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Although it is traditionally believed that lossy image compression, such as JPEG compression, has a negative impact on the performance of deep neural networks (DNNs), it is shown by recent works that well-crafted JPEG compression can actually improve the performance of deep learning (DL). Inspired by this, we propose JPEG-DL, a novel DL framework that prepends any underlying DNN architecture with a trainable JPEG compression layer. To make the quantization operation in JPEG compression trainable, a new differentiable soft quantizer is employed at the JPEG layer, and then the quantization operation and underlying DNN are jointly trained. Extensive experiments show that in comparison with the standard DL, JPEG-DL delivers significant accuracy improvements across various datasets and model architectures while enhancing robustness against adversarial attacks. Particularly, on some fine-grained image classification datasets, JPEG-DL can increase prediction accuracy by as much as 20.9%. Our code is available on https://github.com/JpegInspiredDl/JPEG-Inspired-DL.git.

The Quantum Approximate Optimization Algorithm (QAOA) represents a significant opportunity for practical quantum computing applications, particularly in the era before error correction is fully realized. This algorithm is especially relevant for addressing constraint satisfaction problems (CSPs), which are critical in various fields such as supply chain management, energy distribution, and financial modeling. In our study, we conduct a numerical comparison of three different strategies for incorporating linear constraints into QAOA: transforming them into an unconstrained format, introducing penalty dephasing, and utilizing the quantum Zeno effect. We assess the efficiency and effectiveness of these methods using the knapsack problem as a case study. Our findings provide insights into the potential applicability of different encoding methods for various use cases.

Transparent object depth perception poses a challenge in everyday life and logistics, primarily due to the inability of standard 3D sensors to accurately capture depth on transparent or reflective surfaces. This limitation significantly affects depth map and point cloud-reliant applications, especially in robotic manipulation. We developed a vision transformer-based algorithm for stereo depth recovery of transparent objects. This approach is complemented by an innovative feature post-fusion module, which enhances the accuracy of depth recovery by structural features in images. To address the high costs associated with dataset collection for stereo camera-based perception of transparent objects, our method incorporates a parameter-aligned, domain-adaptive, and physically realistic Sim2Real simulation for efficient data generation, accelerated by AI algorithm. Our experimental results demonstrate the model's exceptional Sim2Real generalizability in real-world scenarios, enabling precise depth mapping of transparent objects to assist in robotic manipulation. Project details are available at https://sites.google.com/view/cleardepth/ ., Comment: 7 pages, 7 figures

Developing high-entropy ceramics (HECs) with ultra-high melting points (Tm) is crucial for their applications in ultra-high-temperature environments. However, related research has seldom been reported. Here, taking high-entropy diborides (HEBs) as an example, we develop a data-driven method to efficiently explore HEBs with ultra-high Tm via transferable machine-learning-potential-based molecular dynamics (MD). Specifically, a moment tensor potential (MTP) for HEBs with nine transition metal elements of group IVB, VB, and VIB is first constructed based on unary and binary diborides. Further studies on the performance of our constructed MTP have confirmed its remarkable accuracy, transferability, and reliability across both equimolar and non-equimolar HEB systems. Tm of HEBs are then accurately simulated through MD simulations based on the constructed MTP, and 24 features are simultaneously collected to enable reliable machine learning training. Five descriptors with the gradient boosting regression model are derived as the optimal combination for accurate Tm predictions in HEBs with genetic algorithms. Based on our established model, Tm of 32563 HEBs are eventually determined, achieving the maximum Tm of 3688 K in (Ti0.1Zr0.1Hf0.6Ta0.2)B2. The work presents a feasible approach to develop HECs with ultra-high Tm., Comment: 27 pages, 6 figures

