Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of Energy-Optimal Impedance Control of Cooperative Robot Manipulators.

An impedance-based control scheme is introduced for cooperative manipulators grasping a rigid load. The position and orientation of the load are to be maintained close to a desired trajectory, trading off tracking accuracy by low energy consumption and maintaining stability. To this end, the augmented dynamics of the robots, their actuators and the load is formed, and an impedance control is adopted. A virtual control strategy is used to decouple torque control from actuator control. An optimization problem is then formulated using energy balance equations. The optimization finds the damping and stiffness gains of the impedance relation such that the energy consumption is minimized. Furthermore, L2 stability techniques are used to allow for time-varying damping and stiffness in the desired impedance. A numerical example is provided to demonstrate the results.
Self-supervised Metric Learning in Multi-View Data: A Downstream Task Perspective

Abstract Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is used in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction’s weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, k-means clustering, and k-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation’s accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the article. Supplementary materials for this article are available online.
Memory-efficient Transformers via Top-k Attention

Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length. While these variants are memory and compute efficient, it is not possible to directly use them with popular pre-trained language models trained using vanilla attention, without an expensive corrective pre-training stage. In this work, we propose a simple yet highly accurate approximation for vanilla attention. We process the queries in chunks, and for each query, compute the top-*k* scores with respect to the keys. Our approach offers several advantages: (a) its memory usage is linear in the input size, similar to linear attention variants, such as Performer and RFA (b) it is a drop-in replacement for vanilla attention that does not require any corrective pre-training, and (c) it can also lead to significant memory savings in the feed-forward layers after casting them into the familiar query-key-value framework. We evaluate the quality of top-*k* approximation for multi-head attention layers on the Long Range Arena Benchmark, and for feed-forward layers of T5 and UnifiedQA on multiple QA datasets. We show our approach leads to accuracy that is nearly-identical to vanilla attention in multiple setups including training from scratch, fine-tuning, and zero-shot inference.
Solving Graph-based Public Good Games with Tree Search and Imitation Learning

Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society. In this work, we adopt the perspective of a central planner with a global view of a network of self-interested agents and the goal of maximizing some desired property in the context of a best-shot public goods game. Existing algorithms for this known NP-complete problem find solutions that are sub-optimal and cannot optimize for criteria other than social welfare. In order to efficiently solve public goods games, our proposed method directly exploits the correspondence between equilibria and the Maximal Independent Set (mIS) structural property of graphs. In particular, we define a Markov Decision Process which incrementally generates an mIS, and adopt a planning method to search for equilibria, outperforming existing methods. Furthermore, we devise a graph imitation learning technique that uses demonstrations of the search to obtain a graph neural network parametrized policy which quickly generalizes to unseen game instances. Our evaluation results show that this policy is able to reach 99.5% of the performance of the planning method while being three orders of magnitude faster to evaluate on the largest graphs tested. The methods presented in this work can be applied to a large class of public goods games of potentially high societal impact and more broadly to other graph combinatorial optimization problems.
Go Small and Similar: A Simple Output Decay Brings Better Performance

Regularization and data augmentation methods have been widely used and become increasingly indispensable in deep learning training. Researchers who devote themselves to this have considered various possibilities. But so far, there has been little discussion about regularizing outputs of the model. This paper begins with empirical observations that better performances are significantly associated with output distributions, that have smaller average values and variances. By audaciously assuming there is causality involved, we propose a novel regularization term, called Output Decay, that enforces the model to assign smaller and similar output values on each class. Though being counter-intuitive, such a small modification result in a remarkable improvement on performance. Extensive experiments demonstrate the wide applicability, versatility, and compatibility of Output Decay.
Is it a click bait? Let's predict using Machine Learning

