Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient MIMO Integrated Sensing and Communications With On–Off Nontransmission Power

This article investigates the energy efficiency of a multiple-input–multiple-output (MIMO) integrated sensing and communication (ISAC) system for Internet of Things (IoT), in which one multiantenna IoT transceiver transmits unified ISAC signals to a multiantenna communication user (CU) and at the same time use the echo signals to estimate an extended target. We focus on one particular ISAC transmission block and take into account the practical on–off nontransmission power at the IoT transceiver. Under this setup, we minimize the energy consumption at the transceiver while ensuring a minimum average data rate requirement for communication and a maximum Cramér–Rao bound (CRB) requirement for target estimation, by jointly optimizing the transmit covariance matrix and the “on” duration for active transmission. We obtain the optimal solution to the rate-and-CRB-constrained energy minimization problem in a semi-closed form. Interestingly, the obtained optimal solution is shown to unify the spectrum-efficient and energy-efficient communications and sensing designs. In particular, for the special MIMO sensing case with rate constraint inactive, the optimal solution follows the isotropic transmission with the shortest “on” duration, in which the IoT transceiver radiates the required sensing energy by using sufficiently high power over the shortest duration. For the general ISAC case, the optimal transmit covariance solution is of full rank and follows the eigenmode transmission based on the communication channel, while the optimal “on” duration is determined based on both the rate and CRB constraints. Numerical results show that the proposed ISAC design achieves significantly reduced energy consumption as compared to the benchmark schemes based on isotropic transmission, always-on transmission, and sensing or communications only designs, especially when the rate and CRB constraints become stringent.
Learning non-Markovian Decision-Making from State-only Sequences

Conventional imitation learning assumes access to the actions of demonstrators, but these motor signals are often non-observable in naturalistic settings. Additionally, sequential decision-making behaviors in these settings can deviate from the assumptions of a standard Markov Decision Process (MDP). To address these challenges, we explore deep generative modeling of state-only sequences with non-Markov Decision Process (nMDP), where the policy is an energy-based prior in the latent space of the state transition generator. We develop maximum likelihood estimation to achieve model-based imitation, which involves short-run MCMC sampling from the prior and importance sampling for the posterior. The learned model enables \textit{decision-making as inference}: model-free policy execution is equivalent to prior sampling, model-based planning is posterior sampling initialized from the policy. We demonstrate the efficacy of the proposed method in a prototypical path planning task with non-Markovian constraints and show that the learned model exhibits strong performances in challenging domains from the MuJoCo suite.
Large saturation moment and high ferromagnetic transition temperature in a structurally disordered inverse Heusler alloy Fe2RuGe

We report the successful synthesis of a new 4$d$ based polycrystalline inverse Heusler alloy Fe$_2$RuGe by an arc melting process and have studied in detail its structural, magnetic and transport properties complemented with first principle calculations. X-ray and neutron diffraction, Extended X-ray Absorption Fine Structure and $^{57}$Fe M\"{o}ssbauer spectroscopic studies confirm the single phase nature of the system where the Fe and Ru atoms are randomly distributed in the 4$c$ and 4$d$ Wyckoff positions in a ratio close to 50:50. The formation of the disordered structure is also confirmed by the theoretical energy minimization calculation. Despite the random cross-site disorder of Fe and Ru atoms, magnetic measurements suggest not only a high Curie temperature of $\sim$860\,K, but also a large saturation magnetic moment $\sim$4.9\,$\mu_B$ per formula unit at 5\,K, considerably exceeding the theoretical limit (4\,$\mu_B$ per formula unit) predicted by the Slater-Pauling rule. Only a few Fe-based inverse Heusler alloys are known to exhibit such high Curie temperatures. Neutron diffraction analysis coupled with the isothermal magnetization value indicates that the magnetic moments in Fe$_2$RuGe are associated with Fe-atoms only, which is also confirmed by M\"ossbauer spectrometry. Interestingly, in comparison to the cubic or hexagonal phase of the parent compound, Fe$_3$Ge, the Curie temperature of Fe$_2$RuGe has increased significantly despite the substitution of the nonmagnetic, yet isoelectronic element Ru in this structurally disordered compound. Our theoretical calculation reveals that the large Fe moment ($\sim2.8\mu_B$/Fe) on the 4$b$ site can be attributed to a charge transfer from this Fe site towards its Ru neighbours. Such a substantial increase in magnetic moment due to electron charge transfer has not previously been reported in a Heusler alloy system.
Vision-based interface for grasping intention detection and grip selection : towards intuitive upper-limb assistive devices

Assistive devices for indivuals with upper-limb movement often lack controllability and intuitiveness, in particular for grasping function. In this work, we introduce a novel user interface for grasping movement control in which the user delegates the grasping task decisions to the device, only moving their (potentially prosthetic) hand toward the targeted object.
What Truly Matters in Trajectory Prediction for Autonomous Driving?

