Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Data Transformations on Scalar Field Topological Analysis of High-Order FEM Solutions

High-order finite element methods (HO-FEM) are gaining popularity in the simulation community due to their success in solving complex flow dynamics. There is an increasing need to analyze the data produced as output by these simulations. Simultaneously, topological analysis tools are emerging as powerful methods for investigating simulation data. However, most of the current approaches to topological analysis have had limited application to HO-FEM simulation data for two reasons. First, the current topological tools are designed for linear data (polynomial degree one), but the polynomial degree of the data output by these simulations is typically higher (routinely up to polynomial degree six). Second, the simulation data and derived quantities of the simulation data have discontinuities at element boundaries, and these discontinuities do not match the input requirements for the topological tools. One solution to both issues is to transform the high-order data to achieve low-order, continuous inputs for topological analysis. Nevertheless, there has been little work evaluating the possible transformation choices and their downstream effect on the topological analysis. We perform an empirical study to evaluate two commonly used data transformation methodologies along with the recently introduced L-SIAC filter for processing high-order simulation data. Our results show diverse behaviors are possible. We offer some guidance about how best to consider a pipeline of topological analysis of HO-FEM simulations with the currently available implementations of topological analysis.
Constraining the initial planetary population in the gravitational instability model

Direct imaging (DI) surveys suggest that gas giants beyond 20 au are rare around FGK stars. However, it is not clear what this means for the formation frequency of gravitational instability (GI) protoplanets due to uncertainties in gap opening and migration efficiency. Here we combine state-of-the-art calculations of homogeneous planet contraction with a population synthesis code. We find DI constraints to be satisfied if protoplanet formation by GI occurs in tens of per cent of systems if protoplanets ‘supermigrate’ to small separations. In contrast, GI may occur in only a few per cent of systems if protoplanets remain stranded at wide orbits because their migration is ‘quenched’ by efficient gap opening. We then use the frequency of massive giants in radial velocity surveys inside 5 au to break this degeneracy – observations recently showed that this population does not correlate with the host star metallicity and is therefore suspected to have formed via GI followed by inward migration. We find that only the supermigration scenario can sufficiently explain this population while simultaneously satisfying the DI constraints and producing the right mass spectrum of planets inside 5 au. If massive gas giants inside 5 au formed via GI, then our models imply that migration must be efficient and that the formation of GI protoplanets occurs in at least tens of per cent of systems.
Tactile Model O: Fabrication and testing of a 3d-printed, three-fingered tactile robot hand

Bringing tactile sensation to robotic hands will allow for more effective grasping, along with a wide range of benefits of human-like touch. Here, we present a three-dimensional-printed, three-fingered tactile robot hand comprising an OpenHand ModelO customized to house a TacTip soft biomimetic tactile sensor in the distal phalanx of each finger. We expect that combining the grasping capabilities of this underactuated hand with sophisticated tactile sensing will result in an effective platform for robot hand research-the Tactile Model O (T-MO). The design uses three JeVois machine vision systems, with each comprising a miniature camera in the tactile fingertip with a processing module in the base of the hand. To evaluate the capabilities of the T-MO, we benchmark its grasping performance by using the Gripper Assessment Benchmark on the Yale-CMU-Berkeley object set. Tactile sensing capabilities are evaluated by performing tactile object classification on 26 objects and predicting whether a grasp will successfully lift each object. Results are consistent with the state of the art, taking advantage of advances in deep learning applied to tactile image outputs. Overall, this work demonstrates that the T-MO is an effective platform for robot hand research and we expect it to open up a range of applications in autonomous object handling.
Inference-Based Resource Allocation for Multi-Cell Backscatter Sensor Networks

This work studies inference-based resource allocation in ultra low-power, large-scale backscatter sensor networks (BSNs). Several ultra-low cost and power sensor devices (tags) are illuminated by a carrier and reflect the measured information towards a wireless core that uses conventional Marconi radio technology. The development of multi-cell BSNs requires few multi-antenna cores and several low-cost scatter radio devices, targeting at maximum possible coverage. The average signal-to-interference-plus-noise ratio (SINR) of maximum-ratio combining (MRC) and zero-forcing (ZF) linear detectors is found and harnessed for frequency sub-channel allocation at tags, exploiting long-term SINR information. The resource allocation problem is formulated as an integer programming optimization problem and solved through the Max-Sum message-passing algorithm. The proposed algorithm is fully parallelizable and adheres to simple message-passing update rules, requiring mainly addition and comparison operations. In addition, the convergence to the optimal solution is attained within very few iteration steps. Judicious simulation study reveals that ZF detector is more suitable for large scale BSNs, capable to cancel out the intra-cell interference. It is also found that the proposed algorithm offers at least an order of magnitude decrease in execution time compared to conventional convex optimization methods.
Spin-polarized multiple Andreev reflections in spin-split superconductors

