Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of 
4f
 states in infinite-layer 
NdNiO2

Atomic $4f$ states have been found to be essential players in the physical behavior of lanthanide compounds, at the Fermi level $E_F$ as in the proposed topological Kondo insulator SmB$_6$, or further away as in the magnetic superconductor system ${\cal R}$Ni$_2$B$_2$C (${\cal R}$=rare earth ion) and in Y$_{1-x}$Pr$_x$Ba$_2$Cu$_3$O$_7$, where the $4f$ shell of Pr has a devastating effect on superconductivity. In hole-doped ${\cal R}$NiO$_2$, the ${\cal R}$=Nd member is found to be superconducting while ${\cal R}$=La is not, in spite of the calculated electronic structures being nearly identical. We report first principles results that indicate that the Nd $4f$ moment affects states at $E_F$ in infinite-layer NdNiO$_2$, an effect that will not occur for LaNiO$_2$. Treating 20% hole-doping in the virtual crystal approach indicates that 0.15 holes empty the $\Gamma$-centered Nd-derived electron pocket while leaving the other electron pocket unchanged; hence Ni only absorbs 0.05 holes; the La counterpart would behave similarly. However, coupling of $4f$ states to the electron pockets at $E_F$ arises through the Nd intra-atomic $4f-5d$ exchange coupling $K\approx 0.5$ eV and is ferromagnetic (FM), i.e. anti-Kondo, in sign. This interaction causes spin-disorder broadening of the electron pockets and should be included in models of the normal and superconducting states of Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ The Ni moments differ by 0.2$\mu_B$ for FM and antiferromagnetic alignment (the latter are larger), reflecting some itineracy and indicating that Heisenberg coupling of the moments may not provide a quantitative modeling of Ni-Ni exchange coupling.
Neural Graph Embedding Methods for Natural Language Processing

Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them. Recent research has resulted in the development of several large KGs. However, all of them tend to be sparse with very few facts per entity. In the first part of the thesis, we propose two solutions to alleviate this problem: (1) KG Canonicalization, i.e., identifying and merging duplicate entities in a KG, (2) Relation Extraction which involves automating the process of extracting semantic relationships between entities from unstructured text. Traditional Neural Networks like CNNs and RNNs are constrained to handle Euclidean data. However, graphs in Natural Language Processing (NLP) are prominent. Recently, Graph Convolutional Networks (GCNs) have been proposed to address this shortcoming and have been successfully applied for several problems. In the second part of the thesis, we utilize GCNs for Document Timestamping problem and for learning word embeddings using dependency context of a word instead of sequential context. In this third part of the thesis, we address two limitations of existing GCN models, i.e., (1) The standard neighborhood aggregation scheme puts no constraints on the number of nodes that can influence the representation of a target node. This leads to a noisy representation of hub-nodes which coves almost the entire graph in a few hops. (2) Most of the existing GCN models are limited to handle undirected graphs. However, a more general and pervasive class of graphs are relational graphs where each edge has a label and direction associated with it. Existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representation of nodes only.
Beyond Adaptive Submodularity: Adaptive Influence Maximization with Intermediary Constraints

We consider a brand with a given budget that wants to promote a product over multiple rounds of influencer marketing. In each round, it commissions an influencer to promote the product over a social network, and then observes the subsequent diffusion of the product before adaptively choosing the next influencer to commission. This process terminates when the budget is exhausted. We assume that the diffusion process follows the popular Independent Cascade model. We also consider an online learning setting, where the brand initially does not know the diffusion parameters associated with the model, and has to gradually learn the parameters over time. Unlike in existing models, the rounds in our model are correlated through an intermediary constraint: each user can be commissioned for an unlimited number of times. However, each user will spread influence without commission at most once. Due to this added constraint, the order in which the influencers are chosen can change the influence spread, making obsolete existing analysis techniques that based on the notion of adaptive submodularity. We devise a sample path analysis to prove that a greedy policy that knows the diffusion parameters achieves at least $1-1/e - \epsilon$ times the expected reward of the optimal policy. In the online-learning setting, we are the first to consider a truly adaptive decision making framework, rather than assuming independent epochs, and adaptivity only within epochs. Under mild assumptions, we derive a regret bound for our algorithm. In our numerical experiments, we simulate information diffusions on four Twitter sub-networks, and compare our UCB-based learning algorithms with several baseline adaptive seeding strategies. Our learning algorithm consistently outperforms the baselines and achieves rewards close to the greedy policy that knows the true diffusion parameters.
Question Generation from Paragraphs: A Tale of Two Hierarchical Models