In the autonomous driving system, trajectory prediction plays a vital role in ensuring safety and facilitating smooth navigation. However, we observe a substantial discrepancy between the accuracy of predictors on fixed datasets and their driving performance when used in downstream tasks. This discrepancy arises from two overlooked factors in the current evaluation protocols of trajectory prediction: 1) the dynamics gap between the dataset and real driving scenario; and 2) the computational efficiency of predictors. In real-world scenarios, prediction algorithms influence the behavior of autonomous vehicles, which, in turn, alter the behaviors of other agents on the road. This interaction results in predictor-specific dynamics that directly impact prediction results. As other agents' responses are predetermined on datasets, a significant dynamics gap arises between evaluations conducted on fixed datasets and actual driving scenarios. Furthermore, focusing solely on accuracy fails to address the demand for computational efficiency, which is critical for the real-time response required by the autonomous driving system. Therefore, in this paper, we demonstrate that an interactive, task-driven evaluation approach for trajectory prediction is crucial to reflect its efficacy for autonomous driving.
Toward Carrollian quantization: Renormalization of Carrollian electrodynamics

Field-theoretic description of Carrollian theories has largely remained classical so far. In this paper, we attempt to study the renormalization of Carrollian gauge field theories via path integral techniques. The case of Carrollian electrodynamics minimally coupled to a massive Carrollian scalar is considered. We report potential problems such as IR divergences and mass shell singularity cropping up at the first order in the perturbation. Perhaps, the most important result that we report is how conventional arguments for gauge independence for mass and coupling are invalidated for a gauge theory in a Carrollian setting. As of now, the renormalization of Carrollian gauge field theories seems to suffer from unphysical ramifications. Possible cures to resolve these issues are suggested.
A Study of Finite Amplitude Barotropic Instability

We report cases with a series of disturbances tilted upshear along the shear zone during the Mei-Yu season. The scale involved is much smaller than the local Rossby radius of deformation; we hypothesize the relevance of barotropic instability, and have explored the nonlinear evolution of barotropic instability. The small and finite amplitude theories are reviewed; their relevance to the observations are briefly discussed. Eigenvalues of ideal three and four region models are calculated analytically. A new Fourier Chebyshev nondivergent barotropic model is constructed. With the initial value problem approach, our experiments on barotropic instability with a vorticity strip either have hyperbolic tangent or Bickerly jet type of wind profiles. We studied the time evolution of the shear layers in terms of the formation of fundamental eddies and successive pairing or merging of eddies. The mutual intensification of counter-propagating Rossby waves (vorticity gradient waves) across the vorticity strip, breakdown of the vorticity strip, local concentration of vorticity and vortex mergering processes were simulated. While the barotropic instability is an efficient way to concentrate vorticity in a small region, the later vortex merging can enlarge the vortex sizes but not the intensity of the resultant vortex. We propose that the concentration and the merging of vortices can create a favorable localized environment within the shear zone for the moist baroclinic processes to operate. As far as the intensity, shape and evolution of the individual vortices are concerned, they are very sensitive to initial background noise(1/100 of mean vorticity). In other words, there is no predictability in the nonlinear evolution. However, the maximum growth rate and the dominant wavelength of vortex can be predicted from the linear analysis. The Bickerly jet possesses higher growth rate than the hyperbolic tangent case on the f plane. We document chaotic behavior of sudden breakup of shear zone and associated vortex merging after a couple of regular cycles of wave-mean flow interaction with shear zone maintained. In the thirty-day integration, the vortices on the β plane become disorganized and scattered while they still remain well organized on the f plane.
Baryon Transition in Holographic QCD

We propose a mechanism of holographic baryon transition in the Sakai-Sugimoto (SS) model: baryons in this model can jump to different states under the mediated effect of gravitons (or glueballs by holography). We consider a time-dependent gravitational perturbation from M5-brane solution of D=11 supergravity and by employing the relations between 11D M-theory and IIA string theory, we get its 10 dimensional counterpart in the SS model. Such a perturbation is received by the D4-branes wrapped on the $S^{4}$ part of the 10D background, namely the baryon vertex. Technically, baryons in the SS model are described by BPST instanton ansatz and their dynamics can be analyzed using the quantum mechanical system in the instanton's moduli space. In this way, different baryonic states are marked by quantum numbers of moduli space quantum mechanics. By holographic spirit, the gravitational perturbation enters the Hamiltonian as a time-dependent perturbation and it is this time-dependent perturbative Hamiltonian produces the transition between different baryonic states. We calculate the transition probability and get the selection rule and also compute the condition for baryon transition and give the possible transition processes in the limit $\omega\gg\left|\vec{k}\right|^{2}$. Since in 10D language, the fluctuation from 11D metric are the perturbation of 10D metric and dilaton which are the modes carried by close strings, thus from the string theory point of view, our proposition can be accounted as the baryonic D4 brane jumps to different states by emitting or absorbing close strings coming from the bulk. In the viewpoints of QCD, it could be interpreted as that baryons transform to different states by interacting with glueballs as a low energy effective theory.
Effective fabric defect detection using contrastive learning and layered fusion network

