Miracle-dz/UniFAR-FTUs huggingface dataset:
A Revised Generative Evaluation of Visual Dialogue

Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge. The current evaluation scheme of the VisDial dataset computes the ranks of ground-truth answers in predefined candidate sets, which Massiceti et al. (2018) show can be susceptible to the exploitation of dataset biases. This scheme also does little to account for the different ways of expressing the same answer--an aspect of language that has been well studied in NLP. We propose a revised evaluation scheme for the VisDial dataset leveraging metrics from the NLP literature to measure consensus between answers generated by the model and a set of relevant answers. We construct these relevant answer sets using a simple and effective semi-supervised method based on correlation, which allows us to automatically extend and scale sparse relevance annotations from humans to the entire dataset. We release these sets and code for the revised evaluation scheme as DenseVisDial, and intend them to be an improvement to the dataset in the face of its existing constraints and design choices.
Blood biochemical profile in normal cyclical and anoestrus cows

Blood serum biochemical constituents were analyzed in 6 normal cyclical cows and 6 anoestrus Jersey crossbred cows. Total protein, albumin, globulins, cholesterol, glucose, calcium, inorganic phosphorus, sodium, chloride, potassium, alkaline phosphatases and SGOT were significantly (P < 0.01) lower in anoestrus cows than that of normal cyclical cows. The possible role of these blood serum constituents in reproduction is discussed.
AdaCoSeg: Adaptive Shape Co-Segmentation With Group Consistency Loss

We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-instance segmentation problem, co-segmentation is intrinsically contextual: how a shape is segmented can vary depending on the set it is in. Hence, our network features an adaptive learning module to produce a consistent shape segmentation which adapts to a set. Specifically, given an input set of unsegmented shapes, we first employ an offline pre-trained part prior network to propose per-shape parts. Then the co-segmentation network iteratively and jointly optimizes the part labelings across the set subjected to a novel group consistency loss defined by matrix ranks. While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts. Overall, our method is weakly supervised, producing segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from AdaSeg and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods.
Optimal transport in Lorentzian synthetic spaces, synthetic timelike Ricci curvature lower bounds and applications

The goal of the present work is three-fold. The first goal is to set foundational results on optimal transport in Lorentzian synthetic spaces, including cyclical monotonicity, stability of optimal couplings and Kantorovich duality (several results are new even for smooth Lorentzian manifolds). The second one is to give a synthetic notion of "timelike Ricci curvature bounded below and dimension bounded above" for a Lorentzian space using optimal transport. The key idea being to analyse convexity properties of Entropy functionals along future directed timelike geodesics of probability measures. Such a notion is proved to be stable under a suitable weak convergence of Lorentzian synthetic spaces. The third goal is to draw applications, most notably extending volume comparisons and Hawking singularity Theorem (in sharp form) to the synthetic setting. The framework of Lorentzian synthetic spaces includes as remarkable classes of examples: space-times endowed with a causally plain (or, more strongly, locally Lipschitz) continuous Lorentzian metric, closed cone structures, some approaches to quantum gravity (e.g. causal Fermion systems).
Self-interacting dark matter and the delay of supermassive black hole growth

Using cosmological hydrodynamic simulations with physically motivated models of supermassive black hole (SMBH) formation and growth, we compare the assembly of Milky Way-mass (Mvir ≈ 7 × 1011 M⊙ at z = 0) galaxies in cold dark matter (CDM) and self-interacting dark matter (SIDM) models. Our SIDM model adopts a constant cross-section of 1 cm2 g−1. We find that SMBH formation is suppressed in the early Universe due to SIDM interactions. SMBH–SMBH mergers are also suppressed in SIDM as a consequence of the lower number of SMBHs formed. Lack of initial merger-driven SMBH growth in turn delays SMBH growth by billions of years in SIDM compared to CDM. Further, we find that this delayed growth suppresses SMBH accretion in the largest progenitors of the main SIDM galaxies during the first 5 Gyr of their evolution. Nonetheless, by z = 0.8 the CDM and SIDM SMBH masses differ only by around 0.2 dex, so that both remain compatible with the MBH–M* relation. We show that the reduced accretion causes the SIDM SMBHs to less aggressively regulate star formation in their host galaxies than their CDM counterparts, resulting in a factor of 3 or more stars being produced over the lifetime of the SIDM galaxies compared to the CDM galaxies. Our results highlight a new way in which SIDM can affect the growth and merger history of SMBHs and ultimately give rise to very different galaxy evolution compared to the classic CDM model.
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks.

