Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface Hof: Surface Reconstruction From A Single Image Using Higher Order Function Networks

We address the problem of reconstructing a high-resolution surface representing an object from a single image. We present Surface HOF, which takes an image of an object as input and generates a mapping function for surface generation. The mapping function takes samples from a canonical domain and maps each sample to a local tangent plane on the 3D reconstruction of the object. By efficiently learning a continuous mapping function, the surface can be generated at arbitrary resolution in contrast to other methods which generate fixed resolution outputs. Experiments show that Surface HOF is more accurate and uses more efficient representations than other state of the art methods for surface reconstruction. Surface HOF is also easier to train: it requires minimal input pre-processing and output post-processing and generates surface representations that are more parameter efficient. Its accuracy and convenience make Surface HOF an appealing method for single image reconstruction.
Instance-wise Depth and Motion Learning from Monocular Videos

We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we propose a differentiable forward rigid projection module that plays a key role in our instance-wise depth and motion learning. Second, we design an instance-wise photometric and geometric consistency loss that effectively decomposes background and moving object regions. Lastly, we introduce a new auto-annotation scheme to produce video instance segmentation maps that will be utilized as input to our training pipeline. These proposed elements are validated in a detailed ablation study. Through extensive experiments conducted on the KITTI dataset, our framework is shown to outperform the state-of-the-art depth and motion estimation methods. Our code and dataset will be available at this https URL.
A Maximum Entropy approach to Massive Graph Spectra

Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing. The choice of kernel function and bandwidth are typically chosen in an ad-hoc manner and heavily affect the resulting output. We prove that kernel smoothing biases the moments of the spectral density. We propose an information theoretically optimal approach to learn a smooth graph spectral density, which fully respects the moment information. Our method's computational cost is linear in the number of edges, and hence can be applied to large networks, with millions of nodes. We apply our method to the problems to graph similarity and cluster number learning, where we outperform comparable iterative spectral approaches on synthetic and real graphs.
Observations of the Orion Source I Disk and Outflow Interface

We imaged the continuum and molecular line emission from Orion Source I (SrcI) with up to 30 mas (12 au) resolution at 43, 99, 223, and 340 GHz in an attempt to probe the structure and chemistry of the circumstellar disk and bipolar outflow associated with this high-mass protostar. The continuum spectral index ranges from ∼2 along the midplane of the disk to ∼3 along the edges, consistent with dust that is optically thick in the midplane but becomes optically thin at the periphery. Salt (NaCl) emission is visible where the dust is optically thin; it provides a unique tracer of the velocity field within the disk. All other molecules that we have mapped—H2O, AlO, SiO, SiS, SO, and SO2—appear to originate primarily in the bipolar outflow. The base of the outflow is corotating with the disk. SiS shows a filamentary structure that is most prominent along the edges of the outflow. The molecular distributions suggest that Si and Al released from dust grains in the disk react with oxygen derived from H2O to form SiO and AlO, and with SO and SO2 to form SiS.
Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is nonrandomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.
Tight Bounds on the Simultaneous Estimation of Incompatible Parameters

The estimation of multiple parameters in quantum metrology is important for a vast array of applications in quantum information processing. However, the unattainability of fundamental precision bounds for incompatible observables has greatly diminished the applicability of estimation theory in many practical implementations. The Holevo Cramer-Rao bound (HCRB) provides the most fundamental, simultaneously attainable bound for multi-parameter estimation problems. A general closed form for the HCRB is not known given that it requires a complex optimisation over multiple variables. In this work, we develop an analytic approach to solving the HCRB for two parameters. Our analysis reveals the role of the HCRB and its interplay with alternative bounds in estimation theory. For more parameters, we generate a lower bound to the HCRB. Our work greatly reduces the complexity of determining the HCRB to solving a set of linear equations that even numerically permits a quadratic speedup over previous state-of-the-art approaches. We apply our results to compare the performance of different probe states in magnetic field sensing, and characterise the performance of state tomography on the codespace of noisy bosonic error-correcting codes. The sensitivity of state tomography on noisy binomial codestates can be improved by tuning two coding parameters that relate to the number of correctable phase and amplitude damping errors. Our work provides fundamental insights and makes significant progress towards the estimation of multiple incompatible observables.
Synthesizing Observations and Theory to Understand Galactic Magnetic Fields: Progress and Challenges

