Miracle-dz/UniFAR-FTUs huggingface dataset:
Open-Set Facial Expression Recognition

Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes. However, recent research works (Cowen et al. 2021; Bryant et al. 2022; Kollias 2023) point out that there are far more expressions than the basic ones. Thus, when these models are deployed in the real world, they may encounter unknown classes, such as compound expressions that cannot be classified into existing basic classes. To address this issue, we propose the open-set FER task for the first time. Though there are many existing open-set recognition methods, we argue that they do not work well for open-set FER because FER data are all human faces with very small inter-class distances, which makes the open-set samples very similar to close-set samples. In this paper, we are the first to transform the disadvantage of small inter-class distance into an advantage by proposing a new way for open-set FER. Specifically, we find that small inter-class distance allows for sparsely distributed pseudo labels of open-set samples, which can be viewed as symmetric noisy labels. Based on this novel observation, we convert the open-set FER to a noisy label detection problem. We further propose a novel method that incorporates attention map consistency and cycle training to detect the open-set samples. Extensive experiments on various FER datasets demonstrate that our method clearly outperforms state-of-the-art open-set recognition methods by large margins. Code is available at https://github.com/zyh-uaiaaaa.
Reward-Relevance-Filtered Linear Offline Reinforcement Learning

This paper studies offline reinforcement learning with linear function approximation in a setting with decision-theoretic, but not estimation sparsity. The structural restrictions of the data-generating process presume that the transitions factor into a sparse component that affects the reward and could affect additional exogenous dynamics that do not affect the reward. Although the minimally sufficient adjustment set for estimation of full-state transition properties depends on the whole state, the optimal policy and therefore state-action value function depends only on the sparse component: we call this causal/decision-theoretic sparsity. We develop a method for reward-filtering the estimation of the state-action value function to the sparse component by a modification of thresholded lasso in least-squares policy evaluation. We provide theoretical guarantees for our reward-filtered linear fitted-Q-iteration, with sample complexity depending only on the size of the sparse component.
Framework for the Quantum Mechanical Sum of Possibilities and Meaning for Field Theory and Gravity

In quantum mechanics, the measureable quantities of a given theory are predicted by performing a weighted sum over possibilities. We show how to arrange the possibilities into bundles such that the associated subsums can be viewed as well-defined theories on their own right. These bundles are submani$\textit{folds}$ of $\textit{possi}$bilities which we call possifolds. We collect and prove some basic facts about possifolds. Especially, we show that possifolds are ensembles of what in a certain broadly defined sense that we explain can be regarded as soliton excitations (soliton-possifold correspondence). We provide an outlook on some applications. Among other things, we illustrate the use of the developed framework for the example of the Lieb-Liniger model. It describes non-relativistic bosons with an attractive interaction. We derive a dual theory describing the lowest-lying energy excitation modes. While the standard Bogoliubov-approximation breaks down at the critical point, our derived summation prescription stays regular. In the Bogoliubov-limit we observe the summation to possess an enhanced symmetry at this point while the summation cannot be ignored there. We finally provide a glimpse on the restrictions black hole physics implies in this context for the gravitational path integral.
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies

The aspiration of the next generation's autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology. Recent advancement in AD simulation, closed-loop model training, and AD big data engine have gained some valuable experience. However, there is a lack of systematic knowledge and deep understanding regarding how to build efficient data-centric AD technology for AD algorithm self-evolution and better AD big data accumulation. To fill in the identified research gaps, this article will closely focus on reviewing the state-of-the-art data-driven autonomous driving technologies, with an emphasis on the comprehensive taxonomy of autonomous driving datasets characterized by milestone generations, key features, data acquisition settings, etc. Furthermore, we provide a systematic review of the existing benchmark closed-loop AD big data pipelines from the industrial frontier, including the procedure of closed-loop frameworks, key technologies, and empirical studies. Finally, the future directions, potential applications, limitations and concerns are discussed to arouse efforts from both academia and industry for promoting the further development of autonomous driving. The project repository is available at: https://github.com/LincanLi98/Awesome-Data-Centric-Autonomous-Driving.
Persona-centric Metamorphic Relation guided Robustness Evaluation for Multi-turn Dialogue Modelling

