Gradient-Guided Dynamic Efficient Adversarial Training

Adversarial training is arguably an effective but time-consuming way to train robust deep neural networks that can withstand strong adversarial attacks. As a response to the inefficiency, we propose the Dynamic Efficient Adversarial Training (DEAT), which gradually increases the adversarial iteration during training. Moreover, we theoretically reveal that the connection of the lower bound of Lipschitz constant of a given network and the magnitude of its partial derivative towards adversarial examples. Supported by this theoretical finding, we utilize the gradient's magnitude to quantify the effectiveness of adversarial training and determine the timing to adjust the training procedure. This magnitude based strategy is computational friendly and easy to implement. It is especially suited for DEAT and can also be transplanted into a wide range of adversarial training methods. Our post-investigation suggests that maintaining the quality of the training adversarial examples at a certain level is essential to achieve efficient adversarial training, which may shed some light on future studies.Comment: 14 pages, 8 figure

Development of Polymeric Porous Membrane for Mediator-Less Microbial Fuel Cells: An Electrochemical Study

In this work, gold nanoparticles (AuNPs) are embedded on the proton exchange membrane in a straightforward manner and are made highly stable. Nanoparticles provide high surface-to-volume ratio with excellent biocompatibility, using appropriate ligands, which allows for a biocompatible environment for bacterial functions. High conductivity, high surface area and catalytic properties of AuNPs make them excellent materials for MFCs. We employed layer-by-layer (LbL) self-assembly technique to prepare multilayered thin-films of polycation poly(allylamine hydrochloride) (PAH) and negatively functionalized AuNPs. The (PAH/AuNP) thin-films act as the catalyst layers and are to provide means for high porosity and high electrical conductive in the LbL thin-films when the polycation serve to assist LbL thin-film formation through ionic bonds. Scanning electron microscopy was used to investigate the morphology and nano/microstructure of the porous membrane catalyst. Samples consisting of different thickness thin-films were tested for their performance over five-day periods. Bioelectricity was generated using Shewanella oneidensis MR-1 cultivated on organic substrate with trypticase soy broth medium. Trypticase soy broth and ferricyanide were injected into the anode and cathode chambers as anolyte and catholyte respectively. Generated voltage and current were monitored and recorded using LabView though NI-DMM, over five-day periods

Professional Network Matters: Connections Empower Person-Job Fit

Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing works leverage historical or contextual information, they often disregard a crucial aspect: job seekers' social relationships in professional networks. This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. Our innovative approach consists of two stages: (1) defining a Workplace Heterogeneous Information Network (WHIN) to capture heterogeneous knowledge, including professional connections and pre-training representations of various entities using a heterogeneous graph neural network; (2) designing a Contextual Social Attention Graph Neural Network (CSAGNN) that supplements users' missing information with professional connections' contextual information. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks, leveraging pre-trained entity representations from WHIN. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn, showing superior performance compared to baseline models.Comment: Accepted at WSDM 202

Ultra-fast self-assembly and stabilization of reactive nanoparticles in reduced graphene oxide films.

Nanoparticles hosted in conductive matrices are ubiquitous in electrochemical energy storage, catalysis and energetic devices. However, agglomeration and surface oxidation remain as two major challenges towards their ultimate utility, especially for highly reactive materials. Here we report uniformly distributed nanoparticles with diameters around 10 nm can be self-assembled within a reduced graphene oxide matrix in 10 ms. Microsized particles in reduced graphene oxide are Joule heated to high temperature (∼1,700 K) and rapidly quenched to preserve the resultant nano-architecture. A possible formation mechanism is that microsized particles melt under high temperature, are separated by defects in reduced graphene oxide and self-assemble into nanoparticles on cooling. The ultra-fast manufacturing approach can be applied to a wide range of materials, including aluminium, silicon, tin and so on. One unique application of this technique is the stabilization of aluminium nanoparticles in reduced graphene oxide film, which we demonstrate to have excellent performance as a switchable energetic material

TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained large language models have further enhanced the effectiveness by leveraging richer textual information in user profiles and job descriptions apart from user behavior features and job metadata. However, the general domain-oriented design struggles to capture the unique structural information within user profiles and job descriptions, leading to a loss of latent semantic correlations. We propose TAROT, a hierarchical multitask co-pretraining framework, to better utilize structural and semantic information for informative text embeddings. TAROT targets semi-structured text in profiles and jobs, and it is co-pretained with multi-grained pretraining tasks to constrain the acquired semantic information at each level. Experiments on a real-world LinkedIn dataset show significant performance improvements, proving its effectiveness in person-job fit tasks.Comment: ICASSP 2024 camera ready. 5 pages, 1 figure, 3 table

Epithelial-to-mesenchymal transition drives a pro-metastatic Golgi compaction process through scaffolding protein PAQR11

Tumor cells gain metastatic capacity through a Golgi phosphoprotein 3-dependent (GOLPH3-dependent) Golgi membrane dispersal process that drives the budding and transport of secretory vesicles. Whether Golgi dispersal underlies the prometastatic vesicular trafficking that is associated with epithelial-to-mesenchymal transition (EMT) remains unclear. Here, we have shown that, rather than causing Golgi dispersal, EMT led to the formation of compact Golgi organelles with improved ribbon linking and cisternal stacking. Ectopic expression of the EMT-activating transcription factor ZEB1 stimulated Golgi compaction and relieved microRNA-mediated repression of the Golgi scaffolding protein PAQR11. Depletion of PAQR11 dispersed Golgi organelles and impaired anterograde vesicle transport to the plasma membrane as well as retrograde vesicle tethering to the Golgi. The N-terminal scaffolding domain of PAQR11 was associated with key regulators of Golgi compaction and vesicle transport in pull-down assays and was required to reconstitute Golgi compaction in PAQR11-deficient tumor cells. Finally, high PAQR11 levels were correlated with EMT and shorter survival in human cancers, and PAQR11 was found to be essential for tumor cell migration and metastasis in EMT-driven lung adenocarcinoma models. We conclude that EMT initiates a PAQR11-mediated Golgi compaction process that drives metastasis

Recent Development of Nano-Materials Used in DNA Biosensors

As knowledge of the structure and function of nucleic acid molecules has increased, sequence-specific DNA detection has gained increased importance. DNA biosensors based on nucleic acid hybridization have been actively developed because of their specificity, speed, portability, and low cost. Recently, there has been considerable interest in using nano-materials for DNA biosensors. Because of their high surface-to-volume ratios and excellent biological compatibilities, nano-materials could be used to increase the amount of DNA immobilization; moreover, DNA bound to nano-materials can maintain its biological activity. Alternatively, signal amplification by labeling a targeted analyte with nano-materials has also been reported for DNA biosensors in many papers. This review summarizes the applications of various nano-materials for DNA biosensors during past five years. We found that nano-materials of small sizes were advantageous as substrates for DNA attachment or as labels for signal amplification; and use of two or more types of nano-materials in the biosensors could improve their overall quality and to overcome the deficiencies of the individual nano-components. Most current DNA biosensors require the use of polymerase chain reaction (PCR) in their protocols. However, further development of nano-materials with smaller size and/or with improved biological and chemical properties would substantially enhance the accuracy, selectivity and sensitivity of DNA biosensors. Thus, DNA biosensors without PCR amplification may become a reality in the foreseeable future

Genetic Removal of the CH1 Exon Enables the Production of Heavy Chain-Only IgG in Mice

Nano-antibodies possess great potential in many applications. However, they are naturally derived from heavy chain-only antibodies (HcAbs), which lack light chains and the CH1 domain, and are only found in camelids and sharks. In this study, we investigated whether the precise genetic removal of the CH1 exon of the γ1 gene enabled the production of a functional heavy chain-only IgG1 in mice. IgG1 heavy chain dimers lacking associated light chains were detected in the sera of the genetically modified mice. However, the genetic modification led to decreased expression of IgG1 but increased expression of other IgG subclasses. The genetically modified mice showed a weaker immune response to specific antigens compared with wild type mice. Using a phage-display approach, antigen-specific, single domain VH antibodies could be screened from the mice but exhibited much weaker antigen binding affinity than the conventional monoclonal antibodies. Although the strategy was only partially successful, this study confirms the feasibility of producing desirable nano-bodies with appropriate genetic modifications in mice