Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine-learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses.Comment: 21 pages, submitted to TKDD. arXiv admin note: text overlap with arXiv:2209.08044, arXiv:2205.1452

RecycleGPT: An Autoregressive Language Model with Recyclable Module

Existing large language models have to run K times to generate a sequence of K tokens. In this paper, we present RecycleGPT, a generative language model with fast decoding speed by recycling pre-generated model states without running the whole model in multiple steps. Our approach relies on the observation that adjacent tokens in a sequence usually have strong correlations and the next token in a sequence can be reasonably guessed or inferred based on the preceding ones. Experiments and analysis demonstrate the effectiveness of our approach in lowering inference latency, achieving up to 1.4x speedup while preserving high performance.Comment: Technical Repor

Self-Optimizing Feature Transformation

Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality, enhance model generalization, overcome data sparsity, and expand the availability of classic models. Current research focuses on domain knowledge-based feature engineering or learning latent representations; nevertheless, these methods are not entirely automated and cannot produce a traceable and optimal representation space. When rebuilding a feature space for a machine learning task, can these limitations be addressed concurrently? In this extension study, we present a self-optimizing framework for feature transformation. To achieve a better performance, we improved the preliminary work by (1) obtaining an advanced state representation for enabling reinforced agents to comprehend the current feature set better; and (2) resolving Q-value overestimation in reinforced agents for learning unbiased and effective policies. Finally, to make experiments more convincing than the preliminary work, we conclude by adding the outlier detection task with five datasets, evaluating various state representation approaches, and comparing different training strategies. Extensive experiments and case studies show that our work is more effective and superior.Comment: Under review of TKDE. arXiv admin note: substantial text overlap with arXiv:2205.1452

Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify masked regions. We identify the performance bottleneck of this paradigm to be the pre-trained CLIP model, since it does not perform well on masked images. To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions. We collect training data by mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to match masked image regions to nouns in the image captions. Compared with the more precise and manually annotated segmentation labels with fixed classes (e.g., COCO-Stuff), we find our noisy but diverse dataset can better retain CLIP's generalization ability. Along with finetuning the entire model, we utilize the "blank" areas in masked images using a method we dub mask prompt tuning. Experiments demonstrate mask prompt tuning brings significant improvement without modifying any weights of CLIP, and it can further improve a fully finetuned model. In particular, when trained on COCO and evaluated on ADE20K-150, our best model achieves 29.6% mIoU, which is +8.5% higher than the previous state-of-the-art. For the first time, open-vocabulary generalist models match the performance of supervised specialist models in 2017 without dataset-specific adaptations.Comment: Project page: https://jeff-liangf.github.io/projects/ovse

A thrombin-triggered self-regulating anticoagulant strategy combined with anti-inflammatory capacity for blood-contacting implants

Interrelated coagulation and inflammation are impediments to endothelialization, a prerequisite for the longterm function of cardiovascular materials. Here, we proposed a self-regulating anticoagulant coating strategy combined with anti-inflammatory capacity, which consisted of thrombin-responsive nanogels with anticoagulant and anti-inflammatory components. As an anticoagulant, rivaroxaban was encapsulated in nanogels cross-linked by thrombin-cleavable peptide and released upon the trigger of environmental thrombin, blocking the further coagulation cascade. The superoxide dismutase mimetic Tempol imparted the antioxidant property. Polyphenol epigallocatechin gallate (EGCG), in addition to its anti-inflammatory function in synergy with Tempol, also acted as a weak cross-linker to stabilize the coating. The effectiveness and versatility of this coating were validated using two typical cardiovascular devices as models, biological valves and vascular stents. It was demonstrated that the coating worked as a precise strategy to resist coagulation and inflammation, escorted reendothelialization on the cardiovascular devices, and provided a new perspective for designing endothelium-like functional coatings

Functional Analysis of MsepOR13 in the Oriental Armyworm Mythimna separata (Walker)

