Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Band Gap on TiO 2 Thin Film with Different Layer using Dip Coating Technique

Titania or titanium dioxide (TiO2) thin films were prepared using a simple sol-gel dip-coating method on a glass substrate. Titanium (IV) tetraisopropoxide, (TTIP) was used as a titania precursor. The purpose of this work is to study the behavior of optical band gap with the different number of layer and the phase present of each sample. From the result, all samples are amorphous phase have their value of optical band gap in the range of 3.21 eV until 3.80 eV depends on transition. The number of layers increased caused the surface morphology of TiO2 to agglomerate and the roughness of the samples increased. The single layer sample shows higher transmittance value than multiple layer samples which is the best for optical sensor application. Keywords—Dip Coating; Sol-gel; TiO2; Optical Sensor
Success criteria not met, but valuable information gained: monitoring a reintroduction of the tammar wallaby

Abstract Context. Fauna translocations are a tool for improving the conservation status of threatened species. Reviews of translocations undertaken in Australia and globally have reported that many fail because of predation by exotic predators. The outcome of ∼40% of translocations was unknown, often owing to inadequate post-release monitoring. Monitoring methods such as global positioning system data-loggers can provide valuable information on survival, habitat use and sociality, and can be particularly useful for cryptic species. Aims. The present study used global positioning system (GPS) data-loggers and VHF radio-transmitters to evaluate the success of a reintroduction of the tammar wallaby and measured survival, short-term home-range, habitat use and proximity between reintroduced individuals (as a proxy for association). Methods. Sixty-nine tammar wallabies of captive and wild stock were reintroduced to Kalbarri National Park (KNP) following long-term and broad-scale fox control, with nine receiving GPS data-loggers, and 16 receiving VHF radio-transmitters. Wallabies were intensively monitored for up to 11 months post-release. Mortalities were investigated using DNA identification and field necropsies. Key results. In total, 16 of the 25 collared wallabies died within 11–319 days of release. Ten of the sixteen deaths were from predation. Home-range areas were larger than those reported elsewhere. Wallabies utilised long-undisturbed vegetation with a dense canopy cover during crepuscular periods. These areas were likely sought as refuge from predation and thermal extremes. During the main feeding period, a mosaic of recently burnt (i.e. ∼1 year) and >10 years since last disturbance was important. Conclusions. The reintroduction was not considered successful because two-thirds of the collared wallabies died within 1 year of release and, therefore, the success criteria were not met. Implications. Despite long-term fox control in KNP, the majority of collared wallaby deaths were a result of fox predation. This highlights the inherent difficulty of establishing populations of some species in the presence of introduced predators. Additional research could assist in determining appropriate control levels for introduced predators, to help ensure the success of future translocations of this species. Consideration should be given to the prey naivety of source animals, prey-switching by introduced predators, and short-term supplementary feeding to assist population establishment.
Quintessenz Journals

Material and methods: Twenty-four post-mortem samples were equally divided into young (12-21 yr), adult (32-45 yr), and old (72-97 yr) age groups. Thirty acinar and ductal nuclei from each sample were assessed for the number and area of AgNORs (nNOR and TVNOR, respectively). PCNA-, Ki-67and p53-stained nuclei were recorded in 10 high power fields and expressed as a percentage from the total number of nuclei of each cell type. One-way ANOVA and Kruskal-Wallis one-way ANOVA statistical tests were used.
Alcohol-Responsive Hyperkinetic Movement Disorders—a Mechanistic Hypothesis

Patients with essential tremor, vocal tremor, torticollis, myoclonus-dystonia and posthypoxic myoclonus often benefit in a surprisingly rapid and robust manner from ingestion of a modest amount of alcohol (ethanol). Despite considerable investigation, the mechanism of ethanol’s ability to produce this effect remains a mystery. In this paper, we review the pharmacology of ethanol and its analogue GHB (or sodium oxybate), summarize the published literature of alcohol-responsive hyperkinetic movement disorders, and demonstrate videos of patients we have treated over the last fifteen years with either an ethanol challenge or with chronic sodium oxybate therapy. We then propose a novel explanation for this phenomenon—namely, that ingestion of modest doses of ethanol (or sodium oxybate) normalizes the aberrant motor networks underling these disorders. We propose that alcohol and its analogues improve clinical symptoms and their physiologic correlate by restoring the normal firing pattern of the major outflow pathways of the cerebellum (the Purkinje cells and deep cerebellar nuclei), We present evidence to support this hypothesis in animal models and in affected patients, and suggest future investigations to test this model.
DECISIONS ABOUT POSTSECONDARY EDUCATION, THEIR RETURNS AND HETEROGENEITY IN COLOMBIA

