Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression analysis of genes for cytochrome P450 CYP86 and glycerol-3-phosphate acyltransferase related to suberin biosynthesis in rice roots under stagnant deoxygenated conditions

: The radial oxygen loss (ROL) barrier formed on the outer cell layers of roots of rice ( Oryza sativa ) contributes to efficient oxygen trans fer through the aerenchyma from the aerial parts to the root apex. It is hypothesized that suberin accumulation in the exodermis contributes to an apoplastic barrier and plays a vital role in ROL barrier formation. A previous study reported that some genes encoding cytochrome P450 family 86 ( CYP86 ) and glycerol-3-phosphate acyltransferase ( GPAT ) might be involved in suberin biosynthesis during ROL barrier development of rice roots. However, how these genes are expressed and their contribution to the sequential development of suberin accumulation in rice roots remains unclear. In this study, four CYP86 and five GPAT genes of rice were identified as candidate genes involved in suberin biosynthesis in roots using sequence homology alignment with Arabidopsis suberin and cutin biosynthesis genes. Gene expression analyses revealed that expression of the candidate genes was induced at the region where suberin biosynthesis occurred under stagnant deoxygenated conditions. These genes showed two types of spatiotemporal expression patterns, at the regions of 5–25 mm and 25–35 mm from the root apex. Tissue-specific expression analyses using laser microdissection and histochemical GUS staining revealed that candidate gene expression was similar in the cell layer of the root exodermis. These results suggest that the selected CYP86 and GPAT genes are involved in suberin biosynthesis in the exodermis, and that suberin biosynthesis in the root may be controlled by the spatiotemporal expression of two groups of genes.
Solving a Mystery in Levels-of-Processing Research: The Word-First Paradigm

In the standard levels-of-processing (LOP) paradigm, subjects are presented with a question directing them to attend to surface features (case), phonetic features (rhyme), or semantic features (category) of an upcoming word. Answering the question greatly affects recall and recognition for the word, and this outcome is attributed to differing LOP of the word. Craik (1977) reasoned that if the words appeared before the questions (i.e., the word-first paradigm) under intentional learning instructions, the LOP effect would disappear. However, when he conducted experiments using the word-first paradigm, the effect remained. We conducted 2 experiments to try to explain this puzzle. In the first experiment, we replicated Craik’s findings in one condition, and in other conditions we asked subjects to make judgments of learning (JOLs) in an attempt to eliminate the LOP effect because JOLs seem to promote deep processing. However, the LOP effect still occurred, albeit in attenuated form. In Experiment 2, we tested 2 new conditions to overcome and explain the LOP effect in the word-first paradigm. In one case, subjects were instructed to use idiosyncratic study strategies to memorize the word; the second condition required subjects to perform pleasantness judgments on the words. The pleasantness rating condition eliminated the LOP effect, but the first did not. We conclude that in the word-first paradigm, subjects typically keep the word in mind without semantic processing using rote rehearsal, despite intentional learning instructions.
Decoupling of a Wideband Dual-Polarized Large-Scale Antenna Array With Dielectric Stubs

A technique of decoupling dielectric stubs (DDS) is proposed for mutual coupling reduction of wideband, dual-polarized, and large-scale antenna arrays in this paper, where a low dielectric constant DDS is mounting on a dual-polarized and wideband antenna array seamlessly. The decoupling mechanism of the proposed DDS is qualitatively explained. By properly optimizing the dielectric constant and dimensions of the dielectric stubs, the DDS can reduce the mutual couplings between array elements efficiently. The dielectric stubs are considered as perturbations to slightly localize the electromagnetic fields emitted from the antenna element to lower the space wave coupling. The advantages of the proposed decoupling technique are wideband, low cost, high efficiency, and suitable for large-scale antenna arrays. For demonstration, 4 × 4 dual-polarized and wideband antenna arrays with and without the DDS are simulated, measured, and compared. The simulated results show that the antenna array with the DDS can achieve all port-to-port isolations over 25 dB from 4.4 to 5.0 GHz for all the coupling paths, which are 7 dB higher than the counterparts without the DDS. Moreover, the total efficiencies of the antenna elements with the proposed DDS are also improved. The measured results agree well with the simulated, verifying the effectiveness of the proposed method of DDS for decoupling of large-scale antenna arrays.
Learning Shared Encoding Representation for End-to-End Speech Recognition Models

