Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite polynomial cohomology with coefficients

We introduce a theory of finite polynomial cohomology with coefficients in this paper. We prove several basic properties and introduce an Abel-Jacobi map with coefficients. As applications, we use such a cohomology theory to study arithmetics of compact Shimura curves over $\mathbb{Q}$, and simplify proofs of the works of Darmon-Rotger and Bertolini-Darmon-Prasanna.
Visual Orbits of Spectroscopic Binaries with the CHARA Array. IV. HD 61859, HD 89822, HD 109510, and HD 191692

We present the visual orbits of four spectroscopic binary stars, HD 61859, HD 89822, HD 109510, and HD 191692, using long baseline interferometry with the CHARA Array. We also obtained new radial velocities from echelle spectra using the APO 3.5 m, CTIO 1.5 m, and Fairborn Observatory 2.0 m telescopes. By combining the astrometric and spectroscopic observations, we solve for the full, three-dimensional orbits and determine the stellar masses to 1%–12% uncertainty and distances to 0.4%–6% uncertainty. We then estimate the effective temperature and radius of each component star through Doppler tomography and spectral energy distribution analyses. We found masses of 1.4–3.5 M ⊙, radii of 1.5–4.7 R ⊙, and temperatures of 6400–10,300 K. We then compare the observed stellar parameters to the predictions of the stellar evolution models, but found that only one of our systems fits well with the evolutionary models.
Predicting Drug-Drug Interactions using Deep Generative Models on Graphs

Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on two multimodal networks: (1) a multi-graph consisting of drug and protein nodes, and (2) a multi-graph consisting of drug and cell line nodes. Our source code is publicly available at https://github.com/HySonLab/drug-interactions.
Becoming A Secondary School Science Teacher

I. DEVELOPING A PRACTICAL PHILOSOPHY. 1. Windows into Science Education. 2. Science and Scientific Inquiry for Understanding. 3. Conceptions and Models of Teaching and Learning. 4. Developing Learners' Multiple Intelligences. II. STRATEGIES FOR SCIENCE DISCOURSE. 5. Strategies for Scientific Inquiry. 6. Journal Writing and Research Reporting. 7. Novel Teaching and Learning Ideas. 8. Explanation, Evidence, Relevance. III. SCIENCE CURRICULAR CONTENT, ISSUES, AND TRENDS. 9. Contextual Science from a Historical Perspective. 10. Interactive Learning with Computer Technology. 11. The Science-Technology-Society-Environment Connection. 12. Science for All Students. 13. Authentic-Assessment Practices in Science. IV. Planning for Teaching Science. 14. Interactive Phases of Teaching and Learning. 15. Multiple Voices in Unit Planning. Parting Message: Teacher Transformation. Appendix A: Some Ideas for Your Teaching Portfolio. Appendix B: Your Professional Portfolio. Appendix C: Suggested PEOE Activities. Appendix D: Practicing Science Safety. References. Name Index. Subject Index.
Approximate sampling and estimation of partition functions using neural networks

We consider the closely related problems of sampling from a distribution known up to a normalizing constant, and estimating said normalizing constant. We show how variational autoencoders (VAEs) can be applied to this task. In their standard applications, VAEs are trained to fit data drawn from an intractable distribution. We invert the logic and train the VAE to fit a simple and tractable distribution, on the assumption of a complex and intractable latent distribution, specified up to normalization. This procedure constructs approximations without the use of training data or Markov chain Monte Carlo sampling. We illustrate our method on three examples: the Ising model, graph clustering, and ranking.
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution

Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images. To alleviate this issue, we propose a model-driven deep neural network, called KXNet, for blind SISR. Specifically, to solve the classical SISR model, we propose a simple-yet-effective iterative algorithm. Then by unfolding the involved iterative steps into the corresponding network module, we naturally construct the KXNet. The main specificity of the proposed KXNet is that the entire learning process is fully and explicitly integrated with the inherent physical mechanism underlying this SISR task. Thus, the learned blur kernel has clear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current representative state-of-the-art blind SISR methods. Code is available at: https://github.com/jiahong-fu/KXNet.
A New Approach to Temperature Shift Functions in Modeling Complex Modulus Damping Data

