Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport

Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art probabilistic latent network model to forecast multilayer dynamic graphs that are increasingly common in transportation and proposes a community-based extension to reduce the computational burden. Flexible time series analysis is obtained by modeling the probability of edges between vertices through latent Gaussian processes. The models and Bayesian inference are illustrated on a sample of 10-year data from four major airlines within the US air transportation system. Results show how the estimated latent parameters from the models are related to the airline's connectivity dynamics, and their ability to project the multilayer graph into the future for out-of-sample full network forecasts, while stochastic blockmodeling allows for the identification of relevant communities. Reliable network predictions would allow policy-makers to better understand the dynamics of the transport system, and help in their planning on e.g. route development, or the deployment of new regulations.
Space-time metamorphosis

We study the problem of registering images. The framework we use is metamorphosis and we construct a variational Eulerian space-time setting and pose the registration problem as an infinite-dimensional optimisation problem. The geodesic equations correspond to a system of advection and continuity equations and are solved analytically. Well-posedness of a primal conforming finite element method is established and its convergence is investigated numerically. This provides a discrete forward operator for the matching parameterized by a space-time velocity field. We propose a gradient descent method on this control variable and show several promising numerical results for this approach.
Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals

Can artificial neural networks learn to represent inflectional morphology and generalize to new words as human speakers do? Kirov and Cotterell (2018) argue that the answer is yes: modern Encoder-Decoder (ED) architectures learn human-like behavior when inflecting English verbs, such as extending the regular past tense form /-(e)d/ to novel words. However, their work does not address the criticism raised by Marcus et al. (1995): that neural models may learn to extend not the regular, but the most frequent class — and thus fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized. To investigate this question, we first collect a new dataset from German speakers (production and ratings of plural forms for novel nouns) that is designed to avoid sources of information unavailable to the ED model. The speaker data show high variability, and two suffixes evince ‘regular’ behavior, appearing more often with phonologically atypical inputs. Encoder-decoder models do generalize the most frequently produced plural class, but do not show human-like variability or ‘regular’ extension of these other plural markers. We conclude that modern neural models may still struggle with minority-class generalization.
Lagartos brasileiros da família Gekkonidae (Sauria)

This paper contains: (i) a general discussion of gekkonid systematics; (ii) a key to the South American genera; (iii) diagnoses of South American genera; (iv) a check list of continental South American species, with bibliographical indications helpful to identification; (v) a systematic account of species known from or expected to occur in Brasil.
Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis

Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise. In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. Acknowledging the importance of contextual and speaker-specific cues for accurate lip-reading, we take a different path from existing works. We focus on learning accurate lip sequences to speech mappings for individual speakers in unconstrained, large vocabulary settings. To this end, we collect and release a large-scale benchmark dataset, the first of its kind, specifically to train and evaluate the single-speaker lip to speech task in natural settings. We propose a novel approach with key design choices to achieve accurate, natural lip to speech synthesis in such unconstrained scenarios for the first time. Extensive evaluation using quantitative, qualitative metrics and human evaluation shows that our method is four times more intelligible than previous works in this space.
Reducibility and Statistical-Computational Gaps from Secret Leakage

Inference problems with conjectured statistical-computational gaps are ubiquitous throughout modern statistics, computer science and statistical physics. While there has been success evidencing these gaps from the failure of restricted classes of algorithms, progress towards a more traditional reduction-based approach to computational complexity in statistical inference has been limited. Existing reductions have largely been limited to inference problems with similar structure -- primarily mapping among problems representable as a sparse submatrix signal plus a noise matrix, which are similar to the common hardness assumption of planted clique. The insight in this work is that a slight generalization of the planted clique conjecture -- secret leakage planted clique -- gives rise to a variety of new average-case reduction techniques, yielding a web of reductions among problems with very different structure. Using variants of the planted clique conjecture for specific forms of secret leakage planted clique, we deduce tight statistical-computational tradeoffs for a diverse range of problems including robust sparse mean estimation, mixtures of sparse linear regressions, robust sparse linear regression, tensor PCA, variants of dense $k$-block stochastic block models, negatively correlated sparse PCA, semirandom planted dense subgraph, detection in hidden partition models and a universality principle for learning sparse mixtures. In particular, a $k$-partite hypergraph variant of the planted clique conjecture is sufficient to establish all of our computational lower bounds. Our techniques also reveal novel connections to combinatorial designs and to random matrix theory. This work gives the first evidence that an expanded set of hardness assumptions, such as for secret leakage planted clique, may be a key first step towards a more complete theory of reductions among statistical problems.
Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellations

With numerous ongoing deployments owned by private companies and startups, dense satellite constellations deployed in low Earth orbit (LEO) will play a major role in the near future of wireless communications. In addition, the 3rd Generation Partnership Project (3GPP) has ongoing efforts to integrate satellites into 5G and beyond-5G networks. Nevertheless, numerous challenges must be overcome to fully exploit the connectivity capabilities of satellite constellations. These challenges are mainly a consequence of the low capabilities of individual small satellites, along with their high orbital speeds and small coverage due to the low altitude of deployment. In particular, inter-plane inter-satellite links (ISLs), which connect satellites from different orbital planes, are greatly dynamic and may be considerably affected by the Doppler shift. In this paper, we present a framework and the corresponding algorithms for the dynamic establishment of the inter-plane ISLs in LEO constellations. Our results show that the proposed algorithms increase the sum of rates in the constellation 1) by up to 115% with respect to the state-of-the-art benchmark schemes in an interference-free environment and 2) by up to 71% when compared to random resource allocation in a worst-case scenario for interference.
ML-assisted versatile approach to Calorimeter R&D

Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy to details. The approach covers entire detector representation from the event generation to the evaluation of the physics performance. The approach allows the use of arbitrary modules arrangement, different signal and background conditions, tunable reconstruction algorithms, and desired physics performance metrics. While combined with properties of detector and electronics prototypes obtained from beam tests, the approach becomes even more versatile. We focus on the Phase II Upgrade of the LHCb Calorimeter under the requirements on operation at high luminosity. We discuss the general design of the approach and particular estimations, including spatial and energy resolution for the future LHCb Calorimeter setup at different pile-up conditions.
High-Dimensional Convolutional Networks for Geometric Pattern Recognition

High-dimensional geometric patterns appear in many computer vision problems. In this work, we present high-dimensional convolutional networks for geometric pattern recognition problems that arise in 2D and 3D registration problems. We first propose high-dimensional convolutional networks from 4 to 32 dimensions and analyze the geometric pattern recognition capacity in high-dimensional linear regression problems. Next, we show that the 3D correspondences form hyper-surface in a 6-dimensional space and validate our network on 3D registration problems. Finally, we use image correspondences, which form a 4-dimensional hyper-conic section, and show that the high-dimensional convolutional networks are on par with many state-of-the-art multi-layered perceptrons.
How Biased is the Population of Facebook Users? Comparing the Demographics of Facebook Users with Census Data to Generate Correction Factors

Censuses and representative sampling surveys around the world are key sources of data to guide government investments and public policies. However, these sources are very expensive to obtain and are collected relatively infrequently. Over the last decade, there has been growing interest in the use of data from social media to complement more traditional data sources. However, social media users are not representative of the general population. Thus, analyses based on social media data require statistical adjustments, like post-stratification, in order to remove the bias and make solid statistical claims. These adjustments are possible only when we have information about the frequency of demographic groups using social media. These data, when compared with official statistics, enable researchers to produce appropriate statistical correction factors. In this paper, we leverage the Facebook advertising platform to compile the equivalent of an aggregate-level census of Facebook users. Our compilation includes the population distribution for seven demographic attributes such as gender, political leaning, and educational attainment at different geographic levels for the U.S. (country, state, and city). By comparing the Facebook counts with official reports provided by the U.S. Census and Gallup, we found very high correlations, especially for political leaning and race. We also identified instances where official statistics may be underestimating population counts as in the case of immigration. We use the information collected to calculate bias correction factors for all computed attributes in order to evaluate the extent to which different demographic groups are more or less represented on Facebook, and to derive the actual distributions for specific audiences of interest. We provide the first comprehensive analysis for assessing biases in Facebook users across several dimensions. This information can be used to generate bias-adjusted population estimates and demographic counts in a timely way and at fine geographic granularity in between data releases of official statistics.
Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency

