Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making

Pre-trained language models (PLMs) have been widely used to underpin various downstream tasks. However, the adversarial attack task has found that PLMs are vulnerable to small perturbations. Mainstream methods adopt a detached two-stage framework to attack without considering the subsequent influence of substitution at each step. In this paper, we formally model the adversarial attack task on PLMs as a sequential decision-making problem, where the whole attack process is sequential with two decision-making problems, i.e., word finder and word substitution. Considering the attack process can only receive the final state without any direct intermediate signals, we propose to use reinforcement learning to find an appropriate sequential attack path to generate adversaries, named SDM-Attack. Extensive experimental results show that SDM-Attack achieves the highest attack success rate with a comparable modification rate and semantic similarity to attack fine-tuned BERT. Furthermore, our analyses demonstrate the generalization and transferability of SDM-Attack. The code is available at https://github.com/fduxuan/SDM-Attack

Fast Color-guided Depth Denoising for RGB-D Images by Graph Filtering

Depth images captured by off-the-shelf RGB-D cameras suffer from much stronger noise than color images. In this paper, we propose a method to denoise the depth images in RGB-D images by color-guided graph filtering. Our iterative method contains two components: color-guided similarity graph construction, and graph filtering on the depth signal. Implemented in graph vertex domain, filtering is accelerated as computation only occurs among neighboring vertices. Experimental results show that our method outperforms state-of-art depth image denoising methods significantly both on quality and efficiency.Comment: 5 pages, 4 figure

Efficient removal of tetracycline from aqueous solution by K2CO3 activated penicillin fermentation residue biochar

In this study, biochar was prepared using penicillin fermentation residue (PR) as the raw material by different methods. The adsorption behavior and adsorption mechanism of biochar on tetracycline (TC) in an aqueous environment were investigated. The results showed that K2CO3 as an activator could effectively make porous structures, and that biochar with mesoporous or microporous could be prepared in a controlled manner with two kinds of different activation methods, the dry mixing method and the impregnation method. The dry mixing method could create more mesopores, while the impregnation method could prepare more micropores. Microporous biochar (IKBCH) with a high specific surface area could be prepared by the impregnation method combined with HCl soaking, which has an excellent adsorption effect on tetracycline. When the concentration of tetracycline was 200 mg/L, the removal rate of 99.91% could be achieved with the dosage of microporous biochar at 1 g/L. The adsorption process was in accordance with the Langmuir model and the pseudo-second-order model, respectively. The maximum adsorption capacity of IKBCH was 268.55 mg/g (25°C). The adsorption mechanisms were pore filling, π-π interaction, electrostatic adsorption, and hydrogen bond. Its stable and wide applicability adsorption process does not cause ecological pollution in the aqueous environment, and it is a promising biochar adsorbent

An improved cutting force prediction model in the milling process with a multi-blade face milling cutter based on FEM and NURBS

Abstract(#br)Multi-blade face milling cutters are widely used in the finish machining of mechanical parts. The cutting force in the milling process is a crucial factor that promotes the chatter of the machine spindles, which can be used to predict the machined surface roughness. In this paper, a novel cutting force prediction model based on non-uniform rational basis splines (NURBS) and finite element method (FEM) is proposed. Single blade cutting forces under different parameters are simulated by FEM, and a cutting force model of the single blade is established by the NURBS interpolation method. Then, combined with the tool tip motion model, the cutting force of the multi-blade face milling cutter can be predicted. To verify the correctness of the cutting force predicted by the proposed..

The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis

ObjectiveMachine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD.MethodsEnglish databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity.ResultsThirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity.ConclusionML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings

LEGO-Net: Learning Regular Rearrangements of Objects in Rooms

Humans universally dislike the task of cleaning up a messy room. If machines were to help us with this task, they must understand human criteria for regular arrangements, such as several types of symmetry, co-linearity or co-circularity, spacing uniformity in linear or circular patterns, and further inter-object relationships that relate to style and functionality. Previous approaches for this task relied on human input to explicitly specify goal state, or synthesized scenes from scratch -- but such methods do not address the rearrangement of existing messy scenes without providing a goal state. In this paper, we present LEGO-Net, a data-driven transformer-based iterative method for learning regular rearrangement of objects in messy rooms. LEGO-Net is partly inspired by diffusion models -- it starts with an initial messy state and iteratively "de-noises'' the position and orientation of objects to a regular state while reducing the distance traveled. Given randomly perturbed object positions and orientations in an existing dataset of professionally-arranged scenes, our method is trained to recover a regular re-arrangement. Results demonstrate that our method is able to reliably rearrange room scenes and outperform other methods. We additionally propose a metric for evaluating regularity in room arrangements using number-theoretic machinery.Comment: Project page: https://ivl.cs.brown.edu/projects/lego-ne

High strength mullite-bond SiC porous ceramics fabricated by digital light processing

Fabricating SiC ceramics via the digital light processing (DLP) technology is of great challenge due to strong light absorption and high refractive index of deep-colored SiC powders, which highly differ from those of resin, and thus significantly affect the curing performance of the photosensitive SiC slurry. In this paper, a thin silicon oxide (SiO2) layer was in-situ formed on the surface of SiC powders by pre-oxidation treatment. This method was proven to effectively improve the curing ability of SiC slurry. The SiC photosensitive slurry was fabricated with solid content of 55 vol% and viscosity of 7.77 Pa s (shear rate of 30 s-1). The curing thickness was 50 μm with exposure time of only 5 s. Then, a well-designed sintering additive was added to completely convert low-strength SiO2 into mullite reinforcement during sintering. Complexshaped mullite-bond SiC ceramics were successfully fabricated. The flexural strength of SiC ceramics sintered at 1550 °C in air reached 97.6 MPa with porosity of 39.2 vol%, as high as those prepared by spark plasma sintering (SPS) techniques.</p

V2C MXene-modified g-C3N4 for enhanced visible-light photocatalytic activity

Increasing the efficiency of charge transfer and separation efficiency of photogenerated carriers are still the main challenges in the field of semiconductor-based photocatalysts. Herein, we synthesized g-C3N4@V2C MXene photocatalyst by modifying g-C3N4 using V2C MXene. The prepared photocatalyst exhibited outstanding photocatalytic performance under visible light. The degradation efficiency of methyl orange by g-C3N4@V2C MXene photocatalyst was as high as 94.5%, which is 1.56 times higher than that by g-C3N4. This was attributed to the V2C MXene inhibiting the rapid recombination of photogenerated carriers and facilitating rapid transfer of photogenerated electrons (e) from g-C3N4 to MXene. Moreover, g-C3N4@V2C MXene photocatalyst showed good cycling stability. The photocatalytic performance was higher than 85% after three cycles. Experiments to capture free radicals revealed that superoxide radicals (02) are the main contributors to the photocatalytic activity. Thus, the proposed g-C3N4@V2C MXene photocatalyst is a promising visible-light catalyst.Comment: 20 pages, 9 figure