Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Emotion Recognition in Context Debiasing

Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation, causing severe performance bottlenecks and confounding valuable context priors. In this paper, we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically, we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference, we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes, resulting in bias mitigation and robust prediction. As a model-agnostic framework, CLEF can be readily integrated into existing methods, bringing consistent performance gains.
Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread

The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such disparities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.
On the topology of the moduli of tropical unramified $p$-covers

We study the topology of the moduli space of tropical unramified $\mathbb{Z}/p$-covers of tropical curves of genus $g \geq 2$ where $p$ is a prime number. We use recent techniques by Chan--Galatius--Payne to identify a contractible subcomplex of the moduli space. We then use this contractibility result to show that this moduli space is simply connected for all $g$ and $p$. In the case of genus $2$ we fully determine the homotopy type of this moduli space for all primes $p$. This work is motivated by prospective applications to the top-weight cohomology of the space of prime cyclic \'etale covers of smooth algebraic curves.
FMPAF: How Do Fed Chairs Affect the Financial Market? A Fine-grained Monetary Policy Analysis Framework on Their Language

The effectiveness of central bank communication is a crucial aspect of monetary policy transmission. While recent research has examined the influence of policy communication by the chairs of the Federal Reserve on various financial variables, much of the literature relies on rule-based or dictionary-based methods in parsing the language of the chairs, leaving nuanced information about policy stance contained in nonverbal emotion out of the analysis. In the current study, we propose the Fine-Grained Monetary Policy Analysis Framework (FMPAF), a novel approach that integrates large language models (LLMs) with regression analysis to provide a comprehensive analysis of the impact of the press-conference communications of chairs of the Federal Reserve on financial markets. We conduct extensive comparisons of model performance under different levels of granularity, modalities, and communication scenarios. Based on our preferred specification, a one-unit increase in the sentiment score is associated with an increase of the price of S\&P 500 Exchange-Traded Fund by approximately 500 basis points, a 15-basis-point decrease in the policy interest rate, while not leading to a significant response in exchange rates.
Derivation of Mutual Information and Linear Minimum Mean-Square Error for Viterbi Decoding of Convolutional Codes Using the Innovations Method

We see that convolutional coding/Viterbi decoding has the structure of the Kalman filter (or the linear minimum variance filter). First, we calculate the covariance matrix of the innovation (i.e., the soft-decision input to the main decoder in a Scarce-State-Transition (SST) Viterbi decoder). Then a covariance matrix corresponding to that of the one-step prediction error in the Kalman filter is obtained. Furthermore, from that matrix, a covariance matrix corresponding to that of the filtering error in the Kalman filter is derived using the formula in the Kalman filter. As a result, the average mutual information per branch for Viterbi decoding of convolutional codes is given using these covariance matrices. Also, the trace of the latter matrix represents the linear minimum mean-square error (LMMSE). We show that an approximate value of the average mutual information is sandwiched between half the SNR times the average filtering and one-step prediction LMMSEs. In the case of QLI codes, from the covariance matrix of the soft-decision input to the main decoder, we can get a matrix. We show that the trace of this matrix has some connection with the linear smoothing error.
Genetic Stability of Different Genotypes of Bread Wheat (Triticum aestivum L.) Grown Under Levels of Nitrogen Fertilizer

