Miracle-dz/UniFAR-FTUs huggingface dataset:
New Growth Mechanism of Dust Grains in Protoplanetary Disks with Magnetically Driven Disk Winds

We discovered a new growth mode of dust grains to kilometer-size bodies in protoplanetary disks that evolve via viscous accretion and magnetically driven disk winds (MDWs). We solved an approximate coagulation equation of dust grains with time-evolving disks that consist of both gas and solid components using a one-dimensional model. With grain growth, all solid particles initially drift inward toward the central star due to the gas drag force. However, the radial profile of gas pressure, P, is modified by the MDW that disperses the gas in an inside-out manner. Consequently, a local concentration of solid particles is created by the converging radial flux of drifting dust grains at the location with a convex-upward profile of P. When the dimensionless stopping time, St, exceeds unity there, the solid particles spontaneously reach the growth-dominated state because of the positive feedback between the suppressed radial drift and the enhanced accumulation of dust particles that drift from the outer part. Once the solid particles are in the drift-limited state, the above-mentioned condition of St for dust growth is equivalent to Σd/Σg ≳ η, where Σd/Σg is the dust-to-gas surface-density ratio and η is the dimensionless radial pressure-gradient force. As a consequence of the successful growth of dust grains, a ring-like structure containing planetesimal-size bodies is formed at the inner part of the protoplanetary disks. Such a ring-shaped concentration of planetesimals is expected to play a vital role in the subsequent planet formation.
Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, which is weak in describing detailed intrinsic information from modalities and easily being ineffective when the domain shifts (e.g., cross attack and cross ethnicity). In this paper, we extend the central difference convolutional networks (CDCN) [39] to a multimodal version, intending to capture intrinsic spoofing patterns among three modalities (RGB, depth and infrared). Meanwhile, we also give an elaborate study about singlemodal based CDCN. Our approach won the first place in "Track Multi-Modal" as well as the second place in "Track Single-Modal (RGB)" of ChaLearn Face Antispoofing Attack Detection Challenge@CVPR2020 [20]. Our final submission obtains 1.02±0.59% and 4.84±1.79% ACER in "Track Multi-Modal" and "Track Single-Modal (RGB)", respectively. The codes are available at https://github.com/ZitongYu/CDCN.
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging

Low fluence illumination sources can facilitate clinical transition of photoacoustic imaging because they are rugged, portable, affordable, and safe. However, these sources also decrease image quality due to their low fluence. Here, we propose a denoising method using a multi-level wavelet-convolutional neural network to map low fluence illumination source images to its corresponding high fluence excitation map. Quantitative and qualitative results show a significant potential to remove the background noise and preserve the structures of target. Substantial improvements up to 2.20, 2.25, and 4.3-fold for PSNR, SSIM, and CNR metrics were observed, respectively. We also observed enhanced contrast (up to 1.76-fold) in an in vivo application using our proposed methods. We suggest that this tool can improve the value of such sources in photoacoustic imaging.
PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation

Code-mixing is the phenomenon of using more than one language in a sentence. In the multilingual communities, it is a very frequently observed pattern of communication on social media platforms. Flexibility to use multiple languages in one text message might help to communicate efficiently with the target audience. But, the noisy user-generated code-mixed text adds to the challenge of processing and understanding natural language to a much larger extent. Machine translation from monolingual source to the target language is a well-studied research problem. Here, we demonstrate that widely popular and sophisticated translation systems such as Google Translate fail at times to translate code-mixed text effectively. To address this challenge, we present a parallel corpus of the 13,738 code-mixed Hindi-English sentences and their corresponding human translation in English. In addition, we also propose a translation pipeline build on top of Google Translate. The evaluation of the proposed pipeline on PHINC demonstrates an increase in the performance of the underlying system. With minimal effort, we can extend the dataset and the proposed approach to other code-mixing language pairs.
Anomalous fractional quantum Hall effect and multi-valued Hamiltonians