In recent years generative adversarial network (GAN) based models have been successfully applied for unsupervised speech-to-speech conversion.The rich compact harmonic view of the magnitude spectrogram is considered a suitable choice for training these models with audio data. To reconstruct the speech signal first a magnitude spectrogram is generated by the neural network, which is then utilized by methods like the Griffin-Lim algorithm to reconstruct a phase spectrogram. This procedure bears the problem that the generated magnitude spectrogram may not be consistent, which is required for finding a phase such that the full spectrogram has a natural-sounding speech waveform. In this work, we approach this problem by proposing a condition encouraging spectrogram consistency during the adversarial training procedure. We demonstrate our approach on the task of translating the voice of a male speaker to that of a female speaker, and vice versa. Our experimental results on the Librispeech corpus show that the model trained with the TF consistency provides a perceptually better quality of speech-to-speech conversion.
Bati Karadeniz Havzasinin Guney Bolumunde Akcakoca-Cide Aciklarinda Sikismali Tektonik Rejime Ait Yeni Bulgular

The earthquake occurred on October 15, 2016 (Ml=5.0) re-attracted attention to the tectonic activity of Western Black Sea Basin. The focal mechanism solution of this earthquake indicates reverse faulting, similar to the Bartin Earthquake of September 3, 1968 (MS=6.6), which is the strongest instrumentally recorded earthquake along the Turkish margin of Black Sea, and reveals another seismological evidence for the active thrusting in the region. In this study, the fault structures that considered to be formed by the effect of compressional tectonic regime and the structures formed by the activities of these faults beneath the shelf and slope areas between the region offshore Akcakoca-Cide at the southern part of the Western Black Sea Basin revealed by using marine seismic reflection data and the composite well log data. By means of the geological sections in previous geological studies, the land-offshore geological sections were prepared and findings about the continuation of the geological features from land to offshore in the study area were presented. These geological sections developed for the study area and the geological features recognized from the seismic migration sections, support the presence of the N-S directional compressional tectonic regime in Western Black Sea.
β-amino acids: Versatile peptidomimetics

The use of peptidomimetics has emerged as a powerful means for overcoming the limitations inherent in the physical characteristics of peptides thus improving their therapeutic potential. A peptidomimetic approach that has emerged in recent years with significant potential, is the use of β-amino acids. β-Amino acids are similar to α-amino acids in that they contain an amino terminus and a carboxyl terminus. However, in β-amino acids two carbon atoms separate these functional termini. β-amino acids, with a specific side chain, can exist as the R or S isomers at either the α (C2) carbon or the β (C3) carbon. This results in a total of 4 possible diastereoisomers for any given side chain. The flexibility to generate a vast range of stereo- and regioisomers, together with the possibility of disubstitution, significantly expands the structural diversity of β-amino acids thereby providing enormous scope for molecular design. The incorporation of β-amino acids has been successful in creating peptidomimetics that not only have potent biological activity, but are also resistant to proteolysis. This article reviews the rapidly expanding applications of β-amino acids in the design of bioactive peptide analogues ranging from receptor agonists and antagonists, MHC-binding peptides, antimicrobial peptides and peptidase inhibitors. Given their structural diversity taken together with the ease of synthesis and incorporation into peptide sequences using standard solid-phase peptide synthesis techniques, β-amino acids have the potential to form a new platform technology for peptidomimetic design and synthesis.
PRKCSH Alternative Splicing Involves in Silica-Induced Expression of Epithelial–Mesenchymal Transition Markers and Cell Proliferation

Background: Mounting evidence suggests that alternative splicing is one of the ways for cells to adapt to environmental stress insults. The aim of this study was firstly to examine the effect of silica on the alternative splicing of lung fibrosis–associated genes. Methods: Microarray analysis was used to construct the alternative splicing profile. Functional experiments were conducted using Cell Counting Kit-8, cell cycle, apoptosis, and epithelial–mesenchymal transition (EMT) analyses. Alternative splicing variants were verified by quantitative real-time polymerase chain reaction (qRT-PCR) polymerase chain reaction method. Results: A total of 1850 genes that have alternative splices in response to silica insult were identified. PCDHB11, MALAT1, MT2A, RP11-126D17.1, and RP11-415I12.2 are the top 5 upregulated genes with occurrence of alternative splice, whereas NDE1, RNPEPL1, TREML2, CSF2RB, and PRKCSH are the top 5 downregulated genes with occurrence of alternative splice. Bioinformatic analysis showed these genes with the occurrence of alternative splice mainly are associated with EMT pathway, N-Glycan biosynthesis, and leukocyte transendothelial migration. Further study indicated that PRKCSH-2 knockdown promotes A549 cell proliferation potential by partially promoting EMT signals. Conclusions: Significant changes in alternative splicing of silicosis-associated genes occur in patients with silicosis in silica conditions. Our study provides basic founding for further investigation into the detail molecular mechanisms underlying silica-induced silicosis.
STAT4 sequence variant and elevated gene expression are associated with type 1 diabetes in Polish children

