Miracle-dz/UniFAR-FTUs huggingface dataset:
Inflow modeling for wind farm flows in RANS

Wind turbine interaction in wind farms can lead to energy loss and increased wind turbine loads, with the magnitude of these effects strongly depending on atmospheric conditions. One-dimensional Reynolds-averaged Navier-Stokes (RANS) models are able to represent the atmospheric boundary layer (ABL) over a range of model fidelity, and can be used for steady-state inflow profiles in three-dimensional RANS simulations of wind farms. In the present work, an overview of existing and recently developed atmospheric inflow models is presented. The inflow models are applied to separately demonstrate impacts on the velocity deficit of a row of ten wind turbines, due to: turbulence intensity and atmospheric stability in the atmospheric surface layer; ABL depth; and Coriolis-induced veer.
Synergistic enhancement of photoluminesent intensity in monolayer molybdenum disulfide embedded with plasmonic nanostructures for catalytic sensing

Enhancing photoluminescence (PL) of semiconducting 2D materials is proven essential for many applications related to optoelectronics and sensing. Here, we demonstrate synergistic PL enhancement in 2D materials by incorporating silver plasmonic nanodiscs in defect-induced monolayer molybdenum disulfide (MoS2) for luminescent quench sensing of dopamine with high accuracy and selectivity. We develop a hole-array perforated 2D MoS2 embedded with plasmonic silver dimers to harmonize the surface plasmon resonance with the PL wavelength of monolayer MoS2, which enhance it by 56-fold. We implemented the optimized perforated MoS2/dimers platform as a catalytic assay for on-chip PL quenching detection of dopamine, achieving a low limit of detection of 9.3 nM. This approach opens avenues of high-performance molecular sensing applications by improving the PL emission of 2D materials.
Evaluating wind farm wakes in large eddy simulations and engineering models

We study wind farm wakes with large eddy simulations (LES) and use these results for the evaluation of engineering models such as the Jensen model, the coupled wake boundary layer model (CWBL), the Turbulence Optimized Park model (TurbOPark), and the wind farm model developed by Niayifar and Porté-Agel (Energies 9, 741 (2016)). We study how well these models capture the wake effects between two aligned wind farms with 72 turbines separated by 10 kilometers in a neutral boundary layer. We find that all considered models over-predict the wind farm wake recovery compared to what is observed in LES. The TurbOPark model predictions on the wind farm wake effect are closest to the LES results for the scenario considered here.
Does Insider Trading Activity Separate Winners and Losers when a Peer Firm Restates?

Peer restatements are an ambiguous signal for investors following non-restating firms in the same industry. The literature, however, consistently documents that non-restating firms experience negative returns when a peer restates, on average. Based on a simple single agent model, we predict that prior insider trading activity provides information that investors use to condition their response to a peer-firm restatement. Consistent with our prediction, we find that the negative returns for non-restating firms are mitigated when insiders have been buying and amplified when insiders have been selling. The effect varies cross-sectionally with more weight being placed on prior insider trading activities when non-restating firms have higher information uncertainty, lower costs of biased reporting, or operate in less-concentrated industries. Finally, we report evidence that recent insider trades prior to a peer restatement help investors correctly interpret implications of the peer restatement for future outcomes of the non-restating firm.
OF-VO: Efficient Navigation Among Pedestrians Using Commodity Sensors

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D Lidar, to explicitly predict the velocities and positions of surrounding obstacles through optical flow estimation, object detection, and sensor fusion. A key aspect of our work is coupling the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm using learning-based perception and model-based planning methods offers better performance than prior algorithms in terms of navigation time and success rate of collision avoidance. Our method also provides bounds on the probabilistic collision avoidance algorithm. We highlight the realtime performance of OF-VO on a Turtlebot navigating among pedestrians in both simulated and real-world scenes. A demo video is available at https://gamma.umd. edu/ofvo/
FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization

We develop two new algorithms, called, FedDR and asyncFedDR, for solving a fundamental nonconvex composite optimization problem in federated learning. Our algorithms rely on a novel combination between a nonconvex Douglas-Rachford splitting method, randomized block-coordinate strategies, and asynchronous implementation. They can also handle convex regularizers. Unlike recent methods in the literature, e.g., FedSplit and FedPD, our algorithms update only a subset of users at each communication round, and possibly in an asynchronous manner, making them more practical. These new algorithms also achieve communication efficiency and more importantly can handle statistical and system heterogeneity, which are the two main challenges in federated learning. Our convergence analysis shows that the new algorithms match the communication complexity lower bound up to a constant factor under standard assumptions. Our numerical experiments illustrate the advantages of our methods compared to existing ones on several datasets.
Neural Extractive Search

Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called “extractive search”, in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available at https://spike.neural-sim.apps. allenai.org/ and a video demonstration is available at https://vimeo.com/559586687.
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs

Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuristic algorithms which are usually fast but may sacrifice the solution quality. Currently, machine learning for combinatorial optimization (MLCO) has become a trending research topic, but most existing MLCO methods treat CO as a single-level optimization by directly learning the end-to-end solutions, which are hard to scale up and mostly limited by the capacity of ML models given the high complexity of CO. In this paper, we propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph (e.g. add, delete or modify edges in a graph), fused with a lower-level heuristic algorithm solving on the optimized graph. Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity. The experiments and results on several popular CO problems like Directed Acyclic Graph scheduling, Graph Edit Distance and Hamiltonian Cycle Problem show its effectiveness over manually designed heuristics and single-level learning methods.
Artificial Hawking Radiation in Non-Hermitian Parity-Time Symmetric Systems

We show that the exceptional surfaces of linear three-dimensional non-Hermitian parity-time-symmetric twoband models attain the form of topologically stable tilted exceptional cones. By relating the exceptional cones to energy cones of two-dimensional Hermitian parity-time-symmetric two-band models, we find a connection between the exceptional cone and the light cone of an observer in the vicinity of a Schwarzschild black hole. When the cone overtilts, light-like particle-antiparticle pairs are created resembling Hawking radiation. We also investigate dissipative features of the non-Hermitian Hamiltonian related to the latter and comment on potential realizations in laboratory setups.
Solving Structured Hierarchical Games Using Differential Backward Induction

From large-scale organizations to decentralized political systems, hierarchical strategic decision making is commonplace. We introduce a novel class of structured hierarchical games (SHGs) that formally capture such hierarchical strategic interactions. In an SHG, each player is a node in a tree, and strategic choices of players are sequenced from root to leaves, with root moving first, followed by its children, then followed by their children, and so on until the leaves. A player's utility in an SHG depends on its own decision, and on the choices of its parent and all the tree leaves. SHGs thus generalize simultaneous-move games, as well as Stackelberg games with many followers. We leverage the structure of both the sequence of player moves as well as payoff dependence to develop a gradient-based back propagation-style algorithm, which we call Differential Backward Induction (DBI), for approximating equilibria of SHGs. We provide a sufficient condition for convergence of DBI and demonstrate its efficacy in finding approximate equilibrium solutions to several SHG models of hierarchical policy-making problems.
Information flow based defensive chain for data leakage detection and prevention: a survey

NING XI, Department of Cyber Engineering, Xidian University, China CHAO CHEN, College of Science and Engineering, James Cook University, Australia JUN ZHANG, School of Software and Electrical Engineering, Swinburne University of Technology, Australia CONG SUN, Department of Cyber Engineering, Xidian University, China SHIGANG LIU, School of Software and Electrical Engineering, Swinburne University of Technology, Australia PENGBIN FENG, Department of Cyber Engineering, Xidian University, China JIANFENG MA, Department of Cyber Engineering, Xidian University, China
A novel Cosmic Filament catalogue from SDSS data