While Convolutional Neural Networks (CNNs) trained for image and video super-resolution (SR) regularly achieve new state-of-the-art performance, they also suffer from significant drawbacks. One of their limitations is their lack of robustness to unseen image formation models during training. Other limitations include the generation of artifacts and hallucinated content when training Generative Adversarial Networks (GANs) for SR. While the Deep Learning literature focuses on presenting new training schemes and settings to resolve these various issues, we show that one can avoid training and correct for SR results with a fully self-supervised fine-tuning approach. More specifically, at test time, given an image and its known image formation model, we fine-tune the parameters of the trained network and iteratively update them using a data fidelity loss. We apply our fine-tuning algorithm on multiple image and video SR CNNs and show that it can successfully correct for a sub-optimal SR solution by entirely relying on internal learning at test time. We apply our method on the problem of fine-tuning for unseen image formation models and on removal of artifacts introduced by GANs.
ETC: Encoding Long and Structured Data in Transformers

Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach.
Novel insights into parathyroid hormone: report of The Parathyroid Day in Chronic Kidney Disease

Abstract Chronic kidney disease (CKD) is often associated with a mineral and bone disorder globally described as CKD-Mineral and Bone Disease (MBD), including renal osteodystrophy, the latter ranging from high bone turnover, as in case of secondary hyperparathyroidism (SHPT), to low bone turnover. The present article summarizes the important subjects that were covered during ‘The Parathyroid Day in Chronic Kidney Disease’ CME course organized in Paris in September 2017. It includes the latest insights on parathyroid gland growth, parathyroid hormone (PTH) synthesis, secretion and regulation by the calcium-sensing receptor, vitamin D receptor and fibroblast growth factor 23 (FGF23)–Klotho axis, as well as on parathyroid glands imaging. The skeletal action of PTH in early CKD stages to the steadily increasing activation of the often downregulated PTH receptor type 1 has been critically reviewed, emphasizing that therapeutic strategies to decrease PTH levels at these stages might not be recommended. The effects of PTH on the central nervous system, in particular cognitive functions, and on the cardiovascular system are revised, and the reliability and exchangeability of second- and third-generation PTH immunoassays discussed. The article also reviews the different circulating biomarkers used for the diagnosis and monitoring of CKD-MBD, including PTH and alkaline phosphatases isoforms. Moreover, it presents an update on the control of SHPT by vitamin D compounds, old and new calcimimetics, and parathyroidectomy. Finally, it covers the latest insights on the persistence and de novo occurrence of SHPT in renal transplant recipients.
Distinguishing dipolar and octupolar quantum spin ices using contrasting magnetostriction signatures

Recently there have been a number of experiments on Ce$_2$Zr$_2$O$_7$ and Ce$_2$Sn$_2$O$_7$, suggesting that these materials host a three-dimensional quantum spin liquid with emergent photons and fractionalized spinon excitations. However, the interpretation of the data to determine the precise nature of the quantum spin liquids is still under debate. The Kramers doublet in Ce$^{3+}$ local moment offers unusual pseudo-spin degrees of freedom as the $x$ and $z$ components transform as a dipole and $y$ component as an octupole. This leads to a variety of possible quantum spin liquid (or quantum spin ice) phases on the pyrochlore lattice of Ce$^{3+}$ moments. In this work, we theoretically propose that magnetostriction would be able to distinguish the dipolar (D-QSI) and octupolar (O-QSI) quantum spin ice, where the dipolar or octupolar components possess the respective spin ice correlations. We show that the magnetostriction in various configurations can be used as a selection rule to differentiate not only D-QSI and O-QSI, but also a number of competing broken symmetry states.
Multiwavelength cross-correlation analysis of the simulated cosmic web

