An ideal anti-counterfeiting technique has to be inexpensive, mass-producible, nondestructive, unclonable and convenient for authentication. Although many anti-counterfeiting technologies have been ...developed, very few of them fulfill all the above requirements. Here we report a non-destructive, inkjet-printable, artificial intelligence (AI)-decodable and unclonable security label. The stochastic pinning points at the three-phase contact line of the ink droplets is crucial for the successful inkjet printing of the unclonable security labels. Upon the solvent evaporation, the three-phase contact lines are pinned around the pinning points, where the quantum dots in the ink droplets deposited on, forming physically unclonable flower-like patterns. By utilizing the RGB emission quantum dots, full-color fluorescence security labels can be produced. A convenient and reliable AI-based authentication strategy is developed, allowing for the fast authentication of the covert, unclonable flower-like dot patterns with different sharpness, brightness, rotations, amplifications and the mixture of these parameters.
Bilingual web pages are widely used to mine translations of unknown terms. This study focused on an effective solution for obtaining relevant web pages, extracting translations with correct lexical ...boundaries, and ranking the translation candidates. This research adopted co-occurrence information to obtain the subject terms and then expanded the source query with the translation of the subject terms to collect effective bilingual search engine snippets. Afterwards, valid candidates were extracted from small-sized, noisy bilingual corpora using an improved frequency change measurement that combines adjacent information. This research developed a method that considers surface patterns, frequency–distance, and phonetic features to elect an appropriate translation. The experimental results revealed that the proposed method performed remarkably well for mining translations of unknown terms.
The performance of a machine translation system (MTS) depends on the quality and size of the training data. How to extend the training dataset for the MTS in specific domains with effective methods ...to enhance the performance of machine translation needs to be explored. A method for selecting in-domain bilingual sentence pairs based on the topic information is proposed. With the aid of the topic relevance of the bilingual sentence pairs to the target domain, subsets of sentence pairs related to the texts to be translated are selected from a large-scale bilingual corpus to train the translation system in specific domains to improve the translation quality for in-domain texts. Through the test, the bilingual sentence pairs are selected by using the proposed method, and further the MTS is trained. In this way, the translation performance is greatly enhanced.
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Optical security labels play a significant role in protecting both our wealth and health. However, simultaneously meeting the requirements including low-cost fabrication, easy detection, and ...high-level security is still challenging for security labels. Here, we design an unclonable anti-counterfeiting system with triple-level security by using the inkjet printing technique, which can be authenticated by naked eyes, a portable microscope, and a fluorescence microscope. These labels are achieved by printing microscale quantum dot (QD) ink droplets on premodified substrates with random-distributed glass microspheres. Due to the unique capillary action induced by the glass microspheres, QDs in the ink droplets are deposited around the microspheres, forming microscale multicircular patterns. Multiple pinning of QDs at the three-phase contact lines appears during the evaporation of the droplet, resulting in the formation of a nanoscale labyrinthine pattern around the microspheres. The nanoscale labyrinth pattern and the microscale multicircular microsphere array, together with the printed macroscopic image, constitute a triple-level progressive anti-counterfeiting system. Moreover, the system is compatible with an artificial intelligence-based identification strategy that allows rapid identification and verification of the unclonable security labels.
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Continuous developments of innovative anticounterfeiting strategies are vital to restrain the fast-growing counterfeit markets. Physical unclonable function (PUF)-based taggants allow for a practical ...solution to provide irreproducible codes for strong authentication. Herein, an advanced anticounterfeiting strategy with multiple security levels was successfully developed using screen printing and atomic layer infiltration (ALI) techniques. Macroscale poly(dimethylsiloxane) (PDMS) patterns were fabricated for primary verification. Spontaneous formation of random wrinkles with size in the micrometer scale was achieved on the top surface of screen-printed PDMS patterns due to the anisotropic relief and redistribution of extra compressive stress after Al2O3 infiltration, which can be used for senior authentication by image identification using the artificial intelligence (AI) technique. Furthermore, the complexity and security level of a code, which are proportional to the minutia density, can be adjusted by the morphology of the wrinkles in terms of amplitude and wavelength via the degree of Al2O3 permeation depending on ALI conditions. These spontaneously formed random wrinkles were demonstrated for validation and decoding with AI, exhibiting the merits of being unclonable, nondestructive, universally adaptable, environmentally stable, and mass-producible, and sufficiently adaptable for an industry-suitable authentication strategy.
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It is important to help researchers find valuable papers from a large literature collection. To this end, many graph‐based ranking algorithms have been proposed. However, most of these algorithms ...suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph‐based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less‐biased ranking than previous methods. MutualRank provides a unified model that involves both intra‐ and inter‐network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well‐known universities and two well‐known textbooks. The experimental results show that MutualRank greatly outperforms the state‐of‐the‐art competitors, including PageRank, HITS, CoRank, Future Rank, and P‐Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.
