Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities

A key challenge for robotic systems is to figure out the behavior of another agent. The capability to draw correct inferences is crucial to derive human behavior from examples. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally (observational evidence). For this reason, robots that rely on inferences that are correlational risk a biased interpretation of the evidence. We propose equipping robots with the necessary tools to conduct observational studies on people. Specifically, we propose and explore the feasibility of structural causal models with non-parametric estimators to derive empirical estimates on hand behavior in the context of object manipulation in a virtual kitchen scenario. In particular, we focus on inferences under (the weaker) conditions of partial confounding (the model covering only some factors) and confront estimators with hundreds of samples instead of the typical order of thousands. Studying these conditions explores the boundaries of the approach and its viability. Despite the challenging conditions, the estimates inferred from the validation data are correct. Moreover, these estimates are stable against three refutation strategies where four estimators are in agreement. Furthermore, the causal quantity for two individuals reveals the sensibility of the approach to detect positive and negative effects. The validity, stability and explainability of the approach are encouraging and serve as the foundation for further research.
Galaxy Flows within 8000 km s−1 from Numerical Action Methods

The trajectories since z = 4 of systems of galaxies (“halos”) with cz < 8000 km s−1 are found through numerical action reconstructions. A set of 9719 halos from a Two Micron All Sky Survey group catalog and Cosmicflows-3 catalogs are given attention. Present distances are adjusted to minimize departures from observed redshifts. For those with the most precisely determined distances, compromises are made between distance and redshift agreement. H 0 is varied from 69 to 77 km s−1 Mpc−1, with Ω m set by the baryon acoustic oscillation constraint from the Planck satellite. A best-fitting amplitude of the mass-to-light relation is found. A uniform density associated with the interhalo medium accounts for the matter not in halos. The solution paths provide the histories of the formation of the nearby large structures and depict how the voids emptied. Assuming no local over/underdensity, the best model has H 0 = 73 km s−1 Mpc−1, with nearly the same density arising from interhalo matter (IHM) as from halos. We examine local over/underdensities by varying the IHM density and find a valley of best-fit models along H 0 = 73.0(1 + 0.165δ) km s−1 Mpc−1. Friedmann models with distinct densities internal and external to the study region give a similar relationship. The fraction of matter in the IHM seen in n-body simulations roughly matches that in our H 0 = 72 scenario. Videos have been created to visualize the complexities of formation of large-scale structures. Standard n-body calculations, starting from the first time steps as tests of the numerical action method solutions and continuing until cosmic scale factor a = 2, provide glimpses into the future.
Factors that control stability, variability, and reliability issues of endurance cycle in ReRAM devices: A phase field study

The morphological evolution of the conducting filament (CF) predominantly controls the electric response of the resistive random access memory (ReRAM) devices. However, the parameters—in terms of the material and the processing—which control the growth of such CF are plenty. Extending the phase field technique for ReRAM systems presented by Roy and Cha [J. Appl. Phys. 128, 205102 (2020)], we could successfully model the complete SET (to attain low resistance state) and RESET (to attain high resistance state) processes due to the application of sweeping voltage. The key parameters that influence the stability of the multi-cycle I-V response or the endurance behavior are identified. The computational findings of the presented model ReRAM system are practical in correlating the multi-parametric influence with the stability, variability, and reliability of the endurance cycle that affect the device performance and also lead to the device failure. We believe that our computational approach of connecting the morphological changes of the CF with the electrical response has the potential to further understand and optimize the performance of the ReRAM devices.
How Design of the Physical Environment Impacts on Early Learning: Educators' and Parents' Perspectives

THIS RESEARCH EXPLORES THE quality and importance of the physical environment of two early learning centres on the Sunshine Coast in Queensland, utilising qualitative interviews with parents (n = 4) and educators (n = 4) to understand how design might impact on children's development and a quantitative rating (the Early Childhood Physical Environment Rating Scale; ECPERS) to assess the quality of the physical built environment and infrastructure. With an average ECPERS quality rating, thematic analysis of the interviews revealed that educators and parents viewed the physical environment as important to a child's development, although the quality of staff was predominant. Early learning centres should be ‘homely’, inviting, bright and linked to the outdoors, with participants describing how space ‘welcomes the child, makes them feel safe and encourages learning’. Four key themes characterised views: Emotional Connection (quality of staff and physical environment), Experiencing Design (impact of design on child development), Hub for Community Integration (relationships and resources) and Future Vision (ideal physical environment, technology and ratings). With participants often struggling to clearly articulate their thoughts on design issues, a collaborative and jargon-free approach to designing space is required. These findings will help facilitate discussion about the role and design of the physical environment in early childhood centres, with the tangible examples of ‘ideal space’ enhancing communication between architects and educators about how best to design and reconfigure space to enhance learning outcomes.
Rethinking Attention-Model Explainability through Faithfulness Violation Test