Introduction Type 1 diabetes (T1D) is caused by the autoimmune destruction of pancreatic β cells, resulting from coincident genetic predisposition and some environmental triggers. Signal transducer and activator of transcription 4 (STAT4) gene encodes a transcription factor, which promotes Th1 cell differentiation, interferon γ production, and development of Th17 cells. Polymorphisms of STAT4 are associated with several autoimmune conditions, while studies in T1D provided inconsistent results. This analysis was designed to investigate the association of STAT4 rs7574865 with T1D in Polish children and to assess STAT4 expression in newly diagnosed subjects. Material and methods Rs7574865 was genotyped in 656 T1D children and 782 healthy individuals. STAT4 mRNA expression was analyzed in peripheral blood mononuclear cells (PBMCs) from 29 children with T1D and 27 age-matched controls. β-cell and thyroid-specific serum autoantibodies were assessed with radioimmunoassays. Results The distribution of rs7574865 genotypes and alleles demonstrated significant difference (p = 0.002, p < 0.001, respectively) between patients vs. controls. Carriers of the minor T allele presented earlier T1D onset (p = 0.017). No differences were found in γ-cell autoantibody in genotype-stratified patients (p > 0.050), while anti-thyroid antibodies were more frequent in carriers of the minor allele(p = 0.039 for anti-thyroperoxidase, p = 0.007 for anti-thyroglobulin antibodies, respectively). STAT4 was overexpressed in PBMCs from T1D patients (p = 0.008), especially subjects with two/three circulating β-cell antibodies (p < 0.001). Conclusions The study confirms an association of STAT4 rs7574865 with T1D in Polish patients, and provides an evidence for its relationship with an earlier disease onset and concomitant thyroid autoimmunity. STAT4 expression appears elevated in T1D, especially with more severe reaction against β-cell antigens.
From soaking wet to bone dry: predicting plant community composition along a steep hydrological gradient

Question Why do plants grow where they grow? Prediction of species' occurrence and abundance in relation to the environment is a core aim of ecology, as is understanding the link between environmental stressors and adaptive traits. Community assembly may be viewed as a sequence of filters, sorting species according to their functional traits. We ask if the strength of filtering changes along a strong hydrological gradient? Can we predict species′ relative abundance using few, but biologically relevant traits? And are strongly filtered traits better predictors of community assembly? Location A hydrological gradient from pond to dry limestone pavements on the Island of Oland, South Sweden. Methods Plant community composition and six morpho-physiogical plant traits were measured along a pronounced gradient in water supply and soil depth. The strength of filtering was quantified using a trait dispersion index, while the prediction of species′ relative abundance and importance of individual traits was assessed with the community assembly by trait selection (CATS) model. Results We show that species are filtered by the hydrological environment through the traits root porosity, specific leaf area and resistance to water loss on drying. For individual traits, the strength of filtering waxes and wanes along the gradient. This strongly suggests that the mechanism, through which species are filtered into communities, acts through different traits as environmental conditions change along the gradient. The CATS model predicted 66% of the variation in species' relative abundances using six traits. In general, the traits subject to filtering also were most important in predicting species abundance. Conclusions Few plant traits are exposed to environmental filtering across the entire hydrological gradient, and most traits are strongly filtered only in parts of the gradient (e.g. root porosity in wet soils and water loss on drying on thin dry soils). Evidence for congruence between trait dispersion indices and the CATS model was established, underpinning the importance to plant community assembly of environmental filtering of species through their traits. New functional traits relevant to a specific environmental gradient – and not just some standard traits from a public database – can contribute significantly to resolve how plant communities are assembled.