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Fine-grained simulation of floor construction processes is essential for supporting lean management and the integration of information technology. However, existing research does not adequately address the on-site decision-making of constructors in selecting tasks and determining their sequence within the entire construction process. Moreover, decision-making frameworks from computer science and robotics are not directly applicable to construction scenarios. To facilitate intelligent simulation in construction, this study introduces the Construction Markov Decision Process (CMDP). The primary contribution of this CMDP framework lies in its construction knowledge in decision, observation modifications and policy design, enabling agents to perceive the construction state and follow policy guidance to evaluate and reach various range of targets for optimizing the planning of construction activities. The CMDP is developed on the Unity platform, utilizing a two-stage training approach with the multi-agent proximal policy optimization algorithm. A case study demonstrates the effectiveness of this framework: the low-level policy successfully simulates the construction process in continuous space, facilitating policy testing and training focused on reducing conflicts and blockages among crews; and the high-level policy improving the spatio-temporal planning of construction activities, generating construction patterns in distinct phases, leading to the discovery of new construction insights.

In this paper, we interpret the dark energy as an effect caused by small scale inhomogeneities of the universe with the use of the spatial averaged approach of Buchert. The model considered here adopts the Chevallier-Polarski-Linder(CPL) parameterizations of the equation of state of the effective perfect fluid from the backreaction effect. Thanks to the effective geometry introduced by Larena et. al.\cite{larena2009testing} in their previous work, we confront such backreaction model with latest type Ia supernova and Hubble parameter observations, coming out with results that reveal the difference between the Friedmann-Lema\^{\i}tre-Robertson-Walker model and backreaction model., Comment: 13 pages, 4 figures, 2 table, published on mpla. arXiv admin note: substantial text overlap with arXiv:1707.00111, arXiv:2205.09326

The theory of quantum information processing for macroscopic qubits is based on the fact that every macroscopic qubit has a conserved number of particles. However, from an experimental point of view, every such qubit experiences processes of decoherence that impact the possibilities for entanglement generation between such qubits and use in quantum information processing efficiently. One of the most prospective methods for generating entanglement between distant atomic BECs is quantum nondemolition measurements. Here, we study how the effects of photon measurement impact the entanglement when photon loss decoherence is included. We employ the thermally entangled state representation (TESR) and integral within the ordered operator(IWOP) approach to obtain the accurate density matrix in a photon loss channel. We demonstrate that varying outcomes of photon number measurements lead to the generation of distinct entangled states, each exhibiting unique characteristics. We find that using the Hofmann-Takeuchi and Duan-Giedke-Cirac-Zoller criterion provides advantages in entanglement detection compared to the Wineland squeezing and EPR steering criterion in such settings., Comment: 13 pages, 9 figures

This paper introduces an automatic affordance reasoning paradigm tailored to minimal semantic inputs, addressing the critical challenges of classifying and manipulating unseen classes of objects in household settings. Inspired by human cognitive processes, our method integrates generative language models and physics-based simulators to foster analytical thinking and creative imagination of novel affordances. Structured with a tripartite framework consisting of analysis, imagination, and evaluation, our system "analyzes" the requested affordance names into interaction-based definitions, "imagines" the virtual scenarios, and "evaluates" the object affordance. If an object is recognized as possessing the requested affordance, our method also predicts the optimal pose for such functionality, and how a potential user can interact with it. Tuned on only a few synthetic examples across 3 affordance classes, our pipeline achieves a very high success rate on affordance classification and functional pose prediction of 8 classes of novel objects, outperforming learning-based baselines. Validation through real robot manipulating experiments demonstrates the practical applicability of the imagined user interaction, showcasing the system's ability to independently conceptualize unseen affordances and interact with new objects and scenarios in everyday settings.

Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve these goals. We propose an adaptive multi-delta coding as a spiking coding method to improve recognition accuracy. We propose two additive solvers for SNN, which can reduce inference energy consumption and amount of parameters significantly, and improve the robustness of temporal differences. In addition, we propose a linear action detection method TAD-LIF, which is suitable for SNNs. TAD-LIF is an improved LIF neuron that can detect transient-state gestures quickly and accurately. We collected two datasets from 20 subjects including 6 micro gestures. The collection devices are two designed lightweight consumer-level sEMG wristbands (3 and 8 electrode channels respectively). Compared to CNN, FCN, and normal SNN-based methods, the proposed SNN has higher recognition accuracy. The accuracy of the proposed SNN is 83.85% and 93.52% on the two datasets respectively. In addition, the inference latency of the proposed SNN is about 1% of CNN, the power consumption is about 0.1% of CNN, and the memory occupation is about 20% of CNN. The proposed methods can be used for precise, high-speed, and low-power micro-gesture recognition tasks, and are suitable for consumer-level intelligent wearable devices, which is a general way to achieve ubiquitous computing.