Constraining dynamo theories of magnetic field origin by observation is indispensable but challenging, in part because the basic quantities measured by observers and predicted by modelers are different. We clarify these differences and sketch out ways to bridge the divide. Based on archival and previously unpublished data, we then compile various important properties of galactic magnetic fields for nearby spiral galaxies. We consistently compute strengths of total, ordered, and regular fields, pitch angles of ordered and regular fields, and we summarize the present knowledge on azimuthal modes, field parities, and the properties of non-axisymmetric spiral features called magnetic arms. We review related aspects of dynamo theory, with a focus on mean-field models and their predictions for large-scale magnetic fields in galactic discs and halos. Furthermore, we measure the velocity dispersion of H i gas in arm and inter-arm regions in three galaxies, M 51, M 74, and NGC 6946, since spiral modulation of the root-mean-square turbulent speed has been proposed as a driver of non-axisymmetry in large-scale dynamos. We find no evidence for such a modulation and place upper limits on its strength, helping to narrow down the list of mechanisms to explain magnetic arms. Successes and remaining challenges of dynamo models with respect to explaining observations are briefly summarized, and possible strategies are suggested. With new instruments like the Square Kilometre Array (SKA), large data sets of magnetic and non-magnetic properties from thousands of galaxies will become available, to be compared with theory.
Cosmic flows in the nearby Universe: new peculiar velocities from SNe and cosmological constraints

The peculiar velocity field offers a unique way to probe dark matter density field on large scales at low redshifts. In this work, we have compiled a new sample of 465 peculiar velocities from low redshift $(z < 0.067)$ Type Ia supernovae. We compare the reconstructed velocity field derived from the 2M++ galaxy redshift compilation to the supernovae, the SFI++ and the 2MTF Tully-Fisher distance catalogues. We used a forward method to jointly infer the distances and the velocities of distance indicators by comparing the observations to the reconstruction. Comparison of the reconstructed peculiar velocity fields to observations allows us to infer the cosmological parameter combination $f\sigma_8$, and the bulk flow velocity arising from outside the survey volume. The residual bulk flow arising from outside the 2M++ volume is inferred to be $171^{+11}_{-11}$ km s$^{-1}$ in the direction $l=301^{\circ} \pm 4^{\circ}$ and $b=0^{\circ} \pm 3^{\circ}$. We obtain $f\sigma_{8} = 0.400 \pm 0.017$, equivalent to $S_8 \approx \sigma_8(\Omega_m/0.3)^{0.55}=0.776\pm0.033$, which corresponds to an approximately $ 4\%\,$ statistical uncertainty on the value of $f\sigma_8$. Our inferred value is consistent with other low redshift results in the literature.
Dietary fiber and intestinal health

Nowadays,due to processed and refined foods in our diet and lifestyle changes and insufficient exercise,intestinal health status of the population is on the ebb and incidences of intestinal diseases are on the rise,which has been affecting people's health and everyday life. Toxic substances overstay in our intestines may deteriorate intestinal environment and then harm intestinal cells and trigger off various types of inflammations. 59 studies with respect to the interaction between dietary fiber and intestines in this literature review. These results indicated that consuming dietary fiber provides multiple health benefits such as improved intestinal motility,regular bowel movements without constipation,removal of toxic and / or harmful substances in the intestines,cleaning bowels and growth of intestinal probiotic bacteria. Furthermore,dietary fiber plays some part in prevention and therapy of common intestinal diseases such as Irritable Bowel Syndrome( IBS),inflammatory intestinal diseases and colon cancer. In conclusion,dietary fiber intake is beneficial to physiological intestinal function,repairing and improvement of damaged intestinal environment and maintaining intestinal health.
Modes and states in quantum optics

Quantum states of light are at the same time endowed with two superposition principles: the one of the classical Maxwell waves and the one of the quantum states occupying these waves. This article reviews the interplay between these two aspects of quantum optics. A summary of the description of multimode quantum states is presented along with an example of the characterization of correlations and entanglement with applications in metrology and quantum computation.
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply

In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success. The theory has been applied to various neural network structures, including CNNs, RNNs, Residual networks, and Batch normalization. Inevitably, recent work has also covered the use of dropout. The mean field theory shows that the existence of depth scales that limit the maximum depth of signal propagation and gradient backpropagation. However, the gradient backpropagation is derived under the gradient independence assumption that weights used during feed forward are drawn independently from the ones used in backpropagation. This is not how neural networks are trained in a real setting. Instead, the same weights used in a feed-forward step needs to be carried over to its corresponding backpropagation. Using this realistic condition, we perform theoretical computation on linear dropout networks and a series of experiments on dropout networks. Our empirical results show an interesting phenomenon that the length gradients can backpropagate for a single input and a pair of inputs are governed by the same depth scale. Besides, we study the relationship between variance and mean of statistical metrics of the gradient and shown an emergence of universality. Finally, we investigate the maximum trainable length for deep dropout networks through a series of experiments using MNIST and CIFAR10 and provide a more precise empirical formula that describes the trainable length than original work.
Callisto: Entropy-based Test Generation and Data Quality Assessment for Machine Learning Systems

