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The evolution of vehicle-to-everything (V2X) communication brings significant challenges, such as data integrity and vulnerabilities stemming from centralized management. This paper presents an innovative integration of decentralized blockchain technology with V2X communication through a multi-layered architecture that combines the Simulation of Urban Mobility (SUMO) traffic simulator and the BlockSim blockchain simulator. In addition, as the Social Internet of Vehicles (SIoV) emerges, efficient resource management becomes indispensable for ensuring seamless communication. We also propose a reference multi-connectivity management method named Enhanced MAX-SINR, designed to advance research in blockchain-specific approaches, taking into account retransmission successfull rates. We evaluate blockchain performance in diverse environments such as urban, suburban, and rural areas, demonstrating that enhancing the success rate of retransmitted blockchain-related messages significantly boosts blockchain transaction performance and provides a foundation for developing intelligent SIoV systems.

With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campaigns), a novel approach for generating find-grained labeled synthetic audit logs that mimic real-world system logs while embedding stealthy APT attacks. SAGA generates configurable audit logs for arbitrary duration, blending benign logs from normal operations with malicious logs based on the definitions the MITRE ATT\&CK framework. Malicious audit logs follow an APT lifecycle, incorporating various attack techniques at each stage. These synthetic logs can serve as benchmark datasets for training machine learning models and assessing diverse APT detection methods. To demonstrate the usefulness of synthetic audit logs, we ran established baselines of event-based technique hunting and APT campaign detection using various synthetic audit logs. In addition, we show that a deep learning model trained on synthetic audit logs can detect previously unseen techniques within audit logs.

We present a machine-learning interatomic potential (MLIP) framework, which substantially accelerates the prediction of lattice thermal conductivity for both particle-like and wave-like thermal transport, including three-phonon and four-phonon scattering processes, and achieves speedups of five orders of magnitude compared to the conventional DFT calculations. We illustrate our approach through an in-depth study of Mg2GeSe4 as an exemplar thermoelectric material. Four phonon scattering is found to reduce lattice thermal conductivity by 22.5% at 300K and 26.7% at 900 K. The particle-like contribution to lattice thermal conductivity decreases, while the wave-like component increases with increasing temperature. The maximum figure of merit zT at 900K is found to be 0.49 for the n-type and 0.45 for the p-type Mg2GeSe4, respectively. Our analysis reveals that a substantial contribution to the Seebeck coefficient is driven by multi-band features in the valence band. Our study gives insight into the promising thermoelectric performance of Mg2GeSe4 and provides an efficient and accurate scheme for developing rational materials discovery strategies and practical applications of thermoelectric materials.

As Advanced Persistent Threats (APTs) grow increasingly sophisticated, the demand for effective detection methods has intensified. This study addresses the challenge of identifying APT campaign attacks through system event logs. A cascading approach, name SFM, combines Technique hunting and APT campaign attribution. Our approach assumes that real-world system event logs contain a vast majority of normal events interspersed with few suspiciously malicious ones and that these logs are annotated with Techniques of MITRE ATT&CK framework for attack pattern recognition. Then, we attribute APT campaign attacks by aligning detected Techniques with known attack sequences to determine the most likely APT campaign. Evaluations on five real-world APT campaigns indicate that the proposed approach demonstrates reliable performance.

We propose three transition-metal adatom systems on 3C-SiC(111) surfaces as a versatile platform to realize massless Dirac fermions and flat bands with strong electronic correlations. Using density functional theory combined with the constrained random phase approximation and dynamical mean-field theory, we investigate the electronic properties of Ti, V, and Cr adatoms. The triangular surface lattices exhibit narrow bandwidths and effective two-band Hubbard models near the Fermi level, originating from partially filled, localized d-orbitals of the adatoms. Our study reveals a materials trend from a flat band Fermi liquid (Cr) via a paramagnetic Mott insulator with large local moments (V) to a Mott insulator on the verge to a heavy Dirac semimetal (Ti) showcasing the diverse nature of these strongly correlated systems. Specifically, the flat bands in the Cr and the well-defined Dirac cones in the strained metallic~Ti lattice indicate high potential for realizing topological and correlated phases.

