Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics and Origins of the Near-resonant Kepler Planets

Short-period super-Earths and mini-Neptunes encircle more than ∼50% of Sun-like stars and are relatively amenable to direct observational characterization. Despite this, environments in which these planets accrete are difficult to probe directly. Nevertheless, pairs of planets that are close to orbital resonances provide a unique window into the inner regions of protoplanetary disks, as they preserve the conditions of their formation, as well as the early evolution of their orbital architectures. In this work, we present a novel approach toward quantifying transit timing variations within multiplanetary systems and examine the near-resonant dynamics of over 100 planet pairs detected by Kepler. Using an integrable model for first-order resonances, we find a clear transition from libration to circulation of the resonant angle at a period ratio of ≈0.6% wide of exact resonance. The orbital properties of these systems indicate that they systematically lie far away from the resonant forced equilibrium. Cumulatively, our modeling indicates that while orbital architectures shaped by strong disk damping or tidal dissipation are inconsistent with observations, a scenario where stochastic stirring by turbulent eddies augments the dissipative effects of protoplanetary disks reproduces several features of the data.
Computing maximum likelihood estimators of a log-concave density function

We consider the problem of estimating a density function that is assumed to be log-concave. This semi-parametric model includes many well-known parametric classes; such as Normal, Gamma, Laplace, Logistic, Beta or Extreme value distributions, for specific parameter ranges. It is known that the maximum likelihood estimator for the log-density is always a piecewise linear function with at most as many knots as observations, but typically much less. We show that this property can be exploited to design a linearly constrained optimization problem whose iteratively calculated solution yields the estimator. We compare several standard and one recently proposed algorithm regarding their performance on this problem.
Accelerated Computations of Sensitivities for xVA

Exposure simulations are fundamental to many xVA calculations and are a nested expectation problem where repeated portfolio valuations create a significant computational expense. Sensitivity calculations which require shocked and unshocked valuations in bump-and-revalue schemes exacerbate the computational load. A known reduction of the portfolio valuation cost is understood to be found in polynomial approximations, which we apply in this article to interest rate sensitivities of expected exposures. We consider a method based on the approximation of the shocked and unshocked valuation functions, as well as a novel approach in which the difference between these functions is approximated. Convergence results are shown, and we study the choice of interpolation nodes. Numerical experiments with interest rate derivatives are conducted to demonstrate the high accuracy and remarkable computational cost reduction. We further illustrate how the method can be extended to more general xVA models using the example of CVA with wrong-way risk.
Secure Software Development Methodologies: A Multivocal Literature Review

In recent years, the number of cyber attacks has grown rapidly. An effective way to reduce the attack surface and protect software is adoption of methodologies that apply security at each step of the software development lifecycle. While different methodologies have been proposed to address software security, recent research shows an increase in the number of vulnerabilities in software and data breaches. Therefore, the security practices incorporated in secure software development methodologies require investigation. This paper provides an overview of security practices involved in 28 secure software development methodologies from industry, government, and academia. To achieve this goal, we distributed the security practices among the software development lifecycle stages. We also investigated auxiliary (non-technical) practices, such as organizational, behavioral, legal, policy, and governance aspects that are incorporated in the secure software development methodologies. Furthermore, we explored methods used to provide evidence of the effectiveness of the methodologies. Finally, we present the gaps that require attention in the scientific community. The results of our survey may assist researchers and organizations to better understand the existing security practices integrated into the secure software development methodologies. In addition, our bridge between"technical"and"non-technical"worlds may be useful for non-technical specialists who investigate software security. Moreover, exploring the gaps that we found in current research may help improve security in software development and produce software with fewer number of vulnerabilities.
Study of Impact Ionization Coefficients in Silicon With Low Gain Avalanche Diodes

