Miracle-dz/UniFAR-FTUs huggingface dataset:
Postcranial axial skeleton of Europasaurus holgeri (Dinosauria, Sauropoda) from the Upper Jurassic of Germany: implications for sauropod ontogeny and phylogenetic relationships of basal Macronaria

Neosauropods are well represented in the Late Jurassic fossil record, both in Laurasia and Gondwana. Among Macronaria, Europasaurus represents one of the most basal forms of this group. In addition to its systematic importance, Europasaurus is also the first unequivocal dwarf sauropod from which adult and juvenile material is available. Despite the abundance of sauropods in the fossil record, early juvenile specimens are rare, limiting knowledge about sauropod ontogeny. Therefore, the great amount of material of Europasaurus provides an excellent opportunity to improve our knowledge on the early evolution of Macronaria, as well as to shed light on some morphological changes through ontogeny. The postcranial axial skeleton of sauropods is extremely modified with respect to the anatomy observed in its ancestors, the ‘prosauropods’, proving to be one of the most informative regions of the body. Here we provide a detailed description of the axial skeleton of Europasaurus, including adult and juvenile elements, discussing its systematic and ontogenetic importance. We also analyse the phylogenetic position of Europasaurus through a cladistic analysis using TNT, which retrieves this taxon in a basal position among Camarasauromorpha. Additionally, the presence/absence of discrete characters and the comparison of juvenile elements with adult specimens allowed us to recognize different morphological ontogenetic stages (MOS). Whereas early stages lack derived characters (e.g. spinodiapophyseal lamina and prespinal lamina on dorsal vertebrae), all derived characters (including autapomorphies) are present in late immature specimens. Therefore, while late immature specimens provide the same phylogenetic signal as adult specimens of Europasaurus, more immature stages are recovered in a basal position among sauropods. Finally, we apply the MOS to other maturity criteria (e.g. neurocentral closure, sexual maturity) in a search for a wider definition of maturity.
A New Species and a Molecular Phylogenetic Analysis of the Antarctic Fish Genus Pogonophryne (Notothenioidei: Artedidraconidae)

Abstract Pogonophryne stewarti, new species, is described from collections made at 1700 m on the continental slope of the Indian Ocean Sector of the Southern Ocean, off Wilkes Land, Antarctica. The new species is assigned to the unspotted P. albipinna species group and is related to P. immaculata. It is distinguished from all other species of the P. albipinna species group by the following combination of characters: a long (about 15–20% SL) mental barbel without a terminal expansion, a shallow head and slender trunk with only slight taper to caudal peduncle, 8–13 middle lateral-line pores, and divergence in the mitochondrial ND2 gene. We discuss the composition and validity of the P. albipinna group, which, up to this point, has been based on three holotype specimens. We provide meristic and morphometric data for seven recently collected adult specimens of P. immaculata. Bayesian analyses of mitochondrial gene sequences for 11 of the now 19 recognized species of Pogonophryne, sampled from all five of the proposed species groups, resulted in reciprocal monophyly of the monotypic species groups. The P. albipinna and P. mentella species groups were each monophyletic and resolved as sister lineages. We provide a revised key for the four species of Pogonophryne classified in the P. albipinna group.
Online mirror descent and dual averaging: keeping pace in the dynamic case

Online mirror descent (OMD) and dual averaging (DA)---two fundamental algorithms for online convex optimization---are known to have very similar (and sometimes identical) performance guarantees when used with a fixed learning rate. Under dynamic learning rates, however, OMD is provably inferior to DA and suffers a linear regret, even in common settings such as prediction with expert advice. We modify the OMD algorithm through a simple technique that we call stabilization. We give essentially the same abstract regret bound for OMD with stabilization and for DA by modifying the classical OMD convergence analysis in a careful and modular way that allows for straightforward and flexible proofs. Simple corollaries of these bounds show that OMD with stabilization and DA enjoy the same performance guarantees in many applications---even under dynamic learning rates. We also shed light on the similarities between OMD and DA and show simple conditions under which stabilized-OMD and DA generate the same iterates.
Combining ability, gene action and yielding performance in maize

Recombinant lines developed from combining Half-sib/S1 evaluation on widely-spaced plants in the direction of high yielding per se, were used in combining ability tests in order to determine gene action under three-way crossing and possible commercial exploitation of such crosses. Combining ability tests consisted of crosses between: a) recombinant lines of common pedigree, b) recombinant lines and freely available inbred lines and c) recombinant lines and commercial F1 maize hybrids (three-way crosses). Heterosis was found to be acceptable, since the best three-way crosses reached the performance of corresponding single-cross hybrids and yielding performance of these three-way crosses was stable across years and experimental fields. Rapid line development from combining Half-sib/S1 evaluation may ensure high and stable crossing performance, based on additive gene action. Stability and uniformity of performance of three-way crosses was due to proper breeding incorporated in single-cross hybrid and to high and stable inbred line performance
Entanglement dynamics in dispersive optomechanics: Nonclassicality and revival

