was developed to assess the SSS variations due to the accumulation of rainfall prior to the time of the AQ observation. RIM uses ocean surface salinities from hybrid coordinate ocean model and the NOAA global precipitation product, climate prediction center morphing, to estimate changes in the near-surface salinity profile. Also, the RIM analysis has been applied to soil moisture and ocean salinity with similar results observed. The Soil Moisture Active Passive (SMAP) satellite carries an L-band radiometer, which measures SSS over a swath of 1000 km at 40-km resolution. SMAP can extend AQ salinity data record with improved temporal/spatial sampling. This paper describes RIM that simulates the effects of rain accumulation on SMAP SSS, showing good correlation between the model and the observed SSS values. Given the better resolution of SMAP, the goal of this paper is to continue the previous analysis of AQ to better understand the effects of the instantaneous and accumulated rain on the salinity measurements.
2-D Grating Couplers for Vertical Fiber Coupling in Two Polarizations

We demonstrate highly efficient couplers in a two-dimensional grating configuration for a vertical fiber-coupling fabricated in the silicon-on-insulator platform. A combination of sub-wavelength reflectors and blazed gratings enables both high coupling efficiency and vertical grating coupling. We experimentally achieve coupling efficiencies of −2.6 dB at 1544 nm and −3.4 dB at 1536 nm for x- and y-polarized LP01 modes, respectively. The experimental results are in good agreement with the simulated −2.4 dB coupling efficiency for both polarizations at a wavelength of 1550 nm. Simulated and measured crosstalk between two polarizations were −19 dB and <−16 dB, respectively.
Virtual Network Function Placement in IoT Network

This paper investigates an efficient placing mechanism for placing Virtual Network Function (VNF) on the cloud networks to enable the construction of Internet of Things (IoT) service chains with minimal resource usage cost. In particular, we propose a model taking IoT network infrastructure into account. Such the network composed of numerous sensors with constrained resource, dynamic connectivity toward multi-homing IoT gateways makes the problem of optimally placing VNFs with expected Quality of Service (QoS) more challenging and has not been considered in prior works. From the model, we formulate a non-convex Integer Programming (IP) placement problem and devise a batching Markov approximation placement (BMAP) algorithm to find the optimal solution. Simulation results show that the proposed approach improves the cost compared to those that do not consider the IoT network.
Rethinking the Grammaticalization of Future be going to: A Corpus-based Approach

Abstract The development of future be going to has been widely studied as an instance of grammaticalization. However, the present researches are limited to qualitative description and hence lack quantitative verification. This paper intends to conduct a corpus-based quantitative study on the grammaticalization of be going to to investigate the realization time of its full grammaticalization from (1) the application of mental verbs, (2) the appearance of non-animate subjects and (3) the use of passive voice in the corpus of Google Books and the diachronic distribution of be going to during the 200 years from the 1810s to 2000s in COHA. The findings include (1) the full grammaticalization of be going to is realized at the late 17th century, (2) the diachronic distribution of future be going to has been increasing compared with that of future will, and (3) future be going to belongs to the grammatical category of modal verbs.
Weighted Tensor Nuclear Norm Minimization for Color Image Restoration

Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance. On the other hand, a number of color extensions of NLSS-based grayscale image restoration methods have been developed. These extensions focus on the pixel-wise correlation among color channels. However, a natural color image also has a complex dependency, known as an inter-channel dependency, among local regions from different color channels. As a result, color artifacts appear in a denoised image obtained by using the existing methods. In this paper, we propose a novel non-local and inter-channel dependency-aware prior called the weighted tensor nuclear norm (WTNN). The proposed prior is derived by incorporating inter-channel dependency to low-rank-based NLSS prior. The WTNN is a low-rankness-of-the-third-order patch tensor, and we apply it to the tensors constructed with non-local similar patches. It enables us to naturally represent the higher-order dependencies among similar color patches. We propose an image denoising algorithm using the WTNN and image restoration algorithm by using a non-trivial generalization of this algorithm. The experimental results clearly show that the proposed WTNN-based color image denoising and restoration algorithms outperform state-of-the-art methods.
Full reciprocity-gap waveform inversion enabling sparse-source acquisition