The Hubble tension persists as a challenge in cosmology. Even early dark energy (EDE) models, initially considered the most promising for alleviating the Hubble tension, fall short of addressing the issue without exacerbating other tensions, such as the $S_8$ tension. Considering that a negative dark matter (DM) equation of state (EoS) parameter is conducive to reduce the value of $\sigma_8$ parameter, we extend the axion-like EDE model in this paper by replacing the cold dark matter (CDM) with DM characterized by a constant EoS $w_{\rm dm}$ (referred as WDM hereafter). We then impose constraints on this axion-like EDE extension model, along with three other models: the axion-like EDE model, $\Lambda$WDM, and $\Lambda$CDM. These constraints are derived from a comprehensive analysis incorporating data from the Planck 2018 cosmic microwave background (CMB), baryon acoustic oscillations (BAO), the Pantheon compilation, as well as a prior on $H_0$ (i.e., $H_0=73.04\pm1.04$, based on the latest local measurement by Riess et al.) and a Gaussianized prior on $S_8$ (i.e., $S_8=0.766\pm0.017$, determined through the joint analysis of KID1000+BOSS+2dLenS). We find that although the new model maintains the ability to alleviate the Hubble tension to $\sim$ 1.4$\sigma$, it still exacerbate the $S_8$ tension to a level similar to that of the axion-like EDE model., Comment: 14 pages, 3 figures, 1 table, accepted by Communications in Theoretical Physics

In this letter, a paradigm for the classification and manipulation of previously unseen objects is established and demonstrated through a real example of chairs. We present a novel robot manipulation method, guided by the understanding of object stability, perceptibility, and affordance, which allows the robot to prepare previously unseen and randomly oriented chairs for a teddy bear to sit on. Specifically, the robot encounters an unknown object and first reconstructs a complete 3D model from perceptual data via active and autonomous manipulation. By inserting this model into a physical simulator (i.e., the robot's "imagination"), the robot assesses whether the object is a chair and determines how to reorient it properly to be used, i.e., how to reorient it to an upright and accessible pose. If the object is classified as a chair, the robot reorients the object to this pose and seats the teddy bear onto the chair. The teddy bear is a proxy for an elderly person, hospital patient, or child. Experiment results show that our method achieves a high success rate on the real robot task of chair preparation. Also, it outperforms several baseline methods on the task of upright pose prediction for chairs.

This paper proposes a learning-from-demonstration method using probability densities on the workspaces of robot manipulators. The method, named "PRobabilistically-Informed Motion Primitives (PRIMP)", learns the probability distribution of the end effector trajectories in the 6D workspace that includes both positions and orientations. It is able to adapt to new situations such as novel via poses with uncertainty and a change of viewing frame. The method itself is robot-agnostic, in which the learned distribution can be transferred to another robot with the adaptation to its workspace density. The learned trajectory distribution is then used to guide an optimization-based motion planning algorithm to further help the robot avoid novel obstacles that are unseen during the demonstration process. The proposed methods are evaluated by several sets of benchmark experiments. PRIMP runs more than 5 times faster while generalizing trajectories more than twice as close to both the demonstrations and novel desired poses. It is then combined with our robot imagination method that learns object affordances, illustrating the applicability of PRIMP to learn tool use through physical experiments., Comment: 17 pages, 19 figures

We introduce a novel dataset consisting of images depicting pink eggs that have been identified as Pomacea canaliculata eggs, accompanied by corresponding bounding box annotations. The purpose of this dataset is to aid researchers in the analysis of the spread of Pomacea canaliculata species by utilizing deep learning techniques, as well as supporting other investigative pursuits that require visual data pertaining to the eggs of Pomacea canaliculata. It is worth noting, however, that the identity of the eggs in question is not definitively established, as other species within the same taxonomic family have been observed to lay similar-looking eggs in regions of the Americas. Therefore, a crucial prerequisite to any decision regarding the elimination of these eggs would be to establish with certainty whether they are exclusively attributable to invasive Pomacea canaliculata or if other species are also involved. The dataset is available at https://www.kaggle.com/datasets/deeshenzhen/pinkeggs

This paper concerns the existence of positive ground state solutions for generalized quasilinear Schr\"odinger equations in $\mathbb{R}^N$ with critical growth which arise from plasma physics, as well as high-power ultrashort laser in matter. By applying a variable replacement, the quasilinear problem reduces to a semilinear problem which the associated functional is well defined in the Sobolev space $H^1(\mathbb{R}^N)$. We use the method of Nehari manifold for the modified equation, establish the minimax characterization, then obtain each Palais-Smale sequence of the associated energy functional is bounded. By combining Lions's concentration-compactness lemma together with some classical arguments developed by Br\'ezis and Nirenberg \cite{bn}, we establish that the bounded Palais-Smale sequence has a nonvanishing behaviour. Finally, we obtain the existence of a positive ground state solution under some appropriate assumptions. Our results extend and generalize some known results.

