Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations

In this work, we present a comprehensive investigation of the elastic properties (the full elastic tensor, bulk, shear and Young’s moduli, and Poisson’s ratio) of 23 well-known ceramic alkali superionic conductor electrolytes (SICEs) using first principles calculations. We find that the computed elastic moduli are in good agreement with experimental data (wherever available) and chemical bonding nature. The anion species and structural framework have a significant influence on the elastic properties, and the relative elastic moduli of the various classes of SICEs follow the order thiophosphate < antiperovskite < phosphate < NASICON < garnet < perovskite. Within the same framework structure, we observe that Na SICEs are softer than their Li analogs. We discuss the implications of these findings in the context of fabrication, battery operation, and enabling a Li metal anode. The data computed in this work will also serve as a useful reference for future experiments as well as theoretical modeling of SICEs for rechargeable alkali-ion batteries. © 2015 The Electrochemical Society. [DOI: 10.1149/2.0061602jes] All rights reserved.
RL-CNN: Reinforcement Learning-designed Convolutional Neural Network for Urban Traffic Flow Estimation

Accurate prediction of urban traffic flows brings enormous advantages to big cities. Therefore, many urban traffic flow predictors have been developed in recent years. Urban traffic flow predictors aim to identify complex mobility patterns and capture urban traffic flow characteristics from large-scale historical datasets. Afterward, trained models are used to predict the future traffic volume in terms of the number of moving objects (e.g., vehicles). Convolutional Neural Networks (CNN) and other deep learning approaches are brilliant choices because of their ability to learn Spatio-temporal dependencies. Nevertheless, the extensive set of hyper-parameters tends to make these neural networks overly complex and challenging to design. In this work, we introduce RL-CNN, a framework based on Reinforcement Learning whose objective is to autonomously discover highperformance CNN architectures for the given traffic prediction task without human intervention. We examine the proposed RL-CNN model as a traffic flow estimator on a real-world and large-scale vehicular network dataset. We observe improvements of 5% - 10% in the average traffic flow prediction accuracy over the state-of-art approaches.
A Comparative Study on CSR Disclosure between Indonesian Islamic Banks and Conventional Banks: the Application of GRI and ISR Indexes

This study aims to determine whether or not there is a discrepancy in Corporate Social Responsibility (CSR) disclosure between sharia and conventional banks in Indonesia, and which CSR aspects are better between those two types of the banks. This study applied a quantitative approach. The analysis method used in this study to test the hypothesis was independent sample t-test. The method was applied to determine whether there is a significant difference in the average value between sharia and conventional banks. The number of samples in this study were 40 consisting of 20 Islamic banks and 20 conventional banks. The data taken were those between 2011 and 2014 which contained the information related to CSR. The results of this study indicated that there were significant differences in CSR disclosure between sharia and conventional banks. On average, CSR disclosure rate of sharia banks was higher than that of conventional banks. The weaknesses of CSR disclosure of sharia banks were in the aspects of environment and general information, while the weaknesses of CSR disclosure of conventional banks were in the aspects of energy, health and safety of the employee, product, and society involvement. Due to the limitation of this study in compiling the aspects of variables presented in Global Reporting Initiative (GRI) and Islamic Social Reporting (ISR), it is suggested for the future study to do further detail analysis to compile and to combine with the primary data in order to get the precise quality of the data disclosed by each bank.
Sound Event Detection and Localization Using ResNet RNN and Time-Delay DOA Technical

This paper presents a deep learning approach for sound events detection and time-delay direction-of-arrival (TDOA) for localization, which is also a part of detection and classification of acoustic scenes and events (DCASE) challenge 2019 Task 3. Deep residual nets originally used for image classification are adapted and combined with recurrent neural networks (RNN) to estimate the onset-offset of sound events, sound events class. Data augmentation and postprocessing techniques are applied to generalize the system performance to unseen data. Direction of sound events in a rever-berant environment is estimated using a time-delay direction-of-arrival TDOA algorithm. Using our best model on validation dataset, sound events detection achieves F1-score of 0.84 and error rate of 0.25, whereas sound source localization task achieves angular error of 16.56 degree and 0.82 frame recall.
Anticancer And Antiangiogenic Activities Of Alkaloids Isolated From Lantana Camara By Adsorption On The Magnetic Nanoparticles

