Miracle-dz/UniFAR-FTUs huggingface dataset:
Production Strategies of Vocal Attitudes

Humans have an impressive ability to communicate precise social intentions and desires with their voice — through vocal attitudes. Previous studies have shown how isolated acoustic features such as pitch can convey social attitudes, but have mostly worked with single attitudes and have not controlled for inter-speaker variability. Thus, the vocal behaviours used to produce social attitudes remain mostly unknown. That is the aim of the current study, to uncover the anatomic production strategies that speakers use to communicate vocal attitudes. To do this, we analysed recordings from N=20 French speakers producing dominant, friendly, seductive and distant speech. For each of these attitudes, we investigated their vocal fold behaviour, vocal tract actuation and phonetic speech structure, with the sup-port of deep alignment methods, and compared them with group statistics. We notably produced high-level representations of speakers’ articulation (e.g. Vowel Space Density) and speech rhythm. Our results reveal speakers’ prototypical strategies to produce vocal attitudes, and highlight how vocal behaviours can communicate social signals. We expect these results to provide an objective validation method for deep voice attitude conver-sions.
Lightening CNN architectures by regularization driven weights' pruning

Deep learning models are getting increasingly big, leading towards overparametrized architectures with high computational and storage requirements. This hinders the possibility to train/deploy them on IoT or mobile devices, while also creating concerns about their environmental ﬁn-gerprint. We propose a regularization technique which allows to selectively shrink the norm of non signiﬁcant weights in order to subsequently prune them, generating highly compressed models. We tested the proposed technique on three well known image classiﬁcation tasks, obtaining results on par or better than competitors in terms of sparsity and metrics.
Salmonella Exhibit Altered Cellular Localization in the Presence of HLA-B27 and Codistribute with Endo-Reticular Membrane

Salmonella enteritica (S. enteritica) induce and require unfolded protein response (UPR) pathways for intracellular replication. Salmonella infections can lead to reactive arthritis (ReA), which can exhibit associations with Human Leucocyte Antigen (HLA)-B∗27 : 05. S. enteritica normally reside in a juxtanuclear position to the Golgi apparatus, representing the formation and residence within the Salmonella-containing vacuole (SCV). Changes in cellular localization of infecting Salmonella can alter their ability to replicate. We therefore used isogenic epithelial cell lines expressing physiological levels of HLA-B∗27 : 05 heavy chain (HC) and a control HLA-B allele, HLA-B∗35 : 01.HC to determine any changes in Salmonella localization within epithelial cells. Expression of HLA-B∗27 : 05 but not HLA-B∗35 : 01 was associated with a quantifiable change in S. enteritica cellular distribution away from the Golgi apparatus. Furthermore, the Salmonella requirements for UPR induction and the consequences of the concomitant endoplasmic reticulum (ER) membrane expansion were determined. Using confocal imaging, S. enteritica bacteria exhibited a significant and quantifiable codistribution with endo-reticular membrane as determined by ER tracker staining. Isogenic S. enterica Typhimurium mutant strains, which can infect but exhibit impaired intracellular growth, demonstrated that the activation of the UPR was dependent on an integral intracellular niche. Therefore, these data identify cellular changes accompanying Salmonella induction of the UPR and in the presence of HLA-B27.
Single-channel speech enhancement using Graph Fourier Transform

This paper presents combination of Graph Fourier Trans-form (GFT) and U-net, proposes a deep neural network (DNN) named G-Unet for single channel speech enhancement. GFT is carried out over speech data for creating inputs of U-net. The GFT outputs are combined with the mask estimated by U-net in time-graph (T-G) domain to reconstruct enhanced speech in time domain by Inverse GFT. The G-Unet outperforms the combination of Short time Fourier Transform (STFT) and magnitude estimation U-net in improving speech quality and de-reverberation, and outperforms the combination of STFT and complex U-net in improving speech quality in some cases, which is validated by testing on LibriSpeech and NOISEX92 dataset.
Oncological outcomes in fertility-sparing treatment in stage IA-G2 endometrial cancer