We used magnetohydrodynamical cosmological simulations to investigate the cross-correlation between different observables (i.e. X-ray emission, Sunyaev–Zeldovich (SZ) signal at 21 cm, H i temperature decrement, diffuse synchrotron emission, and Faraday Rotation) as a probe of the diffuse matter distribution in the cosmic web. We adopt a uniform and simplistic approach to produce synthetic observations at various wavelengths, and we compare the detection chances of different combinations of observables correlated with each other and with the underlying galaxy distribution in the volume. With presently available surveys of galaxies and existing instruments, the best chances to detect the diffuse gas in the cosmic web outside of haloes is by cross-correlating the distribution of galaxies with SZ observations. We also find that the cross-correlation between the galaxy network and the radio emission or the Faraday Rotation can already be used to limit the amplitude of extragalactic magnetic fields, well outside of the cluster volume usually explored by existing radio observations, and to probe the origin of cosmic magnetism with the future generation of radio surveys.
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks

Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated. These methods enjoy great advantages in terms of computational costs, but the sampled sub-networks are not guaranteed to be estimated precisely unless an individual training process is taken. This paper owes such inaccuracy to the inevitable mismatch between assembled network layers, so that there is a random error term added to each estimation. We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates, which consequently leads to better performance of the final architecture. In addition, our approach also enjoys the flexibility of being used under different hardware constraints, since the graph convolutional network has provided an efficient lookup table of the performance of architectures in the entire search space.
Advanced Equivalence Checking for Quantum Circuits

In the not-so-distant future, quantum computing will change the way we tackle certain problems. It promises to dramatically speed-up many chemical, financial, cryptographical, and machine-learning applications. However, in order to capitalize on those promises, complex design flows composed of steps, such as compilation, decomposition, mapping, or transpilation need to be employed before being able to execute a conceptual quantum algorithm on an actual device. This results in many descriptions at various levels of abstraction which may significantly differ from each other. The complexity of the underlying design problems makes it ever more important to not only provide efficient solutions for the single steps but also to verify that the originally intended functionality is preserved throughout all levels of abstraction. This motivates methods for equivalence checking of quantum circuits. However, most existing methods for this are inspired by equivalence checking in the classical realm and have merely been extended to support quantum circuits (i.e., circuits which do not only rely on 0’s and 1’s but also employ superposition and entanglement). In this work, we propose an advanced methodology that takes the different paradigms of quantum circuits not only as a burden but as an opportunity. In fact, the proposed methodology explicitly utilizes characteristics unique to quantum computing in order to overcome the shortcomings of existing approaches. We show that by exploiting the reversibility of quantum circuits, complexity can be kept feasible in many cases. Moreover, we show that, in contrast to the classical realm, simulation is very powerful in verifying quantum circuits. Experimental evaluations confirm that the resulting methodology allows one to conduct equivalence checking dramatically faster than ever before—in many cases just a single simulation run is sufficient. An implementation of the proposed equivalence checking flow is publicly available at https://iic.jku. at/eda/research/quantum_verification/.
Predictability: Can the turning point and end of an expanding epidemic be precisely forecast?