Lantana camara L. (L. camara) is a perennial shrub that contains low amounts of alkaloids. In the present study, the magnetic nanoparticles (MNPs) were used to isolate positively charged alkaloids from the methanolic extract of L. camara leaves. The crude alkaloid was fractionated using HPLC to separate the highest peak of the alkaloid fraction (HPAF). The crude alkaloids (CA) and HPAF were tested for their antiproliferative effect against cancer cell lines (MCF-7, HCT-116, and HeLa) and endothelial cells line (EA.hy926) as a standard cell line. Antiangiogenic properties were examined using rat aortic ring assay. HPAF exhibited a profound anticancer effect against MCF-7 and HeLa cell lines (with IC50 = 0.027 μg / mL and 5.90 μg / mL, respectively, while it displayed reasonably mild cytotoxicity against the HCT-116 cell line (IC50 = 8.38 μg / mL). The CA also demonstrated a significant anticancer effect against MCF-7 and HeLa and a weak cytotoxic effect against colon cancer HCT-116 cells. Cationic alkaloids displayed selective antiproliferative activity against HeLa while it was utterly safe on the normal test cell line. HPAF demonstrated remarkable antiangiogenic activity in non-toxic doses. Also, cationic alkaloids showed significant antiangiogenic effects. The use of magnetic nanoparticles (MNPs) to separate small quantities of precious compounds is handy and cost-effective.
Restrictive Cardiomyopathies: Evaluation Using Cardiac MRI and Multidetector CT

Cardiomyopathies are conditions that affect the myocardium and cause alteration in the cardiac function. Within the cardiomyopathies, the restrictive subgroup has as a main finding the decrease in the ventricular filling. In this manuscript we will review the restrictive cardiomyopathies and discuss their main causes, as well as their imaging findings on cardiac magnetic resonance and computed tomography. We will also include imaging signs that helps to differentiate restrictive cardiomyopathies from constrictive pericarditis.
BUPTMM Submission for YouMakeUp VQA Challenge in Step Ordering Task

The ﬁrst YouMakeUp VQA Challenge, hosted at CVPR 2020 Workshop on Language & Vision with applications to Video Understanding (LVVU), focuses on ﬁne-grained action understanding in makeup instructional videos. This challenge presents two question-answering tasks: image ordering and step ordering. In this paper, we propose an overview and comparative analysis of our system designed for the step ordering task. First, we regard the step ordering task as the moment retrieval problem. Then, we compute the Levenshtein distance between the predicted order and each candidate. We select the candidate with the shortest distance as the ﬁnal answer. Speciﬁcally, we adopt two techniques to improve performance. To retrieve the moments accurately, a late guidance module is designed to further integrate the videos and sentences tightly. We then combine multiple moment retrieval models to improve classiﬁcation accuracy. Finally, our approach obtains the 85.19% accuracy on the testing set.
ASSESSING VACCINATION STRATEGIES FOR THE COVID-19 EPIDEMIC IN MINAS GERAIS (BRAZIL)

In this work we analyze the effectiveness of vaccina- tion strategies for the COVID-19 epidemic in the Brazilian state of Minas Gerais. Firstly we study the effectiveness of general vaccination in the decreasing of the number of infected individuals using a traditional non structured SEIR model. Secondly we consider an age-structured SEIR model with 3 age groups (youngster, adult and elderly) and we analyze the current strategy in the Brazilian state of Minas Gerais, of focusing the vaccination on the elderly group. We conclude by showing this strategy to be mistaken and that a vaccination focusing on the age group of the adults would be much more efficient in decreasing the total number of infected individuals.
Opleiding Informatica Fusion approaches for Monocular Depth Estimation

Estimating depth using a single image is a very useful research ﬁeld with applications in augmented reality, self-driving cars and computational photography. Recently signiﬁcant progress has been made due to the innovative Deep Convolutional Neural Networks. In this thesis we will not innovate by producing a new DCNN architecture but instead we fuse diﬀerent existing DCNN models using multiple fusion methods; the average, weighted average and median. The performance of these fusions is compared using two datasets; NYU Depth-v2 and KITTI. We show that fusing models will always give better performance but choosing the right models to fuse is very important. Models that have signiﬁcant diﬀerence in the architecture are more likely to fuse well. We also show that fusing more models yield better results but will quickly give diminishing returns. Fusing only two well chosen models will give performance close to the performance of a three model fusion. 1
Determination of some metal concentrations in water and sediment samples in Yedigöller region ( k üta hya)

Yedigoller, known for its eutrophic features, is located 2 km away from the center of Kutahya. This region consists of seven lakes but has unfortunately been used as landfill since 1977. What is more, two of these seven lakes have been purposefully dried out. This splendid region is sadly under threat of extinction due to heavy pollutant factors such as domestic, industrial and medical wastes. 2. In the present study, water and sediment samples of two different stations, apart from investigating and measuring temperature, pH, dissolved oxygen, biological oxygen demand, aluminum, calcium, iron, potassium, magnesium, sodium, phosphorus, sulfur, silicon, zinc, silver, boron, cadmium, chromium, copper, mercury, manganese, nickel, lead and selenium in 2009. 3. Our results demonstra ted that the levels of metal concentration occurring in the sediment samples were higher than those cultivated from the water samples. Furthermore, our physicochemical parameters determined that while the water quality of the lakes occurring in Yedigoller was of III. class, it was of IV. class in terms of Se and B metals.
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metrics cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.
Conforming Finite Elements for $H(\text{sym}\,\text{Curl})$ and $H(\text{dev}\,\text{sym}\,\text{Curl})$

