Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly-Supervised Disentanglement Without Compromises

Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i. d. images sharing at least one of the underlying factors of variation. First, we theoretically show that only knowing how many factors have changed, but not which ones, is sufficient to learn disentangled representations. Second, we provide practical algorithms that learn disentangled representations from pairs of images without requiring annotation of groups, individual factors, or the number of factors that have changed. Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets. Finally, we evaluate our learned representations and find that they are simultaneously useful on a diverse suite of tasks, including generalization under covariate shifts, fairness, and abstract reasoning. Overall, our results demonstrate that weak supervision enables learning of useful disentangled representations in realistic scenarios.
Multi-source Deep Gaussian Process Kernel Learning

For many problems, relevant data are plentiful but explicit knowledge is not. Predictions about target variables may be informed by data sources that are noisy but plentiful, or data which the target variable is merely some function of. Intrepretable and flexible machine learning methods capable of fusing data across sources are lacking. We generalize the Deep Gaussian Processes so that GPs in intermediate layers can represent the posterior distribution summarizing the data from a related source. We model the prior-posterior stacking DGP with a single GP. The exact second moment of DGP is calculated analytically, and is taken as the kernel function for GP. The result is a kernel that captures effective correlation through function composition, reflects the structure of the observations from other data sources, and can be used to inform prediction based on limited direct observations. Therefore, the approximation of prior-posterior DGP can be considered a novel kernel composition which blends the kernels in different layers and have explicit dependence on the data. We consider two synthetic multi-source prediction problems: a) predicting a target variable that is merely a function of the source data and b) predicting noise-free data using a kernel trained on noisy data. Our method produces better prediction and tighter uncertainty on the synthetic data when comparing with standard GP and other DGP method, suggesting that our data-informed approximate DGPs are a powerful tool for integrating data across sources.
Non-detection of TiO and VO in the atmosphere of WASP-121b using high-resolution spectroscopy

Thermal inversions have long been predicted to exist in the atmospheres of ultra-hot Jupiters. However, the detection of two species thought to be responsible – titanium oxide and vanadium oxide – remains elusive. We present a search for TiO and VO in the atmosphere of the ultra-hot Jupiter WASP-121b (Teq ≳ 2400 K), an exoplanet with evidence of VO in its atmosphere at low resolution which also exhibits water emission features in its dayside spectrum characteristic of a temperature inversion. We observed its transmission spectrum with the UV-Visual Echelle Spectrograph at the Very Large Telescope and used the cross-correlation method – a powerful tool for the unambiguous identification of the presence of atomic and molecular species – in an effort to detect whether TiO or VO were responsible for the observed temperature inversion. No evidence for the presence of TiO or VO was found at the terminator of WASP-121b. By injecting signals into our data at varying abundance levels, we set rough detection limits of [VO] ≲−7.9 and [TiO] ≲−9.3. However, these detection limits are largely degenerate with scattering properties and the position of the cloud deck. Our results may suggest that neither TiO or VO are the main drivers of the thermal inversion in WASP-121b; however, until a more accurate line list is developed for VO, we cannot conclusively rule out its presence. Future works will consist of a search for other strong optically-absorbing species that may be responsible for the excess absorption in the red-optical.
Compressive Massive Access for Internet of Things: Cloud Computing or Fog Computing?

This paper considers the support of grant-free massive access and solves the challenge of active user detection and channel estimation in the case of a massive number of users. By exploiting the sparsity of user activities, the concerned problems are formulated as a compressive sensing problem, whose solution is acquired by approximate message passing (AMP) algorithm. Considering the cooperation of multiple access points, for the deployment of AMP algorithm, we compare two processing paradigms, cloud computing and fog computing, in terms of their effectiveness in guaranteeing ultra reliable low-latency access. For cloud computing, the access points are connected in a cloud radio access network (C-RAN) manner, and the signals received at all access points are concentrated and jointly processed in the cloud baseband unit. While for fog computing, based on fog radio access network (F-RAN), the estimation of user activity and corresponding channels for the whole network is split, and the related processing tasks are performed at the access points and fog processing units in proximity to users. Compared to the cloud computing paradigm based on traditional C-RAN, simulation results demonstrate the superiority of the proposed fog computing deployment based on F-RAN.
Unsupervised Pretraining Transfers Well Across Languages

Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting. This assumes the existence of a parallel corpus of speech and orthographic transcriptions. Recently, contrastive predictive coding (CPC) algorithms have been proposed to pretrain ASR systems with unlabelled data. In this work, we investigate whether unsupervised pretraining transfers well across languages. We show that a slight modification of the CPC pretraining extracts features that transfer well to other languages, being on par or even outperforming supervised pretraining. This shows the potential of unsupervised methods for languages with few linguistic resources.
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting

