Miracle-dz/UniFAR-FTUs huggingface dataset:
Hidden Order of Boolean Networks

It is a common belief that the order of a Boolean network is mainly determined by its attractors, including fixed points and cycles. Using the semi-tensor product (STP) of matrices and the algebraic state-space representation (ASSR) of the Boolean networks, this article reveals that in addition to this explicit order, there is a certain implicit or hidden order, which is determined by the fixed points and limit cycles of their dual networks. The structure and certain properties of dual networks are investigated. Instead of a trajectory, which describes the evolution of a state, the hidden order provides a global horizon to describe the evolution of the overall network. We conjecture that the order of networks is mainly determined by the dual attractors via their corresponding hidden orders. Then these results about the Boolean networks are further extended to the k -valued case.
Chemical Stratification in a Long Gamma-Ray Burst Cocoon and Early-time Spectral Signatures of Supernovae Associated with Gamma-Ray Bursts

We present the results of 3D hydrodynamic simulations of a gamma-ray burst (GRB) jet emanating from a massive star with a particular focus on the formation of high-velocity quasi-spherical ejecta and the jet-induced chemical mixing. Recent early-time optical observations of supernovae associated with GRBs (e.g., GRB 171205A/SN 2017iuk) indicate a considerable amount of heavy metals in the high-velocity outer layers of the ejecta. Using our jet simulations, we show that the density and chemical structure of the outer ejecta implied by observations can be naturally reproduced by a powerful jet penetrating the progenitor star. We consider three representative jet models with a stripped massive star, a standard jet, a weak jet, and a jet choked by an extended circumstellar medium, to clarify the differences in the dynamical evolution and the chemical properties of the ejected materials. The standard jet successfully penetrates the progenitor star and creates a quasi-spherical ejecta component (cocoon). The jet-induced mixing significantly contaminates the cocoon with heavy elements that have been otherwise embedded in the inner layer of the ejecta. The weak and choked jet models fail to produce an ultrarelativistic jet but produce a quasi-spherical cocoon with different chemical properties. We discuss the impact of the different jet−star interactions on the expected early-time electromagnetic signatures of long GRBs and how to probe the jet dynamics from observations.
Stars Crushed by Black Holes. II. A Physical Model of Adiabatic Compression and Shock Formation in Tidal Disruption Events

We develop a Newtonian model of a deep tidal disruption event (TDE), for which the pericenter distance of the star, r p, is well within the tidal radius of the black hole, r t, i.e., when β ≡ r t/r p ≫ 1. We find that shocks form for β ≳ 3, but they are weak (with Mach numbers ∼1) for all β, and that they reach the center of the star prior to the time of maximum adiabatic compression for β ≳ 10. The maximum density and temperature reached during the TDE follow much shallower relations with β than the previously predicted ρmax∝β3 and Tmax∝β2 scalings. Below β ≃ 10, this shallower dependence occurs because the pressure gradient is dynamically significant before the pressure is comparable to the ram pressure of the free-falling gas, while above β ≃ 10, we find that shocks prematurely halt the compression and yield the scalings ρmax∝β1. 62 and Tmax∝β1. 12 . We find excellent agreement between our results and high-resolution simulations. Our results demonstrate that, in the Newtonian limit, the compression experienced by the star is completely independent of the mass of the black hole. We discuss our results in the context of existing (affine) models, polytropic versus non-polytropic stars, and general relativistic effects, which become important when the pericenter of the star nears the direct capture radius, at β ∼ 12.5 (2.7) for a solar-like star disrupted by a 106 M ⊙ (107 M ⊙) supermassive black hole.
Properties of correlated fission fragments from neutron induced fission of Np-237 at incident neutron energies between 200 keV and 100 MeV

Neutron-induced fission of $^{237}$Np has been measured over a wide range of incident neutron energies using a twin Frisch-gridded ionization chamber (TFGIC) and a thin-backed $^{237}$Np target. These measurements were performed at the Los Alamos Neutron Science Center - Weapons Neutron Research (LANSCE - WNR) facility, which provides a collimated beam of neutrons with energies ranging from 100s of keV to 100s of MeV. The data were analyzed using the double-energy ($2E$) method, with mass-dependent corrections for prompt-fission neutrons and pulse height defect. Pre- and post-neutron evaporation average total kinetic energy ($\langle TKE\rangle$) values are reported for 54 incident neutron energies in the energy range $0.20 \le E_n \le 100.0$ MeV and compared to existing data and evaluations. Pre- and post-neutron evaporation mass yields were extracted with a full width at half maximum (FWHM) resolution of 4u and compared to existing data and evaluations. The present $\langle TKE\rangle$ and mass yield data agree with previous results and also with statistical models of $^{237}$Np$(n,f)$ at incident neutron energies between $E_n = 0.2 - 20.0$ MeV. A flattening of the $\langle TKE\rangle$ data is observed (relative to the prediction of the GEF model) above $E_n = 20.0$ MeV. However, the interpretation of this discrepancy is unclear as the analysis method's neglect of incomplete momentum transfer at high energies, as well as pre-equilibrium pre-fission phenomena likely have a significant impact on the measurement at such high incident neutron energies.
Comparison of annual maximum series and flood-type-differentiated mixture models of partial duration series