Over the years, the way people reads news is changing. Instead of newspapers in printed form, millennials prefer to read them online. Another reason for the massive popularity of digitised news is unlike traditional newspapers, that they provide instant information about the happenings around the globe. Online news has its’ own nuisances. A major chunk of users visits news websites by reading titles / short descriptions of news on social media platforms. ‘All that glitters is not gold’ Many times, social media posts of digital marketing executives regarding news articles does not reflect the actual content of news. They are mainly written in a way capable of inciting users to click on them thereby increasing the overall traffic of the news websites. Clickbait content refers to those content which is mainly written for attracting the attention of masses. These alluring contents provoke users to click on links and redirect them to land on a particular web page. The objective of click-bait articles is to increase traffic of a website rather than providing quality content to users. It is of utmost importance to check that the nature of social media posts done by digital marketing executives regarding news articles published is not clickbait. This project aims at detecting social media posts which tend to be click-bait.
Simultaneous microscopic imaging of thickness and refractive index of thin layers by heterodyne interferometric reflectometry (HiRef)

The detection of spatial or temporal variations in very thin samples has important applications in the biological sciences. For example, cellular membranes exhibit changes in lipid composition and order, which in turn modulate their function in space and time. Simultaneous measurement of thickness and refractive index would be one way to observe these variations, yet doing it noninvasively remains an elusive goal. Here we present a microscopic-imaging technique to simultaneously measure the thickness and refractive index of thin layers in a spatially resolved manner using reflectometry. A coherent laser beam, focussed by a high-numerical-aperture microscope objective and reflected by the sample, is measured using its heterodyne interference with a reference in order to determine the amplitude and phase of the reflected field and thus the complex reflection coefficient. Comparing the results with the theoretically calculated reflection of a thin layer under coherent illumination of high numerical aperture by the microscope objective, the refractive index and thickness of the layer are determined. We present results on a layer of polyvinylacetate with a thickness of approximately 80 nm. These results have a precision better than 10% in the thickness and better than 1% in the refractive index. The measured refractive index is consistent with literature values, and the measured thickness is close to measurements of the same sample by quantitative differential interference contrast. We discuss the significance of these results and the possibility of performing accurate measurements on nanometric layers. Notably, the shot-noise limit of the technique is below 0.5 nm in thickness and 0.0005 in refractive index for millisecond measurement times.
Distribution level battery storage valuation framework

The growing demand for electricity in emerging markets and developing economies such as India is causing loading and congestion problems on distribution networks, particularly in urban locations. Electric utilities in these regions face unique constraints regarding raising capital required to upgrade their congested networks. Battery storage has emerged as a non-wire alternative to feeder-level upgrades. This article presents a valuation framework by optimally sizing and placing battery storage on the distribution network. We evaluate the value of storage using a real options analysis through a Markov Chain Monte Carlo to identify the least-cost network upgrade strategy, given demand growth uncertainty. When applied to urban distribution network feeders typical of those found in congested cities in India, the approach highlights the economic value of network investment deferrals by making use of battery storage. We find that storage costs below 261 USD/kWh justify investments in distribution level storage and storage as a non-wire alternative only makes sense on moderately loaded feeders where storage charging is still feasible without violating network thermal capacity limits.
ASASSN-14ms: The Most Energetic Known Explosion of a Type Ibn Supernova and Its Physical Origin

ASASSN-14ms may represent the most luminous Type Ibn supernova (SN Ibn) ever detected, with an absolute U-band magnitude brighter than −22.0 mag and a total bolometric luminosity >1.0 × 1044 erg s−1 near maximum light. The early-time spectra of this SN are characterized by a blue continuum on which are superimposed narrow P Cygni profile lines of He i, suggesting the presence of slowly moving (∼1000 km s−1), He-rich circumstellar material (CSM). At 1–2 months after maximum brightness, the He i line profiles become only slightly broader, with blueshifted velocities of 2000–3000 km s−1, consistent with the CSM shell being continuously accelerated by the SN light and ejecta. Like most SNe Ibn, the light curves of ASASSN-14ms show rapid post-peak evolution, dropping by ∼7 mag in the V band over three months. Such a rapid post-peak decline and high luminosity can be explained by interaction between SN ejecta and helium-rich CSM of 0.9 M ⊙ at a distance of ∼1015 cm. The CSM around ASASSN-14ms is estimated to originate from a pre-explosion event with a mass-loss rate of 6.7 M ⊙ yr−1 (assuming a velocity of ∼1000 km s−1), which is consistent with abundant He-rich material violently ejected during the late Wolf–Rayet (WN9-11 or Opfe) stage. After examining the light curves for a sample of SNe Ibn, we find that the more luminous ones tend to have slower post-peak decline rates, reflecting that the observed differences may arise primarily from discrepancies in the CSM distribution around the massive progenitors.
Decreasing scaling transition from adaptive gradient descent to stochastic gradient descent

