Miracle-dz/UniFAR-FTUs huggingface dataset:
Imagining Future Digital Assistants at Work: A Study of Task Management Needs

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings, and we offer some recommendations for reduction to practice.
Full Set of Superconducting Parameters of K3c60

The superconducting parameters are the key for building or identifying the theory responsible for the mechanism of superconductivity. Such parameters for fulleride superconductors have not been well established despite the tremendous efforts over the past 30 years. Here we provide a full set of parameters through a systematic study on a well-characterized K$_{3}$C$_{60}$ sample. The obtained high upper critical field of 33.0$\pm$0.5 T from the direct electrical transport measurements together with the relatively high critical temperature and large critical current density classifies K$_{3}$C$_{60}$ as a promising three-dimensional superconducting magnet material with the advantage of the rich carbon abundance on the Earth. This high upper critical field along with the large reduced superconducting energy gap and strong phonon self-energy effect supports the strong electron-phonon coupling interactions in this superconductor. The evaluation of all self-consistently obtained parameters suggests the unconventional nature of the superconductivity for K$_3$C$_{60}$ with the joint contributions from the strong electron-phonon coupling and electron correlations. These results and findings are important not only for fundamentally understanding the superconductivity in fullerides but also for future superconducting magnet developments and applications.
Generative Models of Multichannel Data from a Single Example—Application to Dust Emission

The quest for primordial B-modes in the cosmic microwave background has emphasized the need for refined models of the Galactic dust foreground. Here we aim at building a realistic statistical model of the multifrequency dust emission from a single example. We introduce a generic methodology relying on microcanonical gradient descent models conditioned by an extended family of wavelet phase harmonic (WPH) statistics. To tackle the multichannel aspect of the data, we define cross-WPH statistics, quantifying non-Gaussian correlations between maps. Our data-driven methodology could apply to various contexts, and we have updated the software PyWPH, on which this work relies, accordingly. Applying this to dust emission maps built from a magnetohydrodynamics simulation, we construct and assess two generative models: (1) a (I, E, B) multi-observable input, and (2) a {Iν}ν multifrequency input. The samples exhibit consistent features compared to the original maps. A statistical analysis of model 1 shows that the power spectra, distributions of pixels, and Minkowski functionals are captured to a good extent. We analyze model 2 by fitting the spectral energy distribution (SED) of both the synthetic and original maps with a modified blackbody (MBB) law. The maps are equally well fitted, and a comparison of the MBB parameters shows that our model succeeds in capturing the spatial variations of the SED from the data. Besides the perspectives of this work for dust emission modeling, the introduction of cross-WPH statistics opens a new avenue to characterize non-Gaussian interactions across different maps, which we believe will be fruitful for astrophysics.
Linking Properties to Microstructure in Liquid Metal Embedded Elastomers via Machine Learning

Liquid metals (LM) are embedded in an elastomer matrix to obtain soft composites with unique thermal, dielectric, and mechanical properties. They have applications in soft robotics, biomedical engineering, and wearable electronics. By linking the structure to the properties of these materials, it is possible to perform material design rationally. Liquid-metal embedded elastomers (LMEEs) have been designed for targeted electro-thermo-mechanical properties by semi-supervised learning of structure-property (SP) links in a variational autoencoder network (VAE). The design parameters are the microstructural descriptors that are physically meaningful and have affine relationships with the synthetization of the studied particulate composite. The machine learning (ML) model is trained on a generated dataset of microstructural descriptors with their multifunctional property quantities as their labels. Sobol sequence is used for in-silico Design of Experiment (DoE) by sampling the design space to generate a comprehensive dataset of 3D microstructure realizations via a packing algorithm. The mechanical responses of the generated microstructures are simulated using a previously developed Finite Element (FE) model, considering the surface tension induced by LM inclusions, while the linear thermal and dielectric constants are homogenized with the help of our in-house Fast Fourier Transform (FFT) package. Following the training by minimization of an appropriate loss function, the VAE encoder acts as the surrogate of numerical solvers of the multifunctional homogenizations, and its decoder is used for the material design. Our results indicate the satisfactory performance of the surrogate model and the inverse calculator with respect to high-fidelity numerical simulations validated with LMEE experimental results.
Box dimension of generalized affine fractal interpolation functions

Let $f$ be a generalized affine fractal interpolation function with vertical scaling function $S$. In this paper, we study $\dim_B \Gamma f$, the box dimension of the graph of $f$, under the assumption that $S$ is a Lipschtz function. By introducing vertical scaling matrices, we estimate the upper bound and the lower bound of oscillations of $f$. As a result, we obtain explicit formula of $\dim_B \Gamma f$ under certain constraint conditions.
Sparse semiparametric discriminant analysis for high-dimensional zero-inflated data