We perform quantitative sub-surface Earth-imaging by setting up the Full Reciprocity-gap Waveform Inversion (FRgWI ) method. The reconstruction relies on iterative minimization of a misfit functional which is specifically designed for data obtained from dual-sensors devices. In addition to the pressure field, the dual-sensors devices are able to measure the normal velocity of the waves and have been deployed in exploration geophysics. The use of reciprocity-based misfit functional provides additional features compared to the more traditional least-squares approaches with, in particular, that the observational and computational acquisitions can be different. Therefore, the source positions and wavelets that generate the measurements are not needed for the reconstruction procedure and, in fact, the numerical acquisition (for the simulations) can be arbitrarily chosen. We illustrate our method with three-dimensional experiments, where we first show that the reciprocity-gap inversion behaves better than the Full Waveform Inversion (FWI) in the same context. Next, we investigate arbitrary numerical acquisitions in two ways: firstly, when few measurements are given, we use a dense numerical acquisition (compared to the observational one). On the other hand, with a dense observational acquisition, we employ a sparse computational acquisition, with multiple-point sources, to reduce the numerical cost. We highlight that the reciprocity-gap functional is very efficient in both situations and is much more robust with respect to cross-talk than shot-stacking.
Discourse-Based Evaluation of Language Understanding

We introduce DiscEval, a compilation of $11$ evaluation datasets with a focus on discourse, that can be used for evaluation of English Natural Language Understanding when considering meaning as use. We make the case that evaluation with discourse tasks is overlooked and that Natural Language Inference (NLI) pretraining may not lead to the learning really universal representations. DiscEval can also be used as supplementary training data for multi-task learning-based systems, and is publicly available, alongside the code for gathering and preprocessing the datasets.
On the adoption, usage and evolution of Kotlin features in Android development

Background: Google announced Kotlin as an Android official programming language in 2017, giving developers an option of writing applications using a language that combines object-oriented and functional features. Aims: The goal of this work is to understand the usage of Kotlin features considering four aspects: i) which features are adopted, ii) what is the degree of adoption, iii) when are these features added into Android applications for the first time, and iv) how the usage of features evolves along with applications' evolution. Method: Exploring the source code of 387 Android applications, we identify the usage of Kotlin features on each version application's version and compute the moment that each feature is used for the first time. Finally, we identify the evolution trend that better describes the usage of these features. Results: 15 out of 26 features are used on at least 50% of applications. Moreover, we found that type inference, lambda and safe call are the most used features. Also, we observed that the most used Kotlin features are those first included on Android applications. Finally, we report that the majority of applications tend to add more instances of 24 out of 26 features along with their evolution. Conclusions: Our study generates 7 main findings. We present their implications, which are addressed to developers, researchers and tool builders in order to foster the use of Kotlin features to develop Android applications.
Stochastic-Greedy++: Closing the Optimality Gap in Exact Weak Submodular Maximization

Many problems in discrete optimization can be formulated as the task of maximizing a monotone and weak submodular function subject to a cardinality constraint. For such problems, a simple greedy algorithm is guaranteed to find a solution with a value no worse than $1-1/e$ of the optimal. Although the computational complexity of Greedy is linear in the size of the data $m$ and cardinality constraint ($k$), even this linear complexity becomes prohibitive for large-scale datasets. Recently, Mirzasoleyman et al. propose a randomized greedy algorithm, Stochastic-Greedy, with the expected worst case approximation ratio of $1-1/e-\epsilon$ where $0<\epsilon<1$ is a parameter controlling the trade-off between complexity and performance. We consider the following question: Given the small $\epsilon$ gap between the worst-case performance guarantees of Stochastic-Greedy and Greedy, can we expect nearly equivalent conditions for the exact identification of the optimal subset? In this paper we show that in general there is an unbounded gap between the exact performance of Stochastic-Greedy and Greedy by considering the problem of sparse support selection. Tropp and Gilbert show Greedy finds the optimal solution with high probability assuming $n=O(k\log\frac{m}{k})$, the information theoretic lowerbound on minimum number of measurements for exact identification of the optimal subset. By contrast, we show that irrespective of the number of measurements, Stochastic-Greedy with overwhelming probability fails to find the optimal subset. We reveal that the failure of Stochastic-Greedy can be circumvented by progressively increasing the size of the stochastic search space. Employing this insight, we present the first sparse support selection algorithm that achieves exact identification of the optimal subset from $O(k\log\frac{m}{k})$ measurements with complexity $\tilde{O}(m)$ for arbitrary sparse vectors.
Modelling the polarised emission from black holes on event horizon-scales

Abstract Upcoming VLBI observations will resolve nearby supermassive black holes, most notably Sagittarius A* and M87, on event horizon-scales. Recent observations of Sagittarius A* with the Event Horizon Telescope have revealed horizon-scale structure. Accordingly, the detection and measurement of the back hole “shadow” is expected to enable the existence of astrophysical black holes to be verified directly. Although the theoretical description of the shadow is straightforward, its observational appearance is largely determined by the properties of the surrounding accretion flow, which is highly turbulent. We introduce a new polarised general-relativistic radiative transfer code, BHOSS, which accurately solves the equations of polarised radiative transfer in arbitrary strong-gravity environments, providing physically-realistic images of astrophysical black holes on event horizon-scales, as well as also providing insight into the fundamental properties and nature of the surrounding accretion flow environment.
Rate of propagation of chaos for diffusive stochastic particle systems via Girsanov transformation.

