Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite-size behavior in phase transitions and scaling in the progress of an epidemic (preprint)

Analytical descriptions of patterns concerning spread and fatality during an epidemic, covering natural as well as restriction periods, are important for reducing damage. We employ a scaling model to investigate this aspect in the real data of COVID-19. It transpires from our study that the statistics of fatality in many geographical regions follow unique behavior, implying the existence of universality. Via Monte Carlo simulations in finite boxes, we also present results on coarsening dynamics in a generic model that is used frequently for the studies of phase transitions in various condensed matter systems. It is shown that the overall growth, in a large set of regions, concerning disease dynamics and that during phase transitions in a broad class of finite systems are closely related to each other and can be described via a single and compact mathematical function, thereby providing a new angle to the studies of the former. In the case of disease we have demonstrated that the employed scaling model has predictive capability, even for periods with complex social restrictions. These results are of much general epidemiological relevance.
MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain

Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. Prior work has demonstrated the efficacy of information extraction models in various scientific areas. Recently, several datasets have been released for the yet understudied materials science domain. However, these datasets focus on sub-problems such as parsing synthesis procedures or on sub-domains, e.g., solid oxide fuel cells. In this resource paper, we present MuLMS, a new dataset of 50 open-access articles, spanning seven sub-domains of materials science. The corpus has been annotated by domain experts with several layers ranging from named entities over relations to frame structures. We present competitive neural models for all tasks and demonstrate that multi-task training with existing related resources leads to benefits.
On Adaptive confidence Ellipsoids for sparse high dimensional linear models

In high-dimensional linear models the problem of constructing adaptive confidence sets for the full parameter is known to be generally impossible. We propose re-weighted loss functions under which constructing fully adaptive confidence sets for the parameter is shown to be possible. We give necessary and sufficient conditions on the weights for adaptive confidence sets to exist, and exhibit a concrete rate-optimal procedure in the feasible regime.
The Quantum Monadology

The modern theory of functional programming languages uses monads for encoding computational side-effects and side-contexts, beyond bare-bone program logic. Even though quantum computing is intrinsically side-effectful (as in quantum measurement) and context-dependent (as on mixed ancillary states), little of this monadic paradigm has previously been brought to bear on quantum programming languages. Here we systematically analyze the (co)monads on categories of parameterized module spectra which are induced by Grothendieck's"motivic yoga of operations"-- for the present purpose specialized to HC-modules and further to set-indexed complex vector spaces. Interpreting an indexed vector space as a collection of alternative possible quantum state spaces parameterized by quantum measurement results, as familiar from Proto-Quipper-semantics, we find that these (co)monads provide a comprehensive natural language for functional quantum programming with classical control and with"dynamic lifting"of quantum measurement results back into classical contexts. We close by indicating a domain-specific quantum programming language (QS) expressing these monadic quantum effects in transparent do-notation, embeddable into the recently constructed Linear Homotopy Type Theory (LHoTT) which interprets into parameterized module spectra. Once embedded into LHoTT, this should make for formally verifiable universal quantum programming with linear quantum types, classical control, dynamic lifting, and notably also with topological effects.
Graph Deep Learning for Time Series Forecasting

Graph-based deep learning methods have become popular tools to process collections of correlated time series. Differently from traditional multivariate forecasting methods, neural graph-based predictors take advantage of pairwise relationships by conditioning forecasts on a (possibly dynamic) graph spanning the time series collection. The conditioning can take the form of an architectural inductive bias on the neural forecasting architecture, resulting in a family of deep learning models called spatiotemporal graph neural networks. Such relational inductive biases enable the training of global forecasting models on large time-series collections, while at the same time localizing predictions w.r. t. each element in the set (i.e., graph nodes) by accounting for local correlations among them (i.e., graph edges). Indeed, recent theoretical and practical advances in graph neural networks and deep learning for time series forecasting make the adoption of such processing frameworks appealing and timely. However, most of the studies in the literature focus on proposing variations of existing neural architectures by taking advantage of modern deep learning practices, while foundational and methodological aspects have not been subject to systematic investigation. To fill the gap, this paper aims to introduce a comprehensive methodological framework that formalizes the forecasting problem and provides design principles for graph-based predictive models and methods to assess their performance. At the same time, together with an overview of the field, we provide design guidelines, recommendations, and best practices, as well as an in-depth discussion of open challenges and future research directions.
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach

We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise~\citep{tsybakov2004optimal} under structured unlabeled data distributions. Inspired by~\cite{diakonikolas2020learning}, we prove that any approximate first-order stationary point of a smooth nonconvex loss function yields a halfspace with a low excess error guarantee. In light of the above structural result, we design a nonconvex optimization-based algorithm with a label complexity of $\tilde{O}(d (\frac{1}{\epsilon})^{\frac{8-6\alpha}{3\alpha-1}})$\footnote{In the main body of this work, we use $\tilde{O}(\cdot), \tilde{\Theta}(\cdot)$ to hide factors of the form $\polylog(d, \frac{1}{\epsilon}, \frac{1}{\delta})$}, under the assumption that the Tsybakov noise parameter $\alpha \in (\frac13, 1]$, which narrows down the gap between the label complexities of the previously known efficient passive or active algorithms~\citep{diakonikolas2020polynomial,zhang2021improved} and the information-theoretic lower bound in this setting.
Region-controlled Style Transfer

Image style transfer is a challenging task in computational vision. Existing algorithms transfer the color and texture of style images by controlling the neural network's feature layers. However, they fail to control the strength of textures in different regions of the content image. To address this issue, we propose a training method that uses a loss function to constrain the style intensity in different regions. This method guides the transfer strength of style features in different regions based on the gradient relationship between style and content images. Additionally, we introduce a novel feature fusion method that linearly transforms content features to resemble style features while preserving their semantic relationships. Extensive experiments have demonstrated the effectiveness of our proposed approach.
Complexity of Puiseux solutions of differential and $q$-difference equations of order and degree one

We relate the complexity of both differential and $q$-difference equations of order one and degree one and their solutions. Our point of view is to show that if the solutions are complicated, the initial equation is complicated too. In this spirit, we bound from below an invariant of the differential or $q$-difference equation, the height of its Newton polygon, in terms of the characteristic factors of a solution. The differential and the $q$-difference cases are treated in a unified way.
Mapping the Galactic Disk with the LAMOST and Gaia Red Clump Sample. VIII. Mapping the Kinematics of the Galactic Disk Using Mono-age and Mono-abundance Stellar Populations

We present a comprehensive study of the kinematic properties of the different Galactic disk populations, as defined by the chemical abundance ratios and stellar ages, across a large disk volume (4.5 ≤ R ≤ 15.0 kpc and ∣Z∣ ≤ 3.0 kpc), by using the LAMOST-Gaia red clump sample stars. We determine the median velocities for various spatial and population bins, finding large-scale bulk motions; for example, the wave-like behavior in radial velocity, the north–south discrepancy in azimuthal velocity and the warp signal in vertical velocity, and the amplitudes and spatial dependences of these bulk motions show significant variations for different mono-age and mono-abundance populations. The global spatial behaviors of the velocity dispersions clearly show a signal of spiral arms and a signal of the disk perturbation event within 4 Gyr, as well as disk flaring in the outer region (i.e., R ≥ 12 kpc), mostly for young or alpha-poor stellar populations. Our detailed measurements of age/[α/Fe]-velocity dispersion relations for different disk volumes indicate that young/α-poor populations are likely to originate from dynamic heating by both giant molecular clouds and spiral arms, while old/α-enhanced populations require an obvious contribution from other heating mechanisms, such as merger and accretion, or are born in the chaotic mergers of gas-rich systems and/or turbulent interstellar medium.
GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection

Geometry plays a significant role in monocular 3D object detection. It can be used to estimate object depth by using the perspective projection between object's physical size and 2D projection in the image plane, which can introduce mathematical priors into deep models. However, this projection process also introduces error amplification, where the error of the estimated height is amplified and reflected into the projected depth. It leads to unreliable depth inferences and also impairs training stability. To tackle this problem, we propose a novel Geometry Uncertainty Propagation Network (GUPNet++) by modeling geometry projection in a probabilistic manner. This ensures depth predictions are well-bounded and associated with a reasonable uncertainty. The significance of introducing such geometric uncertainty is two-fold: (1). It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning. (2). It can be derived to a highly reliable confidence to indicate the quality of the 3D detection result, enabling more reliable detection inference. Experiments show that the proposed approach not only obtains (state-of-the-art) SOTA performance in image-based monocular 3D detection but also demonstrates superiority in efficacy with a simplified framework.
Linear MIM-width of the Square of Trees

