Miracle-dz/UniFAR-FTUs huggingface dataset:
Interstellar Formamide (NH2CHO), a Key Prebiotic Precursor

Formamide (NH2CHO) has been identified as a potential precursor of a wide variety of organic compounds essential to life, and many biochemical studies propose it likely played a crucial role in the context of the origin of life on our planet. The detection of formamide in comets, which are believed to have—at least partially— inherited their current chemical composition during the birth of the Solar System, raises the question whether a non-negligible amount of formamide may have been exogenously delivered onto a very young Earth about four billion years ago. A crucial part of the effort to answer this question involves searching for formamide in regions where stars and planets are forming today in our Galaxy, as this can shed light on its formation, survival, and chemical reprocessing along the different evolutionary phases leading to a star and planetary system such as our own. The present review primarily addresses the chemistry of formamide in the interstellar medium, from the point of view of (i) ast.
Strong Modification of Radiative Transition Rates due to a Breit-Interaction-Induced Avoided Crossing

We present the observations of x-rays emitted from the $1s2s^{2}2p_{1/2}2p_{3/2}$ inner shell excited state of B-like W and Bi ions. The relative transition rates are obtained for two dominant radiative transitions to $1s^{2}2s^{2}2p_{1/2}$ and $1s^{2}2s^{2}2p_{3/2}$. The experimental results and the comparison with rigorous relativistic calculations show that the rates of the strong electric dipole allowed $1s^22s^22p$ -- $1s2s^22p^2$ transitions are strongly modified due to a drastic change in the wavefunction caused by the Breit interaction.
PairNorm: Tackling Oversmoothing in GNNs

The performance of graph neural nets (GNNs) is known to gradually decrease with increasing number of layers. This decay is partly attributed to oversmoothing, where repeated graph convolutions eventually make node embeddings indistinguishable. We take a closer look at two different interpretations, aiming to quantify oversmoothing. Our main contribution is PairNorm, a novel normalization layer that is based on a careful analysis of the graph convolution operator, which prevents all node embeddings from becoming too similar. What is more, PairNorm is fast, easy to implement without any change to network architecture nor any additional parameters, and is broadly applicable to any GNN. Experiments on real-world graphs demonstrate that PairNorm makes deeper GCN, GAT, and SGC models more robust against oversmoothing, and significantly boosts performance for a new problem setting that benefits from deeper GNNs. Code is available at this https URL.
Green Tea Extract Along with Rumen Protected Choline improves Immune Status by Modulating Oxidative Stress in Transition Karan Fries Cows

The present experiment was carried out on thirty-two pregnant Karan Fries (KF) cows. In control group, cows were fed basal diet. In T1 each cow was fed rumen protected choline (RPC) (55g/day), in T2 - green tea extract (GTE) (3g/d) and in T3- RPC + GTE (55+3)g/day along with basal diet. The treatment was given 30 days before calving to 60 days after calving. No significant (p ≥ 0.05) difference was observed across the groups for somatic cell count. The concentration of total immunoglobulin concentration increased significantly (p≤ 0.01) in T1, T2 and T3 as compared to control. Serum level of TBARS (P
Multichannel Speech Enhancement by Raw Waveform-Mapping Using Fully Convolutional Networks

In recent years, waveform-mapping-based speech enhancement (SE) methods have garnered significant attention. These methods generally use a deep learning model to directly process and reconstruct speech waveforms. Because both the input and output are in waveform format, the waveform-mapping-based SE methods can overcome the distortion caused by imperfect phase estimation, which may be encountered in spectral-mapping-based SE systems. So far, most waveform-mapping-based SE methods have focused on single-channel tasks. In this article, we propose a novel fully convolutional network (FCN) with Sinc and dilated convolutional layers (termed SDFCN) for multichannel SE that operates in the time domain. We also propose an extended version of SDFCN, called the residual SDFCN (termed rSDFCN). The proposed methods are evaluated on three multichannel SE tasks, namely the dual-channel inner-ear microphones SE task, the distributed microphones SE task, and the CHiME-3 dataset. The experimental results confirm the outstanding denoising capability of the proposed SE systems on the three tasks and the benefits of using the residual architecture on the overall SE performance.
Learning to combine primitive skills: A step towards versatile robotic manipulation §

Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision. Traditional task and motion planning (TAMP) methods can solve complex tasks but require full state observability and are not adapted to dynamic scene changes. Recent learning methods can operate directly on visual inputs but typically require many demonstrations and/or task-specific reward engineering. In this work we aim to overcome previous limitations and propose a reinforcement learning (RL) approach to task planning that learns to combine primitive skills. First, compared to previous learning methods, our approach requires neither intermediate rewards nor complete task demonstrations during training. Second, we demonstrate the versatility of our vision-based task planning in challenging settings with temporary occlusions and dynamic scene changes. Third, we propose an efficient training of basic skills from few synthetic demonstrations by exploring recent CNN architectures and data augmentation. Notably, while all of our policies are learned on visual inputs in simulated environments, we demonstrate the successful transfer and high success rates when applying such policies to manipulation tasks on a real UR5 robotic arm.
Local gravitational energy in higher dimensions

In general relativity, the local gravitational energy is best characterised by the quasilocal mass. The small sphere limit of quasilocal mass provides us the most local notion of gravitational energy. In four dimensions, the limits were shown be the stress tensor in non-vacuum and the Bel-Robinson tensor in vacuum. We study the local gravitational energy in higher dimensions through the lens of the small sphere limits of various quasilocal mass proposals which can be appropriately generalised beyond four dimensions, and report a new quantity $\Q$ which potentially characterises the local gravitational energy content in vacuum. We find that the limits at presence of matter yield the stress tensor as expected, but the vacuum limits are not proportional to the Bel-Robinson superenergy $Q$ in dimensions $n>4$. The result defies the role of the Bel-Robinson superenergy as characterising the gravitational energy in higher dimensions, albeit the fact that it uniquely generalises. More surprisingly, $\Q$ replace the Bel-Robinson superenergy $Q$ in all three quasilocal mass proposals that we study. However, $\Q$ cannot be directly interpreted as a local gravitational energy because of its non-positivity. The physical meaning of $\Q$ awaits more evidence from investigating other quasilocal masses in higher dimensions.
A Symmetric Equilibrium Generative Adversarial Network with Attention Refine Block for Retinal Vessel Segmentation

Objective: Recognizing retinal fundus vessel abnormity is vital to early diagnosis of ophthalmological diseases and cardiovascular events. However, segmentation results are highly influenced by elusive thin vessels. In this work, we present a synthetic network, including a symmetric equilibrium generative adversarial network (SEGAN), mul-ti-scale features refine blocks (MSFRB), and attention mechanism (AM) to enhance the performance on vessel segmentation especially for thin vessels. Method: The proposed network is granted powerful multi-scale repre-sentation capability. First, SEGAN is proposed to construct a symmetric adversarial architecture, which forces gener-ator to produce more realistic images with local details. Second, MSFRB are devised to prevent high-resolution features from being obscured, thereby preserving multi-scale features. Finally, the AM is employed to encourage the network to concentrate on discriminative features. Results: On public dataset DRIVE, STARE, and CHASEDB1, we evaluate our network quantitatively and compare it with state-of-the-art works. The ablation experiment shows that SEGAN, MSFRB, and AM both contribute to the desirable performance of our network. Conclusion: The proposed network outperforms other strategies and effectively functions in elusive vessels segmentation, achieving highest scores in Sensitivity, G-Mean, Precision, and F1-Score while maintaining the top level in other metrics. Significance: The appreciable per-formance and high computational efficiency offer great potential in clinical retinal vessel segmentation application. Meanwhile, the network could be utilized to extract detail information on other biomedical issues.
A Model for the Optimal Management of Inflation

Consider a central bank that can adjust the inflation rate by increasing and decreasing the level of the key interest rate. Each intervention gives rise to proportional costs, and the central bank faces also a running penalty, e.g., due to misaligned levels of inflation and interest rate. We model the resulting minimization problem as a Markovian degenerate two-dimensional bounded-variation stochastic control problem. Its characteristic is that the mean-reversion level of the diffusive inflation rate is an affine function of the purely controlled interest rate's current value. By relying on a combination of techniques from viscosity theory and free-boundary analysis, we provide the structure of the value function and we show that it satisfies a second-order smooth-fit principle. Such a regularity is then exploited in order to determine a system of functional equations solved by the two monotone curves that split the control problem's state space in three connected regions.
DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation

We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 3000-5000 data frames. We generate the sequences using realistic home layouts. For that we sample trajectories that simulate motions of a simple home robot, and then render the frames along the trajectories. Each data frame contains a) RGB images generated using physically-based rendering, b) simulated depth measurements, c) simulated IMU readings and d) ground truth occupancy grid of a house. Our dataset serves a wider range of purposes compared to existing datasets and is the first large-scale benchmark focused on the mapping component of SLAM. The dataset is split into train/validation/test parts sampled from different sets of virtual houses. We present benchmarking results for both classical geometry-based [1], [2] and recent learning-based [3] SLAM algorithms, a baseline mapping method [4], semantic segmentation [5] and panoptic segmentation [6]. The dataset and source code for reproducing our experiments will be publicly available at the time of publication.
DCTD: Deep Conditional Target Densities for Accurate Regression

