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

Voyage optimization combining genetic algorithm and dynamic programming for fuel/emissions reduction

Deterministic optimization algorithms generate optimal routes/paths and speeds along ship voyages. However, a ship can rarely follow pre-defined speeds because dynamic sea environments lead to continuous speed variation. In this paper, a voyage optimization method is proposed to optimize ship engine power to reduce fuel and air emissions. It is a combination of dynamic programming and genetic algorithm to solve voyage planning in three-dimensions. In this method, the engine power is discretized into several levels. The potential benefit of using this algorithm is investigated by a medium-size chemical tanker. A ship\u27s actual sailing is used to demonstrate benefits of the proposed method. On average 3.4% of fuel-saving and emission reduction can be achieved than state-of-the-art deterministic methods. If compared with the actual full-scale measurements, on average 5.6% reduction of fuel consumption and GHG emissions (about 275 tons) can be expected by the proposed method for the six case study voyages

Benchmark study of five optimization algorithms for weather routing

Safety and energy efficiency are two of the key issues in the maritime transport community. A sail plan system, which combines the concepts of weather routing and voyage optimization, are recognized by the shipping industry as an efficient measure to ensure a ship’s safety, gain more economic benefit, and reduce negative effects on our environment. In such a system, the key component is to develop a proper optimization algorithm to generate potential ship routes between a ship’s departure and destination. In the weather routing market, four routing optimization algorithms are commonly used. They are the so-called modified Isochrone and Isopone methods, dynamic programming, three dimensional dynamic programming, and Dijkstra’s algorithm, respectively. Each optimization algorithm has its own advantages and disadvantages to estimate a ship routing with shortest sailing time or/and minimum fuel consumption. This paper will present a benchmark study that compares these algorithms for routing optimization aiming at minimum fuel consumption. A merchant ship sailing in the North Atlantic with full-scale performance measurements are employed as the case study vessels for the comparison. The ship’s speed/power performance is based on the ISO2015 methods combined with the measurement data. It is expected to demonstrate the pros and cons of different algorithms for the ship’s sail planning

Efficiency of a voluntary speed reduction algorithm for a ship’s great circle sailing

The great-circle is the shortest distance between two points on the surface of the earth. When planning a ship’s sailing route (waypoints and forward speeds) for a specific voyage, the great circle route is commonly considered as a reference route, especially for ocean-crossing seaborne transport. During the planning process, the upcoming sea weather condition is one of the most important factors affecting the ship’s route optimization/planning results. To avoid encountering harsh conditions, conventional routing optimization algorithms, such as Isochrone method and Dynamic Programming method, have been developed/implemented to schedule a ship’s optimal routes by selecting waypoints around the great circle reference route based on the ship’s operational performances at sea. Due to large uncertainties in sea weather forecast that used as inputs of these optimization algorithms, the optimized routes may have worse performances than the traditional great circle sailing. In addition, some shipping companies are still sailing in or making charting contracts based on the great circle routes. Therefore, in this study, a new optimization algorithm is proposed to consider the voluntary speed reduction with optimal speed configuration along the great circle course. The efficiency of this method is investigated by comparing these two methods for optimal route planning with respect to ETA and minimum fuel consumption. A container ship sailing in the North Atlantic with full-scale performance measurements are employed as the case study vessels for the comparison

Contrastive Transformation for Self-supervised Correspondence Learning

In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. The intra-video learning transforms the image contents across frames within a single video via the frame pair-wise affinity. To obtain the discriminative representation for instance-level separation, we go beyond the intra-video analysis and construct the inter-video affinity to facilitate the contrastive transformation across different videos. By forcing the transformation consistency between intra- and inter-video levels, the fine-grained correspondence associations are well preserved and the instance-level feature discrimination is effectively reinforced. Our simple framework outperforms the recent self-supervised correspondence methods on a range of visual tasks including video object tracking (VOT), video object segmentation (VOS), pose keypoint tracking, etc. It is worth mentioning that our method also surpasses the fully-supervised affinity representation (e.g., ResNet) and performs competitively against the recent fully-supervised algorithms designed for the specific tasks (e.g., VOT and VOS).Comment: To appear in AAAI 202

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