Miracle-dz/UniFAR-FTUs huggingface dataset:
Autoregressive Transformers for Disruption Prediction in Nuclear Fusion Plasmas

The physical sciences require models tailored to specific nuances of different dynamics. In this work, we study outcome predictions in nuclear fusion tokamaks, where a major challenge are \textit{disruptions}, or the loss of plasma stability with damaging implications for the tokamak. Although disruptions are difficult to model using physical simulations, machine learning (ML) models have shown promise in predicting these phenomena. Here, we first study several variations on masked autoregressive transformers, achieving an average of 5\% increase in Area Under the Receiving Operating Characteristic metric above existing methods. We then compare transformer models to limited context neural networks in order to shed light on the ``memory'' of plasma effected by tokamaks controls. With these model comparisons, we argue for the persistence of a memory throughout the plasma \textit{in the context of tokamaks} that our model exploits.
Nonlinear charge transport induced by gate voltage oscillation in few-layer MnBi2Te4

Nonlinear charge transport, including nonreciprocal longitudinal resistance and nonlinear Hall effect, has garnered significant attention due to its ability to explore inherent symmetries and topological properties of novel materials. An exciting recent progress along this direction is the discovery of significant nonreciprocal longitudinal resistance and nonlinear Hall effect in the intrinsic magnetic topological insulator MnBi2Te4 induced by the quantum metric dipole. Given the importance of this finding, the inconsistent response with charge density, and conflicting requirement of C3z symmetry, it is imperative to elucidate every detail that may impact the nonlinear transport measurement. In this study, we reveal an intriguing experimental factor that inevitably gives rise to sizable nonlinear transport signal in MnBi2Te4. We demonstrate that this effect stems from the gate voltage oscillation caused by the application of a large alternating current to the sample. Furthermore, we propose a methodology to significantly suppress this effect by individually grounding the voltage electrodes during the second-harmonic measurements. Our investigation emphasizes the critical importance of thoroughly assessing the impact of gate voltage oscillation before determining the intrinsic nature of nonlinear transport in all 2D material devices with an electrically connected operative gate electrode.
Evolutionary Dynamics with Randomly Distributed Benevolent Individuals

Understanding the evolution of cooperation is pivotal in biology and social science. Public resources sharing is a common scenario in the real world. In our study, we explore the evolutionary dynamics of cooperation on a regular graph with degree $k$, introducing the presence of a third strategy, namely the benevolence, who does not evolve over time, but provides a fixed benefit to all its neighbors. We find that the presence of the benevolence can foster the development of cooperative behavior and it follows a simple rule: $b/c>k - p_S(k-1)$. Our results provide new insights into the evolution of cooperation in structured populations.
Machine learning approaches to close the filtered two-fluid model for gas-solid flows: Models for subgrid drag force and solid phase stress

Gas-particle flows are commonly simulated through two-fluid model at industrial-scale. However, these simulations need very fine grid to have accurate flow predictions, which is prohibitively demanding in terms of computational resources. To circumvent this problem, the filtered two-fluid model has been developed, where large-scale flow field is numerically resolved and small-scale fluctuations are accounted for through subgrid-scale modeling. In this study, we have performed fine-grid two-fluid simulations of dilute gas-particle flows in periodic domains and applied explicit filtering to generate datasets. Then, these datasets have been used to develop artificial neural network (ANN) models for closures such as the filtered drag force and solid phase stress for the filtered two-fluid model. The set of input variables for the subgrid drag force ANN model that has been found previously to work well for dense flow regimes is found to work as well for the dilute regime. In addition, we present a Galilean invariant tensor basis neural network (TBNN) model for the filtered solid phase stress which can capture nicely the anisotropic nature of the solid phase stress arising from subgrid-scale velocity fluctuations. Finally, the predictions provided by this new TBNN model are compared with those obtained from a simple eddy-viscosity ANN model.
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges

Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies. Traditional approaches to decision-making suffer from sample efficiency and generalization, while large-scale self-supervised pretraining has enabled fast adaptation with fine-tuning or few-shot learning in language and vision. We thus argue to integrate knowledge acquired from generic large-scale self-supervised pretraining into downstream decision-making problems. We propose Pretrain-Then-Adapt pipeline and survey recent work on data collection, pretraining objectives and adaptation strategies for decision-making pretraining and downstream inference. Finally, we identify critical challenges and future directions for developing decision foundation model with the help of generic and flexible self-supervised pretraining.
Bayesian principal stratification with longitudinal data and truncation by death