While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x, y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences often lack a natural probabilistic meaning. We address these issues by proposing Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from (x, y). This model of p(y|x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our regression model achieves a 1.9% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-of-the-art on visual tracking when applied for bounding box regression.
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations

Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyze the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We then identify the conditions that allow consistency regularization to work even without such low-density regions. This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant, CowMix, leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets.
Convergence Diagnostics for Markov Chain Monte Carlo

Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is indispensable for performing Bayesian analysis. Two critical questions that MCMC practitioners need to address are where to start and when to stop the simulation. Although a great amount of research has gone into establishing convergence criteria and stopping rules with sound theoretical foundation, in practice, MCMC users often decide convergence by applying empirical diagnostic tools. This review article discusses the most widely used MCMC convergence diagnostic tools. Some recently proposed stopping rules with firm theoretical footing are also presented. The convergence diagnostics and stopping rules are illustrated using three detailed examples.
Ankle overuse injuries in soccer players. Morphological adaptation of the talus in the anterior impingement.

: The ankle joints of 88 professional soccer players have been studied. The high presence of radiographic changes unchained in its great number by impingement phenomena among the articular areas, being 59.1% for the right ankle and 52.3% for the left one, has been confirmed. These radiographic images seem to indicate in the ankle joint the existance of osseous remodeling phenomena to face overuse and overstress, having in the major part of cases no clinical translation, so that we might denominate them as the "radiographic arthrosis of sportsman". Two types of morphological adaptation of the neck of the talus are described to face the impingement unchained in it by the anterior articular border of the distal epiphysis of the tibia. The Type A, in which changes of the "squatting facet" of the talus are detected and Type B characterized by talotibial exostosis.
Molecules in the Cep E-mm jet: evidence for shock-driven photochemistry?

The chemical composition of protostellar jets and its origin are still badly understood. More observational constraints are needed to make progress. With that objective, we have carried out a systematic search for molecular species in the jet of Cep E-mm, a template for intermediate-mass Class 0 protostars, associated with a luminous, high-velocity outflow. We made use of an unbiased spectral line survey in the range 72–350 GHz obtained with the IRAM 30-m telescope, complementary observations of the CO J = 3–2 transition with the JCMT, and observations at 1 arcsec angular resolution of the CO J = 2–1 transition with the IRAM Plateau de Bure Interferometer. In addition to CO, we have detected rotational transitions from SiO, SO, H2CO, CS, HCO+, and HCN. A strong chemical differentiation is observed in the southern and northern lobes of the jet. Radiative transfer analysis in the large velocity gradient approximation yields typical molecular abundances of the order of 10−8 for all molecular species other than CO. Overall, the jets exhibit an unusual chemical composition, as CS, SO, and H2CO are found to be the most abundant species, with a typical abundance of (3–4)× 10−8. The transverse size of the CO jet emission estimated from interferometric observations is about 1000 au, suggesting that we are detecting emission from a turbulent layer of gas entrained by the jet in its propagation and not the jet itself. We propose that some molecular species could be the signatures of the specific photochemistry driven by the UV radiation field generated in the turbulent envelope.
Quantum and semiclassical exceptional points of a linear system of coupled cavities with losses and gain within the Scully-Lamb laser theory

In the past few decades, many works have been devoted to the study of exceptional points (EPs), i.e., exotic degeneracies of non-Hermitian systems. The usual approach in those studies involves the introduction of a phenomenological effective non-Hermitian Hamiltonian (NHH), where the gain and losses are incorporated as the imaginary frequencies of fields and from which the Hamiltonian EPs (HEPs) are derived. Although this approach can provide valid equations of motion for the fields in the classical limit, its application in the derivation of EPs in the quantum regime is questionable. Recently, a framework [Minganti {\it et al.}, \href{https://doi.org/10.1103/PhysRevA.100. 062131}{Phys. Rev. A {\bf 100}, 062131 (2019)}], which allows one to determine quantum EPs from a Liouvillian EP (LEP), rather than from an NHH, has been proposed. Compared to the NHHs, a Liouvillian naturally includes quantum noise effects via quantum-jump terms, thus allowing one to consistently determine its EPs purely in the quantum regime. In this work we study a non-Hermitian system consisting of coupled cavities with unbalanced gain and losses, where the gain is far from saturation, i.e, the system is assumed to be linear. We apply both formalisms, based on an NHH and a Liouvillian within the Scully-Lamb laser theory, to determine and compare the corresponding HEPs and LEPs in the semiclassical and quantum regimes. {Our results indicate that, although the overall spectral properties of the NHH and the corresponding Liouvillian for a given system can differ substantially, their LEPs and HEPs occur for the same combination of system parameters. }
RLBench: The Robot Learning Benchmark & Learning Environment