This study analyzes the economic returns to schooling decisions made by high school graduates in Colombia. We wanted to verify if the economic returns (wages) obtained by newly postsecondary education graduates compensate the economic and psychological investment they made in order to get that academic degree. To answer that question, we estimated these economic returns for each type of postsecondary degree available in Colombia by origin of the institution (public or private). Our methodological strategy includes the generation of a micro-data base that contains agents’ socio-economic background and also their individual labor market outcomes. Because agents with very similar characteristics and the same schooling decisions might get different economic returns from education, we considered as part of our empirical strategy the inclusion of an approximation of agents’ cognitive abilities.
Effects of Predator Avoidance Behavior on the Coexistence of Competing Prey

Predator avoidance behavior, in which prey limit foraging activities in the presence of predation threats, affects the dynamics of many ecological communities. Despite the growing theoretical appreciation of the role predation plays in coexistence, predator avoidance behavior has yet to be incorporated into the theory in a general way. We introduce adaptive avoidance behavior to a consumer-resource model with three trophic levels to ask whether the ability of prey—the middle trophic level—to avoid predators alters their ability to coexist. We determine the characteristics of cases in which predator avoidance behavior changes prey coexistence or the order of competitive dominance. The mechanism underlying such changes is the weakening of apparent competition relative to resource competition in determining niche overlap, even with resource intake costs. Avoidance behavior thus generally promotes coexistence if prey partition resources but not predators, whereas it undermines coexistence if prey partition predators but not resources. For any given case, the changes in the average fitness difference between two species resulting from avoidance behavior interact with changes in niche overlap to determine coexistence. These results connect the substantial body of theoretical work on avoidance behavior and population dynamics with the body of theory on competitive coexistence.
Revisiting the Role of Mountains in the Northern Hemisphere Winter Atmospheric Circulation

The impact of global orography on Northern Hemisphere wintertime climate is revisited using the Whole Atmosphere Community Climate Model, WACCM6. A suite of experiments explores the roles of both resolved orography, and the parameterized effects of unresolved orographic drag (hereafter parameterized orography), including gravity waves and boundary layer turbulence. Including orography reduces the extra-tropical tropospheric and stratospheric zonal mean zonal wind, , by up to 80%; this is substantially greater than previous estimates. Ultimately parameterized orography accounts for 60-80% of this reduction; however, away from the surface most of the forcing of by parameterized orography is accomplished by resolved planetary waves. We propose that a catalytic wave-mean-flow positive feedback in the stratosphere makes the stratospheric flow particularly sensitive to parameterized orography. Orography and land-sea contrast contribute approximately equally to the strength of the mid-latitude stationary waves in the free troposphere, although orography is the dominant cause of the strength of the Siberian high and Aleutian low at the surface, and of the position of the Icelandic low. We argue that precisely quantifying the role of orography on the observed stationary waves is an almost intractable problem, and in particular should not be approached with linear stationary wave models in which is prescribed. We show that orography has less impact on stationary waves, and therefore on , on a backwards rotating Earth. Lastly, we show that atmospheric meridional heat transport shows remarkable constancy across our simulations, despite vastly different climates and stationary wave strengths.
10. GINGIVAL DEPIGMENTATION BY

Cosmetic desires have improved with time and current trends speak volumes about gingival esthetics and smile designing. Gingival hyperpigmentation plays a negative role in an otherwise acceptable 'smile window'. In the present world, people are concerned more about their esthetics. Several techniques have been employed such as scalpel, electrosurgery, cryosurgery, chemical agents, abrasion, and laser method for the removal of melanin hyperpigmentation. The present case series were aimed to assess the efficacy of gingival soft tissue ceramic trimmer in gingival hyperpigmentation. Pain index (VAS), healing index (Landry, Turnbull & Howley index) and pigmentation index (Takashi et al. 2005) were evaluated to assess its efficacy. The results showed that ceramic trimmer has a lower pain index and a better healing index and delayed re-pigmentation index than rest all other procedures. Thus, it can be concluded that ceramic trimmer might prove to be a boon in achieving aesthetic satisfaction and could be used for depigmentation as it is very cost effective, readily available and acceptable to the patient.
Methods for Numeracy-Preserving Word Embeddings

