Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Full-Body Avatar Movement Predictions in Virtual Reality using Neural Networks

Motion tracking technologies and avatars in virtual reality (VR) showing the movements of the own body enable high levels of presence and a strong illusion of body ownership (IBO) – key features of immersive systems and gaming experiences in virtual environments. Previous work suggests using software-based algorithms that can not only compensate system latency but also predict future movements of the user to increase input performance. However, the effects of movement prediction in VR on input performance are largely unknown. In this paper, we investigate neural network-based predictions of full-body avatar movements in two scenarios: In the first study, we used a standardized 2D Fitts’ Law task to examine the information throughput in VR. In the second study, we utilized a full-body VR game to determine the users’ performance. We found that both performance and subjective measures in a standardized 2D Fitts’ law task could not benefit from the predicted avatar movements. In an immersive gaming scenario, however, the perceived accuracy of the own body location improved. Presence and body assessments remained more stable and were higher than during the Fitts’ task. We conclude that machine-learning-based predictions could be used to compensate system-related latency but participants only subjectively benefit under certain conditions.
Comparative Foraging Behaviour of Three Species of Shrike in Southern Africa

Utilisation of food resources in migrant and resident birds living sympatrically often leads to differences in feeding strategy. For sit-and-wait hunters such as shrikes, it is connected with competition for food and hunting places. In this study, foraging behaviour of three shrike species was studied in savanna-like habitat in southern Africa. The fieldwork was carried out in December 2017 during wintering season of Lesser Grey Shrike Lanius minor and Red-backed Shrike Lanius collurio and breeding season of Common Fiscal Lanius collaris. Details of the bird's behaviour were recorded during 30 min observations of each bird. A general linear mixed model with logit link function and binomial error variance was used to compare the species' behaviour. Although the type of perch-site and perching time did not differ between species, Red-backed Shrike used places situated significantly lower than other shrikes. This species also more often used bushes and attacked from lower perch-sites than Lesser Grey Shrike. This may possibly be due to interspecific competition and antipredator behaviour of Red-backed Shrike. No differences in foraging behaviour between Common Fiscal and Lesser Grey Shrike were observed. All three species utilised the same category of prey. Hunting success (the ratio of successful to all attacks) was low and similar in all three species (47%–57%). The lack of statistically significant differences in behaviour, hunting success and size of prey items among shrike species may be a consequence of the relatively short observation time and small sample sizes.
State Sequence Pooling Training of Acoustic Models for Keyword Spotting

We propose a new training method to improve HMM-based keyword spotting. The loss function is based on a score computed with the keyword/ﬁller model from the entire input sequence. It is equivalent to max/attention pooling but is based on prior acoustic knowledge. We also employ a multi-task learning setup by predicting both LVCSR and keyword posteriors. We compare our model to a baseline trained on frame-wise cross entropy, with and without per-class weighting. We employ a low-footprint TDNN for acoustic modeling. The proposed training yields signiﬁcant and consistent improvement over the baseline in adverse noise conditions. The FRR on cafeteria noise is reduced from 13.07% to 5.28% at 9 dB SNR and from 37.44% to 6.78% at 5 dB SNR. We obtain these results with only 600 unique training keyword samples. The training method is independent of the frontend and acoustic model topology.
Improving Replay Detection System with Channel Consistency DenseNeXt for the ASVspoof 2019 Challenge

In this paper we describe a novel replay detection system for the ASVspoof 2019 challenge. The objective of this challenge is to distinguish arbitrarily audio ﬁles from bona ﬁde or spooﬁng attacks, where spooﬁng attacking includes replay attacks, text-to-speech and voice conversions. Our replay detection system is a pipeline system with three aspects: feature engineering, DNN models, and score fusion. Firstly, logspec is extracted as input features according to previous research works where spectrum augmentation is applied during training stage to boost performance under limited training data. Secondly, DNN models part includes three major models: SEnet, DenseNet, and our proposed model, channel consistency DenseNeXt, where binary cross entropy loss and center loss are applied as training objectives. Finally, score fusion is applied to all three DNN models in order to obtain primary system results. The experiment re-sults show that for our best single system, channel consistency DenseNeXt, t-DCF and EER are 0.0137 and 0.46% on physical access evaluation set respectively. The performance of primary system obtains 0.00785 and 0.282% in terms of t-DCF and EER respectively. This is a 96.8% improvement compared to the baseline system CQCC-GMM and it achieves state-of-the-art performance in PA challenge.
Endovascular recanalization for symptomatic non-acute middle cerebral artery occlusion: proposal of a new angiographic classification