Sequencing-based technologies provide an abundance of high-dimensional biological datasets with skewed and zero-inflated measurements. Classification of such data with linear discriminant analysis leads to poor performance due to the violation of the Gaussian distribution assumption. To address this limitation, we propose a new semiparametric discriminant analysis framework based on the truncated latent Gaussian copula model that accommodates both skewness and zero inflation. By applying sparsity regularization, we demonstrate that the proposed method leads to the consistent estimation of classification direction in high-dimensional settings. On simulated data, the proposed method shows superior performance compared to the existing method. We apply the method to discriminate healthy controls from patients with Crohn’s disease based on microbiome data and to identify genera with the most influence on the classification rule. Keyword: Classification; Latent Gaussian copula; Microbiome data; Probit regression; Variable selection hchung13@uncc.edu yni@stat.tamu. edu Corresponding author, irinag@stat.tamu. edu 1 ar X iv :2 20 8. 03 73 4v 1 [ st at . M E ] 7 A ug 2 02 2
Deep Learning for Material Decomposition in Photon-Counting CT

—Photon-counting CT (PCCT) offers improved diagnostic performance through better spatial and energy resolution, but developing high-quality image reconstruction methods that can deal with these large datasets is challenging. Model-based solutions incorporate models of the physical acquisition in order to reconstruct more accurate images, but are dependent on an accurate forward operator and present difﬁcul-ties with ﬁnding good regularization. Another approach is deep-learning reconstruction, which has shown great promise in CT. However, fully data-driven solutions typically need large amounts of training data and lack interpretability. To combine the beneﬁts of both methods, while minimizing their respective drawbacks, it is desirable to develop reconstruction algorithms that combine both model-based and data-driven approaches. In this work, we present a novel deep-learning solution for material decomposition in PCCT, based on an unrolled/unfolded iterative network. We evaluate two cases: a learned post-processing, which implicitly utilizes model knowledge, and a learned gradient-descent, which has explicit model-based components in the architecture. With our proposed techniques, we solve a challenging PCCT simulation case: three-material decomposition in abdomen imaging with low dose, iodine contrast, and a very small training sample support. In this scenario, our approach outperforms a maximum likelihood estimation, a variational method, as well as a fully-learned network.
Born-Infeld theory with higher derivatives

We present new models of non-linear electromagnetism which satisfy the Noether-Gaillard-Zumino current conservation and are, therefore, self-dual. The new models differ from the Born-Infeld-type models in that they deform the Maxwell theory starting with terms like $\lambda (\partial F)^{4}$. We provide a recursive algorithm to find all higher order terms in the action of the form $\lambda^{n} \partial ^{4n} F^{2n+2} $, which are necessary for the U(1) duality current conservation. We use one of these models to find a self-dual completion of the $\lambda (\partial F)^{4}$ correction to the open string action. We discuss the implication of these findings for the issue of UV finiteness of ${\cal N}=8$ supergravity.
Discovery of a Premerger Shock in an Intercluster Filament in Abell 98

We report the first unambiguous detection of an axial merger shock in the early-stage merging cluster Abell 98 using deep (227 ks) Chandra observations. The shock is about 420 kpc south from the northern subcluster of Abell 98, in between the northern and central subclusters, with a Mach number of  ≈ 2.3 ± 0.3. Our discovery of the axial merger shock front unveils a critical epoch in the formation of a massive galaxy cluster, when two subclusters are caught in the early phase of the merging process. We find that the electron temperature in the postshock region favors the instant collisionless model, where electrons are strongly heated at the shock front, by interactions with the magnetic field. We also report on the detection of an intercluster gas filament, with a temperature of kT = 1.07 ± 0.29 keV, along the merger axis of Abell 98. The measured properties of the gas in the filament are consistent with previous observations and numerical simulations of the hottest, densest parts of the warm–hot intergalactic medium (WHIM), where WHIM filaments interface with the virialization regions of galaxy clusters.
On the uniqueness result for the BSDE with continuous coefficient

In this paper, we study one-dimensional backward stochastic diﬀerential equation (BSDE, for short), whose coeﬃcient f is Lipschitz in y but only continuous in z . In addition, if the terminal condition ξ has bounded Malliavin derivative, we prove some uniqueness results for the BSDE with quadratic and linear growth in z , respectively.
A Tractable Approach to Coverage Analysis in Downlink Satellite Networks