Background The gold standard treatment for early-stage endometrial cancer (EC) is hysterectomy with bilateral salpingo-oophorectomy (BSO) with lymphadenectomy. In selected patients desiring pregnancy, fertility-sparing treatment (FST) can be adopted. Our review aims to collect the most incisive studies about the possibility of conservative management for patients with grade 2, stage IA EC. Different approaches can be considered beyond demolition surgery, such as local treatment with levonorgestrel-releasing intra-uterine device (LNG-IUD) plus systemic therapy with progestins. Study design Our systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. PubMed, EMBASE, and Scopus databases were consulted, and five studies were chosen based on the following criteria: patients with a histological diagnosis of EC stage IA G2 in reproductive age desiring pregnancy and at least one oncological outcome evaluated. Search imputes were “endometrial cancer” AND “fertility sparing” AND “oncologic outcomes” AND “G2 or stage IA”. Results A total of 103 patients were included and treated with a combination of LNG-IUD plus megestrol acetate (MA) or medroxyprogesterone acetate (MPA), gonadotrophin-releasing hormone (GnRH) plus MPA/MA, hysteroscopic resectoscope (HR), and dilation and curettage (D&C). There is evidence of 70% to 85% complete response after second-round therapy prolongation to 12 months. Conclusions Conservative measures must be considered temporary to allow pregnancy and subsequently perform specific counseling to adopt surgery. Fertility-sparing management is not the current standard of care for young women with EC. It can be employed for patients with early-stage diseases motivated to maintain reproductive function. Indeed, the results are encouraging, but the sample size must be increased.
Extending Compositional Attention Networks for Social Reasoning in Videos

We propose a novel deep architecture for the task of reasoning about social interactions in videos. We leverage the multi-step reasoning capabilities of Compositional Attention Networks (MAC) [1], and propose a multimodal extension (MAC-X). MAC-X is based on a recurrent cell that performs iterative mid-level fusion of input modalities (visual, auditory, text) over multiple reasoning steps, by use of a temporal attention mechanism. We then combine MAC-X with LSTMs for temporal input processing in an end-to-end architecture. Our ablation studies show that the proposed MAC-X architecture can effectively leverage multimodal input cues using mid-level fusion mechanisms. We apply MAC-X to the task of Social Video Question Answering in the Social IQ dataset and obtain a 2 . 5% absolute improvement in terms of binary accuracy over the current state-of-the-art.
Multiple Enhancements to LSTM for Learning Emotion-Salient Features in Speech Emotion Recognition

Emotion-relevant feature extraction is key to the speech emotion recognition (SER) task. Although neural network for extracting features has achieved excellent results, in particular long short-term memory (LSTM) based models, there is still ample space for improvement. In this paper, from the perspective of utilizing advantages of multiple models, we propose an approach of multiple enhancements for learning emotion-salient features in SER, which is based on the combination of LSTM, one-dimensional convolution and transformer networks. Firstly, we introduce residual-BLSTM (Bidirectional LSTM) module to make the network deeper and to increase the learning ability of the model by adding feed-forward network (FFN) to the output of BLSTM and building residual connections at the same time. Secondly, time pooling employed in residual-BLSTM module is proposed to reduce features redundancy and overcome training overfitting. Finally, we propose an E-transformer module by combining transformer and convolution neural network. This approach enables it to learn local information while capturing global dependencies. We conduct evaluations on the IEMOCAP dataset using the proposed meth-ods, and it shows the state-of-the-art performances.
Late Holocene earthquakes on the Papatea Fault and its role in past earthquake cycles, Marlborough, New Zealand

