Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Emergence of Grammar and Image Schemas : A Cognitive Linguistic View

This paper presents a cognitive-linguistic approach to the issue of “syntactic hierarchy” and its evolution studied mainly in generative grammar, and offers a plausible picture of how humans have developed the abilities of combining symbols to form complex constructions. Since generative grammar has been reducing its central construct of “Universal Grammar (UG)” to simpler operations, UG as it assumes currently approximates some domain-general cognitive abilities. One of such moves can be seen in “action grammar,” which hypothesizes the motor origin of syntax (Greenfield 1991, Fujita 2017). It claims that syntactic hierarchies are rooted in the strategies of combining multiple objects into a united whole, like nesting cups. According to Greenfield (1991), the combining strategies of objects develop from “pairing” of two objects to “pot” and “subassembly” of multiple objects. In a generative-grammar view (Fujita 2017), such combining strategies give rise to what they call “merge,” as part of hard-wired innate UG, which produces syntactic hierarchy. Noteworthy enough, the idea of action grammar is compatible with the cognitive-linguistic view of embodiment, especially of image schemas that are recurrent patterns extracted from sensori-motor experiences (Johnson 1987); in the case of seriating objects, what underlie is the “container” schema. However, the container schema and inference patterns it evokes are not so self-evident as they seem; one needs to recognize the relative sizes of objects (the container should be bigger than the content), the relationship of inclusion, the dependency of the content’s location on the container and so on to utilize the schema. In fact, Greenfield (1991) suggests the possibilities of non-successful instances of pairings, as we can see younger children and animals try to put a big object into a small container. This leads us to assume that the container schema is acquired in a heuristic way through trial-and-error experiences of combining objects of different sizes (cf. Fragaszy et al. 2002), and that the container schema is not available until later stages of development and evolution; primitive syntagmatic relations between items are rather accounted for by “linking” schema. This paper demonstrates the assumption observed above in terms of usage-based, constructiongrammar view of syntactic development (Tomasello 2003) where constructions are extracted from specific chunks composed of multiple lexical item. In light of this, what appears to be “syntactic hierarchy” is brought only after one establishes constructions which function as containers to accommodate lexical items, not only in ontogenetic but in phylogenetic terms. The present paper will show that the sensorimotor development from the linking schema to the container schema correlates with emergence of grammar, through close examinations of the results of experiments concerning nesting cups presented so far (Greenfield 1991, Fragaszy et al. 2002 and so forth).
Effects of Waveform PMF on Anti-spoofing Detection for Replay Data - ASVspoof 2019

On speaker recognition identity impersonation recent chal-lenges, we have observed that the probability mass function (PMF) of the waveform of genuine speech differs signiﬁcantly from the PMF of identity theft extracts. In our previous works, we presented the analysis of the inﬂuence of the waveform on the logical access (LA) spooﬁng condition, where the spooﬁng extracts are composed of synthesized or converted speech. In this work we extend our analysis to physical access (PA) condition, which focuses on replayed speech. We show that for replayed data, changes in PMF signiﬁcantly inﬂuence performance in terms of spooﬁng detection. Next, we suggest a way to reduce the observed gap between bona ﬁd and replayed speech waveforms. By analogy with the process used to gaussianize the acoustic parameters, we propose a spooﬁng speech genuinization , which moves the PMF of the spooﬁng speech near the PMF of the genuine speech. The proposed genuinization process is assessed on the ASVspoof 2019 challenge datasets, using the baseline system provided by the challenge organiza-tion. In terms of spooﬁng detecion equal error rate (EER), both linear frequency Cepstral coefﬁcient (LFCC) and constant Q cepstral coefﬁcients (CQCC) features based systems lead to better results when applied on non-genuanized replayed data. On the other hand, when the spooﬁng detection systems are trained on genuanized data, the results on genuanized replayed data are very good compared to the results obtained without applying genuinization on the data. As observed previously in LA case, the performance is not consistent and it opens problematic ques-tions on generalization capabilities of anti-spooﬁng systems.
Neighborhood Watch : Using CNNs to Predict Income Brackets from Google Street View Images

