Miracle-dz/UniFAR-FTUs huggingface dataset:
Observations of cold extragalactic gas clouds at $z = 0.45$ towards PKS 1610-771

We present results from MUSE observations of a 21-cm H i absorption system detected with the Australian Square Kilometre Array Pathfinder radio telescope at redshift z = 0.4503 towards the z = 1.71 quasar PKS 1610-771. We identify four galaxies (A, B, X and Y) at the same redshift as the 21-cm H i Damped Lyman-α (DLA) absorption system, with impact parameters ranging from less than 10 kpc to almost 200 kpc from the quasar sightline. Ca ii and Na i absorption is seen in the MUSE spectrum of the background QSO, with velocities coinciding with the initial H i 21-cm detection, but tracing less dense and warmer gas. This metal-line component aligns with the rotating ionised disc of galaxy B (impact parameter 18 kpc from the QSO) and appears to be co-rotating with the galaxy disc. In contrast, the 21-cm H i absorber is blueshifted relative to the galaxies nearest the absorber and has the opposite sign to the velocity field of galaxy B. Since galaxies A and B are separated by only 17 kpc on the sky and 70 km s−1 in velocity, it appears likely that the 21-cm detection traces extragalactic clouds of gas formed from their interaction. This system reveals that the cold 100 K neutral gas critical for star formation can be associated with complex structures beyond the galaxy disc, and is a first case study made in preparation for future large 21-cm absorption surveys like the ASKAP First Large Absorption Survey in H i.
Using Evolutionary Coupling to Establish Relevance Links Between Tests and Code Units. A case study on fault localization

Many software engineering techniques, such as fault localization, operate based on relevance relationships between tests and code. These relationships are often inferred through the use of dynamic test execution information (test execution traces) that approximate the link between relevant code units and asserted, by the tests, program behaviour. Unfortunately, in practice dynamic information is not always available due to the overheads introduced by the instrumentation or the nature of the production environments. To deal with this issue, we propose CEMENT, a static technique that automatically infers such test and code relationships given the projects' evolution. The key idea is that developers make relevant changes on test and code units at the same period of time, i.e., co-evolution of tests and code units reflects a probable link between them. We evaluate CEMENT on 15 open source projects and show that it indeed captures relevant links. Additionally, we perform a fault localization case study where we compare CEMENT with an existing Information Retrieval-based Fault Localization (IRFL) technique and show that it achieves comparable performance. A further analysis of our results reveals a small overlap between the faults successfully localized by the two approaches suggesting complementarity. In particular, out of the 39 successfully localized faults, two are common while CEMENT and IRFL localize 16 and 21. These results demonstrate that test and code evolutionary coupling can effectively support test and debugging activities.
Rendering Layered Materials with Diffuse Interfaces

In this work, we introduce a novel method to render, in real-time, Lambertian surfaces with a rough dieletric coating. We show that the appearance of such configurations is faithfully represented with two microfacet lobes accounting for direct and indirect interactions respectively. We numerically fit these lobes based on the first order directional statistics (energy, mean and variance) of light transport using 5D tables and narrow them down to 2D + 1D with analytical forms and dimension reduction. We demonstrate the quality of our method by efficiently rendering rough plastics and ceramics, closely matching ground truth. In addition, we improve a state-of-the-art layered material model to include Lambertian interfaces.
Explainable Landscape Analysis in Automated Algorithm Performance Prediction

Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance. For this purpose, recent studies learn a supervised machine learning (ML) model using a set of problem landscape features linked to the performance achieved by the optimization algorithm. However, these models are black-box with the only goal of achieving good predictive performance, without providing explanations which landscape features contribute the most to the prediction of the performance achieved by the optimization algorithm. In this study, we investigate the expressiveness of problem landscape features utilized by different supervised ML models in automated algorithm performance prediction. The experimental results point out that the selection of the supervised ML method is crucial, since different supervised ML regression models utilize the problem landscape features differently and there is no common pattern with regard to which landscape features are the most informative.
A Perspective on Neural Capacity Estimation: Viability and Reliability

Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks. These estimators ar referred to as neural mutual information estimation (NMIE)s. NMIEs differ from other approaches as they are data-driven estimators. As such, they have the potential to perform well on a large class of capacity problems. In order to test the performance across various NMIEs, it is desirable to establish a benchmark encompassing the different challenges of capacity estimation. This is the objective of this paper. In particular, we consider three scenarios for benchmarking:i the classic AWGN channel, ii channels continuous inputs optical intensity and peak-power constrained AWGN channel iii channels with a discrete output, i.e., Poisson channel. We also consider the extension to the multi-terminal case with iv the AWGN and optical MAC models. We argue that benchmarking a certain NMIE across these four scenarios provides a substantive test of performance. In this paper we study the performance of mutual information neural estimator (MINE), smoothed mutual information lower-bound estimator (SMILE), and directed information neural estimator (DINE). and provide insights on the performance of other methods as well. To summarize our benchmarking results, MINE provides the most reliable performance.
Galois Covers of Singular Curves in Positive Characteristics

We study the étale fundamental groups of singular reduced connected curves defined over an algebraically closed field of arbitrary prime characteristic. It is shown that when the curve is projective, the étale fundamental group is a free product of the étale fundamental group of its normalization with a free finitely generated profinite group whose rank is well determined. As a consequence of this result and the known results for the smooth case, necessary conditions are given for a finite group to appear as a quotient of the étale fundamental group. Next, we provide similar results for an affine integral curve U. We provide a complete group theoretic classification on which finite groups occur as the Galois groups for Galois étale connected covers of U. In fact, when U is a seminormal curve embedded in a connected seminormal curve X such that X −U consists of smooth points, the tame fundamental group π1(U ⊂ X) is shown to be isomorphic to a free product of the tame fundamental group of the normalization of U with a free finitely generated profinite group whose rank is known. An analogue of the Inertia Conjecture is also posed for certain singular curves.
Provable Constrained Stochastic Convex Optimization with XOR-Projected Gradient Descent

Provably solving stochastic convex optimization problems with constraints is essential for various problems in science, business, and statistics. Recently proposed XOR-Stochastic Gradient Descent (XOR-SGD) provides a convergence rate guarantee solving the constraints-free version of the problem by leveraging XOR-Sampling. However, the task becomes more difficult when additional equality and inequality constraints are needed to be satisfied. Here we propose XOR-PGD, a novel algorithm based on Projected Gradient Descent (PGD) coupled with the XOR sampler, which is guaranteed to solve the constrained stochastic convex optimization problem still in linear convergence rate by choosing proper step size. We show on both synthetic stochastic inventory management and real-world road network design problems that the rate of constraints satisfaction of the solutions optimized by XOR-PGD is $10\%$ more than the competing approaches in a very large searching space. The improved XOR-PGD algorithm is demonstrated to be more accurate and efficient than both XOR-SGD and SGD coupled with MCMC based samplers. It is also shown to be more scalable with respect to the number of samples and processor cores via experiments with large dimensions.
An Information-Theoretic View of Mixed-Delay Traffic in 5G and 6G

Fifth generation mobile communication systems (5G) have to accommodate both Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) services. While eMBB applications support high data rates, URLLC services aim at guaranteeing low-latencies and high-reliabilities. eMBB and URLLC services are scheduled on the same frequency band, where the different latency requirements of the communications render their coexistence challenging. In this survey, we review, from an information theoretic perspective, coding schemes that simultaneously accommodate URLLC and eMBB transmissions and show that they outperform traditional scheduling approaches. Various communication scenarios are considered, including point-to-point channels, broadcast channels, interference networks, cellular models, and cloud radio access networks (C-RANs). The main focus is on the set of rate pairs that can simultaneously be achieved for URLLC and eMBB messages, which captures well the tension between the two types of communications. We also discuss finite-blocklength results where the measure of interest is the set of error probability pairs that can simultaneously be achieved in the two communication regimes.
The Conceptual VAE

In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data. The model is inspired by, and closely related to, the Beta-VAE model of concepts, but is designed to be more closely connected with language, so that the names of concepts form part of the graphical model. We provide evidence that our model -- which we call the Conceptual VAE -- is able to learn interpretable conceptual representations from simple images of coloured shapes together with the corresponding concept labels. We also show how the model can be used as a concept classifier, and how it can be adapted to learn from fewer labels per instance. Finally, we formally relate our model to Gardenfors' theory of conceptual spaces, showing how the Gaussians we use to represent concepts can be formalised in terms of"fuzzy concepts"in such a space.
Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization

Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interaction with uncertainty in physical properties of the object. In this paper, we study robust optimization for control of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for the inaccuracies in the estimates of the physical properties during manipulation. In particular, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a bilevel trajectory optimization algorithm to design a controller that maximizes this stability margin to provide robustness against uncertainty in physical properties of the object. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects.
Bringing Linearly Transformed Cosines to Anisotropic GGX

Linearly Transformed Cosines (LTCs) are a family of distributions that are used for real-time area-light shading thanks to their analytic integration properties. Modern game engines use an LTC approximation of the ubiquitous GGX model, but currently this approximation only exists for isotropic GGX and thus anisotropic GGX is not supported. While the higher dimensionality presents a challenge in itself, we show that several additional problems arise when fitting, post-processing, storing, and interpolating LTCs in the anisotropic case. Each of these operations must be done carefully to avoid rendering artifacts. We find robust solutions for each operation by introducing and exploiting invariance properties of LTCs. As a result, we obtain a small 84 look-up table that provides a plausible and artifact-free LTC approximation to anisotropic GGX and brings it to real-time area-light shading.
Linear convergence of a policy gradient method for finite horizon continuous time stochastic control problems

Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for general continuous space-time stochastic control problems has been elusive. This paper closes the gap by proposing a proximal gradient algorithm for feedback controls of ﬁnite-time horizon stochastic control problems. The state dynamics are continuous time nonlinear diﬀusions with controlled drift and possibly degenerate noise, and the objectives are nonconvex in the state and nonsmooth in the control. We prove under suitable conditions that the algorithm converges linearly to a stationary point of the control problem, and is stable with respect to policy updates by approximate gradient steps. The convergence result justiﬁes the recent reinforcement learning heuristics that adding entropy regularization or a ﬁctitious discount factor to the optimization objective accelerates the convergence of policy gradient methods. The proof exploits careful regularity estimates of backward stochastic diﬀerential equations.
Are You Misinformed? A Study of Covid-Related Fake News in Bengali on Facebook

Our opinions and views of life can be shaped by how we perceive the opinions of others on social media like Facebook. This dependence has increased during COVID-19 periods when we have fewer means to connect with others. However, fake news related to COVID-19 has become a significant problem on Facebook. Bengali is the seventh most spoken language worldwide, yet we are aware of no previous research that studied the prevalence of COVID-19 related fake news in Bengali on Facebook. In this paper, we develop machine learning models to detect fake news in Bengali automatically. The best performing model is BERT, with an F1-score of 0.97. We apply BERT on all Facebook Bengali posts related to COVID-19. We find 10 topics in the COVID-19 Bengali fake news grouped into three categories: System (e.g., medical system), belief (e.g., religious rituals), and social (e.g., scientific awareness).
BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis

Aspect-based sentiment analysis (ABSA) task aims to associate a piece of text with a set of aspects and meanwhile infer their respective sentimental polarities. Up to now, the state-of-the-art approaches are built upon ﬁne-tuning of various pre-trained language models. They commonly aim to learn the aspect-speciﬁc representation in the corpus. Unfortunately, the aspect is often expressed implicitly through a set of representatives and thus renders implicit mapping process unattainable unless sufﬁcient labeled examples. In this paper, we propose to jointly address aspect categorization and aspect-based sentiment subtasks in a uniﬁed framework. Specifically, we ﬁrst introduce a simple but effective mechanism that collaborates the semantic and syntactic information to construct auxiliary-sentences for the implicit aspect. Then, we encourage BERT to learn the aspect-speciﬁc representation in response to the automatically constructed auxiliary-sentence instead of the aspect itself. Finally, we empirically evaluate the performance of the proposed solution by a comparative study on real benchmark datasets for both ABSA and Targeted-ABSA tasks. Our extensive experiments show that it consistently achieves state-of-the-art performance in terms of aspect categorization and aspect-based sentiment across all datasets and the improvement margins are considerable. The code of BERT-ASC is available in Github 1 .
Word-of-Mouth on Action: Analysis of Operational Decisions When Customers are Resentful

