Miracle-dz/UniFAR-FTUs huggingface dataset:
New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition

We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor. These algorithms exploit a non-Euclidean metric on the product space of the factor matrices of the low-rank tensor in the polyadic decomposition form. This new metric is designed using an approximation of the diagonal blocks of the Hessian of the tensor completion cost function, thus has a preconditioning effect on these algorithms. We prove that the proposed Riemannian gradient descent algorithm globally converges to a stationary point of the tensor completion problem, with convergence rate estimates using the $\L{}$ojasiewicz property. Numerical results on synthetic and real-world data suggest that the proposed algorithms are more efficient in memory and time compared to state-of-the-art algorithms. Moreover, the proposed algorithms display a greater tolerance for overestimated rank parameters in terms of the tensor recovery performance, thus enable a flexible choice of the rank parameter.
Visible spectra of 
W8+
 in an electron-beam ion trap

Q. Lu (陆祺峰), C. L. Yan (严成龙), J. Meng (孟举), G. Q. Xu (许帼芹), Y. Yang (杨洋), C.Y. Chen (陈重阳), J. Xiao (肖君),, J.G. Li (李冀光),, J.G. Wang (王 建国), and Y. Zou (邹亚明) 1 Shanghai EBIT Laboratory, Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China; Institute of Applied Physics and Computational Mathematics, Beijing 100088, China Corresponding author: xiao_jun@fudan.edu. cn, Li_Jiguang@iapcm.ac. cn
Constraining gravitational wave amplitude birefringence and Chern-Simons gravity with GWTC-2

We perform a new test of general relativity (GR) with signals from GWTC-2, the LIGO and Virgo catalog of gravitational wave detections. We search for the presence of amplitude birefringence, in which left versus right circularly polarized modes of gravitational waves are exponentially enhanced and suppressed during propagation. Such an effect is present in various beyond-GR theories but is absent in GR. We constrain the amount of amplitude birefringence consistent with the data through an opacity parameter $\kappa$, which we bound to be $\kappa \lesssim 0.74 \textrm{ Gpc}^{-1}$. We then use these theory-agnostic results to constrain Chern-Simons gravity, a beyond-GR theory with motivations in quantum gravity. We bound the canonical Chern-Simons lengthscale to be $\ell_0 \lesssim 1.0 \times 10^3$ km, improving on previous long-distance measurement results by a factor of two.
Offline Correction of Atmospheric Effects on Single-dish Radio Spectroscopy

We present a method to mitigate the atmospheric effects (residual atmospheric lines) in single-dish radio spectroscopy caused by the elevation difference between the target and reference positions. The method is developed as a script using the Atmospheric Transmission at Microwaves (ATM) library built into the Common Astronomy Software Applications (CASA) package. We apply the method to the data taken with the Total Power Array of the Atacama Large Millimeter/submillimeter Array. The intensities of the residual atmospheric (mostly O3) lines are suppressed by, typically, an order of magnitude for the tested cases. The parameters for the ATM model can be optimized to minimize the residual line and, for a specific O3 line at 231.28 GHz, a seasonal dependence of a best-fitting model parameter is demonstrated. The method will be provided as a task within the CASA package in the near future. The atmospheric removal method we developed can be used by any radio/millimeter/submillimeter observatory to improve the quality of its spectroscopic measurements.
Electrothermal vaporization for sample introduction in atomic absorption, atomic emission and plasma mass spectrometry—a critical review with focus on solid sampling and slurry analysis

The aim of this work is to review and compare the present possibilities of electrothermal atomic absorption spectrometry (ETAAS), electrothermal vaporization-inductively coupled plasma-optical emission spectrometry (ETV-ICP-OES) and electrothermal vaporization-inductively coupled plasma-mass spectrometry (ETV-ICP-MS) for analysis of solid samples, slurries and complex matrixes. The paper keeps in perspective the historical evolution of these techniques, providing an overview of the most relevant advances in instrumentation, methodology and fundamentals and highlighting the most relevant applications carried out during the last decade. The benefits of continuum source-AAS, the possible impact of diode lasers on this technique, the potential of ETV-ICP-methods for fractionation/speciation studies, as well as for resolving spectral interferences, and the influence of the different types of ICP-MS instrumentation currently available on the overall performance of ETV-ICP-MS will be some of the aspects discussed in more detail.
Functional inequalities for perturbed measures with applications to log-concave measures and to some Bayesian problems

We study functional inequalities (Poincar\'e, Cheeger, log-Sobolev) for probability measures obtained as perturbations. Several explicit results for general measures as well as log-concave distributions are given.The initial goal of this work was to obtain explicit bounds on the constants in view of statistical applications for instance. These results are then applied to the Langevin Monte-Carlo method used in statistics in order to compute Bayesian estimators.
ViaPPS: A Mobile Pavement Profiling System