We construct conforming finite elements for the spaces $H(\text{sym}\,\text{Curl})$ and $H(\text{dev}\,\text{sym}\,\text{Curl})$. Those are spaces of matrix-valued functions with symmetric or deviatoric-symmetric $\text{Curl}$ in a Lebesgue space, and they appear in various models of nonstandard solid mechanics. The finite elements are not $H(\text{Curl})$-conforming. We show the construction, prove conformity and unisolvence, and point out optimal approximation error bounds.
DPT-FSNet: Dual-Path Transformer Based Full-Band and Sub-Band Fusion Network for Speech Enhancement

Sub-band models have achieved promising results due to their ability to model local patterns in the spectrogram. Some studies further improve the performance by fusing sub-band and full-band information. However, the structure for the full-band and sub-band fusion model was not fully explored. This paper proposes a dual-path transformer-based full-band and sub-band fusion network (DPT-FSNet) for speech enhancement in the frequency domain. The intra and inter parts of the dual-path transformer model sub-band and full-band information, respectively. The features utilized by our proposed method are more interpretable than those utilized by the time-domain dual-path transformer. We conducted experiments on the Voice Bank + DEMAND and Interspeech 2020 Deep Noise Suppression (DNS) datasets to evaluate the proposed method. Experimental results show that the proposed method outperforms the current state-of-the-art.
Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi-domain setting. The intuition is that user-item interactions in a source domain can augment the recommendation quality in a target domain. The intuition can be taken to its extreme, where, in a cross-domain setup, the user history in a source domain is enough to generate high-quality recommendations in a target one. We thus create a Product-of-Experts (POE) architecture for recommendations that jointly models user-item interactions across multiple domains. The method is resilient to missing data for one or more of the domains, which is a situation often found in real life. We present results on two widely-used datasets - Amazon and Yelp, which support the claim that holistic user preference knowledge leads to better recommendations. Surprisingly, we find that in some cases, a POE recommender that does not access the target domain user representation can surpass a strong VAE recommender baseline trained on the target domain.
Collective Monte Carlo updates through tensor network renormalization

We introduce a Metropolis-Hastings Markov chain for Boltzmann distributions of classical spin systems. It relies on approximate tensor network contractions to propose correlated collective updates at each step of the evolution. We present benchmark computations for a wide variety of instances of the two-dimensional Ising model, including ferromagnetic, antiferromagnetic, (fully) frustrated and Edwards-Anderson spin glass instances, and we show that, with modest computational effort, our Markov chain achieves sizeable acceptance rates, even in the vicinity of critical points. In each of the situations we have considered, the Markov chain compares well with other Monte Carlo schemes such as the Metropolis or Wolff’s algorithm: equilibration times appear to be reduced by a factor that varies between 4040 and 20002000, depending on the model and the observable being monitored. We also present an extension to three spatial dimensions, and demonstrate that it exhibits fast equilibration for finite ferro- and antiferromagnetic instances. Additionally, and although it is originally designed for a square lattice of finite degrees of freedom with open boundary conditions, the proposed scheme can be used as such, or with slight modifications, to study triangular lattices, systems with continuous degrees of freedom, matrix models, a confined gas of hard spheres, or to deal with arbitrary boundary conditions.
Prospects for estimating parameters from gravitational waves of superspinar binaries

To date, close to fifty black hole binary mergers were observed by the LIGO and Virgo detectors. The analyses have been done with an assumption that these objects are black holes by limiting the spin prior to the Kerr bound. However, the above assumption is not valid for superspinars, which have the Kerr geometry but rotate beyond the Kerr bound. In this study, we investigate whether and how the limited spin prior range causes a bias in parameter estimation for superspinars if they are detected. To this end, we estimate binary parameters of the simulated inspiral signals of the gravitational waves of compact binaries by assuming that at least one component of them is a superspinar. We have found that when the primary is a superspinar, both mass and spin parameters are biased in parameter estimation due to the limited spin prior range. In this case, the extended prior range is strongly favored compared to the limited one. On the other hand, when the primary is a black hole, we do not see much bias in parameter estimation due to the limited spin prior range, even though the secondary is a superspinar. We also apply the analysis to black hole binary merger events GW170608 and GW190814, which have a long and loud inspiral signal. We do not see any preference of superspinars from the model selection for both events. We conclude that the extension of the spin prior range is necessary for accurate parameter estimation if highly spinning primary objects are found, while it is difficult to identify superspinars if they are only the secondary objects. Nevertheless, the bias in parameter estimation of spin for the limited spin prior range can be a clue of the existence of superspinars.
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion

Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing models employ a few-shot relation's multi-hop neighbor information to enhance its semantic representation. However, noise neighbor information might be amplified when the neighborhood is excessively sparse and no neighbor is available to represent the few-shot relation. Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances. Thus, inferring complex relations in the few-shot scenario is difficult for FKGC models due to limited training instances. In this paper, we propose a few-shot relational learning with global-local framework to address the above issues. At the global stage, a novel gated and attentive neighbor aggregator is built for accurately integrating the semantics of a few-shot relation's neighborhood, which helps filtering the noise neighbors even if a KG contains extremely sparse neighborhoods. For the local stage, a meta-learning based TransH (MTransH) method is designed to model complex relations and train our model in a few-shot learning fashion. Extensive experiments show that our model outperforms the state-of-the-art FKGC approaches on the frequently-used benchmark datasets NELL-One and Wiki-One. Compared with the strong baseline model MetaR, our model achieves 5-shot FKGC performance improvements of 8.0% on NELL-One and 2.8% on Wiki-One by the metric Hits@10.
Carbofunctional sulfur-containing organosilicon compounds

This review summarizes literature data and the authors’ own research results from the past 14–15 years relating to practical, valuable organosilicon carbofunctional sulfur-containing compounds of general formula R4−nSi(SxR1)n, where R is alkyl, arylalkoxyl, aroxyl or even sylatranyl fragment, R1 is hydrogen, alkyl, aryl, alkenyl, etc., n = 1–3, x = 1–10, having sulfur functional groups such as thiol, sulfide, di- and polysulfide, as well as sulfur heteroatomic groups such as thiocarbamide, dioxothiocarbamide, dithiourethane, thiuramdisulfide, etc. The compounds reviewed have been found to be effective, for example, as ingredients for rubber compositions for non-flammable, water- and wear-proof tires or as ion-exchanging and complexing sorbents of heavy and noble metals.
K-Clique Counting on GPUs

Counting 𝑘 -cliques in a graph is an important problem in graph analysis with many applications. Counting 𝑘 -cliques is typically done by traversing search trees starting at each vertex in the graph. An important optimization is to eliminate search tree branches that discover the same clique redundantly. Eliminating redundant clique discovery is typically done via graph orientation or pivoting. Parallel implementations for both of these approaches have demonstrated promising performance on CPUs. In this paper, we present our GPU implementations of 𝑘 -clique counting for both the graph orientation and pivoting approaches. Our implementations explore both vertex-centric and edge-centric parallelization schemes, and replace recursive search tree traversal with iterative traversal based on an explicitly-managed shared stack. We also apply various optimizations to reduce memory consumption and improve the utilization of parallel execution resources. Our evaluation shows that our best GPU implementation out-performs the best state-of-the-art parallel CPU implementation by a geometric mean speedup of 12.39 × , 6.21 × , and 18.99 × for 𝑘 = 4, 7, and 10, respectively. We also evaluate the impact of the choice of parallelization scheme and the incremental speedup of each optimization. Our code will be open-sourced to enable further research on parallelizing 𝑘 -clique counting on GPUs.
A unified framework for Hamiltonian deep neural networks

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/exploding gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The time-invariant version of the corresponding Hamiltonian models enjoys marginal stability, a property that, as shown in previous works and for specific DNNs architectures, can mitigate convergence to zero or divergence of gradients. In the present paper, we formally study this feature by deriving and analysing the backward gradient dynamics in continuous time. The proposed Hamiltonian framework, besides encompassing existing networks inspired by marginally stable ODEs, allows one to derive new and more expressive architectures. The good performance of the novel DNNs is demonstrated on benchmark classification problems, including digit recognition using the MNIST dataset.
Chain Entanglements and Hydrogen Bonds in Carbopol Microgel Reinforced Hydrogel

Physical interaction between the microgel and the hydrogel matrix is a key issue in the rational design of microgel reinforced hydrogels. A pentaerythritol-crosslinked poly(acrylic acid) (PAAc) microgel, named Carbopol, is composited with polyacrylamide (PAAm) hydrogel. The toughness of the hydrogel is significantly improved by constructing an interpenetrating network of Carbopol and PAAm. Chain entanglements within the composited hydrogel result in a strain-hardening phenomenon and an efficient stress transfer from the PAAm matrix to the sacrificing network of Carbopol. Hydrogen bonds between PAAc and PAAm can be formed in an acidic environment, which function as reversible sacrificing bonds and further improve the stretchability of the hydrogel.