Miracle-dz/UniFAR-FTUs huggingface dataset:
Sixth Order Compact Finite Difference Method for 2D Helmholtz Equations with Singular Sources and Reduced Pollution Effect

Due to its highly oscillating solution, the Helmholtz equation is numerically challenging to solve. To obtain a reasonable solution, a mesh size that is much smaller than the reciprocal of the wavenumber is typically required (known as the pollution effect). High order schemes are desirable, because they are better in mitigating the pollution effect. In this paper, we present a high order compact finite difference method for 2D Helmholtz equations with singular sources, which can also handle any possible combinations of boundary conditions (Dirichlet, Neumann, and impedance) on a rectangular domain. Our method achieves a sixth order consistency for a constant wavenumber, and a fifth order consistency for a piecewise constant wavenumber. To reduce the pollution effect, we propose a new pollution minimization strategy that is based on the average truncation error of plane waves. Our numerical experiments demonstrate the superiority of our proposed finite difference scheme with reduced pollution effect to several state-of-the-art finite difference schemes, particularly in the critical pre-asymptotic region where $\textsf{k} h$ is near $1$ with $\textsf{k}$ being the wavenumber and $h$ the mesh size.
A Simple Standard for Sharing Ontological Mappings (SSSOM)

Abstract Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec
Conceptual designs of multi-degree of freedom compliant parallel manipulators composed of wire-beam based compliant mechanisms

This paper proposes conceptual designs of multi-degree(s) of freedom (DOF) compliant parallel manipulators (CPMs) including 3-DOF translational CPMs and 6-DOF CPMs using a building block based pseudo-rigid-body-model (PRBM) approach. The proposed multi-DOF CPMs are composed of wire-beam based compliant mechanisms (WBBCMs) as distributed-compliance compliant building blocks (CBBs). Firstly, a comprehensive literature review for the design approaches of compliant mechanisms is conducted, and a building block based PRBM is then presented, which replaces the traditional kinematic sub-chain with an appropriate multi-DOF CBB. In order to obtain the decoupled 3-DOF translational CPMs (XYZ CPMs), two classes of kinematically decoupled 3-PPPR (P: prismatic joint, R: revolute joint) translational parallel mechanisms (TPMs) and 3-PPPRR TPMs are identified based on the type synthesis of rigid-body parallel mechanisms, and WBBCMs as the associated CBBs are further designed. Via replacing the traditional actuated P joint and the traditional passive PPR/PPRR sub-chain in each leg of the 3-DOF TPM with the counterpart CBBs (i.e. WBBCMs), a number of decoupled XYZ CPMs are obtained by appropriate arrangements. In order to obtain the decoupled 6-DOF CPMs, an orthogonally-arranged decoupled 6-PSS (S: spherical joint) parallel mechanism is first identified, and then two example 6-DOF CPMs are proposed by the building block based PRBM method. It is shown that, among these designs, two types of monolithic XYZ CPM designs with extended life have been presented.
Coupled quintessence with a generalized interaction term

We investigate a cosmological model in which dark energy, represented by a quintessential scalar ﬁeld, is directly coupled to a dark-matter perfect ﬂuid. We are interested in solutions of cosmological relevance, namely those for which a dark-matter-dominated era long enough to allow for structure formation is followed by an era of accelerated expansion driven by dark energy. For the coupling between these two dark components of the Universe, we choose forms that generalize the one most commonly used in the literature. Resorting to powerful methods of qualitative analysis of dynamical systems, we show that, for certain generalized forms of the coupling, ﬁnal states of our coupled quintessential model correspond to solutions in which the evolution of the Universe is completely dominated by dark energy. In this case, there are no scaling solutions. Interestingly, however, for certain values of a relevant parameter, during the approach to the ﬁnal state of evolution, the cosmological parameters change so slowly that, for all practical purposes, the solution appears to be stuck in a state corresponding to a scaling solution.
Estimating evaporation from bare soil using soil moisture data

