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Simple Summary: The aim of our study was to investigate the effects of dietary supplementation with different protein levels after grazing on growth performance as well as on the proteomics of the LL muscle of Jersey-yak. The results showed that a certain amount of crude protein supplementation to Jersey-yak at the end of grazing significantly improved growth performance and increased their economic efficiency. In addition, we analyzed the proteomic differences in the longest dorsal muscle of Jersey-yak. From our analyses, we identified differences in the abundance of 434 proteins involved in pathways related to muscle growth and development and energy metabolism, among others. Several of the proteins enriched in these pathways, such as MYH8, are associated with myoblast development and differentiation as well as muscle development. In conclusion, the results of this study provide proteomic insights into the different feeding patterns of yak crossbred progeny, suggesting that a greater economic income can be achieved by improving their feeding conditions. Jersey-yak is a hybrid offspring of Jersey cattle and yak (Bos grunniens). Changing the feeding system of Jersey-yak can significantly improve its growth performance. In this study, tandem mass tag (TMT) proteomics technology was used to determine the differentially expressed proteins (DEPs) of the longissimus lumborum (LL) muscle of Jersey-yak fed different protein levels of diet. The results showed that compared with the traditional grazing feeding, the growth performance of Jersey-yaks was significantly improved by crude protein supplementation after grazing. A total of 3368 proteins were detected in these muscle samples, of which 3365 were quantified. A total of 434 DEPs were identified. Through analyses, it was found that some pathways related to muscle growth and development were significantly enriched, such as Rap1 signaling pathway, mTOR signaling pathway, and TGF-beta signaling pathway. A number of DEPs enriched in these pathways are related to muscle cell development, differentiation, and muscle development, including integrin subunit alpha 7 (ITGA7), myosin heavy chain 8 (MYH8), and collagen type XII alpha 1 chain (COL12A1). In conclusion, the results of this study provide insights into the proteomics of different feeding patterns of Jersey-yak, providing a stronger basis for further understanding the biological mechanism of hybrid varieties. [ABSTRACT FROM AUTHOR]

Affine equivalence of Boolean functions has various applications in computer science and modern cryptography, such as circuit design and S-boxes. Existing methods for detecting affine equivalence of Boolean functions work in some cases but not when the truth table of a Boolean function is sparse. To improve previous methods and overcome this limitation, we propose a method by transforming the Boolean function to a function with the property that its function values at the orthonormal basis are all equal to 1 or 0, which narrows down the search space of affine transformations. Our first algorithm has the advantage of getting a smaller search space than previous methods and is especially useful for sparse functions. Specifically, when the Boolean functions are sparse, the search space can be reduced exponentially in average and experiments show the efficiency of our first algorithm. We then present another algorithm to transform one circuit into its equivalent affine circuit by synthesizing a reversible circuit and inserting it in front of the original circuit. To our knowledge, this is the first work to automatically synthesize an affine equivalent circuit for any given circuit and the first to do this by combining reversible circuit and non-reversible circuit. [ABSTRACT FROM AUTHOR]

Affine equivalent classes of Boolean functions have many applications in modern cryptography and circuit design. Previous publications have shown that affine equivalence on the entire space of Boolean functions can be computed up to 10 variables, but not on the quotient Boolean function space modulo functions of different degrees. Computing the number of equivalent classes of cosets of Reed-Muller code R (1 , n) is equivalent to classifying Boolean functions modulo linear functions, which can be computed only when n ≤ 7. Based on the linear representation of the affine group A G L (n , 2) on the quotient space R (s , n) / R (k , n) , we obtain a useful counting formula to compute the number of equivalent classes. Instead of computing the conjugacy classes and representatives directly in A G L (n , 2) , we reduce the computation complexity by introducing an isomorphic permutation group Pn and performing the computation in Pn. With the proposed algorithm, the number of equivalent classes of cosets of R(1,n) can be computed up to 10 variables. Furthermore, the number of equivalent classes on R (s , n) / R (k , n) can also be computed when − 1 ≤ k < s ≤ n ≤ 10, which is a major improvement and advancement comparing to previous methods. [ABSTRACT FROM AUTHOR]

We propose a non-transversal but pieceable fault tolerant conversion circuit that is used to convert encoded information between five-qubit code and seven-qubit CSS code. Since a syndrome extraction circuit requiring fewer ancillary qubit resources would facilitate the realization of large-scale quantum computations, we further adapt a flag-assisted fault tolerant syndrome measurement scheme to reduce the cost of ancillary preparation. Numerical simulations are also performed to further analyze the performance of our conversion method. [ABSTRACT FROM AUTHOR]