Prior knowledge of face shape and structure plays an important role in face inpainting. However, traditional face inpainting methods mainly focus on the generated image resolution of the missing portion without consideration of the special particularities of the human face explicitly and generally produce discordant facial parts. To solve this problem, we present a domain embedded multi-model generative adversarial model for inpainting of face images with large cropped regions. We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents. Two adversarial discriminators are finally used to judge whether the generated distribution is close to the real distribution or not. It can not only synthesize novel image structures but also explicitly utilize the embedded face domain knowledge to generate better predictions with consistency on structures and appearance. Experiments on both CelebA and CelebA-HQ face datasets demonstrate that our proposed approach achieved state-of-the-art performance and generates higher quality inpainting results than existing ones.
How to Answer Why - Evaluating the Explanations of AI Through Mental Model Analysis

To achieve optimal human-system integration in the context of user-AI interaction it is important that users develop a valid representation of how AI works. In most of the everyday interaction with technical systems users construct mental models (i.e., an abstraction of the anticipated mechanisms a system uses to perform a given task). If no explicit explanations are provided by a system (e.g. by a self-explaining AI) or other sources (e.g. an instructor), the mental model is typically formed based on experiences, i.e. the observations of the user during the interaction. The congruence of this mental model and the actual systems functioning is vital, as it is used for assumptions, predictions and consequently for decisions regarding system use. A key question for human-centered AI research is therefore how to validly survey users' mental models. The objective of the present research is to identify suitable elicitation methods for mental model analysis. We evaluated whether mental models are suitable as an empirical research method. Additionally, methods of cognitive tutoring are integrated. We propose an exemplary method to evaluate explainable AI approaches in a human-centered way.
The Utility of Graph Clustering of 5S Ribosomal DNA Homoeologs in Plant Allopolyploids, Homoploid Hybrids, and Cryptic Introgressants

Introduction Ribosomal DNA (rDNA) loci have been widely used for identification of allopolyploids and hybrids, although few of these studies employed high-throughput sequencing data. Here we use graph clustering implemented in the RepeatExplorer (RE) pipeline to analyze homoeologous 5S rDNA arrays at the genomic level searching for hybridogenic origin of species. Data were obtained from more than 80 plant species, including several well-defined allopolyploids and homoploid hybrids of different evolutionary ages and from widely dispersed taxonomic groups. Results (i) Diploids show simple circular-shaped graphs of their 5S rDNA clusters. In contrast, most allopolyploids and other interspecific hybrids exhibit more complex graphs composed of two or more interconnected loops representing intergenic spacers (IGS). (ii) There was a relationship between graph complexity and locus numbers. (iii) The sequences and lengths of the 5S rDNA units reconstituted in silico from k-mers were congruent with those experimentally determined. (iv) Three-genomic comparative cluster analysis of reads from allopolyploids and progenitor diploids allowed identification of homoeologous 5S rRNA gene families even in relatively ancient (c. 1 Myr) Gossypium and Brachypodium allopolyploids which already exhibit uniparental partial loss of rDNA repeats. (v) Finally, species harboring introgressed genomes exhibit exceptionally complex graph structures. Conclusion We found that the cluster graph shapes and graph parameters (k-mer coverage scores and connected component index) well-reflect the organization and intragenomic homogeneity of 5S rDNA repeats. We propose that the analysis of 5S rDNA cluster graphs computed by the RE pipeline together with the cytogenetic analysis might be a reliable approach for the determination of the hybrid or allopolyploid plant species parentage and may also be useful for detecting historical introgression events.
Global burden of larynx cancer, 1990-2017: estimates from the global burden of disease 2017 study

Larynx cancer is one of the most common cancers in head and neck. This study aimed to investigate the health burden of larynx cancer at global, regional, and national levels. We collected data of larynx cancer between 1990 and 2017 from the Global Burden of Disease study, including incidence, mortality, and disability adjusted life-years (DALYs). Estimated annual percentage changes (EAPCs) were calculated to assess the changes in age-standardized rate (ASR) of larynx cancer. From 1990 to 2017, LC incident cases increased by 58.67%; however, age-standardized incidence rate (ASIR) decreased, with an EAPC of -0.99. Additionally, the incident cases and ASIR of LC were 6-fold higher for male than those for female in 2017. Over the past 28 years, deaths and DALYs of larynx cancer increased by 33.84% and 25%. Contrarily, age-standardized death and DALY rate showed a downward trend. Incidence, death, and DALYs of larynx cancer were always the highest in people aged 50-69 years. Overall, all the ASRs showed downward trends globally. The majority of larynx cancer burden was observed in men, especially among male aged 50-69 years. South and East Asia carried the heaviest burden of larynx cancer worldwide.
Insect responses to heat: physiological mechanisms, evolution and ecological implications in a warming world