No, they can't. Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained;neither can the end of the epidemic after the turning point. An SIR model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slow-down in the number of newly infected individuals during the expansion phase allows to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the mid-term is not due to wrong or incomplete data, since it persists in error-free, synthetically produced data sets, and does not necessarily improve by using larger data sets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective or phenomenological models, and supports that only probabilities of different outcomes can be confidently given.
Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel

In this paper, a method to remove the smoke effects in laparoscopic images is presented. The proposed method is based on an image-to-image conditional generative adversarial network endowed with a dark channel’s embedded guide mask. The obtained experimental results were evaluated and quantitatively compared with desmoking state-of-art methods using the Peak Signal-to-Noise Ratio (PSNR) metrics and Structural Similarity (SSIM) index. Those results throw an improved performance compared with relevant works. Also, the processing time required by our method is 92 frames per second; a processing time that sets the foundation for a possible real-time implementation in a more modest embedded system.
Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

System identification is a key discipline within the field of automation that deals with inferring mathematical models of dynamic systems based on input-output measurements. Conventional identification methods require extensive data generation and are thus not suitable for real-time applications. In this paper, a novel real-time approach for the parametric identification of linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT) is proposed. The proposed approach requires only a single steady-state cycle of MRFT, and guarantees stability and performance in the identification and control phases. The MRFT output is passed to a trained DL model that identifies the underlying process parameters in milliseconds. A novel modification to the Softmax function is derived to better conform the DL model for the process identification task. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented approach. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.
Chemodynamics of barred galaxies in cosmological simulations: On the Milky Way’s quiescent merger history and in-situ bulge

We explore the chemodynamical properties of a sample of barred galaxies in the Auriga magnetohydrodynamical cosmological zoom-in simulations, which form boxy/peanut (b/p) bulges, and compare these to the Milky Way (MW). We show that the Auriga galaxies which best reproduce the chemodynamical properties of stellar populations in the MW bulge have quiescent merger histories since redshift z ∼ 3.5: their last major merger occurs at $t_{\rm lookback}\gt 12\, \rm Gyr$, while subsequent mergers have a stellar mass ratio of ≤1:20, suggesting an upper limit of a few per cent for the mass ratio of the recently proposed Gaia Sausage/Enceladus merger. These Auriga MW-analogues have a negligible fraction of ex-situ stars in the b/p region ($\lt 1{{\ \rm per\ cent}}$), with flattened, thick disc-like metal-poor stellar populations. The average fraction of ex-situ stars in the central regions of all Auriga galaxies with b/p’s is 3 per cent – significantly lower than in those which do not host a b/p or a bar. While the central regions of these barred galaxies contain the oldest populations, they also have stars younger than 5 Gyr (>30 per cent) and exhibit X-shaped age and abundance distributions. Examining the discs in our sample, we find that in some cases a star-forming ring forms around the bar, which alters the metallicity of the inner regions of the galaxy. Further out in the disc, bar-induced resonances lead to metal-rich ridges in the Vϕ − r plane – the longest of which is due to the Outer Lindblad Resonance. Our results suggest the Milky Way has an uncommonly quiet merger history, which leads to an essentially in-situ bulge, and highlight the significant effects the bar can have on the surrounding disc.
On the role of reduced wind mass-loss rate in enabling exoplanets to shape planetary nebulae

We use the stellar evolution code MESA-binary and follow the evolution of three exoplanets and two brown dwarfs (BDs) to determine their potential role in the future evolution of their parent star on the red giant branch (RGB) and on the asymptotic giant branch (AGB). We limit this study to exoplanets and BDs with orbits that have semi-major axis of 1AU 0.25, and having a parent star of mass M>1Mo. We find that the star HIP 75458 will engulf its planet HIP 75458 b during its RGB phase. The planet will remove the envelope and terminate the RGB evolution, leaving a bare helium core of mass 0.4Mo that will evolve to form a helium white dwarf. Only in one system out of five, the planet beta Pic c will enter the envelope of its parent star during the AGB phase. For that to occur, we have to reduce the wind mass-loss rate by a factor of about four from its commonly used value. This strengthens an early conclusion, which was based on exoplanets with circular orbits, that states that to have a non-negligible fraction of AGB stars that engulf planets we should consider lower wind mass-loss rates of isolated AGB stars (before they are spun-up by a companion). Such an engulfed planet might lead to the shaping of the AGB mass-loss geometry to form an elliptical planetary nebula.