Purpose: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samples with clear labels. In general, biopsy results can be used as benign/malignant labels. However, most clinical samples generally do not have biopsy results. Previous works have proposed generating benign/malignant labels according to Breast Imaging, Reporting and Data System (BI‐RADS) ratings. However, this approach will cause noisy labels, which means that the benign/malignant labels produced from BI‐RADS diagnoses may be inconsistent with the ground truths. Consequently, deep models will overfit the noisy labels and hence obtain poor generalization performance. In this work, we mainly focus on how to reduce the negative effect of noisy labels when they are used to train breast tumor classification models. Methods: We propose an effective approach called noise filter network (NF‐Net) to address the problem of noisy labels when training breast tumor classification models. Specifically, to prevent deep models from overfitting the noisy labels, we propose incorporating two softmax layers for classification. Additionally, to strengthen the effect of clean labels, we design a teacher–student module for distilling the knowledge of clean labels. Results: We conduct extensive comparisons with the existing works on addressing noisy labels. Our method achieves a classification accuracy of 73%, with a precision of 69%, recall of 80%, and F1‐score of 0.74. This result is significantly better than those of the existing state‐of‐the‐art works on addressing noisy labels. Conclusions: This work provides a means to overcome the label shortage problem in training breast tumor classification models. Specifically, we can generate benign/malignant labels according to the BI‐RADS ratings. Although this approach will cause noisy labels, the design of NF‐Net can effectively reduce the negative effect of such labels. [ABSTRACT FROM AUTHOR]

The classification of Boolean functions plays an underpinning role in logic design and synthesis of VLSI circuits. This paper considers a underpinning question in Boolean function classification: how many distinct classes are there for k-input Boolean functions. We exploit various group algebraic properties to efficiently compute the number of unique equivalent classes. We have reduced the computation complexity from 2mm! to (m + 1)!. We present our analysis for NPN classification of Boolean functions with up to ten variables and compute the number of NP and NPN equivalence classes for 3-10 variables. This is the first time to report the number of NP and NPN classifications for Boolean functions with 9-10 variables. We demonstrate the effectiveness of our method by both theoretical proofs and numeric experiments. [ABSTRACT FROM AUTHOR]

An efficient pairwise Boolean matching algorithm for solving the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the structural signature (SS) vector, which comprises a first-order signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the SS is more effective than the traditional signature. A symmetry mark can distinguish symmetric variables and asymmetric variables and be used to search for multiple variable mappings in a single variable-mapping search operation, which reduces the search space significantly. Updating the SS vector via Shannon decomposition provides benefits in distinguishing unidentified variables, and the group mark and phase collision check can be used to discover incorrect variable mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we test both equivalent and non-equivalent matching speeds on the MCNC benchmark circuit sets and random circuit sets. In the experiment, our algorithm is shown to be 4.2 times faster than competitors when testing equivalent circuits and 172 times faster, on average, when testing non-equivalent circuits. [ABSTRACT FROM AUTHOR]

In many machine learning applications such as computer-aided diagnosis, gene sequence analysis or natural language processing, categorical data appears. For small-scale data set with high dimensions, since relatively small proportion of possible categorical configurations are covered by training samples, conventional methods based on frequency information such as Dirichlet Compound Multinomial distribution usually runs into problems of over-fitting. Latent gaussian process is an effective bayesian non-parametric technique for categorical data modeling, which was proposed as an unsupervised method to embed unlabelled categorical data into a continuous and low-dimensional space through gaussian process. As a probabilistic generative model, latent gaussian process owns the ability of density estimation. In this paper, we propose a generative classification model as a supervised method for labelled categorical data, in which we use latent gaussian process to estimate the class-conditional densities. Since the complexity of gaussian process model can adapt to the size of training data, our method is able to effectively model small-sale categorical data. Experimental results show that our proposal can achieve better classification performance compared with other classification models for categorical data. [ABSTRACT FROM AUTHOR]

Quantum-inspired evolutionary algorithm (QEA) has proved to be an effective method to design neural networks with few connections and high classification performance. When a quantum-inspired evolutionary neural network (QENN) converges in the training phase, subsequent training is fruitless and time-wasting. Therefore, it is important to control the number of generations of QENN. The analysis on the convergence property of quantum bit evolution can contribute to designing a safe termination criterion that can always be reached. This paper proposes an appropriate termination criterion based on the average convergence rate (ACR). Experiments on classification tasks are conducted to demonstrate the effectiveness of our method. The results show that the termination criterion based on ACR can duly stop the training process of QENN and overcome the limitations of the termination criterion based on the probability of generating the best solution (PBS). [ABSTRACT FROM AUTHOR]

Affine equivalence classification of Boolean functions has significant applications in logic synthesis and cryptography. Previous studies for classification have been limited by the large set of Boolean functions and the complex operations on the affine group. Although there are many research on affine equivalence classification for parts of Boolean functions in recent years, there are very few results for the entire set of Boolean functions. The best existing result has been achieved by Harrison with 15768919 affine equivalence classes for 6-variable Boolean functions. This paper presents a concise formula for affine equivalence classification of the entire set of Boolean functions as well as a formula for affine classification of Boolean functions with distinct ON-set size respectively. The method outlined in this paper greatly simplifies the affine group's action by constructing an isomorphism mapping from the affine group to a permutation group. By this method, we can compute the affine equivalence classes for up to 10 variables. Experiment results indicate that our scheme for calculating the affine equivalence classes for more than 6 variables is a significant advancement over previous published methods. [ABSTRACT FROM PUBLISHER]

The most crucial issue of quantum cryptography protocols is its security. There exists many ways to attack the quantum communication process. In this paper, we present a model checking method for modeling the eavesdropping in quantum information protocols. So when the security properties of a certain protocol are needed to be verified, we can directly use the models which are already built. Here we adopt the probabilistic model checking tool-PRISM to model these attack methods. The verification results show that the detection rate of eavesdropping is approximately close to 1 when enough photons are transmitted. [ABSTRACT FROM AUTHOR]

