DeepCheck is a symbolic execution-based method to attack feed-forward neural networks. However, in the untargeted attack, DeepCheck suffers from a low success rate due to the limitation of preserving neuron activation patterns and the weakness of solving the constraint by SMT solvers. Therefore, this paper proposes a method to improve the success rate of DeepCheck. Compared to DeepCheck, the proposed method has two main differences including (i) does not force to preserve neuron activation patterns and (ii) uses a heuristic solver rather than SMT solvers. The experimental results on MNIST, Fashion-MNIST, and A-Z handwritten alphabets show three promising results. In the 1-pixel attack, while DeepCheck obtains an average of 0.7% success rate, the proposed method could achieve an average of 54.3% success rate. In the n-pixel attack, while DeepCheck obtains an average of at most 16.9% success rate for using the Z3 solver and at most 26.8% for using the SMTInterpol solver, the proposed method achieves an average of at most 98.7% success rate. In terms of solving cost, while the average running time of the proposed heuristic solver is around 0.4 s per attack, the average running time of DeepCheck is usually larger significantly. These results show the effectiveness of the proposed method to deal with the limitation of DeepCheck.
Beta-zeolite supported ruthenium catalysts for reductive amination of 5-hydroxymethyl-2-furaldehyde (HMF) with an aqueous solution of ammonia (NH3 aq.) and molecular hydrogen (H2) are examined to synthesize the corresponding primary amine of 5-aminomethyl-2-furylmethanol (FAA). Various SiO2/Al2O3 (Si/2Al) ratios of the beta-zeolite support were used to prepare the Ru-based catalysts. It was observed that the Si/2Al ratio was contributed to the catalytic activity, and the Si/2Al = 150 of beta-zeolite was found to be the most active for Ru catalyzed reductive amination of HMF, affording ca. 70% yield. Characterization techniques were taken to analysis the factors that influence the reactivity of catalysts, and which revealed that not only the ruthenium nanoparticle size but also the ratio of RuO2 against metallic Ru species were crucial factors for the reactivity of reductive amination of HMF to FAA. Graphical Abstract
A noise-enhanced super-resolution generative adversarial network plus (nESRGAN+) was proposed to improve the enhanced super-resolution GAN (ESRGAN). The contributions of nESRGAN+ generate an impressive reconstructed image with more texture details and greater sharpness. However, the perceptual quality of the output lacks hallucinated details and undesirable artifacts and takes a long time to converge. To address these problems, we propose four types of parametric regularization algorithms as loss functions of the model to enable the iterative weight adjustment of the network gradient. Several experiments were conducted to confirm that the generator can achieve a better-quality reconstructed image, including restoring the unseen texture. Our method accomplished the average peak signal-to-noise ratio (PSNR) of the reconstructed image at 27.96 dB, the average Structural Similarity Index Measure (SSIM) at 0.8303, and the average Learned Perceptual Image Patch Similarity (LPIPS) at 0.1949. It took seven times less training time than the state of the art. In addition to the better visual quality of the reconstructed result, the proposed loss functions allow the generator to converge faster.
The security of lattice‐based cryptosystems is generally based on the hardness of the Shortest Vector Problem (SVP). The original enumeration (ENUM) algorithm solving SVP runs in exponential time due to the exhaustive search, which is used as a subroutine for the block Korkin–Zolotarev (BKZ) algorithm. It is a critical issue to reduce the computational complexity of ENUM. In this paper, first, we improve the reordering method proposed by Wang et al. in ACISP 2018. We call our proposed method DPR, which permutates the projected dual lattice vectors by decreasing norms. Preliminary experimental results show that the proposed reordering methods can reduce the ENUM complexity compared to the predecessor; for instance, DPR reduces around 32.8% on average in 45‐dimensional lattices. Moreover, the authors’ simulation shows that the higher the lattice dimension, the more DPR can reduce the ENUM complexity. In addition, we study a condition for deciding when the reordering method shall be executed or not. Finally, we improve the BKZ algorithm with DPR methods and the proposed condition.
Dempster rule of combination is a powerful combination tool. It has been widely used in many fields, such as information fusion and decision-making. However, the computational complexity of Dempster rule of combination increases exponentially with the increase of frame of discernment. To address this issue, leveraging the parallel advantage of quantum computing, we present a quantum algorithm of Dempster rule of combination. The new method includes four steps. First, the quantum superposition states corresponding to arbitrary mass functions are prepared. Next, the superposition states corresponding to the two mass functions are combined by the tensor product. Effective qubits are then measured. Finally, the measurement results are normalized to obtain the combined results. The new method not only realizes most of the functions of Dempster rule of combination, but also effectively reduces the computational complexity of Dempster rule of combination in the quantum computer. Finally, we carry out the simulation experiments on the quantum cloud platform of IBM, and the experimental results show that the new method is reasonable. Compared with the traditional combination rule, this method effectively reduces the computational complexity. As the frame of discernment becomes larger, the advantages of the proposed approach in terms of running time become larger and larger.
