Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of the Genus Distictella (Bignoniaceae)1,,2

Abstract Distictella Kuntze is a genus of 18 species in the tribe Bignonieae. The species are lianas or less frequently shrubs, and can be recognized by their terete branchlets without interpetiolar glandular fields; usually bifoliolate or less frequently unifoliolate or trifoliolate leaves, often with a trifid terminal tendril; terminal or less frequently lateral inflorescences; usually glandular, campanulate, ± truncate calyces; tubular-infundibular or tubular-campanulate, strongly curved corollas that are white or less frequently purple, often with a yellow throat, pubescent externally and usually internally; ovaries and styles sericeous and poorly demarcated; woody capsules that are non-echinate, and often with both valves convex (slightly compressed) or one convex and one concave and the fruits curving; and bialate seeds (wings sometimes greatly reduced), brown to black, irregularly ridged, and glabrous. Relationships with similar genera are discussed, and a key to the species of Distictella, species descriptions, and species distribution maps are provided. The relationships of the species are also discussed, and a new species, D. lohmanniae A. Pool, and new variety, D. racemosa (Bureau & K. Schum.) Urb. var. translucida A. Pool, are proposed. Bignonia rusbyi Britton ex Rusby, Distictis angustifolia K. Schum. ex Sprague, Distictella lutescens C. V. Freire & A. Samp. , and Distictella negrensis C. V. Freire & A. Samp. are presented as new synonyms of Distictella racemosa var. racemosa. Distictella broadwayana Urb. is presented as a new synonym of Distictella racemosa var. translucida, and a lectotype is designated for Distictis racemosa Bureau & K. Schum.
A Context Retrieval Method for Context-awareness Using Ontology-Based Approach in Internet of Things Environments

A context-aware system is required for providing context-aware services to users in the Internet of things (IoT) environment. It consists of three primary tasks: gathering context data, abstracting the collected data, and providing services to users. In IoT environments, context data are generated by a large number of sensors, and this context data are part of the context-awareness services provided to the users. When the context-aware system provides context-aware services with their service descriptions, it is necessary to process the data gathered by abstracting and contextualizing them. Generally, the context-aware system encounters a problem in that the context representations between contextualized sensor data and their related service descriptions do not match well. We herein propose a context retrieval method that facilitates in obtaining context information for the context-aware system in IoT environments. The context-aware system communicates with an ontology module that handles input data described in a set of universal resource identifiers, as a triplet. The ontology module resolves each representation request from the context-aware system. The proposed method provides an ontology-based mapping procedure for the context representation problem described above.
A Knowledge Perspective on Quality in Complex Citizen Science

This article examines how project leaders in complex citizen science projects ensure the quality of project outcomes given the challenges of involving citizens, whose knowledge is diverse and unknown beforehand. To this aim, a qualitative multiple-case study was carried out to compare the practices of five collaborative online citizen science projects in the humanities in which citizens transcribe, translate, and annotate handwritten manuscripts from the 17th, 18th, and 19th centuries. The choices made to access knowledge, thus to recruit participants either through open or targeted calls, seem to be fundamental for the different configurations of knowledge management activities that project leaders apply to ensure quality outcomes. Other factors influencing these knowledge management configurations are citizens’ proximity, knowledge characteristics, technology affordances, and the extent to which project leaders are aware of citizens’ backgrounds and skills. This study adds to earlier frameworks proposed to advise the design and management of citizen science projects. By taking a knowledge perspective, this article provides practical directions for project leaders involved in citizen science and highlights the need to put time and effort in managing knowledge processes.
Computational and Experimental Tools of miRNAs in Cancer