Because ternary computer has more superiority than other d-ary number systems, we focus on the investigation of ternary elementary quantum gates and the synthesis algorithm of ternary quantum logic circuits. Above all, Pauli operators and their matrices on qutrit are introduced. Then eight qutrit operators are selected as elementary operators and eight qutrit quantum logic gates are defined. Permutation groups are introduced to characterize the quantum gates and quantum logic circuits. Some important qutrit quantum logic gates are defined also, such as QNOT, QKCXi, EQKCXi, QSwap, QCNOT and EQCNOT. Based on these elementary gates, we prove two very important theorems: (1) all qutrit quantum reversible logic circuit can be generated by Xi gate and QKCXi gate; (2) all qutrit quantum reversible logic circuits can be generated by Xi gate and QCNOT gate. The two theorems indicate that any complicated qutrit quantum reversible circuit can be constructed by the simplest ternary quantum gate. This will greatly simplify the implementation difficulty of quantum circuit. Subsequently, we propose a synthesis algorithm for qutrit quantum reversible logic circuit, which is verified through simulation experiment by the computer program we have designed. [ABSTRACT FROM AUTHOR]

This paper presents an analysis method for quantum information protocols based on model checking, with special focus on the quantum privacy comparison (QPC). The security properties of these protocols can be proved but in ways with much difficulty. Here we will discuss a probabilistic model checking tool - PRISM to verify specific properties of QPC protocol with multi-body and PRISM to verify specific properties of quantum private comparison (QPC) protocol with multi-party and d-dimensional entangled states. [ABSTRACT FROM AUTHOR]

Mapping for network-on-chip (NoC) is one of the key steps of NoC design. To improve the performance and reliability of NoC architectures, we present a comprehensive optimization algorithm with multiple objectives. We propose to find the Pareto optimal solutions, rather than a single solution usually obtained through scalarization, e.g. weighting the objective functions. In order to meet the NoC mapping requests and strengthen the capability of searching solutions, the standard particle swarm optimization (PSO) algorithm is improved and a fault-tolerant routing is proposed. These methods help to solve the tradeoff between high performance and system reliability. We present a mathematical analysis of the convergence of the improved algorithms, and prove sufficient conditions of convergence. The improved algorithms are implemented on the Embedded Systems Synthesis Benchmarks Suite (E3S). Experimental results show our algorithms achieve high performance and reliability compared with the standard PSO. [ABSTRACT FROM AUTHOR]

Mapping IP cores to an on-chip network is an important step in Network-on-Chip (NoC) design and affects the performance of NoC systems. A mapping optimisation algorithm and a fault-tolerant mechanism are proposed in this article. The fault-tolerant mechanism and the corresponding routing algorithm can recover NoC communication from switch failures, while preserving high performance. The mapping optimisation algorithm is based on scatter search (SS), which is an intelligent algorithm with a powerful combinatorial search ability. To meet the requests of the NoC mapping application, the standard SS is improved for multiple objective optimisation. This method helps to obtain high-performance mapping layouts. The proposed algorithm was implemented on the Embedded Systems Synthesis Benchmarks Suite (E3S). Experimental results show that this optimisation algorithm achieves low-power consumption, little communication time, balanced link load and high reliability, compared to particle swarm optimisation and genetic algorithm. [ABSTRACT FROM PUBLISHER]

Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications. [ABSTRACT FROM AUTHOR]

Parallel adders are extensively used in high-performance computer design and hardware acceleration for large-scale data processing. In the adder design theory, a key property of the group propagated carry and the group generated carry is based on the two recurrence equations. The property is fundamental to many parallel prefix adders. However, there is no proof of the property in the literature. This article presents a rigorous and complete proof for it. The proof can leverage a solid ground for a formal verification methodology for parallel adder-based chip design. [ABSTRACT FROM AUTHOR]

In this paper, we address an NPN Boolean matching algorithm. The proposed structural difference signature (SDS) of a Boolean function significantly reduces the search space in the Boolean matching process. The paper analyses the size of the search space from three perspectives: the total number of possible transformations, the number of candidate transformations and the number of decompositions. We test the search space and run time on a large number of randomly generated circuits and Microelectronics Center of North Carolina (MCNC) benchmark circuits with 7–22 inputs. The experimental results show that the search space of Boolean matching is greatly reduced and the matching speed is obviously accelerated. [ABSTRACT FROM AUTHOR]

• We propose to address ZSL as a standard domain adaptation task. • A novel domain-specific batch normalization component is proposed to align domain gaps in semantic-visual fusions. • Extensive experiments show the effectiveness and robustness of our approach. Zero-Shot Learning (ZSL) learns models for recognizing new classes. One of the main challenges in ZSL is the domain discrepancy caused by the category inconsistency between training and testing data. Domain adaptation is the most intuitive way to address this challenge. However, existing domain adaptation techniques cannot be directly applied into ZSL due to the disjoint label space between source and target domains. This work proposes the Transferrable Semantic-Visual Relation (TSVR) approach towards transductive ZSL. TSVR redefines image recognition as predicting the similarity/dissimilarity labels for semantic-visual fusions consisting of class attributes and visual features. After the above transformation, the source and target domains can have the same label space, which hence enables to quantify domain discrepancy. For the redefined problem, the number of similar semantic-visual pairs is significantly smaller than that of dissimilar ones. To this end, we further propose to use Domain-Specific Batch Normalization to align the domain discrepancy. [ABSTRACT FROM AUTHOR]

