Miracle-dz/UniFAR-FTUs huggingface dataset:
Axiomatization of Interventional Probability Distributions

Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that has several advantages: it does not need to make use of any modeling assumptions such as those imposed by structural causal models; it only relies on interventions on single variables; it includes most cases with latent variables and causal cycles; and more importantly, it does not assume the existence of an underlying true causal graph as we do not take it as the primitive object--in fact, a causal graph is derived as a by-product of our theory. We show that, under our axiomatizations, the intervened distributions are Markovian to the defined intervened causal graphs, and an observed joint probability distribution is Markovian to the obtained causal graph; these results are consistent with the case of structural causal models, and as a result, the existing theory of causal inference applies. We also show that a large class of natural structural causal models satisfy the theory presented here. We note that the aim of this paper is axiomatization of interventional families, which is subtly different from"causal modeling. "
First-Choice Maximality Meets Ex-ante and Ex-post Fairness

For the assignment problem where multiple indivisible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-choice maximality (FCM), i.e., maximizing the number of agents assigned their first choices, together with Pareto efficiency (PE). Our mechanisms also provide guarantees of ex-ante and ex-post fairness. The generalized eager Boston mechanism is ex-ante envy-free, and ex-post envy-free up to one item (EF1). The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness. We also show that no strategyproof mechanism satisfies ex-post PE, EF1, and FCM simultaneously. In doing so, we expand the frontiers of simultaneously providing efficiency and both ex-ante and ex-post fairness guarantees for the assignment problem.
THE ROLE OF THE MATHEMATICAL SCIENCES IN SUPPORTING THE COVID-19 RESPONSE IN AUSTRALIA AND NEW ZEALAND

Abstract Mathematical modelling has been used to support the response to the COVID-19 pandemic in countries around the world including Australia and New Zealand. Both these countries have followed similar pandemic response strategies, using a combination of strict border measures and community interventions to minimize infection rates until high vaccine coverage was achieved. This required a different set of modelling tools to those used in countries that experienced much higher levels of prevalence throughout the pandemic. In this article, we provide an overview of some of the mathematical modelling and data analytics work that has helped to inform the policy response to the pandemic in Australia and New Zealand. This is a reflection on our experiences working at the modelling–policy interface and the impact this has had on the pandemic response. We outline the various types of model outputs, from short-term forecasts to longer-term scenario models, that have been used in different contexts. We discuss issues relating to communication between mathematical modellers and stakeholders such as health officials and policymakers. We conclude with some future challenges and opportunities in this area.
CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning

Commonsense reasoning, aiming at endowing machines with a human-like ability to make situational presumptions, is extremely challenging to generalize.For someone who barely knows about “meditation,” while is knowledgeable about “singing,” he can still infer that “meditation makes people relaxed” from the existing knowledge that “singing makes people relaxed” by first conceptualizing “singing” as a “relaxing event” and then instantiating that event to “meditation. ”This process, known as conceptual induction and deduction, is fundamental to commonsense reasoning while lacking both labeled data and methodologies to enhance commonsense modeling.To fill such a research gap, we propose CAT (Contextualized ConceptuAlization and InsTantiation),a semi-supervised learning framework that integrates event conceptualization and instantiation to conceptualize commonsense knowledge bases at scale.Extensive experiments show that our framework achieves state-of-the-art performances on two conceptualization tasks, and the acquired abstract commonsense knowledge can significantly improve commonsense inference modeling.Our code, data, and fine-tuned models are publicly available at [https://github.com/HKUST-KnowComp/CAT](https://github.com/HKUST-KnowComp/CAT).
On the nature of the planet-powered transient event ZTF SLRN-2020

The Red Nova ZTF SLRN-2020 is the third transient event with properties that are compatible with the merger of a planet with a main-sequence (or close to) star on a dynamical time-scale. While the two first transient events occurred in young stellar objects, ZTF SLRN-2020 occurred in an old system. None the less, I show that the three star–planet intermediate luminosity optical transients (ILOTs, also termed Red Novae) occupy the same area in the energy–time diagram of ILOTs. Based on models for ILOTs that are power by stellar binary interaction, I suggest that the planet in ZTF SLRN-2020 launched jets at about its escape speed before it was engulfed by the star. Interestingly, the escape speed from the planet is similar to the orbital speed of the planet. This leads to an outflow with a very low terminal velocity, much below the escape velocity from the star, and in concentration around ≈45° to the equatorial plane. As well, the planet might have lost back some of the accreted mass just before engulfment, forming an accretion disc around the star. This disc might have launched jets during the main outburst of the event. The jets form a bipolar expanding nebula.
A fully conservative and shift-invariant formulation for Galerkin discretizations of incompressible variable density flow