Automatic question generation from paragraphs is an important and challenging problem, particularly due to the long context from paragraphs. In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs. Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs. We model a paragraph in terms of its constituent sentences, and a sentence in terms of its constituent words. While the introduction of the attention mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer, with its inherent attention and positional encoding mechanisms also performs better than flat transformer model. We conducted empirical evaluation on the widely used SQuAD and MS MARCO datasets using standard metrics. The results demonstrate the overall effectiveness of the hierarchical models over their flat counterparts. Qualitatively, our hierarchical models are able to generate fluent and relevant questions
Small deformations of spherical biomembranes

In this contribution to the proceedings of the 11th Mathematical Society of Japan (MSJ) Seasonal Institute (July 2018) we give an overview of some recent work on a mathematical model for small deformations of a spherical membrane. The idea is to consider perturbations to minimisers of a surface geometric energy. The model is obtained from consideration of second order approximations to a perturbed energy. In particular, the considered problems involve particle constraints and surface phase field energies.
Word Embedding Algorithms as Generalized Low Rank Models and their Canonical Form

Word embedding algorithms produce very reliable feature representations of words that are used by neural network models across a constantly growing multitude of NLP tasks. As such, it is imperative for NLP practitioners to understand how their word representations are produced, and why they are so impactful. The present work presents the Simple Embedder framework, generalizing the state-of-the-art existing word embedding algorithms (including Word2vec (SGNS) and GloVe) under the umbrella of generalized low rank models. We derive that both of these algorithms attempt to produce embedding inner products that approximate pointwise mutual information (PMI) statistics in the corpus. Once cast as Simple Embedders, comparison of these models reveals that these successful embedders all resemble a straightforward maximum likelihood estimate (MLE) of the PMI parametrized by the inner product (between embeddings). This MLE induces our proposed novel word embedding model, Hilbert-MLE, as the canonical representative of the Simple Embedder framework. We empirically compare these algorithms with evaluations on 17 different datasets. Hilbert-MLE consistently observes second-best performance on every extrinsic evaluation (news classification, sentiment analysis, POS-tagging, and supersense tagging), while the first-best model depends varying on the task. Moreover, Hilbert-MLE consistently observes the least variance in results with respect to the random initialization of the weights in bidirectional LSTMs. Our empirical results demonstrate that Hilbert-MLE is a very consistent word embedding algorithm that can be reliably integrated into existing NLP systems to obtain high-quality results.
Unmasking DeepFakes with simple Features

Deep generative models have recently achieved impressive results for many real-world applications, successfully generating high-resolution and diverse samples from complex datasets. Due to this improvement, fake digital contents have proliferated growing concern and spreading distrust in image content, leading to an urgent need for automated ways to detect these AI-generated fake images. Despite the fact that many face editing algorithms seem to produce realistic human faces, upon closer examination, they do exhibit artifacts in certain domains which are often hidden to the naked eye. In this work, we present a simple way to detect such fake face images - so-called DeepFakes. Our method is based on a classical frequency domain analysis followed by basic classifier. Compared to previous systems, which need to be fed with large amounts of labeled data, our approach showed very good results using only a few annotated training samples and even achieved good accuracies in fully unsupervised scenarios. For the evaluation on high resolution face images, we combined several public datasets of real and fake faces into a new benchmark: Faces-HQ. Given such high-resolution images, our approach reaches a perfect classification accuracy of 100% when it is trained on as little as 20 annotated samples. In a second experiment, in the evaluation of the medium-resolution images of the CelebA dataset, our method achieves 100% accuracy supervised and 96% in an unsupervised setting. Finally, evaluating a low-resolution video sequences of the FaceForensics++ dataset, our method achieves 91% accuracy detecting manipulated videos. Source Code: this https URL
The productivity and quality of alfalfa (Medicago sativa L.) in Romanian forest steppe.