Electron-pair density wave (PDW) states are now an intense focus of research in the field of cuprate correlated superconductivity. PDWs exhibit periodically modulating superconductive electron pairing that can be visualized directly using scanned Josephson tunneling microscopy (SJTM). Although from theory, intertwining the d -wave superconducting (DSC) and PDW order parameters allows a plethora of global electron-pair orders to appear, which one actually occurs in the various cuprates is unknown. Here, we use SJTM to visualize the interplay of PDW and DSC states in Bi 2 Sr 2 CaCu 2 O 8+x at a carrier density where the charge density wave modulations are virtually nonexistent. Simultaneous visualization of their amplitudes reveals that the intertwined PDW and DSC are mutually attractive states. Then, by separately imaging the electron-pair density modulations of the two orthogonal PDWs, we discover a robust nematic PDW state. Its spatial arrangement entails Ising domains of opposite nematicity, each consisting primarily of unidirectional and lattice commensurate electron-pair density modulations. Further, we demonstrate by direct imaging that the scattering resonances identifying Zn impurity atom sites occur predominantly within boundaries between these domains. This implies that the nematic PDW state is pinned by Zn atoms, as was recently proposed [Lozano et al. , Phys. Rev. B 103, L020502 (2021)]. Taken in combination, these data indicate that the PDW in Bi 2 Sr 2 CaCu 2 O 8+x is a vestigial nematic pair density wave state [Agterberg et al. Phys. Rev. B 91, 054502 (2015); Wardh and Granath arXiv:2203.08250].
The combination of genetic algorithm-based global search and local geometry optimization enables nonempirical structure determination for complex materials such as practical solid catalysts. However, premature convergence in the genetic algorithm hinders the determination of the global minimum for complicated molecular systems. Here, we implemented a distributed genetic algorithm based on the migration from a structure database for avoiding the premature convergence, and thus we realized the structure determination for TiCl4-capped MgCl2 nanoplates with experimentally consistent sizes. The obtained molecular models are featured with a realistic size and nonideal surfaces, representing actual primary particles of catalysts.
The Moderate Resolution Imaging Spectroradiometer (MODIS) of the National Aeronautics and Space Administration (NASA) offers numerous land products of the Earth’s datasets. On the other hand, researchers find it difficult to retrieve this data for specific places. The methods for extracting and analyzing land surface temperature (LST), land use and land cover (LULC), and elevation are presented in this study. The R commands provided make the time-consuming process of extracting data for specific places much more accessible. As a result, a statistical study of LST over Bali is shown as an example. Over the 15 regions of Bali, a quadratic polynomial identified five possible warming patterns, while a logistic regression model assessed the probability of warming. The findings suggest that 25.2% of Bali has warmed during the last two decades, with temperatures being highest in urban and built-up areas and deciduous forests and inversely associated with elevation. Global warming has sparked a lot of academic interest and has become a serious climate problem. The techniques proposed in this work simplify the extraction of LST, LULC, and elevation data from MODIS satellites. These approaches can also be used on other datasets with identical topologies, such as the normalized difference vegetation index (NDVI), aerosol optical depth (AOD), and night light data.
Information modeling and handling in uncertain environments is an important topic in the field of modern artificial intelligence. In practical applications of classification problems, the data harvested by the agent is usually not precise. Based on multi-valued mapping of probabilities expressed by Basic Probability Assignment (BPA), Dempster-Shafer Theory (DST) has a strong ability to model and handle uncertain information. In this paper, we propose a method of fusing attributes to enhance the quality of uncertain data under the framework of DST. The fusion method is based on proposed uncertainty and dissimilarity measures, which performs consistent transformations on belief information in DST. We simulate uncertain data by adding different noises to precise datasets and classify the improved data using common classifiers. With the increasing uncertainty degree of data, the proposed method has higher accuracy and robustness than other methods.
The biological functions of polysaccharides are influenced by their chemistry and chain conformation, which have resulted in various functional applications and new uses for polysaccharides in recent years. Sacran is an intriguing ampholytic polysaccharide with several key properties such as metal adsorption, anti‐inflammatory nature, and transdermal drug‐carrying capacity. It has an extremely high molecular weight over 107 g/mol, which is much higher than those of the previously reported microbial polysaccharides. In particular, it has a remarkable self‐orienting characteristic over a large length scale, which could produce a bundle with twisted morphologies from the nanoscale to the microscale with diameters of ~1 μm and lengths of >800 μm. In this review, morphological variations, as well as novel self‐organization and hierarchical self‐assembly are comprehensively discussed. Sacran fibers deform into various forms, such as two‐ and three‐dimensional flexible fibers and micro–nano fragments, during their evaporation. The self‐assembly and disassembly of the sacran are explained in terms of the preparation process and factors that influence the morphology. This review will pave the way for the development of novel modules such as humidity‐sensitive actuators, micro‐patterned cell scaffolds, and uniaxially oriented membranes.