We present a challenging new benchmark and learning-environment for robot learning: RLBench. The benchmark features 100 completely unique, hand-designed tasks, ranging in difficulty from simple target reaching and door opening to longer multi-stage tasks, such as opening an oven and placing a tray in it. We provide an array of both proprioceptive observations and visual observations, which include rgb, depth, and segmentation masks from an over-the-shoulder stereo camera and an eye-in-hand monocular camera. Uniquely, each task comes with an infinite supply of demos through the use of motion planners operating on a series of waypoints given during task creation time; enabling an exciting flurry of demonstration-based learning possibilities. RLBench has been designed with scalability in mind; new tasks, along with their motion-planned demos, can be easily created and then verified by a series of tools, allowing users to submit their own tasks to the RLBench task repository. This large-scale benchmark aims to accelerate progress in a number of vision-guided manipulation research areas, including: reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and in particular, few-shot learning. With the benchmark's breadth of tasks and demonstrations, we propose the first large-scale few-shot challenge in robotics. We hope that the scale and diversity of RLBench offers unparalleled research opportunities in the robot learning community and beyond. Benchmarking code and videos can be found at https://sites.google. com/view/rlbench.
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks

Recent theoretical work has guaranteed that overparameterized networks trained by gradient descent achieve arbitrarily low training error, and sometimes even low test error. The required width, however, is always polynomial in at least one of the sample size $n$, the (inverse) target error $1/\epsilon$, and the (inverse) failure probability $1/\delta$. This work shows that $\widetilde{\Theta}(1/\epsilon)$ iterations of gradient descent with $\widetilde{\Omega}(1/\epsilon^2)$ training examples on two-layer ReLU networks of any width exceeding $\mathrm{polylog}(n,1/\epsilon,1/\delta)$ suffice to achieve a test misclassification error of $\epsilon$. We also prove that stochastic gradient descent can achieve $\epsilon$ test error with polylogarithmic width and $\widetilde{\Theta}(1/\epsilon)$ samples. The analysis relies upon the separation margin of the limiting kernel, which is guaranteed positive, can distinguish between true labels and random labels, and can give a tight sample-complexity analysis in the infinite-width setting
Evolution of the Stellar Mass–Metallicity Relation. II. Constraints on Galactic Outflows from the Mg Abundances of Quiescent Galaxies

We present the stellar mass–[Fe/H] and mass–[Mg/H] relation of quiescent galaxies in two galaxy clusters at z ∼ 0.39 and z ∼ 0.54. We derive the age, [Fe/H], and [Mg/Fe] for each individual galaxy using a full-spectrum fitting technique. By comparing with the relations for z ∼ 0 Sloan Digital Sky Survey galaxies, we confirm our previous finding that the mass–[Fe/H] relation evolves with redshift. The mass–[Fe/H] relation at higher redshift has lower normalization and possibly steeper slope. However, based on our sample, the mass–[Mg/H] relation does not evolve over the observed redshift range. We use a simple analytic chemical evolution model to constrain the average outflow that these galaxies experience over their lifetime, via the calculation of mass-loading factor. We find that the average mass-loading factor η is a power-law function of galaxy stellar mass, . The measured mass-loading factors are consistent with the results of other observational methods for outflow measurements and with the predictions where outflow is caused by star formation feedback in turbulent disks.
Categorifying the algebra of indexing systems

The homotopy category of $N_\infty$ operads is equivalent to a finite lattice, and as the ambient group varies, there are various image constructions between these lattices. In this paper, we explain how to lift this algebraic structure back to the operad level. We show that lattice joins and meets correspond to derived operadic coproducts and products, and we show that the image constructions correspond to derived operadic induction, restriction, and coinduction, at least when taken along an injective homomorphism. We also prove that a derived variant of the Boardman-Vogt tensor product lifts the join. Our result does not resolve Blumberg and Hill's conjecture on the usual tensor product, but it does imply that every $N_\infty$ ring spectrum can be replaced with an equivalent spectrum, which is equipped with a self-interchanging operad action.