Miracle-dz/UniFAR-FTUs huggingface dataset:
Catalysis of entanglement and other quantum resources

In chemistry, a catalyst is a substance which enables a chemical reaction or increases its rate, while remaining unchanged in the process. Instead of chemical reactions, quantum catalysis enhances our ability to convert quantum states into each other under physical constraints. The nature of the constraints depends on the problem under study and can arise, e.g. from energy preservation. This article reviews the most recent developments in quantum catalysis and gives a historical overview of this research direction. We focus on the catalysis of quantum entanglement and coherence, and also discuss this phenomenon in quantum thermodynamics and general quantum resource theories. We review applications of quantum catalysis and also discuss the recent efforts on universal catalysis, where the quantum state of the catalyst does not depend on the states to be transformed. Catalytic embezzling is also considered, a phenomenon that occurs if the catalyst’s state can change in the transition.
The Rate and Spatial Distribution of Novae in M31 as Determined by a 20 Year Survey

A long-term (1995–2016) survey for novae in the nearby Andromeda galaxy (M31) was conducted as part of the Research-Based Science Education initiative. During the course of the survey 180 nights of observation were completed at Kitt Peak, Arizona. A total of 262 novae were either discovered or confirmed, 40 of which have not been previously reported. Of these, 203 novae form a spatially complete sample detected by the KPNO/WIYN 0.9 m telescope within a 20′×20′ field centered on the nucleus of M31. An additional 50 novae are part of a spatially complete sample detected by the KPNO 4 m telescope within a larger 36′×36′ field. Consistent with previous studies, it is found that the spatial distribution of novae in both surveys follows the bulge light of M31 somewhat more closely than the overall background light of the galaxy. After correcting for the limiting magnitude and the spatial and temporal coverage of the surveys, a final nova rate in M31 is found to be R=40−4+5 yr−1, which is considerably lower than recent estimates. When normalized to the K-band luminosity of M31, this value yields a luminosity-specific nova rate, ν K = 3.3 ± 0.4 yr −1[1010L⊙,K]−1 . By scaling the M31 nova rate using the relative infrared luminosities of M31 and our Galaxy, a nova rate of RG=28−4+5 yr−1 is found for the Milky Way.
Compound Prototype Matching for Few-shot Action Recognition

Few-shot action recognition aims to recognize novel action classes using only a small number of labeled training samples. In this work, we propose a novel approach that first summarizes each video into compound prototypes consisting of a group of global prototypes and a group of focused prototypes, and then compares video similarity based on the prototypes. Each global prototype is encouraged to summarize a specific aspect from the entire video, for example, the start/evolution of the action. Since no clear annotation is provided for the global prototypes, we use a group of focused prototypes to focus on certain timestamps in the video. We compare video similarity by matching the compound prototypes between the support and query videos. The global prototypes are directly matched to compare videos from the same perspective, for example, to compare whether two actions start similarly. For the focused prototypes, since actions have various temporal variations in the videos, we apply bipartite matching to allow the comparison of actions with different temporal positions and shifts. Experiments demonstrate that our proposed method achieves state-of-the-art results on multiple benchmarks.
University of Birmingham Development of a novel model to estimate bedding factors to ensure the economic and robust design of rigid pipes under soil loads

Buried concrete pipes are load bearing structures that need to resist the loads imposed by the surrounding ground. The common approach to design buried concrete pipes is based on an empirical method called the Indirect Design Method, which uses the laboratory capacity of the buried pipe linked to the field capacity using an empirical factor known as the bedding factor. However, limited published studies have investigated this bedding factor or tried to improve the current bedding factor values. Therefore, this study investigated the bending moment and bedding factor for concrete pipes under soil loads by conducting a parametric study investigating the effect of the installation condition, pipe diameter, pipe thickness and backfill height. A validated finite element model has been used for this purpose. The bedding factors obtained from the analyses have been compared with the bedding factors currently adopted by the AASHTO and British Standard (BS) design standards. The results showed that the BS design standard is conservative. In addition, the AASHTO design standard has been shown not to be safe for pipes with a diameter of 0.3 m and becomes more conservative as the diameter increases or the installation quality decreases. Therefore, new bedding factor models have been proposed using the results of the finite element modelling utilising an evolutionary polynomial regression (EPR) method. The paper demonstrates that the new models could be used for the economic and robust design of concrete pipes. The proposed models in this paper have the potential to significantly reduce the costs involved in either construction or maintenance of buried concrete pipes.
An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications

Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In this work, we give an information-theoretic analysis on the generalization error and excess risk of transfer learning algorithms, following a line of work initiated by Russo and Xu. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence $D(\mu||\mu')$ plays an important role in the characterizations where $\mu$ and $\mu'$ denote the distribution of the training data and the testing test, respectively. Specifically, we provide generalization error upper bounds for the empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase. We further apply the analysis to approximated ERM methods such as the Gibbs algorithm and the stochastic gradient descent method. We then generalize the mutual information bound with $\phi$-divergence and Wasserstein distance. These generalizations lead to tighter bounds and can handle the case when $\mu$ is not absolutely continuous with respect to $\mu'$. Furthermore, we apply a new set of techniques to obtain an alternative upper bound which gives a fast (and optimal) learning rate for some learning problems. Finally, inspired by the derived bounds, we propose the InfoBoost algorithm in which the importance weights for source and target data are adjusted adaptively in accordance to information measures. The empirical results show the effectiveness of the proposed algorithm.
Influence of Human Resource Planning Practices on Lecturers’ Performance in Kenyan Public Universities

Kenya Vision 2030 envisions provision of globally competitive quality education, training and research to citizens. Despite public universities’ contribution to this mandate, stakeholders such as World Bank, Commission for University Education, employers and scholars fault the quality and relevance of some academic programmes. The decline in quality is attributed to increased workload, reduced rigour in recruitment and promotion criteria, inability to attract and retain quality staff, and lecturers’ alleged incompetence. Previous studies relating human resource planning (HRP) practices to employee performance (EP) do not use descriptors of HRP like business plans, scenario planning, demand and supply forecasting, forecasting future requirements and action planning in entirety in addition to exhibiting mixed findings. This study intended to establish influence of HRP on lecturers’ performance in Kenyan public universities. It is anchored on Simon’s Decision Making Theory and supported by Barney’s Resource Based View; Adam Smith’s Human Capital Theory; and Herzberg’s Two-Factor Theory and implemented using descriptive cross-sectional survey research design. Target population comprised 1,653 lecturers in 14 chartered public universities established between 2009 and 2019. Multi-stage purposeful sampling technique was adopted to finally take a census of 158 academic heads of departments (lecturers) from 13 out of the 14 universities, having used 20 in piloting. Public universities account for 72% of the total university teaching staff hence the interest. Primary data were collected using semi-structured questionnaires while secondary data obtained from relevant secondary sources. Cronbach’s alpha coefficient was used to examine the internal consistency at 0.796 ( SD = 0.067). While content validity was ascertained by reviewing literature within the domain of study concepts and corroborated by a panel of experts, Pearson’s r was used to measure construct validity. Data were analyzed using Pearson’s r and regression analysis. The correlation between human resource planning (HRP) practice and lecturers’ performance was established to be positive and significant (r =.528, p =.000) HRP practice accounted for 38.0% (Adjusted R 2 =.380) of the variation in the lecturers’ performance in Kenyan public universities implying that it can predict their performance. The findings are useful to the management of public universities, HRM practitioners, researchers, organizational behaviourists and
Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout

Designing an automatic checkout system for retail stores at the human level accuracy is challenging due to similar appearance products and their various poses. This paper addresses the problem by proposing a method with a two-stage pipeline. The ﬁrst stage detects class-agnostic items, and the second one is dedicated to classify product cate-gories. We also track the objects across video frames to avoid duplicated counting. One major challenge is the domain gap because the models are trained on synthetic data but tested on the real images. To reduce the error gap, we adopt domain generalization methods for the ﬁrst-stage detector. In addition, model ensemble is used to enhance the robustness of the 2nd-stage classiﬁer. The method is evaluated on the AI City challenge 2022 – Track 4 and gets the F1 score 40% on the test A set. Code is released at the link.
PseudoClick: Interactive Image Segmentation with Click Imitation

The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i.e., by a minimal number of user clicks. Existing methods require users to provide all the clicks: by first inspecting the segmentation mask and then providing points on mislabeled regions, iteratively. We ask the question: can our model directly predict where to click, so as to further reduce the user interaction cost? To this end, we propose {\PseudoClick}, a generic framework that enables existing segmentation networks to propose candidate next clicks. These automatically generated clicks, termed pseudo clicks in this work, serve as an imitation of human clicks to refine the segmentation mask.
PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners

Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature. To fill this gap, we propose the first PAC-Bayesian adaptation bounds for multiclass learners. We facilitate practical use of our bounds by also proposing the first approximation techniques for the multiclass distribution divergences we consider. For divergences dependent on a Gibbs predictor, we propose additional PAC-Bayesian adaptation bounds which remove the need for inefficient Monte-Carlo estimation. Empirically, we test the efficacy of our proposed approximation techniques as well as some novel design-concepts which we include in our bounds. Finally, we apply our bounds to analyze a common adaptation algorithm that uses neural networks.
The hp-FEM applied to the Helmholtz equation with PML truncation does not suffer from the pollution effect

We consider approximation of the variable-coefficient Helmholtz equation in the exterior of a Dirichlet obstacle using perfectly-matched-layer (PML) truncation; it is well known that this approximation is exponentially accurate in the PML width and the scaling angle, and the approximation was recently proved to be exponentially accurate in the wavenumber $k$ in [Galkowski, Lafontaine, Spence, 2021]. We show that the $hp$-FEM applied to this problem does not suffer from the pollution effect, in that there exist $C_1,C_2>0$ such that if $hk/p\leq C_1$ and $p \geq C_2 \log k$ then the Galerkin solutions are quasioptimal (with constant independent of $k$), under the following two conditions (i) the solution operator of the original Helmholtz problem is polynomially bounded in $k$ (which occurs for"most"$k$ by [Lafontaine, Spence, Wunsch, 2021]), and (ii) either there is no obstacle and the coefficients are smooth or the obstacle is analytic and the coefficients are analytic in a neighbourhood of the obstacle and smooth elsewhere. This $hp$-FEM result is obtained via a decomposition of the PML solution into"high-"and"low-frequency"components, analogous to the decomposition for the original Helmholtz solution recently proved in [Galkowski, Lafontaine, Spence, Wunsch, 2022]. The decomposition is obtained using tools from semiclassical analysis (i.e., the PDE techniques specifically designed for studying Helmholtz problems with large $k$).
Adversarial Robustness Assessment of NeuroEvolution Approaches

NeuroEvolution automates the generation of Artificial Neural Networks through the application of techniques from Evolutionary Computation. The main goal of these approaches is to build models that maximize predictive performance, some-times with an additional objective of minimizing computational complexity. Although the evolved models achieve competitive results performance-wise, their robustness to adversarial examples, which becomes a concern in security-critical scenarios, has received limited attention. In this paper, we evaluate the adversarial robustness of models found by two prominent Neu-roEvolution approaches on the CIFAR-10 image classification task: DENSER and NSGA-Net. Since the models are publicly available, we consider white-box untargeted attacks, where the perturbations are bounded by either the $L_{2}$ or the $L_{\infty}$ -norm. Similarly to manually-designed networks, our results show that when the evolved models are attacked with iterative methods, their accuracy usually drops to, or close to, zero under both distance metrics. The DENSER model is an exception to this trend, showing some resistance under the $L_{2}$ threat model, where its accuracy only drops from 93.70% to 18.10% even with iterative attacks. Additionally, we analyzed the impact of pre-processing applied to the data before the first layer of the network. Our observations suggest that some of these techniques can exacerbate the perturbations added to the original inputs, potentially harming robustness. Thus, this choice should not be neglected when automatically designing networks for applications where adversarial attacks are prone to occur.
Twin Identification over Viewpoint Change: A Deep Convolutional Neural Network Surpasses Humans

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N = 87) viewed pairs of face images of three types: same-identity, general imposters (different identities from similar demographic groups), and twin imposters (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45° profile, and frontal to 90° profile. Accuracy for discriminating matched-identity pairs from twin-imposter pairs and general-imposter pairs was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r = 0.38 to r = 0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.
Optical Field Recovery in Jones Space

