Miracle-dz/UniFAR-FTUs huggingface dataset:
Word Closure-Based Metamorphic Testing for Machine Translation

With the wide application of machine translation, the testing of Machine Translation Systems (MTSs) has attracted much attention. Recent works apply Metamorphic Testing (MT) to address the oracle problem in MTS testing. Existing MT methods for MTS generally follow the workflow of input transformation and output relation comparison, which generates a follow-up input sentence by mutating the source input and compares the source and follow-up output translations to detect translation errors, respectively. These methods use various input transformations to generate test case pairs and have successfully triggered numerous translation errors. However, they have limitations in performing fine-grained and rigorous output relation comparison and thus may report false alarms and miss true errors. In this paper, we propose a word closure-based output comparison method to address the limitations of the existing MTS MT methods. Specifically, we first build a new comparison unit called word closure, where each closure includes a group of correlated input and output words in the test case pair. Word closures suggest the linkages between the appropriate fragment in the source output translation and its counterpart in the follow-up output for comparison. Next, we compare the semantics on the level of word closure to identify the translation errors. In this way, we perform a fine-grained and rigorous semantic comparison for the outputs and thus realize more effective violation identification. We evaluate our method with the test cases generated by five existing input transformations and translation outputs from three popular MTSs. Results show that our method significantly outperforms the existing works in violation identification by improving the precision and recall and achieving an average increase of 29.8% in F1 score. It also helps to increase the F1 score of translation error localization by 35.9%.
Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques

We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity, which therefore yield multifidelity Monte Carlo estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.
An urban ecological synthesis of socio-ecological systems dynamics in Potchefstroom, South Africa

ii Uittreksel . iv Acknowledgements . vi Preface . vii General Introduction . 1 Paper 1: Development and current directions of urban ecological research in South Africa . 9 Supplementary Information . 62 Paper 2: Landscape history, time-lags and drivers of change: remnant natural grasslands in Potchefstroom, South Africa . 109 Supplementary Information . 132 Paper 3: The impact of urbanization on temperate natural grasslands, quantifying 17 years of change in Potchefstroom, South Africa . 136 Supplementary Information . 155 Paper 4: Local-scale sustainability? Insights from the interface between urban ecological research, socio-economic gradients, public opinion, municipal governance and future planning in Potchefstroom, South Africa . 177 General Conclusions . 204
Topological complexity of spiked random polynomials and finite-rank spherical integrals

We study the annealed complexity of a random Gaussian homogeneous polynomial on the $N$-dimensional unit sphere in the presence of deterministic polynomials that depend on fixed unit vectors and external parameters. In particular, we establish variational formulas for the exponential asymptotics of the average number of total critical points and of local maxima. This is obtained through the Kac-Rice formula and the determinant asymptotics of a finite-rank perturbation of a Gaussian Wigner matrix. More precisely, the determinant analysis is based on recent advances on finite-rank spherical integrals by [Guionnet, Husson 2022] to study the large deviations of multi-rank spiked Gaussian Wigner matrices. The analysis of the variational problem identifies a topological phase transition. There is an exact threshold for the external parameters such that, once exceeded, the complexity function vanishes into new regions in which the critical points are close to the given vectors. Interestingly, these regions also include those where critical points are close to multiple vectors.
Nonequilibrium Dyson equations for strongly coupled light and matter: spin glass formation in multi-mode cavity QED

Light-matter interfaces have now entered a new stage marked by the ability to engineer quantum correlated states under driven-dissipative conditions. To propel this new generation of experiments, we are confronted with the need to model non-unitary many-body dynamics in strongly coupled regimes, by transcending traditional approaches in quantum optics. In this work, we contribute to this program by adapting a functional integral technique, conventionally employed in high-energy physics, in order to derive nonequilibrium Dyson equations for interacting light-matter systems. Our approach is grounded in constructing two-particle irreducible (2PI) effective actions, which provide a non-perturbative and conserving framework for describing quantum evolution at a polynomial cost in time. One of the aims of the article is to offer a pedagogical introduction designed to bridge readers from diverse scientific communities, including those in quantum optics, condensed matter, and high-energy physics. We apply our method to complement the analysis of spin glass formation in the context of frustrated multi-mode cavity quantum electrodynamics, initiated in our accompanying work [H. Hosseinabadi, D. Chang, J. Marino, arXiv:2311.05682]. Finally, we outline the capability of the technique to describe other near-term platforms in many-body quantum optics, and its potential to make predictions for this new class of experiments.
PNG-UNITsims: the response of halo clustering to Primodial Non-Gaussianities as a function of mass