Abstract : A new approach to determining the temperature shift function and its utility in smoothing, interpolating, and modeling a set of complex modulus data are described in detail and examples are given. A set of experimental data to characterize the dynamic mechanical properties of a viscoelastic damping polymer typically includes the real component of complex modulus and the material loss factor as well as the experimental frequency and temperature. The set of data must be made useful for design purposes. The present approach analytically represents the wicket plot loss factor as a function of the modulus magnitude. A ratio of polynomials is then used to model the complex modulus as a function of reduced radian frequency. The perpendicular distance from each experimental point to the wicket plot is used to calculate the temperature shift function.
GP-net: Grasp Proposal for Mobile Manipulators

—We present the Grasp Proposal Network (GP-net), a Convolutional Neural Network model which can generate 6-DOF grasps for mobile manipulators. To train GP-net, we synthetically generate a dataset containing depth-images and ground-truth grasp information for more than 1400 objects. In real-world experiments we use the EGAD! grasping benchmark to evaluate GP-net against two commonly used algorithms, the Volumetric Grasping Network (VGN) and the Grasp Pose Detection package (GPD), on a PAL TIAGo mobile manipulator. GP-net achieves grasp success rates of 82 . 2% compared to 57 . 8% for VGN and 63 . 3% with GPD. In contrast to the state-of-the-art methods in robotic grasping, GP-net can be used out-of-the-box for grasping objects with mobile manipulators without limiting the workspace, requiring table segmentation or needing a high-end GPU. To encourage the usage of GP-net, we provide a ROS package along with our code and pre-trained models at https://aucoroboticsmu.github. io/GP-net/.
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks

Volume data is found in many important scientific and engineering applications. Rendering this data for visualization at high quality and interactive rates for demanding applications such as virtual reality is still not easily achievable even using professional-grade hardware. We introduce FoVolNet—a method to significantly increase the performance of volume data visualization. We develop a cost-effective foveated rendering pipeline that sparsely samples a volume around a focal point and reconstructs the full-frame using a deep neural network. Foveated rendering is a technique that prioritizes rendering computations around the user's focal point. This approach leverages properties of the human visual system, thereby saving computational resources when rendering data in the periphery of the user's field of vision. Our reconstruction network combines direct and kernel prediction methods to produce fast, stable, and perceptually convincing output. With a slim design and the use of quantization, our method outperforms state-of-the-art neural reconstruction techniques in both end-to-end frame times and visual quality. We conduct extensive evaluations of the system's rendering performance, inference speed, and perceptual properties, and we provide comparisons to competing neural image reconstruction techniques. Our test results show that FoVolNet consistently achieves significant time saving over conventional rendering while preserving perceptual quality.
Hybrid Learning- and Model-Based Planning and Control of In-Hand Manipulation

This paper presents a hierarchical framework for planning and control of in-hand manipulation of a rigid object involving grasp changes using fully-actuated multifin-gered robotic hands. While the framework can be applied to the general dexterous manipulation, we focus on a more complex definition of in-hand manipulation, where at the goal pose the hand has to reach a grasp suitable for using the object as a tool. The high level planner determines the object trajectory as well as the grasp changes, i.e. adding, removing, or sliding fingers, to be executed by the low-level controller. While the grasp sequence is planned online by a learning-based policy to adapt to variations, the trajectory planner and the low-level controller for object tracking and contact force control are exclusively model-based to robustly realize the plan. By infusing the knowledge about the physics of the problem and the low-level controller into the grasp planner, it learns to successfully generate grasps similar to those generated by model-based optimization approaches, obviating the high computation cost of online running of such methods to account for variations. By performing experiments in physics simulation for realistic tool use scenarios, we show the success of our method on different tool-use tasks and dexterous hand models. Additionally, we show that this hybrid method offers more robustness to trajectory and task variations compared to a model-based method.
Quantum Fluid Dynamics on the Hypersphere

