Intelligent Reflecting Surfaces Assisted Secure Transmission Without Eavesdropper's CSI

In this letter, improving the security of an intelligent reflecting surface (IRS) assisted multiple-input single-output (MISO) communication system is studied. Different from the ideal assumption in existing literatures that full eavesdropper's (Eve's) channel state information (CSI) is available, we consider a more practical scenario without Eve's CSI. To enhance the security of this system given a total transmit power at transmitter (Alice), we propose a joint beamforming and jamming approach, in which a minimum transmit power is firstly optimized at Alice so as to meet the quality of service (QoS) at legitimate user (Bob), and then artificial noise (AN) is emitted to jam the eavesdropper by using the residual power at Alice. Two efficient algorithms exploiting oblique manifold (OM) and minorizationmaximization (MM) algorithms, respectively, are developed for solving the resulting non-convex optimization problem. Simulation results have been provided to validate the performance and convergence of the proposed algorithms

Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations

In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations. The randomly structural heterogeneity, temperature-dependent nonlinearity and material property uncertainty of heterogeneous materials are considered within the proposed self-optimization wavelet-learning framework. Firstly, meso- and micro-structural modeling of random heterogeneous materials are achieved by the proposed computer representation method, whose simulated hierarchical configurations have relatively high volume ratio of material inclusions. Moreover, temperature-dependent nonlinearity and material property uncertainties of random heterogeneous materials are modeled by a polynomial nonlinear model and Weibull probabilistic model, which can closely resemble actual material properties of heterogeneous materials. Secondly, an innovative stochastic three-scale homogenized method (STSHM) is developed to compute the macroscopic nonlinear thermal conductivity of random heterogeneous materials. Background meshing and filling techniques are devised to extract geometry and material features of random heterogeneous materials for establishing material databases. Thirdly, high-dimensional and highly nonlinear material features of material databases are preprocessed and reduced by wavelet decomposition technique. The neural networks are further employed to excavate the predictive models from dimension-reduced low-dimensional data

Higher-order multi-scale deep Ritz method for multi-scale problems of authentic composite materials

The direct deep learning simulation for multi-scale problems remains a challenging issue. In this work, a novel higher-order multi-scale deep Ritz method (HOMS-DRM) is developed for thermal transfer equation of authentic composite materials with highly oscillatory and discontinuous coefficients. In this novel HOMS-DRM, higher-order multi-scale analysis and modeling are first employed to overcome limitations of prohibitive computation and Frequency Principle when direct deep learning simulation. Then, improved deep Ritz method are designed to high-accuracy and mesh-free simulation for macroscopic homogenized equation without multi-scale property and microscopic lower-order and higher-order cell problems with highly discontinuous coefficients. Moreover, the theoretical convergence of the proposed HOMS-DRM is rigorously demonstrated under appropriate assumptions. Finally, extensive numerical experiments are presented to show the computational accuracy of the proposed HOMS-DRM. This study offers a robust and high-accuracy multi-scale deep learning framework that enables the effective simulation and analysis of multi-scale problems of authentic composite materials

A robotic learning and generalization framework for curved surface based on modified DMP

Learning from demonstration (LfD) can enable robots to quickly obtain reference trajectory information. How to reproduce and generalize the skills acquired by demonstrating is a hot topic for researchers. Firstly, aiming at the drawback that many industrial robots were difficult to continuously and smoothly drag and demonstrate, a compliant continuous drag demonstration system based on discrete admittance model was designed. Then, in order to solve the problem of poor generalization ability of the classical dynamic movement primitive (DMP) on curved surface, the modified DMP contained the scaling factor and the force coupling term. Finally, the curve drawing experiments were carried out on a 6-DoF robot. Experimental results show the effectiveness of our proposed learning and generalization framework

Domain-dependent evolution explains functional homology of protostome and deuterostome complement C3-like proteins