A method is presented that uses continuous soil moisture measurements and hourly reference evapotranspiration data to estimate a soil hydraulic factor (β) for modeling soil evaporation. The β factor is used to assess the end of the energy limited soil evaporation phase (Stage 1) and the evaporation rate during the soil hydraulic limited phase (Stage 2) of a two-stage soil evaporation model. A previously developed and tested method to determine β uses an energy balance approach with sensible heat flux density estimated using the surface renewal method to obtain the continuous soil evaporation. A new method is presented, which uses a hydroprobe soil moisture measuring device to estimate the continuous soil evaporation. The estimation of evaporation with soil moisture sensors was simpler and less expensive when compared to the energy balance technique. The methods, evaluated in two field experiments, showed good agreement with evaporation data. Using the evaporation model and β derived from either method pro.
A Nascent Tidal Dwarf Galaxy Forming within the Northern H i Streamer of M82

We identify a ∼600 pc wide region of active star formation located within a tidal streamer of M82 via Hα emission (F Hα ∼ 6.5 × 10−14 erg s−1 cm−2), using a pathfinder instrument based on the Dragonfly Telephoto Array. The object is kinematically decoupled from the disk of M82 as confirmed via Keck/LRIS spectroscopy and is spatially and kinematically coincident with an overdensity of H i and molecular hydrogen within the “northern H i streamer” induced by the passage of M81 several hundred Myr ago. From H i data, we estimate that ∼5 × 107 M ⊙ of gas is present in the specific overdensity coincident with the Hα source. The object’s derived metallicity (12+ log(O/H)≃8.6 ), position within a gas-rich tidal feature, and morphology (600 pc diameter with multiple star-forming clumps), indicate that it is likely a tidal dwarf galaxy in the earliest stages of formation.
NEORL: NeuroEvolution Optimization with Reinforcement Learning

We present an open-source Python framework for NeuroEvolution Optimization with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology. NEORL offers a global optimization interface of state-of-the-art algorithms in the field of evolutionary computation, neural networks through reinforcement learning, and hybrid neuroevolution algorithms. NEORL features diverse set of algorithms, user-friendly interface, parallel computing support, automatic hyperparameter tuning, detailed documentation, and demonstration of applications in mathematical and real-world engineering optimization. NEORL encompasses various optimization problems from combinatorial, continuous, mixed discrete/continuous, to high-dimensional, expensive, and constrained engineering optimization. NEORL is tested in variety of engineering applications relevant to low carbon energy research in addressing solutions to climate change. The examples include nuclear reactor control and fuel cell power production. The results demonstrate NEORL competitiveness against other algorithms and optimization frameworks in the literature, and a potential tool to solve large-scale optimization problems. More examples and benchmarking of NEORL can be found here: https://neorl.readthedocs. io/en/latest/index.html
Triangulation candidates for Bayesian optimization

Bayesian optimization involves"inner optimization"over a new-data acquisition criterion which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart local numerical optimizers. In such cases it is common to replace continuous search with a discrete one over random candidates. Here we propose using candidates based on a Delaunay triangulation of the existing input design. We detail the construction of these"tricands"and demonstrate empirically how they outperform both numerically optimized acquisitions and random candidate-based alternatives, and are well-suited for hybrid schemes, on benchmark synthetic and real simulation experiments.
Three-dimensional stacked MOS transistors by localized silicon epitaxial overgrowth

Three-dimensionally integrated silicon-on-insulator MOS transistors built employing localized silicon epitaxy are discussed. Key parameters for the growth of single-crystal silicon over oxidized polysilicon gates to form channel regions for stacked devices were obtained. The interface between the buried oxide and the silicon overgrowth was characterized by C-V measurements, exhibiting interface state densities as low as 2*10/sup 11//eV-cm/sup 2/ at mid-gap. A self-limiting planarization technique to thin the overgrowth to less than 1 mu m to facilitate the implementation of active devices was developed. The quality of the crystalline material and the planarized surface was characterized by means of MOS transistors that exhibited hole mobilities (165 cm/sup 2//V-s) comparable to those of bulk material. Field-effect operation of the buried interface composed of the oxidized polysilicon and the overgrowth was demonstrated. >
Towards the ultimate PMT waveform analysis

Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the performance of all the waveform analysis algorithms known to us and find fast stochastic matching pursuit the best in accuracy. Significant time (up to ×2) and energy (up to ×1.04) resolution boosts are attainable with fast stochastic matching pursuit, approaching theoretical limits. Other methods also outperform the traditional threshold crossing approach in time resolution.
Why you should learn functional basis