Background Endovascular recanalization for medically refractory non-acute middle cerebral artery (MCA) occlusion remains a clinical dilemma, and limited data are available. We report the multicenter clinical results of endovascular recanalization for symptomatic non-acute MCA occlusion and propose a new angiographic classification to explore which subgroups of patients are most suitable for this treatment. Methods From January 2015 to December 2019, 50 consecutive patients who underwent endovascular recanalization for recurrent symptomatic non-acute MCA occlusion were analyzed retrospectively. All patients were divided into three types according to the angiographic classification. The technical success rate, periprocedural complications, rate of stroke or death within 30 days, and follow-up results were evaluated. Results The overall technical success rate was 84.0% (42/50). The perioperative complication rate was 14.0% (7/50), and the rate of stroke or death within 30 days was 12.0% (6/50). The revascularization success rate was higher in patients with type I occlusion than in those with type II or type III occlusion (95.5%, 83.3%, and 60%, respectively; p=0.014), and the opposite was true for the perioperative complication rate (4.5%, 11.1%, and 40.0%, respectively; p=0.013). The median clinical follow-up period was 13.4 months (IQR 12.5–15.6), and the rate of stroke or death beyond 30 days was 8.3%. Conclusions Endovascular recanalization for non-acute MCA occlusion is technically feasible in reasonably selected patients, especially type I patients, and has potential as an alternative option for patients with recurrent stroke or transient ischemic attack in the short term despite optimal medical therapy.
F0 Slope and Mean: Cues to Speech Segmentation in French

his paper evaluates the use of intonational cues during word segmentation in French. Specifically, we aim to examine how the characteristicsof the fundamental frequency (F0)that can be observed at the beginning of wordsinfluence theirprocessing. Native speakers of French were presented with phonemically identical sequences, such as /selami/ (c’est l’amie/la mie“it’s the friend/the crumb”). To test which propertiesof the F0 affect the perceived segmentation,we manipulated the F0 slope and/or the mean value of the first vowel /a/ in consonant-initial items(e.g., lamie). To assess differences in off-line vs online processing, we used a two-alternative,forced-choice task in Experiment 1 and a lexical decision task in Experiment2. A previous study showed that vowel-initial segmentation was enhanced when the F0 mean value increased. However, the present study shows that modifying the F0 slope while keeping the F0 mean value constantalso influencesspeech segmentation in both off-line and online tasks. This suggests that listeners usethe F0 slope as a cue atthe beginning of content words.
Impact of opioid use disorders on outcomes and readmission following cardiac operations

Objective While opioid use disorder (OUD) has been previously associated with increased morbidity and resource use in cardiac operations, its impact on readmissions is understudied. Methods Patients undergoing coronary artery bypass grafting and valve repair or replacement, excluding infective endocarditis, were identified in the 2010–16 Nationwide Readmissions Database. Using International Classification of Diseases 9/10, we tabulated OUD and other characteristics. Multivariable regression was used to adjust for differences. Results Of an estimated 1 978 276 patients who had cardiac surgery, 5707 (0.3%) had OUD. During the study period, the prevalence of OUD increased threefold (0.15% in 2010 vs 0.53% in 2016, parametric trend<0.001). Patients with OUD were more likely to be younger (54.0 vs 66.0 years, p<0.001), insured by Medicaid (28.2 vs 6.2%, p<0.001) and of the lowest income quartile (33.6 vs 27.1%, p<0.001). After multivariable adjustment, OUD was associated with decreased mortality (1.5 vs 2.7%, p=0.001). Although these patients had similar rates of overall complications (36.1 vs 35.1%, p=0.363), they had increased thromboembolic (1.3 vs 0.8%, p<0.001) and infectious (4.1 vs 2.8%, p<0.001) events, as well as readmission at 30 days (19.0 vs 13.2%, p<0.001). While patients with OUD had similar hospitalisation costs ($50 766 vs $50 759, p=0.994), they did have longer hospitalisations (11.4 vs 10.3 days, p<0.001). Conclusion The prevalence of OUD among cardiac surgical patients has steeply increased over the past decade. Although the presence of OUD was not associated with excess mortality at index hospitalisation, it was predictive of 30-day readmission. Increased attention to predischarge interventions and care coordination may improve outcomes in this population.
Whisper Augmented End-to-End/Hybrid Speech Recognition System - CycleGAN Approach