Superconductors with spin-split energy bands are an important building block for superconducting spintronic designs. Motivated by the recent measurement of tunneling quasiparticle currents between spin-split superconductors, this work investigates theoretically voltage-biased Josephson junctions of arbitrary transparency. The authors show that an exotic subharmonic gap structure arises due to spin-polarized multiple Andreev reflections (MARs), where the odd-order MARs split, but the even-order ones remain unaffected by the spin-split fields. These findings provide a tool to determine the amplitude and alignment of Zeeman fields in spin-split superconductors.
Bugs in Infrastructure as Code

Infrastructure as code (IaC) scripts are used to automate the maintenance and configuration of software development and deployment infrastructure. IaC scripts can be complex in nature, containing hundreds of lines of code, leading to defects that can be difficult to debug, and lead to wide-scale system discrepancies such as service outages at scale. Use of IaC scripts is getting increasingly popular, yet the nature of defects that occur in these scripts have not been systematically categorized. A systematic categorization of defects can inform practitioners about process improvement opportunities to mitigate defects in IaC scripts. The goal of this paper is to help software practitioners improve their development process of infrastructure as code (IaC) scripts by categorizing the defect categories in IaC scripts based upon a qualitative analysis of commit messages and issue report descriptions. We mine open source version control systems collected from four organizations namely, Mirantis, Mozilla, Openstack, and Wikimedia Commons to conduct our research study. We use 1021, 3074, 7808, and 972 commits that map to 165, 580, 1383, and 296 IaC scripts, respectively, collected from Mirantis, Mozilla, Openstack, and Wikimedia Commons. With 89 raters we apply the defect type attribute of the orthogonal defect classification (ODC) methodology to categorize the defects. We also review prior literature that have used ODC to categorize defects, and compare the defect category distribution of IaC scripts with 26 non-IaC software systems. Respectively, for Mirantis, Mozilla, Openstack, and Wikimedia Commons, we observe (i) 49.3%, 36.5%, 57.6%, and 62.7% of the IaC defects to contain syntax and configuration-related defects; (ii) syntax and configuration-related defects are more prevalent amongst IaC scripts compared to that of previously-studied non-IaC software.
Experimental Syntax and Island Effects: Computational models of acquisition for islands

1. Why look at language acquisition? Though it is not always directly stated, the debate at the center of this volume is in many ways driven by language acquisition considerations. Long-­‐distance dependencies are themselves relatively complex, as they involve context-­‐sensitive grammatical operations (e.g., wh-­‐movement or slash-­‐passing). The existence of context-­‐sensitive operations alone increases the complexity of the hypothesis space of possible grammars that must be considered by children during the acquisition process. If island effects are indeed the result of grammatical constraints, then the hypothesis space increases yet again, as the grammar must also contain complex constraints on context-­‐sensitive operations. A common hypothesis in the generative syntax literature is that this level of complexity (constraints on context-­‐sensitive grammatical operations) cannot be learned directly from the input that children receive (i.e., this is a poverty of the stimulus problem). As such, many generative syntacticians have postulated the existence of innate domain-­‐specific knowledge about the form that such constraints must take. In other words, the grammatical approach to island effects is often correlated with a nativist, or Universal Grammar (UG) based, view of language acquisition. In this way, a reductionist approach to island effects could be seen as a type of simplifying approach to the grammar, as it could eliminate the need for one set of innate constraints on the shape of human grammars. Because of this, it seems to us that discussions of " parsimony " and " simplification " in the
Unbiased Learning to Rank: Counterfactual and Online Approaches

This tutorial is about Unbiased Learning to Rank, a recent research field that aims to learn unbiased user preferences from biased user interactions. We will provide an overview of the two main families of methods in Unbiased Learning to Rank: Counterfactual Learning to Rank (CLTR) and Online Learning to Rank (OLTR) and their underlying theory. First, the tutorial will start with a brief introduction to the general Learning to Rank (LTR) field and the difficulties user interactions pose for traditional supervised LTR methods. The second part will cover Counterfactual Learning to Rank (CLTR), a LTR field that sprung out of click models. Using an explicit model of user biases, CLTR methods correct for them in their learning process and can learn from historical data. Besides these methods, we will also cover practical considerations, such as how certain biases can be estimated. In the third part of the tutorial we focus on Online Learning to Rank (OLTR), methods that learn by directly interacting with users and dealing with biases by adding stochasticity to displayed results. We will cover cascading bandits, dueling bandit techniques and the most recent pairwise differentiable approach. Finally, in the concluding part of the tutorial, both approaches are contrasted, highlighting their relative strengths and weaknesses, and presenting future directions of research. For LTR practitioners our comparison gives guidance on how the choice between methods should be made. For the field of Information Retrieval (IR) we aim to provide an essential guide on unbiased LTR to understanding and choosing between methodologies.
Towards Markerless Grasp Capture