Attention mechanisms are dominating the explainability of deep models. They produce probability distributions over the input, which are widely deemed as feature-importance indicators. However, in this paper, we find one critical limitation in attention explanations: weakness in identifying the polarity of feature impact. This would be somehow misleading -- features with higher attention weights may not faithfully contribute to model predictions; instead, they can impose suppression effects. With this finding, we reflect on the explainability of current attention-based techniques, such as Attentio$\odot$Gradient and LRP-based attention explanations. We first propose an actionable diagnostic methodology (henceforth faithfulness violation test) to measure the consistency between explanation weights and the impact polarity. Through the extensive experiments, we then show that most tested explanation methods are unexpectedly hindered by the faithfulness violation issue, especially the raw attention. Empirical analyses on the factors affecting violation issues further provide useful observations for adopting explanation methods in attention models.
Primordial black hole dark matter in the context of extra dimensions

Theories of large extra dimensions (LEDs) such as the Arkani-Hamed, Dimopoulos&Dvali scenario predict a"true"Planck scale $M_\star$ near the TeV scale, while the observed $M_{pl}$ is due to the geometric effect of compact extra dimensions. These theories allow for the creation of primordial black holes (PBHs) in the early Universe, from the collisional formation and subsequent accretion of black holes in the high-temperature plasma, leading to a novel cold dark matter (sub)component. Because of their existence in a higher-dimensional space, the usual relationship between mass, radius and temperature is modified, leading to distinct behaviour with respect to their 4-dimensional counterparts. Here, we derive the cosmological creation and evolution of such PBH candidates, including the greybody factors describing their evaporation, and obtain limits on LED PBHs from direct observation of evaporation products, effects on big bang nucleosynthesis, and the cosmic microwave background angular power spectrum. Our limits cover scenarios of 2 to 6 extra dimensions, and PBH masses ranging from 10 to $10^{21}$ g. We find that for two extra dimensions, LED PBHs represent a viable dark matter candidate with a range of possible black hole masses between $10^{17}$ and $10^{23}$ g depending on the Planck scale and reheating temperature. For $M_\star = 10$ TeV, this corresponds to PBH dark matter with a mass of $M \simeq 10^{21}$ g, unconstrained by current observations. We further refine and update constraints on"ordinary"four-dimension black holes.
Generative Cooperative Networks for Natural Language Generation

Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation. However, for discrete outputs such as language, optimizing GANs remains an open problem with many instabilities, as no gradient can be properly back-propagated from the discriminator output to the generator parameters. An alternative is to learn the generator network via reinforcement learning, using the discriminator signal as a reward, but such a technique suffers from moving rewards and vanishing gradient problems. Finally, it often falls short compared to direct maximum-likelihood approaches. In this paper, we introduce Generative Cooperative Networks, in which the discriminator architecture is cooperatively used along with the generation policy to output samples of realistic texts for the task at hand. We give theoretical guarantees of convergence for our approach, and study various efficient decoding schemes to empirically achieve state-of-the-art results in two main NLG tasks.
The CARE Dataset for Affective Response Detection

Social media plays an increasing role in our communication with friends and family, and our consumption of information and entertainment. Hence, to design effective ranking functions for posts on social media, it would be useful to predict the affective response to a post (e.g., whether the user is likely to be humored, inspired, angered, informed). Similar to work on emotion recognition (which focuses on the affect of the publisher of the post), the traditional approach to recognizing affective response would involve an expensive investment in human annotation of training data. We introduce CARE db , a dataset of 230k social media posts annotated according to 7 affective responses using the Common Affective Response Expression (CARE) method. The CARE method is a means of leveraging the signal that is present in comments that are posted in response to a post, providing high-precision evidence about the affective response of the readers to the post without human annotation. Unlike human annotation, the annotation process we describe here can be iterated upon to expand the coverage of the method, particularly for new affective responses. We present experiments that demonstrate that the CARE annotations compare favorably with crowd-sourced annotations. Finally, we use CARE db to train competitive BERT-based models for predicting affective response as well as emotion detection, demonstrating the utility of the dataset for related tasks.
Identifying Emergent Leadership in OSS Projects Based on Communication Styles