The mechanical and thermal performance of high-entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high-entropy ceramic significantly hinders their development with desired mechanical and thermal properties. Herein, taking high-entropy carbides (HECs) as the model, we show the efficiency and effectiveness of exploring the mechanical and thermal properties via machine-learning-potential-based molecular dynamics (MD). Specifically, a general neuroevolution potential (NEP) with broad compositional applicability for HECs of ten transition metal elements from group IIIB-VIB is efficiently constructed from the small dataset comprising unary and binary carbides with an equal amount of ergodic chemical compositions. Based on this well-established NEP, MD simulations on mechanical and thermal properties of different HECs have shown good agreement with the results of first-principles calculations and experimental measurements, validating the accuracy, generalization, and reliability of using the developed general NEP in investigating mechanical and thermal performance of HECs. Our work provides an efficient solution to accelerate the search for high-entropy ceramics with desirable mechanical and thermal properties., Comment: 37 pages, 6 figures

Revealing the hardening and strengthening mechanisms is crucial for facilitating the design of superhard and high-strength high-entropy ceramics (HECs). Here, we take high-entropy diborides (HEB$_2$) as the prototype to thoroughly investigate the hardening and strengthening mechanisms of HECs. Specifically, the equiatomic 4- to 9-cation single-phase HEB$_2$ ceramics (4-9HEB$_2$) are fabricated by an ultra-fast high-temperature sintering method. The as-fabricated 4-9HEB$_2$ samples possess similar grain sizes, comparable relative densities (up to ~98%), uniform compositions, and clean grain boundaries without any impurities. The experimental results show that the hardness and flexural strength of the as-fabricated 4-9HEB$_2$ samples have an increasing tendency with the increase of metal components. The first-principles calculations find that lattice distortion is essential to the hardness and strength of HEB$_2$. With the increase of metal components, an aggravation of lattice distortion accompanied by B-B bond strengthening is determined, resulting in the enhancement of the hardness and flexural strength. Moreover, the correlation between other potential indicators and the hardness/flexural strength of HEB$_2$ has been disproved, including valence electron concentration, electronegativity mismatch, and metallic states. Our results unravel the hardening and strengthening mechanisms of HECs by intensifying lattice distortion, which may provide guidance for developing superhard and high-strength HECs.

In this paper, we investigate the effectiveness of contrastive learning methods for predicting grasp outcomes in an unsupervised manner. By utilizing a publicly available dataset, we demonstrate that contrastive learning methods perform well on the task of grasp outcomes prediction. Specifically, the dynamic-dictionary-based method with the momentum updating technique achieves a satisfactory accuracy of 81.83% using data from one single tactile sensor, outperforming other unsupervised methods. Our results reveal the potential of contrastive learning methods for applications in the field of robot grasping and highlight the importance of accurate grasp prediction for achieving stable grasps., Comment: Manuscript accepted to RCAR 2023

When tackling binary optimization problems using quantum algorithms, the conventional Ising representation and Quantum Approximate Optimization Algorithm (QAOA) encounter difficulties in efficiently handling errors for large-scale problems involving multiple constraints. To address these challenges, this paper presents a hybrid framework that combines the use of standard Ising Hamiltonians to solve a subset of the constraints, while employing non-Ising formulations to represent and address the remaining constraints. The resolution of these non-Ising constraints is achieved through either penalty dephasing or the quantum Zeno effect. This innovative approach leads to a collection of quantum circuits with adaptable structures, depending on the chosen representation for each constraint. Furthermore, this paper introduces a novel technique that utilizes the quantum Zeno effect by frequently measuring the constraint flag, enabling the resolution of any optimization constraint. Theoretical properties of these algorithms are discussed, and their performance in addressing practical aircraft loading problems is highly promising, showcasing significant potential for a wide range of industrial applications., Comment: 21 pages, 15 figures

The construction of quantum networks requires long-distance teleportation of multi-qubit entangled states. Here, we investigate the entanglement dynamics of GHZ and W states in fiber channels. In a fiber channel, the two most important phenomena that affect polarization entanglement are polarization mode dispersion (PMD) and polarization-dependent loss (PDL). We theoretically characterize how PMD and PDL vectors affect three-qubit states. In particular, upon quantifying the entanglement at the output states using concurrence and entanglement witness, we reveal the occurrence of entanglement sudden death (ESD) and the appearance of decoherence-free subspaces (DSFs) in tripartite systems. Finally, we explore the evolution of GHZ and W state with an arbitrary number of photons in a fiber network and evaluate the expectation value of the entanglement witness.