Defect detection is a very important topic in industrial inspection. Due to the rapid expansion of computer computing power, manual-based defect detection approaches is no longer satisfactory, and deep learning-based detection techniques are increasingly being applied to defect detection tasks. contrastive learning is better for defect detection tasks due to the advantages of its label-free training method. So the Efficient Fabric Defect Detector (EFDD), a high-efficiency defect detector based on contrastive learning approach and layered fusion network, is proposed in our paper. First, we propose the Layered Fusion network to encode defect types with different scale sizes and improve the feature extraction ability of the model. Second, a universal structure with an attention module is introduced, this module can be integrated into ResNet with little overhead. Third, we investigate the impact of parallel enhancement strategies on model training in the data imbalance, different combinations can affect the model to different degrees. Finally, we evaluate the model using three classical public datasets and the average improvement of EFDD over the three datasets is AP 2.1%, AR 1.5% compared to other contrastive learning methods. Compared to the supervised methods, our model accuracy is close to or even exceeds that of the supervised methods.
CSF-Net: color space fusion network for color constancy

Color constancy is the ability of the human visual system to correctly perceive colors in a scene under different illuminants. To mimic this ability, computational color constancy has been proposed to estimate illuminant color and correct the color bias for a given image. Existing computational color constancy approaches focus on the widely used RGB color space. However, RGB color space could not explicitly reflect color characteristics, so it is necessary to investigate other color spaces that better express color features. HSV color space separates the color channel from others, and hence helps us to distinguish different colors. In order to better predict illuminant colors, we design a color space fusion network CSF-Net that integrates the RGB and HSV color spaces at both the feature level and the prediction level. At the feature level, color features extracted from RGB and HSV are combined to estimate an illuminant color. At the prediction level, two illuminant colors are predicted respectively by the color features from RGB and HSV. By merging all the three predicted colors, the illuminant color is finally predicted. Experiments conducted on the public Gehler-Shi and NUS 8-camera datasets show that the proposed CSF-Net performs better than most of the prior works in illuminant color estimation and achieves better color correction in the case of complex illumination.
Hyperspectral image classification aided by LiDAR data

Spectral variability and shadow effects can limit the hyperspectral image (HSI) classification performance. Compared with HSI, the LiDAR data is an excellent complement with its abundant elevation information. In this study, a procedure including pre-processing, deep residual network classification and post-processing is investigated for classification of HSI aided by the LiDAR data to release the problem of identifying shaded objects and spectral variability. Specifically, three aspects with respect to spectral band selection using Archetypal Analysis (AA), feature level fusion based classification by deploying a residual network associated approach and label correction utilizing the elevation information, are explored to realize more accurate classification. Experiments on three public multi-source (hyperspectral and LiDAR) remote sensing datasets show more promising classification can be achieved via fusion two-source of remote sensing data than that using only independent hyperspectral image. In particular, on the Houston 2017 dataset, OA and Kappa achieved significant gains of 2.47% and 2.79% respectively after incorporating LiDAR information. Moreover, the results demonstrate the elevation information used independently in the post-processing stage can help with effective refinement of classification results.
Adaptive scale based u-shape transformer network for ischemic stroke lesion segmentation in CTP images

Ischemic stroke lesion segmentation in Computed Tomography Perfusion (CTP) images is crucial for the quantitative diagnosis of stroke. However, it remains a challenging problem due to the poor image quality of CTP and the complex appearance of the lesions. In this study, we develop a U-shape transformer network with an adaptive scale for ischemic stroke lesion segmentation in CTP images. The state-of-the-art nnU-Net structure is used as the backbone, and a transformer block with self-adapting scale is introduced. The proposed network adopts the advantage of transformer in capturing global information and retains the advantage of convolutional neural network (CNN) in extracting local correlation features. In order to obtain better adaptation of transformer block to ischemic stroke segmentation task, we propose a self-adapting scale selection strategy that offer better patch size and window size to assist the transformer block capture more global information and avoid semantic information being corrupted. Five-fold cross-validation was used in training of the networks, and nnUNet was used as a baseline model in the performance evaluation. The results showed that after involving the proposed method, the mean DICE of the segmentation improved from 0.72 to 0.78 in the ISLES public dataset. For the independent test set, the proposed method achieved a mean DICE of 0.48, a mean precision of 0.60, and a mean recall of 0.46, compared to 0.46, 0.57 and 0.43 by the baseline model. The proposed framework has the potential for improving diagnosis and treatment of ischemic stroke in CTP.
Improved YOLOv5-based detection method for oilfield safety warning bands