It is known from quantum mechanics that particles are associated with wave functions, and that the probability of observing a particle at some future location is proportional to the squared modulus of the amplitude of its wave function. Although this statistical relationship is well quantified, the interpretations have remained controversial, with many split between the classical Copenhagen interpretation and some variation of the de Broglie-Bohm pilot wave models. Recent experiments with Hydrodynamic Quantum Analogs (HQAs) have demonstrated that droplets of real fluid may achieve stable dynamical states, where interaction of the droplets with their own ripples results in motion analogous to the motion of particles subject to the guiding equation under the pilot wave models. Indeed, many effects previously thought to be exclusively quantum have now been observed as emergent phenomena in these macroscopic HQAs. This has motivated us to explore the possibility that quantum mechanics may actually be the result of fluid dynamics on some real quantum scale fluid. In this paper, we show that if there is a real quantum scale fluid having dynamics analogous to the fluid in HQAs, then this fluid must be a superfluid, and the dynamics of that fluid must take place on the surface of a 4-dimensional hypersphere. Under the influence of cosmological inflation, we further show that these bouncing droplets would have the illusion of a property analogous to rest mass, and that the principles of inertia, momentum, mass-energy equivalence, general relativity, the uncertainty principle and the appearance of a time-like dimension can all be derived for droplets as purely emergent phenomena from the fluid dynamics of this system. As such, we believe that this model merits consideration as a potential foundation for a new unifying theory of physics at all scales.
Hartree--Fock Theory, Lieb's Variational Principle, and their Generalizations

Abstract: Hartree–Fock theory in quantum mechanics is reviewed, from the proposal of the Hartree–Fock approximation right after quantum mechanics was formulated to its applications in modern physics. This includes the description of traditional Hartree–Fock theory in quantum chemistry, its generalizations of various kinds, and its importance for predicting the presence of symmetry breaking, or the absence thereof. Hartree–Fock theory in quantum mechanics is reviewed, from the proposal of the Hartree–Fock approximation right after quantum mechanics was formulated to its applications in modern physics. This includes the description of traditional Hartree–Fock theory in quantum chemistry, its generalizations of various kinds, and its importance for predicting the presence of symmetry breaking, or the absence thereof. Dedicated to Elliott H. Lieb MSC: 81-02, 81Q05, 81V45, 81V55, 81V74
Construction of sulfur-free gel breaker agent system and investigation on gel-breaking mechanism for association fracturing fluid

ABSTRACT Sulfur is usually contained in existing gel breaker systems for fracturing fluids, which leads to the generation of hydrogen sulfide gas. In the process of fracturing development. The presence of hydrogen sulfide gas would cause equipment corrosion and environmental pollution. In this paper, a system of sulfur-free gel breaker agents for association fracturing fluid was constructed by evaluating the gel-forming and breaking properties of the fracturing fluid applied with this formula. The results showed that the combinations of 0.02% chlorine dioxide and 0.12% to 0.2% citric acid were chosen as the sulfur-free gel breaker formulas at 80 to 100°C. The polymer molecular chains were broken by synergistic effects of low concentration chlorine dioxide and citric acid to reduce the viscosity of association fracturing fluid, achieving gel-breaking. Simultaneously, the influence of hydrogen sulfide on the development process could be inhibited through bactericidal and oxidation actions of chlorine dioxide. The viscosity of the sulfur-free fracturing fluid remains above 50 mPa·s within 60 minutes, the value of the flow regime index was between 0.2593 and 0.3125, and the settling rate in this system was 0.024 to 0.042 mm/s. The temperature resistance, shear resistance, rheological properties, and sand carrying capacity of the sulfur-free fracturing fluid were not inferior to that of the conventional fracturing fluids, which guaranteed the effect of fracturing reformation. This study is of great significance to not only the development of environmental-friendly fracturing fluid but also the prevention and control of toxic and harmful gases, promoting sustainable development of oilfields.
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data