ABSTRACT The north-striking sinistral reverse Papatea Fault ruptured with a very large (up to 12 m) oblique slip as part of the 2016 M W 7.8 Kaikōura earthquake in the northeastern South Island. Paleoseismic studies were undertaken at three sites along the Papatea Fault, named Murray’s roadcut, Jacqui’s Gully (both on the main strand), and Wharekiri trench (western strand). These sites provide evidence for up to three Late Holocene paleoearthquakes prior to 2016 (=E0) on this previously unmapped active fault, with preferred OxCal-modelled timings of 98–149 (E1), 546–645 cal yr BP (E2), and >738 cal yr BP (E3). Event correlations between the sites are generally consistent across these past events, implying that the two strands of the Papatea Fault link at depth and rupture together co-seismically as in 2016. Comparisons of its paleoseismic record with the Kekerengu Fault and uplift data from Waipapa Bay and Kaikōura, suggest that the Papatea Fault may have three distinct rupture modes: (i) Kaikōura-type multi-fault ruptures with multi-metre, anelastic block displacements and associated major landscape change; (ii) multi-fault earthquake ruptures with other regional fault combinations; and (iii) single-fault Papatea ruptures with metre-scale displacement. OxCal models offer the possibility that the E1 fault rupture occurred in 1855 CE.
Ontogenetic, dietary, and environmental shifts in Mesosauridae

Mesosaurs are the first secondarily aquatic amniotes and one of the most enigmatic clades of reptiles from the early Permian. They have long puzzled paleontologists with their unique morphologies: possessing an elongated skull with thin needle-like teeth, a long neck, large webbed hindlimbs, banana-shaped pachyosteosclerotic ribs, and a long tail. Here, we look at a large dataset of morphometric measurements from 270 mesosaur specimens in collections around the world. These measurements characterize skull, tooth, and limb proportions and their variation with size. This data presents evidence of surprising ontogenetic changes in these animals as well as new insights into their taxonomy. Our results support the recent hypothesis that Mesosaurus tenuidens is the only valid species within Mesosauridae and suggest that “Stereosternum tumidum” and “Brazilosaurus sanpauloensis” represent immature stages or incomplete specimens of Mesosaurus by showing that all three species occupy an incomplete portion of the overall size range of mesosaurs. Under the single-species hypothesis, we highlight a number of ontogenetic trends: (1) a reduction in skull length accompanied by an elongation of the snout within the skull, (2) an elongation of teeth, (3) a reduction in hind limb length, and (4) a reduction in manus length. Concurrent with these changes, we hypothesize that mesosaurs went through a progressive ecological shift during their growth, with juveniles being more common in shallow water deposits, whereas large adults are more frequent in pelagic sediments. These parallel changes suggest that mesosaurs underwent a diet and lifestyle transition during ontogeny, from an active predatory lifestyle as juveniles to a more filter-feeding diet as adults. We propose that this change in lifestyle and environments may have been driven by the pursuit of different food sources, but a better understanding of the Irati Sea fauna will be necessary to obtain a more definitive answer to the question of young mesosaur diet.
Texas Elementary School Teacher and Student Demography: A Multiyear Investigation

In this longitudinal descriptive investigation, an analysis of the ethnic/racial diversity of teachers and of students in Texas public elementary schools from the 2010-2011 school year through the 2018-2019 school year was conducted. Descriptive statistics revealed decreases in the average percentages of White teachers and of White students in Texas public elementary school over 9 school years of data examined herein. The average percentages of Asian, Black, and Hispanic student and teachers increased from 2010-2011 to 2018-2019. Through comparisons of the ethnic/racial diversity of teachers and of students, clear disparities were evident. Higher percentages of White teachers were present than percentages of teachers of color and percentages of students of color. Implications for policy and for practice, as well as recommendations for future research, were discussed.
A 3D transient CFD model to predict heat and moisture transfer in on-farm stored grain silo through parallel computing using compiler directives: Impact of discretization methods on solution efficacy