Optical full-field recovery allows for fiber impairments compensation such as chromatic dispersion and polarization mode dispersion (PMD) in the digital signal processing. For cost-sensitive short-reach optical networks, some advanced single-polarization (SP) optical field recovery schemes are recently proposed to avoid chromatic dispersion-induced power fading effect and improve the spectral efficiency for larger potential capacity. Polarization division multiplexing (PDM) can further double both the spectral efficiency and the system capacity of these SP carrier-assisted direct detection (DD) schemes. However, the so-called polarization fading phenomenon induced by random polarization rotation is a fundamental obstacle that prevents SP carrier-assisted DD systems from polarization diversity. In this paper, we propose a receiver of Jones-space field recovery (JSFR) to realize polarization diversity with SP carrier-assisted DD schemes. Different receiver structures and simplified recovery procedures for JSFR are explored theoretically. The proposed JSFR pushes SP DD schemes towards PDM without an extra optical signal-to-noise ratio (OSNR) penalty. In addition, the JSFR shows good tolerance to PMD since the optical field recovery is conducted before polarization recovery. In the concept-of-proof experiment, we demonstrate 448-Gb/s reception over 80-km single-mode fiber using the proposed JSFR based on 2 × 2 couplers. Furthermore, we qualitatively compare the optical field recovery in Jones space and Stokes space in terms of the modulation dimension and hardware complexity.
Precise determination of pair interactions from pair statistics of many-body systems in and out of equilibrium.

The determination of the pair potential v(r) that accurately yields an equilibrium state at positive temperature T with a prescribed pair correlation function g_{2}(r) or corresponding structure factor S(k) in d-dimensional Euclidean space R^{d} is an outstanding inverse statistical mechanics problem with far-reaching implications. Recently, Zhang and Torquato [Phys. Rev. E 101, 032124 (2020)2470-004510.1103/PhysRevE.101. 032124] conjectured that any realizable g_{2}(r) or S(k) corresponding to a translationally invariant nonequilibrium system can be attained by a classical equilibrium ensemble involving only (up to) effective pair interactions. Testing this conjecture for nonequilibrium systems as well as for nontrivial equilibrium states requires improved inverse methodologies. We have devised an optimization algorithm to precisely determine effective pair potentials that correspond to pair statistics of general translationally invariant disordered many-body equilibrium or nonequilibrium systems at positive temperatures. This methodology utilizes a parameterized family of pointwise basis functions for the potential function whose initial form is informed by small-, intermediate- and large-distance behaviors dictated by statistical-mechanical theory. Subsequently, a nonlinear optimization technique is utilized to minimize an objective function that incorporates both the target pair correlation function g_{2}(r) and structure factor S(k) so that the small intermediate- and large-distance correlations are very accurately captured. To illustrate the versatility and power of our methodology, we accurately determine the effective pair interactions of the following four diverse target systems: (1) Lennard-Jones system in the vicinity of its critical point, (2) liquid under the Dzugutov potential, (3) nonequilibrium random sequential addition packing, and (4) a nonequilibrium hyperuniform "cloaked" uniformly randomized lattice. We found that the optimized pair potentials generate corresponding pair statistics that accurately match their corresponding targets with total L_{2}-norm errors that are an order of magnitude smaller than that of previous methods. The results of our investigation lend further support to the Zhang-Torquato conjecture. Furthermore, our algorithm will enable one to probe systems with identical pair statistics but different higher-body statistics, which will shed light on the well-known degeneracy problem of statistical mechanics.
Observations of Magnetic Reconnection and Particle Acceleration Locations in Solar Coronal Jets

We present a multiwavelength analysis of two flare-related jets on 2014 November 13, using data from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA), the Reuven High Energy Solar Spectroscopic Imager (RHESSI), the Hinode/X-ray Telescope (XRT), and the Interface Region Imaging Spectrograph (IRIS). Unlike most coronal jets, where hard X-ray (HXR) emissions are usually observed near the jet base, in these events HXR emissions are found at several locations, including in the corona. We carry out the first differential emission measure analysis that combines both AIA (and XRT, when available) bandpass filter data and RHESSI HXR measurements for coronal jets, and obtain self-consistent results across a wide temperature range and into nonthermal energies. In both events, hot plasma first appears at the jet base, but as the base plasma gradually cools, hot plasma also appears near the jet top. Moreover, nonthermal electrons, while only mildly energetic, are found in multiple HXR locations and contain large amounts of total energy. In particular, the energetic electrons that produce the HXR sources at the jet top are accelerated near the top location, rather than traveling from a reconnection site at the jet base. This means that there is more than one particle acceleration site in each event. Jet velocities are consistent with previous studies, including the upward and downward velocities around ∼200 km s−1 and ∼100 km s−1, respectively, and fast outflows of 400–700 km s−1. We also examine the energy partition in the later event, and find that the nonthermal energy in the accelerated electrons is most significant compared to the other energy forms considered. We discuss the interpretations and provide constraints on the mechanisms for coronal jet formation.