Alternative use of alfalfa, for various purposes, including the production of biofuels or food supplement for human alimentation, is a study topic still in its early stages of research. Studying and understanding the biology of alfalfa and the factors with a major influence on it are very important activities. The productivity and quality of alfalfa are two indicators that help determine, in addition to economic value, the way in which alfalfa can be used. Evolution of alfalfa yield and quality depends on many factors, such as the growth stage of alfalfa plants at harvesting. It was observed over three years of vegetation the influence of alfalfa plant growth stage at harvest on plant height, leaves/stems ratio, production of leaves, stems and whole plant (DM - dry matter) per hectare and on quality indicators (CP - crude protein, NDF - neutral detergent fiber and ADF - acid detergent fiber). The results showed that, with the advancement of phenological phases, from early bud stage to complete flowering, the total biomass output raised from 2.79 MgÂ·ha -1 to 4.60 MgÂ·ha -1 , the neutral detergent fiber raised from 48.4-50.6% to 62.0-67.7%, while crude protein content decreased from 21.2-24.0% to 13.3-16.5%. The parameter values were correlated with alfalfa growth stage during the harvesting (significant at the 0.05 and 0.01 probability levels).
Effect of Leaf Characteristics on Different Brinjal Genotypes and their Correlation on Insects Pests Infestation

Brinjal or eggplant, Solanum melongena L. is one of the important vegetable crops grown in India and other parts of the world. Insect pests are the main constraint in the successful cultivation of brinjal. Among them, leafhopper and whitefly are cosmopolitan in distribution and are found wherever brinjal is grown. The use of resistance genotypes is familiar as the imperative tool for the biointensive pest management system. The morphological and physical characteristics of plants and fruits are associated with attraction, feeding and oviposition of the insect pests. Development of varieties resistant to the insect pests is an important strategy of integrated pest management (Bhatti et al., 1976). The identification of biophysical characteristics from insect resistant varieties is of most practical significance.
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

Pretrained transformer-based language models have achieved state of the art across countless tasks in natural language processing. These models are highly expressive, comprising at least a hundred million parameters and a dozen layers. Recent evidence suggests that only a few of the final layers need to be fine-tuned for high quality on downstream tasks. Naturally, a subsequent research question is, "how many of the last layers do we need to fine-tune? " In this paper, we precisely answer this question. We examine two recent pretrained language models, BERT and RoBERTa, across standard tasks in textual entailment, semantic similarity, sentiment analysis, and linguistic acceptability. We vary the number of final layers that are fine-tuned, then study the resulting change in task-specific effectiveness. We show that only a fourth of the final layers need to be fine-tuned to achieve 90% of the original quality. Surprisingly, we also find that fine-tuning all layers does not always help.
Allelic Diversity of Polymorphic AMA-1 (Apical Membrane Antigen 1) Vaccine Candidate Antigen of Plasmodium falciparum in Two Population of Imported and Indigenous Cases in South-East of Iran using Nested-PCR and RFLP

The Plasmodium falciparum Apical Membrane Antigen 1 (AMA1) is a leading Malaria Vaccine Candidate Antigen. Antigenic variation is one of the main obstacles in the development of a universal effective malaria vaccine. Antibodies against AMA1 have been shown to block parasite invasion of human erythrocytes. Therefore, detailed studies on the molecular polymorphism PFAMA1 a Geographic area before each experiment or design the vaccine is administered. This study was designed to determine the distribution of AMA1 allele class of Plasmodium falciparum in two population of imported cases and indigenous cases in South-East of Iran . We used the Nested PCR and RFLPs methods with specific primers and restriction enzymes, which improves the three AMA1 allele class(K1-3D7-HB3). Overall the 94 confirmed P. falciparum samples obtained from four different districts, in two population of imported cases(46) and indigenous cases(48) in the south East of Iran. There are three classes of allelic AMA1 were compared in two populations of Indigenous and Imported, 3D7 in Indigenous Population was the higher prevalence than Imported Population. Considering on these results, 3D7 alleles suitable candidates for malaria vaccine design can be. The data reported here will be valuable for the development of AMA1 based malaria vaccine.
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE.
In vitro induction of tuber formation for the synthesis of secondary metabolites in Chlorophytum borivilianum Sant. et Fernand