With the development of deep learning technology, image-based object detection algorithms have been widely used in oilfield safety behavior regulation. However, the accuracy of identifying safety warning bands in oilfields is low, mainly due to the extreme aspect ratios. To solve the above problems, this paper proposes an improved YOLOv5-based method for detecting rotating targets of oilfield warning bands. By adding an additional angle prediction task to the original object detection framework and using a cyclic smooth labelling algorithm to transform the angle regression problem into a classification problem, the horizontal and predicted angle decoders can be combined to obtain the rotation bounding box of the target. This provides a more accurate spatial position representation of the warning band target, making it easier for the network to extract strong discriminative features of the target. Compared with traditional object detection algorithms annotated with horizontal bounding boxes, the rotation bounding box annotated object detection algorithm proposed in this paper significantly improves the recognition performance of safety warning bands and meets practical application requirements.
Low-resolution radar target classification algorithm based on one-dimensional densely connected network

To address the problem of low accuracy of traditional low-resolution radar target classification and recognition. In this paper, a low-resolution radar target classification algorithm based on a one-dimensional Densely Connected Convolutional Network (DenseNet) is proposed. The algorithm first directly downscales the Densely Connected Convolutional Network, then takes the original 1D radar target signal as the input for training, uses a segmented loss function for the characteristics of different classes of signals, makes the network use different loss functions in different training stages, and then back-propagates the loss to optimize the weights to improve the recognition effect of the network. The experimental results show that the recognition rate of the proposed method is higher than that of traditional radar target classification methods and simple one-dimensional convolutional neural networks (CNN) for low-spectral radar target classification, especially under low signal-to-noise ratio conditions, which fully demonstrates the effectiveness of the proposed method.
Bias reductions for beta kernel estimation

The beta kernel estimator for a density with support was discussed by Chen [(1999) ‘Beta Kernel Estimators for Density Functions’, Computational Statistics and Data Analysis, 31, 131–145]. In this paper, when the underlying density has a fourth-order derivative, we improve the beta kernel estimator using the bias correction techniques based on two beta kernel estimators with different smoothing parameters. As a result, we propose new bias corrected beta kernel estimators involving the digamma functions, and then establish their asymptotic properties. Simulation studies are conducted to illustrate the finite sample performance of the proposed estimators.
Design of high-resolution and wide-range time-to-digital converter

Time digital conversion technology has been widely used in the field of measurement technology. A high-resolution, widerange Time-to-Digital-Converter (TDC) circuit is proposed based on Multiplier Delay-Locked Loop (MDLL) and Time Amplifier (TA). In this paper, a two-segment combination structure is adopted to obtain a wide range by using the cycle counting method in the high segment, and to take into account the high resolution by using the time amplification technique in the middle and low segments. Finally, a TDC circuit with a range of 500 ps ~ 4 us and a resolution of 18 ps is presented.
Research on the processing algorithm of drilling image

In geological exploration, the texture and context information generated by different rocks on the inner wall of the drilling hole is of great significance to understand the geological condition. Drilling camera technology can obtain digital drilling video and hole wall images. For a long time, the method of manual interpretation is the main method of studying hole wall images. This method has problems such as poor real-time performance and low work efficiency . In this paper, the processing algorithm is designed based on the imaging characteristics of hole wall image and the method of digital image processing, including image preprocessing, image expansion and image stitching. First, the radius of the area is determined according to the image features, and then the concentric circle expansion method is used to obtain the rectangular expansion image. Finally, the SIFT feature point is used to complete the accurate matching with the Ransac algorithm, and use weighted average method to complete image fusion. The entire algorithm can obtain a panoramic expansion image of the hole wall area, laying the foundation for the quantitative analysis of drilling data.
Power Internet of Things traffic classification system based on reinforcement learning

Network traffic classification technology is an important means for power Internet of things to carry out network management and maintain network security. However, there are many existing traffic classification methods. Different traffic classification methods face different data sets, and the data sets used for training are limited, the update is slow, and the change of traffic characteristics is not obvious. Therefore, based on passive detection technology, this paper uses traffic analysis as a tool to collect the lossless traffic data of the target network, and then uses reinforcement learning Q-learning algorithm to classify the traffic and design the corresponding return function, and adopt ε-greedy exploration strategy and delayed return strategy to improve the learning effect of agents and improve the accuracy and efficiency of classification to a greater extent. Finally, the feasibility of the system is verified by experimental simulation. After 100 days of training, the classification accuracy has exceeded 85%, and with the increase of training time, the classification accuracy will be further improved.