Ensuring safety levels on roads is an imperative task for road authorities. Significant amounts of time and money are spent on performing road inspections every year. In order to ensure efficiency of road inventories, road practitioners are in need of reliable systems that are fast and are designed to comply with high standards at lower costs. To date, the 3D Mobile Mapping Systems is the most efficient, productive and accurate system used during evaluation of pavement inventories. This paper presents ViaPPS, a 3D Mobile Mapping System from ViaTech AS. ViaPPS offers accurate georeferenced data by combining the perception and navigation sensors. This paper showcases results from multiple mobile systems after the harmonization process which is held once a year to evaluate reliability and repeatability of the measurements.
Optimal control problems governed by two dimensional convective Brinkman-Forchheimer equations

The convective Brinkman-Forchheimer (CBF) equations describe the motion of incompressible viscous fluids through a rigid, homogeneous, isotropic, porous medium and is given by \begin{document}$ \partial_t{\boldsymbol{u}}-\mu \Delta{\boldsymbol{u}}+({\boldsymbol{u}}\cdot\nabla){\boldsymbol{u}}+\alpha{\boldsymbol{u}}+\beta|{\boldsymbol{u}}|^{r-1}{\boldsymbol{u}}+\nabla p = {\boldsymbol{f}},\ \nabla\cdot{\boldsymbol{u}} = 0. $\end{document} In this work, we consider some distributed optimal control problems like total energy minimization, minimization of enstrophy, etc governed by the two dimensional CBF equations with the absorption exponent \begin{document}$ r = 1,2 $\end{document} and \begin{document}$ 3 $\end{document} . We show the existence of an optimal solution and the first order necessary conditions of optimality for such optimal control problems in terms of the Euler-Lagrange system. Furthermore, for the case \begin{document}$ r = 3 $\end{document} , we show the second order necessary and sufficient conditions of optimality. We also investigate an another control problem which is similar to that of the data assimilation problems in meteorology of obtaining unknown initial data, when the system under consideration is 2D CBF equations, using optimal control techniques.
Measuring time delays: II. Using observations of the unresolved flux and astrometry

Lensed quasars and supernovae can be used to study galaxies’ gravitational potential and measure cosmological parameters. The typical image separation of objects lensed by galaxies is of the order of 0.5″. Finding the ones with small separations, and measuring their time-delays using ground-based observations is challenging. We suggest a new method to identify lensed quasars and simultaneously measure their time-delays, using seeing-limited synoptic observations in which the lensed quasar images and the lensing galaxy are unresolved. We show that using the light curve of the combined flux, and the astrometric measurements of the center-of-light position of the lensed images, the lensed nature of a quasar can be identified, and its time-delay can be measured. We provide the analytic formalism to do so, taking into account the measurement errors and the fact that the power spectra of quasar light curves is red. We demonstrate our method on simulated data, while its implementation to real data will be presented in future papers. Our simulations suggest that, under reasonable assumptions, the new method has the potential to detect unresolved lensed quasars and measure their time delays, even when the image separation is about 0.2″, or the flux ratio between the faintest and brightest images is as low as 0.05. Python and MATLAB implementations are provided. In a companion paper, we present a method for measuring the time delay using the combined flux observations. This method may be useful in cases in which the astrometric information is not relevant (e.g., reverberation mapping).
Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion

Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence. The taxonomy expansion task aims to find a position for a new term in an existing taxonomy to capture the emerging knowledge in the world and keep the taxonomy dynamically updated. Previous taxonomy expansion solutions neglect valuable information brought by the hierarchical structure and evaluate the correctness of merely an added edge, which downgrade the problem to node-pair scoring or mini-path classification. In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully exploits the hierarchical structure’s properties to maximize the coherence of expanded taxonomy. HEF makes use of taxonomy’s hierarchical structure in multiple aspects: i) HEF utilizes subtrees containing most relevant nodes as self-supervision data for a complete comparison of parental and sibling relations; ii) HEF adopts a coherence modeling module to evaluate the coherence of a taxonomy’s subtree by integrating hypernymy relation detection and several tree-exclusive features; iii) HEF introduces the Fitting Score for position selection, which explicitly evaluates both path and level selections and takes full advantage of parental relations to interchange information for disambiguation and self-correction. Extensive experiments show that by better exploiting the hierarchical structure and optimizing taxonomy’s coherence, HEF vastly surpasses the prior state-of-the-art on three benchmark datasets by an average improvement of 46.7% in accuracy and 32.3% in mean reciprocal rank.
Tutorial on implementing Hoare logic for imperative programs in Haskell