Complement proteins emerged early in evolution but outside the vertebrate clade they are poorly characterized. An evolutionary model of C3 family members revealed that in contrast to vertebrates the evolutionary trajectory of C3-like genes in cnidarian, protostomes and invertebrate deuterostomes was highly divergent due to independent lineage and species-specific duplications. The deduced C3-like and vertebrate C3, C4 and C5 proteins had low sequence conservation, but extraordinarily high structural conservation and 2-chain and 3-chain protein isoforms repeatedly emerged. Functional characterization of three C3-like isoforms in a bivalve representative revealed that in common with vertebrates complement proteins they were cleaved into two subunits, b and a, and the latter regulated inflammation-related genes, chemotaxis and phagocytosis. Changes within the thioester bond cleavage sites and the a-subunit protein (ANATO domain) explained the functional differentiation of bivalve C3-like. The emergence of domain-related functions early during evolution explains the overlapping functions of bivalve C3-like and vertebrate C3, C4 and C5, despite low sequence conservation and indicates that evolutionary pressure acted to conserve protein domain organization rather than the primary sequence.info:eu-repo/semantics/publishedVersio

Durable superhydrophobic polyvinylidene fluoride membranes via facile spray-coating for effective membrane distillation

Membrane wetting and fouling substantially limits application and deployment of membrane distillation process. Designing high-performance superhydrophobic membranes offers an effective solution to solve the challenge. In this work, a highly durable superhydrophobic surface (water contact angle of 170.8 ± 1.3°) was constructed via a facile and rapid spray-coating of extremely hydrophobic SiO2 nanoparticles onto a porous polyvinylidene fluoride (PVDF) substrate for membrane distillation. The superhydrophobic membrane coated by fluorinated SiO2 nanoparticles exhibited a superior physicochemical stability in a wide range of extreme environments (i.e., NaOH, HCl, hot water, rust water, humic acid solution, ultrasonication, and high-speed water scouring). During 8-h continuous membrane distillation desalination experiment, the coated superhydrophobic membrane experienced a consistently stable water vapor flux (ca. 19.1 kg·m−2·h−1) and desalination efficiency (99.99 %). Additionally, such a stable superhydrophobicity endowed the spray-coated PVDF membrane to overcome membrane wetting and fouling during membrane distillation of highly saline solutions containing foulants (i.e., humic acid and rust). Results reported in this study provides a useful concept and strategy in facile construction of robust superhydrophobic membranes via spray-coating for effective membrane distillation.</p

ICESsuHN105, a Novel Multiple Antibiotic Resistant ICE in Streptococcus suis Serotype 5 Strain HN105

Streptococcussuis serotype 5, an emerging zoonosis bacterial pathogen, has been isolated from infections in both pigs and humans. In this study, we sequenced the first complete genome of a virulent, multidrug-resistant SS5 strain HN105. The strain HN105 displayed enhanced pathogenicity in zebrafish and BABL/c mouse infection models. Comparative genome analysis identified a novel 80K integrative conjugative element (ICE), ICESsuHN105, as required for the multidrug resistance phenotype. Six corresponding antibiotic resistance genes in this ICE were identified, namely tet (O), tet (M), erm (two copies), aph, and spc. Phylogenetic analysis classified the element as a homolog of the ICESa2603 family, containing the typical family backbone and insertion DNA. DNA hybrids mediated by natural transformation between HN105 and ZY05719 verified the antibiotic resistant genes of ICESsuHN105 that could be transferred successfully, while they were dispersedly inserted with a single gene in different genomic locations of ZY05719(HN105) transformants. To further identify the horizontal transfer of ICESsuHN105 as a whole mobile genetic element, a circular intermediate form of ICESsuHN105 was detected by PCR. However, the effective conjugation using serotype 2 S. suis as recipients was not observed in current assays in vitro. Further studies confirmed the presence of the complete lantibiotic locus encoded in ICESsuHN105 that effectively inhibits the growth of other streptococci. In summary, this study demonstrated the presence of antibiotic resistance genes in ICE that are able to transfer between different clinical isolates and adapt to a broader range of Streptococcus serotype or species

CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation

Since the natural language processing (NLP) community started to make large language models (LLMs), such as GPT-4, act as a critic to evaluate the quality of generated texts, most of them only train a critique generation model of a specific scale on specific datasets. We argue that a comprehensive investigation on the key factor of LLM-based evaluation models, such as scaling properties, is lacking, so that it is still inconclusive whether these models have potential to replace GPT-4's evaluation in practical scenarios. In this paper, we propose a new critique generation model called CritiqueLLM, which includes a dialogue-based prompting method for high-quality referenced / reference-free evaluation data. Experimental results show that our model can achieve comparable evaluation performance to GPT-4 especially in system-level correlations, and even outperform GPT-4 in 3 out of 8 tasks in a challenging reference-free setting. We conduct detailed analysis to show promising scaling properties of our model in the quality of generated critiques. We also demonstrate that our generated critiques can act as scalable feedback to directly improve the generation quality of LLMs.Comment: 18 pages, 5 figure

The Effects of Transcranial Direct Current Stimulation (tDCS) on Working Memory Training in Healthy Young Adults

Working memory (WM) is a fundamental cognitive ability to support complex thought, but it is limited in capacity. WM training has shown the potential benefit for those in need of a higher WM ability. Many studies have shown the potential of transcranial direct current stimulation (tDCS) to transiently enhance WM performance by delivering a low current to the brain cortex of interest, via electrodes on the scalp. tDCS has also been revealed as a promising intervention to augment WM training in a few studies. However, those few tDCS-paired WM training studies, focused more on the effect of tDCS on WM enhancement and its transferability after training and paid less attention to the variation of cognitive performance during the training procedure. The current study attempted to explore the effect of tDCS on the variation of performance, during WM training, in healthy young adults. All the participants received WM training with the load-adaptive verbal N-back task, for 5 days. During the training procedure, active/sham anodal high-definition tDCS (HD-tDCS) was used to stimulate the left dorsolateral prefrontal cortex (DLPFC). To examine the training effect, pre- and post-tests were performed, respectively, 1 day before and after the training sessions. At the beginning of each training session, stable-load WM tasks were performed, to examine the performance variation during training. Compared to the sham stimulation, higher learning rates of performance metrics during the training procedure were found when WM training was combined with active anodal HD-tDCS. The performance improvements (post–pre) of the active group, were also found to be higher than those of the sham group and were transferred to a similar untrained WM task. Further analysis revealed a negative relationship between the training improvements and the baseline performance. These findings show the potential that tDCS may be leveraged as an intervention to facilitate WM training, for those in need of a higher WM ability

Golgi Phosphoprotein 3 Promotes Wls Recycling and Wnt Secretion in Glioma Progression

Background/Aims: Golgi phosphoprotein 3 (GOLPH3) plays pro-malignancy roles in several types of cancer. However, the molecular mechanism underlying GOLPH3 promoting tumor progression remains poorly understood. Methods: The expression of GOLPH3 and Wntless (Wls) in glioma tissues was examined by western blotting and immunohistochemistry. EdU incorporation assay and colony formation assay was used to examine the cell growth ability. The effect of GOLPH3 on Wls recycling, Wnt secretion and β-catenin activity was detected using western blotting, immunofluorescence, RT-PCR, ELISA or luciferase assay. Results: The protein levels of GOLPH3 and Wls were upregulated and positively correlated with each other in human glioma tissues. The promoting effect of GOLPH3 on glioma cell proliferation was partially mediated by Wls. In addition, GOLPH3 interacted with Wls and GOLPH3 down-regulation drove Wls into lysosome for degradation, inhibiting its recycling to golgi and the plasma membrane. Importantly, GOLPH3 down-regulation inhibited Wnt2b secretion and decreased β-catenin level and transcription activity. Conclusions: This study provides a brand new evidence that GOLPH3 promotes glioma cell proliferation by facilitating Wls recycling and Wnt/β-catenin signaling. Our findings suggest a rationale for targeting the GOLPH3-Wls-Wnt axis as a promising therapeutic approach for glioblastoma