The US government spends millions of dollars every year collecting income-level data, a time-intensive and expensive process. Due to recent advancements in computing technology and increased availability of open-source images, our group proposes a deep learning framework for predicting the income level of a neighborhood given a Google Street View image. Using transfer learning on pre-trained VGG16 and ResNet18 models, we experiment with retraining different layers and hyperparameter tuning to determine the model that will yield the highest accuracy. We then perform a post-processing procedure to encode geographic information to our classifications. Our best model attained a validation accuracy of 72.0% prior to post-processing, which is on par with results in related papers[7, 16, 10]; it consisted of a transfer learned ResNet18 model with layers 3 and 4, as well as the final fully connected layer, retrained. Training was conducted using an Adam optimizer and softmax cross entropy loss. Generating saliency maps, confusion matrices, and other visualizations provide valuable insights into improving the model and understanding its behavior.
DeriNet 2.0: Towards an All-in-One Word-Formation Resource

DeriNet is a large linguistic resource containing over 1 million lexemes of Czech connected by almost 810 thousand links that correspond to derivational relations. In the previous version, DeriNet 1.7, it only contained very sparse annotations of features other than derivations – it listed the lemma and part-of-speech category of each lexeme and since version 1.5, a true/false ﬂag with lexemes created by compounding. The paper presents an extended version of this network, labelled DeriNet 2.0, which adds a number of features, namely annotation of morphological categories (aspect, gender and animacy) with all lexemes in the database, identiﬁcation of root morphemes in 250 thousand lexemes, annotation of ﬁve semantic labels (diminutive, possessive, female, iterative, and aspect) with 150 thousand derivational relations, a pilot annotation of parents of compounds, and another pilot annotation of so-called ﬁctitious lexemes, which connect related derivational families without a common synchronous parent. The new pieces of annotation could be added thanks to a new ﬁle format for storing the network, which aims to be general and extensible, and therefore possibly usable to other similar projects.
Assessment of the Effect of Fibrillatory Waves in the Analysis of Spatial Heterogeneity of Ventricular Repolarization

In providing proper treatment for Atrial Fibrillation (AF) patients, the clinician must establish the pattern of arrhythmia, determine associated symptoms, and asses for underlying comorbidities in order to define short- and long-term management strategies. To do so, this paper describes a preliminary study in which a novel ECG-based estimator of the standard deviation of ventricular myocytes’ repolarization times (sϑ ) called the V-index was computed in simulated data, assessing whether the presence of f-waves may corrupt the V-index. The analysis was performed in simulated ECGs generated by combining synthetic bundles of 300 T-waves with seven different values of sϑ, with QRS template and 50 f-waves extracted from real data. The results show a strong correlation between the calculated target V-index (tV-index) and the sϑ (r = 0.99, p-value < 0.001). They also show the effect of the f-waves in the computation of the V-index. The V-index calculated with f-waves (fV-index) and after the f-waves were removed (cV-index) had highly significant differences (p-value < 0.001) and significant differences (p-value < 0.05) with the tV-index for values below 53.3 ms, which proves the corruptive effect the f-waves have on the computation of the V-index and stresses the importance of removing them before attempting further analysis.
CROSSING THE PARALLEL: EFFECTS OF GENDER AND LENGTH OF RESIDENCE IN NORTH KOREAN REFUGEES’ BACK VOWELS