Humans excel at grasping objects and manipulating them. Capturing human grasps is important for understanding grasping behavior and reconstructing it realistically in Virtual Reality (VR). However, grasp capture - capturing the pose of a hand grasping an object, and orienting it w.r. t. the object - is difficult because of the complexity and diversity of the human hand, and occlusion. Reflective markers and magnetic trackers traditionally used to mitigate this difficulty introduce undesirable artifacts in images and can interfere with natural grasping behavior. We present preliminary work on a completely marker-less algorithm for grasp capture from a video depicting a grasp. We show how recent advances in 2D hand pose estimation can be used with well-established optimization techniques. Uniquely, our algorithm can also capture hand-object contact in detail and integrate it in the grasp capture process. This is work in progress, find more details at https://contactdb. this http URL.
Topological transformations of speckles

Deterministic control of coherent random light is highly important for information transmission through complex media. However, only a few simple speckle transformations can be achieved through diffusers without prior characterization. As recently shown, spiral wavefront modulation of the impinging beam allows permuting intensity maxima and intrinsic $\pm 1$-charged optical vortices. Here, we study this cyclic-group algebra when combining spiral phase transforms of charge $n$, with $D_3$- and $D_4$-point-group symmetry star-like amplitude modulations. This combination allows statistical strengthening of permutations and controlling the period to be 3 and 4, respectively. Phase saddle-points are shown to complete the cycle. These results offer new tools to manipulate critical points in speckles.
A Survey of Automatic Methods for Nutritional Assessment

Nutritional assessment is key in order to make decisions about the nature and cause of nutrition related health issues that affect an individual. The systematic process of collecting and interpreting relevant nutrition information, however, is still in its technological infancy. Despite technological advances in storage and analysis of nutritional data, methods for collecting data are largely unchanged over the past two decades. It is well documented that these methods have issues that cause under-reporting. Meanwhile, new developments in wearable biometric logging devices have seen increased traction among individuals. This is sometimes referred to as the Quantified Self movement. One part of this movement is the development of technological means for objectively collecting nutritional data. Nutritional assessment, however, is about to be heavily impacted by emerging computer science technologies, and this survey provides an overview of promising technology approaches supporting nutritional assessment. Both academic and commercial systems are reviewed and categorized.
Redistributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective

Abstract Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing.
Dephasing dynamics of an impurity coupled to an anharmonic environment

We analyze the dephasing dynamics of an impurity coupled to an anharmonic environment. We show that a strong anharmonicity produces two different effects depending on the environment temperature: for high temperatures, the system suffers a strong dephasing, while for low temperatures there is a strong information back-flow (as measured by the Breuer-Laine-Piilo (BLP) non-Markovianity measure). Both dephasing and back-flow are particularly significant when the anharmonic potential allows environment states very close to the dissociation limit. In contrast, the information back-flow is suppressed when assuming the environment to be Gaussian. In this regard, we find that the Gaussian approximation is particularly poor at low temperatures.
Symbolic Reachability Analysis of Hybrid Systems

A restricted constraint system called hybrid zone is formalized for the representation and manipulation of rectangular automata state-spaces. Hybrid zones are proved to be closed over reachability operations of rectangular hybrid systems. In addition, rectangular hybrid systems are used to simulate nonlinear hybrid systems, which enables us to use hybrid zones for reachability analysis of nonlinear hybrid systems. After the hybrid zone has been converted to the canonical form, reachability operations for hybrid systems can be implemented straightforwardly. Hence, the main computation is the operation for obtaining the canonical form of hybrid zones. Finding the canonical form can be automated by an algorithm for linear programming.
High-resolution Transmission Spectroscopy of Four Hot Inflated Gas Giant Exoplanets