Automatic speech recognition (ASR) systems are known to perform poorly under whispered speech conditions. One of the primary reasons is the lack of large annotated whisper corpora. To address this challenge, we propose data augmentation with synthetic whisper corpus generated from normal speech using Cycle-Consistent Generative Adversarial Network (CycleGAN). We train CycleGAN model with a limited corpus of parallel whispered and normal speech, aligned using Dynamic Time Warping (DTW). The model learns frame-wise mapping from feature vectors of normal speech to those of whisper. We then augment ASR systems with the generated synthetic whisper corpus. In this paper, we validate our proposed approach using state-of-the-art end-to-end (E2E) and hybrid ASR systems trained on publicly available Librispeech, wTIMIT and internally recorded far-field corpora. We achieved 23% relative reduction in word error rate (WER) compared to baseline on whisper test sets. In addition, we also achieved WER reductions on Librispeech and far-field test sets.
Tongue and Lip Motion Patterns in Alaryngeal Speech

A laryngectomy is the surgical removal of the larynx which results in the loss of phonation. The aim of this study was to characterize tongue and lip movements during speech produced by individuals who have had a laryngectomy. EMA (electromagnetic articulography) was used to derive movement data from the tongue and lips of nine speakers (four alaryngeal and five typical). The kinematic metrics included movement duration, range, speed, and cumulative path distance. We also used a support vector machine (SVM) to classify alaryngeal and healthy speech movement patterns. Our preliminary results indicated that alaryngeal articulation is longer in duration than healthy speakers. Alaryngeal speakers also use larger lateral tongue movements and move the tongue back at a slower speed than healthy speakers. The results from the SVM model also indicates that alaryngeal articulatory movement patterns are distinct from healthy speakers. Taken together, these findings suggest that there are differences in articulatory behavior that occur after the removal of the larynx. It may be helpful to consider the distinct articulatory motion patterns of alaryngeal speech in clinical practice and in the development of technologies (e.g., silent speech interfaces) that assist to provide an intelligible form of speech for this patient population.
An Evaluation of Manual and Semi-Automatic Laughter Annotation

With laughter research seeing a development in recent years, there is also an increased need in materials having laughter annotations. We examine in this study how one can leverage existing spontaneous speech resources to this goal. We first analyze the process of manual laughter annotation in corpora, by establishing two important parameters of the process: the amount of time required and its inter-rater reliability. Next, we propose a novel semi-automatic tool for laughter annotation, based on a signal-based representation of speech rhythm. We test both annotation approaches on the same recordings, containing German dyadic spontaneous interactions, and employing a larger pool of annotators than previously done. We then compare and discuss the obtained results based on the two aforementioned parameters, highlighting the benefits and costs associated to each approach.
Mobile-Assisted Prosody Training for Limited English Proficiency: Learner Background and Speech Learning Pattern

