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Deployed SAE level 4+ Automated Driving Systems (ADS) without a human driver are currently operational ride-hailing fleets on surface streets in the United States. This current use case and future applications of this technology will determine where and when the fleets operate, potentially resulting in a divergence from the distribution of driving of some human benchmark population within a given locality. Existing benchmarks for evaluating ADS performance have only done county-level geographical matching of the ADS and benchmark driving exposure in crash rates. This study presents a novel methodology for constructing dynamic human benchmarks that adjust for spatial and temporal variations in driving distribution between an ADS and the overall human driven fleet. Dynamic benchmarks were generated using human police-reported crash data, human vehicle miles traveled (VMT) data, and over 20 million miles of Waymo's rider-only (RO) operational data accumulated across three US counties. The spatial adjustment revealed significant differences across various severity levels in adjusted crash rates compared to unadjusted benchmarks with these differences ranging from 10% to 47% higher in San Francisco, 12% to 20% higher in Maricopa, and 7% lower to 34% higher in Los Angeles counties. The time-of-day adjustment in San Francisco, limited to this region due to data availability, resulted in adjusted crash rates 2% lower to 16% higher than unadjusted rates, depending on severity level. The findings underscore the importance of adjusting for spatial and temporal confounders in benchmarking analysis, which ultimately contributes to a more equitable benchmark for ADS performance evaluations.

Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-computer interaction. In this paper, we propose a Dialogue-Aware Transformer framework (DAT) with Modality-Group Fusion (MGF), which relies solely on audio-visual input and is language-independent, for estimating human engagement in conversations. Specifically, our method employs a modality-group fusion strategy that independently fuses audio and visual features within each modality for each person before inferring the entire audio-visual content. This strategy significantly enhances the model's performance and robustness. Additionally, to better estimate the target participant's engagement levels, the introduced Dialogue-Aware Transformer considers both the participant's behavior and cues from their conversational partners. Our method was rigorously tested in the Multi-Domain Engagement Estimation Challenge held by MultiMediate'24, demonstrating notable improvements in engagement-level regression precision over the baseline model. Notably, our approach achieves a CCC score of 0.76 on the NoXi Base test set and an average CCC of 0.64 across the NoXi Base, NoXi-Add, and MPIIGI test sets., Comment: 1st Place on the NoXi Base dataset in the Multi-Domain Engagement Estimation Challenge held by MultiMediate 24, accepted by ACM Multimedia 2024. The source code is available at \url{https://github.com/MSA-LMC/DAT}

Intent is a significant latent factor influencing user-item interaction sequences. Prevalent sequence recommendation models that utilize contrastive learning predominantly rely on single-intent representations to direct the training process. However, this paradigm oversimplifies real-world recommendation scenarios, attempting to encapsulate the diversity of intents within the single-intent level representation. SR models considering multi-intent information in their framework are more likely to reflect real-life recommendation scenarios accurately.

Dynamic facial expression recognition (DFER) is essential for understanding human emotions and behavior. However, conventional DFER methods, which primarily use dynamic facial data, often underutilize static expression images and their labels, limiting their performance and robustness. To overcome this, we introduce UniLearn, a novel unified learning paradigm that integrates static facial expression recognition (SFER) data to enhance DFER task. UniLearn employs a dual-modal self-supervised pre-training method, leveraging both facial expression images and videos to enhance a ViT model's spatiotemporal representation capability. Then, the pre-trained model is fine-tuned on both static and dynamic expression datasets using a joint fine-tuning strategy. To prevent negative transfer during joint fine-tuning, we introduce an innovative Mixture of Adapter Experts (MoAE) module that enables task-specific knowledge acquisition and effectively integrates information from both static and dynamic expression data. Extensive experiments demonstrate UniLearn's effectiveness in leveraging complementary information from static and dynamic facial data, leading to more accurate and robust DFER. UniLearn consistently achieves state-of-the-art performance on FERV39K, MAFW, and DFEW benchmarks, with weighted average recall (WAR) of 53.65\%, 58.44\%, and 76.68\%, respectively. The source code and model weights will be publicly available at \url{https://github.com/MSA-LMC/UniLearn}.