Impact ionization in silicon devices has been extensively studied and several models for a quantitative description of the impact ionization coefficients have been proposed. We evaluate those models against gain measurements on low-gain avalanche diodes (LGADs) and derive new parameterizations for the impact ionization coefficients optimized to describe a large set of experimental data. We present pulsed infrared (IR)-laser-based gain measurements on five different types of <inline-formula> <tex-math notation="LaTeX">$50 \boldsymbol {\mu }\text{m}$ </tex-math></inline-formula>-thick LGADs from two different producers centro nacional de microelectrónica (CNM) and Hamamatsu Photonics (HPK) performed in a temperature range from <inline-formula> <tex-math notation="LaTeX">$- 15\,\,{^{\circ} }\text{C}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$40 {^{\circ} }\text{C}$ </tex-math></inline-formula>. Detailed technology computer-aided design (TCAD) device models are conceived based on secondary ion mass spectrometry (SIMS) doping profile measurements and tuning of the device models to measure <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characteristics. Electric field profiles are extracted from the TCAD simulations and used as input to an optimization procedure (least squares fit) of the impact ionization model parameters to the experimental data. It is demonstrated that the new parameterizations give a good agreement between all measured data and TCAD simulations which is not achieved with the existing models. Finally, we provide an error analysis and compare the obtained values for the electron and hole impact ionization coefficients against existing models.
Targets in Reinforcement Learning to solve Stackelberg Security Games

Reinforcement Learning (RL) algorithms have been success- fully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc. These scenarios can be framed as Stackelberg security games (SSGs) where defenders and attackers compete to control tar- get resources. The algorithm’s competency is assessed by which agent is controlling the targets. This review investi- gates modeling of SSGs in RL with a focus on possible im-provements of target representations in RL algorithms.
NeAF: Learning Neural Angle Fields for Point Normal Estimation

Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors or learning local surface fitting using neural networks. However, these methods are not generalized well to unseen scenarios and are sensitive to parameter settings. To resolve these issues, we propose an implicit function to learn an angle field around the normal of each point in the spherical coordinate system, which is dubbed as Neural Angle Fields (NeAF). Instead of directly predicting the normal of an input point, we predict the angle offset between the ground truth normal and a randomly sampled query normal. This strategy pushes the network to observe more diverse samples, which leads to higher prediction accuracy in a more robust manner. To predict normals from the learned angle fields at inference time, we randomly sample query vectors in a unit spherical space and take the vectors with minimal angle values as the predicted normals. To further leverage the prior learned by NeAF, we propose to refine the predicted normal vectors by minimizing the angle offsets. The experimental results with synthetic data and real scans show significant improvements over the state-of-the-art under widely used benchmarks. Project page: https://lisj575.github. io/NeAF/.
Robust and Fast Measure of Information via Low-rank Representation

The matrix-based Rényi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi's entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi's entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n³) to O(n²s) or even O(ns²), where n is the number of data samples and s ≪ n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi's entropy in terms of both performance and computational efficiency.
A linear filter regularization for POD-based reduced order models of the quasi-geostrophic equations

We propose a regularization for Reduced Order Models (ROMs) of the quasi-geostrophic equations (QGE) to increase accuracy when the Proper Orthogonal Decomposition (POD) modes retained to construct the reduced basis are insufficient to describe the system dynamics. Our regularization is based on the so-called BV-alpha model, which modifies the nonlinear term in the QGE and adds a linear differential filter for the vorticity. To show the effectiveness of the BV-alpha model for ROM closure, we compare the results computed by a POD-Galerkin ROM with and without regularization for the classical double-gyre wind forcing benchmark. Our numerical results show that the solution computed by the regularized ROM is more accurate, even when the retained POD modes account for a small percentage of the eigenvalue energy. Additionally, we show that, although computationally more expensive that the ROM with no regularization, the regularized ROM is still a competitive alternative to full order simulations of the QGE.
Towards Training GNNs using Explanation Directed Message Passing

With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions. However, there has been no work in generating explanations on the fly during model training and utilizing them to improve the expressive power of the underlying GNN models. In this work, we introduce a novel explanation-directed neural message passing framework for GNNs, EXPASS (EXplainable message PASSing), which aggregates only embeddings from nodes and edges identified as important by a GNN explanation method. EXPASS can be used with any existing GNN architecture and subgraph-optimizing explainer to learn accurate graph embeddings. We theoretically show that EXPASS alleviates the oversmoothing problem in GNNs by slowing the layer wise loss of Dirichlet energy and that the embedding difference between the vanilla message passing and EXPASS framework can be upper bounded by the difference of their respective model weights. Our empirical results show that graph embeddings learned using EXPASS improve the predictive performance and alleviate the oversmoothing problems of GNNs, opening up new frontiers in graph machine learning to develop explanation-based training frameworks.
Identification of the Breach of Short-term Rental Regulations in Irish Rent Pressure Zones