Abstract Computational fluid dynamics model parameters highly depend on the storage structure, grain conditions, and airflow properties. Solution methods obtained for the lab-scale bins cannot be extrapolated for a relatively large on-farm silo. To validate the above hypothesis, a model was developed and validated with stored barley aeration in a 1000t silo. A mathematical function was used to initialize the discrete initial conditions in the silo followed by using DEFINE_macros to execute parallel computing. Time-step analysis was conducted followed by optimization of the solution methods in terms of the accuracy and time frame of the model’s output. Results showed that with a time-step of 4 s, temperature and moisture error were 1.1–1.7 °C and 0.3% wb while mean relative deviation were 3.1–4.4% and 2.2%, respectively. COUPLED algorithm resulted in a similar accuracy as of SIMPLE scheme except within spatial and transient formulations. However, the former algorithm was observed to take almost 190% more time than the latter scheme, limiting the simulation efficiency in an on-farm silo. Green Gauss Node-based gradient technique was found to be the appropriate for discretizing large silos. Model showed 45 kJ kg−1 energy emission that decreased the cooling potential of air in silo. As field evaluation of aeration strategies are time-consuming, this model can be used to obtain results that could shape the stored grain management practice.
Reducing interface recombination for Cu(In,Ga)Se2 by atomic layer deposited buffer layers

Partial CuInGaSe2 (CIGS) solar cell stacks with different atomic layer deposited buffer layers and pretreatments were analyzed by photoluminescence (PL) and capacitance voltage (CV) measurements to investigate the buffer layer/CIGS interface. Atomic layer deposited ZnS, ZnO, and SnOx buffer layers were compared with chemical bath deposited CdS buffer layers. Band bending, charge density, and interface state density were extracted from the CV measurement using an analysis technique new to CIGS. The surface recombination velocity calculated from the density of interface traps for a ZnS/CIGS stack shows a remarkably low value of 810 cm/s, approaching the range of single crystalline II–VI systems. Both the PL spectra and its lifetime depend on the buffer layer; thus, these measurements are not only sensitive to the absorber but also to the absorber/buffer layer system. Pretreatment of the CIGS prior to the buffer layer deposition plays a significant role on the electrical properties for the same buffer layer/.
Notice of Retraction A novel porosity simulation method using the Markov model

Porosity is quite significant for the characterization and prediction of transport properties. To simulate the porosity values, a porosity simulation method using Markov model based on cokriging is proposed to simulate the continuous values of porosity. Although cokriging is an unbiased prediction method with minimum prediction variance, original cokriging cannot solve the problem of instability in matrix. However, according to the hypothesis of screening effect provided by a Markov model, cokriging can be successfully approximated. The screening hypothesis indicates that the hard (primary) datum screens the influence of any other datum on the soft (secondary) collocated datum. Experimental results show that our method is better than the original full cokriging without Markov model, and regenerates the characteristics of soft data well.
Causality, structural modelling, and exogeneity

This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based on (i) model fit, (ii) invariance under a large variety of environmental changes, and (iii) congruence with background knowledge. We also tackle the issue of confounding and show how latent confounders can play havoc with exogeneity. This framework avoids making untestable metaphysical claims about causal relations and yet remains useful for cognitive and action-oriented goals.
Pemodelan Indeks Pembangunan Manusia di Indonesia dengan Geographically Weighted Regression (GWR)

The Human Development Index (HDI) is an important indicator to measure success in efforts to build the quality of human life. The HDI explains how the population can access development outcomes in terms of income, health and education. This study aims to obtain HDI modeling and see what factors affect HDI in Indonesia in 2020. The data in this study are secondary data obtained from the Central Statistics Agency (CSA). Modeling using linear regression is not necessarily suitable to be applied in all provinces in Indonesia are different. Therefore, in this study, a geographical approach was used, namely Geographically Weighted Regression (GWR) in modeling the HDI using the independent variables namely Expected Length of School (ELS), Average Length of School (ALS), Life Expectancy (LE) and Per Capita Expenditure (PCE). The GWR model is a development of the spatial regression model where each parameter is calculated for each observation location so that each location will have a different interpretations. GWR modeling requires a weighting function, while the weighting function used in this study ia the Adaptive Kernel Gaussian. The result of this study indicate that all independent variables affect HDI. The GWR model is the best compared to linear regression with the standard selection of the largest value and smallest value.
Using cross-model learnings for the Gram Vaani ASR Challenge 2022