This paper introduces a formulation of the variable density incompressible Navier-Stokes equations by modifying the nonlinear terms in a consistent way. For Galerkin discretizations, the formulation leads to full discrete conservation of mass, squared density, momentum, angular momentum and kinetic energy without the divergence-free constraint being strongly enforced. In addition to favorable conservation properties, the formulation is shown to make the density field invariant to global shifts. The effect of viscous regularizations on conservation properties is also investigated. Numerical tests validate the theory developed in this work. The new formulation shows superior performance compared to other formulations from the literature, both in terms of accuracy for smooth problems and in terms of robustness.
HACK: Learning a Parametric Head and Neck Model for High-fidelity Animation

Significant advancements have been made in developing parametric models for digital humans, with various approaches concentrating on parts such as the human body, hand, or face. Nevertheless, connectors such as the neck have been overlooked in these models, with rich anatomical priors often unutilized. In this paper, we introduce HACK (Head-And-neCK), a novel parametric model for constructing the head and cervical region of digital humans. Our model seeks to disentangle the full spectrum of neck and larynx motions, facial expressions, and appearance variations, providing personalized and anatomically consistent controls, particularly for the neck regions. To build our HACK model, we acquire a comprehensive multi-modal dataset of the head and neck under various facial expressions. We employ a 3D ultrasound imaging scheme to extract the inner biomechanical structures, namely the precise 3D rotation information of the seven vertebrae of the cervical spine. We then adopt a multi-view photometric approach to capture the geometry and physically-based textures of diverse subjects, who exhibit a diverse range of static expressions as well as sequential head-and-neck movements. Using the multi-modal dataset, we train the parametric HACK model by separating the 3D head and neck depiction into various shape, pose, expression, and larynx blendshapes from the neutral expression and the rest skeletal pose. We adopt an anatomically-consistent skeletal design for the cervical region, and the expression is linked to facial action units for artist-friendly controls. We also propose to optimize the mapping from the identical shape space to the PCA spaces of personalized blendshapes to augment the pose and expression blendshapes, providing personalized properties within the framework of the generic model. Furthermore, we use larynx blendshapes to accurately control the larynx deformation and force the larynx slicing motions along the vertical direction in the UV-space for precise modeling of the larynx beneath the neck skin. HACK addresses the head and neck as a unified entity, offering more accurate and expressive controls, with a new level of realism, particularly for the neck regions. This approach has significant benefits for numerous applications, including geometric fitting and animation, and enables inter-correlation analysis between head and neck for fine-grained motion synthesis and transfer.
Behavior Contrastive Learning for Unsupervised Skill Discovery

In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual information (MI) between states and skills. However, such an MI objective tends to learn simple and static skills and may hinder exploration. In this paper, we propose a novel unsupervised skill discovery method through contrastive learning among behaviors, which makes the agent produce similar behaviors for the same skill and diverse behaviors for different skills. Under mild assumptions, our objective maximizes the MI between different behaviors based on the same skill, which serves as an upper bound of the previous MI objective. Meanwhile, our method implicitly increases the state entropy to obtain better state coverage. We evaluate our method on challenging mazes and continuous control tasks. The results show that our method generates diverse and far-reaching skills, and also obtains competitive performance in downstream tasks compared to the state-of-the-art methods.
Gravity of gluonic fluctuations and the value of the cosmological constant

We analyze the classical linear gravitational effect of idealized pion-like dynamical systems, consisting of light quarks connected by attractive gluonic material with a stress–energy p=−ρc2 in one or more dimensions. In one orbit of a system of total mass M, quarks of mass m<<M expand apart initially with v/c∼1 , slow due to the gluonic attraction, reach a maximum size R0∼ℏ/Mc , then recollapse. We solve the linearized Einstein equations and derive the effect on freely falling bodies for two systems: a gluonic bubble model where uniform gluonic stress–energy fills a spherical volume bounded by a 2D surface comprising the quarks’ rest mass, and a gluonic string model where a thin string connects two pointlike quarks. The bubble model is shown to produce a secular mean outward residual velocity of test particles that lie within its orbit. It is shown that the mean gravitational repulsion of bubble-like virtual-pion vacuum fluctuations agrees with the measured value of the cosmological constant, for a bubble with a radius equal to about twice the pion de Broglie length. These results support the view that the gravity of standard QCD vacuum fluctuations is the main source of cosmic acceleration.
On steady convection in a porous medium