Olfaction in insects has a critical role in recognizing the host, finding food, and choosing mating partners, as well as avoiding predators. Odorant receptors (ORs), which are housed in the dendritic membrane of sensory neurons and extended into the lymph of sensilla on insect antennae, are participating in the detection of volatile compounds in insects. In the present study, we identified an OR gene, named MsepOR13, in the oriental armyworm Mythimna separata (Walker). Quantitative real-time polymerase chain reaction revealed that MsepOR13 was expressed mainly in the antennae of male and female moths. In in vitro heterologous expression experiments, MsepOR13 was widely tuned to 32 of the 67 different compounds tested. Furthermore, MsepOR13 responded to eugenol at a low concentration of 10-9 M, with an EC50 value of 3.91 × 10-6 M. The high sensitivity suggests an important role for the OR13 gene in the moth olfactory system

Microwave-Assisted Synthesis of Co/CoOx Supported on Earth-Abundant Coal-Derived Carbon for Electrocatalysis of Oxygen Evolution

The evident demand for hydrogen as the ultimate energy fuel for posterity calls for the development of low-cost, efficient and stable electrocatalysts for water splitting. Herein, we report the synthesis of Co/CoOx supported on coal-derived N-doped carbon via a simple microwave-assisted method and demonstrate its application as an efficient catalyst for the oxygen evolution reaction (OER). With the optimal amount of cobalt introduced into the N-doped coal-derived, the developed catalyst achieved overpotentials of 0.370 and 0.429 V during water oxidation at current densities of 1 mA cm(-2) and 10 mA cm(-2), respectively. There was no noticeable loss in the activity of the catalyst during continuous galvanostatic polarization at a current density of 10 mA cm(-2) for a test period of 66 h. The synergistic interaction of the Co/CoOx moieties with the pyridinic and pyrollic nitrogen functional groups in the N-doped carbon, as well with the other heteroatoms species in the pristine coal favored enhancement of the OER electrocatalytic performance. (C) The Author(s) 2019. Published by ECS

Transcatheter arterial chemoembolization combined with apatinib and camrelizumab for unresectable advanced gastric or gastroesophageal junction cancer: a single-arm, single-center, retrospective study

PurposeThis study aims to investigate the efficacy and safety of transcatheter arterial chemoembolization (TACE) combined with Apatinib and Camrelizumab for treating unresectable advanced gastric or gastroesophageal junction (G/GEJ) cancer.Material and methodsIn this study, data of patients with unresectable advanced G/GEJ cancer who received TACE combined with Apatinib and Camrelizumab from August 2018 to December 2021 was evaluated. After TACE, patients were given intravenous Camrelizumab 200mg every three weeks and oral apatinib 250mg/day for treatment. The primary endpoint was overall survival (OS), and the secondary endpoints were objective response rate (ORR), disease control rate (DCR), and adverse events (AEs).ResultsA total of 49 patients were enrolled in this study. The median follow-up time was 14.0 months, and the median OS was 20.0 months (95% CI = 13.6-26.4). Two patients (4.08%) achieved complete remission, 28 patients (57.14%) achieved partial remission, 18 patients (36.73%) had stable disease, and 1 patient (2.04%) had disease progression. The ORR was 61.22%, and the DCR was 97.96%. Multivariate Cox regression analysis indicated that age (HR 4.74, 95% CI = 1.674-13.440, P=0.003) and multiple distant metastases (HR 20.916, 95% CI = 4.094-106.808, P = 0.001) were independent risk factors for OS. Most AEs were classified as grade 1-2, the most common being RCCEP (69.39%). There were 5 cases of grade 3-4 adverse events (10.20%). No patients discontinued or reduced the treatment dose due to AEs, and all patients received symptomatic treatment.ConclusionTACE combined with Apatinib and Camrelizumab is a safe and effective therapeutic option for patients with unresectable advanced G/GEJ cancer, which can significantly improve the median OS and ORR of patients. And the adverse events (AEs) are tolerable and manageable