The use of Mobile-Assisted Pronunciation Training (MAPT) has been increasing drastically due to the personal and interactive nature of mobile devices. However, MAPT applications lack support from empirical evidence as research on MAPT-based acquisition, particularly related to prosody, has been rare. The present study employs a MAPT application with lessons on lexical stress and prominence with Limited English Proficiency (LEP) users (n = 31) of mixed ages and first languages. Then, 16 experienced raters conducted discoursebased prosodic analysis on unconstrained speech collected at the beginning and the end of the intervention. A series of mixedeffect model analyses were conducted on learner effort, improvement and learner background to investigate their relationship with accentedness and comprehensibility. The results indicated that present MAPT prosody interventions were effective for comprehensibility but not accentedness, however, learner effort on lexical stress and prominence exhibit differing patterns. Similar to previous findings, learner age impacts production more than the length of residency or history of language study. Implications include a prosody-based MAPT application; support for the treatment of accentedness and comprehensibility as separate, but related constructs; and a further understanding of the role of learner-related factors in prosody intervention.
Sequence-to-Sequence Articulatory Inversion Through Time Convolution of Sub-Band Frequency Signals

We propose a new acoustic-to-articulatory inversion (AAI) sequence-to-sequence neural architecture, where spectral sub-bands are independently processed in time by 1-dimensional (1-D) convolutional ﬁlters of different sizes. The learned features maps are then combined and processed by a recurrent block with bi-directional long short-term memory (BLSTM) gates for preserving the smoothly varying nature of the articulatory trajectories. Our experimental evidence shows that, on a speaker dependent AAI task, in spite of the reduced number of parameters, our model demonstrates better root mean squared error (RMSE) and Pearson’s correlation coefﬁcient (PCC) than a both a BLSTM model and an FC-BLSTM model where the ﬁrst stages are fully connected layers. In particular, the average RMSE goes from 1.401 when feeding the ﬁlterbank features directly into the BLSTM, to 1.328 with the FC-BLSTM model, and to 1.216 with the proposed method. Similarly, the average PCC increases from 0.859 to 0.877, and 0.895, respectively. On a speaker independent AAI task, we show that our convolutional features outperform the original ﬁlterbank features, and can be combined with phonetic features bringing independent information to the solution of the problem. To the best of the authors’ knowledge, we report the best results on the given task and data.
Dysarthria Detection and Severity Assessment Using Rhythm-Based Metrics

Dysarthria refers to a range of speech disorders mainly affecting articulation. However, impairments are also seen in suprasegmental elements of speech such as prosody. In this study, we examine the effect of using rhythm metrics on detecting dysarthria, and for assessing severity level. Previous studies investigating prosodic irregularities in dysarthria tend to focus on pitch or voice quality measurements. Rhythm is another aspect of prosody which refers to the rhythmic division of speech units into relatively equal time. Speakers with dysarthria tend to have irregular rhythmic patterns that could be useful for detecting dysarthria. We compare the classification accuracy between solely using standard prosodic features against using both standard prosodic features and rhythm-based features, using random forest, support vector machine, and feed-forward neural network. Our best performing classifiers achieved a relative percentage increase of 7.5% and 15% in detection and severity assessment respectively for the QoLT Korean dataset, while the TORGO English dataset had an increase of 4.1% and 3.2%. Results indicate that including rhythmic information can increase accuracy performance regardless of the classifier. Furthermore, we show that rhythm metrics are useful in both Korean and English.
ERK phosphorylates chromosomal axis component HORMA domain protein HTP-1 to regulate oocyte numbers

ERK phosphorylates meiosis component HTP-1 to regulate oocyte numbers. Oocyte numbers, a critical determinant of female reproductive fitness, are highly regulated, yet the mechanisms underlying this regulation remain largely undefined. In the Caenorhabditis elegans gonad, RAS/extracellular signal–regulated kinase (ERK) signaling regulates oocyte numbers; mechanisms are unknown. We show that the RAS/ERK pathway phosphorylates meiotic chromosome axis protein HTP-1 at serine-325 to control chromosome dynamics and regulate oocyte number. Phosphorylated HTP-1(S325) accumulates in vivo in an ERK-dependent manner in early-mid pachytene stage germ cells and is necessary for synaptonemal complex extension and/or maintenance. Lack of HTP-1 phosphorylation leads to asynapsis and persistence of meiotic double-strand breaks, causing delayed meiotic progression and reduced oocyte number. In contrast, early onset of ERK activation causes precocious meiotic progression, resulting in increased oocyte number, which is reversed by removal of HTP-1 phosphorylation. The RAS/ERK/HTP-1 signaling cascade thus functions to monitor formation and maintenance of synapsis for timely resolution of double-strand breaks, oocyte production, and reproductive fitness.
Assessment of an HIV-prevention intervention for couples in peri-urban Uganda: pervasive challenges to relationship quality also challenge intervention effectiveness