MicroRNAs (miRNAs) are short non-protein coding and single-stranded small RNA molecules with a critical role in the regulation of gene expression. These molecules are crucial regulatory elements in diverse biological processes such as apoptosis, development, and progression. miRNA genes have been associated with various human diseases, particularly cancer, and considered as a new biomarker. After the discovery of miRNAs, many researches have focused on identifying and characterizing miRNA genes in cancer. The various expression levels of miRNAs between cancer cells and normal cells are very crucial to diagnosis, prognosis, and treatment of many cancers. Many computational and experimental tools have been employed to characterize miRNAs. However, there exist some challenges in identifying miRNA using both computational and experimental tools due to miRNA features. The present review briefly introduced miRNA biology and certain computational and experimental tools for identifying and profiling miRNAs in cancer. Furthermore, we presented the advantages and challenges of these tools.
A Schatten-q Matrix Perturbation Theory via Perturbation Projection Error Bound

This paper studies the Schatten-$q$ error of low-rank matrix estimation by singular value decomposition under perturbation. Specifically, we establish a tight perturbation bound on the low-rank matrix estimation via a perturbation projection error bound. This new proof technique has provable advantages over the classic approaches. Then, we establish lower bounds to justify the tightness of the upper bound on the low-rank matrix estimation error. Based on the matrix perturbation projection error bound, we further develop a unilateral and a user-friendly sin$\Theta$ bound for singular subspace perturbation. Finally, we demonstrate the advantage of our results over the ones in the literature by simulation.
Phenolic Composition, Antibacterial And Antioxidant Activities Of Brazilian Red Propolis [composição Fenólica, Atividade Antibacteriana E Antioxidante Da Própolis Vermelha Brasileira]

PHENOLIC COMPOSITION, ANTIBACTERIAL AND ANTIOXIDANT ACTIVITIES OF BRAZILIAN RED PROPOLIS. Propolis is a resinous hive product collected by honeybees from various plant sources. It has a complex chemical composition, constituted by various phenolic compounds. Extracts of increasing polarity (n-hexane, chloroform, and ethanol) were obtained from a sample of red propolis from the state of Alagoas. Assays were carried out for determination of contents of phenolics, along with antibacterial and antioxidant activities. The EEP, fractions and sub-fractions showed strong biological activities and were related with phenolic the content compounds contents. The sub-fractions were more bioactive than the EEP and fractions, demonstrating that the antioxidant and antibacterial activities are not a result of synergistic effect between the various chemical compounds in propolis.Keywords: red propolis; antioxidant; antibacterial activity.
Optimization-Driven Hierarchical Learning Framework for Wireless Powered Backscatter-Aided Relay Communications

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays’ radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep $Q$ -network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.
ContentWise Impressions: An Industrial Dataset with Impressions Included

In this article, we introduce the \dataset dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet. The dataset is distinguished from other already available multimedia recommendation datasets by the availability of impressions, \idest the recommendations shown to the user, its size, and by being open-source. We describe the data collection process, the preprocessing applied, its characteristics, and statistics when compared to other commonly used datasets. We also highlight several possible use cases and research questions that can benefit from the availability of user impressions in an open-source dataset. Furthermore, we release software tools to load and split the data, as well as examples of how to use both user interactions and impressions in several common recommendation algorithms.
Alerts in High-resolution TEM characterization of perovskite material

High-resolution TEM (HRTEM) is a powerful tool for structure characterization. However, MAPbI3 perovskite is highly sensitive to electron beams and easily decompose into PbI2. Universal mistakes that PbI2 is incorrectly labeled as perovskite are widely exist in HRTEM characterizations, which negatively affect the development of perovskite research field. Here complete errors in MAPbI3 perovskite calibration are statisticed, classified and identified based on corresponding electron diffraction (ED) simulations. Corrected material phases, ED patterns of original PbI2 and obvious crystallographic parameters are also presented. This approach paves the way to avoid misleadings in HRTEM characterization of perovskite and other electron beam-sensitive materials in the future.
PDE-Driven Spatiotemporal Disentanglement

A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory. Following this direction, we propose in this article a novel and general paradigm for this task based on a resolution method for partial differential equations: the separation of variables. This inspiration allows us to introduce a dynamical interpretation of spatiotemporal disentanglement. It induces a principled model based on learning disentangled spatial and temporal representations of a phenomenon to accurately predict future observations. We experimentally demonstrate the performance and broad applicability of our method against prior state-of-the-art models on physical and synthetic video datasets.
Parallel Newton-Chebyshev Polynomial Preconditioners for the Conjugate Gradient method