Background: The advantages of γ-cyclodextrin (γ-CD) include its high solubility, ability to form inclusion complexes with various poorly water-soluble molecules, and favorable toxicological profile; thus, γ-CD is an attractive functional excipient widely used in many industrial settings. Unfortunately, the high cost of γ-CD caused by the low activity and stability of γ-cyclodextrin glycosyltransferase (γ-CGTase) has hampered large-scale production and application. Results: This study reports the in vivo one-step production of immobilized γ-CGTase decorated on the surface of polyhydroxyalkanoate (PHA) nanogranules by the N-terminal fusion of γ-CGTase to PHA synthase via a designed linker. The immobilized γ-CGTase-PHA nanogranules showed outstanding cyclization activity of 61.25 ± 3.94 U/mg (γ-CGTase protein) and hydrolysis activity of 36,273.99 ± 1892.49 U/mg, 44.74% and 18.83% higher than that of free γ-CGTase, respectively. The nanogranules also exhibited wider optimal pH (cyclization activity 7.0–9.0, hydrolysis activity 10.0–11.0) and temperature (55–60 °C) ranges and remarkable thermo- and pH-stability, expanding its utility to adapt to wider and more severe reaction conditions than the free enzyme. A high yield of CDs (22.73%) converted from starch and a high ratio (90.86%) of γ-CD in the catalysate were achieved at pH 9.0 and 50 °C for 10 h with 1 mmol/L K+, Ca2+, and Mg2+ added to the reaction system. Moreover, γ-CGTase-PHA beads can be used at least eight times, retaining 82.04% of its initial hydrolysis activity and 75.73% of its initial cyclization activity. Conclusions: This study provides a promising nanobiocatalyst for the cost-efficient production of γ-CD, which could greatly facilitate process control and economize the production cost. [ABSTRACT FROM AUTHOR]

Abstract The recent successes of deep neural networks heavily rely on massive amounts of data annotations. For a certain task that is short of labeled data, the cost of large-scale data annotation is usually prohibitively expensive. Through transferring the already learnt knowledge from a different but related source domain that is sufficiently labeled, domain adaptation aims to relieve such a burden for the target domain. The recent studies reveal that learning domain-invariant features has gained great successes in unsupervised domain adaptation. However, domain invariance does not necessarily imply discriminative representations in the target domain due to the lack of labeled target data. To address this issue, we propose a new unsupervised domain adaptation method named TarGAN, which can successfully disentangle the class and the style codes of the target generator and generate Tar get samples with given class labels based on the recent advances of G enerative A dversarial N etworks. This is achieved by tying the high-level layers of the source generator and the target generator, and concurrently enforcing high mutual information between the class code and the generated images. Accompanied with given class labels, these generated target images can significantly improve the classifier's discriminative ability on the target domain. Experiments on several standard benchmarks demonstrate that our proposed TarGAN can outperform the existing state-of-the-art domain adaptation methods. [ABSTRACT FROM AUTHOR]

Abstract Generalized Symbolic Trajectory Evaluation (GSTE) is an alternative model checking technique based on particular automata to specify the properties. Despite the success of GSTE, its state explosion remains a major hurdle when applying it to large industrial designs. This paper presents two efficient theoretical underpinning abstraction algorithms based on assertion graph to combat the state explosion problem. We implement these two algorithms as a prototype system for discrete models. Experimental results show that the prototype system is 10 × faster than the former without abstraction. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threat (APT) is a serious threat against sensitive information. Current detection approaches are time-consuming since they detect APT attack by in-depth analysis of massive amounts of data after data breaches. Specifically, APT attackers make use of DNS to locate their command and control (C&C) servers and victims’ machines. In this paper, we propose an efficient approach to detect APT malware C&C domain with high accuracy by analyzing DNS logs. We first extract 15 features from DNS logs of mobile devices. According to Alexa ranking and the VirusTotal’s judgement result, we give each domain a score. Then, we select the most normal domains by the score metric. Finally, we utilize our anomaly detection algorithm, called Global Abnormal Forest (GAF), to identify malware C&C domains. We conduct a performance analysis to demonstrate that our approach is more efficient than other existing works in terms of calculation efficiency and recognition accuracy. Compared with Local Outlier Factor (LOF), k-Nearest Neighbor (KNN), and Isolation Forest (iForest), our approach obtains more than 99% F-M and R for the detection of C&C domains. Our approach not only can reduce data volume that needs to be recorded and analyzed but also can be applicable to unsupervised learning. [ABSTRACT FROM AUTHOR]