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Reconstruction of total facial deformities and defects has been a major challenge of reconstructive surgery. Allotransplantation is limited by the number of donors and the need for life-long ...immunosuppression. Autotransplantation, where multiple autogenous tissue grafts from various donor sites are used to repair facial defects, inevitably leaves conspicuous patchwork scars. A prefabricated monoblock flap, although the preferred treatment modality, is limited by insufficient blood supply and the large size of the flap. In the Journal of Craniofacial Surgery (2014;25:21-25), Li et al applied the technique of flap prefabrication and stem cell-assisted tissue expansion to reconstruct total facial injuries, but the operations were complicated and the final expanded flap area was also uncertain. This article introduces an approach to reconstruct total facial injuries with a prefabricated expanded thoracic flap combined with an expanded scalp flap (called combined flaps), which not only solves the limitations of blood supply and expanded volume but also reduces patchwork scars. The approach is a simple, feasible, and effective surgical method for total face resurfacing.
We tackle the problems of both event and entity relation extraction, and come up with a novel method to implement joint extraction: iteratively interactive learning. This method is motivated by the ...empirical findings as below: the extracted event attributes (e.g., trigger and event type) can be used as the reliable features for the recognition of entity relation types, and vice versa. Accordingly, on one hand, we utilize the predicted event attributes (by a certain event extraction system) to remodel the distributed representations of features for entity relation extraction, and on the other hand, we use entity relations (recognized by a certain relation extraction system) to remodel the features for event extraction. This enables a double-channel task-independent joint model with an interactive learning: learning events for relation extraction, and meanwhile learning relations for event extraction. In practice, we perform the interactive learning in an iterative manner, so as to boost the joint model progressively. Methodologically, we take the neural network of bidirectional long short-term memory (Bi-LSTM) for learning event and relation respectively. And as usual, the attention mechanism is used. In our experiments, the automatic content extraction corpus is used for the evaluation of the proposed method. Such a corpus consists of event, entity and relation samples with gold-standard attribute tags. Experimental results show that our method outperforms the baselines (Bi-LSTMs with attention without interactive learning) in both event and relation extraction tasks, yielding performance gains of about 1.6% and 1.8% F-scores respectively, at the condition of low-resource setting.
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Activation of protein kinase C (PKC) is a critical intracellular signaling triggered by ischemic preconditioning (IPC), but the precise mechanisms underlying the actions of PKC in IPC-mediated ...cardioprotection remain unclear. Here, we investigated the role of PKC activation on the antioxidant activity by IPC in rabbit hearts. Isolated rabbit hearts were subjected to 60 min of global ischemia by cold cardioplegic arrest (4 °C) and 60 min of reperfusion (37 °C). IPC was induced by three cycles of 2-min ischemia following 3 min of reperfusion (37 °C) before cardioplegic arrest. IPC resulted in a better recovery of mechanical function, increased tissue reduced glutathione-to-oxidized glutathione ratio (GSH/GSSG), superoxide dismutase and catalase content, and decreased tissue malondialdehyde (MDA) content compared to control hearts subjected to 60 min of cardioplegic ischemia and 60 min of reperfusion. IPC also significantly induced activation of nuclear factor erythroid 2-related factor 2 (Nrf2) and the inductions of antioxidant genes heme oxygenase-1 (HO-1) and manganese superoxide dismutase (MnSOD). Injection of phorbol 12-myristate 13 acetate, an activator of PKC, before cardioplegic ischemia induced translocation of PKC-δ and -ε isoforms to membrane fraction, nuclear accumulation of Nrf2, and conferred cardioprotection similar to IPC. Polymyxin B, an inhibitor of PKC, blocked the membrane translocation of PKC-δ and -ε during IPC, inhibited Nrf2 nuclear accumulation, and significantly diminished the IPC-induced cardioprotection when administrated before IPC. These results indicate that the activation of PKC induces the translocation of Nrf2 and the enhancement of endogenous antioxidant defenses in the IPC hearts and suggest that PKC may target Nrf2 to confer cardioprotection.
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Aiming at image target recognition, a novel algorithm of Gabor wavelet neural network based on parallel structure is proposed in this paper, and the system of neural network for multi-target ...recognition is designed. Based on the characteristics of multi-CPU parallel structure and the parallel property of neural network, the algorithm of Gabor wavelet neural network is proved theoretically. The relevant algorithm structure is designed; the training and recognizing algorithms for image target recognition are given out. Finally, the simulation experiment for 4 types of plane targets indicated that recognition rate reached 98%+, recognizing time was 40ms.
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