Miracle-dz/UniFAR-FTUs huggingface dataset:
Giant molecular cloud formation at the interface of colliding supershells in the large magellanic cloud

We investigate the H i envelope of the young, massive GMCs in the star-forming regions N48 and N49, which are located within the high column density H i ridge between two kpc-scale supergiant shells, LMC 4 and LMC 5. New long-baseline H i 21 cm line observations with the Australia Telescope Compact Array (ATCA) were combined with archival shorter baseline data and single dish data from the Parkes telescope, for a final synthesized beam size of 24.75′′ by 20.48′′, which corresponds to a spatial resolution of ∼ 6 pc in the LMC. It is newly revealed that the H i gas is highly filamentary, and that the molecular clumps are distributed along filamentary H i features. In total 39 filamentary features are identified and their typical width is ∼ 21 (8–49) [pc]. We propose a scenario in which the GMCs were formed via gravitational instabilities in atomic gas which was initially accumulated by the two shells and then further compressed by their collision. This suggests that GMC formation involves the filamentary nature of the atomic medium.
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation

Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR). In this paper, we report on a program of research that applies CBR solutions to the problem of Explainable AI (XAI) in the DL. We describe a series of twin-systems pairings of opaque DL models with transparent CBR models that allow the latter to explain the former using factual, counterfactual and semi-factual explanation strategies. This twinning shows that functional abstractions of DL (e.g., feature weights, feature importance and decision boundaries) can be used to drive these explanatory solutions. We also raise the prospect that this research also applies to the problem of Data Augmentation in DL, underscoring the fecundity of these DeepCBR ideas.
Hand Gesture Recognition Based on a Nonconvex Regularization

Recognition of hand gestures is one of the most fundamental tasks in human-robot interaction. Sparse representation based methods have been widely used due to their efficiency and low demands on the training data. Recently, nonconvex regularization techniques including the l1-2 regularization have been proposed in the image processing community to promote sparsity while achieving efficient performance. In this paper, we propose a vision-based hand gesture recognition model based on the l1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on binary and gray-scale data sets have demonstrated the effectiveness of this method in identifying hand gestures.
Bias field correction of MPRAGE by an external reference -- The poor man's MP2RAGE

Purpose: To implement and evaluate a sequential approach to obtain semi-quantitative T1weighted MPRAGE images, unbiased by B1 inhomogeneities at 7T. Methods: In the reference gradient echo used for normalization of the MPRAGE image, flip angle (αGE) and acquisition voxel size (Vref) was varied to optimize tissue contrast and acquisition time (Tacq). The finalized protocol was implemented at three different resolutions and the reproducibility was evaluated. Maps of T1 were derived based on the normalized MPRAGE through forward signal modelling. Results: A good compromise between tissue contrast and SNR was reached at αGE=3°. A reduction of the reference GE Tacq by a factor of 4, at the cost of negligible bias, was obtained by increasing Vref with a factor of 8 relative the MPRAGE resolution. The coefficient-ofvariation in segmented WM was 9±5% after normalization, compared to 24±12% before. The T1 maps showed no obvious bias and had reasonable values with regard to literature, especially after optional B1 correction through separate flip angle mapping. Conclusion: A non-interleaved acquisition for normalization of MPRAGE offers a simple alternative to MP2RAGE to obtain semi-quantitative purely T1-weighted images. These images can be converted to T1 maps analogously to the established MP2RAGE approach. Scan time can be reduced by increasing Vref which has a miniscule effect on image quality.
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specic Robotic Grasping in Clutter

Despite the impressive progress achieved in robotic grasping, robots are not skilled in sophisticated tasks (e.g. search and grasp a specied target in clutter). Such tasks involve not only grasping but the comprehensive perception of the world (e.g. the object relationships). Recently, encouraging results demonstrate that it is possible to understand high-level concepts by learning. However, such algorithms are usually data-intensive, and the lack of data severely limits their performance. In this paper, we present a new dataset named REGRAD for the learning of relationships among objects and grasps. We collect the annotations of object poses, segmentations, grasps, and relationships for the target-driven relational grasping tasks. Our dataset is collected in both forms of 2D images and 3D point clouds. Moreover, since all the data are generated automatically, it is free to import new objects for data generation. We also released a real-world validation dataset to evaluate the sim-to-real performance of models trained on REGRAD. Finally, we conducted a series of experiments, showing that the models trained on REGRAD could generalize well to the realistic scenarios, in terms of both relationship and grasp detection. Our dataset and code could be found at: https://github.com/poisonwine/REGRAD.
Using Adaptive Gradient for Texture Learning in Single-View 3D Reconstruction