Automatic hardware/software partitioning relies on characterization, estimation and design space exploration of the system performance and cost metrics. In real world situations, such estimates are complicated and cannot be 100 percent accurate. Furthermore, hardware/software co-design is so complicated nowadays that simply considering the bipartitioning between hardware and software is not sufficient. It is important to consider some of the other key design parameters and resource sharing together with the hardware/software partitioning problem. Under variable requirements of smart systems, more flexibility on the resource usage should be incorporated in system modelling. This paper considers uncertainty modeling for system partitioning with an enhanced set of parameters for hardware/software resource sharing. We harness state-of-the-art uncertainty theory for linear uncertain distribution and normal uncertain distribution. Our derivations convert the uncertainty model back to a regular constraint optimization problem. Experimental results show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

As the rapid and accurate screening of infectious diseases can provide meaningful information for outbreak prevention and control, as well as owing to the existing limitations of the polymerase chain reaction (PCR), it is imperative to have new and validated detection techniques for SARS-CoV-2. Therefore, the rationale for outlining the techniques used to detect SARS-CoV-2 proteins and performing a comprehensive comparison to serve as a practical benchmark for future identification of similar viral proteins is clear. This review highlights the urgent need to strengthen pandemic preparedness by emphasizing the importance of integrated measures. These include improved tools for pathogen characterization, optimized societal precautions, the establishment of early warning systems, and the deployment of highly sensitive diagnostics for effective surveillance, triage, and resource management. Additionally, with an improved understanding of the virus' protein structure, considerable advances in targeted detection, treatment, and prevention strategies are expected to greatly improve our ability to respond to future outbreaks. [Display omitted] • This review features meaningful information for preventing and controlling outbreaks. • This is a clear summary of the structure and function of SARS-CoV-2 proteins. • A comprehensive comparison of relevant SARS-CoV-2 protein assays is revealed. • This review aims to provide a future reference for detecting similar viral proteins. [ABSTRACT FROM AUTHOR]

As the rapid and accurate screening of infectious diseases can provide meaningful information for outbreak prevention and control, as well as owing to the existing limitations of the polymerase chain reaction (PCR), it is imperative to have new and validated detection techniques for SARS-CoV-2. Therefore, the rationale for outlining the techniques used to detect SARS-CoV-2 proteins and performing a comprehensive comparison to serve as a practical benchmark for future identification of similar viral proteins is clear. This review highlights the urgent need to strengthen pandemic preparedness by emphasizing the importance of integrated measures. These include improved tools for pathogen characterization, optimized societal precautions, the establishment of early warning systems, and the deployment of highly sensitive diagnostics for effective surveillance, triage, and resource management. Additionally, with an improved understanding of the virus' protein structure, considerable advances in targeted detection, treatment, and prevention strategies are expected to greatly improve our ability to respond to future outbreaks. [Display omitted] • This review features meaningful information for preventing and controlling outbreaks. • This is a clear summary of the structure and function of SARS-CoV-2 proteins. • A comprehensive comparison of relevant SARS-CoV-2 protein assays is revealed. • This review aims to provide a future reference for detecting similar viral proteins. [ABSTRACT FROM AUTHOR]

Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focus on improving recommendation accuracy instead of efficiency. In this paper, we propose a Deep Pairwise Hashing (DPH) to map users and items to binary vectors in Hamming space, where a user's preference for an item can be efficiently calculated by Hamming distance, which significantly improves the efficiency of online recommendation. To alleviate data sparsity and cold-start problems, the user-item interactive information and item content information are unified to learn effective representations of items and users. Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation. Finally, we adopt the alternating optimization method to optimize the proposed model with discrete constraints. Extensive experiments on three different datasets show that DPH can significantly advance the state-of-the-art frameworks regarding data sparsity and item cold-start recommendation. [ABSTRACT FROM AUTHOR]

The norm properties of Lyapunov mappings and their restrictions on symmetric and skew-symmetric subspaces are investigated. For non-negative, non-positive, and tridiagonal matrices, this paper gives an affirmative answer to an open conjecture which says that the norm of a Lyapunov mapping over the space of real square matrices equals that of its restricted map over the subspace of real symmetric matrices. In addition, matrix transformation and norm relationships on certain matrices arising from the Lyapunov mapping and its restrictions are provided. [ABSTRACT FROM AUTHOR]

In recent times, deep learning models have achieved state-of-the-art performances in image classification. However, the classification and generalization ability of most achievements have highly relied on the availability of large-scale accurate labeled training data, which are time-consuming, laborious, and expensive to collect. Moreover, in the generalizing process of deep learning models, the noisy labels are the challenges for multi-category classification. Hence, it is essential to explore effective methods that can efficiently and correctly train deep models under label noise to conduct multi-category classification. This paper proposes using robust binary loss functions to train deep models under label noise to address this problem. Specifically, we suggest handling the K -class classification task by using K binary classifiers, which can be completed by a joint adoption of multi-category large margin classification approaches, e.g., Pairwise-Comparison (PC) or One-versus-All (OVA). We also theoretically demonstrate that our method is inherently tolerant to label noise by using symmetric binary loss functions in multi-category classification tasks. Moreover, we designed a truncated CCE loss to combine with the proposed losses to improve the learning ability. Finally, we test our method on three different benchmark datasets with different types of label noise. The experimental results have clearly confirmed the effectiveness of our method, which can reduce the negative effect of noisy labels and improve the generalization ability. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN) has shown profound power in machine learning. It inspires many researchers from other fields to create powerful tools for various tasks, including quantum state preparation, quantum circuit translation, and so on. It is known as classical techniques cannot efficiently simulate the quantum system, and the existing works haven't investigated the quantum version of maximum mean discrepancy as the metric in learning models and applied it to quantum data. In this paper, we propose a metric named quantum maximum mean discrepancy (qMMD), which can be used to measure the distance between quantum data in Hilbert space. Based on the qMMD, we then design a quantum generative adversarial model, named qMMD-GAN, under the hybrid quantum–classical methods. We also provide the construction of qMMD-GAN that can be easily implemented on a quantum device. We demonstrate the power of our qMMD-GAN by applying it to a crucial real-world application that is generating an unknown quantum state. Our numerical experiments show that qMMD-GAN has a competitive performance compared to existing results. We believe that the hybrid-based models will not only be applied to physics research but provide a new direction for improving classical data processing tasks. [ABSTRACT FROM AUTHOR]