We present the largest full N-body simulation to date with local primordial non-Gaussianities (L-PNG), the \textsc{PNG-UNITsim}. It tracks the evolution of $4096^3$ particles within a periodic box with $L_{\rm box} = 1 \; h^{-1}\,{\rm Gpc}$, leading to a mass resolution of $m_{p} = 1.24\times 10^{9}\; h^{-1}\,M_\odot$. This is enough to resolve galaxies targeted by stage-IV spectroscopic surveys. The \textsc{PNG-UNIT} has \textit{Fixed} initial conditions whose phases are also \textit{Matched} to the pre-existing \textsc{UNIT} simulation. These two features in the simulations reduce our uncertainty significantly so we use 100 \textsc{FastPM} mocks to estimate this reduction. The amplitude of the non-Gaussianities used to set the initial conditions of this new simulation is $f_{\rm NL}^{\rm local} = 100$. In this first study, we use mass selected dark matter haloes from the \textsc{PNG-UNIT} simulation to constrain the local PNG parameters. PNG induce a scale dependent bias, parameterised through \bp or $p$, which might depend on the type of cosmological tracer. Those cases when $p=1$ are referred to as the {\it universality relation}. We measure $p$ as a function of the halo mass. Haloes with masses between $1\times 10^{12}$ and $2\times 10^{13} \, h^{-1} M_\odot$ are well described by the {\it universality relation}. For haloes with masses between $2\times 10^{10}$ and $1\times 10^{12} \, h^{-1} M_\odot$ we find that $p<1$ at $3\sigma$. Combining all the mass bins, we find $p$ consistent with a value of $0.955\pm0.013$, which is $3\sigma$ away from \textit{universality}, as low mass haloes are more numerous. We also study the effect of using priors on $p$ when constraining $f_{\rm NL}$. Using the values we obtain for $b_\phi$ as priors, we forecast that a DESI-like (stage-IV) survey will be able to constrain $f_{\rm NL}$ better than if the universality relation is assumed.
Cavity-resonated detection of spin polarization in a microfabricated atomic vapor cell

We demonstrate continuous Pound-Drever-Hall (PDH) nondestructive monitoring of the electron spin polarization of an atomic vapor in a microfabricated vapor cell within an optical resonator. The two-chamber silicon and glass cell contains $^{87}$Rb and 1.3 amagat of N$_{2}$ buffer gas, and is placed within a planar optical resonator formed by two mirrors with dichroic dielectric coatings to resonantly enhance the coupling to phase-modulated probe light near the D$_2$ line at 780 nm. We describe the theory of signal generation in this system, including the spin-dependent complex refractive index, cavity optical transfer functions, and PDH signal response to spin polarization. We observe cavity transmission and PDH signals across $\approx 200$ GHz of detuning around the atomic resonance line. By resonant optical pumping on the 795 nm D$_1$ line, we observe spin-dependent cavity line shifts, in good agreement with theory. We use the saturation of the line shift vs. optical pumping power to calibrate the number density and efficiency of the optical pumping. In the unresolved sideband regime, we observe quantum-noise-limited PDH readout of the spin polarization density, with a flat noise floor of $9 \times 10^9$ spins cm$^{-3}$ Hz$^{-1/2}$ for frequencies above 700 Hz. We note possible extensions of the technique.
Tuning for fluidity using fluctuations in biological tissue models

How do biological systems tune emergent properties at the scale of tissues? One class of such emergent behaviors, important to biological functions such as body-axis elongation, involves rigidity transitions, in which a tissue changes from a fluid-like state to a solid-like state or vice versa. Here, we explore the idea that tissues might tune ``learning degrees of freedom"to affect this emergent behavior. We study tissue fluidity in the 2D vertex model, using the vertex model energy as a learning cost function and the cell stiffnesses, target shapes, and target areas as sets of learning degrees of freedom that can be varied to minimize the energy. We show that the rigidity transition is unaffected when cell stiffnesses are treated as learning degrees of freedom. When preferred perimeters or areas are treated as learning degrees of freedom, however, energy minimization introduces spatial correlations in target cell shapes or areas that shift the rigidity transition. There is an optimal heterogeneity of target cell shapes or areas to enable learning. These observations suggest that biological tissues can learn tissue-scale behaviors by tuning their individual cell properties.
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit

Information Pursuit (IP) is an explainable prediction algorithm that greedily selects a sequence of interpretable queries about the data in order of information gain, updating its posterior at each step based on observed query-answer pairs. The standard paradigm uses hand-crafted dictionaries of potential data queries curated by a domain expert or a large language model after a human prompt. However, in practice, hand-crafted dictionaries are limited by the expertise of the curator and the heuristics of prompt engineering. This paper introduces a novel approach: learning a dictionary of interpretable queries directly from the dataset. Our query dictionary learning problem is formulated as an optimization problem by augmenting IP's variational formulation with learnable dictionary parameters. To formulate learnable and interpretable queries, we leverage the latent space of large vision and language models like CLIP. To solve the optimization problem, we propose a new query dictionary learning algorithm inspired by classical sparse dictionary learning. Our experiments demonstrate that learned dictionaries significantly outperform hand-crafted dictionaries generated with large language models.
SUSY QED with Lorentz-asymmetric fermionic matter and a glance at the electron's EDM

This contribution sets out to pursue the investigation of a supersymmetric electrodynamics model with Lorentz-symmetry violation (LSV) manifested by a space-time unbalance in the propagation of the fermionic charged matter. Despite violation of Lorentz symmetry, the supersymmetry algebra is kept untouched. We then adopt a superspace approach to build up an $\mathcal{N}=1$-supersymmetric Abelian gauge theory in presence of a Lorentz-violating background supermultiplet that accommodates the space-time asymmetry parameter of the charged matter. We describe, in this scenario, how the particular Lorentz-symmetry breaking, brought about by the fermionic matter, affects its (matter) scalar partners and the photon/photino that minimally couple to charged matter. From the (modified) Dirac, Klein-Gordon and Maxwell field equations, we work out the corresponding dispersion relations to inspect and discuss the physical effects of the LSV Majorana fermion condensates that naturally emerge from the background supermultiplet. Finally, we target efforts to investigate the Gordon decomposition of the charged lepton electromagnetic current. This is carried out by iterating the (fermion and scalar) matter field equations, which points to an effective contribution to the electron's electric dipole moment (EDM). This result allows us to attain an estimate of the pseudo-vector condensate of the (LSV) Majorana background fermion.
Hopf orbits and the first ECH capacity

We consider dynamically convex (star-shaped) domains in a symplectic vector space of dimension 4. For such a domain, a"Hopf orbit"is a closed characteristic in the boundary which is unknotted and has self-linking number -1. We show that the minimum action among Hopf orbits exists (without any nondegeneracy hypothesis) and defines a symplectic capacity for dynamically convex domains in four dimensions. We further show that this capacity agrees with the first ECH capacity for dynamically convex domains. Combined with a result of Edtmair, this implies that for dynamically convex domains in four dimensions, the first ECH capacity agrees with the cylinder capacity.
KGLens: A Parameterized Knowledge Graph Solution to Assess What an LLM Does and Doesn't Know

Measuring the alignment between a Knowledge Graph (KG) and Large Language Models (LLMs) is an effective method to assess the factualness and identify the knowledge blind spots of LLMs. However, this approach encounters two primary challenges including the translation of KGs into natural language and the efficient evaluation of these extensive and complex structures. In this paper, we present KGLens--a novel framework aimed at measuring the alignment between KGs and LLMs, and pinpointing the LLMs' knowledge deficiencies relative to KGs. KGLens features a graph-guided question generator for converting KGs into natural language, along with a carefully designed sampling strategy based on parameterized KG structure to expedite KG traversal. We conducted experiments using three domain-specific KGs from Wikidata, which comprise over 19,000 edges, 700 relations, and 21,000 entities. Our analysis across eight LLMs reveals that KGLens not only evaluates the factual accuracy of LLMs more rapidly but also delivers in-depth analyses on topics, temporal dynamics, and relationships. Furthermore, human evaluation results indicate that KGLens can assess LLMs with a level of accuracy nearly equivalent to that of human annotators, achieving 95.7% of the accuracy rate.
Differential graded enhancements of singularity categories

The singularity category of a ring detects the homological singularity of the given ring, and appears in many different contexts. We describe two different dg enhancements of the singularity category, that is, the Vogel dg category and the singular Yoneda dg category. These two dg enhancements turn out to be quasi-equivalent. We report some progress on the Singular Presilting Conjecture.
Emergence of In-Context Reinforcement Learning from Noise Distillation

Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks. Current in-context RL methods are limited by their strict requirements for data, which needs to be generated by RL agents or labeled with actions from an optimal policy. In order to address this prevalent problem, we propose AD$^\varepsilon$, a new data acquisition approach that enables in-context Reinforcement Learning from noise-induced curriculum. We show that it is viable to construct a synthetic noise injection curriculum which helps to obtain learning histories. Moreover, we experimentally demonstrate that it is possible to alleviate the need for generation using optimal policies, with in-context RL still able to outperform the best suboptimal policy in a learning dataset by a 2x margin.
VectorTalker: SVG Talking Face Generation with Progressive Vectorisation

High-fidelity and efficient audio-driven talking head generation has been a key research topic in computer graphics and computer vision. In this work, we study vector image based audio-driven talking head generation. Compared with directly animating the raster image that most widely used in existing works, vector image enjoys its excellent scalability being used for many applications. There are two main challenges for vector image based talking head generation: the high-quality vector image reconstruction w.r. t. the source portrait image and the vivid animation w.r. t. the audio signal. To address these, we propose a novel scalable vector graphic reconstruction and animation method, dubbed VectorTalker. Specifically, for the highfidelity reconstruction, VectorTalker hierarchically reconstructs the vector image in a coarse-to-fine manner. For the vivid audio-driven facial animation, we propose to use facial landmarks as intermediate motion representation and propose an efficient landmark-driven vector image deformation module. Our approach can handle various styles of portrait images within a unified framework, including Japanese manga, cartoon, and photorealistic images. We conduct extensive quantitative and qualitative evaluations and the experimental results demonstrate the superiority of VectorTalker in both vector graphic reconstruction and audio-driven animation.
Provably Convergent Federated Trilevel Learning

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach; 2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes u-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed u-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the u-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by O(1/ϵ²). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%.
Pitch-axis supermanoeuvrability in a biomimetic morphing-wing UAV

Birds and bats are extraordinarily adept flyers: whether in hunting prey, or evading predators, agility and manoeuvrability in flight are vital. In conventional high-performance aircraft, approaches to extreme manoeuvrability, such as post-stall manoeuvring, have often focused on thrust-vectoring technology - the domain of classical supermanoeuvrability - rather than biomimicry. In this work, however, we show that these approaches are not incompatible: biomimetic wing morphing is an avenue both to classical supermanoeuvrability, and to new forms of biologically-inspired supermanoeuvrability. Using a flight simulator equipped with a multibody model of lifting surface motion and a Goman-Khrabrov dynamic stall model for all lifting surfaces, we demonstrate the capability of a biomimetic morphing-wing unmanned aerial vehicles (UAV) for two key forms of supermanoeuvrability: the Pugachev cobra, and ballistic transition. Conclusions are drawn as to the mechanism by which these manoeuvres can be performed, and their feasibility in practical biomimetic unmanned aerial vehicle (UAV). These conclusions have wide relevance to both the design of supermanoeuvrable UAVs, and the study of biological flight dynamics across species.
Differenzierung zwischen Alzheimer-Demenz und Depression im Alter: Übersicht und Konzeptionierung eines Angehörigen-Fragebogens

Zusammenfassung Hintergrund Die Differentialdiagnostik von Alzheimer-Demenz und Major Depression im Alter ist aufgrund überlappender und sich ähnelnder Symptome erschwert. Mit Hilfe eines Angehörigenfragebogens könnte die Diagnostik deutlich vereinfacht werden. Material und Methode Anhand einer Auswahl von klinischen Kriterien, einer Expertenbefragung sowie der statistische Auswertung der Expertenbefragung wird ein Fragebogen für Angehörige konzeptioniert. Ergebnisse 15 von 48 vorgeschlagenen klinischen Kriterien werden von Neuropsychologen für die Differentialdiagnostik als geeignet bewertet und in dem Angehörigen-Fragebogen (ADAD) zusammengefasst. Erste Ergebnisse der Validierung des Fragebogens geben ebenfalls einen Hinweis auf die Tauglichkeit der klinischen Kriterien. Schlussfolgerungen Der ADAD-Fragebogen könnte in der klinischen Praxis die Differentialdiagnostik von Demenz und Altersdepression schnell und einfach ergänzen, muss jedoch noch in einer größeren Patientengruppe evaluiert werden. Schlüsselwörter