Efﬁcient and practical representation of geometric data is a ubiquitous problem for several applications in geometry processing. A widely used choice is to encode the 3D objects through their spectral embedding, associating to each surface point the values assumed at that point by a truncated subset of the eigenfunctions of a differential operator (typically the Laplacian). Several attempts to deﬁne new, preferable embeddings for different applications have seen the light during the last decade. Still, the standard Laplacian eigenfunctions remain solidly at the top of the available solutions, despite their limitations, such as being limited to near-isometries for shape matching. Recently, a new trend shows advantages in learning substitutes for the Laplacian eigenfunctions. At the same time, many research questions remain unsolved: are the new bases better than the LBO eigenfunctions, and how do they relate to them? How do they act in the functional perspective? And how to exploit these bases in new conﬁgurations in conjunction with additional features and descriptors? In this study, we properly pose these questions to improve our understanding of this emerging research direction. We show their applicative relevance in different contexts revealing some of their insights and exciting future directions.
In-Kernel Control-Flow Integrity on Commodity OSes using ARM Pointer Authentication

This paper presents an in-kernel, hardware-based control-flow integrity (CFI) protection, called PAL, that utilizes ARM's Pointer Authentication (PA). It provides three important benefits over commercial, state-of-the-art PA-based CFIs like iOS's: 1) enhancing CFI precision via automated refinement techniques, 2) addressing hindsight problems of PA for in kernel uses such as preemptive hijacking and brute-forcing attacks, and 3) assuring the algorithmic or implementation correctness via post validation. PAL achieves these goals in an OS-agnostic manner, so could be applied to commodity OSes like Linux and FreeBSD. The precision of the CFI protection can be adjusted for better performance or improved for better security with minimal engineering efforts if a user opts in to. Our evaluation shows that PAL incurs negligible performance overhead: e.g.,<1% overhead for Apache benchmark and 3~5% overhead for Linux perf benchmark on the latest Mac mini (M1). Our post-validation approach helps us ensure the security invariant required for the safe uses of PA inside the kernel, which also reveals new attack vectors on the iOS kernel. PAL as well as the CFI-protected kernels will be open sourced.
Multi-Leader Congestion Games with an Adversary

We study a multi-leader single-follower congestion game where multiple users (leaders) choose one resource out of a set of resources and, after observing the realized loads, an adversary (single-follower) attacks the resources with maximum loads causing additional costs for the leaders. For the resulting strategic game among the leaders, we show that pure Nash equilibria fail to exist and therefore, we consider approximate equilibria instead. As our first main result, we show that the existence of a K-approximate equilibrium can always be guaranteed, where K (approximately equal to 1.1974) is the unique solution of a cubic polynomial equation. To this end, we give a polynomial time combinatorial algorithm which computes a K-approximate equilibrium. The factor K is tight, meaning that there is an instance that does not admit an A-approximate equilibrium for any A < K. Thus A = K is the smallest possible value of A such that the existence of an A-approximate equilibrium can be guaranteed for any instance of the considered game. Secondly, we focus on approximate equilibria of a given fixed instance. We show how to compute efficiently a best approximate equilibrium, that is, with smallest possible A among all A-approximate equilibria of the given instance.
Algorithms with improved delay for enumerating connected induced subgraphs of a large cardinality

The problem of enumerating all connected induced subgraphs of a given order $k$ from a given graph arises in many practical applications: bioinformatics, information retrieval, processor design,to name a few. The upper bound on the number of connected induced subgraphs of order $k$ is $n\cdot\frac{(e\Delta)^{k}}{(\Delta-1)k}$, where $\Delta$ is the maximum degree in the input graph $G$ and $n$ is the number of vertices in $G$. In this short communication, we first introduce a new neighborhood operator that is the key to design reverse search algorithms for enumerating all connected induced subgraphs of order $k$. Based on the proposed neighborhood operator, three algorithms with delay of $O(k\cdot min\{(n-k),k\Delta\}\cdot(k\log{\Delta}+\log{n}))$, $O(k\cdot min\{(n-k),k\Delta\}\cdot n)$ and $O(k^2\cdot min\{(n-k),k\Delta\}\cdot min\{k,\Delta\})$ respectively are proposed. The first two algorithms require exponential space to improve upon the current best delay bound $O(k^2\Delta)$\cite{4} for this problem in the case $k>\frac{n\log{\Delta}-\log{n}-\Delta+\sqrt{n\log{n}\log{\Delta}}}{\log{\Delta}}$ and $k>\frac{n^2}{n+\Delta}$ respectively.
VPVnet: a velocity-pressure-vorticity neural network method for the Stokes' equations under reduced regularity