Introduction to Nonlinear Dynamical Systems Analysis, R.A.M. Gregson and S.J. Guastello Principles of Time Series Analysis, R.A.M. Gregson Frequency Distributions and Error Functions, S.J. Guastello Phase Space Analysis and Unfolding, M. Shelhamer Nonlinear Dynamical Analysis of Noisy Time Series, A. Heathcote and D. Elliott The Effects of the Irregular Sample and Missing Data in Time Series Analysis, D.M. Kreindler and C.J. Lumsden A Dynamical Analysis via the Extended-Return-Map, J.-S. Li, J. Krauth, and J.P. Huston Adjusting Behavioral Methods When Applying Nonlinear Dynamical Measures to Stimulus Rates, B.B. Frey Entropy, S.J. Guastello Analysis of Recurrence: Overview and Application to Eye-Movement Behavior, D.J. Aks Discontinuities and Catastrophes with Polynomial Regression, S.J. Guastello Nonlinear Regression and Structural Equations, S.J. Guastello Catastrophe Models with Nonlinear Regression, S.J. Guastello Catastrophe Model for the Prospect-Utility Theory Question, T.A. Oliva and S.R. McDade Measuring the Scaling Properties of Temporal and Spatial Patterns: From the Human Eye to the Foraging Albatross, M.S. Fairbanks and R.P. Taylor Oscillators with Differential Equations, J. Butner and T.N. Story Markov Chains for Identifying Nonlinear Dynamics, S.J. Merrill Markov Chain Example: Transitions between Two Pictorial Attractors, R.A.M. Gregson Identifying Ill-Behaved Nonlinear Processes without Metrics: Use of Symbolic Dynamics, R.A.M. Gregson Information Hidden in Signals and Macromolecules I: Symbolic Time-Series Analysis, M.A. Jimenez-Montano, R. Feistel, and O. Diez-Martinez Orbital Decomposition: Identification of Dynamical Patterns in Categorical Data, S.J. Guastello Orbital Decomposition for Multiple Time-Series Comparisons, D. Pincus, D.L. Ortega, and A.M. Metten The Danger of Wishing for Chaos, P.E. McSharry Methodological Issues in the Application of Monofractal Analyses in Psychological and Behavioral Research, D. Delignieres, K. Torre, and L. Lemoine Frontiers of Nonlinear Methods, R.A.M. Gregson Index
Toward Cooperative Level Generation in Multiplayer Games: A User Study in Overcooked!

Multiplayer contents play an essential role in extending the lifespan of the game and positively affect the player’s experience. However, previous studies on procedural content generation focused on single-player games and few for multiplayer games. Multiplayer games have different ethics and mechanics associated with competition or cooperation. Thus, content designers are concerned about this interaction. In this paper, we propose a new method for generating multiplayer levels that encourage diverse cooperation among game players. Our contributions are summarized as follows: 1) We designed four cooperation patterns usable in controllable generation literature. 2) We proposed a controllable level generator to deploy the proposed patterns in the two-player cooperative cooking game, Overcooked! 3) We discussed effective methods for leading players’ cooperation experience. Consequently, we found that the players’ interaction is most fundamental at the multiplayer level. The results of our study will lead to diverse cooperation experiences in future multiplayer game content generation studies.
Angular Response Analysis of Broadband RCS Reduction Checkerboard Metasurfaces