Distributed learning is essential to train machine learning algorithms across heterogeneous agents while maintaining data privacy. We conduct an asymptotic analysis of Unified Distributed SGD (UD-SGD), exploring a variety of communication patterns, including decentralized SGD and local SGD within Federated Learning (FL), as well as the increasing communication interval in the FL setting. In this study, we assess how different sampling strategies, such as i.i.d. sampling, shuffling, and Markovian sampling, affect the convergence speed of UD-SGD by considering the impact of agent dynamics on the limiting covariance matrix as described in the Central Limit Theorem (CLT). Our findings not only support existing theories on linear speedup and asymptotic network independence, but also theoretically and empirically show how efficient sampling strategies employed by individual agents contribute to overall convergence in UD-SGD. Simulations reveal that a few agents using highly efficient sampling can achieve or surpass the performance of the majority employing moderately improved strategies, providing new insights beyond traditional analyses focusing on the worst-performing agent., Comment: To appear in NeurIPS 2024

We study behavior change-based visual risk object identification (Visual-ROI), a critical framework designed to detect potential hazards for intelligent driving systems. Existing methods often show significant limitations in spatial accuracy and temporal consistency, stemming from an incomplete understanding of scene affordance. For example, these methods frequently misidentify vehicles that do not impact the ego vehicle as risk objects. Furthermore, existing behavior change-based methods are inefficient because they implement causal inference in the perspective image space. We propose a new framework with a Bird's Eye View (BEV) representation to overcome the above challenges. Specifically, we utilize potential fields as scene affordance, involving repulsive forces derived from road infrastructure and traffic participants, along with attractive forces sourced from target destinations. In this work, we compute potential fields by assigning different energy levels according to the semantic labels obtained from BEV semantic segmentation. We conduct thorough experiments and ablation studies, comparing the proposed method with various state-of-the-art algorithms on both synthetic and real-world datasets. Our results show a notable increase in spatial and temporal consistency, with enhancements of 20.3% and 11.6% on the RiskBench dataset, respectively. Additionally, we can improve computational efficiency by 88%. We achieve improvements of 5.4% in spatial accuracy and 7.2% in temporal consistency on the nuScenes dataset., Comment: 8 pages, 4 figures

We introduce Synthetic Playground (SynPlay), a new synthetic human dataset that aims to bring out the diversity of human appearance in the real world. We focus on two factors to achieve a level of diversity that has not yet been seen in previous works: i) realistic human motions and poses and ii) multiple camera viewpoints towards human instances. We first use a game engine and its library-provided elementary motions to create games where virtual players can take less-constrained and natural movements while following the game rules (i.e., rule-guided motion design as opposed to detail-guided design). We then augment the elementary motions with real human motions captured with a motion capture device. To render various human appearances in the games from multiple viewpoints, we use seven virtual cameras encompassing the ground and aerial views, capturing abundant aerial-vs-ground and dynamic-vs-static attributes of the scene. Through extensive and carefully-designed experiments, we show that using SynPlay in model training leads to enhanced accuracy over existing synthetic datasets for human detection and segmentation. The benefit of SynPlay becomes even greater for tasks in the data-scarce regime, such as few-shot and cross-domain learning tasks. These results clearly demonstrate that SynPlay can be used as an essential dataset with rich attributes of complex human appearances and poses suitable for model pretraining. SynPlay dataset comprising over 73k images and 6.5M human instances, is available for download at https://synplaydataset.github.io/., Comment: Project Page: https://synplaydataset.github.io/