Reducing multiple and concurrent partnerships has been identified as a priority in generalised HIV epidemics, yet developing successful interventions to bring about such behaviour change has proven challenging. We offered a three-session intervention aimed to improve couple relationship quality and address HIV risk factors, particularly concurrent sexual partnerships (CSP), in a peri-urban community of Kampala, Uganda. Before launching the intervention, a different group of community members participated in eight single-gender focus group discussions (FGDs) which explored issues of couple relationship quality and satisfaction. Findings from the FGDs guided the intervention. All 162 couples invited to the intervention completed a survey pre- and post-intervention. In FGDs, women and men discussed challenges faced in their relationships, including pervasive dissatisfaction, financial constraints, deception and lack of trust, poor communication, lack of sexual satisfaction, and concurrent sexual partnerships. A difference-in-difference analysis showed no measurable impact of the intervention on relationship quality or sexual risk behaviours over a six-month follow-up among 183 individuals who participated in the intervention, although many stated in response to open-ended questions that they had experienced positive relationship changes. Qualitative findings suggest high demand for couple-focused interventions but also reveal many individual-, couple-, community- and structural-level factors which contribute to women and men seeking concurrent sexual partnerships. More intensive interventions may be needed to overcome these barriers to behaviour change and reduce HIV risk. These findings also raise questions about how to interpret divergent qualitative and quantitative data, a topic which has received little attention in the literature.
RefDiff4Go: Detecting Refactorings in Go

Refactoring is a key software development practice that seeks to improve the internal structure of the code without changing its external behavior. In this way, the identification of refactorings is a key source of information for researchers, practitioners, and tool builders. However, existing approaches and tools documented in the literature do not support emerging ecosystems, such as the one of the Go programming language, which is nowadays widely used to develop robust and popular projects. To address these challenges, we present in this paper RefDiff4Go, a Go extension for RefDiff, which is a multi-language refactoring detection tool. RefDiff4Go detects 13 refactoring types in Go projects. We also evaluated RefDiff4Go with six well-known Go open-source projects, achieving 92% of precision and 80% of recall. Finally, we show that RefDiff4Go runtime performance supports its adoption in professional software projects.
Hematological characteristics of patients in coronavirus 19 infection: a systematic review and meta-analysis

ABSTRACT Background COVID-19 infection has become a pandemic and a global health issue since its origin in Wuhan, China in December 2019. The present systematic review and meta-analysis aim to assess hematological changes seen in COVID-19 infection and their association with the severity of the disease. Methods Pooled proportions were calculated using both fixed effects model and random effects model. Weighted mean difference and 95% CI were calculated and reported. Results Initial search identified 84 reference articles, 23 relevant articles were selected and reviewed. Compared to general population, the weighted mean difference of WBC count in all COVID-19 patients was lower by 0.97 × 109 mm3 (95% CI = –1.29 to –0.66). In severe COVID-19 patients, the weighted mean difference of platelet count was lower by 23.85 × 109/liter (95% CI = –35.18 to –9.53), as compared to general population. The weighted mean difference of prothrombin time, D-Dimer, and fibrinogen in severe COVID-19 patients was higher by 1.92 seconds (95% CI = 0.01 to 3.84), 6.23 mg/liter (95% CI = 0.11 to 12.36) and 1.88 g/liter (95% CI = 1.18 to 2.48) respectively, as compared to general population. Pooled proportion showed D-Dimer to be elevated in 80.00% (95 % CI = 50.00 to 99.00) of severe patients. Conclusions Our meta-analysis shows that patients with COVID-19 have significant thrombocytopenia, leukopenia along with elevated D-dimer, fibrinogen and prothrombin time. These laboratory findings are marked in severe COVID–19 infections and could be helpful in early recognition of severe infection.