This paper focus on investigating the explicit rate of convergence for the propagation of chaos, in a pathwise sense a family of interacting stochastic particle related to some Brownian driven McKean-Vlasov dynamics. Precisely the McKean form of nonlinearity is concentrated on a path dependent drift component and satisfies a particular sub-gaussian moment control. Such control enables to derive a uniform estimate of the cost in terms of exponential martingale between the particle and its McKean/mean-field limit system which in turn provide an optimal rate of propagation of chaos in terms of the total variation distance. As a by-product, we deepen some recent propagation of chaos results due to Lacker 2018 and provides a partial stochastic interpretation of the entropy control technique introduced in Jabin and Wang 2016.
Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images

Detecting objects in a two-dimensional setting is often insufficient in the context of real-life applications where the surrounding environment needs to be accurately recognized and oriented in three-dimension (3D), such as in the case of autonomous driving vehicles. Therefore, accurately and efficiently detecting objects in the three-dimensional setting is becoming increasingly relevant to a wide range of industrial applications, and thus is progressively attracting the attention of researchers. Building systems to detect objects in 3D is a challenging task though, because it relies on the multi-modal fusion of data derived from different sources. In this paper, we study the effects of anchoring using the current state-of-the-art 3D object detector and propose Class-specific Anchoring Proposal (CAP) strategy based on object sizes and aspect ratios based clustering of anchors. The proposed anchoring strategy significantly increased detection accuracy's by 7.19%, 8.13% and 8.8% on Easy, Moderate and Hard setting of the pedestrian class, 2.19%, 2.17% and 1.27% on Easy, Moderate and Hard setting of the car class and 12.1% on Easy setting of cyclist class. We also show that the clustering in anchoring process also enhances the performance of the regional proposal network in proposing regions of interests significantly. Finally, we propose the best cluster numbers for each class of objects in KITTI dataset that improves the performance of detection model significantly.
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization

We consider a generic empirical composition optimization problem, where there are empirical averages present both outside and inside nonlinear loss functions. Such a problem is of interest in various machine learning applications, and cannot be directly solved by standard methods such as stochastic gradient descent. We take a novel approach to solving this problem by reformulating the original minimization objective into an equivalent min-max objective, which brings out all the empirical averages that are originally inside the nonlinear loss functions. We exploit the rich structures of the reformulated problem and develop a stochastic primal-dual algorithm, SVRPDA-I, to solve the problem efficiently. We carry out extensive theoretical analysis of the proposed algorithm, obtaining the convergence rate, the computation complexity and the storage complexity. In particular, the algorithm is shown to converge at a linear rate when the problem is strongly convex. Moreover, we also develop an approximate version of the algorithm, named SVRPDA-II, which further reduces the memory requirement. Finally, we evaluate our proposed algorithms on several real-world benchmarks, and experimental results show that the proposed algorithms significantly outperform existing techniques.
High-resolution spectroscopic study of dwarf stars in the northern sky

Context. New space missions, such as NASA TESS or ESA PLATO, will focus on bright stars, which have been largely ignored by modern large surveys, especially in the northern sky. Spectroscopic information is of paramount importance in characterising the stars and analysing planets possibly orbiting them, and in studying the Galactic disc evolution. Aims. The aim of this work was to analyse all bright (V < 8 mag) F, G, and K dwarf stars using high-resolution spectra in the selected sky fields near the northern celestial pole. Methods. The observations were carried out with the 1.65 m diameter telescope at the Molėtai Astronomical Observatory and a fibre-fed high-resolution spectrograph covering a full visible wavelength range (4000–8500 Å). The atmospheric parameters were derived using the classical equivalent width approach while the individual chemical element abundances were determined from spectral synthesis. For both tasks the one-dimensional plane-parallel LTE MARCS stellar model atmospheres were applied. The NLTE effects for the majority of elemental abundances in our sample were negligible; however, we did calculate the NLTE corrections for the potassium abundances, as they were determined from the large 7698.9 Å line. For manganese and copper we have accounted for a hyperfine splitting. Results. We determined the main atmospheric parameters, kinematic properties, orbital parameters, and stellar ages for 109 newly observed stars and chemical abundances of Na I, Mg I, Al I, Si I, Si II, S I, K I, Ca I, Ca II, Sc I, Sc II, Ti I, Ti II, V I, Cr I, Cr II, Mn I, Fe I, Fe II, Co I, Ni I, Cu I, and Zn I for 249 F, G, and K dwarf stars observed in the present study and in our previous study. The [Mg I/Fe I] ratio was adopted to define the thin-disc (α-poor) and thick-disc (α-rich) stars in our sample. We explored the behaviour of 21 chemical species in the [El/Fe I] versus [Fe I/H] and [El/Fe I] versus age planes, and compared the results with the latest Galactic chemical evolution models. We also explored [El/Fe I] gradients according to the mean Galactocentric distances and maximum height above the Galactic plane. Conclusions. We found that in the Galactic thin-disc [El/Fe I] ratios of α-elements and aluminium have a positive trend with respect to age while the trend of Mn is clearly negative. Abundances of other species do not display significant trends. While the current theoretical models are able to reproduce the generic trends of the elements, they often seem to overestimate or underestimate the observational abundances. We found that the α-element and zinc abundances have slightly positive or flat radial and vertical gradients, while gradients for the odd-Z element Na, K, V, and Mn abundances are negative.
Structure-Invariant Testing for Machine Translation