In this note we exploit polynomial preconditioners for the Conjugate Gradient method to solve large symmetric positive definite linear systems in a parallel environment. We put in connection a specialized Newton method to solve the matrix equation X^{-1} = A and the Chebyshev polynomials for preconditioning. We propose a simple modification of one parameter which avoids clustering of extremal eigenvalues in order to speed-up convergence. We provide results on very large matrices (up to 8 billion unknowns) in a parallel environment showing the efficiency of the proposed class of preconditioners.
The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures

This paper presents the Jazz Transformer, a generative model that utilizes a neural sequence model called the Transformer-XL for modeling lead sheets of Jazz music. Moreover, the model endeavors to incorporate structural events present in the Weimar Jazz Database (WJazzD) for inducing structures in the generated music. While we are able to reduce the training loss to a low value, our listening test suggests however a clear gap between the average ratings of the generated and real compositions. We therefore go one step further and conduct a series of computational analysis of the generated compositions from different perspectives. This includes analyzing the statistics of the pitch class, grooving, and chord progression, assessing the structureness of the music with the help of the fitness scape plot, and evaluating the model's understanding of Jazz music through a MIREX-like continuation prediction task. Our work presents in an analytical manner why machine-generated music to date still falls short of the artwork of humanity, and sets some goals for future work on automatic composition to further pursue.
Intra-class variation reduction of speaker representation in disentanglement framework

In this paper, we propose an effective training strategy to ex-tract robust speaker representations from a speech signal. Oneof the key challenges in speaker recognition tasks is to learnlatent representations or embeddings containing solely speakercharacteristic information in order to be robust in terms of intra-speaker variations. By modifying the network architecture togenerate both speaker-related and speaker-unrelated representa-tions, we exploit a learning criterion which minimizes the mu-tual information between these disentangled embeddings. Wealso introduce an identity change loss criterion which utilizes areconstruction error to different utterances spoken by the samespeaker. Since the proposed criteria reduce the variation ofspeaker characteristics caused by changes in background envi-ronment or spoken content, the resulting embeddings of eachspeaker become more consistent. The effectiveness of the pro-posed method is demonstrated through two tasks; disentangle-ment performance, and improvement of speaker recognition ac-curacy compared to the baseline model on a benchmark dataset,VoxCeleb1. Ablation studies also show the impact of each cri-terion on overall performance.
Ubicomp Digital 2020 - Handwriting classification using a convolutional recurrent network

The Ubicomp Digital 2020 -- Time Series Classification Challenge from STABILO is a challenge about multi-variate time series classification. The data collected from 100 volunteer writers, and contains 15 features measured with multiple sensors on a pen. In this paper,we use a neural network to classify the data into 52 classes, that is lower and upper cases of Arabic letters. The proposed architecture of the neural network a is CNN-LSTM network. It combines convolutional neural network (CNN) for short term context with along short term memory layer (LSTM) for also long term dependencies. We reached an accuracy of 68% on our writer exclusive test set and64.6% on the blind challenge test set resulting in the second place.
Land Use and Land Cover Classification using a Human Group based Particle Swarm Optimization Algorithm with a LSTM classifier on hybrid-pre-processing Remote Sensing Images

Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-based particle swarm optimization (PSO) algorithm is applied to select the optimal features, whose benefits are a fast convergence rate and ease of implementation. After selecting the optimal feature values, a long short-term memory (LSTM) network is utilized to classify the LULC classes. Experimental results showed that the human group-based PSO algorithm with a LSTM classifier effectively well differentiates the LULC classes in terms of classification accuracy, recall and precision. A maximum improvement of 6.03% on Sat 4 and 7.17% on Sat 6 in LULC classification is reached when the proposed human group-based PSO with LSTM is compared to individual LSTM, PSO with LSTM, and Human Group Optimization (HGO) with LSTM. Moreover, an improvement of 2.56% in accuracy is achieved, compared to the existing models, GoogleNet, Visual Geometric Group (VGG), AlexNet, ConvNet, when the proposed method is applied.
Construction of Protograph-Based LDPC Codes With Chordless Short Cycles