Surviving changing climate conditions is particularly difficult for organisms such as insects that depend on environmental temperature to regulate their physiological functions. Insects are extremely threatened by global warming, since many do not have enough physiological tolerance even to survive continuous exposure to the current maximum temperatures experienced in their habitats. Here, we review literature on the physiological mechanisms that regulate responses to heat and provide heat tolerance in insects: (i) neuronal mechanisms to detect and respond to heat; (ii) metabolic responses to heat; (iii) thermoregulation; (iv) stress responses to tolerate heat; and (v) hormones that coordinate developmental and behavioural responses at warm temperatures. Our review shows that, apart from the stress response mediated by heat shock proteins, the physiological mechanisms of heat tolerance in insects remain poorly studied. Based on life‐history theory, we discuss the costs of heat tolerance and the potential evolutionary mechanisms driving insect adaptations to high temperatures. Some insects may deal with ongoing global warming by the joint action of phenotypic plasticity and genetic adaptation. Plastic responses are limited and may not be by themselves enough to withstand ongoing warming trends. Although the evidence is still scarce and deserves further research in different insect taxa, genetic adaptation to high temperatures may result from rapid evolution. Finally, we emphasize the importance of incorporating physiological information for modelling species distributions and ecological interactions under global warming scenarios. This review identifies several open questions to improve our understanding of how insects respond physiologically to heat and the evolutionary and ecological consequences of those responses. Further lines of research are suggested at the species, order and class levels, with experimental and analytical approaches such as artificial selection, quantitative genetics and comparative analyses.
Piyano Eğitiminde Video Kamera Kullanimi 1 Use of Videotapes in Piano Education

The purpose of this research is to give information about use of video tapes in music education, adapt it for piano education and show the effect of videotaped lesson performances on students self evaluation skills. The research was made by relation surveillance model in 2004-2005 instructional year during seven weeks at Gazi University Music Education Department where 12 students were chosen by random way. Mann Withney U-test was used in order to compare students and specialists points. At the end of the research, it was determined that video tapes used in piano education in some way increase students self evaluation skills.
How useful is [~(123)I]β-CIT SPECT in clinical practice?

Objective: To assess the accuracy and clinical usefulness of [ 123I]β-CIT (2β-carbomethoxy-3β-(4-iodophenyl)tropane)-SPECT in the differential diagnosis of Parkinson's disease. Subjects: 185 consecutive patients with symptoms of movement disorder were studied. The diagnoses were Parkinson's disease (92), essential tremor (16), vascular parkinsonism (15), various Parkinson plus syndromes (P+) (12), dementia with Lewy bodies (DLB) (5), dystonia (5), drug induced movement disorder (12), and other diagnoses (8). A reference group (psychogenic parkinsonism) comprised 20 subjects with complaints suggesting extrapyramidal disease but with no unequivocal signs on clinical examination and no abnormalities on brain imaging. Results: β-CIT uptake was significantly lower in the whole striatum as well as separately in the putamen and in the caudate nucleus in Parkinson's disease than in the reference group or in drug induced movement disorder, essential tremor, or dystonia. The uptake of β-CIT in the vascular parkinsonism group was heterogeneous and mean β-CIT uptake fell between the reference group and the Parkinson's disease group. In the P+and DLB groups the striatal uptake ratios overlapped those of the Parkinson's disease group. Conclusions: [123I]β-CIT SPECT may not be as useful a tool in the clinical differential diagnosis of Parkinson's disease as was previously believed, but it was 100%sensitive and specific for the diagnosis in younger patients (age 55 years). In older patients (age 55 years)-specificity was substantially lower (68.5%). This differential specificity reflected the different distribution of differential diagnostic disorders (P+, DLB, vascular parkinsonism) in the older and younger age groups.
Number of Remaining Teeth and Its Association with Educational Level in Chilean Adults: Data from the National Health Survey 2016-2017