Obtaining highly productive and stable varieties through different environments is one of the most important goals sought by plant breeders. A field experiment was carried out to investigate the effect of different nitrogen fertilizer levels on the stability of growth and grain yield in eight cultivars of bread wheat during two growing seasons (2020-2021 and 2021-2022), using split plot arrangement in randomized complete block design (RCBD) with three replications. The results showed that there was a significant response of some genotypes to the levels of nitrogen fertilizer for grain yield and its components during the two growing seasons. The Iba99 genotype was characterized by the highest mean of grain yield, number of grains. spike-1 and spike length, followed by the Baghdad2 genotype. The genotypes under study differed in the stability of traits in different environments. Baghdad2 showed the highest stability for important traits such as grain yield, weight of 1000 grains, number of grains spike-1 and spike length, while unstable and responded to different environments for plant height and a number of spikes.m-2. Whereas, Iba99 showed good stability for the spike length and the number of spikes.m-2, with its response to the environments for the other traits under study. Both stability phenotypic and genotypic resultant method and the stability triangle method showed very similar results with Eberhart and Russell method, therefore it is possible to rely on these two methods to estimate the stability of genotypes due to their lower complexity. Consequently, the stable genotypes traits in different environments can be selected and introduced directly into future breeding programs for the purpose of adopting new varieties. Although other genotypes showed superior characteristics of grain yield and its important components, they suffer by responding to different environments, thus they can be included in hybridization breeding programs with local varieties to transfer their desirable traits.
V3D: Video Diffusion Models are Effective 3D Generators

Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency. Our code is available at https://github.com/heheyas/V3D
Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches

In recent years, there has been a growing interest in using learning-based approaches for solving combinatorial problems, either in an end-to-end manner or in conjunction with traditional optimization algorithms. In both scenarios, the challenge lies in encoding the targeted combinatorial problems into a structure compatible with the learning algorithm. Many existing works have proposed problem-specific representations, often in the form of a graph, to leverage the advantages of \textit{graph neural networks}. However, these approaches lack generality, as the representation cannot be easily transferred from one combinatorial problem to another one. While some attempts have been made to bridge this gap, they still offer a partial generality only. In response to this challenge, this paper advocates for progress toward a fully generic representation of combinatorial problems for learning-based approaches. The approach we propose involves constructing a graph by breaking down any constraint of a combinatorial problem into an abstract syntax tree and expressing relationships (e.g., a variable involved in a constraint) through the edges. Furthermore, we introduce a graph neural network architecture capable of efficiently learning from this representation. The tool provided operates on combinatorial problems expressed in the XCSP3 format, handling all the constraints available in the 2023 mini-track competition. Experimental results on four combinatorial problems demonstrate that our architecture achieves performance comparable to dedicated architectures while maintaining generality. Our code and trained models are publicly available at \url{https://github.com/corail-research/learning-generic-csp}.
Voiculescu's Theorem in Properly Infinite Factors

This paper investigates Voiculescu's theorem on approximate equivalence in separable properly infinite factors. We establish the norm-denseness of the set of all reducible operators and prove Voiculescu's bicommutant theorem. Additionally, we extend these results to the multiplier algebras within separable type $\mathrm{III}$ factors.
FedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning

Instruction tuning has proven essential for enhancing the performance of large language models (LLMs) in generating human-aligned responses. However, collecting diverse, high-quality instruction data for tuning poses challenges, particularly in privacy-sensitive domains. Federated instruction tuning (FedIT) has emerged as a solution, leveraging federated learning from multiple data owners while preserving privacy. Yet, it faces challenges due to limited instruction data and vulnerabilities to training data extraction attacks. To address these issues, we propose a novel federated algorithm, FedPIT, which utilizes LLMs' in-context learning capability to self-generate task-specific synthetic data for training autonomously. Our method employs parameter-isolated training to maintain global parameters trained on synthetic data and local parameters trained on augmented local data, effectively thwarting data extraction attacks. Extensive experiments on real-world medical data demonstrate the effectiveness of FedPIT in improving federated few-shot performance while preserving privacy and robustness against data heterogeneity.
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning

As AI assistance is increasingly infused into decision-making processes, we may seek to optimize human-centric objectives beyond decision accuracy, such as skill improvement or task enjoyment of individuals interacting with these systems. With this aspiration in mind, we propose offline reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize such human-centric objectives. Our approach seeks to optimize different objectives by adaptively providing decision support to humans -- the right type of assistance, to the right person, at the right time. We instantiate our approach with two objectives: human-AI accuracy on the decision-making task and human learning about the task, and learn policies that optimize these two objectives from previous human-AI interaction data. We compare the optimized policies against various baselines in AI-assisted decision-making. Across two experiments (N = 316 and N = 964), our results consistently demonstrate that people interacting with policies optimized for accuracy achieve significantly better accuracy -- and even human-AI complementarity -- compared to those interacting with any other type of AI support. Our results further indicate that human learning is more difficult to optimize than accuracy, with participants who interacted with learning-optimized policies showing significant learning improvement only at times. Our research (1) demonstrates offline RL to be a promising approach to model dynamics of human-AI decision-making, leading to policies that may optimize various human-centric objectives and provide novel insights about the AI-assisted decision-making space, and (2) emphasizes the importance of considering human-centric objectives beyond decision accuracy in AI-assisted decision-making, while also opening up the novel research challenge of optimizing such objectives.
Understanding Parents' Perceptions and Practices Toward Children's Security and Privacy in Virtual Reality

Recent years have seen a sharp increase in underage users of virtual reality (VR), where security and privacy (S\&P) risks such as data surveillance and self-disclosure in social interaction have been increasingly prominent. Prior work shows children largely rely on parents to mitigate S\&P risks in their technology use. Therefore, understanding parents' S\&P knowledge, perceptions, and practices is critical for identifying the gaps for parents, technology designers, and policymakers to enhance children's S\&P. While such empirical knowledge is substantial in other consumer technologies, it remains largely unknown in the context of VR. To address the gap, we conducted in-depth semi-structured interviews with 20 parents of children under the age of 18 who use VR at home. Our findings highlight parents generally lack S\&P awareness due to the perception that VR is still in its infancy. To protect their children's interaction with VR, parents currently primarily rely on active strategies such as verbal education about S\&P. Passive strategies such as parental controls in VR are not commonly used among our interviewees, mainly due to their perceived technical constraints. Parents also highlight that a multi-stakeholder ecosystem must be established towards more S\&P support for children in VR. Based on the findings, we propose actionable S\&P recommendations for critical stakeholders, including parents, educators, VR companies, and governments.
Yu-Shiba-Rusinov states in the s-wave superconducting kagome Hubbard model: Self-consistent Bogoliubov-de Gennes calculations

The Yu-Shiba-Rusinov (YSR) states in kagome superconductors have been studied recently in theory and experiments. However, more details of local superconductivity around the YSR states in kagome superconductors and the existence of the related first-order phase transition remain unclear. Based on the self-consistent Bogoliubov-de Gennes equations for the $s-$wave superconducting kagome Hubbard model, we show that first-order phase transitions occur when varying the spin-magnetic interaction $J$ of the magnetic impurity, and discontinuities of hysteresis loops in the plots of the local pair potential versus $J$ happen at the upper and lower critical interactions $J_{\rm c1}$ and $J_{\rm c2}$, rather than the conventional critical interaction $J_c$. The minimal energy of the stable YSR state $E_{\rm YSR, min}$ is non-zero for the chemical potential at the van Hove singularities, Dirac point, and the flat band. Furthermore, $J_{\rm c1}$, $J_{\rm c2}$, and $E_{\rm YSR, min}$ are dependent on the chemical potential and the superconducting coupling interaction.
What is different between these datasets?

The performance of machine learning models heavily depends on the quality of input data, yet real-world applications often encounter various data-related challenges. One such challenge could arise when curating training data or deploying the model in the real world - two comparable datasets in the same domain may have different distributions. While numerous techniques exist for detecting distribution shifts, the literature lacks comprehensive approaches for explaining dataset differences in a human-understandable manner. To address this gap, we propose a suite of interpretable methods (toolbox) for comparing two datasets. We demonstrate the versatility of our approach across diverse data modalities, including tabular data, language, images, and signals in both low and high-dimensional settings. Our methods not only outperform comparable and related approaches in terms of explanation quality and correctness, but also provide actionable, complementary insights to understand and mitigate dataset differences effectively.
Is Vanilla MLP in Neural Radiance Field Enough for Few-shot View Synthesis?