Word-of-Mouth (WoM) communication, via online reviews, plays an important role in customers’ purchasing decisions. As such, retailers must consider the impact of WoM to manage customer perceptions and future demand. In this paper, we consider an online shopping system with premium and regular customers. Building on the behavioral and operations management literature, we model customer preferences based on the perceived service quality indicated by WoM and integrate this into the retailer’s operational problem to determine a shipment policy. First, we study the e-tailer’s problem when she has no knowledge of WoM, and only reacts to the changes in demand. We analyze the long-term behavior of customer demand and show that potential market size and customer sensitivity are the key parameters determining this behavior. Then, we build a model to integrate knowledge of WoM into operational decision making and partially characterize the optimal solution. We show that (i) beating the competition in the market may not always benefit the company under strict operational constraints as it can result in undesired customer switching behavior, (ii) relaxations in operational constraints may hurt profitability due to the associated difficulties of managing perceptions, and (iii) seeking a stationary policy can lead to suboptimal solutions, therefore cyclic policies should also be considered when appropriate.
Was that so hard? Estimating human classification difficulty

When doctors are trained to diagnose a specific disease, they learn faster when presented with cases in order of increasing difficulty. This creates the need for automatically estimating how difficult it is for doctors to classify a given case. In this paper, we introduce methods for estimating how hard it is for a doctor to diagnose a case represented by a medical image, both when ground truth difficulties are available for training, and when they are not. Our methods are based on embeddings obtained with deep metric learning. Additionally, we introduce a practical method for obtaining ground truth human difficulty for each image case in a dataset using self-assessed certainty. We apply our methods to two different medical datasets, achieving high Kendall rank correlation coefficients, showing that we outperform existing methods by a large margin on our problem and data.
CNNs and Transformers Perceive Hybrid Images Similar to Humans

Hybrid images is a technique to generate images with two interpretations that change as a function of viewing distance. It has been utilized to study multiscale processing of images by the human visual system. Using 63,000 hybrid images across 10 fruit categories, here we show that predictions of deep learning vision models qualitatively matches with the human perception of these images. Our results provide yet another evidence in support of the hypothesis that Convolutional Neural Networks (CNNs) and Transformers are good at modeling the feedforward sweep of information in the ventral stream of visual cortex. Code and data is available at https://github.com/aliborji/hybrid_images. git.
High-resolution Iterative Feedback Network for Camouflaged Object Detection

Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner, essentially a global loop-based connection among the multi-scale resolutions. To design better feedback feature ﬂow and avoid the feature corruption caused by recurrent path, an iterative feedback strategy is proposed to impose more constraints on each feedback connection. Extensive experiments on four challenging datasets demonstrate that our HitNet breaks the performance bottleneck and achieves significant improvements compared with 29 state-of-the-art methods. In addition, to address the data scarcity in camouflaged scenarios, we provide an application example to convert the salient objects to camouflaged objects, thereby generating more camouflaged training samples from the diverse salient object datasets. Code will be made publicly available.
Simulation and analysis of electromagnetic force on shield of canned motor

As one of the key parts inside canned motor,the shield functions as isolation physically,which integrity may affect operation safety of primary loop in plant.The shield is installed inside RCP,who is influenced by complex loads,such as alternating magnetic field and coolant flow field.Based on the harmonic analysis in electromagnetic field of the motor,the distribution of magnetic induction and electromagnetic force on the shield are obtained,which results show that,the electromagnetic force borne upon the shield within the iron core is distributed periodically in circumferential direction of the motor with the tangential force as main force.Meanwhile the electromagnetic force borne on the shield is larger than that on rotor shield,who shall decrease little by little starting from iron core along winding end.However,the decreasing rate shall be the maximum as departing from the axial position corresponding to iron core end.
Title: Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals Short title: A neural network model of human continual learning

Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting. We augment standard stochastic gradient descent with two algorithmic motifs, socalled “sluggish” task units and a Hebbian training step that strengthens connections between task units and hidden units that encode task-relevant information. We found that the “sluggish” units introduce a switch-cost during training, which biases representations under interleaved training towards a joint representation that ignores the contextual cue, while the Hebbian step promotes the formation of a gating scheme from task units to the hidden layer that produces orthogonal representations which are perfectly guarded against interference. Validating the model on previously published human behavioural data revealed that it matches performance of participants who had been trained on blocked or interleaved curricula, and that these performance differences were driven by misestimation of the true category boundary.