Semantic segmentation, which aims to acquire pixel-level understanding about images, is among the key components in computer vision. To train a good segmentation model for real-world images, it usually requires a huge amount of time and labor effort to obtain sufficient pixel-level annotations of real-world images beforehand. To get rid of such a nontrivial burden, one can use simulators to automatically generate synthetic images that inherently contain full pixel-level annotations and use them to train a segmentation model for the real-world images. However, training with synthetic images usually cannot lead to good performance due to the domain difference between the synthetic images (i.e., source domain) and the real-world images (i.e., target domain). To deal with this issue, a number of unsupervised domain adaptation (UDA) approaches have been proposed, where no labeled real-world images are available. Different from those methods, in this work, we conduct a pioneer attempt by using easy-to-collect image-level annotations for target images to improve the performance of cross-domain segmentation. Specifically, we leverage those image-level annotations to construct curriculums for the domain adaptation problem. The curriculums describe multi-level properties of the target domain, including label distributions over full images, local regions and single pixels. Since image annotations are “weak” labels compared to pixel annotations for segmentation, we coin this new problem as weakly-supervised cross-domain segmentation. Comprehensive experiments on the $\texttt {GTA5}\rightarrow \texttt {Cityscapes}$ and $\texttt {SYNTHIA}\rightarrow \texttt {Cityscapes}$ settings demonstrate the effectiveness of our method over the existing state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

How to design a universal fault-tolerant quantum computation protocol with high fidelity and low resource consumption is a hot topic in the field of quantum computing research. By reducing the effective code distance, the design of a universal fault-tolerant gate set based on concatenation code has been proposed. The auxiliary state required by the error correction of code must be verified before it can be used, so as to avoid the errors spread from the auxiliary state to the data state. But the qubit consumption can be very large due to multiple verification processes for the ancillary block. In this work, we analyze the ancillary consumption of two different verification schemes used in the Steane's error correction process. We take the basic state preparation of a 25-qubit 2-level concatenation code as an example, adopt a simpler verification circuit in its error correction process, and design an efficient decoding strategy by using deep neural network algorithm. Numerical simulations are also performed to analyze our low qubit resource overhead error correction scheme. [ABSTRACT FROM AUTHOR]

In this study, we examined the impact of college students' mental health on their social behavior. A social network was identified based on the behavior of college students sharing a meal. We analyzed the impact of depression on the structure of this network and found that students without obvious depressive symptoms, based on the test data of the SCL-90 Assessment Scale, were better at socializing than students with obvious depressive symptoms. We proposed a public opinion spreading model on social networks and formulated a heterogeneous mean-field theory to describe it. Further, using computer simulation experiments, we studied the impact of students' mental health on the process of information propagation in college. The results of the experiments showed that students without obvious depressive symptoms were more likely to receive information than students with obvious depressive symptoms. Based on the results of our study, college psychological consultants can actively identify students who may be at risk of mental illness and give them attention and guidance. [ABSTRACT FROM AUTHOR]

Abstract Triamcinolone acetonide (TCA) abuse in cosmetics is a common phenomenon. A rapid lateral flow immunochromatographic assay (ICA) was developed for the quantitative detection of TCA using a probe based on upconversion luminescence nanoparticles. Lanthanide-doped upconversion nanoparticles (UCNPs) were synthesized in a system comprising water and ethylene glycol, and a silicon dioxide layer was covered at the carboxyl site. A binding site protection strategy was used to decrease the background signal of UCNPs-ICA. Using dexamethasone derivative as a coating antigen, the optimal UCNPs–ICA exhibits good dynamic linear detection for TCA in the range 1.0–100 ng mL−1 with a median inhibitory concentration of 9.8 ng mL−1. The detection limits for TCA in a cosmetic sample are 20 μg kg−1. The pretreatment of samples only needs dilution with water, suggesting the assay can quantitate TCA on-site using a portable upconversion luminescence reader with a cumulative analysis time of only 10 min. Graphical abstract Unlabelled Image Highlights • SiO 2 covered sensitive UCNPs were explored as probes in the competitive strip assay. • New method allowed easy and controlled preparation of antibody-UCNPs conjugates. • TCA was quantitatively detected by the novel fluorescent reader-based strip test. [ABSTRACT FROM AUTHOR]

