Coherence generating power of unitary transformations via probabilistic average

We study the ability of a quantum channel to generate quantum coherence when it applies to incoherent states. Based on probabilistic averages, we define a measure of such coherence generating power (CGP) for a generic quantum channel, based on the average coherence generated by the quantum channel acting on a uniform ensemble of incoherent states. Explicit analytical formula of the CGP for any unitary channels are presented in terms of subentropy. An upper bound for CGP of unital quantum channels has been also derived. Detailed examples are investigated.Comment: 16 pages, 2 figures, LaTeX, accepted versio

Principal-Agent Problem with Third Party: Information Design from Social Planner's Perspective

We study the principal-agent problem with a third party that we call social planner, whose responsibility is to reconcile the conflicts of interest between the two players and induce socially optimal outcome in terms of some given social utility function. The social planner owns no contractual power but manages to control the information flow between the principal and the agent. We design a simple workflow with two stages for the social planner. In the first stage, the problem is reformulated as an optimization problem whose solution is the optimal utility profile. In the second stage, we investigate information design and show that binary-signal information structure suffices to induce the socially optimal outcome determined in the first stage. The result shows that information plays a key role in social planning in the principal-agent model

On the Landscape of One-hidden-layer Sparse Networks and Beyond

Sparse neural networks have received increasing interests due to their small size compared to dense networks. Nevertheless, most existing works on neural network theory have focused on dense neural networks, and our understanding of sparse networks is very limited. In this paper, we study the loss landscape of one-hidden-layer sparse networks. We first consider sparse networks with linear activations. We show that sparse linear networks can have spurious strict minima, which is in sharp contrast to dense linear networks which do not even have spurious minima. Second, we show that spurious valleys can exist for wide sparse non-linear networks. This is different from wide dense networks which do not have spurious valleys under mild assumptions

FS_YOLOv8: A Deep Learning Network for Ground Fissures Instance Segmentation in UAV Images of the Coal Mining Area

The ground fissures caused by coal mining have seriously affected the ecological environment of the land. Timely and accurate identification and landfill treatment of ground fissures can avoid secondary geological disasters in coal mine areas. At present, the fissure identification methods based on deep learning show excellent performance on roads and walls, etc. Nevertheless, the automatic and reliable segmentation of ground fissures in remote sensing images poses a challenge for deep learning networks, due to the diverse and complex texture information included in the mining ground fissures and background. To overcome these challenges, we propose an improved YOLOv8 instance segmentation network to automatically and efficiently segment the ground fissures in coal mining areas. In detail, a model called FS_YOLOv8 is proposed. The DSPP (Dynamic Snake convolutional Pyramid Pooling) module is incorporated into the FS_YOLOv8 model to establish a multi-scale dynamic snake convolution feature aggregation structure. This module replaces the conventional convolution found in the SPPF module of YOLOv8 and aims to enhance the model's ability to extract features related to fissures with tubular structures. Furthermore, the D-LKA (Deformable Large Kernel Attention) module is employed to autonomously collect fissure context information. To enhance the detection capability of challenging samples in remote sensing images with intricate background and fissure texture, we employ a Slide Loss function. Ultimately, the ground fissure dataset of unmanned aerial vehicle (UAV) images in coal mine areas is subjected to experimental analysis. The experimental findings demonstrate that FS_YOLOv8 exhibits exceptional proficiency in segmenting ground fissures within intricate and expansive mining areas

Single-Curvature Sandwich Panels with Aluminum Foam Cores under Impulsive Loading

Single-curvature sandwich panels combine the advantages of the shell and sandwich structure and are therefore envisaged to possess good potential to resist blast and shock or impact loads. This study presents a comprehensive report on the dynamic response and shock resistance of single-curvature sandwich panels, comprising two aluminum alloy face-sheets and an aluminum foam core, subjected to air-blast loading, in terms of the experimental investigation and numerical simulation. The deformation modes, shock resistance capability, and energy absorption performance are studied, and the influences of specimen curvature, blast impulse, and geometrical configuration are discussed. Results indicate that the deformation/failure, deflection response, and energy absorption of curved sandwich panels are sensitive to the loading intensity and geometric configuration. These results are significant to guide the engineering applications of sandwich structures with metallic foam cores subjected to air-blast loading

OR-Gate: A Noisy Label Filtering Method for Speaker Verification

The deep learning models used for speaker verification are heavily dependent on large-scale data and correct labels. However, noisy (wrong) labels often occur, which deteriorates the system's performance. Unfortunately, there are relatively few studies in this area. In this paper, we propose a method to gradually filter noisy labels out at the training stage. We compare the network predictions at different training epochs with ground-truth labels, and select reliable (considered correct) labels by using the OR gate mechanism like that in logic circuits. Therefore, our proposed method is named as OR-Gate. We experimentally demonstrated that the OR-Gate can effectively filter noisy labels out and has excellent performance.Comment: Submitted to 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023