Miracle-dz/UniFAR-FTUs huggingface dataset:
A high-speed Zernike moments edge operator based on 9×9 masks

Based on the Zernike moments, a high-speed Zernike moments edge operator based on 9×9 masks is developed.Firstly, the calculation steps of Zernike moments masks are derived,and then two 9×9 masks of the new edge operator are given.Two different images are chosen for the new operator testing:one is the original artificial binary image, and the other is the fuzzy image after 5 times of smoothing.The results show that the new operator has strong edge refining ability, and the edge detecting accuracy is satisfied.Although the fuzzy image testing also shows that the new operator has good detection result, it should be noted that there is mismatching between the edge image and the recorded sub-pixel coordinate data.Finally, the two step detection stragety is used to improve the operation speed.The running time of the new operater is no more than 0.19 s.
A Study on Non-Blind Algorithm of Subarray Signal Processing for Desired Signal Estimation

In this paper, a DOA estimation method using subarray and spatial smooth is proposed. The DOA is then estimated based on the computation of the phase shift between the reference signal and its phase shifted signal. To significantly improve the robustness of DOA estimation and of beamforming and to estimate azimuth angle in multipath mobile channel environment, we developed techniques for applying spatial smooth subarray method to arrays of linear geometry. We developed method for applying spatial smooth subarray to arrays of arbitrary geometry. We study the cause of ambiguities in a multiple signal environment and find the necessary and sufficient conditions for an array manifold to be ambiguity free. We show that can be the application of our method MUSIC and adaptive beamforming method. The estimation results are verified by simulations.
Improving the Utility of Differentially Private Clustering through Dynamical Processing

This study aims to alleviate the trade-off between utility and privacy in the task of differentially private clustering. Existing works focus on simple clustering methods, which show poor clustering performance for non-convex clusters. By utilizing Morse theory, we hierarchically connect the Gaussian sub-clusters to fit complex cluster distributions. Because differentially private sub-clusters are obtained through the existing methods, the proposed method causes little or no additional privacy loss. We provide a theoretical background that implies that the proposed method is inductive and can achieve any desired number of clusters. Experiments on various datasets show that our framework achieves better clustering performance at the same privacy level, compared to the existing methods.
Optical variability in Quasars: Scaling with black hole mass and Eddington ratio depend on the observed timescales

Quasars emission is highly variable, and this variability gives us clues to understand the accretion process onto supermassive black holes. We can expect variability properties to correlate with the main physical properties of the accreting black hole, i.e., its mass and accretion rate. It has been established that the relative amplitude of variability anti-correlates with the accretion rate.The dependence of the variance on black hole mass has remained elusive, and contradicting results, including positive, negative, or no correlation, have been reported. In this work, we show that the key to these contradictions lies in the timescales of variability studied (e.g., the length of the light curves available). By isolating the variance on different timescales as well as mass and accretion rate bins we show that there is indeed a negative correlation between black hole mass and variance and that this anti-correlation is stronger for shorter timescale fluctuations. The behavior can be explained in terms of a universal variability power spectrum for all quasars, resembling a broken power law where the variance is constant at low temporal frequencies and then drops continuously for frequencies higher than a characteristic frequency $f_b$, where $f_b$ correlates with the black hole mass. Furthermore, to explain all the variance results presented here, not only the normalization of this power spectrum must anti-correlate with the accretion rate, but also the shape of the power spectra at short timescales must depend on this parameter as well.
Design of a multimodal device to improve well-being of autistic workers interacting with collaborative robots

Several studies report that people diagnosed with autism spectrum disorder (ASD) have low employment rates and major difficulties in maintaining occupation (Hendricks, 2010). Adopting smart and interactive technology, promoted by Industry 4.0, can be a valid solution to improve social inclusion in the workplace. The present work describes the design and development process of the multimodal device (A)MICO (acronym for 'A Multimodal device to improve inclusive Interaction between Cobot and Operator'), aimed to improve the user experience of ASD workers interacting with collaborative robots (cobots) in production lines. (A)MICO proposes a new intuitive mode of communication in which information about the cobot activity is transferred through acoustic and visual feedback. Lights and sounds are designed to make the user aware of the cobot's activity and gain control of the situation. To define the optimal feedback configuration, a co-design process has been performed involving five users with high functioning autism (HFA). The inclusion of users with HFA in the design process allows to analyse the system from different perspectives and increase the acceptance of the final product (Francis et al., 2009). In addition, (A)MICO is designed to be integrated into a pre-existing cobot system and its feedback can be customized making job tasks more human-friendly and accessible to all. To achieve this purpose, Design for All principles were taken into consideration since the preliminary stages of the study, in order to develop a device that could represent a valuable aid in improving the well-being and productivity for both neurotypical and neurodiverse users.
Se Nanopowder Conversion into Lubricious 2D Selenide Layers by Tribochemical Reactions