We use algebraic invariant theory to study three weight enumerators of self-dual quantum codes over finite fields. We show that the weight enumerators of self-dual quantum codes can be expressed algebraically by two polynomials and the double weight enumerators of self-dual quantum codes can be expressed algebraically by five polynomials. We also explicitly compute the complete weight enumerators of some special self-dual quantum codes. Our approach avoids applying the well-known Molien's formula and demonstrates the potential of employing invariant theory to compute weight enumerators of quantum codes., Comment: 17 pages

Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the proliferation of diverse multimodal social media content including text, and images multimodal stance detection (MSD) has become a crucial research area. However, existing MSD studies have focused on modeling stance within individual text-image pairs, overlooking the multi-party conversational contexts that naturally occur on social media. This limitation stems from a lack of datasets that authentically capture such conversational scenarios, hindering progress in conversational MSD. To address this, we introduce a new multimodal multi-turn conversational stance detection dataset (called MmMtCSD). To derive stances from this challenging dataset, we propose a novel multimodal large language model stance detection framework (MLLM-SD), that learns joint stance representations from textual and visual modalities. Experiments on MmMtCSD show state-of-the-art performance of our proposed MLLM-SD approach for multimodal stance detection. We believe that MmMtCSD will contribute to advancing real-world applications of stance detection research., Comment: ACM MM2024

We develop a new cohomology theory for finite-dimensional left-symmetric color algebras and their finite-dimensional bimodules, establishing a connection between Lie color cohomology and left-symmetric color cohomology. We prove that the cohomology of a left-symmetric color algebra $A$ with coefficients in a bimodule $V$ can be computed by a lower degree cohomology of the corresponding Lie color algebra with coefficients in Hom$(A,V)$, generalizing a result of Dzhumadil'daev in right-symmetric cohomology. We also explore the varieties of two-dimensional and three-dimensional left-symmetric color algebras., Comment: 19 pages

Low-energy excitations may manifest intricate behaviors of correlated electron systems and provide essential insights into the dynamics of quantum states and phase transitions. We study a two-orbital Hubbard model featuring the so-called holon-doublon low-energy excitations in the Mott insulating narrow band in the orbital-selective Mott phase (OSMP). We employ an improved dynamical mean-field theory (DMFT) technique to calculate the spectral functions at zero temperature. We show that the holon-doublon bound state is not the sole component of the low-energy excitations. Instead, it should be a bound state composed of a Kondo-like state in the wide band and a doublon in the narrow band, named inter-band Kondo-like (IBK) bound states. Notably, as the bandwidths of the two bands approach each other, we find anomalous IBK bound-state excitations in the metallic {\em wide} band., Comment: 5 pages, 4 figures

High-$T_c$ superconductivity (SC) was recently found in the bilayer material La$_3$Ni$_2$O$_7$ (La327) under high pressures. We study the bilayer two-orbital Hubbard model derived from the band structure of the La327. The model is solved by cluster dynamical mean-field theory (CDMFT) with natural orbitals renormalization group (NORG) as impurity solver at zero temperature, considering only normal states. With hole doping, we have observed sequentially the Mott insulator (Mott), pseudogap (PG), non-Fermi liquid (NFL), and Fermi liquid (FL) phases, with quantum correlations decreasing. The ground state of the La327 is in the NFL phase with Hund spin correlation, which transmits the Ni-$3d_{z^2}$ ($z$) orbital inter-layer AFM correlation to the Ni-$3d_{x^2-y^2}$ orbitals. When the $\sigma$-bonding state of the $z$ orbitals ($z+$) is no longer fully filled, the inter-layer antiferromagnetic (AFM) correlations weaken rapidly. At low pressures, the fully filled $z+$ band supports a strong inter-layer AFM correlations, potentially favoring short-range spin density wave (SDW) and suppressing SC. Hole doping at low pressures may achieve a similar effect to high pressures, under which the $z+$ band intersects with the Fermi level, and consequently the spin correlations weaken remarkably, potentially suppressing the possible short-range SDW and favoring SC., Comment: 8 pages, 9 figures