Recently there has been significant progress in the field of dialogue system thanks to the introduction of training paradigms such as fine-tune and prompt learning. Persona can function as the prior knowledge for maintaining the personality consistency of dialogue systems, which makes it perform well on accuracy. Nonetheless, the conventional reference-based evaluation method falls short in capturing the genuine text comprehension prowess of the model, significantly relying on the quality of data annotation. In contrast, the application of metamorphic testing offers a more profound insight into the model's distinct capabilities without necessitating supplementary annotation labels. This approach furnishes a more comprehensive portrayal of the model's intricacies and exposes intricacies concealed within reference-based validation techniques. Consequently, we introduce a persona-centric metamorphic relation construction for metamorphic testing, aimed at evaluating both the persona consistency and robustness of personalized dialogue models. For that reason, this work evaluates several widely used training paradigms including learning from scratch, pretrain + fine-tune and prompt learning in personalized dialogue retrieval to know if they are more robust or if they have the same flaws as their predecessor. Under three kinds of designed metamorphic relations with consistent outputs, our experimental results reveal that prompt learning shows stronger robustness compared to training from scratch and fine-tune. Although tested retrieval models gain competitively high retrieval accuracy according to the traditional reference-based validation, they are still fragile and demonstrate various unexpected behaviors, thus there is still room for future improvement in personalized dialogue retrieval.
On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge

Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels with intersymbol interference has been introduced. This so-called BCJRNet algorithm utilizes neural networks to calculate channel likelihoods. BCJRNet has demonstrated resilience against inaccurate channel tap estimations when applied to a time-invariant channel with ideal exponential decay profiles. However, its generalization capabilities for practically-relevant time-varying channels, where the receiver can only access incorrect channel parameters, remain largely unexplored. The primary contribution of this paper is to expand upon the results from existing literature to encompass a variety of imperfect channel knowledge cases that appear in real-world transmissions. Our findings demonstrate that BCJRNet significantly outperforms the conventional BCJR algorithm for stationary transmission scenarios when learning from noisy channel data and with imperfect channel decay profiles. However, this advantage is shown to diminish when the operating channel is also rapidly time-varying. Our results also show the importance of memory assumptions for conventional BCJR and BCJRNet. An underestimation of the memory largely degrades the performance of both BCJR and BCJRNet, especially in a slow-decaying channel. To mimic a situation closer to a practical scenario, we also combined channel tap uncertainty with imperfect channel memory knowledge. Somewhat surprisingly, our results revealed improved performance when employing the conventional BCJR with an underestimated memory assumption. BCJRNet, on the other hand, showed a consistent performance improvement as the level of accurate memory knowledge increased.
Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread

We develop a simulation framework for studying misinformation spread within online social networks that blends agent-based modeling and natural language processing techniques. While many other agent-based simulations exist in this space, questions over their fidelity and generalization to existing networks in part hinders their ability to provide actionable insights. To partially address these concerns, we create a 'digital clone' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users. We parse these histories to both extract the structure of the network and model the nuanced ways in which information is shared and spread among its members. Unlike many other agent-based methods in this space, information sharing between users in our framework is sensitive to topic of discussion, user preferences, and online community dynamics. To evaluate the fidelity of our method, we seed our cloned network with a set of posts recorded in the base network and compare propagation dynamics between the two, observing reasonable agreement across the twin networks over a variety of metrics. Lastly, we explore how the cloned network may serve as a flexible, low-cost testbed for misinformation countermeasure evaluation and red teaming analysis. We hope the tools explored here augment existing efforts in the space and unlock new opportunities for misinformation countermeasure evaluation, a field that may become increasingly important to consider with the anticipated rise of misinformation campaigns fueled by generative artificial intelligence.
Hypochaos prevents tragedy of the commons in discrete-time eco-evolutionary game dynamics.

While quite a few recent papers have explored game-resource feedback using the framework of evolutionary game theory, almost all the studies are confined to using time-continuous dynamical equations. Moreover, in such literature, the effect of ubiquitous chaos in the resulting eco-evolutionary dynamics is rather missing. Here, we present a deterministic eco-evolutionary discrete-time dynamics in generation-wise non-overlapping population of two types of harvesters-one harvesting at a faster rate than the other-consuming a self-renewing resource capable of showing chaotic dynamics. In the light of our finding that sometimes chaos is confined exclusively to either the dynamics of the resource or that of the consumer fractions, an interesting scenario is realized: The resource state can keep oscillating chaotically, and hence, it does not vanish to result in the tragedy of the commons-extinction of the resource due to selfish indiscriminate exploitation-and yet the consumer population, whose dynamics depends directly on the state of the resource, may end up being composed exclusively of defectors, i.e., high harvesters. This appears non-intuitive because it is well known that prevention of tragedy of the commons usually requires substantial cooperation to be present.
C2Ideas: Supporting Creative Interior Color Design Ideation with Large Language Model