Mergers have often been considered irreversible. Much sociophonetic work has focused on describing their genesis; in contrast, there has been very little research describing their reversal. This study observes speakers having accomplished a merger reversal, or demerger, resulting in the restoration of a phonemic distinction. North Korean features a well-known merger of /u/-/ɯ/ that is non-merging in South Korean and stigmatized because it is associated with North Korean defector status. This research investigates to what extent North Korean refugees in South Korea continue to exhibit this merger once they are living in South Korea, or if they begin to show signs of demerger. The role of structural categories (vowel dispersion) and social categories (gender and length of residence in South Korea) are considered. Results show main effects for length of residence and gender, whereby female speakers and a longer length of residence in South Korea show preference for the demerger.
Hydroxyapatite / zirconium oxide ceramics obtained by spark-plasma sintering

Powdered samples of hydroxyapatite and oxoapatite were obtained via sol-gel and hydrothermal method, respectively. Powder X-ray diff raction analysis showed absence of substances that could adversely aff ect the biocompatibility of the compounds obtained. Ceramic samples with zirconium oxide were obtained from apatites by spark plasma sintering and their physicochemical and mechanical attestation were carried out.
Make Me Warm : Combining Product Emotions and Usability to Redesign Microwave Products

Microwave oven is one of everyday products with many types and brands offering similar functionalities and capabilities. These similarities create tough competition, and in this case, product usability and users’ emotional responses to product appearance often become key determinants of purchasing decision. This research combines product emotions measurement and usability testing to evaluate and redesign existing microwave products. Modified PrEmo was used to measure positive and negative emotional responses of thirty participants to three different microwaves. Usability testing involving eight participants was then conducted to the product receiving the most negative emotional reactions. Through these procedures, usability problems and product features that evoked positive emotions were identified and analyzed. The redesign of the microwave encompassed its physical form, color, control panel, and display. Three redesign alternatives were generated, each of which was evaluated using the previous methods. It was found that all alternatives resulted in better usability and product emotions scores, where alternative 3 produced the highest total (combined) score.
ield response of chickpea ( Cicer arietinum L . ) to high temperature

High temperature is an important factor affecting chickpea growth, development and grain yield. Understanding the plant response to high temperature is a key strategy in breeding for heat tolerance in chickpea (Cicer arietinum L.). This study assessed genetic variability for heat tolerance in chickpea and identified sources of heat tolerance that could be used for crop improvement. One hundred and sixty-seven genotypes were grown in two environments (heat stressed/late sown and non-stressed/optimal sowing time) in 2 years (2009–2010 and 2010–2011) at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. Large genetic variation was observed for phenology, growth, yield components and grain yield. While phenology (assessed as days to first flower, days to 50% flowering and days to first pod) was negatively correlated with grain yield at high temperature; plant biomass, pod number, filled pod number and seed number per plant were positively correlated. Genotypes were classified into short and long duration groups based on their maturity. Days to first flowering (DFF) of long duration genotypes were negatively associated with grain yield under stressed conditions in both years compared with medium to short duration genotypes. However, genotypes varied in their heat sensitivity and temperatures ≥35 ◦C produced yield losses up to 39%. A heat tolerance index (HTI) classified the genotypes into five groups: (i) stable heat tolerant (>0.5), (ii) moderately heat tolerant (0.1–0.49), (iii) stable heat sensitive (−ve values), (iv) heat tolerant to moderately sensitive (−0.10 to 1) and (v) heat sensitive to moderately tolerant (−0.5 to 0.4). Pod characteristics, including days to first pod and pod number per plant, were correlated with grain yield whereas canopy temperature depression (CTD) was generally not correlated. Heat tolerant genotypes in a range of maturities were identified that could be used to improve the heat tolerance of chickpea. © 2014 Elsevier B.V. All rights reserved.
Automated Emotion Morphing in Speech Based on Diffeomorphic Curve Registration and Highway Networks