This paper is concerned with breather solutions of a radially symmetric curl-curl wave equation with double power nonlinearity. By considering the solutions with a special form, we obtain a family of ordinary differential equations (ODEs) parameterized by the radial variable. Then we characterize periodic behaviors and analyze the joint effects of the double power nonlinear terms on the minimal period and the maximal amplitude. Under certain conditions, we construct a breather solution for the original curl-curl wave equation and find such a solution which can generate a continuum of phase-shifted breathers.

It is well known that there are several long-standing problems implying the discordance of the $\Lambda$CDM model. Although most of the models proposed to resolve these problems assume that dark matter is pressureless, it is still possible that dark matter is not cold, as current observations have not ruled out this possibility yet. Therefore, in this article, we treat the dark matter equation of state parameter as a free parameter, and apply observational data to investigate the non-coldness of dark matter. Impressing by the simplicity of the phenomenological emergent dark energy (PEDE) and its ability to relieve the Hubble tension, we propose the PEDE+$w_{\rm dm}$ model based on PEDE and non-cold dark matter. We then place constraints on this model in light of the Planck 2018 Cosmic Microwave Background (CMB) anisotropies, baryon acoustic oscillation (BAO) measurements, and the Pantheon compilation of Type Ia supernovae. The results indicate a preference for a negative dark matter equation of state parameter at $95\%$ CL for all data sets except CMB alone and CMB+BAO, which suggests that the non-coldness assumption of dark matter worth to be investigated further in order to understand the nature of dark matter. The Hubble tension is alleviated in this scenario compared to the $\Lambda$CDM model, with a significance below 3$\sigma$ level for all data sets except CMB+Pantheon. However, from the analysis based on Bayesian evidence, we clearly see that the data sets favor $\Lambda$CDM over the PEDE+$w_{\rm dm}$ model., Comment: 8 pages, 2 figure, published on PRD

We propose a novel paradigm to vector magnetometry based on machine learning. Unlike conventional schemes where one measured signal explicitly connects to one parameter, here we encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals, i.e., the oscillating optical rotation signal's harmonics of a frequency modulated laser beam traversing the atomic sample. The map between the recorded signals and the vectorial field information is established through a pre-trained deep neural network. We demonstrate experimentally a single-shot all optical vector atomic magnetometer, with a simple scalar-magnetometer design employing only one elliptically-polarized laser beam and no additional coils. Magnetic field amplitude sensitivities of about 100 $\textrm{fT}/\sqrt{\textrm{Hz}}$ and angular sensitivities of about 100 $\mu rad/\sqrt{\textrm{Hz}}$ (for a magnetic field of about 140 nT) are derived from the neural network. Our approach can reduce the complexity of the architecture of vector magnetometers, and may shed light on the general design of multiparameter sensing., Comment: 8 pages,4 figures

Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization. Previous compression algorithms usually start from the pre-trained dense models and only focus on efficient inference, while time-consuming training is still unavoidable. In contrast, this paper points out that the million-scale training data is redundant, which is the fundamental reason for the tedious training. To address the issue, this paper aims to introduce sparsity into data and proposes an end-to-end efficient training framework from three sparse perspectives, dubbed Tri-Level E-ViT. Specifically, we leverage a hierarchical data redundancy reduction scheme, by exploring the sparsity under three levels: number of training examples in the dataset, number of patches (tokens) in each example, and number of connections between tokens that lie in attention weights. With extensive experiments, we demonstrate that our proposed technique can noticeably accelerate training for various ViT architectures while maintaining accuracy. Remarkably, under certain ratios, we are able to improve the ViT accuracy rather than compromising it. For example, we can achieve 15.2% speedup with 72.6% (+0.4) Top-1 accuracy on Deit-T, and 15.7% speedup with 79.9% (+0.1) Top-1 accuracy on Deit-S. This proves the existence of data redundancy in ViT., Comment: AAAI 2023