Interior color design is a creative process that endeavors to allocate colors to furniture and other elements within an interior space. While much research focuses on generating realistic interior designs, these automated approaches often misalign with user intention and disregard design rationales. Informed by a need-finding preliminary study, we develop C2Ideas, an innovative system for designers to creatively ideate color schemes enabled by an intent-aligned and domain-oriented large language model. C2Ideas integrates a three-stage process: Idea Prompting stage distills user intentions into color linguistic prompts; Word-Color Association stage transforms the prompts into semantically and stylistically coherent color schemes; and Interior Coloring stage assigns colors to interior elements complying with design principles. We also develop an interactive interface that enables flexible user refinement and interpretable reasoning. C2Ideas has undergone a series of indoor cases and user studies, demonstrating its effectiveness and high recognition of interactive functionality by designers.
Effect of Gravitational Waves on Yang-Mills condensates

In this article, we investigate the interactions of a Yang-Mills (YM) wave fluctuation of a classical isotropic, homogeneous YM condensate, which models gluon plasma, with a Gravitational Wave (GW). We re-analyse the study of fluctuations of the gluon plasma using vector decomposition of the gauge field into scalar, longitudinal, and transverse modes. We find that there is an energy transfer between isotropic gluon condensate and plasmon modes, but the effect is delayed, and dependent on the initial conditions. We also studied quarks in the background of YM condensate and GW. We find that the quarks break the isotropy of the condensate and the GW couples quarks of different flavours of the SU(2) group. The GW induces flavour fluctuations, and this has interesting implications for experimental observations and quark-gluon plasma physics.
A Robot Expressing Emotions Through Gestures: Everyone Outside of Italy Would Understand this?

In the context of our research activities on affective computing and human-robot interaction we are working on both the recognition of human's emotions and the expression of emotions by robots. In our vision, robots will be increasingly present in schools, factories, and homes, and their empathetic behavior may foster their acceptance. In particular, in one of our research, we sought to replicate gestures associated with specific emotions on a social robot, NAO. Our focus was on Ekman's six primary emotions, along with five emotions selected from Plutchik's wheel of emotions. In our opinion the cultural component linked to the expression of emotions through gestures certainly influenced both us and the participants. Thus, we would like to investigate the influence of our culture in the gestural expression of emotion.
Approximation of Pufferfish Privacy for Gaussian Priors

This paper studies how to approximate pufferfish privacy when the adversary's prior belief of the published data is Gaussian distributed. Using Monge's optimal transport plan, we show that $(\epsilon, \delta)$-pufferfish privacy is attained if the additive Laplace noise is calibrated to the differences in mean and variance of the Gaussian distributions conditioned on every discriminative secret pair. A typical application is the private release of the summation (or average) query, for which sufficient conditions are derived for approximating $\epsilon$-statistical indistinguishability in individual's sensitive data. The result is then extended to arbitrary prior beliefs trained by Gaussian mixture models (GMMs): calibrating Laplace noise to a convex combination of differences in mean and variance between Gaussian components attains $(\epsilon,\delta)$-pufferfish privacy.
CoVault: A Secure Analytics Platform

Analytics on personal data, such as individuals' mobility, financial, and health data can be of significant benefit to society. Such data is already collected by smartphones, apps and services today, but liberal societies have so far refrained from making it available for large-scale analytics. Arguably, this is due at least in part to the lack of an analytics platform that can secure data through transparent, technical means (ideally with decentralized trust), enforce source policies, handle millions of distinct data sources, and run queries on billions of records with acceptable query latencies. To bridge this gap, we present an analytics platform called CoVault which combines secure multi-party computation (MPC) with trusted execution environment (TEE)-based delegation of trust to be able execute approved queries on encrypted data contributed by individuals within a datacenter to achieve the above properties. We show that CoVault scales well despite the high cost of MPC. For example, CoVault can process data relevant to epidemic analytics for a country of 80M people (about 11.85B data records/day) on a continuous basis using a core pair for every 20,000 people. Compared to a state-of-the-art MPC-based platform, CoVault can process queries between 7 to over 100 times faster, as well as scale to many sources and big data.
The FinnBrain Multimodal Neonatal Template and Atlas Collection: T1, T2, and DTI brain templates, and accompanying cortical and subcortical atlases