Many-body eigenstates that are neither thermal nor many-body-localized (MBL) were numerically found in certain interacting chains with moderate quasiperiodic potentials. The energy regime consisting of these non-ergodic but extended (NEE) eigenstates has been extensively studied for being a possible many-body mobility edge between the energy-resolved MBL and thermal phases. Recently, the NEE regime was further proposed to be a prethermal phenomenon that generally occurs when different operators spread at sizably different timescales. Here, we numerically examine the mutual independence among the NEE, MBL, and thermal regimes in the lens of eigenstate entanglement spectra (ES). Given the complexity and rich information embedded in ES, we develop an unsupervised learning approach that is designed to quantify the mutual independence among general phases. Our method is first demonstrated on an illustrative toy example that uses RGB color data to represent phases, then applied to the ES of an interacting generalized Aubry Andre model from weak to strong potential strength. We find that while the MBL and thermal regimes are mutually independent, the NEE regime is dependent on the former two and smoothly appears as the potential strength decreases. We attribute our numerically finding to the fact that the ES data in the NEE regime exhibits both an MBL-like fast decay and a thermal-like long tail., Comment: 6 pages, 5 figures

We consider the well-known phonon scattering induced high-temperature linear-in-$T$ metallic resistivity, showing that a naive extrapolation of the effective linearity from high temperatures to $T=0$ leads to an apparent zero-temperature negative resistivity. The precise magnitude of this extrapolated $T=0$ negative resistivity depends on the temperature regime from where the extrapolation is carried out, and approaches the correct physical result of zero resistivity at $T=0$ only if the extrapolation starts from $T\gg T_D$, where $T_D$ is the Debye temperature. We establish a theoretical relationship between the negative intercept and the slope of the linear-in-$T$ resistivity as a function of the temperature $T$ from where the extrapolation is carried out. Experimental implications of our finding are discussed for the much-discussed Planckian behavior of the transport scattering rate., Comment: 7 pages, 6 figures

Since the last century, considerable efforts have been devoted to the study of valley-degenerate narrow gap semiconductors, such as the Pb$_{1-x}$Sn$_x$Se alloy. This material possesses band minima at the $L$-points of their Brillouin zone, yielding a valley degeneracy of four. However, in (111)-oriented films, it is still not fully understood how differences between the longitudinal valley, oriented along the growth axis, and the oblique valleys, oriented at an angle with respect to that axis, appear in magneto-optical infrared spectroscopy. In this work, we report a magnetooptical study on this family of alloys, focusing on an anomaly in the interband transition of the peak intensity ratio of longitudinal and oblique valleys under a magnetic field applied along the [111] direction. Based on the Mitchell-Wallis model, we provide a theoretical fit for the experimental transmission data, which quantitatively explains the spectral shape of the data at magnetic fields as high as 35T. In particular, we attribute this anomalous peak intensity variation to the carrier density difference between the two types of valleys as well as the field-dependent thin-film interference. Our analysis also allows for the extraction of the real and imaginary parts of the dielectric function., Comment: 11 pages, 9 figures. Comments are welcome

Diffusion models have shown tremendous results in image generation. However, due to the iterative nature of the diffusion process and its reliance on classifier-free guidance, inference times are slow. In this paper, we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half, and only requires 1\% trainable parameters of the base model. Furthermore, once trained, our guide model can be applied to various fine-tuned, domain-specific versions of the base diffusion model without the need for additional training: this "plug-and-play" functionality drastically improves inference computation while maintaining the visual fidelity of generated images. Empirically, we show that our approach is able to produce visually appealing results and achieve a comparable FID score to the teacher with as few as 8 to 16 steps., Comment: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 project page: https://5410tiffany.github.io/plug-and-play-diffusion-distillation.github.io/

We observe that current state-of-the-art (SOTA) methods suffer from the performance imbalance issue when performing multi-task reinforcement learning (MTRL) tasks. While these methods may achieve impressive performance on average, they perform extremely poorly on a few tasks. To address this, we propose a new and effective method called STARS, which consists of two novel strategies: a shared-unique feature extractor and task-aware prioritized sampling. First, the shared-unique feature extractor learns both shared and task-specific features to enable better synergy of knowledge between different tasks. Second, the task-aware sampling strategy is combined with the prioritized experience replay for efficient learning on tasks with poor performance. The effectiveness and stability of our STARS are verified through experiments on the mainstream Meta-World benchmark. From the results, our STARS statistically outperforms current SOTA methods and alleviates the performance imbalance issue. Besides, we visualize the learned features to support our claims and enhance the interpretability of STARS., Comment: The first two authors contribute equally

This paper provides an overview of the Fake-EmoReact 2021 Challenge, held at the 9th SocialNLP Workshop, in conjunction with NAACL 2021. The challenge requires predicting the authenticity of tweets using reply context and augmented GIF categories from EmotionGIF dataset. We offer the Fake-EmoReact dataset with more than 453k as the experimental materials, where every tweet is labeled with authenticity. Twenty-four teams registered to participate in this challenge, and 5 submitted their results successfully in the evaluation phase. The best team achieves 93.9 on Fake-EmoReact 2021 dataset using F1 score. In addition, we show the definition of share task, data collection, and the teams' performance that joined this challenge and their approaches.