The technique of transmission spectroscopy allows us to constrain the chemical composition of the atmospheres of transiting exoplanets. It relies on very high signal-to-noise spectroscopic (or spectrophotometric) observations and is thus most suited for bright exoplanet host stars. In the era of the Transiting Exoplanet Survey Satellite, Next Generation Space Telescope, and PLAnetary Transits and Oscillations of stars (PLATO), more and more suitable targets, even for mid-sized telescopes, are discovered. Furthermore, a wealth of archival data is available that could become a basis for long-term monitoring of exo-atmospheres. We analyzed archival High Accuracy Radial velocity Planet Searcher (HARPS) spectroscopic time series of four host stars to transiting bloated gas exoplanets, namely WASP-76b, WASP-127b, WASP-166b, and KELT-11b, searching for traces of sodium (sodium doublet), hydrogen (Hα, Hβ), and lithium (670.8 nm). The archival data sets include spectroscopic time series taken during transits. Comparing in- and out-of-transit spectra we can filter out the stellar lines and investigate the absorption from the planet. Simultaneously, the stellar activity is monitored using the Mg i and Ca i lines. We detect sodium in the atmosphere of WASP-76b at a 7–9σ level. Furthermore, we report also at a 4–8σ level of significance the detection of sodium in the atmosphere of WASP-127b, confirming earlier results based on low-resolution spectroscopy. The data show no sodium nor any other atom at high confidence levels for WASP-166b nor KELT-11b, hinting at the presence of thick high clouds.
The MOSDEF Survey: Sulfur Emission-line Ratios Provide New Insights into Evolving Interstellar Medium Conditions at High Redshift

We present results on the emission-line properties of 1.3 ≤ z ≤ 2.7 galaxies drawn from the complete the MOSFIRE Deep Evolution Field (MOSDEF) survey. Specifically, we use observations of the emission-line diagnostic diagram of [O iii]λ 5007/Hβ versus [S ii]λλ6717,6731/Hα, i.e., the “[S ii] BPT diagram,” to gain insight into the physical properties of high-redshift star-forming regions. High-redshift MOSDEF galaxies are offset toward lower [S ii]λλ6717,6731/Hα at fixed [O iii]λ5007/Hβ, relative to local galaxies from the Sloan Digital Sky Survey (SDSS). Furthermore, at fixed [O iii]λ5007/Hβ, local SDSS galaxies follow a trend of decreasing [S ii]λλ6717,6731/Hα as the surface density of star formation (ΣSFR) increases. We explain this trend in terms of the decreasing fractional contribution from diffuse ionized gas (fDIG) as ΣSFR increases in galaxies, which causes galaxy-integrated line ratios to shift toward the locus of pure H ii-region emission. The z ∼ 0 relationship between fDIG and ΣSFR implies that high-redshift galaxies have lower fDIG values than typical local systems, given their significantly higher typical ΣSFR. When an appropriate low-redshift benchmark with zero or minimal fDIG is used, high-redshift MOSDEF galaxies appear offset toward higher [S ii]λλ6717,6731/Hα and/or [O iii]λ 5007/Hβ. The joint shifts of high-redshift galaxies in the [S ii] and [N ii] BPT diagrams are best explained in terms of the harder spectra ionizing their star-forming regions at fixed nebular oxygen abundance (expected for chemically young galaxies), as opposed to large variations in N/O ratios or higher ionization parameters. The evolving mixture of H ii regions and diffuse ionized gas is an essential ingredient of our description of the interstellar medium over cosmic time.
Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences

Most previous studies on automatic recognition model for bipolar disorder (BD) were based on both social media and linguistic features. The present study investigates the possibility of adopting only language-based features, namely the syntax and morpheme collocation. We also examine the effect of gender on the results considering gender has long been recognized as an important modulating factor for mental disorders, yet it received little attention in previous linguistic models. The present study collects Twitter posts 3 months prior to the self-disclosure by 349 BD users (231 female, 118 male). We construct a set of syntactic patterns in terms of the word usage based on graph pattern construction and pattern attention mechanism. The factors examined are gender differences, syntactic patterns, and bipolar recognition performance. The performance indicates our F1 scores reach over 91% and outperform several baselines, including those using TF-IDF, LIWC and pre-trained language models (ELMO and BERT). The contributions of the present study are: (1) The features are contextualized, domain-agnostic, and purely linguistic. (2) The performance of BD recognition is improved by gender-enriched linguistic pattern features, which are constructed with gender differences in language usage.
SCHMIDT'S GAME ON HAUSDORFF METRIC AND FUNCTION SPACES: GENERIC DIMENSION OF SETS AND IMAGES

We consider Schmidt's game on the space of compact subsets of a given metric space equipped with the Hausdorff metric, and the space of continuous functions equipped with the supremum norm. We are interested in determining the generic behavior of objects in a metric space, mostly in the context of fractal dimensions, and the notion of “generic” we adopt is that of being winning for Schmidt's game. We find properties whose corresponding sets are winning for Schmidt's game that are starkly different from previously established, and well‐known, properties which are generic in other contexts, such as being residual or of full measure.