Steam condensation on the surface of a solid is a widely observed mode of energy transfer in nature and various industrial applications. The condensation efficiency is closely related to the material properties and geometric morphology of the solid surface, as well as the method of liquid removal. Despite many surface modification strategies at the micro and nano levels having been employed to enhance steam condensation, understanding how to regulate steam condensation and liquid removal on complex surface morphologies remains incomplete. Here, we report a method that uses superhydrophilic and superhydrophobic coatings as well as spiked surfaces to achieve efficient steam condensation and rapid removal of the liquid. We reveal that on a copper plate with millimeter-scale spikes, hydrophobic spiked surfaces facilitate the dropwise condensation, while hydrophilic bottom grooves promote liquid spreading, and the suction in capillary gaps can promptly remove the condensate liquid. This method achieves a high condensation efficiency without relying on gravity. Additionally, we demonstrate that the condensation on spiked surfaces has a significant suction effect, as it continuously attracts the steam flow, thereby altering the flow direction. This finding provides a new approach for efficient steam condensation technology and opens up a promising pathway for providing circulation power and improving condensation efficiency in phase-change heat transfer devices such as heat pipes.

We study the matrix-variate regression problem $Y_i = \sum_{k} \beta_{1k} X_i \beta_{2k}^{\top} + E_i$ for $i=1,2\dots,n$ in the high dimensional regime wherein the response $Y_i$ are matrices whose dimensions $p_{1}\times p_{2}$ outgrow both the sample size $n$ and the dimensions $q_{1}\times q_{2}$ of the predictor variables $X_i$ i.e., $q_{1},q_{2} \ll n \ll p_{1},p_{2}$. We propose an estimation algorithm, termed KRO-PRO-FAC, for estimating the parameters $\{\beta_{1k}\} \subset \Re^{p_1 \times q_1}$ and $\{\beta_{2k}\} \subset \Re^{p_2 \times q_2}$ that utilizes the Kronecker product factorization and rearrangement operations from Van Loan and Pitsianis (1993). The KRO-PRO-FAC algorithm is computationally efficient as it does not require estimating the covariance between the entries of the $\{Y_i\}$. We establish perturbation bounds between $\hat{\beta}_{1k} -\beta_{1k}$ and $\hat{\beta}_{2k} - \beta_{2k}$ in spectral norm for the setting where either the rows of $E_i$ or the columns of $E_i$ are independent sub-Gaussian random vectors. Numerical studies on simulated and real data indicate that our procedure is competitive, in terms of both estimation error and predictive accuracy, compared to other existing methods.

Utilizing the non-equilibrium Green's function method, we study the local heat current flow of phonons in nanoscale ballistic graphene nanoribbon, where boundary scattering leads to the formation of atomic-scale current vortices. We further map out the atomic temperature distribution in the ribbon with B\"uttiker's probe approach. From the heat current and temperature distribution, we observe inverted temperature response in the ribbon, where the heat current direction goes from the colder to the hotter region. Moreover, we show that atomic scale defect can generate heat vortex at certain frequency, but it is averaged out when including contributions from all the phonon modes. Meanwhile, our results have recovered residual-resistivity dipole features manifested at the vicinity of local defects. These results extend the study of local heat vortex and negative temperature response in the bulk hydrodynamic regime to the atomic-scale ballistic regime, further confirming boundary scattering is crucial to generate backflow of heat current.

Let $k$ be a field of any characteristic, $V$ a finite-dimensional vector space over $k$, and $S^d(V^*)$ be the $d$-th symmetric power of the dual space $V^*$. Given a linear map $\varphi$ on $V$ and an eigenvector $w$ of $\varphi$, we prove that the pair $(\varphi, w)$ can be used to construct a new Lie algebra structure on $S^d(V^*)$. We prove that this Lie algebra structure is solvable, and in particular, it is nilpotent if $\varphi$ is a nilpotent map. We also classify the Lie algebras for all possible pairs $(\varphi, w)$, when $k=\mathbb{C}$ and $V$ is two-dimensional., Comment: 11 pages; accepted for publication by American Mathematical Monthly

We use invariant theory of finite groups to study shape enumerators of self-dual linear codes in a finite NRT metric space. We provide a new approach that avoids applying Molien's formula to compute all possible shape enumerators. We also explicitly compute the shape enumerators of some low-dimensional self-dual NRT codes over an arbitrary finite field., Comment: 13 pages; accepted for publication by SIAM J. Discrete Math