Chlorophytum borivilianum Sant. et Fernand an endangered herb is valued for several medicinal properties in its tuberous roots. An efficient and reproducible method for inducing in vitro tubers from stem disc explant has been developed. Stem disc possessing shoot buds were induced to develop multiple shoots in Murashige and Skoog (MS) medium supplemented with vitamins, 3% sucrose, 0.8% agar and 5 mg/L benzylaminopurine (BAP). Healthy regenerated shoots were rooted in MS basal medium containing 3% sucrose (w/v), 0.8% agar supplemented with indole-3-acetic acid (2 mg/L). On further sub culturing, the maximum percentage of tuber formation was obtained in growth hormone free half (½) MS liquid media supplemented with vitamins and 1.5% sucrose after 8 to 9 weeks. The saponin contents of the in vitro and in vivo raised tubers were qualitatively and quantitatively analyzed by highperformance liquid chromatography (HPLC) and liquid chromatography electrospray ionization mass spectrometry (LC-ESI-MS). There was a significant similarity in the saponins in both tubers. The in vitro raised tubers showed similar high metabolite content than in vivo grown tubers which is required for medicinal applications. The rooted plantlets were transferred to peat and sand (2:1) with more than 80% success. This is the 1 st report of in vitro tuber formation and secondary metabolites screening of C. borivilianum. This work will give a strong impetus to the pharmaceutical and neutraceutical sectors.
Building an Aerial–Ground Robotics System for Precision Farming: An Adaptable Solution

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource-use efficiency, reduction of chemical treatments, and optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multipurpose unmanned ground vehicles (UGVs). This article presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multispectral-perception algorithms and aerial and ground-based systems developed to monitor crop density, weed pressure, and crop nitrogen (N)-nutrition status and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment as well as the modules for collaborative mapping. We finally present the ground-intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and perform selective spraying without human intervention.
Impact of AGN feedback on galaxies and their multiphase ISM across cosmic time

We present simulations of galaxy formation, based on the GADGET-3 code, in which a sub-resolution model for star formation and stellar feedback is interfaced with a new model for AGN feedback. Our sub-resolution model describes a multiphase ISM, accounting for hot and cold gas within the same resolution element: we exploit this feature to investigate the impact of coupling AGN feedback energy to the different phases of the ISM over cosmic time. Our fiducial model considers that AGN feedback energy coupling is driven by the covering factors of the hot and cold phases. We perform a suite of cosmological hydrodynamical simulations of disc galaxies (Mhalo, DM ≃ 2 · 1012 M⊙, at z = 0), to investigate: (i) the effect of different ways of coupling AGN feedback energy to the multiphase ISM; (ii) the impact of different prescriptions for gas accretion (i.e. only cold gas, both cold and hot gas, with the additional possibility of limiting gas accretion from cold gas with high angular momentum); (iii) how different models of gas accretion and coupling of AGN feedback energy affect the coevolution of supermassive BHs and their host galaxy. We find that at least a share of the AGN feedback energy has to couple with the diffuse gas, in order to avoid an excessive growth of the BH mass. When the BH only accretes cold gas, it experiences a growth that is faster than in the case in which both cold and hot gas are accreted. If the accretion of cold gas with high angular momentum is reduced, the BH mass growth is delayed, the BH mass at z = 0 is reduced by up to an order of magnitude, and the BH is prevented from accreting below z ≲ 2, when the galaxy disc forms.
Blockwise Self-Attention for Long Document Understanding

We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa.
How I see it: An exploratory study on attributions and emotions in L2 learning

Learners’ attributions have received increasing attention in second/foreign language (L2) learning. Studies have shown that how learners attribute their performance influences not only their self-efficacy, motivation, and goal attainment but also their emotions (Hsieh, 2012; Hsieh & Kang, 2010; Hsieh & Shallert, 2008; Weiner, 2000, 2014). This exploratory study investigated how Japanese adult learners of L2 English attributed changes in their L2 learning attitudes and motivation through a 10-week TOEIC preparation program. It also examined emotions expressed in their attributional statements and the differences between learners with lower and higher L2 proficiency. A content analysis of open-ended questionnaire responses suggested eight attributional categories: perceived L2 improvement, enjoyment, positive feelings, increased L2 exposure, realization of L2 needs and importance, effective L2 instruction, and praise from the teacher for positive changes in attitudes and/or motivation and perceived inefficient L2 skills for negative changes in attitudes and/or motivation. Enjoyment was an emotion the most frequently mentioned by both groups while other emotions, such as joy, happiness, and disappointment, were expressed only by the beginner learners. These results offer important implications for L2 pedagogy and prospects for further research in the area.