In recent years, machine translation software has increasingly been integrated into our daily lives. People routinely use machine translation for various applications, such as describing symptoms to a foreign doctor and reading political news in a foreign language. However, the complexity and intractability of neural machine translation (NMT) models that power modern machine translation make the robustness of these systems difficult to even assess, much less guarantee. Machine translation systems can return inferior results that lead to misunderstanding, medical misdiagnoses, threats to personal safety, or political conflicts. Despite its apparent importance, validating the robustness of machine translation systems is very difficult and has, therefore, been much under-explored. To tackle this challenge, we introduce structure-invariant testing (SIT), a novel metamorphic testing approach for validating machine translation software. Our key insight is that the translation results of “similar” source sentences should typically exhibit similar sentence structures. Specifically, SIT (1) generates similar source sentences by substituting one word in a given sentence with semantically similar, syntactically equivalent words; (2) represents sentence structure by syntax parse trees (obtained via constituency or dependency parsing); (3) reports sentence pairs whose structures differ quantitatively by more than some threshold. To evaluate SIT, we use it to test Google Translate and Bing Microsoft Translator with 200 source sentences as input, which led to 64 and 70 buggy issues with 69.5% and 70% top-1 accuracy, respectively. The translation errors are diverse, including under-translation, over-translation, incorrect modification, word/phrase mistranslation, and unclear logic.
A novel accurate and efficient model for population dynamics and genetics in marine environment

Population Dynamics (PD) is a long standing research field in biology, mathematical physics and statistical mechanics. PD deals with changes in size, density and structure of one or more populations over time. In realistic settings, for population living in a fluid environment, it is largely unknown, both theoretically and experimentally, how the phenomenology of Population Dynamics is changed by the presence of turbulence. Effective compressibility can arise, for example, from the convergence and divergence of water masses at which passively traveling organisms are restricted to live. The flow can significantly diminish the effect of selective advantage on fixation probabilities. Selective advantage means the ability of one species to growth faster than others. However, because of number fluctuations a relatively small selective advantage may not be successful since the characteristic time scale is much smaller than the one associated to turbulent effects. This project, focus on the enhancement or inhibition of selective advantage in PD subject to advection of compressible turbulence. We find that in presence of weak flows compared to the no-flow case, on average, organisms born at sources acquiring a significant advantage.
Constraining Stellar Parameters and Atmospheric Dynamics of the Carbon AGB star V Oph

Molecules and dust produced by the atmospheres of cool evolved stars contribute to a significant amount of the total material found in the interstellar medium. To understand the mechanism behind the mass loss of these stars, it is of pivotal importance to investigate the structure and dynamics of their atmospheres. Our goal is to verify if the extended molecular and dust layers of the carbon-rich asymptotic giant branch (AGB) star V Oph, and their time variations, can be explained by dust-driven winds triggered by stellar pulsation alone, or if other mechanisms are in play. We model V Oph mid-infrared interferometric VLTI-MIDI data (8–13 μm), at phases 0.18, 0.49, and 0.65, together with literature photometric data, using the latest-generation self-consistent dynamic atmosphere models for carbon-rich stars: DARWIN. We determine the fundamental stellar parameters: Teff = 2600 K, Lbol = 3585 L⊙, M = 1.5 M⊙, C/O = 1.35, M⊙ yr−1. We calculate the stellar photospheric radii at the three phases: 479, 494, 448 R⊙; and the dust radii: 780, 853, 787 R⊙. The dynamic models can fairly explain the observed N-band visibility and spectra, although there is some discrepancy between the data and the models, which is discussed in the text. We discuss the possible causes of the temporal variations of the outer atmosphere, deriving an estimate of the magnetic field strength, and computing upper limits for the Alfvén waves velocity. In addition, using period–luminosity sequences, and interferometric modeling, we suggest V Oph as a candidate to be reclassified as a semi-regular star.