Neural Radiance Field (NeRF) has achieved superior performance for novel view synthesis by modeling the scene with a Multi-Layer Perception (MLP) and a volume rendering procedure, however, when fewer known views are given (i.e., few-shot view synthesis), the model is prone to overfit the given views. To handle this issue, previous efforts have been made towards leveraging learned priors or introducing additional regularizations. In contrast, in this paper, we for the first time provide an orthogonal method from the perspective of network structure. Given the observation that trivially reducing the number of model parameters alleviates the overfitting issue, but at the cost of missing details, we propose the multi-input MLP (mi-MLP) that incorporates the inputs (i.e., location and viewing direction) of the vanilla MLP into each layer to prevent the overfitting issue without harming detailed synthesis. To further reduce the artifacts, we propose to model colors and volume density separately and present two regularization terms. Extensive experiments on multiple datasets demonstrate that: 1) although the proposed mi-MLP is easy to implement, it is surprisingly effective as it boosts the PSNR of the baseline from $14.73$ to $24.23$. 2) the overall framework achieves state-of-the-art results on a wide range of benchmarks. We will release the code upon publication.
Discrete Topological Complexities of Simplical Maps

In this study, we delve into the discrete TC of surjective simplicial fibrations, aiming to unravel the interplay between topological complexity, discrete geometric structures, and computational efficiency. Moreover, we examine the properties of the discrete TC number in higher dimensions and its relationship with scat. We also touch on the basic properties of the notion of higher contiguity distance, and show that it is possible to consider discrete TC computations in a simpler sense.
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?

While large language models (LLMs) excel in world knowledge understanding, adapting them to specific subfields requires precise adjustments. Due to the model's vast scale, traditional global fine-tuning methods for large models can be computationally expensive and impact generalization. To address this challenge, a range of innovative Parameters-Efficient Fine-Tuning (PEFT) methods have emerged and achieved remarkable success in both LLMs and Large Vision-Language Models (LVLMs). In the medical domain, fine-tuning a medical Vision-Language Pretrained (VLP) model is essential for adapting it to specific tasks. Can the fine-tuning methods for large models be transferred to the medical field to enhance transfer learning efficiency? In this paper, we delve into the fine-tuning methods of LLMs and conduct extensive experiments to investigate the impact of fine-tuning methods for large models on existing multimodal models in the medical domain from the training data level and the model structure level. We show the different impacts of fine-tuning methods for large models on medical VLMs and develop the most efficient ways to fine-tune medical VLP models. We hope this research can guide medical domain researchers in optimizing VLMs' training costs, fostering the broader application of VLMs in healthcare fields. Code and dataset will be released upon acceptance.
Economic Capacity Withholding Bounds of Competitive Energy Storage Bidders

Economic withholding in electricity markets refers to generators bidding higher than their true marginal fuel cost, and is a typical approach to exercising market power. However, existing market designs require storage to design bids strategically based on their own future price predictions, motivating storage to conduct economic withholding without assuming market power. As energy storage takes up more significant roles in wholesale electricity markets, understanding its motivations for economic withholding and the consequent effects on social welfare becomes increasingly vital. This paper derives a theoretical framework to study the economic capacity withholding behavior of storage participating in competitive electricity markets and validate our results in simulations based on the ISO New England system. We demonstrate that storage bids can reach unbounded high levels under conditions where future price predictions show bounded expectations but unbounded deviations. Conversely, in scenarios with peak price limitations, we show the upper bounds of storage bids are grounded in bounded price expectations. Most importantly, we show that storage capacity withholding can potentially lower the overall system cost when price models account for system uncertainties. Our paper reveals energy storage is not a market manipulator but an honest player contributing to the social welfare. It helps electricity market researchers and operators better understand the economic withholding behavior of storage and reform market policies to maximize storage contributing to a cost-efficient decolonization.