The housing crisis in Ireland has rapidly grown in recent years. To make a more significant profit, many landlords are no longer renting out their houses under long-term tenancies but under short-term tenancies. The shift from long-term to short-term rentals has harmed the supply of private housing rentals. Regulating rentals in Rent Pressure Zones with the highest and rising rents is becoming a tricky issue. In this paper, we develop a breach identifier to check short-term rentals located in Rent Pressure Zones with potential breaches only using publicly available data from Airbnb (an online marketplace focused on short-term home-stays). First, we use a Residual Neural Network to filter out outdoor landscape photos that negatively impact identifying whether an owner has multiple rentals in a Rent Pressure Zone. Second, a Siamese Neural Network is used to compare the similarity of indoor photos to determine if multiple rental posts correspond to the same residence. Next, we use the Haversine algorithm to locate short-term rentals within a circle centered on the coordinate of a permit. Short-term rentals with a permit will not be restricted. Finally, we improve the occupancy estimation model combined with sentiment analysis, which may provide higher accuracy. Because Airbnb does not disclose accurate house coordinates and occupancy data, it is impossible to verify the accuracy of our breach identifier. The accuracy of the occupancy estimator cannot be verified either. It only provides an estimate within a reasonable range. Users should be skeptical of short-term rentals that are flagged as possible breaches.
Subsampling for tensor least squares: Optimization and statistical perspectives

In this paper, we investigate the random subsampling method for tensor least squares problem with respect to the popular t-product. From the optimization perspective, we present the error bounds in the sense of probability for the residual and solution obtained by the proposed method. From the statistical perspective, we derive the expressions of the conditional and unconditional expectations and variances for the solution, where the unconditional ones combine the model noises. Moreover, based on the unconditional variance, an optimal subsampling probability distribution is also found. Finally, the feasibility and eﬀectiveness of the proposed method and the correctness of the theoretical results are veriﬁed by numerical experiments.
A General Deep Learning Speech Enhancement Framework Motivated by Taylor's Theorem

—While deep neural networks greatly facilitate the proliferation of the speech enhancement field, most of the existing methods are developed following either heuristic or blind optimization criteria, which severely hampers interpretability and transparency. Inspired by Taylor’s theorem, we propose a general unfolding framework for both single-and multi-channel speech enhancement tasks. Concretely, we formulate the complex spectrum recovery into the spectral magnitude mapping in the neighboring space of the noisy mixture, in which the sparse prior is introduced for phase modification in advance. Based on that, the mapping function is decomposed into the superimposition of the 0th-order and high-order polynomials in Taylor’s series, where the former coarsely removes the interference in the magnitude domain and the latter progressively complements the remaining spectral detail in the complex spectrum domain. In addition, we study the relation between adjacent order term and reveal that each high-order term can be recursively estimated with its lower-order term, and each high-order term is then proposed to evaluate using a surrogate function with trainable weights, so that the whole system can be trained in an end-to-end manner. Extensive experiments are conducted on WSJ0-SI84, DNS-Challenge, Voicebank+Demand, and spatialized Librispeech datasets. Quantitative results show that the proposed approach not only yields competitive performance over existing top-performed approaches, but also enjoys decent internal transparency and flexibility.
Investigation of Proper Orthogonal Decomposition for Echo State Networks

Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped with a very efficient training procedure, Reservoir Computing strategies, such as the ESN, require high-order networks, i.e., many neurons, resulting in a large number of states that are magnitudes higher than the number of model inputs and outputs. A large number of states not only makes the time-step computation more costly but also may pose robustness issues, especially when applying ESNs to problems such as Model Predictive Control (MPC) and other optimal control problems. One way to circumvent this complexity issue is through Model Order Reduction strategies such as the Proper Orthogonal Decomposition (POD) and its variants (POD-DEIM), whereby we find an equivalent lower order representation to an already trained high dimension ESN. To this end, this work aims to investigate and analyze the performance of POD methods in Echo State Networks, evaluating their effectiveness through the Memory Capacity (MC) of the POD-reduced network compared to the original (full-order) ESN. We also perform experiments on two numerical case studies: a NARMA10 difference equation and an oil platform containing two wells and one riser. The results show that there is little loss of performance comparing the original ESN to a POD-reduced counterpart and that the performance of a POD-reduced ESN tends to be superior to a normal ESN of the same size. Also, the POD-reduced network achieves speedups of around $80\%$ compared to the original ESN.
Evaluation of three-loop self-energy master integrals with four or five propagators