Word embedding models are typically able to capture the semantics of words via the distributional hypothesis, but fail to capture the numerical properties of numbers that appear in the text. This leads to problems with numerical reasoning involving tasks such as question answering. We propose a new methodology to assign and learn embeddings for numbers. Our approach creates Deterministic, Independent-of-Corpus Embeddings (the model is referred to as DICE) for numbers, such that their cosine similarity reflects the actual distance on the number line. DICE outperforms a wide range of pre-trained word embedding models across multiple examples of two tasks: (i) evaluating the ability to capture numeration and magnitude; and (ii) to perform list maximum, decoding, and addition. We further explore the utility of these embeddings in downstream tasks, by initializing numbers with our approach for the task of magnitude prediction. We also introduce a regularization approach to learn model-based embeddings of numbers in a contextual setting.
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation

Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge in semantic networks. In this paper, we propose a Synset Relation-Enhanced Framework (SREF) that leverages sense relations for both sense embedding enhancement and a try-again mechanism that implements WSD again, after obtaining basic sense embeddings from augmented WordNet glosses. Experiments on all-words and lexical sample datasets show that the proposed system achieves new state-of-the-art results, defeating previous knowledge-based systems by at least 5.5 F1 measure. When the system utilizes sense embeddings learned from SemCor, it outperforms all previous supervised systems with only 20% SemCor data.
Active Learning for BERT: An Empirical Study

Real world scenarios present a challenge for text classification, since labels are usually expensive and the data is often characterized by class imbalance. Active Learning (AL) is a ubiquitous paradigm to cope with data scarcity. Recently, pre-trained NLP models, and BERT in particular, are receiving massive attention due to their outstanding performance in various NLP tasks. However, the use of AL with deep pre-trained models has so far received little consideration. Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets. We focus on practical scenarios of binary text classification, where the annotation budget is very small, and the data is often skewed. Our results demonstrate that AL can boost BERT performance, especially in the most realistic scenario in which the initial set of labeled examples is created using keyword-based queries, resulting in a biased sample of the minority class. We release our research framework, aiming to facilitate future research along the lines explored here.
Compositional Phrase Alignment and beyond

Phrase alignment is the basis for modelling sentence pair interactions, such as paraphrase and textual entailment recognition. Most phrase alignments are compositional processes such that an alignment of a phrase pair is constructed based on the alignments of their child phrases. Nonetheless, studies have revealed that non-compositional alignments involving long-distance phrase reordering are prevalent in practice. We address the phrase alignment problem by combining an unordered tree mapping algorithm and phrase representation modelling that explicitly embeds the similarity distribution in the sentences onto powerful contextualized representations. Experimental results demonstrate that our method effectively handles compositional and non-compositional global phrase alignments. Our method significantly outperforms that used in a previous study and achieves a performance competitive with that of experienced human annotators.
Multilingual AMR-to-Text Generation

Generating text from structured data is challenging because it requires bridging the gap between (i) structure and natural language (NL) and (ii) semantically underspecified input and fully specified NL output. Multilingual generation brings in an additional challenge: that of generating into languages with varied word order and morphological properties. In this work, we focus on Abstract Meaning Representations (AMRs) as structured input, where previous research has overwhelmingly focused on generating only into English. We leverage advances in cross-lingual embeddings, pretraining, and multilingual models to create multilingual AMR-to-text models that generate in twenty one different languages. For eighteen languages, based on automatic metrics, our multilingual models surpass baselines that generate into a single language. We analyse the ability of our multilingual models to accurately capture morphology and word order using human evaluation, and find that native speakers judge our generations to be fluent.
Sequence-level Mixed Sample Data Augmentation

Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach to encourage compositional behavior in neural models for sequence-to-sequence problems. Our approach, SeqMix, creates new synthetic examples by softly combining input/output sequences from the training set. We connect this approach to existing techniques such as SwitchOut and word dropout, and show that these techniques are all approximating variants of a single objective. SeqMix consistently yields approximately 1.0 BLEU improvement on five different translation datasets over strong Transformer baselines. On tasks that require strong compositional generalization such as SCAN and semantic parsing, SeqMix also offers further improvements.
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction

Named Entity Recognition (NER) is a fundamental task in natural language processing. In order to identify entities with nested structure, many sophisticated methods have been recently developed based on either the traditional sequence labeling approaches or directed hypergraph structures. Despite being successful, these methods often fall short in striking a good balance between the expression power for nested structure and the model complexity. To address this issue, we present a novel nested NER model named HIT. Our proposed HIT model leverages two key properties pertaining to the (nested) named entity, including (1) explicit boundary tokens and (2) tight internal connection between tokens within the boundary. Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary. Experiments on three public NER datasets demonstrate that the proposed HIT achieves state-of-the-art performance.
On the Sentence Embeddings from BERT for Semantic Textual Similarity

