Charge Trap Memory Based on Few-Layered Black Phosphorus

Atomically thin layered two-dimensional materials, including transition-metal dichacolgenide (TMDC) and black phosphorus (BP), (1) have been receiving much attention, because of their promising physical properties and potential applications in flexible and transparent electronic devices . Here, for the first time we show non-volatile chargetrap memory devices, based on field-effect transistors with large hysteresis, consisting of a few-layer black phosphorus channel and a three dimensional (3D) Al2O3 /HfO2 /Al2O3 charge-trap gate stack. An unprecedented memory window exceeding 12 V is observed, due to the extraordinary trapping ability of HfO2. The device shows a high endurance and a stable retention of ?25% charge loss after 10 years, even drastically lower than reported MoS2 flash memory. The high program/erase current ratio, large memory window, stable retention and high on/off current ratio, provide a promising route towards the flexible and transparent memory devices utilising atomically thin two-dimensional materials. The combination of 2D materials with traditional high-k charge-trap gate stacks opens up an exciting field of nonvolatile memory devices.Comment: 16 pages, 10 figures, 1 table. arXiv admin note: substantial text overlap with arXiv:1407.7432 by other authors; text overlap with arXiv:1505.04859 by other authors without attributio

Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval

The Bag-of-Words (BoW) representation is well applied to recent state-of-the-art image retrieval works. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the intersection set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the intersection set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the intersection set. We evaluate our method through extensive experiments on three benchmark datasets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance compared with the state-of-the-art methods.Comment: 8 pages, 7 figures, 6 tables, accepted to CVPR 201

Research on the performance of buffer for landing gear based on the drop test

Based on the drop test of the articulated main landing gear of Seagull 300 light multifunctional amphibious airplane, a further study has been conducted to establish buffer performance under different air chamber pressures and attitude angles. Through comparative analysis of the test results, the influencing rule of air chamber pressure and attitude angle on the buffer performance parameters (system capacity, vertical load, buffer compression, system efficiency and buffer efficiency) was obtained. The results demonstrate that air chamber pressure has a significant effect on the buffer system efficiency, while the attitude angle influences the system capacity a lot. With air chamber pressure increasing system efficiency decreases first, then gradually increases after reaching its minimum at 2.15 MPa and decreases at last after reaching its maximum at 2.7 MPa. Buffer efficiency decreases first and then increases after reaching its minimum at 2.2 MPa. When the attitude angle is between 3 and 12 degrees, the smaller the attitude angle, the more energy the system absorbs and the better the buffer performance is. The rate of change of performance parameters varies linearly with attitude angle. With the increase of angle, system capacity, maximum vertical load and system efficiency increase, and the change rate of buffer compression decreases correspondingly. The rate of change of system efficiency has the fastest growth

Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination

Visual tracking can be easily disturbed by similar surrounding objects. Such objects as hard distractors, even though being the minority among negative samples, increase the risk of target drift and model corruption, which deserve additional attention in online tracking and model update. To enhance the tracking robustness, in this paper, we propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates via an efficient convolutional regression. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples, which serves as an alternative of fully-connected layers and benefits from the closed-form solver for efficient learning. Extensive experiments are conducted on 11 challenging tracking benchmarks including OTB-2013, OTB-2015, VOT2018, VOT2019, UAV123, Temple-Color, NfS, TrackingNet, LaSOT, UAV20L, and OxUvA. The proposed method achieves state-of-the-art performance on prevalent benchmarks, while running in a real-time speed.Comment: Accepted by IEEE TCSV

Partial entropy in finite-temperature phase transitions

It is shown that the von Neumann entropy, a measure of quantum entanglement, does have its classical counterpart in thermodynamic systems, which we call partial entropy. Close to the critical temperature the partial entropy shows perfect finite-size scaling behavior even for quite small system sizes. This provides a powerful tool to quantify finite-temperature phase transitions as demonstrated on the classical Ising model on a square lattice and the ferromagnetic Heisenberg model on a cubic lattice.Comment: 4 pages, 6 figures, Revised versio

DocScanner: Robust Document Image Rectification with Progressive Learning

Compared with flatbed scanners, portable smartphones are much more convenient for physical documents digitizing. However, such digitized documents are often distorted due to uncontrolled physical deformations, camera positions, and illumination variations. To this end, we present DocScanner, a novel framework for document image rectification. Different from existing methods, DocScanner addresses this issue by introducing a progressive learning mechanism. Specifically, DocScanner maintains a single estimate of the rectified image, which is progressively corrected with a recurrent architecture. The iterative refinements make DocScanner converge to a robust and superior performance, while the lightweight recurrent architecture ensures the running efficiency. In addition, before the above rectification process, observing the corrupted rectified boundaries existing in prior works, DocScanner exploits a document localization module to explicitly segment the foreground document from the cluttered background environments. To further improve the rectification quality, based on the geometric priori between the distorted and the rectified images, a geometric regularization is introduced during training to further improve the performance. Extensive experiments are conducted on the Doc3D dataset and the DocUNet Benchmark dataset, and the quantitative and qualitative evaluation results verify the effectiveness of DocScanner, which outperforms previous methods on OCR accuracy, image similarity, and our proposed distortion metric by a considerable margin. Furthermore, our DocScanner shows the highest efficiency in runtime latency and model size

DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding

This work presents DocPedia, a novel large multimodal model (LMM) for versatile OCR-free document understanding, capable of parsing images up to 2,560$\times$2,560 resolution. Unlike existing work either struggle with high-resolution documents or give up the large language model thus vision or language ability constrained, our DocPedia directly processes visual input in the frequency domain rather than the pixel space. The unique characteristic enables DocPedia to capture a greater amount of visual and textual information using a limited number of visual tokens. To consistently enhance both perception and comprehension abilities of our model, we develop a dual-stage training strategy and enrich instructions/annotations of all training tasks covering multiple document types. Extensive quantitative and qualitative experiments conducted on various publicly available benchmarks confirm the mutual benefits of jointly learning perception and comprehension tasks. The results provide further evidence of the effectiveness and superior performance of our DocPedia over other methods