In recent years, malware with strong concealment uses encrypted protocol to evade detection. Thus, encrypted traffic identification can help security analysts to be more effective in narrowing down those encrypted network traffic. Existing methods are protocol independent, such as statistical-based and machine-learning-based approaches. Statistical-based approaches, however, are confined to payload length and machine-learning-based approaches have a low recognition rate for encrypted traffic using undisclosed protocols. In this paper, we proposed a heuristic statistical testing (HST) approach that combines both statistics and machine learning and has been proved to alleviate their respective deficiencies. We manually selected four randomness tests to extract small payload features for machine learning to improve real-time performances. We also proposed a simple handshake skipping method called HST-R to increase the classification accuracy. We compared our approach with other identification approaches on a testing dataset consisting of traffic that uses two known, two undisclosed, and one custom cryptographic protocols. Experimental results showed that HST-R performs better than other traditional coding-based, entropy-based, and ML-based approaches. We also showed that our handshake skipping method could generalize better for unknown cryptographic protocols. Finally, we also conducted experimental comparisons among different classification algorithms. The results showed that C4.5, with our method, has the highest identification accuracy for secure sockets layer and secure shell traffic. [ABSTRACT FROM AUTHOR]

Abstract Streptococcus pyogenes (Group A Streptococcus , GAS) and Streptococcus agalactiae (Group B Streptococcus , GBS) are common pathogens that threaten public health. In this study, a double recombinase polymerase (RPA) amplification assay was developed to rapidly detect these pathogens. Specificity tests revealed that the GAS and GBS strains were positive for speB and SIP genes, respectively. In clinical samples, the double assay performed similarly to the traditional biochemical method. The limits of detection were both ≤100 copies per reaction. In tests for simulant-contaminated samples, bacterial-culture media containing 103 CFU/mL original concentrations of S. pyogenes and S. agalactiae were positive in RPA assays after incubating for 4 h. Results can be obtained at 37 °C in 20 min. To determine whether propidium monoazide (PMA) can eliminate the influence of DNA extracted from dead cells, a bacterial suspension was treated with PMA before DNA extraction. Findings of RPA assay showed that DNA extracted from dead cells had no fluorescence signal. Therefore, the PMA-RPA assay is a promising technology for field tests and rapid point-of-care diagnosis. Highlights • This study is the first to develop a double real-time RPA system for the detection of Streptococcus pyogenes (GAS) and Streptococcus agalactiae (GBS), and the first PMA-RPA assay, which can weaken or eliminate the effect of dead cells. • We think the system can be used to detected GAS and GBS more quickly in the food separated from Streptococcus spp, for the Chinese national standard (GB, China) GB 4789. 11–2014, and clinical examination. [ABSTRACT FROM AUTHOR]

High-performance liquid chromatography coupled with high-resolution mass spectrometry was used for the determination of polyphenols inLycium barbarumleaves. Twenty compounds extracted by methanol–water were tentatively identified that included chlorogenic acids, flavonoids, and phenolic acids. Neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C were identified in Chinese cultivatedL. barbarumleaves for the first time. Caffeic acid and isoquercitrin were also present. The concentrations of these compounds inL. barbarumleaves were determined. The results showed that all analytes had linear calibration relationships with limits of detection from 0.318 to 3.35 ng mL−1. The polyphenols and flavonoids inL. barbarumleaves provided strong 2,2-diphenyl-1-picrylhydrazyl radical-scavenging capacity (IC50of 23.1 ± 0.4 to 26.0 ± 0.4 µg mL−1). This method is suitable for the determination of polyphenols inL. barbarumleaves, which provide polyphenols with suitable antioxidant activity. [ABSTRACT FROM PUBLISHER]

Traditional Data Field Hierarchical Clustering Algorithm (DFHCA) uses brute force method to compute the forces exert on each object. The computation complexity increases as O ( n 2 ). In this study, we improve the force computation efficiency of DFHCA to O ( n log  n ). We use the Barnes–Hut tree to reduce the number of force computation by approximating far away particles with their center of mass. And compared with traditional method, our method does not need to tune the parameters. In our implementation, we discuss two different merging strategies. Experimental results show that the proposed method could improve the computation efficiency under the same settings. We also find that DFHCA-M merging strategy converges faster than DFHCA-S merging strategy. Finally, we compare and analyze the time complexity and space complexity of our algorithm. [ABSTRACT FROM AUTHOR]

Coconut proteins have attracted increasing attention due to their nutritional and potential health benefits, but there have been few proteomic studies on coconuts. The aims of this study were to generate a proteomic map and identify common coconut proteins in different species. Two-dimensional electrophoresis (2-DE) was used to comprehensively analyze coconut proteins. Approximately 200 protein spots were detected on 2-DE maps of coconut samples from three different sources. The proteins were distributed in seven taxa (labeled a to g) according to their molecular mass. Taxa a, b, e and g contained common proteins, while taxa e and g were thermally stable and, therefore, may be considered characteristic of coconut products. Taxa a, b, d, e and g were subjected to MALDI-TOF/TOF-MS analysis. Taxa a, b and g were found to be homologous to 7S globulin. Taxon d was identified as glutelin, and taxon e was identified as a putative receptor-like protein kinase. [ABSTRACT FROM AUTHOR]

