Early-stage gas identification using convolutional long short-term neural network with sensor array time series data

Gas identification/classification through pattern recognition techniques based on gas sensor arrays often requires the equilibrium responses or the full traces of time-series data of the sensor array. Leveraging upon the diverse gas sensing kinetics behaviors measured via the sensor array, a computational intelligence-based meta-model is proposed to automatically conduct the feature extraction and subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. The time-series data contains implicit temporal dependency/correlation that is worth being characterized to enhance the gas identification performance and reliability. In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network is developed to perform the identification task incorporating temporal characteristics within time-series data. This novel approach shows the enhanced accuracy and robustness as compared to the baseline models, i.e., multilayer perceptron (MLP) and support vector machine (SVM) through the comprehensive statistical examination. Specifically, the classification accuracy of CLSTM reaches as high as 96%, regardless of the operating condition specified. More importantly, the excellent gas identification performance of CLSTM at early stages of gas exposure indicates its practical significance in future real-time applications. The promise of the proposed method has been clearly illustrated through both the internal and external validations in the systematic case investigation

Erasing and Correction of Liquid Metal Printed Electronics Made of Gallium Alloy Ink from the Substrate

Gallium-based liquid metals have recently been found important in a variety of newly emerging applications such as room temperature metal 3D printing, direct writing of electronics and biomedicine etc. In all these practices, one frequently encounters the situations that a printed circuit or track needs to be corrected or the unwanted parts of the device should be removed as desired. However, few appropriate strategies are currently available to tackle such important issues at this stage. Here we have identified several low cost ways toward this goal by comparatively investigating three typical strategies spanning from mechanical, chemical, to electrochemical principles, for removing the gallium-based liquid metal circuits or thin films. Regarding the mechanical approach, we constructed an eraser for removing the liquid metal thin films. It was shown that ethanol (CH3CH2OH) could serve as a good candidacy material for the mechanical eraser. In the chemical category, we adopted alkalis and acids to remove the finely printed liquid metal circuits and sodium hydroxide (NaOH) solution was particularly revealed to be rather efficient in making a chemical eraser. In the electrochemical strategy, we applied a 15 V voltage to a liquid metal thin film (covered with water) and successfully removed the target metal part. These methods were comparatively evaluated with each of the merits and shortcomings preliminarily clarified in the end. The present work is expected to be important for the increasing applications of the liquid metal enabled additive manufactures.Comment: 16 pages, 4 figure

A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

The past years have witnessed many dedicated open-source projects that built and maintain implementations of Support Vector Machines (SVM), parallelized for GPU, multi-core CPUs and distributed systems. Up to this point, no comparable effort has been made to parallelize the Elastic Net, despite its popularity in many high impact applications, including genetics, neuroscience and systems biology. The first contribution in this paper is of theoretical nature. We establish a tight link between two seemingly different algorithms and prove that Elastic Net regression can be reduced to SVM with squared hinge loss classification. Our second contribution is to derive a practical algorithm based on this reduction. The reduction enables us to utilize prior efforts in speeding up and parallelizing SVMs to obtain a highly optimized and parallel solver for the Elastic Net and Lasso. With a simple wrapper, consisting of only 11 lines of MATLAB code, we obtain an Elastic Net implementation that naturally utilizes GPU and multi-core CPUs. We demonstrate on twelve real world data sets, that our algorithm yields identical results as the popular (and highly optimized) glmnet implementation but is one or several orders of magnitude faster.Comment: 10 page

Matrix Metalloproteinase-2 Cleavable Peptide-Based siRNA Delivery System for Cancer Treatment

Among all kinds of gene therapy, siRNA, a class of 20 to 25 nucleotide-long double-stranded RNA molecules, is one of the promising therapeutic solutions to regulate post-transcriptional process for cancer treatment. However, naked siRNA is easily degradable in the body circulation system and cannot efficiently be consumed by cells. To overcome this problem, cell-penetrating peptides (CPPs) have received much attention due to their ability to translocate through plasma membranes along with a low toxicity. In past years, our group has developed a CPP called NP1 (Stearyl-HHHHHHHHHHHHHHHHRRRRRRRR-NH2), aiming to provide highly efficient siRNA delivery. However, although NP1 has outstanding transfection results on various cell line on in vitro tests, it could not provide promising results on serum environment since the presence of serum largely reduces the transfection efficacy and the overall positively charged surface of NP1/siRNA complex is not favored in systematic application. Matrix metalloproteinase-2, a category of gelatinase subgroup of MMPs, has been confirmed playing a critical role in tumor progression, angiogenesis, and metastasis. It has the ability to degrade the surrounding ECM to help cancer cell migrate inside the body. Thus, relatively larger amount of MMP-2 secretion can be detected at tumor site which makes them a universal stimulus for bio-responding. Herein, this thesis focus on increasing the stability of NP1 while maintaining the high transfection efficiency in the presence of serum. The complex surface will be sheltered with polyethylene glycol (PEG) to screen the surface charge and avoid serum protein binding; Furthermore, the linker between NP1 and PEG, with composed of 8 specific amino acid sequence (GPLGIAGQ), will be recognized by matrix metalloproteinase-2 to achieve sensitive cleavage of PEG. In this study, the following objective has been examined: (i) success cleavage of the designed linker and the existence of MMP2 in the cultured environment ; (ii) the physicochemical characterization of the modified peptides, and the interaction between peptides and siRNA molecules; (iii) the evaluation of the silencing efficiency, and toxicity of peptides/siRNA complexes in cultured cells in serum environment versus the results from NP1; (iv) in vitro biocompatibility study of the peptides/siRNA nanocomplexes, and (vi) the stability along with the RNase resistance ability of new modified peptide carrier comparing with NP1

BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT

Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL) fine-tuning, a new paradigm that allows language models to align with human preferences, i.e., InstructGPT. In this study, we propose BadGPT, the first backdoor attack against RL fine-tuning in language models. By injecting a backdoor into the reward model, the language model can be compromised during the fine-tuning stage. Our initial experiments on movie reviews, i.e., IMDB, demonstrate that an attacker can manipulate the generated text through BadGPT.Comment: This paper is accepted as a poster in NDSS202