Transition metal dichalcogenide (TMD) coatings have attracted enormous scientific and industrial interest due to their outstanding tribological behavior. The paradigmatic example is MoS2, even though selenides and tellurides have demonstrated superior tribological properties. Here, an innovative in operando conversion of Se nanopowders into lubricious 2D selenides, by sprinkling them onto sliding metallic surfaces coated with Mo and W thin films, is described. Advanced material characterization confirms the tribochemical formation of a thin tribofilm containing selenides, reducing the coefficient of friction down to below 0.1 in ambient air, levels typically reached using fully formulated oils. Ab initio molecular dynamics simulations under tribological conditions reveal the atomistic mechanisms that result in the shear‐induced synthesis of selenide monolayers from nanopowders. The use of Se nanopowder provides thermal stability and prevents outgassing in vacuum environments. Additionally, the high reactivity of the Se nanopowder with the transition metal coating in the conditions prevailing in the contact interface yields highly reproducible results, making it particularly suitable for the replenishment of sliding components with solid lubricants, avoiding the long‐lasting problem of TMD‐lubricity degradation caused by environmental molecules. The suggested straightforward approach demonstrates an unconventional and smart way to synthesize TMDs in operando and exploit their friction‐ and wear‐reducing impact.
Status of 11 T 2-in-1 Nb$_3$Sn Dipole Development for LHC

The LHC upgrade plans foresee installation of additional collimators in the LHC lattice. To provide the necessary longitudinal space for these collimators, shorter and stronger Nb3Sn dipoles compatible with the LHC lattice and main systems could be used. This paper describes the design and status of the twin-aperture Nb3Sn dipole being developed by FNAL and CERN for the LHC, and reports test results of two collared coils to be used in the first 1 m long twin-aperture dipole model.
GPU accelerated matrix factorization of large scale data using block based approach

Matrix Factorization (MF) on large scale data takes substantial time on a Central Processing Unit (CPU). While Graphical Processing Unit (GPU)s could expedite the computation of MF, the available memory on a GPU is finite. Leveraging GPUs require alternative techniques that allow not only parallelism but also address memory limitations. Synchronization between computation units, isolation of data related to a computational unit, sharing of data between computational units and identification of independent tasks among computational units are some of the challenges while leveraging GPUs for MF. We propose a block based approach to matrix factorization using Stochastic Gradient Descent (SGD) that is aimed at accelerating MF on GPUs. The primary motivation for the approach is to make it viable to factorize extremely large data sets on limited hardware without having to compromise on results. The approach addresses factorization of large scale data by identifying independent blocks, each of which are factorized in parallel using multiple computational units. The approach can be extended to one or more GPUs and even to distributed systems. The RMSE results of the block based approach are with in acceptable delta in comparison to the results of CPU based variant and multi-threaded CPU variant of similar SGD kernel implementation. The advantage, of the block based variant, in-terms of speed are significant in comparison to other variants.
SLoMo: A General System for Legged Robot Motion Imitation From Casual Videos

We present SLoMo: a first-of-its-kind framework for transferring skilled motions from casually captured “in-the-wild” video footage of humans and animals to legged robots. SLoMo works in three stages: 1) synthesize a physically plausible reconstructed key-point trajectory from monocular videos; 2) optimize a dynamically feasible reference trajectory for the robot offline that includes body and foot motion, as well as a contact sequence that closely tracks the key points; and 3) track the reference trajectory online using a general-purpose model-predictive controller on robot hardware. Traditional motion imitation for legged motor skills often requires expert animators, collaborative demonstrations, and/or expensive motion-capture equipment, all of which limit scalability. Instead, SLoMo only relies on easy-to-obtain videos, readily available in online repositories like YouTube. It converts videos into motion primitives that can be executed reliably by real-world robots. We demonstrate our approach by transferring the motions of cats, dogs, and humans to example robots including a quadruped (on hardware) and a humanoid (in simulation).
Diagonalization Based Parallel-in-Time Method for a Class of Fourth Order Time Dependent PDEs

In this paper, we design, analyze and implement efficient time parallel method for a class of fourth order time-dependent partial differential equations (PDEs), namely biharmonic heat equation, linearized Cahn-Hilliard (CH) equation and the nonlinear CH equation. We use diagonalization technique on all-at-once system to develop efficient iterative time parallel methods for investigating the solution behaviour of said equations. We present the convergence analysis of Parallel-in-Time (PinT) algorithms. We verify our findings by presenting numerical results.
A Supervised Machine Learning Approach to Operator Intent Recognition for Teleoperated Mobile Robot Navigation