Introduction Several population studies have addressed oral health inequalities. Edentulism, functional dentition, and number of remaining teeth have been associated with different socioeconomic level measurements. The aim of this study was to evaluate the association between educational level and tooth loss in the Chilean population aged 15 years and above, based on the 2016-2017 National Health Survey (ENS 2016-2017). Material and Methods. The sample for this cross-sectional study comprised 5473 subjects. The main independent variable was educational level (LEL: low, MEL: medium, and HEL: high). To measure tooth loss, we considered the variables number of remaining teeth, edentulism, and functional dentition. We used logistic regressions to assess the condition of dentition according to the subject's EL. As to the number of teeth variable, linear regressions were conducted. The analyses were carried out considering the complex sampling design in SPSS 24.0. Results When comparing LEL subjects with HEL subjects, the adjusted difference in number of remaining teeth was 3.11 for the maxilla and 1.72 for the mandible. An individual with LEL had a 7.51 [3.50–16.10] and 6.06 [2.68–13.68] times higher risk of upper edentulism and lower edentulism than a HEL individual, respectively. Regarding functional dentition, the adjusted OR in HEL subjects was 13.33 [8.02–22.15] and in MEL subjects was 2.81 [2.03–3.87], compared to LEL results. Conclusions LEL was associated with a significant tooth loss in the Chilean population. Subjects with LEL obtained a lower mean of number of remaining teeth and higher prevalence of edentulism and nonfunctional dentition.
A Time-Frequency Perspective on Audio Watermarking

Existing audio watermarking methods usually treat the host audio signals of a function of time or frequency individually, while considering them in the joint time-frequency (TF) domain has received less attention. This paper proposes an audio watermarking framework from the perspective of TF analysis. The proposed framework treats the host audio signal in the 2-dimensional (2D) TF plane, and selects a series of patches within the 2D TF image. These patches correspond to the TF clusters with minimum averaged energy, and are used to form the feature vectors for watermark embedding. Classical spread spectrum embedding schemes are incorporated in the framework. The feature patches that carry the watermarks only occupy a few TF regions of the host audio signal, thus leading to improved imperceptibility property. In addition, since the feature patches contain a neighborhood area of TF representation of audio samples, the correlations among the samples within a single patch could be exploited for improved robustness against a series of processing attacks. Extensive experiments are carried out to illustrate the effectiveness of the proposed system, as compared to its counterpart systems. The aim of this work is to shed some light on the notion of audio watermarking in TF feature domain, which may potentially lead us to more robust watermarking solutions against malicious attacks.
Public discourse and social network echo chambers driven by socio-cognitive biases

In recent years, social media has increasingly become an important platform for political campaigns, especially elections. It remains elusive how exactly public discourse is driven by the intricate interplay between individual socio-cognitive biases, dueling campaign efforts, and social media platforms. We examine this complex socio-political process by integrating observed retweet networks from the 2016 political networks with an agent-based model of political opinion formation and network structure. Here we show that the range of political viewpoints individuals are willing to consider is a key determinant in the formation of polarized networks and the emergence of echo chambers. We also find that winning majority support in public discourse is determined by both the effort exerted by campaigns and the relative ideological positioning of opposing campaigns. Our results demonstrate how public discourse and political polarization can be modeled as an interactive process of shifting individual opinions, evolving social networks, and political campaigns.
Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q network with monocular vision

The fast developing of unmanned aerial vehicle(UAV) brings forward the higher request to the ability of autonomous obstacle avoidance in crowded environment. Small UAVs such as quadrotors usually integrate with simple sensors and computation units because of the limited payload and power supply, which adds difficulties for operating traditional obstacle avoidance method. In this paper, we present a framework to control a quadrotor to fly through crowded environments autonomously. This framework adopts a two-stage architecture, a sensing module based on unsupervised deep learning method and a decision module established on deep reinforcement learning method, which takes the monocular image as inputs and outputs quadrotor actions. And it enables the quadrotor to realize autonomous obstacle avoidance without any prior environment information or labeled datasets. To eliminate the negative effects of limited observation capacity of on-board monocular camera, the decision module uses dueling double deep recurrent Q networks. The trained model shows high success rate as it control a quadrotor to fly through crowded environments in simulation. And it can have good performance after scenario transformed.
Linearly Convergent Algorithm with Variance Reduction for Distributed Stochastic Optimization

This paper considers a distributed stochastic strongly convex optimization, where agents connected over a network aim to cooperatively minimize the average of all agents' local cost functions. Due to the stochasticity of gradient estimation and distributedness of local objective, fast linearly convergent distributed algorithms have not been achieved yet. This work proposes a novel distributed stochastic gradient tracking algorithm with variance reduction, where the local gradients are estimated by an increasing batch-size of sampled gradients. With an undirected connected communication graph and a geometrically increasing batch-size, the iterates are shown to converge in mean to the optimal solution at a geometric rate (achieving linear convergence). The iteration, communication, and oracle complexity for obtaining an $\epsilon$-optimal solution are established as well. Particulary, the communication complexity is $\mathcal{O}(\ln (1/\epsilon))$ while the oracle complexity (number of sampled gradients) is $\mathcal{O}(1/\epsilon^2)$, which is of the same order as that of centralized approaches. Hence, the proposed scheme is communication-efficient without requiring extra sampled gradients. Numerical simulations are given to demonstrate the theoretic results.