In open source software (OSS) communities, existing leadership indicators are dominantly measured by code contribution or community influence. Recent studies on emergent leadership shed light on additional dimensions such as intellectual stimulation in collaborative communications. To that end, this paper proposes an automated approach, named iLead, to mine communication styles and identify emergent leadership behaviors in OSS communities, using issue comments data. We start with the construction of 6 categories of leadership behaviors based on existing leadership studies. Then, we manually label leadership behaviors in 10,000 issue comments from 10 OSS projects, and extract 304 heuristic linguistic patterns which represent different types of emergent leadership behaviors in flexible and concise manners. Next, an automated algorithm is developed to merge and consolidate different pattern sets extracted from multiple projects into a final pattern ranking list, which can be applied for the automatic leadership identification. The evaluation results show that iLead can achieve a median precision of 0.82 and recall of 0.78, outperforming ten machine/deep learning baselines. To demonstrate practical usefulness, we also conduct empirical analysis and human evaluation of the identified leadership behaviors from iLead. We argue that emergent leadership behaviors in issue discussion should be taken into consideration to broaden existing OSS leadership viewpoints. Practical insights on community building and leadership skill development are offered for OSS community and individual developers, respectively.
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of their success, the size of these models has increased rapidly, requiring high-performance hardware, software, and algorithmic techniques to enable training such large models. As the result of a joint effort between Microsoft and NVIDIA, we present details on the training of the largest monolithic transformer based language model, Megatron-Turing NLG 530B (MT-NLG), with 530 billion parameters. In this paper, we first focus on the infrastructure as well as the 3D parallelism methodology used to train this model using DeepSpeed and Megatron. Next, we detail the training process, the design of our training corpus, and our data curation techniques, which we believe is a key ingredient to the success of the model. Finally, we discuss various evaluation results, as well as other interesting observations and new properties exhibited by MT-NLG. We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning accuracies on several NLP benchmarks and establishes new state-of-the-art results. We believe that our contributions will help further the development of large-scale training infrastructures, large-scale language models, and natural language generations.
Human-AI Interactions in Public Sector Decision-Making:"Automation Bias"and"Selective Adherence"to Algorithmic Advice

Artificial intelligence algorithms are increasingly adopted as decisional aides by public bodies, with the promise of overcoming biases of human decision-makers. At the same time, they may introduce new biases in the human–algorithm interaction. Drawing on psychology and public administration literatures, we investigate two key biases: overreliance on algorithmic advice even in the face of “warning signals” from other sources ( automation bias ), and selective adoption of algorithmic advice when this corresponds to stereotypes ( selective adherence ). We assess these via three experimental studies conducted in the Netherlands: In study 1 ( N = 605), we test automation bias by exploring participants’ adherence to an algorithmic prediction compared to an equivalent human-expert prediction. We do not find evidence for automation bias. In study 2 ( N = 904), we replicate these findings, and also test selective adherence . We find a stronger propensity for adherence when the advice is aligned with group stereotypes, with no significant differences between algorithmic and human-expert advice. In study 3 ( N = 1,345), we replicate our design with a sample of civil servants. This study was conducted shortly after a major scandal involving public authorities’ reliance on an algorithm with discriminatory outcomes (the “childcare benefits scandal”). The scandal is itself illustrative of our theory and patterns diagnosed empirically in our experiment, yet in our study 3, while supporting our prior findings as to automation bias, we do not find patterns of selective adherence. We suggest this is driven by bureaucrats’ enhanced awareness of discrimination and algorithmic biases in the aftermath of the scandal. We discuss the implications of our findings for public sector decision making in the age of automation. Overall, our study speaks to potential negative effects of automation of the administrative state for already vulnerable and disadvantaged citizens.
Effect of Measurement Errors on the Multivariate CUSUM CoDa Control Chart for the Manufacturing Process