I obtain identities satisfied by the 3-loop self-energy master integrals with four or five propagators with generic masses, including the derivatives with respect to each of the squared masses and the external momentum invariant. These identities are then recast in terms of the corresponding renormalized master integrals, enabling straightforward numerical evaluation of them by the differential equations approach. Some benchmark examples are provided. The method used to obtain the derivative identities relies only on the general form implied by integration by parts relations, without actually following the usual integration by parts reduction procedure. As a byproduct, I find a simple formula giving the expansion of the master integrals to arbitrary order in the external momentum invariant, in terms of known derivatives of the corresponding vacuum integrals.
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression

Transformers have attained superior performance in natural language processing and computer vision. Their self-attention and feedforward layers are overparameterized, limiting inference speed and energy efficiency. Tensor decomposition is a promising technique to reduce parameter redundancy by leveraging tensor algebraic properties to express the parameters in a factorized form. Prior efforts used manual or heuristic factorization settings without hardware-aware customization, resulting in poor hardware efficiencies and large performance degradation. In this work, we propose a hardware-aware tensor decomposition framework, dubbed HEAT, that enables efficient exploration of the exponential space of possible decompositions and automates the choice of tensorization shape and decomposition rank with hardware-aware co-optimization. We jointly investigate tensor contraction path optimizations and a fused Einsum mapping strategy to bridge the gap between theoretical benefits and real hardware efficiency improvement. Our two-stage knowledge distillation flow resolves the trainability bottleneck and thus significantly boosts the final accuracy of factorized Transformers. Overall, we experimentally show that our hardware-aware factorized BERT variants reduce the energy-delay product by 5.7x with less than 1.1% accuracy loss and achieve a better efficiency-accuracy Pareto frontier than hand-tuned and heuristic baselines.
Statistical Modeling of Spatially Stratified Heterogeneous Data

Spatial statistics is dominated by spatial autocorrelation (SAC) based Kriging and BHM, and spatial local heterogeneity based hotspots and geographical regression methods, appraised as the first and second laws of Geography (Tobler 1970; Goodchild 2004), respectively. Spatial stratified heterogeneity (SSH), the phenomena of a partition that within strata is more similar than between strata, examples are climate zones and landuse classes and remote sensing classification, is prevalent in geography and understood since ancient Greek, is surprisingly neglected in Spatial Statistics, probably due to the existence of hundreds of classification algorithms. In this article, we go beyond the classifications and disclose that SSH is the sources of sample bias, statistic bias, modelling confounding and misleading CI, and recommend robust solutions to overcome the negativity. In the meantime, we elaborate four benefits from SSH: creating identical PDF or equivalent to random sampling in stratum; the spatial pattern in strata, the borders between strata as a specific information for nonlinear causation; and general interaction by overlaying two spatial patterns. We developed the equation of SSH and discuss its context. The comprehensive investigation formulates the statistics for SSH, presenting a new principle and toolbox in spatial statistics.
NGC 7538 IRS 2 in [Ne ii]: shell and cavity kinematics of a compact H ii region

NGC 7538 IRS 2 is a compact H ii region and recent star formation source, with a shell morphology, lying on the border of the visible H ii region NGC 7538. We present a spectral cube of the [Ne ii] 12.8 $\mu$m emission line obtained with the TEXES spectrometer on Gemini North with velocity resolution ∼4 km s−1 and angular resolution ∼0.3 arcsec. The kinematics of the data cube show ionized gas flowing along multiple cavity walls. We have simulated the kinematics and structure of IRS 2 with a model of superimposed cavities created by outflows from embedded stars in a cloud with density gradients. Most of the cavities, including the largest that dominates IRS 2 structure, are associated with B-type stars; the outflow of the bright ionizing O star binary IRS 2a/b is small in extent and lies in a high-density clump. The IRS 2 model shows that the behaviour of an H ii region is not a matter of only the most massive star present; cloud clumpiness and activity of lower mass stars may determine the structure and kinematics.