Machine Learning (ML) has seen massive progress in the last decade and as a result, there is a pressing need for validating ML-based systems. To this end, we propose, design and evaluate CALLISTO– a novel test generation and data quality assessment framework. To the best of our knowledge, CALLISTO is the first black box framework to leverage the uncertainty in the prediction and systematically generate new test cases for ML classifiers. Our evaluation of CALLISTO on four real world data sets reveals thousands of errors. We also show that leveraging the uncertainty in prediction can increase the number of erroneous test cases up to a factor of 20, as compared to when no such knowledge is used for testing.CALLISTO has the capability to detect low quality data in the datasets that may contain mislabelled data. We conduct and present an extensive user study to validate the results of CALLISTO on identifying low quality data from four state-of-the-art real world datasets.
One-Shot Weakly Supervised Video Object Segmentation

Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks. Recent work has shown to perform on par with weaker levels of supervision in terms of scribbles and bounding boxes. However, there has been limited attention given to the problem of few-shot object segmentation with image-level supervision. We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embeddings. It enables our model to achieve 5.1% improvement over previously proposed image-level few-shot object segmentation. Our method compares relatively close to the state of the art methods that use strong supervision, while ours use the least possible supervision. We further propose a novel setup for few-shot weakly supervised video object segmentation(VOS) that relies on image-level labels for the first frame. The proposed setup uses weak annotation unlike semi-supervised VOS setting that utilizes strongly labelled segmentation masks. The setup evaluates the effectiveness of generalizing to novel classes in the VOS setting. The setup splits the VOS data into multiple folds with different categories per fold. It provides a potential setup to evaluate how few-shot object segmentation methods can benefit from additional object poses, or object interactions that is not available in static frames as in PASCAL-5i benchmark.
Equilibria of an anisotropic nonlocal interaction equation: Analysis and numerics

In this paper, we study the equilibria of an anisotropic, nonlocal aggregation equation with nonlinear diffusion which does not possess a gradient flow structure. Here, the anisotropy is induced by an underlying tensor field. While anisotropic forces cannot be associated with a potential in general and stationary solutions of anisotropic aggregation equations generally cannot be regarded as minimizers of an energy functional, we derive equilibrium conditions for stationary line patterns in the setting of spatially homogeneous tensor fields which can be regarded as the minimizers of a regularised energy functional depending on a scalar potential. In particular, this dimension reduction allows us to study the associated one-dimensional problem instead of the two-dimensional setting. For spatially homogeneous tensor fields, we show the existence of energy minimisers, establish $\Gamma$-convergence of the regularised energy functionals as the diffusion coefficient vanishes, and prove the convergence of minimisers of the regularised energy functional to minimisers of the non-regularised energy functional. Further, we investigate properties of stationary solutions on different domains. Finally, we prove weak convergence of a numerical scheme for the numerical solution of the anisotropic, nonlocal aggregation equation with nonlinear diffusion and any underlying tensor field, and show numerical results.
Polar Codes’ Simplicity, Random Codes’ Durability

Over any discrete memoryless channel, we offer error correction codes such that: for one, their block error probabilities and code rates scale like random codes’; and for two, their encoding and decoding complexities scale like polar codes’. Quantitatively, for any constants <inline-formula> <tex-math notation="LaTeX">$\pi,\rho >0$ </tex-math></inline-formula> such that <inline-formula> <tex-math notation="LaTeX">$\pi +2\rho < 1$ </tex-math></inline-formula>, we construct a sequence of block codes with block length <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> approaching infinity, block error probability <inline-formula> <tex-math notation="LaTeX">$\exp (-{N}^\pi)$ </tex-math></inline-formula>, code rate <inline-formula> <tex-math notation="LaTeX">${N}^{-\rho }$ </tex-math></inline-formula> less than the Shannon capacity, and encoding and decoding complexity <inline-formula> <tex-math notation="LaTeX">${O}({N}\log {N})$ </tex-math></inline-formula> per code block. The core theme is to incorporate polar coding (which limits the complexity to polar’s realm) with large, random, dynamic kernels (which boosts the performance to random’s realm). The putative codes are optimal in the following manner: Should <inline-formula> <tex-math notation="LaTeX">$\pi +2\rho >1$ </tex-math></inline-formula>, no such codes exist over generic channels regardless of complexity.
Scale-wise Convolution for Image Restoration