Recently, learning-based approaches for 3D model reconstruction have attracted attention owing to its modern applications such as Extended Reality(XR), robotics and self-driving cars. Several approaches presented good performance on reconstructing 3D shapes by learning solely from images, i.e., without using 3D models in training. Challenges, however, remain in texture generation due to the gap between 2D and 3D modals. In previous work, the grid sampling mechanism from Spatial Transformer Networks was adopted to sample color from an input image to formulate texture. Despite its success, the existing framework has limitations on searching scope in sampling, resulting in flaws in generated texture and consequentially on rendered 3D models. In this paper, to solve that issue, we present a novel sampling algorithm by optimizing the gradient of predicted coordinates based on the variance on the sampling image. Taking into account the semantics of the image, we adopt Frechet Inception Distance (FID) to form a loss function in learning, which helps bridging the gap between rendered images and input images. As a result, we greatly improve generated texture. Furthermore, to optimize 3D shape reconstruction and to accelerate convergence at training, we adopt part segmentation and template learning in our model. Without any 3D supervision in learning, and with only a collection of single-view 2D images, the shape and texture learned by our model outperform those from previous work. We demonstrate the performance with experimental results on a publically available dataset.
Distributed Multigrid Neural Solvers on Megavoxel Domains

We consider the distributed training of large scale neural networks that serve as PDE (partial differential equation) solvers producing full field outputs. We specifically consider neural solvers for the generalized 3D Poisson equation over megavoxel domains. A scalable framework is presented that integrates two distinct advances. First, we accelerate training a large model via a method analogous to the multigrid technique used in numerical linear algebra. Here, the network is trained using a hierarchy of increasing resolution inputs in sequence, analogous to the ‘V’, ‘W’, ‘F’ and ‘Half-V’ cycles used in multigrid approaches. In conjunction with the multi-grid approach, we implement a distributed deep learning framework which significantly reduces the time to solve. We show scalability of this approach on both GPU (Azure VMs on Cloud) and CPU clusters (PSC Bridges2). This approach is deployed to train a generalized 3D Poisson solver that scales well to predict output full field solutions up to the resolution of 512 × 512 × 512 for a high dimensional family of inputs. This strategy opens up the possibility of fast and scalable training of neural PDE solvers on heterogeneous clusters.
NURBS-Diff: A Differentiable NURBS Layer for Machine Learning CAD Applications

Recent deep-learning-based techniques for the reconstruction of geometries from different input representations such as images and point clouds have been instrumental in advancing research in geometric machine learning. Most of these techniques rely on a triangular mesh representation for representing the geometry, with very recent attempts in using B-splines. While Non-Uniform Rational B-splines (NURBS) are the de facto standard in the CAD industry, minimal efforts have been made to bridge the gap between deep-learning frameworks and the NURBS representation for geometry. The backbone of modern deep learning techniques is the use of a fully automatic differentiable deﬁnition for each mathematical operation to enable backpropagation of losses while training. In order to integrate the NURBS representation of CAD models with deep learning methods, we propose a differentiable NURBS layer for evaluating the curve or surface given a set of NURBS parameters. We have developed a NURBS layer deﬁning the forward and backward pass required for automatic differentiation. Our implementation is GPU accelerated and is directly integrated with PyTorch, a popular deep learning framework. We demonstrate the efﬁcacy of our NURBS layer by automatically incorporating it with the stochastic gradient descent algorithm and performing CAD operations such as curve or surface ﬁtting and surface offsetting. Further, we show its utility in deep learning applications such as point cloud reconstruction and structural modeling and analysis of shell structures such as heart valves. These examples show that our layer has better performance for certain deep learning frameworks and can be directly integrated with any CAD deep-learning framework that require the use of NURBS.
Localization of Dirac Fermions in Finite-Temperature Gauge Theory

It is by now well established that Dirac fermions coupled to non-Abelian gauge theories can undergo an Anderson-type localization transition. This transition affects eigenmodes in the lowest part of the Dirac spectrum, the ones most relevant to the low-energy physics of these models. Here we review several aspects of this phenomenon, mostly using the tools of lattice gauge theory. In particular, we discuss how the transition is related to the finite-temperature transitions leading to the deconfinement of fermions, as well as to the restoration of chiral symmetry that is spontaneously broken at low temperature. Other topics we touch upon are the universality of the transition, and its connection to topological excitations (instantons) of the gauge field and the associated fermionic zero modes. While the main focus is on Quantum Chromodynamics, we also discuss how the localization transition appears in other related models with different fermionic contents (including the quenched approximation), gauge groups, and in different space-time dimensions. Finally, we offer some speculations about the physical relevance of the localization transition in these models.
Bounds on the Number of Mass Points of the Capacity Achieving Distribution of the Amplitude Constraint Poisson Noise Channel