We present VPVnet, a deep neural network method for the Stokes' equations under reduced regularity. Different with recently proposed deep learning methods [40,51] which are based on the original form of PDEs, VPVnet uses the least square functional of the first-order velocity-pressure-vorticity (VPV) formulation ([30]) as loss functions. As such, only first-order derivative is required in the loss functions, hence the method is applicable to a much larger class of problems, e.g. problems with non-smooth solutions. Despite that several methods have been proposed recently to reduce the regularity requirement by transforming the original problem into a corresponding variational form, while for the Stokes' equations, the choice of approximating spaces for the velocity and the pressure has to satisfy the LBB condition additionally. Here by making use of the VPV formulation, lower regularity requirement is achieved with no need for considering the LBB condition. Convergence and error estimates have been established for the proposed method. It is worth emphasizing that the VPVnet method is divergence-free and pressure-robust, while classical inf-sup stable mixed finite elements for the Stokes' equations are not pressure-robust. Various numerical experiments including 2D and 3D lid-driven cavity test cases are conducted to demonstrate its efficiency and accuracy.
A cross-domain recommender system using deep coupled autoencoders

Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items without sharing sensitive information regarding the users' behavior, and thus avoiding the leak of user privacy. In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations, thus transferring beneficial information from the source to the target domain. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the item-latent factors across domains. Extensive numerical experiments on two publicly available benchmark datasets are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.
Robust Flight Control Design with Eigenstructure Assignment.

Although the H∞ control method is powerful tool to design robust controller, it is difficult to attain the performances in time domain. A new design method of H∞ state feedback controller is proposed which guarantees the robust stability and assigns eigenstructure in the desired regions. The time responses can be shaped with assigning appropriate eigenstructure. Conditions of robust stability and closed-loop eigenstructure assignments are solved in LMI form with one nonlinear constraint. This method is validated with numerical example of flight control design.
Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System

The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles. Existing algorithms usually optimize the performance of a single channel. So designing the model to achieve the optimal recommendation list which maximize the Click-Through Rate (CTR) of whole homepage is a challenge problem. Other than the accuracy objective, display diversity on the homepage is also important since homogeneous display usually hurts user experience. In this paper, we propose a two-stage architecture of the homepage recommendation system. In the first stage, we develop efficient algorithms for recommending items to proper channels while maintaining diversity. The two methods can be combined: user-channel-item predictive model with diversity constraint. In the second stage, we provide an ordered list of items in each channel. Existing re-ranking models are hard to describe the mutual influence between items in both intrachannel and inter-channel. Therefore, we propose a Deep & Hierarchical Attention Network Re-ranking (DHANR) model for homepage recommender systems. The Hierarchical Attention Network consists of an item encoder, an itemlevel attention layer, a channel encoder and a channel-level attention layer. Our method achieves a significant improvement in terms of precision, intra-list average distance(ILAD) and channel-wise Precision@k in offline experiments and in terms of CTR and ILAD in our online systems.
Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System

We develop methods to learn the correlation potential for a time-dependent Kohn-Sham (TDKS) system in one spatial dimension. We start from a low-dimensional two-electron system for which we can numerically solve the time-dependent Schr ¨ odinger equation; this yields electron densities suitable for training models of the correlation potential. We frame the learning problem as one of optimizing a least-squares objective subject to the constraint that the dynamics obey the TDKS equation. Applying adjoints, we develop efﬁcient methods to compute gradients and thereby learn models of the correlation potential. Our results show that it is possible to learn values of the correlation potential such that the resulting electron densities match ground truth densities. We also show how to learn correlation potential functionals with memory, demonstrating one such model that yields reasonable results for trajectories outside the training set.