In this work, we learn a shared encoding representation for a multi-task neural network model optimized with connectionist temporal classification (CTC) and conventional framewise cross-entropy training criteria. Our experiments show that the multi-task training not only tackles the complexity of optimizing CTC models such as acoustic-to-word but also results in significant improvement compared to the plain-task training with an optimal setup. Furthermore, we propose to use the encoding representation learned by the multi-task network to initialize the encoder of attention-based models. Thereby, we train a deep attention-based end-to-end model with 10 long short-term memory (LSTM) layers of encoder which produces 12.2\% and 22.6\% word-error-rate on Switchboard and CallHome subsets of the Hub5 2000 evaluation.
3DRegNet: A Deep Neural Network for 3D Point Registration

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available.
A structured adaptive mesh refinement strategy with a sharp interface direct-forcing immersed boundary method for moving boundary problems

We develop a versatile and accurate structured adaptive mesh refinement (S-AMR) strategy with a moving least square sharp-direct forcing immersed boundary method (IBM) for incompressible fluid-structure interaction (FSI) simulations. The computational grid consists of several nested blocks in different refinement levels. While blocks with the coarsest grid cover the entire computational domain, the computational domain is locally refined at the location of solid boundary (moving or fixed) by bisecting selected blocks in every coordinate direction. The grid topology and data structure is managed by an extended version of Afivo toolkit (Teunissen and Ebert, 2018), where a novel technique is introduced for conservative data transfer between the coarser and the finer blocks, particularly in velocity transformation for which the mass conservation plays a crucial role. In the present study, the continuity and Navier-Stokes equations for incompressible flows are spatially discretized with a second order central finite difference method using a collocated arrangement and are time-integrated using a semi-implicit second order fractional step method, although the proposed S-AMR strategy can be used with different discretization schemes. An IBM using a moving least square approach is utilized to impose boundary conditions. To handle FSI problems, all the governing equations for the dynamics of fluid and structure are simultaneously advanced in time by a predictorcorrector strategy. Several test cases of increasing complexity are solved in order to demonstrate the robustness and accuracy of the proposed method as well as its capability in simulation-driven mesh adaptivity.
Energy Efficient and Resilient Infrastructure for Fog Computing Health Monitoring Applications

In this paper, we propose a resilient energy efficient and fog computing infrastructure for health monitoring applications. We design the infrastructure to be resilient against server failures under two scenarios; without geographical constraints and with geographical constraints. We consider a heart monitoring application where patients send their 30-seconds recording of Electrocardiogram (ECG) signal for processing, analysis and decision making at both primary and backup servers. A Mixed Integer Linear Programming (MILP) model is used to optimize the number and locations of the primary and backup processing servers so that the energy consumption of both the processing and networking equipment are minimized. The results show that considering geographical constraints yields a network energy consumption increase by up to 9% compared to without geographical constraint. The results also show that, increasing the number of processing servers that can be served at each candidate node can reduce the energy consumption of networking equipment besides reducing the rate of energy increase of networking equipment due to increasing level of demand.
Comparative Analysis of Machine Learning Techniques for Temperature Compensation in Microwave Sensors

The planar nature of microwave sensors leaves them vulnerable to ambient temperature changes with potential impact on the perception of the material under test. A temperature compensation technique is required to consider its direct effect on the dielectric property of materials. In this article, machine learning algorithms are employed in two configurations of classifier and regressor on frequency response of a split-ring resonator operating at 1.19 GHz. A wide range of dielectric constant is covered with concentrations of [0:20%:100%]-methanol/acetone in water with a temperature cycle of 25 °C–50 °C. This broad variety of cases captures the complicacy of entangled trends that are recognized using classifiers regardless of the measurement temperature. In the next step, the ambient temperature is extracted from the same measured data. This is accomplished by cascading the classifier with a regressor pool that contains trained parameters for individual classes. Highly accurate classification of material types followed by their corresponding temperature (using linear regression with $R^{2}=97$ % averaged over tenfold cross validation and a mean absolute error of 0.58 °C) leads to investigating limit of detection in the proposed scheme. This step, through testing of [0:1%:5%] methanol in water, identified multilayer perceptron (MLP) and support vector machine (SVM) as the best performing algorithms. Final hyperparameter optimization yields parameters for these two models that provide accuracy of 0.97 and 0.99, respectively.
Styler: Learning Formatting Conventions to Repair Checkstyle Errors