Currently, researchers have proposed the adaptive gradient descent algorithm and its variants, such as AdaGrad, RMSProp, Adam, AmsGrad, etc. Although these algorithms have a faster speed in the early stage, the generalization ability in the later stage of training is often not as good as the stochastic gradient descent. Recently, some researchers have combined the adaptive gradient descent and stochastic gradient descent to obtain the advantages of both and achieved good results. Based on this research, we propose a decreasing scaling transition from adaptive gradient descent to stochastic gradient descent method(DSTAda). For the training stage of the stochastic gradient descent, we use a learning rate that decreases linearly with the number of iterations instead of a constant learning rate. We achieve a smooth and stable transition from adaptive gradient descent to stochastic gradient descent through scaling. At the same time, we give a theoretical proof of the convergence of DSTAda under the framework of online learning. Our experimental results show that the DSTAda algorithm has a faster convergence speed, higher accuracy, and better stability and robustness. Our implementation is available at: https://github.com/kunzeng/DSTAdam.
User Acceptance of Gender Stereotypes in Automated Career Recommendations

Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we show that a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using career recommendation as a case study, we build a fair AI career recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, our results demonstrate we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans.
Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media

COVID-19 pandemic has generated what public health officials called an infodemic of misinformation. As social distancing and stay-at-home orders came into effect, many turned to social media for socializing. This increase in social media usage has made it a prime vehicle for the spreading of misinformation. This paper presents a mechanism to detect COVID-19 health-related misinformation in social media following an interdisciplinary approach. Leveraging social psychology as a foundation and existing misinformation frameworks, we defined misinformation themes and associated keywords incorporated into the misinformation detection mechanism using applied machine learning techniques. Next, using the Twitter dataset, we explored the performance of the proposed methodology using multiple state-of-the-art machine learning classifiers. Our method shows promising results with at most 78% accuracy in classifying health-related misinformation versus true information using uni-gram-based NLP feature generations from tweets and the Decision Tree classifier. We also provide suggestions on alternatives for countering misinformation and ethical consideration for the study.
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation

Planning provides a framework for optimizing sequential decisions in complex environments. Recent advances in efficient planning in deterministic or stochastic high-dimensional domains with continuous action spaces leverage backpropagation through a model of the environment to directly optimize actions. However, existing methods typically not take risk into account when optimizing in stochastic domains, which can be incorporated efficiently in MDPs by optimizing the entropic utility of returns. We bridge this gap by introducing Risk-Aware Planning using PyTorch (RAPTOR), a novel framework for risk-sensitive planning through end-to-end optimization of the entropic utility objective. A key technical difficulty of our approach lies in that direct optimization of the entropic utility by backpropagation is impossible due to the presence of environment stochasticity. The novelty of RAPTOR lies in the reparameterization of the state distribution, which makes it possible to apply stochastic backpropagatation through sufficient statistics of the entropic utility computed from forward-sampled trajectories. The direct optimization of this empirical objective in an end-to-end manner is called the risk-averse straight-line plan, which commits to a sequence of actions in advance and can be sub-optimal in highly stochastic domains. We address this shortcoming by optimizing for risk-aware Deep Reactive Policies (RaDRP) in our framework. We evaluate and compare these two forms of RAPTOR on three highly stochastic do-mains, including nonlinear navigation, HVAC control, and linear reservoir control, demonstrating the ability to manage risk in complex MDPs.
Reverse-engineer the Distributional Structure of Infant Egocentric Views for Training Generalizable Image Classifiers

We analyze egocentric views of attended objects from infants. This paper shows 1) empirical evidence that children's egocentric views have more diverse distributions compared to adults' views, 2) we can computationally simulate the infants' distribution, and 3) the distribution is beneficial for training more generalized image classifiers not only for infant egocentric vision but for third-person computer vision.
Can Transformer Language Models Predict Psychometric Properties?