This work considers a Poisson noise channel with an amplitude constraint. It is well-known that the capacity-achieving input distribution for this channel is discrete with finitely many points. We sharpen this result by introducing upper and lower bounds on the number of mass points. In particular, the upper bound of order A log(A) and lower bound of order √ A are established where A is the constraint on the input amplitude. In addition, along the way, we show several other properties of the capacity and capacity-achieving distribution. For example, it is shown that the capacity is equal to − logPY ⋆(0) where PY ⋆ is the optimal output distribution. Moreover, an upper bound on the values of the probability masses of the capacity-achieving distribution and a lower bound on the probability of the largest mass point are established.
Modelling Cooperation and Competition in Urban Retail Ecosystems with Complex Network Metrics

Understanding the impact that a new business has on the local market ecosystem is a challenging task as it is multifaceted in nature. Past work in this space has examined the collaborative or competitive role of homogeneous venue types (i.e. the impact of a new bookstore on existing bookstores). However, these prior works have been limited in their scope and explanatory power. To better measure retail performance in a modern city, a model should consider a number of factors that interact synchronously. This paper is the first which considers the multifaceted types of interactions that occur in urban cities when examining the impact of new businesses. We first present a modeling framework which examines the role of new businesses in their respective local areas. Using a longitudinal dataset from location technology platform Foursquare, we model new venue impact across 26 major cities worldwide. Representing cities as connected networks of venues, we quantify their structure and characterise their dynamics over time. We note a strong community structure emerging in these retail networks, an observation that highlights the interplay of cooperative and competitive forces that emerge in local ecosystems of retail establishments. We next devise a data-driven metric that captures the first-order correlation on the impact of a new venue on retailers within its vicinity accounting for both homogeneous and heterogeneous interactions between venue types. Lastly, we build a supervised machine learning model to predict the impact of a given new venue on its local retail ecosystem. Our approach highlights the power of complex network measures in building machine learning prediction models. These models have numerous applications within the retail sector and can support policymakers, business owners, and urban planners in the development of models to characterize and predict changes in urban settings.
Automated Design Space Exploration of CGRA Processing Element Architectures using Frequent Subgraph Analysis

The architecture of a coarse-grained reconfigurable array (CGRA) processing element (PE) has a significant effect on the performance and energy efficiency of an application running on the CGRA. This paper presents an automated approach for generating specialized PE architectures for an application or an application domain. Frequent subgraphs mined from a set of applications are merged to form a PE architecture specialized to that application domain. For the image processing and machine learning domains, we generate specialized PEs that are up to 10.5x more energy efficient and consume 9.1x less area than a baseline PE.
Parallel Projection - A New Return Mapping Algorithm for Finite Element Modeling of Shape Memory Alloys

We present a novel method for finite element analysis of inelastic structures containing Shape Memory Alloys (SMAs). Phenomenological constitutive models for SMAs lead to material nonlinearities, that require substantial computational effort to resolve. Finite element analysis methods, which rely on Gauss quadrature integration schemes, must solve two sets of coupled differential equations: one at the global level and the other at the local, i.e. Gauss point level. In contrast to the conventional return mapping algorithm, which solves these two sets of coupled differential equations separately using a nested Newton procedure, we propose a scheme to solve the local and global differential equations simultaneously. In the process we also derive closed-form expressions used to update the internal state variables, and unify the popular closest-point and cutting plane methods with our formulas. Numerical testing indicates that our method allows for larger thermomechanical loading steps and provides increased computational efficiency, over the standard return mapping algorithm.
Discover the Unknown Biased Attribute of an Image Classifier

Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, the previous bias identification pipeline overly relies on human experts to conjecture potential biases (e.g., gender), which may neglect other underlying biases not realized by humans. To help human experts better find the AI algorithms’ biases, we study a new problem in this work – for a classifier that predicts a target attribute of the input image, discover its unknown biased attribute.To solve this challenging problem, we use a hyperplane in the generative model’s latent space to represent an image attribute; thus, the original problem is transformed to optimizing the hyperplane’s normal vector and offset. We propose a novel total-variation loss within this framework as the objective function and a new orthogonalization penalty as a constraint. The latter prevents trivial solutions in which the discovered biased attribute is identical with the target or one of the known-biased attributes. Extensive experiments on both disentanglement datasets and real-world datasets show that our method can discover biased attributes and achieve better disentanglement w.r. t. target attributes. Furthermore, the qualitative results show that our method can discover unnoticeable biased attributes for various object and scene classifiers, proving our method’s generalizability for detecting biased attributes in diverse domains of images.
STM beyound vacuum tunnelling: Scanning Tunnelling Hydrogen Microscopy as a route to ultra-high resolution