In many causal studies, outcomes are censored by death, in the sense that they are neither observed nor defined for units who die. In such studies, the focus is usually on the stratum of always survivors up to a single fixed time s. Building on a recent strand of the literature, we propose an extended framework for the analysis of longitudinal studies, where units can die at different time points, and the main endpoints are observed and well defined only up to the death time. We develop a Bayesian longitudinal principal stratification framework, where units are cross classified according to the longitudinal death status. Under this framework, the focus is on causal effects for the principal strata of units that would be alive up to a time point s irrespective of their treatment assignment, where these strata may vary as a function of s. We can get precious insights into the effects of treatment by inspecting the distribution of baseline characteristics within each longitudinal principal stratum, and by investigating the time trend of both principal stratum membership and survivor-average causal effects. We illustrate our approach for the analysis of a longitudinal observational study aimed to assess, under the assumption of strong ignorability of treatment assignment, the causal effects of a policy promoting start ups on firms survival and hiring policy, where firms hiring status is censored by death.
Rethinking RAFT for Efficient Optical Flow

Despite significant progress in deep learning-based optical flow methods, accurately estimating large displacements and repetitive patterns remains a challenge. The limitations of local features and similarity search patterns used in these algorithms contribute to this issue. Additionally, some existing methods suffer from slow runtime and excessive graphic memory consumption. To address these problems, this paper proposes a novel approach based on the RAFT framework. The proposed Attention-based Feature Localization (AFL) approach incorporates the attention mechanism to handle global feature extraction and address repetitive patterns. It introduces an operator for matching pixels with corresponding counterparts in the second frame and assigning accurate flow values. Furthermore, an Amorphous Lookup Operator (ALO) is proposed to enhance convergence speed and improve RAFTs ability to handle large displacements by reducing data redundancy in its search operator and expanding the search space for similarity extraction. The proposed method, Efficient RAFT (Ef-RAFT),achieves significant improvements of 10% on the Sintel dataset and 5% on the KITTI dataset over RAFT. Remarkably, these enhancements are attained with a modest 33% reduction in speed and a mere 13% increase in memory usage. The code is available at: https://github.com/n3slami/Ef-RAFT
Single production of an exotic vector-like $Y$ quark at future high energy $pp$ colliders

Vector-like quarks have been predicted in various new physics scenarios beyond the Standard Model (SM). In a simplified modelling of a $(B,Y)$ doublet including a vector-like quark $Y$, with charge $-\frac{4}{3}$e, there are only two free parameters: the $Y$ coupling $\kappa_{Y}$ and mass $m_Y$. In the five flavor scheme, we investigate the single production of the $Y$ state decaying into $Wb$ at the Large Hadron Collider (LHC) Run-III and High-Luminosity LHC (HL-LHC) operating at $\sqrt{s}$ = 14 TeV, the possible High-Energy LHC (HE-LHC) with $\sqrt{s}$ = 27 TeV as well as the Future Circular Collider in hadron-hadron mode (FCC-hh) with $\sqrt{s}$ = 100 TeV. Through detailed signal-to-background analyses and detector simulations, we assess the exclusion capabilities of the $Y$ state at the different colliders. We find that this can be improved significantly with increasing collision energy, especially at the HE-LHC and FCC-hh, both demonstrating an obvious advantage with respect to the HL-LHC case in the case of high $m_Y$. Assuming a 10% systematic uncertainty on the background event rate, the exclusion capabilities are summarized as follows: (1) the LHC Run-III can exclude the correlated regions of $\kappa_{Y} \in [0.044,0.5]$ and $m_{Y} \in [1000\text{ GeV},3099\text{ GeV}]$ with integrated luminosity $L = 300\text{ fb}^{-1}$; (2) the HL-LHC can exclude the correlated regions of $\kappa_{Y} \in [0.027,0.5]$ and $m_{Y} \in [1000\text{ GeV},3653\text{ GeV}]$ with $L = 3$ ab$^{-1}$; (3) the HE-LHC can exclude the correlated regions of $\kappa_Y \in [0.030,0.5]$ and $m_{Y} \in [1000\text{ GeV} , 4936\text{ GeV}]$ with $L = 3$ ab$^{-1}$; (4) the FCC-hh can exclude the correlated regions of $\kappa_{Y} \in [0.051,0.5]$ and $m_{Y} \in [1000\text{ GeV} , 6610\text{ GeV}]$ with $L = 3$ ab$^{-1}$.
Energetic Variational Gaussian Process Regression for Computer Experiments