Graph parameters measure the amount of structure (or lack thereof) in a graph that makes it amenable to being decomposed in a way that facilitates dynamic programming. Graph decompositions and their associated parameters are important both in practice (as a tool for designing robust algorithms for NP-hard problems) and in theory (relating large classes of problems to the graphs on which they are solvable in polynomial time). Linear MIM-width is a variant of the graph parameter MIM-width, introduced by Vatshelle. MIM-width is a parameter that is constant for many classes of graphs. Most graph classes which have been shown to have constant MIM-width also have constant linear MIM-width. However, computing the (linear) MIM-width of graphs, or showing that it is hard, has proven to be a huge challenge. To date, the only graph class with unbounded linear MIM-width, whose linear MIM-width can be computed in polynomial time, is the trees. In this follow-up, we show that for any tree $T$ with linear MIM-width $k$, the linear MIM-width of its square $T^2$ always lies between $k$ and $2k$, and that these bounds are tight for all $k$.
Cross-view Self-localization from Synthesized Scene-graphs

Cross-view self-localization is a challenging scenario of visual place recognition in which database images are provided from sparse viewpoints. Recently, an approach for synthesizing database images from unseen viewpoints using NeRF (Neural Radiance Fields) technology has emerged with impressive performance. However, synthesized images provided by these techniques are often of lower quality than the original images, and furthermore they significantly increase the storage cost of the database. In this study, we explore a new hybrid scene model that combines the advantages of view-invariant appearance features computed from raw images and view-dependent spatial-semantic features computed from synthesized images. These two types of features are then fused into scene graphs, and compressively learned and recognized by a graph neural network. The effectiveness of the proposed method was verified using a novel cross-view self-localization dataset with many unseen views generated using a photorealistic Habitat simulator.
Business va'avanua: cultural hybridisation and indigenous entrepreneurship in the Bouma National Heritage Park, Fiji : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Social Anthropology, Massey University, Palmerston North, New Zealand

This thesis explores the ways community-based ecotourism development in the Boumā National Heritage Park was negotiated at the nexus of Western entrepreneurship and the vanua, an indigenous epistemology. In 1990, the Boumā tribe of Taveuni, Fiji established the Boumā National Heritage Park. A growing dependence on the market economy and a desire to find an economic alternative to commercial logging on their communally-tenured land, led to their decision to approach the New Zealand government for assistance to establish the Park. The four villages involved have since developed their own community-based ecotourism enterprises. Despite receiving first place in a British Airways Tourism for Tomorrow Award category in 2002, there was a growing sense of social dysfunction in Boumā during the research period. According to my participants, this was partly due to the community-based ecotourism development process which had paid little attention to the vanua. Largely through talanoa as discussion, the people of Boumā have become increasingly conscious of references to the vanua values in their own evaluation and management of the projects. This thesis draws on Tim Ingold’s (2000) ‘taskscapes’ as, like the vanua, they relationally link humans with other elements of the environment within their landscape. This contrasts with a common Western epistemological approach of treating humans as independent of other cosmological and physical elements and as positioned against the landscape. Largely due to its communal nature, it may be argued that the vanua is incompatible with values associated with Boumā’s Western, capitalist-based ecotourism models. However, in this thesis I argue that despite numerous obstacles, the Boumā National Heritage Park is one example of a tribe’s endeavours to culturally hybridise the vanua with entrepreneurship to create a locally meaningful form of indigenous entrepreneurship for the wellbeing of its people. The Boumā people call this hybrid ‘business va’avanua’. Informal talanoa is presented in this thesis as a potential tool for political agency in negotiating issues surrounding community-based ecotourism and business va’avanua.
Mott transition and abnormal instability of electronic structure in FeSe

FeSe has been extensively explored as a quantum material, primarily due to the observed highest superconducting transition temperature among Fe-based unconventional superconductors. Nonetheless, the electronic structure and the electron correlations responsible for the remarkable diversity of physical properties in FeSe remain elusive. We undertook a comprehensive investigation of the electronic structure of FeSe, known as a Hund metal, and found that it is not uniquely defined. Through accounting for all two-particle irreducible diagrams constructed from electron Green's function $G$ and screened Coulomb interaction $W$ in a self-consistent manner, a Mott-insulator phase of FeSe is unveiled. The metal-insulator transition is driven by the strong on-site Coulomb interaction in its paramagnetic phase, accompanied by the weakening of both local and non-local screening effects on the Fe-3$d$ orbitals. Our results suggest that Mott physics may play a pivotal role in shaping the electronic, optical, and superconducting properties of monolayer or nanostructured FeSe.
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation

Representation Learning on Knowledge Graphs (KGs) is essential for downstream tasks. The dominant approach, KG Embedding (KGE), represents entities with independent vectors and faces the scalability challenge. Recent studies propose an alternative way for parameter efficiency, which represents entities by composing entity-corresponding codewords matched from predefined small-scale codebooks. We refer to the process of obtaining corresponding codewords of each entity as entity quantization, for which previous works have designed complicated strategies. Surprisingly, this paper shows that simple random entity quantization can achieve similar results to current strategies. We analyze this phenomenon and reveal that entity codes, the quantization outcomes for expressing entities, have higher entropy at the code level and Jaccard distance at the codeword level under random entity quantization. Therefore, different entities become more easily distinguished, facilitating effective KG representation. The above results show that current quantization strategies are not critical for KG representation, and there is still room for improvement in entity distinguishability beyond current strategies. The code to reproduce our results is available at https://github.com/JiaangL/RandomQuantization.
A Unified, Scalable Framework for Neural Population Decoding

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale.
Singlet fission spin dynamics from molecular structure: a modular computational pipeline

Singlet fission, which has applications in areas ranging form solar energy to quantum information, relies critically on transitions within a multi-spin manifold. These transitions are driven by fluctuations in the spin-spin exchange interaction, which have been linked to changes in nuclear geometry or exciton migration. Whilst simple calculations have supported this mechanism, to date little effort has been made to model realistic fluctuations which are informed by the actual structure and properties of physical materials. In this paper, we develop a modular computational pipeline for calculating singlet fission spin dynamics by way of electronic structural calculations, molecular dynamics, and numerical models of spin dynamics. The outputs of this pipeline aid in the interpretation of measured spin dynamics and allow us to place constraints on geometric fluctuations which are consistent with these observations.
Thermal Hall conductivity of electron-doped cuprates: Electrons and phonons

It has recently become clear that phonons generate a sizable thermal Hall effect in cuprates, whether they are undoped, electron-doped or hole-doped (inside the pseudogap phase). At higher doping, where cuprates are reasonably good metals, mobile electrons also generate a thermal Hall effect, the thermal equivalent of the standard electrical Hall effect. Here we show that in the cleanest crystals of the electron-doped cuprate Nd$_{2-x}$Ce$_{x}$CuO$_{4}$, at high doping, the phonon and electron contributions to the thermal Hall conductivity $\kappa_{\rm {xy}}$ are of comparable magnitude, but of opposite sign. In samples of lower quality, phonons dominate $\kappa_{\rm {xy}}$, resulting in a negative $\kappa_{\rm {xy}}$ at all temperatures. The fact that the negative phononic $\kappa_{\rm {xy}}$ in the metallic state is similar in magnitude and temperature dependence to that found in the insulating state at lower doping rules out any mechanism based on skew scattering of phonons off charged impurities, since a local charge should be screened in the metallic regime. The phononic $\kappa_{\rm {xy}}$ is found to persist over the entire doping range where antiferromagnetic correlations are known to be significant, suggesting that such correlations may play a role in generating the phonon thermal Hall effect in electron-doped cuprates. If the same mechanism is also at play in hole-doped cuprates, the presence of a phononic $\kappa_{\rm {xy}}$ below (and only below) the critical doping $p^{\star}$ would be evidence that spin correlations are a property of the pseudogap phase.
Besicovitch-Eggleston sets for finite GLS number systems with redundancy

In this article we study Besicovitch-Eggleston sets for finite GLS number systems with redundancy. These number systems produce number expansions reminiscent of Cantor base expansions. The redundancy refers to the fact that each number $x \in [0,1]$ has uncountably many representations in the system. We distinguish between these representations by adding an extra dimension and describing the system as a diagonally affine IFS on $\mathbb R^2$. For the associated two dimensional level sets of digit frequencies we give the Birkhoff spectrum and an expression for the Hausdorff dimension. To obtain these results we first prove a more general result on the Hausdorff dimension of level sets for Birkhoff averages of continuous potentials for a certain family of diagonally affine IFS's. We also study the Hausdorff dimension of digit frequency sets along fibres.