Video Game Genre Classification Based on Deep Learning

Video games have played a more and more important role in our life. While the genre classification is a deeply explored research subject by leveraging the strength of deep learning, the automatic video game genre classification has drawn little attention in academia. In this study, we compiled a large dataset of 50,000 video games, consisting of the video game covers, game descriptions and the genre information. We explored three approaches for genre classification using deep learning techniques. First, we developed five image-based models utilizing pre-trained computer vision models such as MobileNet, ResNet50 and Inception, based on the game covers. Second, we developed two text-based models, using Long-short Term Memory (LSTM) model and the Universal Sentence Encoder model, based on the game descriptions. For the third approach, we constructed a multi-modal fusion model, which concatenates extracted features from one image-based model and one text-based model. We analysed our results and revealed some challenges that exist in the task of genre classification for video games. Some future works are also proposed

Improved lightweight identification of agricultural diseases based on MobileNetV3

At present, the identification of agricultural pests and diseases has the problem that the model is not lightweight enough and difficult to apply. Based on MobileNetV3, this paper introduces the Coordinate Attention block. The parameters of MobileNetV3-large are reduced by 22%, the model size is reduced by 19.7%, and the accuracy is improved by 0.92%. The parameters of MobileNetV3-small are reduced by 23.4%, the model size is reduced by 18.3%, and the accuracy is increased by 0.40%. In addition, the improved MobileNetV3-small was migrated to Jetson Nano for testing. The accuracy increased by 2.48% to 98.31%, and the inference speed increased by 7.5%. It provides a reference for deploying the agricultural pest identification model to embedded devices.Comment: Accepted by CAIBDA 202

End-to-End nn-ary Relation Extraction for Combination Drug Therapies

Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently there are over 350K articles in PubMed that use the "combination drug therapy" MeSH heading with at least 10K articles published per year over the past two decades. Extracting combination therapies from scientific literature inherently constitutes an $n$-ary relation extraction problem. Unlike in the general $n$-ary setting where $n$ is fixed (e.g., drug-gene-mutation relations where $n=3$), extracting combination therapies is a special setting where $n \geq 2$ is dynamic, depending on each instance. Recently, Tiktinsky et al. (NAACL 2022) introduced a first of its kind dataset, CombDrugExt, for extracting such therapies from literature. Here, we use a sequence-to-sequence style end-to-end extraction method to achieve an F1-Score of $66.7\%$ on the CombDrugExt test set for positive (or effective) combinations. This is an absolute $\approx 5\%$ F1-score improvement even over the prior best relation classification score with spotted drug entities (hence, not end-to-end). Thus our effort introduces a state-of-the-art first model for end-to-end extraction that is already superior to the best prior non end-to-end model for this task. Our model seamlessly extracts all drug entities and relations in a single pass and is highly suitable for dynamic $n$-ary extraction scenarios.Comment: Accepted to appear in IEEE ICHI 2023. Code: https://github.com/bionlproc/end-to-end-CombDrugEx

Block Ionomer Complex Formulations For In Vivo Protein Delivery

Complex coacervation between therapeutic proteins and hydrophilic ionic-neutral block-copolymers leads to the formation of core-shell structured nanoparticles gently packaging the proteins in its core (reviewed in CHAPTER 1). This kind of nanoparticle, termed block ionomer complex (BIC), has great potential as protein delivery vehicles because of its simple and non-denaturing manufacturing procedure. However, reports on in vivo application of protein BICs are not common, primarily because of their instability at physiological ionic strength. The focus of this thesis is to explore the usage of BIC as an in vivo protein delivery vehicle. Our lab previously developed a protein BIC formulation, “SOD1 nanozyme”, formed between the protein superoxide dismutase 1 (SOD1) and the block copolymer poly(ethylene glycol)-poly(L-lysine) (PEG-PLL) followed by crosslinking with 3,3'-dithiobis(sulfosuccinimidyl propionate) (DTSSP). CHAPTER 2 examines the mechanism for SOD1 nanozyme to be effective for stroke treatment. Active incorporation of SOD1 nanozymes into the growing thrombus turns out to retain them at the vicinity of the injured sites on blood vessels after stroke. Although helpful in the retention of SOD1 nanozymes after stroke, polylysine can be toxic in vivo because of their cationic charge which easily disrupts cellular membranes. CHAPTER 3 describes a project that aims to replace the polylysine component with poly (aspartate diethyltriamine) (PAsp(DET)). This polymer is less toxic than polylysine due to its unique two-step protonation behavior, and its BIC with SOD1 has a similar size as the PLL-based SOD1 nanozyme. The therapeutic efficacy of this new formulation is also close to the PLL-based formulation. This dissertation also involves the characterization of a protein BIC formulation without crosslinking. We found that the BIC formed by brain derived neurotrophic factor (BDNF) and PEG-poly (L-glutamic acid) is stable at physiological salt concentrations. While protecting the cargo BDNF from interaction with a variety of mucosal proteins, the complex specifically releases active BDNF in the presence of its receptor, tropomyosin receptor kinase B (TrkB). Compare with native BDNF, The complex delivered significantly higher amounts of protein to different brain regions after intranasal delivery. This work is presented in CHAPTER 4. In summary, BIC is a promising platform for in vivo delivery of therapeutic proteins with careful design of stabilization strategies.Doctor of Philosoph

When Sparsity Meets Dynamic Convolution

Dynamic convolution achieves a substantial performance boost for efficient CNNs at a cost of increased convolutional weights. Contrastively, mask-based unstructured pruning obtains a lightweight network by removing redundancy in the heavy network at risk of performance drop. In this paper, we propose a new framework to coherently integrate these two paths so that they can complement each other compensate for the disadvantages. We first design a binary mask derived from a learnable threshold to prune static kernels, significantly reducing the parameters and computational cost but achieving higher performance in Imagenet-1K(0.6\% increase in top-1 accuracy with 0.67G fewer FLOPs). Based on this learnable mask, we further propose a novel dynamic sparse network incorporating the dynamic routine mechanism, which exerts much higher accuracy than baselines ($2.63\%$ increase in top-1 accuracy for MobileNetV1 with $90\%$ sparsity). As a result, our method demonstrates a more efficient dynamic convolution with sparsity