Control charts, one of the main tools in Statistical Process Control (SPC), have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E’s) are involved in the quality characteristic of interest. The authors explored the impact of a linear covariate error model on the multivariate cumulative sum (CUSUM) control charts for a specific kind of data known as compositional data(CoDa). The average run length (ARL) is used to assess the performance of the proposed chart. The results indicate that M.E’s significantly affects the multivariate CUSUM-CoDa control charts. The authors have used the Markov chain method to study the impact of different involved parameters using four different cases for the variance-covariance matrix (i.e. uncorrelated with equal variances, negatively correlated with equal variances, uncorrelated with unequal variances, positively correlated with unequal variances). The authors concluded that the ARL of the multivariate CUSUM-CoDa chart increase with an increase in the value of error variance-covariance matrix, while the ARL decreases with an increase in the subgroup size m or the constant powering b. For the implementation of the proposal, two illustrated examples have been reported for multivariate CUSUM-CoDa control charts in the presence of M.E’s. One deals with the manufacturing process of uncoated aspirin tablets, and the other is based on monitoring machines in the muesli manufacturing process.
Geometric properties of spin clusters in random triangulations coupled with an Ising Model

We investigate the geometry of a typical spin cluster in random triangulations sampled with a probability proportional to the energy of an Ising configuration on their vertices, both in the finite and infinite volume settings. This model is known to undergo a combinatorial phase transition at an explicit critical temperature, for which its partition function has a different asymptotic behavior than uniform maps. The purpose of this work is to give geometric evidence of this phase transition. In the infinite volume setting, called the Infinite Ising Planar Triangulation, we exhibit a phase transition for the existence of an infinite spin cluster: for critical and supercritical temperatures, the root spin cluster is finite almost surely, while it is infinite with positive probability for subcritical temperatures. Remarkably, we are able to obtain an explicit parametric expression for this probability, which allows to prove that the percolation critical exponent is $\beta=1/4$. We also derive critical exponents for the tail distribution of the perimeter and of the volume of the root spin cluster, both in the finite and infinite volume settings. Finally, we establish the scaling limit of the interface of the root spin cluster seen as a looptree. In particular in the whole supercritical temperature regime, we prove that the critical exponents and the looptree limit are the same as for critical Bernoulli site percolation. Our proofs mix combinatorial and probabilistic arguments. The starting point is the gasket decomposition, which makes full use of the spatial Markov property of our model. This decomposition enables us to characterize the root spin cluster as a Boltzmann planar map in the finite volume setting. We then combine precise combinatorial results obtained through analytic combinatorics and universal features of Boltzmann maps to establish our results.
Ja n 20 22 ON SOME NEW RESULTS ON ANISOTROPIC SINGULAR PERTURBATIONS OF SECOND ORDER ELLIPTIC OPERATOR

Abstract. In this article, we deal with some problems involving a class of singularly perturbed elliptic operator. We prove the asymptotic preserving of a general Galerkin method associated to a semilinear problem. We use a particular Galerkin approximation to estimate the convergence rate on the whole domain, for the linear problem. Finally, we study the asymptotic behavior of the semigroup generated.
Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks

Previous numerical results have shown that a two time-scale update rule (TTUR) using constant learning rates is practically useful for training generative adversarial networks (GANs). Meanwhile, a theoretical analysis of TTUR to ﬁnd a stationary local Nash equilibrium of a Nash equilibrium problem with two players, a discriminator and a generator, has been given using decaying learning rates. In this paper, we give a theoretical analysis of TTUR using constant learning rates to bridge the gap between theory and practice. In particular, we show that, for TTUR using constant learning rates, the number of steps needed to ﬁnd a stationary local Nash equilibrium decreases as the batch size increases. We also provide numerical results to support our theoretical analyzes.
Learning Proximal Operators to Discover Multiple Optima

Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task. Most past algorithms either apply single-solution optimization methods from multiple random initial guesses or search in the vicinity of found solutions using ad hoc heuristics. We present an end-to-end method to learn the proximal operator of a family of training problems so that multiple local minima can be quickly obtained from initial guesses by iterating the learned operator, emulating the proximal-point algorithm that has fast convergence. The learned proximal operator can be further generalized to recover multiple optima for unseen problems at test time, enabling applications such as object detection. The key ingredient in our formulation is a proximal regularization term, which elevates the convexity of our training loss: by applying recent theoretical results, we show that for weakly-convex objectives with Lipschitz gradients, training of the proximal operator converges globally with a practical degree of over-parameterization. We further present an exhaustive benchmark for multi-solution optimization to demonstrate the effectiveness of our method.