There is a concept in graph theory known as a chord which has not been considered before in relation to trapping sets of Tanner graphs. A chord of a cycle is an edge outside the cycle which connects two vertices of that cycle. It is proved that short cycles with a chord are the root of several trapping sets and eliminating them increases the minimum distance <inline-formula> <tex-math notation="LaTeX">$d_{\min }$ </tex-math></inline-formula> of a code. We provide new analytic lower bounds on <inline-formula> <tex-math notation="LaTeX">$d_{\min }$ </tex-math></inline-formula> of LDPC codes with girths 6 and 8 and column weight <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> in which the short cycles are all chordless. We prove, analytically, that <inline-formula> <tex-math notation="LaTeX">$d_{\min }\geq 2\gamma $ </tex-math></inline-formula> for girth 6 and <inline-formula> <tex-math notation="LaTeX">$d_{\min }\geq \frac {3(\gamma -1)^{2}}{\gamma \ln \gamma -\gamma +1}$ </tex-math></inline-formula> for girth 8. Comparing these bounds with the existing bound <inline-formula> <tex-math notation="LaTeX">$\gamma +1$ </tex-math></inline-formula> for girth-6 LDPC codes shows the positive and significant influence of eliminating these cycles. A method to construct protograph-based LDPC codes with different girths and free of short cycles with a chord is given which is applicable to any type of protographs, simple and multi-edge, regular and irregular. The conditions to remove small trapping sets from the Tanner graph of a multi-edge QC-LDPC code are given. Numerical results indicate that the application of our method to QC-LDPC codes improves existing results.
Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution Under Random Designs

Abstract We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i.e., a sort of random Fourier design and Gaussian design). Despite the wide applicability, the theoretical understanding about these two paradigms remains largely inadequate in the presence of random noise. The current article makes two contributions by demonstrating that (i) a two-stage nonconvex algorithm attains minimax-optimal accuracy within a logarithmic number of iterations, and (ii) convex relaxation also achieves minimax-optimal statistical accuracy vis-à-vis random noise. Both results significantly improve upon the state-of-the-art theoretical guarantees. Supplementary materials for this article are available online.
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. The projection methods includes spherical projection, bird-eye view projection, etc. Although this process makes the point cloud suitable for the 2D CNN-based networks, it inevitably alters and abandons the 3D topology and geometric relations. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. In this work, we first perform an in-depth analysis for different representations and backbones in 2D and 3D spaces, and reveal the effectiveness of 3D representations and networks on LiDAR segmentation. Then, we develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds. Moreover, a dimension-decomposition based context modeling module is introduced to explore the high-rank context information in point clouds in a progressive manner. We evaluate the proposed model on a large-scale driving-scene dataset, i.e. SematicKITTI. Our method achieves state-of-the-art performance and outperforms existing methods by 6% in terms of mIoU.
Neural Granular Sound Synthesis

Granular sound synthesis is a popular audio generation technique based on rearranging sequences of small waveform windows. In order to control the synthesis, all grains in a given corpus are analyzed through a set of acoustic descriptors. This provides a representation reflecting some form of local similarities across the grains. However, the quality of this grain space is bound by that of the descriptors. Its traversal is not continuously invertible to signal and does not render any structured temporality. We demonstrate that generative neural networks can implement granular synthesis while alleviating most of its shortcomings. We efficiently replace its audio descriptor basis by a probabilistic latent space learned with a Variational Auto-Encoder. A major advantage of our proposal is that the resulting grain space is invertible, meaning that we can continuously synthesize sound when traversing its dimensions. It also implies that original grains are not stored for synthesis. To learn structured paths inside this latent space, we add a higher-level temporal embedding trained on arranged grain sequences. The model can be applied to many types of libraries, including pitched notes or unpitched drums and environmental noises. We experiment with the common granular synthesis processes and enable new ones.