Formatting coding conventions play an important role on code readability. In this paper, we present Styler, an automatic repair tool dedicated to fix formatting-related errors raised by Checkstyle, a highly configurable format checker for Java. To fix formatting errors in a given project, Styler learns fixes based on the Checkstyle ruleset defined in the project and predicts repairs for the current errors using machine learning. In an empirical evaluation, we found that Styler repaired 24% of 497 real Checkstyle errors mined from five GitHub projects. Moreover, in a comparison of Styler with the state-of-the-art machine learning code formatters Naturalize and CodeBuff, we found that Styler is the tool that fixes more real Checkstyle errors and also generates smaller repairs. Finally, we conclude that Styler is promising to be used in IDEs and in a Continuous Integration environment to repair Checkstyle errors.
Valley Hall In-Plane Edge States as Building Blocks for Elastodynamic Logic Circuits

In this work, we investigate theoretically and demonstrate experimentally the existence of valley-Hall edge states in the in-plane dynamics of honeycomb lattices with bi-valued strut thickness. We exploit these states to achieve non-trivial waveguiding of optical modes that is immune to backscattering from sharp corners. We also present how different types of interfaces can be combined into multi-branch junctions to form complex waveguide paths and realize a variety of structural logic designs with unconventional wave transport capabilities. We illustrate this potential with two applications. The first is a direction-selective energy-splitting waveguide tree featuring a pronounced asymmetric wave transport behavior. The second is an internal waveguide loop along which the energy can be temporarily trapped and periodically released, effectively working as a signal delayer. The modal complexity of in-plane elasticity has important consequences on the regime of manifestation of the edge states, as the availability of viable total bandgaps is shifted to higher frequencies compared to the out-of-plane counterpart problem. It also poses additional experimental challenges, associated with proper acquisition and deciphering of the in-plane modes, the solution of which requires a systematic use of in-plane laser vibrometry.
Analysis of Non-Professional Investor's Intention to Use XBRL-based Financial Statements Using Social Influence and Effort Expectancy Variables from the Unified Theory of Acceptance and Use of Technology (UTAUT) Model

This research aimed to examine non-professional investors' behavior who are confronted with presenting financial statements using XBRL. We try to apply two variables from the unified theory of acceptance and use of technology (UTAUT) model (Social Influence and Effort Expectancy). As we know lately, the current global economic condition, the internet, and derivative products, and other products that use computing technology continue to evolve. The one with rapid progress is applying any kind of technology like XBRL in the presentation of financial statements. It is hoped that globally, the XBRL implementation can be mandatory. XBRL can reduce the cost of capital and information asymmetry in the capital market. It is more easily processed or analyzed, and allowing better decision making also enhances financial information transparency. It will enable users to integrate data from various locations in the financial statements. However, there is a problem due to the nature of productivity of information technology itself. Each new technology will require a sufficient amount of time to be implemented effectively and certainly without forgetting the general obstacles that may occur according to each country's character.
Chandra-HETGS Characterization of an Outflowing Wind in the Accreting Millisecond Pulsar IGR J17591–2342

An accreting millisecond X-ray pulsar, IGR J17591−2342 was discovered in 2018 August in scans of the Galactic bulge and center by the International Gamma-Ray Astrophysics Laboratory X-ray and gamma-ray observatory. It exhibited an unusual outburst profile with multiple peaks in the X-ray, as observed by several X-ray satellites over 3 months. Here we present observations of this source performed in the X-ray/gamma-ray and near-infrared domains and focus on a simultaneous observation performed with the Chandra High Energy Transmission Gratings Spectrometer (HETGS) and the Neutron Star Interior Composition Explorer (NICER). The HETGS provides high-resolution spectra of the Si edge region that yield clues as to the source’s distance and reveal evidence (at 99.999% significance) of an outflow with a velocity of 2800 km s−1. We demonstrate good agreement between the NICER and HETGS continua, provided that one properly accounts for the differing manners in which these instruments view the dust-scattering halo in the source’s foreground. Unusually, we find a possible set of Ca lines in the HETGS spectra (with significances ranging from 97.0% to 99.7%). We hypothesize that IGR J17591−2342 is a neutron star low-mass X-ray binary at the distance of the Galactic bulge or beyond that may have formed from the collapse of a white dwarf system in a rare, calcium-rich Type Ib supernova explosion.
Improving Explainability of Recommendation System by Multi-sided Tensor Factorization