We study entanglement dynamics in a dispersive optomechanical system consisting of two optical modes and a mechanical oscillator inside an optical cavity. The two optical modes interact with the mechanical oscillator, but not directly with each other. The appearance of optical entanglement witnesses non-classicality of the oscillator. We study the dependence of the entanglement dynamics with the optomechanical coupling, the mean cavity photon numbers and temperature. We propose an experimental realization with ultracold atomic ensembles.
A New Discretization Scheme for One Dimensional Stochastic Differential Equations Using Time Change Method

We propose a new numerical method for one dimensional stochastic differential equations (SDEs). The main idea of this method is based on a representation of a weak solution of a SDE with a time changed Brownian motion, dated back to Doeblin (1940). In cases where the diffusion coefficient is bounded and $\beta$-Holder continuous with $0 < \beta \leq 1$, we provide the rate of strong convergence. An advantage of our approach is that we approximate the weak solution, which enables us to treat a SDE with no strong solution. Our scheme is the first to achieve the strong convergence for the case $0 < \beta < 1/2$.
Deep Learning for Dialogue Systems

In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today's virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to a dialogue system is still challenging. Hence, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue.miulab. tw.
Species composition of climbers in seasonal semideciduous forest fragments of Southeastern Brazil

In this study we evaluated floristic composition patterns of communities of climbers within ten inventories carried out in semideciduous forest fragments of southeastern Brazil. One of the inventories is original, being carried out for the present study in Ribeirao Cachoeira forest, Campinas, Sao Paulo State, Southeastern Brazil. This inventory was then pooled together to other nine climbers' inventories made in other forests of Southeastern Brazil to form a data base, which was examined regarding species richness, similarity, species distribution and climbing methods. The total number of species obtained was 355, belonging to 145 genera and 43 families. The ten most diverse families Bignoniaceae (45 species), Fabaceae (42), Malpighiaceae (36), Asteraceae (31), Apocynaceae (29), Sapindaceae (28), Convolvulaceae (21), Cucurbitaceae (14), Passifloraceae (10), and Euphorbiaceae (8) contributed to 74.4% of the total number of species recorded. The commonest climbing method in the studied sites was main stem or branch twining, accounting for 178 species or 50.1% of the total, the second commonest was tendril climbing (121 species, 34.1%), and the least, scrambling (56 species, 15.8%). We found a high percentage of exclusive species i.e., those occurring in only one forest site, which accounted for 49.3% of the total recorded. The mean similarity among forest sites (30%) may be considered low. The climbing species contribution to the total wood plant richness recorded on the forests sites was very high in some of the sites (up to 52.5%). These results indicated the importance of climber communities to plant diversity for semideciduous forests in Southeastern Brazil, enhancing the regional diversity and the conservation value of these forest remnants.
Simple Unsupervised Multi-Object Tracking

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training. Given unlabeled videos, our proposed method (SimpleReID) first generates tracking labels using SORT and trains a ReID network to predict the generated labels using crossentropy loss. We demonstrate that SimpleReID performs substantially better than simpler alternatives, and we recover the full performance of its supervised counterpart consistently across diverse tracking frameworks. The observations are unusual because unsupervised ReID is not expected to excel in crowded scenarios with occlusions, and drastic viewpoint changes. By incorporating our unsupervised SimpleReID with CenterTrack trained on augmented still images, we establish a new state-of-the-art performance on popular datasets like MOT16/17 without using tracking supervision, beating current best (CenterTrack) by 0.2-0.3 MOTA and 4.4-4.8 IDF1 scores. We further provide evidence for limited scope for improvement in IDF1 scores beyond our unsupervised ReID in the studied settings. Our investigation suggests reconsideration towards more sophisticated, supervised, end-to-end trackers by showing promise in simpler unsupervised alternatives.
Online End-To-End Neural Diarization with Speaker-Tracing Buffer