Ophiocordyceps sinensis (Berk.) Sacc. is one of the most well-known fungi in traditional Chinese medicine and is attracting attention because of its nutritious and medicinal properties. The present study aimed to produce a proteomic map to identify common O. sinensis proteins. The caterpillar body and stroma of O. sinensis collected from five locations and four fungal specimens of similar appearance were examined by two-dimensional electrophoresis (2-DE). Five proteins were identified using MALDI-TOF--TOF/MS, and the 2-DE identification pattern was provided. OCS_04585 and β-lactamase domain-containing protein, the two abundant and characteristic proteins, were separated and purified using liquid-phase isoelectric focusing. The products were high-quality materials that can be used for future protein-function studies and immunoassay development. [ABSTRACT FROM AUTHOR]

A highly specific competitive enzyme-linked immunosorbent assay (ELISA) protocol has been developed to identify and classify almond products based on differential proteomic analysis. We applied two-dimensional electrophoresis to compare the differences between almond and apricot kernels to search for almond-specific proteins. The amino acid of apricot Pru-1 was sequenced and aligned to almond Pru-1. One peptide, RQGRQQGRQQQEEGR, which exists in almond but not in apricot, was used as hapten to prepare monoclonal antibody against almond Pru-1. An optimized ELISA method was established using this antibody. The assay did not exhibit cross-reactivity with the tested apricot kernels and other edible plant seeds. The limit of detection (LOD) was 2.5–100 μg/g based on different food samples. The recoveries of fortified samples at levels of twofold and eightfold LOD ranged from 82% to 96%. The coefficients of variation were less than 13.0%. Using 7 M urea as extracting solution, the heat-treated protein loss ratios were 2%, 5% and 15% under pasteurization (65 °C for 30 min), baking (150 °C for 30 min) and autoclaved sterilization (120 °C for 15 min), respectively. [ABSTRACT FROM AUTHOR]

We propose a semi-supervised approach towards anomaly detection in multivariate categorical data. Our goal is to learn a model that can distinguish the anomalous data, given a small set of training data from the normal class. To this end, our approach learns the probability distribution of normal instances with the assumption that the categorical data are generated from a continuous latent space. Gaussian process is adopted to construct the generative model. As a non-parametric Bayesian model, Gaussian process can adapt its model complexity according to the data size. Hence, our approach can be effective when the training dataset is small. Comprehensive experiments over different benchmarks clearly demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Foodborne illnesses associated with foodborne pathogens Listeria monocytogenes and Staphylococcus aureus occur at high frequencies. In this study, species‐specific primers and probes sets for the detection of L. monocytogenes and S. aureus were designed based on the nucleotide sequences of the actin‐assembly inducing protein precursor (actA) and clumping factor A (clfA) genes, respectively. We established a duplex RPA assay that is capable of simultaneously detecting L. monocytogenes and S. aureus within 15 min. With the plasmid standard, the sensitivity of the double RPA to L. monocytogenes and S. aureus can reach 102 copies/μL. The method also detected DNA extracted from samples inoculated with L. monocytogenes and S. aureus. After 4 hr of incubation, the sensitivity of the food model to L. monocytogenes and S. aureus reached 230 and 270 cfu/reaction, respectively. The assay was highly specific and had no cross‐reaction between closely related bacteria. Further assessment of this method on strains isolated within the lab collection showed that the duplex RPA provided the same testing results as microbiological methods. Therefore, the assay is effective for the rapid detection and preliminary screening of L. monocytogenes and S. aureus. Practical applications: Listeria monocytogenes and Staphylococcus aureus are two major foodborne pathogens and have been found in a wide range of foods. We developed a duplex RPA specific targeting actA and clfA genes with high‐amplification efficiency. We evaluated the performance of duplex RPA by building a food model. The detection results by the duplex RPA were compared with PCR and no significant differences were found. This study provides the information of the developed duplex RPA for detecting two specific foodborne pathogens, which will be helpful for further studies about simultaneous detection of several targets using duplex RPA. [ABSTRACT FROM AUTHOR]

Recombinase polymerase amplification (RPA) is an isothermal amplification technique. Because of its short detection cycle and high specificity, it has been applied in various fields. However, the design of probe on the efficiency of RPA is not well understood and the effect of sequence mismatches of oligonucleotides on the performance of RPA is rarely discussed. In this study, we found that different primers with the same probe have a slight effect on the efficiency of fluorescent RPA, and different probes with the same amplified region have a great influence on the efficiency of fluorescent RPA. We summarized the design rules of probes suitable for fluorescent RPA by analyzing the experimental data. The rule is that the best distance between fluorescent groups in the probe is 1–2 bases, and the G content should be reduced as far as possible. In addition, we verified this rule by designing a series of probes. Furthermore, we found the base mismatches of the probe had a significant effect on RPA, which can lead to false positives and can change the amplification efficiency. However, 1–3 mismatches covering the center of the primer sequence only affect the amplification efficiency of RPA, not its specificity. And with an increase in the number of primer mismatches, the efficiency of RPA will decrease accordingly. This study suggests that the efficiency of fluorescent RPA is closely related to the probe. We recommend that when designing a fluorescent probe, one must consider the presence of closely related non-targets and specific bases. [ABSTRACT FROM AUTHOR]