For convection in a porous medium the dependence of the Nusselt number on the Rayleigh number is examined to sixth order using an expansion for the Rayleigh number proposed by Kuo (1961). The results show very good agreement with experiment. Additionally, the abrupt change which is observed in the heat transport at a supercritical Rayleigh number may be explained by a breakdown of Darcy's law.
Imputation of missing values using quantile regression

In this thesis, we consider an imputation method to handle missing response values based on quantile regression estimation. In the proposed method, the missing response values are generated using the estimated conditional quantile regression function at given values of covariates parametrically or semiparametrically. We adopt the generalized method of moments and the empirical likelihood method for estimation of parameters defined through a general estimating equation. We demonstrate that the proposed estimators, which combine both quantile regression imputation (parametric or semiparametric) and general estimating equation methods (generalized method of moments or empirical likelihood), have competitive advantages over some of the most widely used parametric and non-parametric imputation estimators. The consistency and the asymptotic normality of our estimators are established and variance estimation is provided. Results from a limited simulation study and an empirical study are presented to show the adequacy of the proposed methods.
Energy levels and transition rates for helium-like ions with Z = 10–36

Aims. Helium-like ions provide an important X-ray spectral diagnostics in astrophysical and high-temperature fusion plasmas. An interpretation of the observed spectra provides information on temperature, density, and chemical compositions of the plasma. Such an analysis requires information for a wide range of atomic parameters, including energy levels and transition rates. Our aim is to provide a set of accurate energy levels and transition rates for helium-like ions with Z = 10–36. Methods. The second-order many-body perturbation theory (MBPT) was adopted in this paper. To support our MBPT results, we performed an independent calculation using the multiconfiguration Dirac-Hartree-Fock (MCDHF) method. Results. We provide accurate energies for the lowest singly excited 70 levels among 1 snl ( n ≤ 6 ,l ≤ ( n −1)) configurations and the lowest doubly excited 250 levels arising from the K-vacancy 2 l n ′ l ′( n ′ ≤ 6, l ′ ≤ ( n ′−1)) configurations of helium-like ions with Z = 10−36. Wavelengths, transition rates, oscillator strengths, and line strengths are calculated for the E1, M1, E2, and M2 transitions among these levels. The radiative lifetimes are reported for all the calculated levels. Conclusions. Our MBPT results for singly excited n ≤ 2 levels show excellent agreement with other elaborate calculations, while those for singly excited n ≥ 3 and doubly excited levels show significant improvements over previous theoretical results. Our results will be very helpful for astrophysical line identification and plasma diagnostics.
KNEE JOINT FORCES IN CYCLING AT TWO WORKLOADS WITH CIRCULAR AND NON-CIRCULAR CHAINRINGS

The purpose of this study was to investigate the knee structure forces at two power outputs with circular and non-circular chainrings. 14 male cyclists pedalled using three different chainrings with a cadence of 90 rpm at 180 W and 300 W. Kinematics (Vicon) and kinetics (Powertec) were recorded. The knee structure forces were calculated using a 2D knee model. The tangential pedal forces were higher in the oval chainring conditions in both workloads. No differences were observed for the knee forces. The higher workload lead to significantly higher pedal and knee forces in all chainring conditions. Oval chainrings do not affect knee joint forces. Knee joint loading is highly affected by the work load, independently from the chainring systems used. The recommendation for using non-circular chainrings has not to be based on knee joint loading aspects.
Controllable Mixed-Initiative Dialogue Generation through Prompting

Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particular dialogue intents or strategies, prescribed by a policy planner. The standard approach has been fine-tuning pre-trained language models to perform generation conditioned on these intents. However, these supervised generation models are limited by the cost and quality of data annotation.We instead prompt large language models as a drop-in replacement to fine-tuning on conditional generation. We formalize prompt construction for controllable mixed-initiative dialogue. Our findings show improvements over fine-tuning and ground truth responses according to human evaluation and automatic metrics for two tasks: PersuasionForGood and Emotional Support Conversations.
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps

The increasing popularity of deep learning (DL) models and the advantages of computing, including low latency and bandwidth savings on smartphones, have led to the emergence of intelligent mobile applications, also known as DL apps, in recent years. However, this technological development has also given rise to several security concerns, including adversarial examples, model stealing, and data poisoning issues. Existing works on attacks and countermeasures for on-device DL models have primarily focused on the models themselves. However, scant attention has been paid to the impact of data processing disturbance on the model inference. This knowledge disparity highlights the need for additional research to fully comprehend and address security issues related to data processing for on-device models. In this paper, we introduce a data processing-based attacks against real-world DL apps. In particular, our attack could influence the performance and latency of the model without affecting the operation of a DL app. To demonstrate the effectiveness of our attack, we carry out an empirical study on 517 real-world DL apps collected from Google Play. Among 320 apps utilizing MLkit, we find that 81.56% of them can be successfully attacked. The results emphasize the importance of DL app developers being aware of and taking actions to secure on-device models from the perspective of data processing.
Equality in the Miyaoka-Yau inequality and uniformization of non-positively curved klt pairs

Let $(X, \Delta)$ be a compact K\"ahler klt pair, where $K_X + \Delta$ is ample or numerically trivial, and $\Delta$ has standard coefficients. We show that if equality holds in the orbifold Miyaoka-Yau inequality for $(X, \Delta)$, then its orbifold universal cover is either the unit ball (ample case) or the affine space (numerically trivial case).
An Investigation on Word Embedding Offset Clustering as Relationship Classification

Vector representations obtained from word embedding are the source of many groundbreaking advances in natural language processing. They yield word representations that are capable of capturing semantics and analogies of words within a text corpus. This study is an investigation in an attempt to elicit a vector representation of relationships between pairs of word vectors. We use six pooling strategies to represent vector relationships. Different types of clustering models are applied to analyze which one correctly groups relationship types. Subtraction pooling coupled with a centroid based clustering mechanism shows better performances in our experimental setup. This work aims to provide directions for a word embedding based unsupervised method to identify the nature of a relationship represented by a pair of words.
Real-World Denoising via Diffusion Model

Real-world image denoising is an extremely important image processing problem, which aims to recover clean images from noisy images captured in natural environments. In recent years, diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models. However, it has not been widely used in the field of image denoising because it is difficult to control the appropriate position of the added noise. Inspired by diffusion models, this paper proposes a novel general denoising diffusion model that can be used for real-world image denoising. We introduce a diffusion process with linear interpolation, and the intermediate noisy image is interpolated from the original clean image and the corresponding real-world noisy image, so that this diffusion model can handle the level of added noise. In particular, we also introduce two sampling algorithms for this diffusion model. The first one is a simple sampling procedure defined according to the diffusion process, and the second one targets the problem of the first one and makes a number of improvements. Our experimental results show that our proposed method with a simple CNNs Unet achieves comparable results compared to the Transformer architecture. Both quantitative and qualitative evaluations on real-world denoising benchmarks show that the proposed general diffusion model performs almost as well as against the state-of-the-art methods.
Asynchronous multi-class traffic management in wide area networks

The emergence of new applications brings multi-class traffic with diverse quality of service (QoS) requirements to wide area networks (WANs), motivating research in traffic engineering (TE). In recent years, novel centralized and hierarchical TE schemes have used heuristic or machine learning techniques to orchestrate resources in closed systems such as datacenter networks. However, these schemes suffer from long delivery delays and high control overhead when applied to general WANs. To provide low-delay services, this paper proposes an asynchronous multi-class traffic management (AMTM) scheme. We first establish an asynchronous TE paradigm in which distributed nodes locally perform low-complexity and low-delay traffic control based on link prices, and the TE server updates link prices to eliminate decision conflicts between edge nodes. By modeling the asynchronous TE paradigm as a control system with non-negligible control loop delay, we find that the traditional pricing strategy cannot simultaneously achieve a low packet loss rate and a low flow delivery delay. To address this issue, we propose a new pricing strategy based on the observations of virtual queues in intermediate nodes. We also present a system design and related algorithms that utilize a dynamic step size mechanism of link price update. Simulation results show that AMTM can effectively reduce the end-to-end flow delivery delay.
Quasinormal modes of rotating accelerating black holes

This paper studies the quasinormal mode spectrum of the scalar perturbation on the background of the rotating accelerating black holes. The quasinormal frequency $\omega$ and the separation constant $\lambda$ are calculated using two methods: the continued fractions method and the directly integration method. The spectrum is found to include three families of modes: the photon sphere modes, the acceleration modes, and the near extreme modes. We investigate the effects of back hole parameters such as spin and acceleration. Empirical formulas are presented for the numerical results, specifically for the acceleration modes in the small black hole limit or the near extreme modes in the extreme black hole limit. An interesting phenomenon known as eigenvalue repulsion is observed when the acceleration modes intersect with the near extreme modes at certain parameter values. The strong cosmic censorship conjecture for spinning C-metric is respected.