In the diverse and multilingual land of India, Hindi is spoken as a first language by a majority of its population. Efforts are made to obtain data in terms of audio, transcriptions, dictionary, etc. to develop speech-technology applications in Hindi. Similarly, the Gram-Vaani ASR Challenge 2022 provides sponta-neous telephone speech, with natural back-ground and regional variations in Hindi. The challenge provides: 100 hours of labeled train-set, 5 hours of labeled dev-set and 1000 hours of unlabeled data-set. For the ‘Closed Challenge’, we trained an End-to-End (E2E) Conformer model using speed perturbations, SpecAugment techniques and use VTLN to handle any unknown speaker groups in the blind evaluation set. On the dev-set, we achieved a 30.3% WER compared to the 34.8% WER by the Challenge E2E baseline. For the ‘Self Supervised Closed Challenge’, a semi-supervised learning approach is used. We generate pseudo-transcripts for the unlabeled data using a hybrid TDNN-3gram LM model and trained an E2E model. This is then used as a seed for retraining the E2E model with high confidence data. Cross-model learning and refining of the E2E model gave 25.3% WER on the dev-set compared to ∼ 33-35% WER by the Challenge baseline that use wav2vec models.
Effects of the exogenous N-acylhomoserine lactones on the performances of microalgal-bacterial granular consortia

ABSTRACT Chlorella sorokiniana, the dominant microalgal strain with fast growth rate and high nutrients’ adsorption rate, was selected from the mature MBGS system. After adding C6-HSL and 3-oxo-C12-HSL to the MBGS consortia formed by combining it with mature AGS, it could also be found that C6-HSL accelerated the denitrification rate and improved the removal rate of TIN and TP effectively. Besides, the promoting effect of C6-HSL was slightly higher than that of 3-oxo-C12-HSL. By adjusting the concentration of C6-HSL (5 × 10−9, 7.5 × 10−7 and 2.5 × 10−5mol/L), the results revealed that adding 5 × 10−9mol/L C6-HSL significantly improved the efficiency of nitrogen and phosphorus removal in MBGS consortia. However, adding higher concentration of C6-HSL (7.5 × 10−7mol/L) would lead to deterioration of TIN and COD degradation. The content of EPS secreted by bacteria was regulated by different concentrations of AHLs, and the PS/PN value decreased with the increasing trend of C6-HSL concentration. GRAPIHCAL ABSTRACT
GNSS-R: Operational Applications

This paper provides an overview of operational applications of GNSS-R, and describes Oceanpal, an inexpensive, all-weather, passive instrument for remote sensing of the ocean and other water surfaces. This instrument is based on the use of reflected signals emitted from GNSS, and it holds great potential for future applications thanks to the growing, long term GNSS infrastructure. The instrument exploits the fact that, at any given moment, several GNSS emitters are simultaneously in view, providing separated multiple scattering points with different geometries. Reflected signals are affected by surface roughness and motion (i.e., sea state, orbital motion, and currents), mean surface height and dielectric properties (i.e., salinity and pollution). Oceanpal is envisioned as an accurate, "dry" tide gauge and surface roughness monitoring system, and as an important element of a future distributed ocean remote sensing network concept. We also report some results from the Starlab Coastpal campaign, focusing on ground GNSS-R applications.
The Benefit of Narratives for Prediction of the S&P 500 Index

In this article, the authors show that variables from the Global Database of Events, Language, and Tone convey significant information that can improve on a purely macroeconomic approach when modeling the US equity market. Based on these metrics, the authors construct time series that represent and measure how some narratives that appear to be battling each other are changing in the current market environment. Specifically, the authors appraise the strength of the roaring 20s, back to the 70s, secular stagnation, and monetary economic narratives, but they also add topical societal narratives related to environmental or social aspects and a geopolitical risk narrative. The authors formalize an information content framework and show that including quantitative signals that translate into qualitative stories brings added value when determining the stock market’s movement. Indeed, in addition to having higher explanatory power from their underlying variables, narratives can improve the diversification of standard macroeconomic models. As such, the authors’ results advocate a close monitoring of narratives in financial markets.