We introduce VividDream, a method for generating explorable 4D scenes with ambient dynamics from a single input image or text prompt. VividDream first expands an input image into a static 3D point cloud through iterative inpainting and geometry merging. An ensemble of animated videos is then generated using video diffusion models with quality refinement techniques and conditioned on renderings of the static 3D scene from the sampled camera trajectories. We then optimize a canonical 4D scene representation using an animated video ensemble, with per-video motion embeddings and visibility masks to mitigate inconsistencies. The resulting 4D scene enables free-view exploration of a 3D scene with plausible ambient scene dynamics. Experiments demonstrate that VividDream can provide human viewers with compelling 4D experiences generated based on diverse real images and text prompts., Comment: Project page: https://vivid-dream-4d.github.io

We study reward models for long-horizon manipulation tasks by learning from action-free videos and language instructions, which we term the visual-instruction correlation (VIC) problem. Recent advancements in cross-modality modeling have highlighted the potential of reward modeling through visual and language correlations. However, existing VIC methods face challenges in learning rewards for long-horizon tasks due to their lack of sub-stage awareness, difficulty in modeling task complexities, and inadequate object state estimation. To address these challenges, we introduce VICtoR, a novel hierarchical VIC reward model capable of providing effective reward signals for long-horizon manipulation tasks. VICtoR precisely assesses task progress at various levels through a novel stage detector and motion progress evaluator, offering insightful guidance for agents learning the task effectively. To validate the effectiveness of VICtoR, we conducted extensive experiments in both simulated and real-world environments. The results suggest that VICtoR outperformed the best existing VIC methods, achieving a 43% improvement in success rates for long-horizon tasks.

We present a framework for diversifying human poses in a synthetic dataset for aerial-view human detection. Our method firstly constructs a set of novel poses using a pose generator and then alters images in the existing synthetic dataset to assume the novel poses while maintaining the original style using an image translator. Since images corresponding to the novel poses are not available in training, the image translator is trained to be applicable only when the input and target poses are similar, thus training does not require the novel poses and their corresponding images. Next, we select a sequence of target novel poses from the novel pose set, using Dijkstra's algorithm to ensure that poses closer to each other are located adjacently in the sequence. Finally, we repeatedly apply the image translator to each target pose in sequence to produce a group of novel pose images representing a variety of different limited body movements from the source pose. Experiments demonstrate that, regardless of how the synthetic data is used for training or the data size, leveraging the pose-diversified synthetic dataset in training generally presents remarkably better accuracy than using the original synthetic dataset on three aerial-view human detection benchmarks (VisDrone, Okutama-Action, and ICG) in the few-shot regime.

Aerial-view human detection has a large demand for large-scale data to capture more diverse human appearances compared to ground-view human detection. Therefore, synthetic data can be a good resource to expand data, but the domain gap with real-world data is the biggest obstacle to its use in training. As a common solution to deal with the domain gap, the sim2real transformation is used, and its quality is affected by three factors: i) the real data serving as a reference when calculating the domain gap, ii) the synthetic data chosen to avoid the transformation quality degradation, and iii) the synthetic data pool from which the synthetic data is selected. In this paper, we investigate the impact of these factors on maximizing the effectiveness of synthetic data in training in terms of improving learning performance and acquiring domain generalization ability--two main benefits expected of using synthetic data. As an evaluation metric for the second benefit, we introduce a method for measuring the distribution gap between two datasets, which is derived as the normalized sum of the Mahalanobis distances of all test data. As a result, we have discovered several important findings that have never been investigated or have been used previously without accurate understanding. We expect that these findings can break the current trend of either naively using or being hesitant to use synthetic data in machine learning due to the lack of understanding, leading to more appropriate use in future research.