In applications that involve human-robot interaction (HRI), human-robot teaming (HRT), and cooperative human-machine systems, the inference of the human partner's intent is of critical importance. This paper presents a method for the inference of the human operator's navigational intent, in the context of mobile robots that provide full or partial (e.g., shared control) teleoperation. We propose the Machine Learning Operator Intent Inference (MLOII) method, which a) processes spatial data collected by the robot's sensors; b) utilizes a supervised machine learning algorithm to estimate the operator's most probable navigational goal online. The proposed method's ability to reliably and efficiently infer the intent of the human operator is experimentally evaluated in realistically simulated exploration and remote inspection scenarios. The results in terms of accuracy and uncertainty indicate that the proposed method is comparable to another state-of-the-art method found in the literature.
Influence of oxygen on electronic correlation and transport in iron in the outer Earth's core

Knowing the transport properties of iron under realistic conditions present in the Earth's core is essential for the geophysical modeling of Earth's magnetic field generation. Besides by extreme pressures and temperatures, transport may be influenced importantly also by the presence of light elements. Using a combination of molecular dynamics, density functional theory, and dynamical mean-field theory methods we investigate how oxygen impurities influence the electronic correlations and transport in the liquid outer Earth's core. We consider a case with an oxygen content of ~10 atomic%, a value that is believed to be close to the composition of the core. We find that the electronic correlations are enhanced but their effect on conductivities is moderate (compared to pure Fe, electrical conductivity drops by 10% and thermal conductivity by 18%). The effect of electron-electron scattering alone, whereas not large, is comparable to effects of the compositional disorder. We reveal the mechanism behind the larger suppression of the thermal conductivity and associated reduction of the Lorenz ratio and discuss its geophysical significance.
On Pitfalls of RemOve-And-Retrain: Data Processing Inequality Perspective

Approaches for appraising feature importance approximations, alternatively referred to as attribution methods, have been established across an extensive array of contexts. The development of resilient techniques for performance benchmarking constitutes a critical concern in the sphere of explainable deep learning. This study scrutinizes the dependability of the RemOve-And-Retrain (ROAR) procedure, which is prevalently employed for gauging the performance of feature importance estimates. The insights gleaned from our theoretical foundation and empirical investigations reveal that attributions containing lesser information about the decision function may yield superior results in ROAR benchmarks, contradicting the original intent of ROAR. This occurrence is similarly observed in the recently introduced variant RemOve-And-Debias (ROAD), and we posit a persistent pattern of blurriness bias in ROAR attribution metrics. Our findings serve as a warning against indiscriminate use on ROAR metrics. The code is available as open source.
A Variable Structure Point-to-Point Regulation Controller for Uncertain General Linear Systems

In this paper, an alternative variable structure controller is designed for the point-to-point regulation control of uncertain general linear plants so that the output of plants can be controlled from an arbitrarily given initial point to an arbitrarily given reference point in the state space. By using the error between the steady state value of the output and an arbitrarily given reference point and those integral, a transformed integral sliding surface is defined, in advance, as the surface from an initial state to an arbitrarily given reference point without the reaching phase problems. A corresponding control input is suggested to satisfy the existence condition of the sliding mode on the preselected transformed integral sliding surface against matched uncertainties and disturbances. Therefore, the output controlled by the proposed controller is completely robust and identical to that of the preselected transformed integral sliding surface. Through an example, the effectiveness of the suggested controller is verified.
AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays

Background: Recently, ChatGPT and similar generative AI models have attracted hundreds of millions of users and become part of the public discourse. Many believe that such models will disrupt society and will result in a significant change in the education system and information generation in the future. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models -- both lack scientific rigour. Objective: Through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays, we systematically assess the quality of the AI-generated content. Methods: A large corpus of essays was rated using standard criteria by a large number of human experts (teachers). We augment the analysis with a consideration of the linguistic characteristics of the generated essays. Results: Our results demonstrate that ChatGPT generates essays that are rated higher for quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays, e.g., it is characterized by fewer discourse and epistemic markers, but more nominalizations and greater lexical diversity. Conclusions: Our results clearly demonstrate that models like ChatGPT outperform humans in generating argumentative essays. Since the technology is readily available for anyone to use, educators must act immediately. We must re-invent homework and develop teaching concepts that utilize these AI models in the same way as math utilized the calculator: teach the general concepts first and then use AI tools to free up time for other learning objectives.
Measuring and Modeling the Free Content Web

