229 research outputs found
A Visual Representation-guided Framework with Global Affinity for Weakly Supervised Salient Object Detection
Fully supervised salient object detection (SOD) methods have made
considerable progress in performance, yet these models rely heavily on
expensive pixel-wise labels. Recently, to achieve a trade-off between labeling
burden and performance, scribble-based SOD methods have attracted increasing
attention. Previous scribble-based models directly implement the SOD task only
based on SOD training data with limited information, it is extremely difficult
for them to understand the image and further achieve a superior SOD task. In
this paper, we propose a simple yet effective framework guided by general
visual representations with rich contextual semantic knowledge for
scribble-based SOD. These general visual representations are generated by
self-supervised learning based on large-scale unlabeled datasets. Our framework
consists of a task-related encoder, a general visual module, and an information
integration module to efficiently combine the general visual representations
with task-related features to perform the SOD task based on understanding the
contextual connections of images. Meanwhile, we propose a novel global semantic
affinity loss to guide the model to perceive the global structure of the
salient objects. Experimental results on five public benchmark datasets
demonstrate that our method, which only utilizes scribble annotations without
introducing any extra label, outperforms the state-of-the-art weakly supervised
SOD methods. Specifically, it outperforms the previous best scribble-based
method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for
mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method
achieves comparable or even superior performance to the state-of-the-art fully
supervised models
Towards Runtime Customizable Trusted Execution Environment on FPGA-SoC
Processing sensitive data and deploying well-designed Intellectual Property
(IP) cores on remote Field Programmable Gate Array (FPGA) are prone to private
data leakage and IP theft. One effective solution is constructing Trusted
Execution Environment (TEE) on FPGA-SoCs (FPGA System on Chips). Researchers
have integrated this type TEE with Trusted Platform Module (TPM)-based trusted
boot, denoted as FPGA-SoC tbTEE. But there is no effort on secure and trusted
runtime customization of FPGA-SoC TEE. This paper extends FPGA-SoC tbTEE to
build Runtime Customizable TEE (RCTEE) on FPGA-SoC by additive three major
components (our work): 1) CrloadIP, which can load an IP core at runtime such
that RCTEE can be adjusted dynamically and securely; 2) CexecIP, which can not
only execute an IP core without modifying the operating system of FPGA-SoC TEE,
but also prevent insider attacks from executing IPs deployed in RCTEE; 3)
CremoAT, which can provide the newly measured RCTEE state and establish a
secure and trusted communication path between remote verifiers and RCTEE. We
conduct a security analysis of RCTEE and its performance evaluation on Xilinx
Zynq UltraScale+ XCZU15EG 2FFVB1156 MPSoC
Observation of pi/2 modes in an acoustic Floquet system
Topological phases of matter have remained an active area of research in the
last few decades. Periodic driving is known to be a powerful tool for enriching
such exotic phases, which leads to various phenomena with no static analogs.
One such phenomenon is the emergence of the elusive modes, i.e., a type
of topological boundary state pinned at a quarter of the driving frequency. The
latter may lead to the formation of Floquet parafermions in the presence of
interaction, which is known to support more computational power than Majorana
particles. In this work, we experimentally verify the signature of
modes in an acoustic waveguide array, which is designed to simulate a
square-root periodically driven Su-Schrieffer-Heeger model. This is
accomplished by confirming the -periodicity ( being the driving period)
profile of an initial-boundary excitation, which we also show theoretically to
be the smoking gun evidence of modes. Our findings are expected to
motivate further studies of modes in quantum systems for potential
technological applications.Comment: 6 pages, 3 figure. Comments are welcom
Integrated mRNA Sequence Optimization Using Deep Learning
The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5\u27 untranslated region (UTR) and 3\u27UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics
Nonconvex optimization for optimum retrieval of the transmission matrix of a multimode fiber
Transmission matrix (TM) allows light control through complex media such as
multimode fibers (MMFs), gaining great attention in areas like biophotonics
over the past decade. The measurement of a complex-valued TM is highly desired
as it supports full modulation of the light field, yet demanding as the
holographic setup is usually entailed. Efforts have been taken to retrieve a TM
directly from intensity measurements with several representative phase
retrieval algorithms, which still see limitations like slow or suboptimum
recovery, especially under noisy environment. Here, a modified non-convex
optimization approach is proposed. Through numerical evaluations, it shows that
the nonconvex method offers an optimum efficiency of focusing with less running
time or sampling rate. The comparative test under different signal-to-noise
levels further indicates its improved robustness for TM retrieval.
Experimentally, the optimum retrieval of the TM of a MMF is collectively
validated by multiple groups of single-spot and multi-spot focusing
demonstrations. Focus scanning on the working plane of the MMF is also
conducted where our method achieves 93.6% efficiency of the gold standard
holography method when the sampling rate is 8. Based on the recovered TM, image
transmission through the MMF with high fidelity can be realized via another
phase retrieval. Thanks to parallel operation and GPU acceleration, the
nonconvex approach can retrieve an 86851024 TM (sampling rate=8) with
42.3 s on a regular computer. In brief, the proposed method provides optimum
efficiency and fast implementation for TM retrieval, which will facilitate wide
applications in deep-tissue optical imaging, manipulation and treatment
Identification and analysis of the secretome of plant pathogenic fungi reveals lifestyle adaptation
The secretory proteome plays an important role in the pathogenesis of phytopathogenic fungi. However, the relationship between the large-scale secretome of phytopathogenic fungi and their lifestyle is not fully understood. In the present study, the secretomes of 150 plant pathogenic fungi were predicted and the characteristics associated with different lifestyles were investigated. In total, 94,974 secreted proteins (SPs) were predicted from these fungi. The number of the SPs ranged from 64 to 1,662. Among these fungi, hemibiotrophic fungi had the highest number (average of 970) and proportion (7.1%) of SPs. Functional annotation showed that hemibiotrophic and necrotroph fungi, differ from biotrophic and symbiotic fungi, contained much more carbohydrate enzymes, especially polysaccharide lyases and carbohydrate esterases. Furthermore, the core and lifestyle-specific SPs orthogroups were identified. Twenty-seven core orthogroups contained 16% of the total SPs and their motif function annotation was represented by serine carboxypeptidase, carboxylesterase and asparaginase. In contrast, 97 lifestyle-specific orthogroups contained only 1% of the total SPs, with diverse functions such as PAN_AP in hemibiotroph-specific and flavin monooxygenases in necrotroph-specific. Moreover, obligate biotrophic fungi had the largest number of effectors (average of 150), followed by hemibiotrophic fungi (average of 120). Among these effectors, 4,155 had known functional annotation and pectin lyase had the highest proportion in the functionally annotated effectors. In addition, 32 sets of RNA-Seq data on pathogen-host interactions were collected and the expression levels of SPs were higher than that of non-SPs, and the expression level of effector genes was higher in biotrophic and hemibiotrophic fungi than in necrotrophic fungi, while secretase genes were highly expressed in necrotrophic fungi. Finally, the secretory activity of five predicted SPs from Setosphearia turcica was experimentally verified. In conclusion, our results provide a foundation for the study of pathogen-host interaction and help us to understand the fungal lifestyle adaptation
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