Using the programming language Haskell, we introduce an implementation for a simple imperative language that can evaluate arithmetic and boolean expressions; step by step, we will expand the implementation starting from arithmetic expressions, to boolean expressions and then to imperative programs. As we expand the language, we will show several evaluation strategies, considering the normalization property and how it affects an implementation. Finally, we will provide a bottom-up implementation of Hoare’s logic which will allow us to deduce facts about programs without the need for a full evaluation
Testing in Global Software Development - A Pattern Approach

Although testing is critical in GSD, its application in this context has not been deeply investigated so far. This work investigates testing in GSD. It provides support for test managers acting in a globally distributed environment. With this it closes a gap. The leading question is"What problems exist in testing in GSD and how can they be addressed in projects? "Decomposing this question we a) identify problems of testing in GSD projects and b) provide good practices to support practitioners in testing in GSD projects. The research is realized in the context of Capgemini Germany. Our contribution to solving the stated research problem is a collection of 16 patterns for testing in GSD projects. For practitioners the usage of the patterns is simplified by various views on the patterns. Herewith we stipulate research and support project managers and test managers in the realization of testing in GSD projects.
Developing for personalised learning: the long road from educational objectives to development and feedback

objectives to development and feedback George Tsatiris, Kostas Karpouzis Artificial Intelligence and Learning Systems Lab National Technical University of Athens gtsatiris@image.ntua. gr, kkarpou@cs.ntua. gr This paper describes the design and implementation work done to support the functional and teaching requirements of iRead [1], a 4-year EU-funded project on personalised learning technologies to support reading skills. The core software applications developed in the project are a reader application, which highlights specific parts of the words contained in the text, given specific criteria, and a serious game, which consists of a series of gamified activities utilising words and sentences. The foundation of these applications and the software infrastructure that provides access to the content consists of language models for English, German, Spanish and Greek (for primary school children), and English as a foreign language, including children with dyslexia; following the definition of extensive phonological and syntactic models for these languages, the linguists in the project worked together with teachers to define the learning objectives for each of the target age groups, as well as the sequence in which each language feature should be taught. The sequencing of these features, including which prerequisites should be taught and mastered by the students before moving on to more advanced features, was encoded in a tree-like hierarchical graph; essentially, this graph encapsulates both the language model (i.e. the features that make up each word or sentence, at least at the given language level) and the teaching model, represented by the selection of necessary features for each school year and the succession in which they should be taught. When a new student registers with the iRead system, this graph is instantiated as a user profile, with different values of mastery for each feature, depending on the students’ age.
Puzzle-CAM: Improved Localization Via Matching Partial And Full Features

Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most advanced approaches are based on class activation maps (CAMs) to generate pseudo-labels to train the segmentation network. The main limitation of WSSS is that the process of generating pseudo-labels from CAMs that use an image classifier is mainly focused on the most discriminative parts of the objects. To address this issue, we propose Puzzle-CAM, a process that minimizes differences between the features from separate patches and the whole image. Our method consists of a puzzle module and two regularization terms to discover the most integrated region in an object. Puzzle-CAM can activate the overall region of an object using image-level supervision without requiring extra parameters. In experiments, Puzzle-CAM outperformed previous state-of-the-art methods using the same labels for supervision on the PASCAL VOC 2012 dataset. Code associated with our experiments is available at https://github.com/OFRIN/PuzzleCAM.
Graphene Decorated with Nickel Nanoparticles as a Sensitive Substrate for Simultaneous Determination of Sunset Yellow and Tartrazine in Food Samples

In this paper, ultrathin graphene (GN) nanosheets were uniformly decorated with nickel (Ni) nanoparticles, as confirmed by scanning electron microscopy and transmission electron microscopy. This GNNi hybrid exhibited excellent accumulation and catalytic capacity for the simultaneous detection of sunset yellow and tartrazine, two colorants commonly found mixed in foodstuffs. The electrochemical reaction mechanism of sunset yellow and tartrazine were studied in detail on the GNNi nanocomposite modified glassy carbon electrode. In square wave voltammetry measurements, the oxidation peak potentials of sunset yellow and tartrazine were separated by about 250 mV. This novel proposed voltammetric method has good selectivity which was successfully applied for the simultaneous determination of sunset yellow and tartrazine in some food sample extracts.
The Epoch of Giant Planet Migration Planet Search Program. I. Near-infrared Radial Velocity Jitter of Young Sun-like Stars