Probing the quantum geometry and topology in condensed matter systems has relied heavily on static electronic transport experiments in magnetic fields. Yet, contact-free optical measurements have rarely been explored. Magnetic dichroism (MCD), the nonreciprocal absorption of circular polarized light, was theoretically linked to the quantized anomalous Hall effect in magnetic insulators and can identify the bands and momenta responsible for the underlying Berry Curvature (BC). Detecting BC through MCD faces two challenges: First, the relevant inter-band transitions usually generate MCD in the infrared (IR) range, requiring large samples with high quality. Second, while most magnetic materials are metallic, the relation between MCD and BC in metals remains unclear. Here, we report the observation of MCD in the IR range along with the anomalous Hall effect in thin film MnBi2Te4. Both phenomena emerge with a field-driven phase transition from an antiferromagnet to a canted ferromagnet. By theoretically relating the MCD to the anomalous Hall effect via BC in a metal, we show that this transition accompanies an abrupt onset of BC, signaling a topological phase transition from a topological insulator to a doped Chern insulator. Our density functional theory calculation suggests the MCD signal mainly originates from an optical transition at the Brillouin zone edge, hinting at a potential new source of BC away from the commonly considered {\Gamma} point. Our findings demonstrate a novel experimental approach for detecting BC and identifying the responsible bands and momenta, generally applicable to magnetic materials.

An out-of-plane magnetic field can always suppress superconductivity. In Bernal-stacked bilayer graphene (BBG), recently observed activation of superconductivity (SC) through either in-plane magnetic fields or proximate spin-orbit coupling (SOC) offers a rare instance of switching superconductivity on. To understand this, we must first examine the non-superconducting state. We propose an incommensurate current density wave (CrDW) driven by van Hove singularities away from the zone corners as a competing order. We note that the two switches, the in-plane field and the SOC, both break spin degeneracy. Our parquet renormalization group analysis reveals that breaking spin degeneracy shifts the balance from CrDW, favored under spin degeneracy, to SC when degeneracy is lifted. Driven by purely repulsive interactions, the pairing symmetry of the resulting SC is $p/d$-wave. The presence of CrDW accounts for the non-linear $I-V$ behavior in the normal state and suggests potential anomalous Hall effects due to time-reversal symmetry breaking. We further predict that reducing screening could enhance SC.

Recent progress in the study of many-body localization (MBL) in strongly disordered interacting spin chains has emphasized the importance of distinguishing finite time prethermal behavior from long time and large volume asymptotics. We re-examine a reported non-ergodic extended (NEE) regime in the interacting quasiperiodic Ganeshan-Pixley-Das Sarma model from this perspective, and propose that this regime is a prethermal feature. Indeed, we argue that the NEE regime may be identified through a change in the functional form of spin-spin autocorrelation functions, demonstrating that the NEE regime is distinguishable within intermediate-time dynamics. This is in contrast with existing conjectures relating the NEE regime to the presence of an asymptotic mobility edge in the single-particle spectrum. Thus, we propose a mechanism for the formation of an NEE regime which does not rely on asymptotic properties of the spin chain. Namely, we propose that the NEE regime emerges due to regularly spaced deep wells in the disorder potential. The highly detuned sites suppress spin transport across the system, effectively cutting the chain, and producing a separation of time scales between the spreading of different operators. To support this proposal, we show that the NEE phenomenology also occurs in random models with deep wells but with no mobility edges, and does not occur in quasiperiodic models with mobility edges but with no deep wells. Our results support the broad conclusion that there is not a sharp distinction between the dynamics of quasiperiodically and randomly disordered systems in the prethermal regime. More specifically, we find that generic interacting quasiperiodic models do not have stable intermediate dynamical phases arising from their single-particle mobility edges, and that NEE phenomenology in such models is transient., Comment: 13 pages, 9 figures

Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry and condensed matter physics. The rapid emergence and development of quantum computers offer a promising route to systematically tackle this problem. Recent work by Huggins et al.[1] proposed a hybrid quantum-classical quantum Monte Carlo (QC-QMC) algorithm using Clifford shadows to determine the ground state of a Fermionic Hamiltonian. This approach displayed inherent noise resilience and the potential for improved accuracy compared to its purely classical counterpart. Nevertheless, the use of Clifford shadows introduces an exponentially scaling post-processing cost. In this work, we investigate an improved QC-QMC scheme utilizing the recently developed Matchgate shadows technique [2], which removes the aforementioned exponential bottleneck. We observe from experiments on quantum hardware that the use of Matchgate shadows in QC-QMC is inherently noise robust. We show that this noise resilience has a more subtle origin than in the case of Clifford shadows. Nevertheless, we find that classical post-processing, while asymptotically efficient, requires hours of runtime on thousands of classical CPUs for even the smallest chemical systems, presenting a major challenge to the scalability of the algorithm.

Due to the diverging density of states (DOS), Van Hove singularities (VHS) near the Fermi level are known to boost the susceptibility to a wide variety of electronic instabilities, including superconductivity. We theoretically show that the number of VHS in the normal state can be qualitatively inferred from the optical absorption spectra Re $\sigma_{ii}(\omega)$ in the superconducting state. The key feature is the absorption peak at frequency $\omega=2\Delta$ from the optical transition across the superconducting gap $\Delta$, which is forbidden in a single-band clean superconductor when the inversion symmetry is preserved and the band is quadratic. Although VHS dispersions are mostly quadratic, we find that a divergent peak occurs when there are multiple VHS on the Fermi surface under an applied current. In contrast, we find non-diverging weak peaks when there is only a single VHS. Depending on whether this single VHS has logarithmically or power-law divergent DOS, the peaks in Re $\sigma_{xx}(\omega)$ and Re $\sigma_{yy}(\omega)$ are nearly isotropic and anisotropic, respectively. Therefore, we propose that experimentally measured peak magnitude and anisotropy in the optical absorption spectra of VH-driven superconductors can be used to determine the number and type of VHS on the Fermi surface. Disorders could even facilitate in distinguishing the multiple and single VHS scenarios since only the diverging peak in the latter case is expected to survive., Comment: 17 pages, 6 figures

We theoretically consider the possibility that phonons may be playing a role in the observed linear-in-$T$ resistivity in cuprates by focusing on the obvious question: How can phonon scattering be consistent with a linear-in-$T$ resistivity with a constant slope given that cuprates have many phonon modes with different energies and electron-phonon couplings (e.g. 21 phonon modes for LSCO)? We show using an arbitrarily large number of independent phonon modes that, within a model Boltzmann transport theory, the emergent high-$T$ linear-in-$T$ resistivity manifests an approximately constant slope independent of the number of phonon modes except in some fine-tuned narrow temperature regimes. We also comment on the quantitative magnitude of the linear-in-$T$ resistivity in cuprates pointing out the constraints on the effective electron-phonon coupling necessary to produce the observed resistivity., Comment: 10 pages, 3 figures

In this study, we unveil an eccentric superradiance phenomenon in molecular aggregates coupled to surface plasmon polaritons. Through the quantization of electromagnetic fields in media, we demonstrate that superradiance can be significantly enhanced by polaritons and its behavior distinguishably surpasses the Dick's $N$ scaling law. To understand the mechanism of this anomalous phenomenon, we derive an analytical expression of the superradiance rate, which is general for molecular aggregates in arbitrary dispersive and absorbing media. Furthermore, we show the importance of intermolecular distance for this extraordinary superradiance.