With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the US, we are now approaching an inflection point, where the process of retrospectively evaluating ADS safety impact can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a "benchmark" crash rate. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identify important dependencies in controlling for geographic region, road type, and vehicle type, and show how failing to control for these features can bias results. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks., Comment: Accepted Manuscript for publication in Traffic Injury Prevention

This paper examines the safety performance of the Waymo Driver, an SAE level 4 automated driving system (ADS) used in a rider-only (RO) ride-hailing application without a human driver, either in the vehicle or remotely. ADS crash data was derived from NHTSA's Standing General Order (SGO) reporting over 7.14 million RO miles through the end of October 2023 in Phoenix, AZ, San Francisco, CA, and Los Angeles, CA. When considering all locations together, the any-injury-reported crashed vehicle rate was 0.6 incidents per million miles (IPMM) for the ADS vs 2.80 IPMM for the human benchmark, an 80% reduction or a human crash rate that is 5 times higher than the ADS rate. Police-reported crashed vehicle rates for all locations together were 2.1 IPMM for the ADS vs. 4.68 IPMM for the human benchmark, a 55% reduction or a human crash rate that was 2.2 times higher than the ADS rate. Police-reported and any-injury-reported crashed vehicle rate reductions for the ADS were statistically significant when compared in San Francisco and Phoenix, as well as combined across all locations (except for any-injury-reported in Phoenix). The any property damage or injury comparison had statistically significant decrease in 3 comparisons, but also non-significant results in 3 other benchmarks. Given imprecision in the benchmark estimate and multiple potential sources of underreporting biasing the benchmarks, caution should be taken when interpreting the results of the any property damage or injury comparison. Together, these crash-rate results should be interpreted as a directional and continuous confidence growth indicator, together with other methodologies, in a safety case approach., Comment: Accepted Manuscript for publication in Traffic Injury Prevention

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations, e.g., insufficient quantity and diversity of pose, occlusion and illumination, as well as the inherent ambiguity of facial expressions. In contrast, static facial expression recognition (SFER) currently shows much higher performance and can benefit from more abundant high-quality training data. Moreover, the appearance features and dynamic dependencies of DFER remain largely unexplored. To tackle these challenges, we introduce a novel Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and dynamic information implicitly encoded in extracted facial landmark-aware features, thereby significantly improving DFER performance. Firstly, we build and train an image model for SFER, which incorporates a standard Vision Transformer (ViT) and Multi-View Complementary Prompters (MCPs) only. Then, we obtain our video model (i.e., S2D), for DFER, by inserting Temporal-Modeling Adapters (TMAs) into the image model. MCPs enhance facial expression features with landmark-aware features inferred by an off-the-shelf facial landmark detector. And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos. Notably, MCPs and TMAs only increase a fraction of trainable parameters (less than +10\%) to the original image model. Moreover, we present a novel Emotion-Anchors (i.e., reference samples for each emotion category) based Self-Distillation Loss to reduce the detrimental influence of ambiguous emotion labels, further enhancing our S2D. Experiments conducted on popular SFER and DFER datasets show that we achieve the state of the art., Comment: The code is available at https://github.com/MSA-LMC/S2D. The paper has been accepted for publication in IEEE Transactions on Affective Computing (TAFFC)

Modeling and simulation of multiphase flows in complex geomerties are challenging due to the complexity in describing the interface topology changes among different phases and the difficulty in implementing the boundary conditions on the irregular solid surface. In this work, we first developed a diffuse-domain (DD) based phase-field model for multiphase flows in complex geometries. In this model, the irregular fluid region is embedded into a larger and regular domain by introducing a smooth characteristic function. Then, the reduction-consistent and conservative phase-field equation for the multiphase field and the consistent and conservative Navier-Stokes equations for the flow field are reformulated as the diffuse-domain based consistent and conservative (DD-CC) equations where some additional source terms are added to reflect the effects of boundary conditions. In this case, there is no need to directly treat the complex boundary conditions on the irregular solid surface, and additionally, based on a matched asymptotic analysis, it is also shown that the DD-CC equations can converge to the original governing equations as the interface width parameter tends to zero. Furthermore, to solve the DD-CC equations, we proposed a novel and simple lattice Boltzmann (LB) method with a Hermite-moment-based collision matrix which can not only keep consistent and conservation properties, but also improve the numerical stability with a flexible parameter. With the help of the direct Taylor expansion, the macroscopic DD-CC equations can be recovered correctly from the present LB method. Finally, to test the capacity of LB method, several benchmarks and complex problems are considered, and the numerical results show that the present LB method is accurate and efficient for the multiphase flows in complex geomerties., Comment: 22 pages, 9 figures