We present early results from the Epoch of Giant Planet Migration program, a precise radial velocity (RV) survey of more than 100 intermediate-age (∼20–200 Myr) G and K dwarfs with the Habitable Zone Planet Finder spectrograph (HPF) at McDonald Observatory’s Hobby–Eberly Telescope. The goals of this program are to determine the timescale and dominant physical mechanism of giant planet migration interior to the water ice line of Sun-like stars. Here, we summarize results from the first 14 months of this program, with a focus on our custom RV pipeline for HPF, a measurement of the intrinsic near-infrared RV activity of young Solar analogs, and modeling the underlying population-level distribution of stellar jitter. We demonstrate on-sky stability at the sub-2 m s−1 level for the K2 standard HD 3765 using a least-squares matching method to extract precise RVs. Based on a subsample of 29 stars with at least three RV measurements from our program, we find a median rms level of 34 m s−1. This is nearly a factor of 2 lower than the median rms level in the optical of 60 m s−1 for a comparison sample with similar ages and spectral types as our targets. The observed near-infrared jitter measurements for this subsample are well reproduced with a log-normal parent distribution with μ = 4.15 and σ = 1.02. Finally, by compiling rms values from previous planet search programs, we show that near-infrared jitter for G and K dwarfs generally decays with age in a similar fashion to optical wavelengths, albeit with a shallower slope and lower overall values for ages ≲1 Gyr.
Signatures of exceptional points in a quantum thermal machine

The concepts and tools from the theory of non-Hermitian quantum systems are used to investigate the dynamics of a quantum thermal machine. This approach allows us to characterize in full generality the analytical time-dependent dynamics of an autonomous quantum thermal machine, by solving a non-Hermitian Liouvillian for an arbitrary initial state. We show that the thermal machine features a number of exceptional points for experimentally realistic parameters. The signatures of a third-order exceptional point, both in the short and long-time regimes are demonstrated. As these points correspond to regimes of critical decay towards the steady state, in analogy with a critically damped oscillator, our work opens interesting possibilities for the precise control of the dynamics of quantum thermal machines.
On the evolution of syntactic information encoded by BERT’s contextualized representations

The adaptation of pretrained language models to solve supervised tasks has become a baseline in NLP, and many recent works have focused on studying how linguistic information is encoded in the pretrained sentence representations. Among other information, it has been shown that entire syntax trees are implicitly embedded in the geometry of such models. As these models are often fine-tuned, it becomes increasingly important to understand how the encoded knowledge evolves along the fine-tuning. In this paper, we analyze the evolution of the embedded syntax trees along the fine-tuning process of BERT for six different tasks, covering all levels of the linguistic structure. Experimental results show that the encoded syntactic information is forgotten (PoS tagging), reinforced (dependency and constituency parsing) or preserved (semantics-related tasks) in different ways along the fine-tuning process depending on the task.
A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis

We propose a new Named entity recognition (NER) method to effectively make use of the results of Part-of-speech (POS) tagging, Chinese word segmentation (CWS) and parsing while avoiding NER error caused by POS tagging error. This paper first uses Stanford natural language process (NLP) tool to annotate large-scale untagged data so as to reduce the dependence on the tagged data; then a new NLP model, g-BERT model, is designed to compress Bidirectional Encoder Representations from Transformers (BERT) model in order to reduce calculation quantity; finally, the model is evaluated based on Chinese NER dataset. The experimental results show that the calculation quantity in g-BERT model is reduced by 60% and performance improves by 2% with Test F1 to 96.5 compared with that in BERT model.
Mining Large-Scale Low-Resource Pronunciation Data From Wikipedia

Pronunciation modeling is a key task for building speech technology in new languages, and while solid grapheme-to-phoneme (G2P) mapping systems exist, language coverage can stand to be improved. The information needed to build G2P models for many more languages can easily be found on Wikipedia, but unfortunately, it is stored in disparate formats. We report on a system we built to mine a pronunciation data set in 819 languages from loosely structured tables within Wikipedia. The data includes phoneme inventories, and for 63 low-resource languages, also includes the grapheme-to-phoneme (G2P) mapping. 54 of these languages do not have easily findable G2P mappings online otherwise. We turned the information from Wikipedia into a structured, machine-readable TSV format, and make the resulting data set publicly available so it can be improved further and used in a variety of applications involving low-resource languages.
Similarity of Classification Tasks

Recent advances in meta-learning has led to remarkable performances on several few-shot learning benchmarks. However, such success often ignores the similarity between training and testing tasks, resulting in a potential bias evaluation. We, therefore, propose a generative approach based on a variant of Latent Dirichlet Allocation to analyse task similarity to optimise and better understand the performance of meta-learning. We demonstrate that the proposed method can provide an insightful evaluation for meta-learning algorithms on two few-shot classification benchmarks that matches common intuition: the more similar the higher performance. Based on this similarity measure, we propose a task-selection strategy for meta-learning and show that it can produce more accurate classification results than methods that randomly select training tasks.