We discuss anomalous fractional quantum Hall effect that exists without external magnetic field. We propose that excitations in such systems may be described effectively by non-interacting particles with the Hamiltonians defined on the Brillouin zone with a branch cut. Hall conductivity of such a system is expressed through the one-particle Green function. We demonstrate that for the Hamiltonians of the proposed type this expression takes fractional values times Klitzing constant. Possible relation of the proposed construction with degeneracy of ground state is discussed as well.
Planar Bichromatic Bottleneck Spanning Trees

Given a set $P$ of $n$ red and blue points in the plane, a \emph{planar bichromatic spanning tree} of $P$ is a spanning tree of $P$, such that each edge connects between a red and a blue point, and no two edges intersect. In the bottleneck planar bichromatic spanning tree problem, the goal is to find a planar bichromatic spanning tree $T$, such that the length of the longest edge in $T$ is minimized. In this paper, we show that this problem is NP-hard for points in general position. Moreover, we present a polynomial-time $(8\sqrt{2})$-approximation algorithm, by showing that any bichromatic spanning tree of bottleneck $\lambda$ can be converted to a planar bichromatic spanning tree of bottleneck at most $8\sqrt{2}\lambda$.
BuGL - A Cross-Language Dataset for Bug Localization

Bug Localization is the process of locating potential error-prone files or methods from a given bug report and source code. There is extensive research on bug localization in the literature that focuses on applying information retrieval techniques or machine learning/deep learning approaches or both, to detect location of bugs. The common premise for all approaches is the availability of a good dataset, which in this case, is the standard benchmark dataset that comprises of 6 Java projects and in some cases, more than 6 Java projects. The existing dataset do not comprise projects of other programming languages, despite of the need to investigate specific and cross project bug localization. To the best of our knowledge, we are not aware of any dataset that addresses this concern. In this paper, we present BuGL, a large-scale cross-language dataset. BuGL constitutes of more than 10,000 bug reports drawn from open-source projects written in four programming languages, namely C, C++, Java, and Python. The dataset consists of information which includes Bug Reports and Pull-Requests. BuGL aims to unfold new research opportunities in the area of bug localization.
Electrical Route to Realising Intensity Simulation of Heavy Rain Events in Tropics

In the backdrop of a revolution in weather prediction by Numerical Weather Prediction (NWP) models, quantitative prediction of intensity of heavy rainfall events and associated disasters has remained a challenge. Encouraged by compelling evidence of electrical influences on cloud/rain microphysical processes, here we propose a hypothesis that modification of raindrop size distribution (RDSD) towards larger drop sizes through enhanced collision-coalescence facilitated by cloud electric fields could be one of the factors responsible for intensity errors in weather/climate models. The robustness of the hypothesis is confirmed through a series of simulations of strongly electrified (SE) rain events and weakly electrified (WE) events with a convection-permitting weather prediction model incorporating the electrically modified RDSD parameters in the model physics. Our results indicate a possible roadmap for improving hazard prediction associated with extreme rainfall events in weather prediction models and climatological dry bias of precipitation simulation in many climate models.
MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition

We present an MatchboxNet - an end-to-end neural network for speech command recognition. MatchboxNet is a deep residual network composed from blocks of 1D time-channel separable convolution, batch-normalization, ReLU and dropout layers. MatchboxNet reaches state-of-the-art accuracy on the Google Speech Commands dataset while having significantly fewer parameters than similar models. The small footprint of MatchboxNet makes it an attractive candidate for devices with limited computational resources. The model is highly scalable, so model accuracy can be improved with modest additional memory and compute. Finally, we show how intensive data augmentation using an auxiliary noise dataset improves robustness in the presence of background noise.
Effects of the COVID-19 Pandemic on Population Mobility under Mild Policies: Causal Evidence from Sweden