Aims. In this work we present a new catalogue of cosmic ﬁlaments obtained from the latest Sloan Digital Sky Survey (SDSS) public data. Methods. In order to detect ﬁlaments, we implement a version of the Subspace-Constrained Mean-Shift algorithm that is boosted by machine learning techniques. This allows us to detect cosmic ﬁlaments as one-dimensional maxima in the galaxy density distribution. Our ﬁlament catalogue uses the cosmological sample of SDSS, including Data Release 16, and therefore inherits its sky footprint (aside from small border e ﬀ ects) and redshift coverage. In particular, this means that, taking advantage of the quasar sample, our ﬁlament reconstruction covers redshifts up to z = 2 . 2, making it one of the deepest ﬁlament reconstructions to our knowledge. We follow a tomographic approach and slice the galaxy data in 269 shells at di ﬀ erent redshift. The reconstruction algorithm is applied to 2D spherical maps. Results. The catalogue provides the position and uncertainty of each detection for each redshift slice. The quality of our detections, which we assess with several metrics, show improvement with respect to previous public catalogues obtained with similar methods. We also detect a highly signiﬁcant correlation between our ﬁlament catalogue and galaxy cluster catalogues built from microwave observations of the Planck Satellite and the Atacama Cosmology Telescope.
On the extraordinary construction of cycle sets by Wolfgang Rump

Cycle sets are algebraic structures introduced by Rump to study set theoretic solutions to the Yang-Baxter equation. While studying cycle sets Rump also introduced braces, which have since overtaken cycle sets as a tool for studying solutions. This survey paper is primarily an introduction to cycle sets, motivating their study and relating them to key results of brace theory and Yang-Baxter theory. It is aimed at anyone from those already very familiar with braces but less familiar with cycle sets, to those with only a basic level of background in ring theory and group theory. We introduce cycle sets following Rump’s original results giving more detailed, easy to follow versions of his proofs and then relate them back to left braces. We also go on to discuss interesting constructions of cycle sets which do not necessarily correspond directly to braces.
Towards the Memorization Effect of Neural Networks in Adversarial Training

Recent studies suggest that ``memorization'' is one important factor for overparameterized deep neural networks (DNNs) to achieve optimal performance. Specifically, the perfectly fitted DNNs can memorize the labels of many atypical samples, generalize their memorization to correctly classify test atypical samples and enjoy better test performance. While, DNNs which are optimized via adversarial training algorithms can also achieve perfect training performance by memorizing the labels of atypical samples, as well as the adversarially perturbed atypical samples. However, adversarially trained models always suffer from poor generalization, with both relatively low clean accuracy and robustness on the test set. In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples. Based on these two findings, we propose Benign Adversarial Training (BAT) which can facilitate adversarial training to avoid fitting ``harmful'' atypical samples and fit as more ``benign'' atypical samples as possible. In our experiments, we validate the effectiveness of BAT, and show it can achieve better clean accuracy vs. robustness trade-off than baseline methods, in benchmark datasets such as CIFAR100 and Tiny~ImageNet.
Comprehension Based Question Answering using Bloom’s Taxonomy

Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom’s Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.
Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsApp

There is currently no easy way to fact-check content on WhatsApp and other end-to-end encrypted platforms at scale. In this paper, we analyze the usefulness of a crowd-sourced"tipline"through which users can submit content ("tips") that they want fact-checked. We compare the tips sent to a WhatsApp tipline run during the 2019 Indian national elections with the messages circulating in large, public groups on WhatsApp and other social media platforms during the same period. We find that tiplines are a very useful lens into WhatsApp conversations: a significant fraction of messages and images sent to the tipline match with the content being shared on public WhatsApp groups and other social media. Our analysis also shows that tiplines cover the most popular content well, and a majority of such content is often shared to the tipline before appearing in large, public WhatsApp groups. Overall, our findings suggest tiplines can be an effective source for discovering content to fact-check.
Towards Defending against Adversarial Examples via Attack-Invariant Features

Deep neural networks (DNNs) are vulnerable to adversarial noise. Their adversarial robustness can be improved by exploiting adversarial examples. However, given the continuously evolving attacks, models trained on seen types of adversarial examples generally cannot generalize well to unseen types of adversarial examples. To solve this problem, in this paper, we propose to remove adversarial noise by learning generalizable invariant features across attacks which maintain semantic classification information. Specifically, we introduce an adversarial feature learning mechanism to disentangle invariant features from adversarial noise. A normalization term has been proposed in the encoded space of the attack-invariant features to address the bias issue between the seen and unseen types of attacks. Empirical evaluations demonstrate that our method could provide better protection in comparison to previous state-of-the-art approaches, especially against unseen types of attacks and adaptive attacks.
Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation

Sequential whole-body 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) scans are regarded as the imaging modality of choice for the assessment of treatment response in the lymphomas because they detect treatment response when there may not be changes on anatomical imaging. Any computerized analysis of lymphomas in whole-body PET requires automatic segmentation of the studies so that sites of disease can be quantitatively monitored over time. State-of-the-art PET image segmentation methods are based on convolutional neural networks (CNNs) given their ability to leverage annotated datasets to derive high-level features about the disease process. Such methods, however, focus on PET images from a single time-point and discard information from other scans or are targeted towards specific organs and cannot cater for the multiple structures in whole-body PET images. In this study, we propose a spatiotemporal ‘dual-stream’ neural network (ST-DSNN) to segment sequential whole-body PET scans. Our STDSNN learns and accumulates image features from the PET images done over time. The accumulated image features are used to enhance the organs / structures that are consistent over time to allow easier identification of sites of active lymphoma. Our results show that our method outperforms the state-of-the-art PET image segmentation methods.
DCA based Algorithm with Extrapolation for Nonconvex Nonsmooth Optimization

In this paper, we focus on the problem of minimizing the sum of a nonconvex differentiable function and a DC (Difference of Convex functions) function, where the differentiable function is not restricted to the global Lipschitz gradient continuity assumption. This problem covers a broad range of applications in machine learning and statistics such as compressed sensing, signal recovery, sparse dictionary learning, and matrix factorization, etc. We take inspiration from the Nesterov's acceleration technique and the DC algorithm to develop a novel algorithm for the considered problem. Analyzing the convergence, we study the subsequential convergence of our algorithm to a critical point. Furthermore, we justify the global convergence of the whole sequence generated by our algorithm to a critical point and establish its convergence rate under the Kurdyka-Lojasiewicz condition. Numerical experiments on the nonnegative matrix completion problem are performed to demonstrate the efficiency of our algorithm and its superiority over well-known methods.
Neural UpFlow

We present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a low-resolution particle-based liquid simulation. The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions. We also propose a particle-based approach to interpolate between liquids generated from varying simulation discretizations using a state-of-the-art bidirectional optical flow solver method for fluids in addition with a novel key-event topological alignment constraint. In conjunction with the neighborhood contributions, our loss formulation allows the inference model throughout epochs to reward important differences in regard to significant gaps in simulation discretizations. Even when applied in an untested simulation setup, our approach is able to generate plausible high-resolution details. Using this interpolation approach and the predicted displacements, our approach combines the input liquid properties with the predicted motion to infer semi-Lagrangian advection. We furthermore showcase how the proposed interpolation approach can facilitate generating large simulation datasets with a subset of initial condition parameters.
GRB 170817A Afterglow from a Relativistic Electron–Positron Pair Wind Observed Off-axis

A relativistic electron–positron (e + e −) pair wind from a rapidly rotating, strongly magnetized neutron star (NS) would interact with a gamma-ray burst (GRB) external shock and reshape afterglow emission signatures. Assuming that the merger remnant of GW170817 is a long-lived NS, we show that a relativistic e + e − pair wind model with a simple top-hat jet viewed off-axis can reproduce multiwavelength afterglow lightcurves and superluminal motion of GRB 170817A. The Markov Chain Monte Carlo (MCMC) method is adopted to obtain the best-fitting parameters, which give the jet half-opening angle θ j ≈ 0.12 rad, and the viewing angle θ v ≈ 0.23 rad. The best-fitting value of θ v is close to the lower limit of the prior that is chosen based on the gravitational-wave and electromagnetic observations. In addition, we also derive the initial Lorentz factor Γ0 ≈ 49 and the isotropic kinetic energy E K,iso ≈ 1× 1052 erg. Consistency between the corrected on-axis values for GRB 170817A and typical values observed for short GRBs indicates that our model can also reproduce the prompt emission of GRB 170817A. An NS with a magnetic field strength B p ≈ 1.6 × 1013 G is obtained in our fitting, indicating that a relatively low thermalization efficiency η ≲ 10−3 is needed to satisfy observational constraints on the kilonova. Furthermore, our model is able to reproduce a late-time shallow decay in the X-ray lightcurve, and predicts that the X-ray and radio flux will continue to decline in the coming years.