Abstract Recently, explainable recommender systems to improve their persuasiveness have attracted attentions. In this regard, some approaches extract information from posts or comments on items and apply them to simple and effective template. These information (e.g., topics and interests), however, are indirectly reflected to the existing recommendation algorithms or models therefore do not directly improve the recommendation accuracy. Moreover, extra resources in deriving information are required. Thereby, we propose a collaborative filtering approach using a tensor which is modeled considering 5Ws aspects and generate explanations by combining factorization results with templates. Quality and explanation of recommendations were evaluated on quantitative/qualitative analyses.
Sparse Bounded Degree Sum of Squares Optimization for Certifiably Globally Optimal Rotation Averaging

Estimating unknown rotations from noisy measurements is an important step in SfM and other 3D vision tasks. Typically, local optimization methods susceptible to returning suboptimal local minima are used to solve the rotation averaging problem. A new wave of approaches that leverage convex relaxations have provided the first formal guarantees of global optimality for state estimation techniques involving SO(3). However, most of these guarantees are only applicable when the measurement error introduced by noise is within a certain bound that depends on the problem instance's structure. In this paper, we cast rotation averaging as a polynomial optimization problem over unit quaternions to produce the first rotation averaging method that is formally guaranteed to provide a certifiably globally optimal solution for \textit{any} problem instance. This is achieved by formulating and solving a sparse convex sum of squares (SOS) relaxation of the problem. We provide an open source implementation of our algorithm and experiments, demonstrating the benefits of our globally optimal approach.
Semantic Bilinear Pooling for Fine-Grained Recognition

Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified than coarse categories. However, most of the recent deep learning based methods neglect the semantic structure of fine-grained objects and do not take advantage of the traditional fine-grained recognition techniques (e.g. coarse-to-fine classification). In this paper, we propose a novel framework with a two-branch network (coarse branch and fine branch), i.e., semantic bilinear pooling, for fine-grained recognition with a hierarchical label tree. This framework can adaptively learn the semantic information from the hierarchical levels. Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes. Furthermore, our method leverages only the fine branch when testing so that it adds no overhead to the testing time. Experimental results show that our proposed method achieves state-of-the-art performance on four public datasets.
Surface Deformation Monitoring in the Republic of Croatia with MT-InSAR

The Multi-Temporal Synthetic Aperture Radar Interferometry (MT-InSAR) has proven to be a very useful tool in providing a high precise detailed characterization of surface deformation processes over wide areas. The first application in the Republic of Croatia was applied over the wider Zagreb area in the project “The Geodynamic GPS Network of the City of Zagreb”. The project was established in 1997 with the aim to answer the need for a better understanding of an ongoing geodynamic processes in the area. The research was carried out with GPS campaigns, whereas the MT-InSAR technique was introduced into the project in 2015. The Persistent Scatterers MT-INSAR technique was applied on Envisat ASAR data in 2015 and on Sentinel-1 data in 2017. The paper provides a revisit of the research with Envisat-ASAR data and preliminary results of the Sentinel-1 data. Furthermore, we discuss other possible MT-InSAR applications in the area.
Recent Advances in Model Reduction of Vibrating Substructures

The depressurization of a vessel containing saturated or subcooled liquid may occur in a variety of industrial processes and often poses a potentially hazardous situation. A numerical model was developed for estimating the thermodynamic and the dynamic state of the boiling liquid during a boiling liquid vapor explosion (BLEVE) event. The model allows calculation of bubble nucleation and growth processes in the liquid at the superheat-limit state, the front velocity of the expending liquid and the shock wave pressure formed by the liquid expansion through the air. Conditions of shock formation were found to be normally associated with high initial temperatures that can bring the liquid to its superheat-limit state during the initial depressurization. Furthermore, the high initial temperature also induces a generation of higher vapor pressures that forces a rapid mixture expansion.
Singularity Conditions for Continuum Parallel Robots

Research on continuum parallel robots has been essentially devoted to the computation of their geometricostatic models and of their performance in terms of workspace size, accuracy, compliance, force transmission, and manipulability. Their singularity analysis has been limited to the identification of a limited number of singular configurations, without any deep investigation of the physical phenomena occurring in these singularities. In this article, we define the singularity conditions for continuum parallel robots. We provide a straightforward interpretation of the phenomena occurring in singularities. Especially, we prove that some singularities appear when the robot potential energy has a local isovalue. Because of this property, we show that these singularities separate the stable configurations from the unstable ones in the workspace. Moreover, on such singularities, the robot can freely move along a given direction without any constraint under the action of small perturbations. We illustrate the singularity phenomena and their effects by simulations performed with two different continuum parallel robots.