Satellite networks are promising to provide ubiquitous and high-capacity global wireless connectivity. Traditionally, satellite networks are modeled by placing satellites on a grid of multiple circular orbit geometries. Such a network model, however, requires intricate system-level simulations to evaluate coverage performance, and analytical understanding of the satellite network is limited. Continuing the success of stochastic geometry in a tractable analysis for terrestrial networks, in this paper, we develop novel models that are tractable for the coverage analysis of satellite networks using stochastic geometry. By modeling the locations of satellites and users using Poisson point processes on the surfaces of concentric spheres, we characterize analytical expressions for the coverage probability of a typical downlink user as a function of relevant parameters, including path-loss exponent, satellite height, density, and Nakagami fading parameter. Then, we also derive a tight lower bound of the coverage probability in tractable expression while keeping full generality. Leveraging the derived expression, we identify the optimal density of satellites in terms of the height and the path-loss exponent. Our key finding is that the optimal average number of satellites decreases logarithmically with the satellite height to maximize the coverage performance. Simulation results verify the exactness of the derived expressions.
AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement

Existing 3D skeleton-based action recognition approaches reach impressive performance by encoding handcrafted action features to image format and decoding by CNNs. However, such methods are limited in two ways: a) the handcrafted action features are difficult to handle challenging actions, and b) they generally require complex CNN models to improve action recognition accuracy, which usually occur heavy computational burden. To overcome these limitations, we introduce a novel AFE-CNN, which devotes to enhance the features of 3D skeleton-based actions to adapt to challenging actions. We propose feature enhance modules from key joint, bone vector, key frame and temporal perspectives, thus the AFE-CNN is more robust to camera views and body sizes variation, and significantly improve the recognition accuracy on challenging actions. Moreover, our AFE-CNN adopts a light-weight CNN model to decode images with action feature enhanced, which ensures a much lower computational burden than the state-of-the-art methods. We evaluate the AFE-CNN on three benchmark skeleton-based action datasets: NTU RGB+D, NTU RGB+D 120, and UTKinect-Action3D, with extensive experimental results demonstrate our outstanding performance of AFE-CNN.
HWGN2: Side-channel Protected Neural Networks through Secure and Private Function Evaluation

Recent work has highlighted the risks of intellectual property (IP) piracy of deep learning (DL) models from the side-channel leakage of DL hardware accelerators. In response, to provide side-channel leakage resiliency to DL hardware accelerators, several approaches have been proposed, mainly borrowed from the methodologies devised for cryptographic implementations. Therefore, as expected, the same challenges posed by the complex design of such countermeasures should be dealt with. This is despite the fact that fundamental cryptographic approaches, specifically secure and private function evaluation, could potentially improve the robustness against side-channel leakage. To examine this and weigh the costs and benefits, we introduce hardware garbled NN (HWGN2), a DL hardware accelerator implemented on FPGA. HWGN2 also provides NN designers with the flexibility to protect their IP in real-time applications, where hardware resources are heavily constrained, through a hardware-communication cost trade-off. Concretely, we apply garbled circuits, implemented using a MIPS architecture that achieves up to 62.5x fewer logical and 66x less memory utilization than the state-of-the-art approaches at the price of communication overhead. Further, the side-channel resiliency of HWGN2 is demonstrated by employing the test vector leakage assessment (TVLA) test against both power and electromagnetic side-channels. This is in addition to the inherent feature of HWGN2: it ensures the privacy of users' input, including the architecture of NNs. We also demonstrate a natural extension to the malicious security modeljust as a by-product of our implementation.
Effect of the Heliospheric State on CME Evolution

The culmination of solar cycle 24 by the end of 2019 has created the opportunity to compare the differing properties of coronal mass ejections (CMEs) between two whole solar cycles: solar cycle 23 (SC 23) and solar cycle 24 (SC 24). We report on the width evolution of limb CMEs in SCs 23 and 24 in order to test the suggestion by Gopalswamy et al. that CME flux ropes attain pressure balance at larger heliocentric distances in SC 24. We measure CME width as a function of heliocentric distance for a significantly large number of limb CMEs (∼1000) and determine the distances where the CMEs reach constant width in each cycle. We introduced a new parameter, the transition height (hc) of a CME, defined as the critical heliocentric distance beyond which the CME width stabilizes to a quasi-constant value. Cycle and phase-to-phase comparisons are based on this new parameter. We find that the average value of hc in SC 24 is 62% higher than that in SC 23. SC 24 CMEs attain their peak width at larger distances from the Sun than SC 23 CMEs do. The enhanced transition height in SC 24 is new observational ratification of the anomalous expansion. The anomalous expansion of SC 24 CMEs, which is caused by the weak state of the heliosphere, accounts for the larger heliocentric distance where the pressure balance between CME flux rope and the ambient medium is attained.
UFNRec: Utilizing False Negative Samples for Sequential Recommendation

Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential component in learning the evolving user preferences through historical records. Except for randomly sampling negative samples from a uniformly distributed subset, many delicate methods have been proposed to mine negative samples with high quality. However, due to the inherent randomness of negative sampling, false negative samples are inevitably collected in model training. Current strategies mainly focus on removing such false negative samples, which leads to overlooking potential user interests, lack of recommendation diversity, less model robustness, and suffering from exposure bias. To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance. We first devise a simple strategy to extract false negative samples and then transfer these samples to positive samples in the following training process. Furthermore, we construct a teacher model to provide soft labels for false negative samples and design a consistency loss to regularize the predictions of these samples from the student model and the teacher model. To the best of our knowledge, this is the first work to utilize false negative samples instead of simply removing them for the sequential recommendation. Experiments on three benchmark public datasets are conducted using three widely applied SOTA models. The experiment results demonstrate that our proposed UFNRec can effectively draw information from false negative samples and further improve the performance of SOTA models. The code is available at https://github.com/UFNRec-code/UFNRec.
Velocity reconstruction in puffing pool fires with physics-informed neural networks

Pool fires are canonical representations of many accidental fires which can exhibit an unstable unsteady behavior, known as puffing, which involves a strong coupling between the temperature and velocity fields. Despite their practical relevance to fire research, their experimental study can be limited due to the complexity of measuring relevant quantities in parallel. In this work, we analyze the use of a recent physics-informed machine learning approach, called hidden fluid mechanics (HFM), to reconstruct unmeasured quantities in a puffing pool fire from measured quantities. The HFM framework relies on a physics-informed neural network (PINN) for this task. A PINN is a neural network that uses both the available data, here the measured quantities, and the physical equations governing the system, here the reacting Navier–Stokes equations, to infer the full fluid dynamic state. This framework is used to infer the velocity field in a puffing pool fire from measurements of density, pressure, and temperature. In this work, the dataset used for this test was generated from numerical simulations. It is shown that the PINN is able to reconstruct the velocity field accurately and to infer most features of the velocity field. In addition, it is shown that the reconstruction accuracy is robust with respect to noisy data, and a reduction in the number of measured quantities is explored and discussed. This study opens up the possibility of using PINNs for the reconstruction of unmeasured quantities from measured ones, providing the potential groundwork for their use in experiments for fire research.
Inverse maximum theorems and some consequences

We deal with inverse maximum theorems, which are inspired by the ones given by Aoyama, Komiya, Li et al. , Park and Komiya, and Yamauchi. As a consequence of our results, we state and prove an inverse maximum Nash theorem and show that any generalized Nash game can be reduced to a classical Nash game, under suitable assumptions. Additionally, we show that a result by Arrow and Debreu, on the existence of solutions for generalized Nash games, is actually equivalent to the one given by Debreu-Fan-Glicksberg for classical Nash games, which in turn is equivalent to Kakutani-Fan-Glisckberg’s ﬁxed point theorem.
Do financial regulators act in the public's interest? A Bayesian latent class estimation framework for assessing regulatory responses to banking crises

When banks fail amidst financial crises, the public criticizes regulators for bailing out or liquidating specific banks, especially the ones that gain attention due to their size or dominance. A comprehensive assessment of regulators, however, requires examining all their decisions, and not just specific ones, against the regulator’s dual objective of preserving financial stability while discouraging moral hazard. In this article, we develop a Bayesian latent class estimation framework to assess regulators on these competing objectives and evaluate their decisions against resolution rules recommended by theoretical studies of bank behavior designed to contain moral hazard incentives. The proposed estimation framework addresses the unobserved heterogeneity underlying regulator’s decisions in resolving failed banks and provides a disciplined statistical approach for inferring if they acted in the public interest. Our results reveal that during the crises of 1980’s, the U.S. banking regulator’s resolution decisions were consistent with recommended decision rules, while the U.S. savings and loans (S&L) regulator, which ultimately faced insolvency in 1989 at a cost of $132 billion to the taxpayer, had deviated from such recommendations. Timely interventions based on this evaluation could have redressed the S&L regulator’s decision structure and prevented losses to taxpayers.