Possible high-$T_c$ superconductivity (SC) has been found experimentally in the bilayer material La$_3$Ni$_2$O$_7$ under high pressure recently, in which the Ni-$3d_{3z^2-r^2}$ and $3d_{x^2-y^2}$ orbitals are expected to play a key role in the electronic structure and the SC. Here we study the two-orbital electron correlations and the nature of the SC using the bilayer two-orbital Hubbard model downfolded from the band structure of La3Ni2O7 in the framework of the dynamical mean-field theory. We find that each of the two orbitals forms $s_\pm$-wave SC pairing. Because of the nonlocal inter-orbital hoppings, the two-orbital SCs are concomitant and they transition to Mott insulating states simultaneously when tuning the system to half filling. The Hund's coupling induced local inter-orbital spin coupling enhances the electron correlations pronouncedly and is crucial to the SC., Comment: 5 pages, 5 figures

In isolated nonlinear optical waveguide arrays with bounded energy spectrum, simultaneous conservation of energy and power of the optical modes enables study of coupled thermal and particle transport in the negative temperature regime. Here, based on exact numerical simulation and rationale from Landauer formalism, we predict generic violation of the Wiedemann-Franz law in such systems. This is rooted in the spectral decoupling of thermal and power current of optical modes, and their different temperature dependence. Our work extends the study of coupled thermal and particle transport into unprecedented regimes, not reachable in natural condensed matter and atomic gas systems.

Solid materials possess both long-range order and some degree of disorder are critical for understanding the nature of crystal and glassy state, but how to controllable introduce specific type of disorder into a crystalline material is a big challenge. Our previous work indicated that weakening the inter-layer interaction is an effective strategy to import disorders between the layers.Here, we illustrated that the inter-layer interaction can be weakened to around 1/60 of that of graphite in the self-assembled material, a two-dimensions frameworks formed by B-C-T-A with Cu nodes, which has an obvious layered-structure.

In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge. ABAW 2023 aims to tackle the challenge of affective behavior analysis in natural contexts, with the ultimate goal of creating intelligent machines and robots that possess the ability to comprehend human emotions, feelings, and behaviors. For the Expression Classification Challenge, we propose a streamlined approach that handles the challenges of classification effectively. However, our main contribution lies in our use of diverse models and tools to extract multimodal features such as audio and video cues from the Hume-Reaction dataset. By studying, analyzing, and combining these features, we significantly enhance the model's accuracy for sentiment prediction in a multimodal context. Furthermore, our method achieves outstanding results on the Emotional Reaction Intensity (ERI) Estimation Challenge, surpassing the baseline method by an impressive 84\% increase, as measured by the Pearson Coefficient, on the validation dataset., Comment: Solutions of HFUT-CVers Team at the 5th ABAW Competition (CVPR 2023 workshop)

The natural orbitals renormalization group (NORG) has previously been proposed as an efficient numerical method for solving zero-temperature properties of multisite and multiorbital quantum impurity systems. Here, we implement the NORG as an impurity solver for dynamical mean-field theory (DMFT). In comparison with the exact diagonalization method, the NORG method can treat much more bath sites in an impurity model to which the DMFT maps a lattice model and can find accurate zero-temperature Matsubara and low-frequency retarded Green's functions. We demonstrate the effectiveness of this method on a two-orbital Hubbard model on the Bethe lattice and find successfully the orbital selective Mott transition with a Kondo resonance peak in the wide band and two holon-doublon bound state excitation peaks in the narrow band., Comment: 7 pages, 6 figures