While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Naively applying those scale-invariant techniques (e.g., multi-scale testing, random-scale data augmentation) to image restoration tasks usually leads to inferior performance. In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. Inspired from spatial-wise convolution for shift-invariance, “scale-wise convolution” is proposed to convolve across multiple scales for scale-invariance. In our scale-wise convolutional network (SCN), we first map the input image to the feature space and then build a feature pyramid representation via bi-linear down-scaling progressively. The feature pyramid is then passed to a residual network with scale-wise convolutions. The proposed scale-wise convolution learns to dynamically activate and aggregate features from different input scales in each residual building block, in order to exploit contextual information on multiple scales. In experiments, we compare the restoration accuracy and parameter efficiency among our model and many different variants of multi-scale neural networks. The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal. Code and models are available at: https://github.com/ychfan/scn_sr.
Markovianity of an emitter coupled to a structured spin-chain bath

We analyze the dynamics of a spin 1/2 subsystem coupled to a spin chain. We simulate numerically the full quantum many-body system for various sets of parameters and initial states of the chain, and characterize the divisibility of the subsystem dynamics, i.e. whether it is Markovian and can be described by a (time dependent) master equation. We identify regimes in which the subsystem admits such Markovian description, despite the many-body setting, and provide insight about why the same is not possible in other regimes. Interestingly, coupling the subsystem at the edge, instead of the center, of the chain gives rise to qualitatively distinct behavior.
Identical or fraternal twins? The chemical homogeneity of wide binaries from Gaia DR2

One of the high-level goals of Galactic archaeology is chemical tagging of stars across the Milky Way to piece together its assembly history. For this to work, stars born together must be uniquely chemically homogeneous. Wide binary systems are an important laboratory to test this underlying assumption. Here, we present the detailed chemical abundance patterns of 50 stars across 25 wide binary systems comprised of main-sequence stars of similar spectral type identified in Gaia DR2 with the aim of quantifying their level of chemical homogeneity. Using high-resolution spectra obtained with McDonald Observatory, we derive stellar atmospheric parameters and precise detailed chemical abundances for light/odd-Z (Li, C, Na, Al, Sc, V, Cu), α (Mg, Si, Ca), Fe-peak (Ti, Cr, Mn, Fe, Co, Ni, Zn), and neutron capture (Sr, Y, Zr, Ba, La, Nd, Eu) elements. Results indicate that 80 per cent (20 pairs) of the systems are homogeneous in [Fe/H] at levels below 0.02 dex. These systems are also chemically homogeneous in all elemental abundances studied, with offsets and dispersions consistent with measurement uncertainties. We also find that wide binary systems are far more chemically homogeneous than random pairings of field stars of similar spectral type. These results indicate that wide binary systems tend to be chemically homogeneous but in some cases they can differ in their detailed elemental abundances at a level of [X/H] ∼ 0.10 dex, overall implying chemical tagging in broad strokes can work.
Establishing a Search String to Detect Secondary Studies in Software Engineering

Context: A tertiary study can be performed to identify related reviews on a topic of interest. However, the elaboration of an appropriate and effective search string to detect secondary studies is challenging for Software Engineering (SE) researchers. Objective: The main goal of this study is to propose a suitable search string to detect secondary studies in SE, addressing issues such as the quantity of applied terms, relevance, recall and precision. Method: We analyzed seven tertiary studies under two perspectives: (1) structure – strings’ terms to detect secondary studies; and (2) field: where searching – titles alone or abstracts alone or titles and abstracts together, among others. We validate our string by performing a twostep validation process. Firstly, we evaluated the capability to retrieve secondary studies over a set of 1537 secondary studies included in 24 tertiary studies in SE. Secondly, we evaluated the general capacity of retrieving secondary studies over an automated search using the Scopus digital library. Results: Our string was capable to retrieve an optimum value of over 90% of the included secondary studies (recall) with a high general precision of almost 60%. Conclusion: The suitable search string for finding secondary studies in SE contains the terms “systematic review”, “literature review”, “systematic mapping”, “mapping study” and “systematic map”.