We present a novel approach for emotion conversion that bridges the domains of speech analysis and computer vision. Our strategy is to warp the pitch contour of a source emotional utterance using diffeomorphic curve registration. The associated dynamical process pushes the original source contour towards that of a target emotional utterance. Mathematically, this warping process is completely speciﬁed by a set of initial momenta . Therefore, we use parallel data to train a highway neural network (HNet) to predict these initial momenta directly from the signal characteristics. The input features to the HNet include contextual pitch and spectral information. Once trained, the HNet is used to obtain the initial momenta for new utterances. From here, the diffeomorphic process takes over and warps the pitch contour accordingly. We validate our framework on the VESUS repository collected at Johns Hopkins University, which contains parallel emotional utterances from 10 actors. The proposed warping is more accurate that three state-of-the-art baselines for emotion conversion. We also evaluate the quality of our emotion manipulations via crowd sourcing.
The Continued Reduction in Dense Fog in the Southern California Region : Possible Climate Change Influences

Dense fog appears to be decreasing in many parts of the world, especially in cities. An earlier study showed that dense fog (visibility < 400 m) was disappearing in the urban southern California area as well. It showed that the decrease in dense fog events could be explained mainly by declining particulate levels, Pacific SSTs, and increased urban warming. Dense fog is most prevalent along the coast and decreases rapidly inland. Using hourly data from 1948 to the present, we looked at the relationship between fog events and contributing factors in the region and trends over time. The relationship between the occurence of dense fog to the phase of two atmosphere–ocean cycles: the Pacific Decadal Oscillation (PDO) and the Southern Oscillation In addition, the influence of the urban heat island and the amount of suspended particulate matter were assessed. Results show a decrease in the occurrence of very low visibilities (<400 m) at the stations in close proximity to the Pacific Ocean. Occurrence of the frequency of low visibilities at these two locations was highly correlated with the phase of the PDO. A downward trend in particulate concentrations coupled with an upward trend in urban temperatures were associated with a decrease in dense fog occurrence at both LAX and LGB. While examining data from LAX, we saw a frequency of dense fog that reached over 300 hours in 1950, but occurrence was down to zero in 1997. Since 1997, there has been a bit of a recovery with both 2008 and 2009 recording over 30 hours of dense fog each. In the present study, we examine the relationships that control the frequency of dense fog (visibility < 400 m) in coastal southern California. To remove urban influence, we also included Vandenberg Air Force Base, located in a relatively sparsely populated area. While particulates, urban heat island and Pacific SSTs are all contributing factors, we now speculate on the direct and indirect influences of climate change on continued decreases in dense fog. Case studies of local and regional dense fog in southern California point to the importance of strong, low inversions and to a lesser contributor, Santa Ana winds. Both are associated with large-scale atmospheric circulation patterns, which have changed markedly over the period of study.
Effects of Base-Frequency and Spectral Envelope on Deep-Learning Speech Separation and Recognition Models

Base-frequencies (F0) and spectral envelopes play an important role in speech separation and recognition by humans. Two experiments were conducted to study how trained networks for multi-speaker speech separation/recognition are affected by difference of F0 and spectral envelopes between source signals. The first experiment examined the effects of natural F0/envelope on the performance of speech separation. Results showed that when the two target signals differed in F0 by ±3 semitones or more or differed in the envelope by a scaling factor larger than 1.08 or less than 0.92, separation performance improved significantly. This is consistent with human listeners and is the first finding for deep learning-network (DNN) models. The second experiment tested the effect of F0/envelope difference on multi-speaker automatic speech recognition(ASR) system’s performance. Results showed that multi-speaker recognition result also significantly rely on F0/envelope differences. The overall results indicated that the dependency of the existing automatic systems on monaural cues is similar to that of human, while automatic systems still perform inferior than human on same tasks.
An asymmetric perceptual dependency between pitch and breathiness