Direct imaging is a fast and reliable method for the characterization of surfaces. When it comes to small surface structures in the size of the features e.g. in todays computer processors, classical optical imaging methods fail in resolving these structures. With the invention of the scanning tunnelling microscope (STM) for the first time it became possible to image the structure of surfaces with atomic precision. However, the STM fails in resolving complex chemical structures like e.g. organic molecules. The lack of chemical sensitivity in STM images can be overcome by the condensation of molecular hydrogen or deuterium in the STM junction. Images recorded in the so-called scanning tunnelling hydrogen microscopy (STHM) closely resemble the chemical structure of different organic molecules. However, the mechanism behind the contrast formation has not been addressed so far. Here we show that the origin of the STHM contrast is a single hydrogen (H2) or deuterium (D2) molecule located directly below the tip apex that acts as a combined sensor and signal transducer. Together with the tip the gas molecule forms a nano-scale force sensor, comparable to sensors in atomic force microscopy (AFM), which probes the total electron density (TED) of the surface trough the Pauli repulsion and converts this signal into variations of the junctions’ conductance again via Pauli repulsion. Other than the sensors in conventional scanning force techniques, due to its size, the sensor of the STHM junction is intrinsically insensitive to long-range forces, usually limiting the image resolution. The insensitivity to long-range forces results in a high image resolution, so that even small changes in the TED leave a mark in obtained STHM images. The resolution hereby reaches an unprecedented level as can be seen by the direct imaging of local intermolecular interactions like e.g. hydrogen bonds appear with remarkable clarity in STHM images of organic layers. Thus, besides the identification of chemical species of different adsorbates, the STHM mode allows the study of interactions between adsorbates which e.g. lead to their self organization on the surface. Therefore, the STHM mode may give important insight in the driving mechanisms behind the formation and composition of matter on the atomic level. However, the STHM mode, in which a single H2 (D2) molecule probes the TED of the surface, is only one example of a broader class of sensors. It is conceivable, that by an appropriate choice of the molecule in the junction, other surface properties can be imaged which are usually inaccessible by other imaging techniques.
Questioning causality on sex, gender and COVID-19, and identifying bias in large-scale data-driven analyses: the Bias Priority Recommendations and Bias Catalog for Pandemics

The COVID-19 pandemic has spurred a large amount of observational studies reporting linkages between the risk of developing severe COVID-19 or dying from it, and sex and gender. By reviewing a large body of related literature and conducting a fine grained analysis based on sex-disaggregated data of 61 countries spanning 5 continents, we discover several confounding factors that could possibly explain the supposed male vulnerability to COVID-19. We thus highlight the challenge of making causal claims based on available data, given the lack of statistical significance and potential existence of biases. Informed by our findings on potential variables acting as confounders, we contribute a broad overview on the issues bias, explainability and fairness entail in data-driven analyses. Thus, we outline a set of discriminatory policy consequences that could, based on such results, lead to unintended discrimination. To raise awareness on the dimensionality of such foreseen impacts, we have compiled an encyclopedia-like reference guide, the Bias Catalog for Pandemics (BCP), to provide definitions and emphasize realistic examples of bias in general, and within the COVID-19 pandemic context. These are categorized within a division of bias families and a 2-level priority scale, together with preventive steps. In addition, we facilitate the Bias Priority Recommendations on how to best use and apply this catalog, and provide guidelines in order to address real world research questions. The objective is to anticipate and avoid disparate impact and discrimination, by considering causality, explainability, bias and techniques to mitigate the latter. With these, we hope to 1) contribute to designing and conducting fair and equitable data-driven studies and research;and 2) interpret and draw meaningful and actionable conclusions from these.
To code, or not to code, at the racetrack: Kelly betting and single-letter codes

For a gambler with side information, Kelly betting gives the optimal log growth rate of the gambler's fortune, which is closely related to the mutual information between the correct winner and the noisy side information. We show conditions under which optimal Kelly betting can be implemented using single-letter codes. We show that single-letter coding is optimal for a wide variety of systems; for example, all systems with diagonal reward matrices admit optimal single-letter codes. We also show that important classes of systems do not admit optimal single-letter codes for Kelly betting, such as when the side information is passed through a Z channel. Our results are important to situations where the computational complexity of the gambler is constrained, and may lead to new insights into the fitness value of information for biological systems.