This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a speaker’s permutation problem due to the possibility to assign speaker regions incorrectly across the recording. To circumvent this inconsistency, we proposed a speaker-tracing buffer mechanism that selects several input frames representing the speaker permutation information from previous chunks and stores them in a buffer. These buffered frames are stacked with the input frames in the current chunk and fed into a self-attention network. Our method ensures consistent diarization outputs across the buffer and the current chunk by checking the correlation between their corresponding outputs. Additionally, we trained SA-EEND with variable chunk-sizes to mitigate the mismatch between training and inference introduced by the speaker-tracing buffer mechanism. Experimental results, including online SA-EEND and variable chunk-size, achieved DERs of 12.54 % for CALLHOME and 20.77 % for CSJ with 1.4 s actual latency.
Modeling the molecular impact of the SARS-CoV-2 infection on the renin-angiotensin system

Infection by the SARS-CoV-2 virus is mediated by the binding of its spike protein to the membrane-bound angiotensin converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS). The understanding of RAS dysregulation due to this viral infection is of fundamental importance to better understand the pathogenic mechanisms and risk factors of the coronavirus disease COVID-19, and to design effective therapeutic strategies. To address this issue, we built a mathematical model of RAS based on data about protein and peptide concentrations in normotensive and hypertensive individuals. We first tested our model on clinical data on the action of antihypertensive RAS-blocking drugs in control individuals. Despite the simplicity of our model, it reproduces very well, without any fitting of additional parameters, the impact of a series of drugs, i.e. angiotensin-converting enzyme inhibitors (ACE-I), direct renin inhibitors (DRI) and angiotensin II receptor blockers (ARB). We applied our model to analyze the impact of SARS-CoV-2 infection on the RAS system, which we modeled through a downregulation of ACE2 related to viral load. Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients. We found that while ACE-I, DRI, rhACE2 and Ang1-7 tend to improve the clinical outcomes in a tension-dependent manner, the use of ARB appears to worsen it. The mathematical model that we developed offers the interesting possibility of testing in silico the RAS dysregulation upon SARS-CoV-2 infection and of predicting how risk factors as well as different drugs, alone or in combination, impact on RAS and disease severity.
Optimization of MR Fingerprinting for Free-Breathing Quantitative Abdominal Imaging

In this work, we propose a free-breathing magnetic resonance fingerprinting method that can be used to obtain $B_1^+$-robust quantitative maps of the abdomen in a clinically acceptable time. A three-dimensional MR fingerprinting sequence with a radial stack-of-stars trajectory was implemented for quantitative abdominal imaging. The k-space acquisition ordering was adjusted to improve motion-robustness. The flip angle pattern was optimized using the Cramer-Rao Lower Bound, and the encoding efficiency of sequences with 300, 600, 900, and 1800 flip angles was evaluated. To validate the sequence, a movable multicompartment phantom was developed. Reference multiparametric maps were acquired under stationary conditions using a previously validated MRF method. Periodic motion of the phantom was used to investigate the motion-robustness of the proposed sequence. The best performing sequence length (600 flip angles) was used to image the abdomen during a free-breathing volunteer scan. When using a series of 600 or more flip angles, the estimated $T_1$ values in the stationary phantom showed good agreement with the reference scan. Phantom experiments revealed that motion-related artefacts can appear in the quantitative maps, and confirmed that a motion-robust k-space ordering is essential in preventing these artefacts. The in vivo scan demonstrated that the proposed sequence can produce clean parameter maps while the subject breathes freely. Using this sequence, it is possible to generate $B_1^+$-robust quantitative maps of proton density, $T_1$, and $B_1^+$ under free-breathing conditions at a clinically usable resolution within 5 minutes.
PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography

Remote photoplethysmography (rPPG) is a non-contact technique for measuring cardiac signals from facial videos. High-quality rPPG pulse signals are urgently demanded in many fields, such as health monitoring and emotion recognition. However, most of the existing rPPG methods can only be used to get average heart rate (HR) values due to the limitation of inaccurate pulse signals. In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse signals through denoising the chrominance (CHROM) signals. Considering that the cardiac signal is quasi-periodic and has apparent time-frequency characteristics, the error losses defined in time and spectrum domains are both employed with the adversarial loss to enforce the model generating accurate pulse waveforms as its reference. The proposed framework is tested on three public databases. The results show that the PulseGAN framework can effectively improve the waveform quality, thereby enhancing the accuracy of HR, the interbeat interval (IBI) and the related heart rate variability (HRV) features. The proposed method significantly improves the quality of waveforms compared to the input CHROM signals, with the mean absolute error of AVNN (the average of all normal-to-normal intervals) reduced by 41.19%, 40.45%, 41.63%, and the mean absolute error of SDNN (the standard deviation of all NN intervals) reduced by 37.53%, 44.29%, 58.41%, in the cross-database test on the UBFC-RPPG, PURE, and MAHNOB-HCI databases, respectively. This framework can be easily integrated with other existing rPPG methods to further improve the quality of waveforms, thereby obtaining more reliable IBI features and extending the application scope of rPPG techniques.
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning

In this paper, we propose a maximum mutual information (MMI) framework for multi-agent reinforcement learning (MARL) to enable multiple agents to learn coordinated behaviors by regularizing the accumulated return with the mutual information between actions. By introducing a latent variable to induce nonzero mutual information between actions and applying a variational bound, we derive a tractable lower bound on the considered MMI-regularized objective function. Applying policy iteration to maximize the derived lower bound, we propose a practical algorithm named variational maximum mutual information multi-agent actor-critic (VM3-AC), which follows centralized learning with decentralized execution (CTDE). We evaluated VM3-AC for several games requiring coordination, and numerical results show that VM3-AC outperforms MADDPG and other MARL algorithms in multi-agent tasks requiring coordination.
Kiloparsec-scale jet-driven feedback in AGN probed by highly ionized gas: A MUSE/VLT perspective

Abstract We employ optical spectroscopy from the Multi Unit Spectroscopic Explorer (MUSE) combined with X-ray and radio data to study the highly-ionized gas (HIG) phase of the feedback in a sample of five local nearby Active Galactic Nuclei (AGN). Thanks to the superb field of view and sensitivity of MUSE, we found that the HIG, traced by the coronal line [Fe vii] λ6089, extends to scales not seen before, from 700 pc in Circinus and up to ∼2 kpc in NGC 5728 and NGC 3393. The gas morphology is complex, following closely the radio jet and the X-ray emission. Emission line ratios suggest gas excitation by shocks produced by the passage of the radio jet. This scenario is further supported by the physical conditions derived for the HIG, stressing the importance of the mechanical feedback in AGN with low-power radio jets.
Spatio-Temporal Characteristics of Dengue Outbreaks

Based on a data set of dengue cases in the Brazilian city of Fortaleza, collected from 2011 to 2016, we study the spatio-temporal characteristics of dengue outbreaks to characterize epidemic and non-epidemic years. First, we identify regions that show a high incidence of dengue cases and mosquito larvae in different years and analyze their corresponding correlations. We find that the characteristic correlation length of the epidemic is at least of the order of the system size, suggesting that factors such as citizen mobility may play a significant role in driving the spatial spread of the disease. We also find that in epidemic years, spatial correlations are substantially higher with a distribution skewed towards large values than non-epidemic years. Inspired by this observation, we perform a mean-field estimation of the basic reproduction number and find that the estimated values agree well with the values reported for other regions in Brazil and other countries, pointing towards similar underlying spreading mechanisms. These findings provide insights into the spreading characteristics of dengue in densely populated areas and may be relevant for improving disease containment strategies.
Meta Dialogue Policy Learning

Dialog policy determines the next-step actions for agents and hence is central to a dialogue system. However, when migrated to novel domains with little data, a policy model can fail to adapt due to insufficient interactions with the new environment. We propose Deep Transferable Q-Network (DTQN) to utilize shareable low-level signals between domains, such as dialogue acts and slots. We decompose the state and action representation space into feature subspaces corresponding to these low-level components to facilitate cross-domain knowledge transfer. Furthermore, we embed DTQN in a meta-learning framework and introduce Meta-DTQN with a dual-replay mechanism to enable effective off-policy training and adaptation. In experiments, our model outperforms baseline models in terms of both success rate and dialogue efficiency on the multi-domain dialogue dataset MultiWOZ 2.0.
Learning Dependency-Based Compositional Semantics

Compositional question answering begins by mapping questions to logical forms, but training a semantic parser to perform this mapping typically requires the costly annotation of the target logical forms. In this paper, we learn to map questions to answers via latent logical forms, which are induced automatically from question-answer pairs. In tackling this challenging learning problem, we introduce a new semantic representation which highlights a parallel between dependency syntax and efﬁ-cient evaluation of logical forms. On two standard semantic parsing benchmarks (G EO and J OBS ), our system obtains the highest published accuracies, despite requiring no annotated logical forms.
An integer program and new lower bounds for computing the strong rainbow connection numbers of graphs

We present an integer programming model to compute the strong rainbow connection number, src(G), of any simple graph G. We introduce several enhancements to the proposed model, including a fast heuristic, and a variable elimination scheme. Moreover, we present a novel lower bound for src(G) which may be of independent research interest. We solve the integer program both directly and using an alternative method based on iterative lower bound improvement, the latter of which we show to be highly effective in practice. To our knowledge, these are the first computational methods for the strong rainbow connection problem. We demonstrate the efficacy of our methods by computing the strong rainbow connection numbers of graphs containing up to 379 vertices.