The Gaussian process (GP) regression model is a widely employed surrogate modeling technique for computer experiments, offering precise predictions and statistical inference for the computer simulators that generate experimental data. Estimation and inference for GP can be performed in both frequentist and Bayesian frameworks. In this chapter, we construct the GP model through variational inference, particularly employing the recently introduced energetic variational inference method by Wang et al. (2021). Adhering to the GP model assumptions, we derive posterior distributions for its parameters. The energetic variational inference approach bridges the Bayesian sampling and optimization and enables approximation of the posterior distributions and identification of the posterior mode. By incorporating a normal prior on the mean component of the GP model, we also apply shrinkage estimation to the parameters, facilitating mean function variable selection. To showcase the effectiveness of our proposed GP model, we present results from three benchmark examples.
A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots

We present a review of personality in neural conversational agents (CAs), also called chatbots. First, we define Personality, Persona, and Profile. We explain all personality schemes which have been used in CAs, and list models under the scheme(s) which they use. Second we describe 21 datasets which have been developed in recent CA personality research. Third, we define the methods used to embody personality in a CA, and review recent models using them. Fourth, we survey some relevant reviews on CAs, personality, and related topics. Finally, we draw conclusions and identify some research challenges for this important emerging field.
Filtrations for $\mathbb{wK4}$ and its relatives

We study the finite model property of subframe logics with expressible transitive reflexive closure modality. For $m>0$, let $\mathrm{L}_m$ be the logic given by axiom $\lozenge^{m+1} p\to \lozenge p\vee p$. We construct filtrations for the logics $\mathrm{L}_m$. It follows that these logics and their tense counterparts have the finite model property. Then we show that every canonical subframe logic that contains $\mathrm{L}_m$ have the finite model property.
Diffusion-HPC: Synthetic Data Generation for Human Mesh Recovery in Challenging Domains

Recent text-to-image generative models have exhibited remarkable abilities in generating high-fidelity and photo-realistic images. However, despite the visually impressive results, these models often struggle to preserve plausible human structure in the generations. Due to this reason, while generative models have shown promising results in aiding downstream image recognition tasks by generating large volumes of synthetic data, they are not suitable for improving downstream human pose perception and understanding. In this work, we propose a Diffusion model with Human Pose Correction (Diffusion-HPC), a text-conditioned method that generates photo-realistic images with plausible posed humans by injecting prior knowledge about human body structure. Our generated images are accompanied by 3D meshes that serve as ground truths for improving Human Mesh Recovery tasks, where a shortage of 3D training data has long been an issue. Furthermore, we show that Diffusion-HPC effectively improves the realism of human generations under varying conditioning strategies.
Predicting Evoked Emotions in Conversations

Understanding and predicting the emotional trajectory in multi-party multi-turn conversations is of great significance. Such information can be used, for example, to generate empathetic response in human-machine interaction or to inform models of pre-emptive toxicity detection. In this work, we introduce the novel problem of Predicting Emotions in Conversations (PEC) for the next turn (n+1), given combinations of textual and/or emotion input up to turn n. We systematically approach the problem by modeling three dimensions inherently connected to evoked emotions in dialogues, including (i) sequence modeling, (ii) self-dependency modeling, and (iii) recency modeling. These modeling dimensions are then incorporated into two deep neural network architectures, a sequence model and a graph convolutional network model. The former is designed to capture the sequence of utterances in a dialogue, while the latter captures the sequence of utterances and the network formation of multi-party dialogues. We perform a comprehensive empirical evaluation of the various proposed models for addressing the PEC problem. The results indicate (i) the importance of the self-dependency and recency model dimensions for the prediction task, (ii) the quality of simpler sequence models in short dialogues, (iii) the importance of the graph neural models in improving the predictions in long dialogues.
Wild2Avatar: Rendering Humans Behind Occlusions

Rendering the visual appearance of moving humans from occluded monocular videos is a challenging task. Most existing research renders 3D humans under ideal conditions, requiring a clear and unobstructed scene. Those methods cannot be used to render humans in real-world scenes where obstacles may block the camera's view and lead to partial occlusions. In this work, we present Wild2Avatar, a neural rendering approach catered for occluded in-the-wild monocular videos. We propose occlusion-aware scene parameterization for decoupling the scene into three parts - occlusion, human, and background. Additionally, extensive objective functions are designed to help enforce the decoupling of the human from both the occlusion and the background and to ensure the completeness of the human model. We verify the effectiveness of our approach with experiments on in-the-wild videos.
Prompt emission of relativistic protons up to GeV energies from M6.4-class solar flare on July 17, 2023

