Illegal Intrusion Detection of Internet of Things Based on Deep Mining Algorithm

In this study, to reduce the influence of The Internet of Things (IoT) illegal intrusion on the transmission effect, and ensure IoT safe operation, an illegal intrusion detection method of the Internet of Things (IoT) based on deep mining algorithm was designed to accurately detect IoT illegal intrusion. Moreover, this study collected the data in the IoT through data packets and carries out data attribute mapping on the collected data, transformed the character information into numerical information, implemented standardization and normalization processing on the numerical information, and optimized the processed data by using a regional adaptive oversampling algorithm to obtain an IoT data training set. The IoT data training set was taken as the input data of the improved sparse auto-encoder neural network. The hierarchical greedy training strategy was used to extract the feature vector of the sparse IoT illegal intrusion data that were used as the inputs of the extreme learning machine classifier to realize the classification and detection of the IoT illegal intrusion features. The experimental results indicate that the feature extraction of the illegal intrusion data of the IoT can effectively reduce the feature dimension of the illegal intrusion data of the IoT to less than 30 and the dimension of the original data. The recall rate, precision, and F1 value of the IoT intrusion detection are 98.3%, 98.7%, and 98.6%, respectively, which can accurately detect IoT intrusion attacks. The conclusion demonstrates that the intrusion detection of IoT based on deep mining algorithm can achieve accurate detection of IoT illegal intrusion and reduce the influence of IoT illegal intrusion on the transmission effect

A Class of Optimal Portfolio Liquidation Problems with a Linear Decreasing Impact

A problem of an optimal liquidation is investigated by using the Almgren-Chriss market impact model on the background that the n agents liquidate assets completely. The impact of market is divided into three components: unaffected price process, permanent impact, and temporary impact. The key element is that the variable temporary market impact is analyzed. When the temporary market impact is decreasing linearly, the optimal problem is described by a Nash equilibrium in finite time horizon. The stochastic component of the price process is eliminated from the mean-variance. Mathematically, the Nash equilibrium is considered as the second-order linear differential equation with variable coefficients. We prove the existence and uniqueness of solutions for the differential equation with two boundaries and find the closed-form solutions in special situations. The numerical examples and properties of the solution are given. The corresponding finance phenomenon is interpreted

Development of Structure-Switching Aptamers for Kanamycin Detection Based on Fluorescence Resonance Energy Transfer

The structure-switching aptamers are designed for the simple and rapid detection of kanamycin based on the signal transduction principle of fluorescence resonance energy transfer (FRET). The structure switch is composed of kanamycin-binding aptamers and the complementary strands, respectively labeled with fluorophore and quencher, denoted as FDNA and QDNA. In the absence of kanamycin, FDNA and QDNA form the double helix structure through the complementary pairing of bases. The fluorophore and the quencher are brought into close proximity, which results in the fluorescence quenching because of the FRET mechanism. In the presence of kanamycin, the FDNA specifically bind to the target due to the high affinity of aptamers, and the QDNA are dissociated. The specific recognition between aptamers and kanamycin will obstruct the formation of structure switch and reduce the efficiency of FRET between FDNA and QDNA, thus leading to the fluorescence enhancement. Therefore, based on the structure-switching aptamers, a simple fluorescent assay for rapid detection of kanamycin was developed. Under optimal conditions, there was a good linear relationship between kanamycin concentration and the fluorescence signal recovery. The linear range of this method in milk samples was 100–600 nM with the detection limit of 13.52 nM (3σ), which is well below the maximum residue limit (MRL) of kanamycin in milk. This method shows excellent selectivity for kanamycin over the other common antibiotics. The structure-switching aptamers have been successfully applied to the detection of kanamycin spiked in milk samples with the satisfying recoveries between 101.3 and 109.1%, which is well-consistent with the results from LC-MS/MS. Due to the outstanding advantages of facile operation, rapid detection, high sensitivity, excellent specificity, and low cost, the application and extension of this strategy for rapid determination of antibiotics in food samples may greatly improve the efficiency in food safety and quality supervision

Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning challenge from a causal theory perspective. Our agent is endowed with two basic yet general theories for transfer learning: (i) a task shares a common abstract structure that is invariant across domains, and (ii) the behavior of specific features of the environment remain constant across domains. We adopt a Bayesian perspective of causal theory induction and use these theories to transfer knowledge between environments. Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment. A hierarchy of Bayesian structures is used to model abstract-level structural causal knowledge, and an instance-level associative learning scheme learns which specific objects can be used to induce state changes through interaction. This model-learning scheme is then integrated with a model-based planner to achieve a task in the OpenLock environment, a virtual ``escape room'' with a complex hierarchy that requires agents to reason about an abstract, generalized causal structure. We compare performances against a set of predominate model-free reinforcement learning(RL) algorithms. RL agents showed poor ability transferring learned knowledge across different trials. Whereas the proposed model revealed similar performance trends as human learners, and more importantly, demonstrated transfer behavior across trials and learning situations.Comment: Accepted to AAAI 2020 as an ora

Human immunodeficiency virus type 1 specific cytotoxic T lymphocyte responses in Chinese infected with HIV-1 B'/C Recombinant (CRF07_BC)

<p>Abstract</p> <p>Background</p> <p>The characterization of HIV-1-specific T cell responses in people infected with locally circulating HIV-1 strain will facilitate the development of HIV-1 vaccine. Sixty intravenous drug users infected with HIV-1 circulating recombinant form 07_BC (CRF07_BC), which has been spreading rapidly in western China from north to south, were recruited from Xinjiang, China to assess the HIV-1-specific T cell responses at single peptide level with overlapping peptides (OLP) covering the whole concensus clades B and C proteome.</p> <p>Results</p> <p>The median of the total magnitude and total number of OLPs recognized by CTL responses were 10925 SFC/million PBMC and 25 OLPs, respectively, when tested by clade C peptides, which was significantly higher than when tested by clade B peptides. The immunodominant regions, which cover 14% (58/413) of the HIV-1 proteome, are widely distributed throughout the HIV-1 proteome except in Tat, Vpu and Pol-PR, with Gag, Pol-RT, Pol-Int and Nef being most frequently targeted. The subdominant epitopes are mostly located in p24, Nef, integrase, Vpr and Vif. Of the responses directed to clade C OLPs, 61.75% (972/1574) can be observed when tested with corresponding clade B OLPs. However, Pol-PR and Vpu tend to be targeted in the clade B sequence rather than the clade C sequence, which is in line with the recombinant pattern of CRF07_BC. Stronger and broader CTL responses in subjects with CD4 cell counts ranging from 200 to 400/mm³were observed when compared to those with less than 200/mm³or more than 400/mm³, though there have been no significant correlations identified between the accumulative CTL responses or overall breadth and CD4 cell count or plasma viral load.</p> <p>Conclusion</p> <p>This is the first study conducted to comprehensively address T cell responses in Chinese subjects infected with HIV-1 CRF07_BC in which subtle differences in cross-reactivity were observed, though similar patterns of overall immune responses were demonstrated with clade B infected populations. The immunodominant regions identified in this population can facilitate future HIV-1 vaccine development in China.</p

Signature Movements Lead to Efficient Search for Threatening Actions

The ability to find and evade fighting persons in a crowd is potentially life-saving. To investigate how the visual system processes threatening actions, we employed a visual search paradigm with threatening boxer targets among emotionally-neutral walker distractors, and vice versa. We found that a boxer popped out for both intact and scrambled actions, whereas walkers did not. A reverse correlation analysis revealed that observers' responses clustered around the time of the “punch", a signature movement of boxing actions, but not around specific movements of the walker. These findings support the existence of a detector for signature movements in action perception. This detector helps in rapidly detecting aggressive behavior in a crowd, potentially through an expedited (sub)cortical threat-detection mechanism

A Class of Optimal Portfolio Liquidation Problems with a Linear Decreasing Impact

A problem of an optimal liquidation is investigated by using the Almgren-Chriss market impact model on the background that the agents liquidate assets completely. The impact of market is divided into three components: unaffected price process, permanent impact, and temporary impact. The key element is that the variable temporary market impact is analyzed. When the temporary market impact is decreasing linearly, the optimal problem is described by a Nash equilibrium in finite time horizon. The stochastic component of the price process is eliminated from the mean-variance. Mathematically, the Nash equilibrium is considered as the second-order linear differential equation with variable coefficients. We prove the existence and uniqueness of solutions for the differential equation with two boundaries and find the closed-form solutions in special situations. The numerical examples and properties of the solution are given. The corresponding finance phenomenon is interpreted