Pre-trained contextual representations like BERT have achieved great success in natural language processing. However, the sentence embeddings from the pre-trained language models without fine-tuning have been found to poorly capture semantic meaning of sentences. In this paper, we argue that the semantic information in the BERT embeddings is not fully exploited. We first reveal the theoretical connection between the masked language model pre-training objective and the semantic similarity task theoretically, and then analyze the BERT sentence embeddings empirically. We find that BERT always induces a non-smooth anisotropic semantic space of sentences, which harms its performance of semantic similarity. To address this issue, we propose to transform the anisotropic sentence embedding distribution to a smooth and isotropic Gaussian distribution through normalizing flows that are learned with an unsupervised objective. Experimental results show that our proposed BERT-flow method obtains significant performance gains over the state-of-the-art sentence embeddings on a variety of semantic textual similarity tasks. The code is available at https://github.com/bohanli/BERT-flow.
Predicting Reference: What Do Language Models Learn about Discourse Models?

Whereas there is a growing literature that probes neural language models to assess the degree to which they have latently acquired grammatical knowledge, little if any research has investigated their acquisition of discourse modeling ability. We address this question by drawing on a rich psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next. The results reveal that, for the most part, the prediction behavior of neural language models does not resemble that of human language users.
Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation

The attention mechanism is the crucial component of the transformer architecture. Recent research shows that most attention heads are not confident in their decisions and can be pruned. However, removing them before training a model results in lower quality. In this paper, we apply the lottery ticket hypothesis to prune heads in the early stages of training. Our experiments on machine translation show that it is possible to remove up to three-quarters of attention heads from transformer-big during early training with an average -0.1 change in BLEU for Turkish→English. The pruned model is 1.5 times as fast at inference, albeit at the cost of longer training. Our method is complementary to other approaches, such as teacher-student, with English→German student model gaining an additional 10% speed-up with 75% encoder attention removed and 0.2 BLEU loss.
Can Emojis Convey Human Emotions? A Study to Understand the Association between Emojis and Emotions

Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role. One conspicuous aspect of emojis is their use to convey affect in ways that may otherwise be non-trivial to achieve. In this paper, we seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings. We additionally conduct experiments to assess to what extent such associations can be inferred from existing data in an unsupervised manner. Our experiments show that this succeeds when high-quality word-level information is available.
Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations

In this paper, we demonstrate that by utilizing sparse word representations, it becomes possible to surpass the results of more complex task-specific models on the task of fine-grained all-words word sense disambiguation. Our proposed algorithm relies on an overcomplete set of semantic basis vectors that allows us to obtain sparse contextualized word representations. We introduce such an information theory-inspired synset representation based on the co-occurrence of word senses and non-zero coordinates for word forms which allows us to achieve an aggregated F-score of 78.8 over a combination of five standard word sense disambiguating benchmark datasets. We also demonstrate the general applicability of our proposed framework by evaluating it towards part-of-speech tagging on four different treebanks. Our results indicate a significant improvement over the application of the dense word representations.
Semantically Inspired AMR Alignment for the Portuguese Language

Abstract Meaning Representation (AMR) is a graph-based semantic formalism where the nodes are concepts and edges are relations among them. Most of AMR parsing methods require alignment between the nodes of the graph and the words of the sentence. However, this alignment is not provided by manual annotations and available automatic aligners focus only on the English language, not performing well for other languages. Aiming to fulfill this gap, we developed an alignment method for the Portuguese language based on a more semantically matched word-concept pair. We performed both intrinsic and extrinsic evaluations and showed that our alignment approach outperforms the alignment strategies developed for English, improving AMR parsers, and achieving competitive results with a parser designed for the Portuguese language.
Improving Bilingual Lexicon Induction for Low Frequency Words

This paper designs a Monolingual Lexicon Induction task and observes that two factors accompany the degraded accuracy of bilingual lexicon induction for rare words. First, a diminishing margin between similarities in low frequency regime, and secondly, exacerbated hubness at low frequency. Based on the observation, we further propose two methods to address these two factors, respectively. The larger issue is hubness. Addressing that improves induction accuracy significantly, especially for low-frequency words.