Miracle-dz/UniFAR-FTUs huggingface dataset:
Additive manufacturing process design with differentiable simulations

We present a novel computational paradigm for process design in manufacturing processes that incorporates simulation responses to optimize manufacturing process parameters in high-dimensional temporal and spatial design spaces. We developed a differentiable finite element analysis framework using automatic differentiation which allows accurate optimization of challenging process parameters such as time-series laser power. We demonstrate the capability of our proposed method through three illustrative case studies in additive manufacturing for: (i) material and process parameter inference using partial observable data, (ii) controlling time-series thermal behavior, and (iii) stabilizing melt pool depth. This research opens new avenues for high-dimensional manufacturing design using solid mechanics simulation tools such as finite element methods. Our codes are made publicly available for the research community at this https URL.
EOR by Low Salinity Water and Surfactant at Low Concentration: Impact of Injection and in Situ Brine Composition

Low salinity water (LSW) and low salinity surfactant (LSS) core flooding experiments were performed to study the impact of ionic composition on oil recovery from aged Berea sandstone cores. Surfactant adsorption in packed beds, contact angles, interfacial tension (IFT), critical micellar concentration (CMC), and end-point relative permeabilities were used to better understand wettability alteration. In the samples aged with the same in situ brine with (Ca2+ + Mg2+)/Na+ = 0.033, both end-point relative permeabilities of LSW flooding and contact angles showed LSW with only sodium chloride made the samples more water-wet than LSW with divalent-contained brines. Oil recovery was also highest in LSW injection with only sodium chloride. In tertiary LSS of the same core samples, according to both flooding and contact angles, LSS with only sodium chloride showed more water wetness. Characterization measurements showed that, at higher Ca2+/Na+ ratio, CMC and IFT were lower whereas surfactant adsorption was higher.
Non-Trivial Lasing Mode From Parity-Time Symmetric Diffraction Gratings

In this research, for the first time, active diffraction gratings as optical PT-symmetric platforms are studied. Through numerical and analytical studies, the bifurcation as the featured property in PT-symmetric systems is indicated. Moreover, it is shown that in addition to the gain/loss value, the period selection can push the system from the PT-symmetry to the symmetry-broken phase. Accomplished numerical simulations by the Rigorous Coupled-Wave Analysis (RCWA) method indicates the existence of Spectral Singularities (SSs) corresponding to single-mode, zero-bandwidth resonant peaks. The guiding procedure for fulfilling SSs in PT-symmetric diffraction gratings is related to the unique behavior of the scattering matrix in these kinds of gratings. The purpose of this study is to fill the research gap related to active PT-symmetric diffraction gratings and introducing new functionalities for these structures, including low-threshold and super-coherent lasers, which can advance the performance of on-chip optoelectronic devices.
Photon-Starved Scene Inference using Single Photon Cameras

Scene understanding under low-light conditions is a challenging problem. This is due to the small number of photons captured by the camera and the resulting low signal-to-noise ratio (SNR). Single-photon cameras (SPCs) are an emerging sensing modality that are capable of capturing images with high sensitivity. Despite having minimal read-noise, images captured by SPCs in photon-starved conditions still suffer from strong shot noise, preventing reliable scene inference. We propose photon scale-space – a collection of high-SNR images spanning a wide range of photons-per-pixel (PPP) levels (but same scene content) as guides to train inference model on low photon flux images. We develop training techniques that push images with different illumination levels closer to each other in feature representation space. The key idea is that having a spectrum of different brightness levels during training enables effective guidance, and increases robustness to shot noise even in extreme noise cases. Based on the proposed approach, we demonstrate, via simulations and real experiments with a SPAD camera, high-performance on various inference tasks such as image classification and monocular depth estimation under ultra low-light, down to < 1 PPP. Project Page: https://wisionlab.cs. wisc.edu/project/photon-net
WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking

Millions of web users directly depend on ad and tracker blocking tools to protect their privacy. However, existing ad and tracker blockers fall short because of their reliance on trivially susceptible advertising and tracking content. In this paper, we first demonstrate that the state-of-the-art machine learning based ad and tracker blockers, such as AdGraph, are susceptible to adversarial evasions deployed in real-world. Second, we introduce WebGraph, the first graph-based machine learning blocker that detects ads and trackers based on their action rather than their content. By building features around the actions that are fundamental to advertising and tracking - storing an identifier in the browser, or sharing an identifier with another tracker - WebGraph performs nearly as well as prior approaches, but is significantly more robust to adversarial evasions. In particular, we show that WebGraph achieves comparable accuracy to AdGraph, while significantly decreasing the success rate of an adversary from near-perfect under AdGraph to around 8% under WebGraph. Finally, we show that WebGraph remains robust to a more sophisticated adversary that uses evasion techniques beyond those currently deployed on the web.
Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO

All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters (ADCs) and spatial equalizer of an all-digital system to the communication scenario (e.g., the number of users, modulation scheme, and propagation conditions) enables orders-of-magnitude power savings for realistic mmWave channels. For example, for a 256-BS-antenna 16-user system supporting 1 GHz bandwidth, a traditional baseline architecture designed for a 64-user worst-case scenario would consume 23 W in 28 nm CMOS for the ADC array and the spatial equalizer, whereas a resolution-adaptive architecture is able to reduce the power consumption by 6.7×.
Where do I rank? Am I happy?: learning income position and subjective-wellbeing in an internet experiment

A tailor-made internet survey experiment provides individuals with information on their income positions to examine their effects on subjective well-being. In the first survey, respondents were asked about their household income and subjective well-being. Based on the data collected, three different respondents’ income positions within the residential locality, within a group of the same educational background, and cohort were obtained. In the follow-up survey for the treatment group, respondents are informed of their income positions and then asked for subjective well-being.
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings

Reinforcement learning (RL) can be used to learn treatment policies and aid decision making in healthcare. However, given the need for generalization over complex state/action spaces, the incorporation of function approximators (e.g., deep neural networks) requires model selection to reduce overfitting and improve policy performance at deployment. Yet a standard validation pipeline for model selection requires running a learned policy in the actual environment, which is often infeasible in a healthcare setting. In this work, we investigate a model selection pipeline for offline RL that relies on off-policy evaluation (OPE) as a proxy for validation performance. We present an in-depth analysis of popular OPE methods, highlighting the additional hyperparameters and computational requirements (fitting/inference of auxiliary models) when used to rank a set of candidate policies. We compare the utility of different OPE methods as part of the model selection pipeline in the context of learning to treat patients with sepsis. Among all the OPE methods we considered, fitted Q evaluation (FQE) consistently leads to the best validation ranking, but at a high computational cost. To balance this trade-off between accuracy of ranking and computational efficiency, we propose a simple two-stage approach to accelerate model selection by avoiding potentially unnecessary computation. Our work serves as a practical guide for offline RL model selection and can help RL practitioners select policies using real-world datasets. To facilitate reproducibility and future extensions, the code accompanying this paper is available online.
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks

We present four different robust transfer learning and data augmentation strategies for robust mobile scene recognition. By training three mobile-ready (EfficientNetB0, MobileNetV2, MobileNetV3) and two large-scale baseline (VGG16, ResNet50) convolutional neural network architectures on the widely available Event8, Scene15, Stanford40, and MIT67 datasets, we show the generalization ability of our transfer learning strategies. Furthermore, we tested the robustness of our transfer learning strategies under viewpoint and lighting changes using the KTH-Idol2 database. Also, the impact of inference optimization techniques on the general performance and the robustness under different transfer learning strategies is evaluated. Experimental results show that when employing transfer learning, Fine-Tuning in combination with extensive data augmentation improves the general accuracy and robustness in mobile scene recognition. We achieved state-of-the-art results using various baseline convolutional neural networks and showed the robustness against lighting and viewpoint changes in challenging mobile robot place recognition.
Evolution of Accretor Stars in Massive Binaries: Broader Implications from Modeling ζ Ophiuchi

Most massive stars are born in binaries close enough for mass transfer episodes. These modify the appearance, structure, and future evolution of both stars. We compute the evolution of a 100-day-period binary, consisting initially of a 25 M ⊙ star and a 17 M ⊙ star, which experiences stable mass transfer. We focus on the impact of mass accretion on the surface composition, internal rotation, and structure of the accretor. To anchor our models, we show that our accretor broadly reproduces the properties of ζ Ophiuchi, which has long been proposed to have accreted mass before being ejected as a runaway star when the companion exploded. We compare our accretor to models of single rotating stars and find that the later and stronger spin-up provided by mass accretion produces significant differences. Specifically, the core of the accretor retains higher spin at the end of the main sequence, and a convective layer develops that changes its density profile. Moreover, the surface of the accretor star is polluted by CNO-processed material donated by the companion. Our models show effects of mass accretion in binaries that are not captured in single rotating stellar models. This possibly impacts the further evolution (either in a binary or as single stars), the final collapse, and the resulting spin of the compact object.
Efficient Fast Multipole Accelerated Boundary Elements via Recursive Computation of Multipole Expansions of Integrals