We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info" to depict its degree of factualness. Similar to the FEVER dataset, claims in the "Supports" and "Refutes" categories are also annotated with corresponding evidence sentences sourced from single or multiple pages in Chinese Wikipedia. Our labeled dataset holds a Fleiss' kappa value of 0.7934 for five-way inter-annotator agreement. In addition, through the experiments with the state-of-the-art approaches developed on the FEVER dataset and a simple baseline for CFEVER, we demonstrate that our dataset is a new rigorous benchmark for factual extraction and verification, which can be further used for developing automated systems to alleviate human fact-checking efforts. CFEVER is available at https://ikmlab.github.io/CFEVER., Comment: AAAI-24

The entertainment industry relies on 3D visual content to create immersive experiences, but traditional methods for creating textured 3D models can be time-consuming and subjective. Generative networks such as StyleGAN have advanced image synthesis, but generating 3D objects with high-fidelity textures is still not well explored, and existing methods have limitations. We propose the Semantic-guided Conditional Texture Generator (CTGAN), producing high-quality textures for 3D shapes that are consistent with the viewing angle while respecting shape semantics. CTGAN utilizes the disentangled nature of StyleGAN to finely manipulate the input latent codes, enabling explicit control over both the style and structure of the generated textures. A coarse-to-fine encoder architecture is introduced to enhance control over the structure of the resulting textures via input segmentation. Experimental results show that CTGAN outperforms existing methods on multiple quality metrics and achieves state-of-the-art performance on texture generation in both conditional and unconditional settings.

We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel environments. The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios. Thus, a robust system is necessary to notify operators when FSI policies are inconsistent with the intent of demonstrations. This task introduces three challenges: (1) detecting behavior errors in novel environments, (2) identifying behavior errors that occur without revealing notable changes, and (3) lacking complete temporal information of the rollout due to the necessity of online detection. However, the existing benchmarks cannot support the development of AED because their tasks do not present all these challenges. To this end, we develop a cross-domain AED benchmark, consisting of 322 base and 153 novel environments. Additionally, we propose Pattern Observer (PrObe) to address these challenges. PrObe is equipped with a powerful pattern extractor and guided by novel learning objectives to parse discernible patterns in the policy feature representations of normal or error states. Through our comprehensive evaluation, PrObe demonstrates superior capability to detect errors arising from a wide range of FSI policies, consistently surpassing strong baselines. Moreover, we conduct detailed ablations and a pilot study on error correction to validate the effectiveness of the proposed architecture design and the practicality of the AED task, respectively. The AED project page can be found at https://aed-neurips.github.io/., Comment: Accepted to NeurIPS2024

We study the zero-temperature phase diagram of a symmetric electron-hole bilayer system by comparing the ground state energies of two distinct limiting cases, characterized by an electron-hole plasma or an exciton gas, respectively. For the electron-hole plasma, the random phase approximation is used; for the exciton gas, we consider three different approximations: the unscreened Coulomb interaction, the statically screened one, and the dynamically screened one under the plasmon-pole approximation. Our results suggest that the exciton gas is stable at small layer separation. However, static screening in general suppresses the formation of excitons, and dynamic screening gives different results depending on the representative energy scale we used in the plasmon-pole approximation. We conclude that energetic considerations alone are very sensitive to the approximation schemes, and the phase diagram of the system may depend crucially on exactly how the electron-hole attraction is treated in the theory. For very small and very large densities, however, all our approximations show the exciton gas to have lower energy than the plasma., Comment: 7 pages, 4 figures

Cavity quantum electrodynamics (CQED) and its extensions are widely used for the description of exciton-polariton systems. However, the exciton-polariton models based on CQED vary greatly within different contexts. One of the most significant discrepancies among these CQED models is whether one should include direct intermolecular interactions in the CQED Hamiltonian. To answer this question, in this article, we derive an effective dissipative CQED model including free-space dipole-dipole interactions (CQED-DDI) from a microscopic Hamiltonian based on macroscopic quantum electrodynamics. Dissipative CQED-DDI successfully captures the nature of vacuum fluctuations in dielectric media and separates it into the free-space effects and the dielectric-induced effects. The former include spontaneous emissions, dephasings and dipole-dipole interactions in free space; the latter include exciton-polariton interactions and photonic losses due to dielectric media. We apply dissipative CQED-DDI to investigate the exciton-polariton dynamics (the population dynamics of molecules above a plasmonic surface) and compare the results with those based on the methods proposed by several previous studies. We find that direct intermolecular interactions are a crucial element when employing CQED-like models to study exciton-polariton systems involving multiple molecules.