Transformer-based language models (LMs) continue to advance state-of-the-art performance on NLP benchmark tasks, including tasks designed to mimic human-inspired “commonsense” competencies. To better understand the degree to which LMs can be said to have certain linguistic reasoning skills, researchers are beginning to adapt the tools and concepts of the field of psychometrics. But to what extent can the benefits flow in the other direction? I.e., can LMs be of use in predicting what the psychometric properties of test items will be when those items are given to human participants? We gather responses from numerous human participants and LMs (transformer- and non-transformer-based) on a broad diagnostic test of linguistic competencies. We then use the responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately. We then determine how well these two sets of predictions match. We find cases in which transformer-based LMs predict psychometric properties consistently well in certain categories but consistently poorly in others, thus providing new insights into fundamental similarities and differences between human and LM reasoning.
The X-rays wind connection in PG 2112+059

We study the connection between the X-ray and UV properties of the broad absorption line (BAL) wind in the highly X-ray variable quasar PG 2112+059 by comparing Chandra-ACIS data with contemporaneous UV HST/STIS spectra in three different epochs. We observe a correlation whereby an increase in the equivalent-widths (EWs) of the BALs is accompanied by a redder UV spectrum. The growth in the BALs EWs is also accompanied by a significant dimming in soft X-ray emission (. 2 keV), consistent with increased absorption. Variations in the hard X-ray emission (& 2 keV) are only accompanied by minor spectral variations of the UV-BALs and do not show significant changes in the EW of BALs. These trends suggest a wind-shield scenario where the outflow inclination with respect to the line of sight is decreasing and/or the wind mass is increasing. These changes elevate the covering fraction and/or column densities of the BALs and are likely accompanied by a nearly contemporaneous increase in the column density of the shield.
A Cluster Model for Growth of Random Trees

We first consider the growth of trees by probabilistic attachment of new vertices to leaves. This leads to a growth model based on vertex clusters and probabilities assigned to clusters. This model turns out to be readily applicable to attachment at any depth of the tree, hence the paper evolves to a general study of tree growth by cluster-based attachment. Drawing inspiration from the concept of intrinsic vertex fitness due to Bianconi and Barabasi, we introduce vertex mass as an additive intrinsic vertex attribute. Unlike Bianconi and Barabasi who used fitness as a vertex degree multiplier in the context of growth by preferential attachment, we treat vertex mass as a fundamental probabilistic construct whose additivity plays a primary role. Notably, independent mass distributions induce a distribution on the sum of such masses through Laplace convolution. In this way, clusters of vertices inherit their mass distributions from vertices within the cluster. Our main contribution is a novel theorem for the joint distribution of cluster masses, conditioned on their respective distributions. As described by Ferguson and Kingman in the context of distributions on general measures, the choice of gamma conditioning distributions leads to the Dirichlet distribution. Beyond gamma conditioning distributions, our theorem allows other choices, such as the fat-tailed stable distributions with infinite mean. We discuss Levy conditioning distributions as a gamma alternative, the Levy distribution being a notable instance of the stable family. We conclude with a theorem giving the analytic marginals of the normalised distribution conditioned on the Levy distribution.
Robust Representation Learning via Perceptual Similarity Metrics

A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream task, without overfitting to spurious input features. Extracting such task-relevant predictive information is particularly difficult for real-world datasets. In this work, we propose Contrastive Input Morphing (CIM), a representation learning framework that learns input-space transformations of the data to mitigate the effect of irrelevant input features on downstream performance. Our method leverages a perceptual similarity metric via a triplet loss to ensure that the transformation preserves task-relevant information.Empirically, we demonstrate the efficacy of our approach on tasks which typically suffer from the presence of spurious correlations: classification with nuisance information, out-of-distribution generalization, and preservation of subgroup accuracies. We additionally show that CIM is complementary to other mutual information-based representation learning techniques, and demonstrate that it improves the performance of variational information bottleneck (VIB) when used together.