This paper begins with the story that systems science, born of modern Western civilization, began to pay attention to traditional Eastern wisdom to deal with management issues. The consequence of the story is that we should complementarily utilize quantitative and rational analysis results and qualitative and intuitive empirical knowledge to solve problems. As a methodology for implementing knowledge synthesis, this paper presents the knowledge construction systems methodology that promotes knowledge management enhanced by systems thinking. Based on this methodology, it introduces the concept of knowledge synthesis enablers, which are conditions or activities that facilitate knowledge synthesis when promoting research and businesses. This paper shows some results of covariance structure analysis using the evaluation data of applied systems research projects and examines the validity of the assumed enabler candidates.
In video game development, creating maps, enemies, and many other elements of game levels is one of the important processes. In order to improve players’ game experiences, game designers need to understand players’ behavioral tendencies and create levels accordingly. Among various components of levels, this paper targets mazes and presents an automatic maze generation method considering difficulty based on human players’ tendencies. We first investigate the tendencies using supervised learning and then create a test player considering human-likeness by exploiting the tendencies. The test player simulates human players’ behaviors when playing mazes and judges difficulty according to the simulation results. Maze evaluation results from subject experiments show that our method succeeds in generating mazes where the difficulty estimated by the test player matches human players’.
This work investigated the isomerization of galactose to tagatose, a low caloric rare sugar, using arginine as a catalyst. Galactose (5% w/v) and arginine (0.10 mol/mol-galactose) in water were treated at 90−120 °C. The results showed that as the temperature and time increased, galactose was continuously consumed. Rare sugars namely tagatose, talose, and sorbose were formed with the highest yield of 16.8, 2.7, and 3.3%, respectively at 120 °C, 20 min. High temperature and short time conditions resulted in lower Maillard reaction extent. The arginine concentrations at 0.05, 0.10, and 0.15 mol/mol-galactose resulted in a slight increase in tagatose yield while an increase of the initial galactose concentration from 5 to 20% resulted in a decrease in tagatose yield, although the tagatose concentration increased. The highest tagatose productivity of 278 g/(L⋅h) was obtained using galactose of 20% w/v and arginine of 0.10 mol/mol-galactose at 120 °C and 4 min.
In this study, we cryopreserved pig spermatozoa using carboxylated poly-L-lysine (CPLL) as the cryoprotectant to determine its efficacy. Pig spermatozoa were placed in a freezing extender containing 3% (v/v) glycerol and different CPLL concentrations. The motility indices of the spermatozoa cryopreserved with 0.25% (v/v) CPLL at 6 (59.3), 9 (53.7), and 12 (26.2) h after thawing were significantly higher (P < 0.01 or P < 0.05) than those of the spermatozoa cryopreserved without CPLL (53.7, 40.1, and 17.5 at 6, 9, and 12 h after thawing, respectively). The concentration of CPLL in the freezing extender did not affect the ability of frozen-thawed spermatozoa to fertilize oocytes in vitro. However, the blastocyst formation rate of embryos derived from spermatozoa cryopreserved with 0.25% CPLL (24.6%) was significantly higher (P < 0.01) than that of embryos derived from spermatozoa cryopreserved without CPLL (11.2%). The conception rate of the sows inseminated with spermatozoa cryopreserved with 0.25% CPLL (72.2%) was not significantly different from that of the sows inseminated with spermatozoa stored at 17°C (81.3%). However, the mean number of total piglets born to the former (10.0) was significantly lower (P < 0.05) than that of total piglets born to the latter (13.4). The results showed that CPLL in the freezing extender maintained the motility of frozen-thawed pig spermatozoa and improved the in vitro development of embryos produced by in vitro fertilization. In addition, we have demonstrated that piglets could be obtained with artificial insemination using spermatozoa cryopreserved with CPLL.
Protein palmitoylation, a post-translational modification, is universally observed in eukaryotic cells. The localization of palmitoylated proteins to highly dynamic, sphingolipid- and cholesterol-rich microdomains (called lipid rafts) on the plasma membrane has been shown to play an important role in signal transduction in cells. However, this complex biological system is not yet completely understood. Here, we used a combined approach where an artificial lipidated protein was applied to biomimetic model membranes and plasma membranes in cells to illuminate chemical and physiological properties of the rafts. Using cell-sized giant unilamellar vesicles, we demonstrated the selective partitioning of enhanced green fluorescent protein modified with a C-terminal palmitoyl moiety (EGFP-Pal) into the liquid-ordered phase consisting of saturated phospholipids and cholesterol. Using Jurkat T cells treated with an immunostimulant (concanavalin A), we observed the vesicular transport of EGFP-Pal. Further cellular studies with the treatment of methyl β-cyclodextrin revealed the cholesterol-dependent internalization of EGFP-Pal, which can be explained by a raft-dependent, caveolae-mediated endocytic pathway. The present synthetic approach using artificial and natural membrane systems can be further extended to explore the potential utility of artificially lipidated proteins at biological and artificial interfaces.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.