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Motivated by a correlation between the distribution of descents over permutations that avoid a consecutive pattern and those avoiding the respective quasi-consecutive pattern, as established in this paper, we obtain a complete $\des$-Wilf classification for quasi-consecutive patterns of length up to 4. For equivalence classes containing more than one pattern, we construct various descent-preserving bijections to establish the equivalences, which lead to the provision of proper versions of two incomplete bijective arguments previously published in the literature. Additionally, for two singleton classes, we derive explicit bivariate generating functions using the generalized run theorem., Comment: 20 pages

The accomplished functions of a variety of quantum devices are closely associated with the controlling of exciton transport. To this end we study the exciton transport of the two-dimensional system consisting of two-level multichains with various coupling configurations in a cavity. Two types of the chains, the homogeneous and heterogeneous coupling chain, as well as two inter-chain coupling conformations, the square and triangle type, are considered. The effects of the inter-chain coupling, the dimerization parameter, the cavity, the length and number of the chains on exciton transport are in detail investigated for different coupling configurations through the spectra, the Hopfield coefficients, and the steady-state dynamics of the system. The results show that in the absence of cavity the exciton transport currents and efficiency are decided by the distribution of exciton on the multichains. However, when the cavity is considered the exciton transport can be significantly enhanced or suppressed by the polariton at the cavity-dressed energy level crossings and anticrossings near zero-energy modes, at which the coherent excitation and Landau-Zener transitions occur. Meanwhile, we discover that the incontinuity and extreme points of the second order partial derivative of the Hopfield coefficients of the photon with the inter-chain coupling and the dimerization parameter correspond respectively to the crossings and anticrossings at the extreme points of the photon occupation number. In addition, the exciton transport efficiency is closely related to the parity of both the length and number of the chains, which behaves as oscillation. This work is important for understanding the exciton transport mechanism in the multichain-cavity system, and provides theoretical basis for excitonic devices with controllable and efficient exciton transport.

This paper addresses the task of 3D clothed human generation from textural descriptions. Previous works usually encode the human body and clothes as a holistic model and generate the whole model in a single-stage optimization, which makes them struggle for clothing editing and meanwhile lose fine-grained control over the whole generation process. To solve this, we propose a layer-wise clothed human representation combined with a progressive optimization strategy, which produces clothing-disentangled 3D human models while providing control capacity for the generation process. The basic idea is progressively generating a minimal-clothed human body and layer-wise clothes. During clothing generation, a novel stratified compositional rendering method is proposed to fuse multi-layer human models, and a new loss function is utilized to help decouple the clothing model from the human body. The proposed method achieves high-quality disentanglement, which thereby provides an effective way for 3D garment generation. Extensive experiments demonstrate that our approach achieves state-of-the-art 3D clothed human generation while also supporting cloth editing applications such as virtual try-on. Project page: http://jtdong.com/tela_layer/

Scene completion and forecasting are two popular perception problems in research for mobile agents like autonomous vehicles. Existing approaches treat the two problems in isolation, resulting in a separate perception of the two aspects. In this paper, we introduce a novel LiDAR perception task of Occupancy Completion and Forecasting (OCF) in the context of autonomous driving to unify these aspects into a cohesive framework. This task requires new algorithms to address three challenges altogether: (1) sparse-to-dense reconstruction, (2) partial-to-complete hallucination, and (3) 3D-to-4D prediction. To enable supervision and evaluation, we curate a large-scale dataset termed OCFBench from public autonomous driving datasets. We analyze the performance of closely related existing baseline models and our own ones on our dataset. We envision that this research will inspire and call for further investigation in this evolving and crucial area of 4D perception. Our code for data curation and baseline implementation is available at https://github.com/ai4ce/Occ4cast., Comment: IROS 2024

As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests. However, its greedy multi-layer learning procedure is prone to overfitting, limiting model effectiveness and generalizability. This paper presents an optimized Deep Forest, featuring learnable, layerwise data augmentation policy schedules. Specifically, We introduce the Cut Mix for Tabular data (CMT) augmentation technique to mitigate overfitting and develop a population-based search algorithm to tailor augmentation intensity for each layer. Additionally, we propose to incorporate outputs from intermediate layers into a checkpoint ensemble for more stable performance. Experimental results show that our method sets new state-of-the-art (SOTA) benchmarks in various tabular classification tasks, outperforming shallow tree ensembles, deep forests, deep neural network, and AutoML competitors. The learned policies also transfer effectively to Deep Forest variants, underscoring its potential for enhancing non-differentiable deep learning modules in tabular signal processing.

Traditional photonuclear reactions primarily excite giant dipole resonances, making the measurement of isovector giant resonances with higher multipolarties a great challenge. In this work, the manipulation of collective excitations of different multipole transitions in nuclei via vortex $\gamma$ photons has been investigated. We develop the calculation method for photonuclear cross sections induced by the vortex $\gamma$ photon beam using the fully self-consistent random-phase approximation plus particle-vibration coupling (RPA+PVC) model based on Skyrme density functional. We find that the electromagnetic transitions with multipolarity $J< m_\gamma$ are forbidden for vortex $\gamma$ photons due to the angular momentum conservation, with $m_\gamma$ being the projection of total angular momentum of $\gamma$ photon on its propagation direction. For instance, this allows for probing the isovector giant quadrupole resonance without interference from dipole transitions using vortex $\gamma$ photons with $m_\gamma=2$. The electromagnetic transitions with $J>m_\gamma$ are strongly suppressed compared with the plane-wave-$\gamma$-photon case, and even vanish at specific polar angles. Therefore, the giant resonances with specific multipolarity can be extracted via vortex $\gamma$ photons. Moreover, the vortex properties of $\gamma$ photons can be meticulously diagnosed by measuring the nuclear photon-absorption cross section. Our method opens new avenues for photonuclear excitations, generation of coherent $\gamma$ photon laser and precise detection of vortex particles, and consequently, has significant impact on nuclear physics, nuclear astrophysics and strong laser physics.

Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. This could be addressed by the adversarial defense, but its training requires the often-unknown attacking mechanism. Differently, we propose ROBOSAC, a novel sampling-based defense strategy generalizable to unseen attackers. Our key idea is that collaborative perception should lead to consensus rather than dissensus in results compared to individual perception. This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus. In such a framework, more teammates in the sampled subset often entail better perception performance but require longer sampling time to reject potential attackers. Thus, we derive how many sampling trials are needed to ensure the desired size of an attacker-free subset, or equivalently, the maximum size of such a subset that we can successfully sample within a given number of trials. We validate our method on the task of collaborative 3D object detection in autonomous driving scenarios., Comment: Accepted by ICCV 2023