The use of the annual maximum series for flood frequency analyses limits the considered information to one event per year and one sample that is assumed to be homogeneous. However, flood may have different generating processes, such as snowmelt, heavy rainfall or long-duration rainfall, which makes the assumption of homogeneity questionable. Flood types together with statistical flood-type-specific mixture models offer the possibility to consider the different flood-generating processes separately and therefore obtain homogeneous sub-samples. The combination of flood types in a mixture model then gives classical flood quantiles for given return periods. This higher flexibility comes to the cost of more distribution parameters, which may lead to a higher uncertainty in the estimation. This study compares the classical flood frequency models such as the annual maximum series with the type-specific mixture model for different scenarios relevant for design flood estimation in terms of Bias and variance. Thee results show that despite the higher number of parameters, the mixture model is preferable compared to the classical models, if a high number of flood events per year occurs and/or the flood types differ significantly in their distribution parameters.
Incorporating Nuclear Quantum Eﬀects in Molecular Dynamics in Molecular Dynamics

The accurate incorporation of nuclear quantum eﬀects in large-scale molecular dynamics simulations remains a signiﬁcant challenge. Here, we develop a new formulation of the equations of motion for molecular dynamics that utilizes an eﬀective energy surface obtained from a constrained energy minimization with given quantum nuclear expectation positions, thus enabling the incorporation of nuclear quantum eﬀects in molecular dynamics simulations. With a series of model systems, we demonstrate that this new approach is signiﬁcantly more accurate in describing vibrations than classical molecular dynamics and can also accurately describe tunneling, which classical molecular dynamics is unable to do. It is comparable to or more accurate than centroid molecular dynamics and ring-polymer molecular dynamics. Through a combination with constrained nuclear electronic orbital density functional theory, this new approach is promising for practical molecular simulations with nuclear quantum eﬀects incorporated.
Negative curvature obstructs acceleration for geodesically convex optimization, even with exact first-order oracles

Hamilton and Moitra (2021) showed that, in certain regimes, it is not possible to accelerate Riemannian gradient descent in the hyperbolic plane if we restrict ourselves to algorithms which make queries in a (large) bounded domain and which receive gradients and function values corrupted by a (small) amount of noise. We show that acceleration remains unachievable for any deterministic algorithm which receives exact gradient and function-value information (unbounded queries, no noise). Our results hold for the classes of strongly and nonstrongly geodesically convex functions, and for a large class of Hadamard manifolds including hyperbolic spaces and the symmetric space $\mathrm{SL}(n) / \mathrm{SO}(n)$ of positive definite $n \times n$ matrices of determinant one. This cements a surprising gap between the complexity of convex optimization and geodesically convex optimization: for hyperbolic spaces, Riemannian gradient descent is optimal on the class of smooth and and strongly geodesically convex functions, in the regime where the condition number scales with the radius of the optimization domain. The key idea for proving the lower bound consists of perturbing the hard functions of Hamilton and Moitra (2021) with sums of bump functions chosen by a resisting oracle.
Time-independent Generalization Bounds for SGLD in Non-convex Settings

We establish generalization error bounds for stochastic gradient Langevin dynamics (SGLD) with constant learning rate under the assumptions of dissipativity and smoothness, a setting that has received increased attention in the sampling/optimization literature. Unlike existing bounds for SGLD in non-convex settings, ours are time-independent and decay to zero as the sample size increases. Using the framework of uniform stability, we establish time-independent bounds by exploiting the Wasserstein contraction property of the Langevin diffusion, which also allows us to circumvent the need to bound gradients using Lipschitz-like assumptions. Our analysis also supports variants of SGLD that use different discretization methods, incorporate Euclidean projections, or use non-isotropic noise.
Incorporating the effect of heterogeneous surface heating into a semi-empirical model of the surface energy balance closure