The accurate processing of neonatal and infant brain MRI data is crucially important for developmental neuroscience, but presents challenges that child and adult data do not. Tissue segmentation and image coregistration accuracy can be improved by optimizing template images and / or related segmentation procedures. Here, we describe the construction of the FinnBrain Neonate (FBN-125) template; a multi-contrast template with T1- and T2-weighted as well as diffusion tensor imaging derived fractional anisotropy and mean diffusivity images. The template is symmetric and aligned to the Talairach-like MNI 152 template and has high spatial resolution (0.5 mm3). In addition, we provide atlas labels, constructed from manual segmentations, for cortical grey matter, white matter, cerebrospinal fluid, brainstem, and cerebellum as well as the bilateral hippocampi, amygdalae, caudate nuclei, putamina, globi pallidi, and thalami. We provide this multi-contrast template along with the labelled atlases for the use of the neuroscience community in the hope that it will prove useful in advancing developmental neuroscience, for example, by helping to achieve reliable means for spatial normalization and measures of neonate brain structure via automated computational methods. Additionally, we provide standard co-registration files that will enable investigators to reliably transform their statistical maps to the adult MNI space, which has the potential to improve the consistency and comparability of neonatal studies or the use of adult MNI space atlases in neonatal neuroimaging.
Epigenetic control of multiple genes with a single lentiviral vector encoding transcriptional repressors fused to compact zinc finger arrays

Gene silencing without gene editing holds great potential for the development of safe therapeutic applications. Here, we describe a novel strategy to concomitantly repress multiple genes using zinc finger proteins fused to Krüppel-Associated Box repression domains (ZF-Rs). This was achieved via the optimization of a lentiviral system tailored for the delivery of ZF-Rs in hematopoietic cells. We showed that an optimal design of the lentiviral backbone is crucial to multiplex up to three ZF-Rs or two ZF-Rs and a chimeric antigen receptor. ZF-R expression had no impact on the integrity and functionality of transduced cells. Furthermore, gene repression in ZF-R-expressing T cells was highly efficient in vitro and in vivo during the entire monitoring period (up to ten weeks), and it was accompanied by epigenetic remodeling events. Finally, we described an approach to improve ZF-R specificity to illustrate the path towards the generation of ZF-Rs with a safe clinical profile. In conclusion, we successfully developed an epigenetic-based cell engineering approach for concomitant modulation of multiple gene expressions that bypass the risks associated with DNA editing.
Dynamic inter-domain transformations mediate the allosteric regulation of human 5, 10-methylenetetrahydrofolate reductase

5,10-methylenetetrahydrofolate reductase (MTHFR) commits folate-derived one-carbon units to generate the methyl-donor S-adenosyl-L-methionine (SAM). Eukaryotic MTHFR appends to the well-conserved catalytic domain (CD) a unique regulatory domain (RD) that confers feedback inhibition by SAM. We determined cryo-electron microscopy structures of human MTHFR bound to SAM and its demethylated product S-adenosyl-L-homocysteine (SAH). In the active state, with the RD bound to a single SAH, the CD is flexible and exposes its active site for catalysis. However, in the inhibited state the RD pocket is remodelled, exposing a second SAM-binding site that was previously occluded. Dual-SAM bound MTHFR demonstrates a substantially rearranged inter-domain linker that reorients the CD, inserts a loop into the active site, positions Tyr404 to bind the cofactor FAD, and blocks substrate access. Our data therefore explain the long-distance regulatory mechanism of MTHFR inhibition, underpinned by the transition between dual-SAM and single-SAH binding in response to cellular methylation status.
SPATIAL VARIABILITY OF MIDTROPOSPHERIC CIRCULATION PATTERNS AND ASSOCIATED SURFACE CLIMATE IN THE UNITED STATES DURING ENSO WINTERS

Numerous studies have documented that the Pacific/North American (PNA) pattern is the dominant extratropical response to ENSO forcing affecting the circulation over North America. However, the PNA is not the sole pattern that occurs during ENSO events. This study identifies the dominant synoptic circulation patterns and associated temperature and precipitation departures that occur during ENSO winters. Using standardized departures of 500 mbar heights over North America and the North Pacific Ocean, a subjective classification of the anomaly maps for winter months identified as warm ENSO events identifies three basic categories of 500 mbar standardized anomaly patterns: Variations of the PNA pattern, the reverse PNA pattern, and patterns with no PNA signature. Composite standardized anomaly maps of the synoptic categories of 500 mbar heights as well as composites of standardized temperature and precipitation departures for the contiguous United States were constructed. Three variations of the PNA, accounti.