Sweden has adopted far less restrictive social distancing policies than most countries following the COVID-19 pandemic. This paper uses data on all mobile phone users, from one major Swedish mobile phone network, to examine the impact of the Coronavirus outbreak under the Swedish mild recommendations and restrictions regime on individual mobility and if changes in geographical mobility vary over different socio-economic strata. Having access to data for January-March in both 2019 and 2020 enables the estimation of causal effects of the COVID-19 outbreak by adopting a Difference-in-Differences research design. The paper reaches four main conclusions: (i) The daytime population in residential areas increased significantly (64 percent average increase); (ii) The daytime presence in industrial and commercial areas decreased significantly (33 percent average decrease); (iii) The distance individuals move from their homes during a day was substantially reduced (38 percent decrease in the maximum distance moved and 36 percent increase in share of individuals who move less than one kilometer from home); (iv) Similar reductions in mobility were found for residents in areas with different socioeconomic and demographic characteristics. These results show that mild government policies can compel people to adopt social distancing behavior.
Causal Effects of Prenatal Drug Exposure on Birth Defects with Missing by Terathanasia

We investigate the causal effects of drug exposure on birth defects, motivated by a recent cohort study of birth outcomes in pregnancies of women treated with a given medication, that revealed a higher rate of major structural birth defects in infants born to exposed versus unexposed women. An outstanding problem in this study was the missing birth defect outcomes among pregnancy losses resulting from spontaneous abortion. This led to missing not at random because, according to the theory of "terathanasia", a defected fetus is more likely to be spontaneously aborted. In addition, the previous analysis stratified on live birth against spontaneous abortion, which was itself a post-exposure variable and hence did not lead to causal interpretation of the stratified results. In this paper we aimed to estimate and provide inference for the causal parameters of scientific interest, including the principal effects, making use of the missing data mechanism informed by terathanasia. During this process we also dealt with complications in the data including left truncation, observational nature, and rare events. We report our findings which shed light on how studies on causal effects of medication or other exposures during pregnancy may be analyzed.
Can axion clumps be formed in a pre-inflationary scenario?

The QCD axion and an axion-like particle (ALP) are compelling candidates of dark matter. For the QCD axion, it is known that when the Peccei-Quinn (PQ) symmetry is spontaneously broken after inflation, the large initial fluctuation can lead to axion clump formation. On the other hand, when the symmetry is already broken during inflation, it has been believed that the axion clump formation does not occur due to the small amplitude of the initial axion fluctuation. We revisit this prevailing understanding, considering both the QCD axion and an ALP. In our analysis, the clump formation was not observed for the QCD axion even if we consider an extremely fine-tuned initial condition. Meanwhile, for an ALP which allows a more general potential form, we show that the clump formation can take place through the tachyonic instability or/and the resonance instability, considering a multiple cosine potential.
Learning Geometric Word Meta-Embeddings

We propose a geometric framework for learning meta-embeddings of words from different embedding sources. Our framework transforms the embeddings into a common latent space, where, for example, simple averaging or concatenation of different embeddings (of a given word) is more amenable. The proposed latent space arises from two particular geometric transformations - source embedding specific orthogonal rotations and a common Mahalanobis metric scaling. Empirical results on several word similarity and word analogy benchmarks illustrate the efficacy of the proposed framework.
Angular momentum balance and vortex production in wall-bounded flows

To produce a vortex, a torque must be applied to the fluid. In viscous fluids, the torques that produce turbulent vortices result from the loss of symmetry of the stress tensor, once the viscous friction exceeds the shear stress resistance of the fluid. In wall-bounded flows, in particular, the turbulent vortices form in a thin layer of fluid adjacent to the wall, practically coinciding with the so-called viscous sublayer, where the viscous friction reaches the largest values. The present paper determines a vortex structure for this sublayer, consistent with the well-known linearity of the diagram of the mean streamwise velocity of this region. The analysis enables us to calculate the diameter, angular velocity, and interaxis of the vortices in the viscous sublayer in steady-state conditions. The lifting force that makes the vortices migrate from the wall towards the mainstream flow is determined, and the crucial role played by gyroscopic precession in the reorientation of the vortex axis is discussed.
Counterfactual confounding adjustment for feature representations learned by deep models: with an application to image classification tasks

Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML). While counterfactual thinking has been leveraged in ML tasks that aim to predict the consequences of actions/interventions, it has not yet been applied to more traditional/static supervised learning tasks, such as the prediction of labels in image classification tasks. Here, we propose a counterfactual approach to remove/reduce the influence of confounders from the predictions generated a deep neural network (DNN). The idea is to remove confounding from the feature representations learned by DNNs in anticausal prediction tasks. By training an accurate DNN using softmax activation at the classification layer, and then adopting the representation learned by the last layer prior to the output layer as our features, we have that, by construction, the learned features will fit well a (multi-class) logistic regression model, and will be linearly associated with the labels. Then, in order to generate classifiers that are free from the influence of the observed confounders we: (i) use linear models to regress each learned feature on the labels and on the confounders and estimate the respective regression coefficients and model residuals; (ii) generate new counterfactual features by adding back to the estimated residuals to a linear predictor which no longer includes the confounder variables; and (iii) train and evaluate a logistic classifier using the counterfactual features as inputs. We validate the proposed methodology using colored versions of the MNIST and fashion-MNIST datasets, and show how the approach can effectively combat confounding and improve generalization in the context of dataset shift. Finally, we also describe how to use conditional independence tests to evaluate if the counterfactual approach has effectively removed the confounder signals from the predictions.
Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills

Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow. In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.
Geodesic and orthogeodesic identities on hyperbolic surfaces

The lengths of geodesics on hyperbolic surfaces satisfy intriguing equations, known as identities, relating these lengths to geometric quantities of the surface. This paper is about a large family of identities that relate lengths of closed geodesics and orthogeodesics to boundary lengths or number of cusps. These include, as particular cases, identities due to Basmajian, to McShane and to Mirzakhani and Tan-Wong-Zhang. In stark contrast to previous identities, the identities presented here include the lengths taken among all closed geodesics.
OSLNet: Deep Small-Sample Classification With an Orthogonal Softmax Layer

A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data. To mitigate overfitting in small-sample classification, learning more discriminative features from small-sample data is becoming a new trend. To this end, this paper aims to find a subspace of neural networks that can facilitate a large decision margin. Specifically, we propose the <italic>Orthogonal Softmax Layer</italic> (OSL), which makes the weight vectors in the classification layer remain orthogonal during both the training and test processes. The Rademacher complexity of a network using the OSL is only <inline-formula> <tex-math notation="LaTeX">$\frac {1}{K}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> is the number of classes, of that of a network using the fully connected classification layer, leading to a tighter generalization error bound. Experimental results demonstrate that the proposed OSL has better performance than the methods used for comparison on four small-sample benchmark datasets, as well as its applicability to large-sample datasets. Codes are available at: <uri>https://github.com/dongliangchang/OSLNet</uri>.
Super-Resolution-based Snake Model - An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical Image

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.
Summarizing Diverging String Sequences, with Applications to Chain-Letter Petitions

Algorithms to find optimal alignments among strings, or to find a parsimonious summary of a collection of strings, are well studied in a variety of contexts, addressing a wide range of interesting applications. In this paper, we consider chain letters, which contain a growing sequence of signatories added as the letter propagates. The unusual constellation of features exhibited by chain letters (one-ended growth, divergence, and mutation) make their propagation, and thus the corresponding reconstruction problem, both distinctive and rich. Here, inspired by these chain letters, we formally define the problem of computing an optimal summary of a set of diverging string sequences. From a collection of these sequences of names, with each sequence noisily corresponding to a branch of the unknown tree $T$ representing the letter's true dissemination, can we efficiently and accurately reconstruct a tree $T' \approx T$? In this paper, we give efficient exact algorithms for this summarization problem when the number of sequences is small; for larger sets of sequences, we prove hardness and provide an efficient heuristic algorithm. We evaluate this heuristic on synthetic data sets chosen to emulate real chain letters, showing that our algorithm is competitive with or better than previous approaches, and that it also comes close to finding the true trees in these synthetic datasets.