While vision transformers (ViTs) have continuously achieved new milestones in the field of computer vision, their sophisticated network architectures with high computation and memory costs have impeded their deployment on resource-limited edge devices. In this paper, we propose a hardware-efficient image-adaptive token pruning framework called HeatViT for efficient yet accurate ViT acceleration on embedded FPGAs. By analyzing the inherent computational patterns in ViTs, we first design an effective attention-based multi-head token selector, which can be progressively inserted before transformer blocks to dynamically identify and consolidate the non-informative tokens from input images. Moreover, we implement the token selector on hardware by adding miniature control logic to heavily reuse existing hardware components built for the backbone ViT. To improve the hardware efficiency, we further employ 8-bit fixed-point quantization, and propose polynomial approximations with regularization effect on quantization error for the frequently used nonlinear functions in ViTs. Finally, we propose a latency-aware multi-stage training strategy to determine the transformer blocks for inserting token selectors and optimize the desired (average) pruning rates for inserted token selectors, in order to improve both the model accuracy and inference latency on hardware. Compared to existing ViT pruning studies, under the similar computation cost, HeatViT can achieve 0.7%$\sim$8.9% higher accuracy; while under the similar model accuracy, HeatViT can achieve more than 28.4%$\sim$65.3% computation reduction, for various widely used ViTs, including DeiT-T, DeiT-S, DeiT-B, LV-ViT-S, and LV-ViT-M, on the ImageNet dataset. Compared to the baseline hardware accelerator, our implementations of HeatViT on the Xilinx ZCU102 FPGA achieve 3.46$\times$$\sim$4.89$\times$ speedup., Comment: HPCA 2023

The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method called the winning ticket, such that it can be trained from scratch to almost as good as the dense counterpart. Meanwhile, the research of LTH in vision transformers (ViTs) is scarcely evaluated. In this paper, we first show that the conventional winning ticket is hard to find at the weight level of ViTs by existing methods. Then, we generalize the LTH for ViTs to input data consisting of image patches inspired by the input dependence of ViTs. That is, there exists a subset of input image patches such that a ViT can be trained from scratch by using only this subset of patches and achieve similar accuracy to the ViTs trained by using all image patches. We call this subset of input patches the em winning tickets, which represent a significant amount of information in the input data. We use a ticket selector to generate the winning tickets based on the informativeness of patches for various types of ViT, including DeiT, LV-ViT, and Swin Transformers. The experiments show that there is a clear difference between the performance of models trained with winning tickets and randomly selected subsets, which verifies our proposed theory. We elaborate on the analogical similarity between our proposed Data-LTH-ViTs and the conventional LTH to further verify the integrity of our theory. The Source codes are available at https://github.com/shawnricecake/vit-lottery-ticket-input., Comment: Accepted by IJCAI 2023

The $H_0$ tension between low- and high- redshift measurements is definitely a serious issue faced by current cosmologists since it ranges from 4$\sigma$ to 6$\sigma$. To relieve this tension, in this paper we propose a new interacting dark energy model with time varying coupling parameter by parameterizing the densities of dark matter and dark energy, this parametric approach for interacting dark sectors are inspired by our previous work concerning the coupled generalized three-form dark energy model in which dark matter and dark energy behave like two uncoupled dark sectors with effective equation of state when the three-form $|\kappa X|\gg1$, for this reason, we reconstruct coupled generalized three-form dark energy from such parametric model under the condition $|\kappa X_0|\gg1$. In the end, we place constraints on three parametric models in light of the Planck 2018 cosmic microwave background (CMB) distance priors, baryon acoustic oscillations (BAO) data and the Pantheon compilation of Type Ia supernovae (SN Ia) data by assuming the parameter $k$ as 0,5,10 respectively. The fitting results show that, for all the observational data sets, the parametric models with $k=0,5,10$ relieve the Hubble tension with the latest local determinations of the Hubble constant from ${\rm SH_0ES}$ team, i.e. the so called R22, to 2.3 $\sigma$, 1.9 $\sigma$ and 1.3 $\sigma$ with $\chi_{\rm min}^2=10.43,10.47,10.48$ respectively, revealing that $k$ is positive correlated to the Hubble constant., Comment: 17 pages, 11 figures, published on Physics of the Dark Universe

We put forward a pressure-parametric model to study the tiny deviation from cosmological constant(CC) behavior of the dark sector accelerating the expansion of the Universe. Data from cosmic microwave background (CMB) anisotropies, baryonic acoustic oscillations (BAO), Type Ia supernovae (SN Ia) observation are applied to constrict the model parameters. The constraint results show that such model suffers with $H_0$ tension as well. To realize this model more physically, we reconstruct it with the quintessence and phantom scalar fields, and find out that although the model predicts a quintessence-induced acceleration of the Universe at past and present, at some moment of the future, dark energy's density have a disposition to increase., Comment: 14 pages, 10 figures, 1 table, accepted by Modern Physics Letters A (Vol. 35, No. 25, 2050209 (2020))