We show evidence of particle acceleration at GEV energies associated directly with protons from the prompt emission of a long-duration M6-class solar flare on July 17, 2023, rather than from protons acceleration by shocks from its associated Coronal Mass Ejection (CME), which erupted with a speed of 1342 km/s. Solar Energetic Particles (SEP) accelerated by the blast have reached Earth, up to an almost S3 (strong) category of a radiation storm on the NOAA scale. Also, we show a temporal correlation between the fast rising of GOES-16 proton and muon excess at ground level in the count rate of the New-Tupi muon detector at the central SAA region. A Monte Carlo spectral analysis based on muon excess at New-Tupi is consistent with the acceleration of electrons and protons (ions) up to relativistic energies (GeV energy range) in the impulsive phase of the flare. In addition, we present another two marginal particle excesses (with low confidence) at ground-level detectors in correlation with the solar flare prompt emission.
Representation of forward performance criteria with random endowment via FBSDE and application to forward optimized certainty equivalent

We extend the notion of forward performance criteria to settings with random endowment in incomplete markets. Building on these results, we introduce and develop the novel concept of forward optimized certainty equivalent (forward OCE), which offers a genuinely dynamic valuation mechanism that accommodates progressively adaptive market model updates, stochastic risk preferences, and incoming claims with arbitrary maturities. In parallel, we develop a new methodology to analyze the emerging stochastic optimization problems by directly studying the candidate optimal control processes for both the primal and dual problems. Specifically, we derive two new systems of forward-backward stochastic differential equations (FBSDEs) and establish necessary and sufficient conditions for optimality, and various equivalences between the two problems. This new approach is general and complements the existing one based on backward stochastic partial differential equations (backward SPDEs) for the related value functions. We, also, consider representative examples for both forward performance criteria with random endowment and forward OCE, and for the case of exponential criteria, we investigate the connection between forward OCE and forward entropic risk measures.
Prospects of detection of subsolar mass primordial black hole and white dwarf binary mergers

The subsolar mass primordial black hole (PBH) attracts attention as robust evidence of its primordial origin against the astrophysical black hole. Not only with themselves, PBHs can also form binaries with ordinary astrophysical objects, catching them by gravitational wave (GW) bremsstrahlung. We discuss the detectability of the inspiral GWs from binaries consisting of a PBH and a white dwarf (WD) by using space-borne gravitational wave interferometers like DECIGO. The conservative assessment shows the expected event number in three years by DECIGO is $\mathcal{O}(10^{-6})$ for $M_\mathrm{PBH} \sim 0. 1M_\odot$. Possible enhancement mechanisms of WD-PBH binary formation may amplify this event rate. We discuss how large enhancement associated with WDs is required to detect WD-PBH merger events without violating the existing constraints on the PBH-PBH merger by the ground-based detector.
Disforming scalar-tensor cosmology

Disformal transformations of Friedmann-Lema\^itre-Robertson-Walker and Bianchi geometries are analyzed in the context of scalar-tensor gravity. Novel aspects discussed are the $3+1$ splitting, the effective fluid equivalent of the gravitational scalar, Bianchi models, stealth solutions and de Sitter solutions with non-constant scalar field (which are signatures of scalar-tensor gravity). Both pure disformal transformations and more general ones are discussed.
A dimension reduction approach for loss valuation in credit risk modelling

This paper addresses the ``curse of dimensionality'' in the loss valuation of credit risk models. A dimension reduction methodology based on the Bayesian filter and smoother is proposed. This methodology is designed to achieve a fast and accurate loss valuation algorithm in credit risk modelling, but it can also be extended to valuation models of other risk types. The proposed methodology is generic, robust and can easily be implemented. Moreover, the accuracy of the proposed methodology in the estimation of expected loss and value-at-risk is illustrated by numerical experiments. The results suggest that, compared to the currently most used PCA approach, the proposed methodology provides more accurate estimation of expected loss and value-at-risk of a loss distribution.
Energy-Efficient Power Control for Multiple-Task Split Inference in UAVs: A Tiny Learning-Based Approach

The limited energy and computing resources of unmanned aerial vehicles (UAVs) hinder the application of aerial artificial intelligence. The utilization of split inference in UAVs garners significant attention due to its effectiveness in mitigating computing and energy requirements. However, achieving energy-efficient split inference in UAVs remains complex considering of various crucial parameters such as energy level and delay constraints, especially involving multiple tasks. In this paper, we present a two-timescale approach for energy minimization in split inference, where discrete and continuous variables are segregated into two timescales to reduce the size of action space and computational complexity. This segregation enables the utilization of tiny reinforcement learning (TRL) for selecting discrete transmission modes for sequential tasks. Moreover, optimization programming (OP) is embedded between TRL's output and reward function to optimize the continuous transmit power. Specifically, we replace the optimization of transmit power with that of transmission time to decrease the computational complexity of OP since we reveal that energy consumption monotonically decreases with increasing transmission time. The replacement significantly reduces the feasible region and enables a fast solution according to the closed-form expression for optimal transmit power. Simulation results show that the proposed algorithm can achieve a higher probability of successful task completion with lower energy consumption.