67 research outputs found
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks
Adversarial training is the most empirically successful approach in improving
the robustness of deep neural networks for image classification.For text
classification, however, existing synonym substitution based adversarial
attacks are effective but not efficient to be incorporated into practical text
adversarial training. Gradient-based attacks, which are very efficient for
images, are hard to be implemented for synonym substitution based text attacks
due to the lexical, grammatical and semantic constraints and the discrete text
input space. Thereby, we propose a fast text adversarial attack method called
Fast Gradient Projection Method (FGPM) based on synonym substitution, which is
about 20 times faster than existing text attack methods and could achieve
similar attack performance. We then incorporate FGPM with adversarial training
and propose a text defense method called Adversarial Training with FGPM
enhanced by Logit pairing (ATFL). Experiments show that ATFL could
significantly improve the model robustness and block the transferability of
adversarial examples.Comment: Accepted by AAAI 2021, code is available at
https://github.com/JHL-HUST/FGP
Study on Oil Pressure Characteristics and Trajectory Tracking Control in Shift Process of Wet-Clutch for Electric Vehicles
Accurate control of oil pressure of wet-clutch is of great importance for improving shift quality. Based on dynamic models of two-gear planetary transmission and hydraulic control system, a trajectory tracking model of oil pressure was built by sliding mode control method. An experiment was designed to verify the validity of hydraulic control system, through which the relationship between duty cycle of on-off valve and oil pressure of clutch was determined. The tracking effect was analyzed by simulation. Results showed that oil pressure could follow well the optimal trajectory and the shift quality was effectively improved
The Effects of Storage Conditions on Lycopene Content and Color of Tomato Hot Pot Sauce
Tomato hot pot sauce (THPS) at different storage temperatures (0, 25, and 37°C) and with two kinds of packaging for 120 days was investigated in this study. High performance liquid chromatography was employed for detecting lycopene and 5-hydroxymethylfurfural (HMF). The changes of lycopene and HMF during storage were regressed with kinetic equation of both zero-order and first-order models, and the latter fitted better. The kinetic equation constant (k value) of lycopene or HMF at 37°C was higher than that at 25°C. The k value of lycopene of PET/PE (P1) packaged THPS was 1.60 times of that of PET/Al/EAA/PE (P2) packaged at 37°C, while it was 2.12 times at 25°C. The k value of HMF of P1 packaged THPS was 1.69 times of that of P2 packaged at 37°C, while it was 1.01 times at 25°C. Significant correlations between color index of L⁎, a⁎, and a⁎/b⁎ and lycopene or HMF were found at storage temperature. Browning color was attributed to both Maillard reaction and degradation of lycopene. In conclusion, lower storage temperature and stronger oxygen barrier property of package could maintain color stability and extend shelf life
Non-Standard Errors
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants
AI is a viable alternative to high throughput screening: a 318-target study
: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery
Optimal Design of Ejector Nozzle Profile with Internal and External Integrated Flow
Based on the orthogonal experimental method, a simulation case of the flow field of the ejector nozzle was designed to investigate the influence of the structural parameters of the ejector nozzle on the internal and external flow. This study explored the effects of throat area, outlet area, throat position, and ejector nozzle length on the ejector flow rate ratio, thrust coefficient, and net thrust coefficient. Subsequently, flow path geometry optimization was conducted to maximize the thrust coefficient or net thrust coefficient. The results revealed that the throat area ratio and the outlet area of the ejector nozzle are the primary factors affecting the aerodynamic performance. Compared to the baseline ejector nozzle model, the optimal model for thrust coefficient exhibited a 16.333% improvement, while the optimal model for net thrust coefficient demonstrated a significant enhancement of 46.674%
A Video Specific Instruction Set Architecture for ASIP design
This paper describes a novel video specific instruction set architecture for ASIP design. With single
instruction multiple data (SIMD) instructions, two destination modes, and video specific
instructions, an instruction set architecture is introduced to enhance the performance for video
applications. Furthermore, we quantify the improvement on H.263 encoding. In this paper, we evaluate
and compare the performance of VS-ISA, other DSPs (digital signal processors), and conventional SIMD
media extensions in the context of video coding. Our evaluation results show that VS-ISA improves the
processor's performance by approximately 5x on H.263 encoding, and VS-ISA outperforms other
architectures by 1.6x to 8.57x in computing IDCT
A Retargetable Compiler of VLIW ASIP for Media Signal Processing
Abstract * In the last decade extensive researches have been carried out in ASIP (Application Specific Instruction Processor) design field. One of the key steps in ASIP design is code generation by a retargetable compiler. In this paper we describe our experience in implementing a retargetable compiler for VLIW ASIP based on ORC (Open Research Compiler) framework. Orienting towards a new register file access architecture model, we narrate the process making modifications on ORC framework to get the compiler. The experimental results indicate that our method is effective to get compilers retargeting at VLIW ASIPs
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