In boundary element methods (BEM) in R 3 , matrix elements and right hand sides are typically computed via integration over line, triangle and tetrahedral volume elements. When the problem size gets large, the resulting linear systems are often solved iteratively via Krylov methods, with fast multipole methods (FMM) used to accelerate the matrix vector products needed. The integrals are often computed via numerical or analytical quadrature. When FMM acceleration is used, most entries of the matrix never need be computed explicitly - they are only needed in terms of their contribution to the multipole expansion coefﬁcients. Furthermore, the two parts of this resulting algorithm - the integration and the FMM matrix vector product - are both approximate, and their errors have to be matched to avoid wasteful computations, or poorly controlled error. We propose a new fast method for generation of multipole expansion coefﬁcients for the ﬁelds produced by the integration of the single and double layer potentials on surface triangles; charge distributions over line segments; and regular functions over tetrahedra in the volume; so that the overall method is well integrated into the FMM, with controlled error. The method is based on recursive computations of the multipole moments for O (1) cost per moment with a low asymptotic constant. The method is developed for the Laplace Green’s function in R 3 . The derived recursions are tested both for accuracy and performance.
Measuring interstellar turbulence in fast radio burst host galaxies

Turbulence is a vital part of the interstellar medium (ISM) of galaxies, contributing significantly to galaxy energy budgets and acting as a regulator of star formation. Despite this, little is understood about ISM turbulence empirically. In the Milky Way, multiple tracers are used to reconstruct the density- and velocity-fluctuation power spectra over an enormous range of scales, but questions remain on the nature of these fluctuations at the smallest scales. Even less is known about the ISM of distant galaxies, where only a few tracers of turbulence, such as non-thermal broadening of optical recombination lines, are accessible. We explore the use of radio-wave scattering of fast radio bursts (FRBs) to add a second probe of turbulence in extragalactic galaxies on scales many orders of magnitude smaller than those probed by emission-line widths. We first develop the formalism to compare the scattering measures of FRBs to alternative probes of density and velocity fluctuations in the host-galaxy ISM. We then apply this formalism to three FRBs with detailed host-galaxy analyses in the literature, with the primary motivation of determining whether FRB scattering within the host galaxy probes the same turbulent cascade as the gas seen in H${\rm \alpha}$ emission. In all cases we consider, we find such an association plausible, although in one of these sources, FRB 20121102A, the radio-scattering limit on the turbulent energy is much less constraining than the H${\rm \alpha}$ line width. We anticipate that future FRB surveys, especially those at frequencies below 1 GHz, will find many FRBs that illuminate the small-scale properties of extragalactic ISM.
Graph Pseudometrics from a Topological Point of View

We explore pseudometrics for directed graphs in order to better understand their topological properties. The directed flag complex associated to a directed graph provides a useful bridge between network science and topology. Indeed, it has often been observed that phenomena exhibited by real-world networks reflect the topology of their flag complexes, as measured, for example, by Betti numbers or simplex counts. As it is often computationally expensive (or even unfeasible) to determine such topological features exactly, it would be extremely valuable to have pseudometrics on the set of directed graphs that can both detect the topological differences and be computed efficiently. To facilitate work in this direction, we introduce methods to measure how well a graph pseudometric captures the topology of a directed graph. We then use these methods to evaluate some well-established pseudometrics, using test data drawn from several families of random graphs.
Predicting the Redshift of γ-Ray-loud AGNs Using Supervised Machine Learning

Active galactic nuclei (AGNs) are very powerful galaxies characterized by extremely bright emissions coming from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems, such as the evolution of the early stars and their formation, along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multiwavelength observations, often involving various astronomical facilities. Here we employ machine-learning algorithms to estimate redshifts from the observed γ-ray properties and photometric data of γ-ray-loud AGNs from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm using a LASSO-selected set of predictors. We obtain a tight correlation, with a Pearson correlation coefficient of 71.3% between the inferred and observed redshifts and an average Δz norm = 11.6 × 10−4. We stress that, notwithstanding the small sample of γ-ray-loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine-learning models.
Human Pose Transfer with Disentangled Feature Consistency

—Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. However, most existing approaches explicitly leverage the pose information extracted from the source images as a conditional input for the generative networks. Meanwhile, they usually focus on the visual ﬁdelity of the synthesized images but neglect the inherent consistency, which further conﬁnes their performance of pose transfer. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates the keypoint ampliﬁer to enhance the pose feature extraction. Additionally, an unpaired support dataset M IXAMO -S UP providing more extra pose information has been further utilized during the training to improve the generality and robustness of DFC-Net. Extensive experimental results on M IXAMO -P OSE and EDN-10 K have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.
Resource Constrained Neural Networks for Direction-of-Arrival Estimation in Micro-controllers

With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains. Narrow beam-forming makes it difficult to have spatial sensing (direction-of-arrival, DoA, estimation) in a centralized manner, and with the evolution of paradigms such as artificial intelligence of Things (AIOT), ultra-reliable low latency communication (URLLC) services and distributed networks, intelligence for edge devices (Edge-AI) is highly desirable. It helps to reduce the data-communication overhead compared to cloud-AI-centric networks and is more secure and free from scalability limitations. However, achieving desired functional accuracy is a challenge on edge devices such as microcontroller units (MCU) due to area, memory, and power constraints. In this work, we propose low complexity neural network-based algorithm for accurate DoA estimation and its efficient mapping on the off-the-self MCUs. An ad-hoc graphical-user interface (GUI) is developed to configure the STM32 NUCLEO-H743ZI2 MCU with the proposed algorithm and to validate its functionality. The performance of the proposed algorithm is analyzed for different signal-to-noise ratios (SNR), word-length, the number of antennas, and DoA resolution. In-depth experimental results show that it outperforms the conventional statistical spatial sensing approach.
OLR 2021 Challenge: Datasets, Rules and Baselines

This paper introduces the sixth Oriental Language Recognition (OLR) 2021 Challenge, which intends to improve the performance of language recognition systems and speech recognition systems within multilingual scenarios. The data profile, four tasks, two baselines, and the evaluation principles are introduced in this paper. In addition to the Language Identification (LID) tasks, multilingual Automatic Speech Recognition (ASR) tasks are introduced to OLR 2021 Challenge for the first time. The challenge this year focuses on more practical and challenging problems, with four tasks: (1) constrained LID, (2) unconstrained LID, (3) constrained multilingual ASR, (4) unconstrained multilingual ASR. Baselines for LID tasks and multilingual ASR tasks are provided, respectively. The LID baseline system is an extended TDNN x-vector model constructed with Pytorch. A transformer-based end-to-end model is provided as the multilingual ASR baseline system. These recipes will be online published, and available for participants to construct their own LID or ASR systems. The baseline results demonstrate that those tasks are rather challenging and deserve more effort to achieve better performance.
Multivariate Methods for Detection of Rubbery Rot in Storage Apples by Monitoring Volatile Organic Compounds: An Example of Multivariate Generalised Mixed Models

This article is a case study illustrating the use of a multivariate statistical method for screening potential chemical markers for early detection of post-harvest disease in storage fruit. We simultaneously measure a range of volatile organic compounds (VOCs) and two measures of severity of disease infection in apples under storage: the number of apples presenting visible symptoms and the lesion area. We use multivariate generalised linear mixed models (MGLMM) for studying association patterns of those simultaneously observed responses via the covariance structure of random components. Remarkably, those MGLMMs can be used to represent patterns of association between quantities of different statistical nature. In the particular example considered in this paper, there are positive responses (concentrations of VOC, Gamma distribution based models), positive responses possibly containing observations with zero values (lesion area, Compound Poisson distribution based models) and binomially distributed responses (proportion of apples presenting infection symptoms). We represent patterns of association inferred with the MGLMMs using graphical models (a network represented by a graph), which allow us to eliminate spurious associations due to a cascade of indirect correlations between the responses. Key-words: Multivariate-Models, Generalised-Linear-Mixed-Models, Graphical-Models, Covariance-Selection-Models ∗Corresponding author: Rodrigo Labouriau, rodrigo.labouriau@math.au. dk J.S. Pelck, H. Holthusen, M. Edelenbos, A. Luca and R. Labouriau 2