A recent, cross-linguistic study shows that, under certain experimental conditions, listeners have trouble shifting perceptual attention from breathiness to pitch but not from pitch to breathiness. The hypothesis put forth to explain this asymmetry is that listeners are associating differences in breathiness with differences in pitch, but they are not associating differences in pitch with differences in breathiness. The current study tests this hypothesis using a cue weighting task. English listeners categorized auditory stimuli in which there was strong evidence that one of the two cues, pitch or breathiness, is informative, and no evidence that the other cue is informative. If the hypothesis is correct, we would expect listeners learning to use breathiness to also attend to pitch, but we would not expect listeners learning to use pitch to also attend to breathiness. Results show a marginal effect in this direction, providing weak support for the hypothesis.
Using of Species-Specific Primers for Molecular Diagnosis of in Vitro Promastigotes of Leishmania donovani

Visceral Leishmaniasis (VL) is a disseminated protozoan infection caused by Leishmania donovani parasites which affects almost half a million persons annually. Classical diagnosis methods of VL still not very sensitive and time consuming. In this study, we reported the success of polymerase chain reaction (PCR) method to identify L. donovani based on kinetoplast deoxyribonucleic acid (kDNA) for the diagnosis of the parasite using in vitro promastigote cultures. LdI species specific primer was used to identify L. donovani and the result showed a single band of about ~600bp. It can be recommended that this primer is to be used for detection the visceral L. donovani.
Forget a Bit to Learn Better: Soft Forgetting for CTC-Based Automatic Speech Recognition

Prior work has shown that connectionist temporal classiﬁcation (CTC)-based automatic speech recognition systems perform well when using bidirectional long short-term memory (BLSTM) networks unrolled over the whole speech utterance. This is because whole-utterance BLSTMs better capture long-term context. We hypothesize that this also leads to overﬁtting and propose soft forgetting as a solution. During training, we unroll the BLSTM network only over small non-overlapping chunks of the input utterance. We randomly pick a chunk size for each batch instead of a ﬁxed global chunk size. In order to retain some utterance-level information, we encourage the hidden states of the BLSTM network to approximate those of a pre-trained whole-utterance BLSTM. Our experiments on the 300-hour English Switchboard dataset show that soft forgetting improves the word error rate (WER) above a competitive whole-utterance phone CTC BLSTM by an average of 7-9% relative. We obtain WERs of 9.1%/17.4% using speaker-independent and 8.7%/16.8% using speaker-adapted models respectively on the Hub5-2000 Switchboard/CallHome test sets. We also show that soft forgetting improves the WER when the model is used with limited temporal context for streaming recognition. Finally, we present some empirical insights into the regularization and data augmentation effects of soft forgetting.
Contextual Factors, Bilingualism and English Language Learning among a Group of Primary School Pupils in Nigeria

Contextual factors are crucial in second language learning and could constitute a factor in developing motivation to learn and attitudes towards the target language (Gardner, 1991; Hamers& Blanc, 2000; Erlenawati, 2005). Children’s sociolinguistic environment also plays an important role in language use, educational achievement and, eventually, life achievement in Nigeria’s ESL (English as a second language) situation. The home and school are the two most important environments in the language learning experience of a Nigerian primary school child, particularly the learning of the English language. The extent to which each environment impacts on the acquisition of English-proficiency could depend on the extent the home supports the school’s effort. Homes that do not use English in everyday conversation but support English literacy activities encourage learners in their path to acquiring competence in the language. Generally, oracy characterises the home environment while literacy skills are largely acquired in school, since home literacy habits are just being acculturated.
Chapter Three Nanostructures of Natural Iron Oxide Nanoparticles

Natural iron oxide nanoparticles have distinctive structures, compositions, and properties in various environments. They not only provide valuable information about the pathway of iron oxides formations, but also signify evolutions of physical and chemical conditions in diverse settings in our planet, and even extraterrestrial environments. These tiny, highly reactive and mobile entities present in many bio/geochemical processes, and regulate the transportation of pollutants and nutrients in soils and waters. This chapter is to cover the nanostructures of ferrihydrite, goethite, maghemite, hematite, schwertmannite, and akaganéite nanocrystals, and their occurrences in nature.