Free content websites that provide free books, music, games, movies, etc., have existed on the Internet for many years. While it is a common belief that such websites might be different from premium websites providing the same content types, an analysis that supports this belief is lacking in the literature. In particular, it is unclear if those websites are as safe as their premium counterparts. In this paper, we set out to investigate, by analysis and quantification, the similarities and differences between free content and premium websites, including their risk profiles. To conduct this analysis, we assembled a list of 834 free content websites offering books, games, movies, music, and software, and 728 premium websites offering content of the same type. We then contribute domain-, content-, and risk-level analysis, examining and contrasting the websites' domain names, creation times, SSL certificates, HTTP requests, page size, average load time, and content type. For risk analysis, we consider and examine the maliciousness of these websites at the website- and component-level. Among other interesting findings, we show that free content websites tend to be vastly distributed across the TLDs and exhibit more dynamics with an upward trend for newly registered domains. Moreover, the free content websites are 4.5 times more likely to utilize an expired certificate, 19 times more likely to be malicious at the website level, and 2.64 times more likely to be malicious at the component level. Encouraged by the clear differences between the two types of websites, we explore the automation and generalization of the risk modeling of the free content risky websites, showing that a simple machine learning-based technique can produce 86.81\% accuracy in identifying them.
A Bayesian Spatio-Temporal Extension to Poisson Auto-Regression: Modeling the Disease Infection Rate of COVID-19 in England

The COVID-19 pandemic provided many modeling challenges to investigate the evolution of an epidemic process over areal units. A suitable encompassing model must describe the spatio-temporal variations of the disease infection rate of multiple areal processes while adjusting for local and global inputs. We develop an extension to Poisson Auto-Regression that incorporates spatio-temporal dependence to characterize the local dynamics while borrowing information among adjacent areas. The specification includes up to two sets of space-time random effects to capture the spatio-temporal dependence and a linear predictor depending on an arbitrary set of covariates. The proposed model, adopted in a fully Bayesian framework and implemented through a novel sparse-matrix representation in Stan, provides a framework for evaluating local policy changes over the whole spatial and temporal domain of the study. It has been validated through a substantial simulation study and applied to the weekly COVID-19 cases observed in the English local authority districts between May 2020 and March 2021. The model detects substantial spatial and temporal heterogeneity and allows a full evaluation of the impact of two alternative sets of covariates: the level of local restrictions in place and the value of the Google Mobility Indices. The paper also formalizes various novel model-based investigation methods for assessing additional aspects of disease epidemiology.
Transferring Procedural Knowledge across Commonsense Tasks

Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and procedural understanding, current AI has no mechanisms to automatically track and explain procedures in unseen stories. To bridge this gap, we study the ability of AI models to transfer procedural knowledge to novel narrative tasks in a transparent manner. We design LEAP: a comprehensive framework that integrates state-of-the-art modeling architectures, training regimes, and augmentation strategies based on both natural and synthetic stories. To address the lack of densely annotated training data, we devise a robust automatic labeler based on few-shot prompting to enhance the augmented data. Our experiments with in- and out-of-domain tasks reveal insights into the interplay of different architectures, training regimes, and augmentation strategies. LEAP's labeler has a clear positive impact on out-of-domain datasets, while the resulting dense annotation provides native explainability.
The observed number counts in luminosity distance space

Next generation surveys will provide us with an unprecedented number of detections of supernovae Type Ia and gravitational wave merger events. Cross-correlations of such objects offer novel and powerful insights into the large-scale distribution of matter in the universe. Both of these sources carry information on their luminosity distance, but remain uninformative about their redshifts; hence their clustering analyses and cross-correlations need to be carried out in luminosity distance space, as opposed to redshift space. In this paper, we calculate the full expression for the number count fluctuation in terms of a perturbation to the observed luminosity distance. We find the expression to differ significantly from the one commonly used in redshift space. Furthermore, we present a comparison of the number count angular power spectra between luminosity distance and redshift spaces. We see a wide divergence between the two at large scales, and we note that lensing is the main contribution to such differences. On such scales and at higher redshifts the difference between the angular power spectra in luminosity distance and redshift spaces can be roughly 50%. We also investigate cross-correlating different redshift bins using different tracers, i.e. one in luminosity distance space and one in redshift, simulating the cross-correlation angular power spectrum between background gravitational waves/supernovae and foreground galaxies. Finally, we show that in a cosmic variance limited survey, the relativistic corrections to the density-only term ought to be included.