It was discovered several decades ago that eddy covariance measurements systematically underestimate sensible and latent heat fluxes, creating an imbalance in the surface energy budget. Since then, many studies have addressed this problem and proposed a variety of solutions to the problem, including improvements to instruments and correction methods applied during data postprocessing. However, none of these measures have led to the complete closure of the energy balance gap. The leading hypothesis is that not only surface-attached turbulent eddies but also sub-mesoscale atmospheric circulations contribute to the transport of energy in the atmospheric boundary layer, and the contribution from organized motions has been grossly neglected. The problem arises because the transport of energy through these secondary circulations cannot be captured by the standard eddy covariance method given the relatively short averaging periods of time (~30 minutes) used to compute statistics. There are various approaches to adjust the measured heat fluxes by attributing the missing energy to the sensible and latent heat flux in different proportions. However, few correction methods are based on the processes causing the energy balance gap. Several studies have shown that the magnitude of the energy balance gap depends on the atmospheric stability and the heterogeneity scale of the landscape around the measurement site. Based on this, the energy balance gap within the surface layer has already been modelled as a function of a nonlocal atmospheric stability parameter by performing a large-eddy simulation study with idealized homogeneous surfaces. We have further developed this approach by including thermal surface heterogeneity in addition to atmospheric stability in the parameterization. Specifically, we incorporated a thermal heterogeneity parameter that was shown to relate to the magnitude of the energy balance gap. For this purpose, we use a Large-Eddy Simulation dataset of 28 simulations with seven different atmospheric conditions and three heterogeneous surfaces with different heterogeneity scales as well as one homogeneous surface. The newly developed model captures very well the variability in the magnitude of the energy balance gap under different conditions. The model covers a wide range of both atmospheric stabilities and landscape heterogeneity scales and is well suited for application to eddy covariance measurements since all necessary information can be modelled or obtained from a few additional measurements.
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Most existing UDA approaches enable knowledge transfer via learning domain-invariant representation and sharing one classifier across two domains. However, ignoring the domain-specific information that are related to the task, and forcing a unified classifier to fit both domains will limit the feature expressiveness in each domain. In this paper, by observing that the Transformer architecture with comparable parameters can generate more transferable representations than CNN counterparts, we propose a Win-Win TRansformer framework (WinTR) that separately explores the domain-specific knowledge for each domain and meanwhile interchanges cross-domain knowledge. Specifically, we learn two different mappings using two individual classification tokens in the Transformer, and design for each one a domain-specific classifier. The cross-domain knowledge is transferred via source guided label refinement and single-sided feature alignment with respect to source or target, which keeps the integrity of domain-specific information. Extensive experiments on three benchmark datasets show that our method outperforms the state-of-the-art UDA methods, validating the effectiveness of exploiting both domain-specific and invariant
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i.e. images whose content cannot be used to improve a classifier during training. In this paper, we reveal the road that researchers should follow for understanding ULEs and improving ULEs as they were originally formulated (ULEOs). The paper makes four contributions. First, we show that ULEOs exploit color and, consequently, their effects can be mitigated by simple grayscale pre-filtering, without resorting to adversarial training. Second, we propose an extension to ULEOs, which is called ULEO-GrayAugs, that forces the generated ULEs away from channel-wise color perturbations by making use of grayscale knowledge and data augmentations during optimization. Third, we show that ULEOs generated using Multi-Layer Perceptrons (MLPs) are effective in the case of complex Convolutional Neural Network (CNN) classifiers, suggesting that CNNs suffer specific vulnerability to ULEs. Fourth, we demonstrate that when a classifier is trained on ULEOs, adversarial training will prevent a drop in accuracy measured both on clean images and on adversarial images. Taken together, our contributions represent a substantial advance in the state of art of unlearnable examples, but also reveal important characteristics of their behavior that must be better understood in order to achieve further improvements.
Value methodology in public engagement for construction development projects

Purpose – The use of public engagement (PE) has been encouraged by the Hong Kong government in recent years, with the construction industry under particular pressure to employ it. However, society as a whole has been suffering as a result of various levels of discontent and conflict in many construction development projects. The logical paths to analyze public need and resolve conflict in the PE process are vague. Value methodology (VM), a logical method, is thus proposed as an initial framework to support PE activities in construction projects. The purpose of this paper is to enhance PE performance for the complicated construction development projects with the application of VM principles. Design/methodology/approach – Based on an extensive literature review, PE logical team process factors and PE outcomes were identified. A questionnaire survey was conducted to investigate the relationships between the logical PE team decision-making process (VM application in the logical process and team behaviors) and.
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms

The high volume of increasingly sophisticated cyber threats is drawing growing attention to cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection, new algorithms that are more robust, effective, and able to use more information are needed. Moreover, the intrusion detection task faces a serious challenge associated with the extreme class imbalance between normal and malicious traffics. Recently, graph-neural network (GNN) achieved state-of-the-art performance to model the network topology in cybersecurity tasks. However, only a few works exist using GNNs to tackle the intrusion detection problem. Besides, other promising avenues such as applying the attention mechanism are still under-explored. This paper presents two novel graph-based solutions for intrusion detection, the modified E-GraphSAGE, and E-ResGATalgorithms, which rely on the established GraphSAGE and graph attention network (GAT), respectively. The key idea is to integrate residual learning into the GNN leveraging the available graph information. Residual connections are added as a strategy to deal with the high-class imbalance, aiming at retaining the original information and improving the minority classes' performance. An extensive experimental evaluation of four recent intrusion detection datasets shows the excellent performance of our approaches, especially when predicting minority classes.
Medial Spectral Coordinates for 3D Shape Analysis

In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects repre-sented by surface meshes, their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision to autonomous driving, medical imaging, and robotics. In these settings, spectral co-ordinates have shown promise for shape representation due to their ability to incorporate both local and global shape properties in a manner that is qualitatively invariant to iso-metric transformations. Yet, surprisingly, such coordinates have thus far typically considered only local surface positional or derivative information. In the present article, we propose to equip spectral coordinates with medial (object width) information, so as to enrich them. The key idea is to couple surface points that share a medial ball, via the weights of the adjacency matrix. We develop a spectral feature using this idea, and the algorithms to compute it. The incorporation of object width and medial coupling has direct benefits, as illustrated by our experiments on object classification, object part segmentation, and surface point correspondence.
Quantised Transforming Auto-Encoders: Achieving Equivariance to Arbitrary Transformations in Deep Networks

In this work we investigate how to achieve equivariance to input transformations in deep networks, purely from data, without being given a model of those transformations. Convolutional Neural Networks (CNNs), for example, are equivariant to image translation, a transformation that can be easily modelled (by shifting the pixels vertically or horizontally). Other transformations, such as out-of-plane rotations, do not admit a simple analytic model. We propose an auto-encoder architecture whose embedding obeys an arbitrary set of equivariance relations simultaneously, such as translation, rotation, colour changes, and many others. This means that it can take an input image, and produce versions transformed by a given amount that were not observed before (e.g. a different point of view of the same object, or a colour variation). Despite extending to many (even non-geometric) transformations, our model reduces exactly to a CNN in the special case of translation-equivariance. Equivariances are important for the interpretability and robustness of deep networks, and we demonstrate results of successful re-rendering of transformed versions of input images on several synthetic and real datasets, as well as results on object pose estimation.
Quantifying Effectiveness of Cathodic Protection in Water Mains: Case Studies

Cathodic protection is a viable measure to extend the residual life of water mains and thus defer capital investments in their rehabilitation and renewal. The effectiveness of cathodic protection varies with the unique set of conditions under which it is applied and is therefore difficult to quantify. Consequently, reported case histories have been largely anecdotal and most often based on the observed reduction of water main breaks following the application of cathodic protection. Models to assess the impact of cathodic protection programs were proposed in a previous companion paper “Quantifying the Effectiveness of Cathodic Protection in Water Mains: Theory. ” This paper describes the application of these models to five groups of water mains from two water utilities that have had on-going programs of hot spot and retrofit cathodic protection. The case histories show that cathodic protection programs (hot spot and retrofit strategies) have had a significant effect on reducing breakage rates, however, in s.
Approximate 3-designs and partial decomposition of the Clifford group representation using transvections

X iv :2 11 1. 13 67 8v 2 [ qu an tph ] 1 9 Ju n 20 22 Approximate 3-designs and partial decomposition of the Clifford group representation using transvections Tanmay Singal∗ Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5/7, 87-100 Toruń, Poland Physics Division, National Center for Theoretical Sciences, Taipei 10617, Taiwan Department of Physics, National Taiwan University, No.1 Sec. 4. , Roosevelt Road, Taipei 106, Taiwan Department of Analysis, Budapest University of Technology and Economics, 1111 Budapest, Egry József u. 1.
An analysis of the improvements in OSNR from distributed Raman amplifiers using modern transmission fibres

We present measurements of the Raman efficiency spectra for recent transmission fibres and model the improvement in DWDM system performance using distributed Raman amplification. For 5 spans of 25 dB fibre loss and 1 dB of connector loss, we predict 4.5-6.5 dB improvement in optical signal to noise ratio (OSNR) when using a 500 mW pump, a variation of up to 2 dB depending on fibre type.