This paper examines the bistatic radar cross-section (RCS) reduction bandwidths of four checkerboard metasurfaces: a square-circle pair and a Jerusalem cross pair designed on Taconic TLY-3 substrate, another Jerusalem cross pair, and a simple cross-slotted cross pair designed on FR4 substrate. This is done by using simulations for various angles of incidence/scattering – from normal to 30°. The performance of each of these structures seems to be stable till 10° angle of incidence/scattering, with the square-circle pair being the only structure that is able to maintain a good fractional bandwidth up to a 30° angle of incidence. The other structures show a substantial reduction in bandwidth beyond 10°.
Computationally-Efficient and Reliable Antenna Design within the System-by-Design ML-Based Framework

The increasing complexity of modern electromagnetic (EM) devices requires advanced solution strategies to reduce the computational burden of the synthesis process. In this work, the machine learning (ML)-based System-by-Design (SbD) framework is exploited to address the design of antennas in a reliable and computationally-efficient fashion. The SbD scheme is based on a "divide-et-impera" paradigm which aims at decomposing the problem at hand into smaller sub-tasks that are addressed by specific interconnected functional blocks. Hence, the overall computational cost can be reduced by choosing and integrating the proper functional blocks, including global optimization techniques and learning-by-examples (LBE) approaches. The effectiveness of the SbD paradigm is shown with an illustrative example concerning the design of a mm-wave slotted substrate integrated wave-guide (SSIW) antenna for 77 GHz automotive radar systems.
RF Sensing for Smoking Detection at Oil Fields

In this paper, an ultra-wideband (UWB) Radar sensor is used to detect human gestures while smoking or vaping in potentially dangerous areas such as an oil field or a gas station. Existing smoking detection systems are primarily camera-based, which has a number of drawbacks, including poor illumination, training issues with longer video sequence data, and major privacy concerns. The data collected from a UWB Radar is represented in the form of spectrograms. Three classes are considered, namely cigarette, vape and when the subject is not smoking. InceptionV3, VGG19, and VGG16 deep learning algorithms are used to extract spatiotemporal information from the Spectrogram. Finally, by classifying the Spectrograms into the considered gestures, the smoking and/or vaping is accurately identified. The simulation results show that InceptionV3 can achieve a maximum classification accuracy of 90.00%.
EMI Modeling for Vehicle Body using Characteristic Mode Analysis

This paper applies characteristic mode analysis to the vehicle body to better understand the coupling mechanism of the vehicle EMI emission. We discussed a few typical modes of the vehicle body. By visualizing the eigen-current distribution of the vehicle modes, the EMI emission of the vehicle could be suppressed significantly by only putting absorbing material on the local area of the vehicle with relatively high eigen-current intensity. The simulation results show that the characteristic mode analysis method can simplify the electromagnetic analysis process and provide a sufficient theoretical basis for subsequent work.
Effect of Nickel-coating on High-speed Connector Transmission Properties

In this paper, the effects of electroplating materials on high-speed connector transmission properties are studied. A large number of experimental data of a coaxial structure show that nickel plating would cause insertion loss resonance at low frequencies as well as an increase in impedance. A simulation model setup with corrections to the magnetic loss tangent is proposed in commercial electromagnetic tool and good correlation is achieved. Such findings are reproduced and verified on a typical type of high-speed backplane connectors. It is also shown that applying Tin coating on top of Ni-plating can improve the insertion loss degradation, which provides a guideline to high-speed connector manufactures.
DOA Estimation Method for Polarization Sensitive Circular Array Based on Reduced-Dimensional MUSIC

This paper proposes a reduced-dimensional multiple signal classification (RD-MUSIC) based on the polarization sensitive array (PSA). With the PAS, the direction of arrival (DOA) and polarization information of the source signal can be obtained simultaneously. RD-MUSIC estimates the two-dimensional DOA instead of calculating the polarization parameter jointly. The simulation results show that compared to the traditional MUSIC, the RD-MUSIC with the PSA has a higher resolution and the capacity of mitigating interference. Moreover, it improves the accuracy of estimated parameters and does not bring additional computational complexity.