Time-resolved Measurement of Quadrupole Wakefields in Corrugated Structures

Corrugated structures have recently been widely used for manipulating electron beam longitudinal phase space and for producing THz radiation. Here we report on time-resolved measurements of the quadrupole wakefields in planar corrugated structures. It is shown that while the time- dependent quadrupole wakefield produced by a planar corrugated structure causes significant growth in beam transverse emittance, it can be effectively canceled with a second corrugated structure with orthogonal orientation. The strengths of the time-dependent quadrupole wakefields for various corrugated structure gaps are also measured and found to be in good agreement with theories. Our work should forward the applications of corrugated structures in many accelerator based scientific facilities

Evaluation of anti-smoking television advertising on tobacco control among urban community population in Chongqing, China

Background China is the largest producer and consumer of tobacco in the world. Considering the constantly growing urban proportion, persuasive tobacco control measures are important in urban communities. Television, as one of the most pervasive mass media, can be used for this purpose. Methods The anti-smoking advertisement was carried out in five different time slots per day from 15 May to 15 June in 2011 across 12 channels of Chongqing TV. A cross-sectional study was conducted in the main municipal areas of Chongqing. A questionnaire was administered in late June to 1,342 native residents aged 18–45, who were selected via street intercept survey. Results Respondents who recognized the advertisement (32.77 %) were more likely to know or believe that smoking cigarettes caused impotence than those who did not recognize the advertisement (26.11 %). According to 25.5 % of smokers, the anti-smoking TV advertising made them consider quitting smoking. However, females (51.7 %) were less likely to be affected by the advertisement to stop and think about quitting smoking compared to males (65.6 %) (OR = 0.517, 95 % CI [0.281–0.950]). In addition, respondents aged 26–35 years (67.4 %) were more likely to try to persuade others to quit smoking than those aged 18–25 years (36.3 %) (OR = 0.457, 95 % CI [0.215–0.974]). Furthermore, non-smokers (87.4 %) were more likely to find the advertisement relevant than smokers (74.8 %) (OR = 2.34, 95 % CI [1.19–4.61]). Conclusions This study showed that this advertisement did not show significant differences on smoking-related knowledge and attitude between non-smokers who had seen the ad and those who had not. Thus, this form may not be the right tool to facilitate change in non-smokers. The ad should instead be focused on the smoking population. Gender, smoking status, and age influenced the effect of anti-smoking TV advertising on the general population in China

Toward Understanding the Dynamics of Over-parameterized Neural Networks

The practical applications of neural networks are vast and varied, yet a comprehensive understanding of their underlying principles remains incomplete. This dissertation advances the theoretical understanding of neural networks, with a particular focus on over-parameterized models. It investigates their optimization and generalization dynamics and sheds light on various deep-learning phenomena observed in practice. This research deepens the understanding of the complex behaviors of these models and establishes theoretical insights that closely align with their empirical behaviours across diverse computational tasks.In the first part of the thesis, we analyze the fundamental properties of over-parameterized neural networks and we demonstrate that these properties can lead to the success of their optimization. We show that feedforward neural networks corresponding to arbitrary directed acyclic graphs undergo transition to linearity. The transition to linearity is characterized by the networks converging to their first-order Taylor expansion of parameters as their ``width'' approaches infinity. The width of these general networks is characterized by the minimum indegree of their neurons, except for the input and first layers. We further demonstrate that the property of transition to linearity plays an important role in the success of the optimization of over-parameterized neural networks.In this second part of the thesis, we investigate the modern training regime of over-parameterized neural networks, particularly focusing on the large learning rate regime. While neural networks can be approximated by linear models as their width increases, certain properties of wide neural networks cannot be captured by linear models. We show that recently proposed Neural Quadratic Models can exhibit the ``catapult phase''~\cite{lewkowycz2020large} that arises when training such models with large learning rates. We then empirically show that the behaviour of neural quadratic models parallels that of neural networks in generalization, especially in the catapult phase regime. Our analysis further demonstrates that quadratic models can be an effective tool for analysis of neural networks.Moreover, we extend the analysis of catapult dynamics to stochastic gradient descent (SGD). We first present an explanation regarding the common occurrence of spikes in the training loss when neural networks are trained with SGD. We provide evidence that the spikes in the training loss of SGD are caused by catapults. Second, we posit an explanation for how catapults lead to better generalization by demonstrating that catapults increase feature learning by increasing alignment with the Average Gradient Outer Product (AGOP) of the true predictor. Furthermore, we demonstrate that a smaller batch size in SGD induces a larger number of catapults, thereby improving AGOP alignment and test performance. Overall, by integrating theoretical insights with empirical validations, this dissertation provides a new understanding of the complex dynamics governing neural network training and generalization

Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning

In this paper, we first present an explanation regarding the common occurrence of spikes in the training loss when neural networks are trained with stochastic gradient descent (SGD). We provide evidence that the spikes in the training loss of SGD are "catapults", an optimization phenomenon originally observed in GD with large learning rates in [Lewkowycz et al. 2020]. We empirically show that these catapults occur in a low-dimensional subspace spanned by the top eigenvectors of the tangent kernel, for both GD and SGD. Second, we posit an explanation for how catapults lead to better generalization by demonstrating that catapults promote feature learning by increasing alignment with the Average Gradient Outer Product (AGOP) of the true predictor. Furthermore, we demonstrate that a smaller batch size in SGD induces a larger number of catapults, thereby improving AGOP alignment and test performance.Comment: ICML 202

Quadratic models for understanding catapult dynamics of neural networks

While neural networks can be approximated by linear models as their width increases, certain properties of wide neural networks cannot be captured by linear models. In this work we show that recently proposed Neural Quadratic Models can exhibit the "catapult phase" [Lewkowycz et al. 2020] that arises when training such models with large learning rates. We then empirically show that the behaviour of neural quadratic models parallels that of neural networks in generalization, especially in the catapult phase regime. Our analysis further demonstrates that quadratic models can be an effective tool for analysis of neural networks.Comment: accepted in ICLR 2024; changed the titl

Influence of Anodic Oxidation Parameters of TiO₂Nanotube Arrays on Morphology and Photocatalytic Performance

Titanium dioxide nanotube arrays (TNTAs) were fabricated by electrochemical anodization of Ti foils. The effects of electrolyte, applied voltage, duration of anodic oxidation to morphology, and photocatalytic performance of TNTAs were investigated. TNTAs formed in electrolyte of glycol and DMSO tend to grow along radial direction with flimsy tube wall and weak adhesion on Ti substrate. Those in glycerol, however, easily achieve balance between growth rate and corrosion rate, form orderly arranged array of nanotubes with uniform diameter, moderate length, and strong adhesiveness with substrates then. Although the photocatalytic activity of Rh B degradation on TNTAs prepared in glycol and DMSO is higher than those prepared in glycerol, their convenience of recycling and recovery shows the opposite. The optimality condition of anodic oxidation for TNTAs with good morphology and photocatalytic performance was present, which may have potential application in the synthesis of composite nanoarrays.</jats:p

Photoresponse of polyaniline-functionalized graphene quantum dots

Polyaniline-functionalized graphene quantum dots (PANI-GQD) and pristine graphene quantum dots (GQDs) were utilized for optoelectronic devices. The PANI-GQD based photodetector exhibited higher responsivity which is about an order of magnitude at 405 nm and 7 folds at 532 nm as compared to GQD-based photodetectors. The improved photoresponse is attributed to the enhanced interconnection of GQD by island-like polymer matrices, which facilitate carrier transport within the polymer matrices. The optically tunable current–voltage (I–V) hysteresis of PANI-GQD was also demonstrated. The hysteresis magnifies progressively with light intensity at a scan range of ±1 V. Both GQD and PANI-GQD devices change from positive to negative photocurrent when the bias reaches 4 V. Photogenerated carriers are excited to the trapping states in GQDs with increased bias. The trapped charges interact with charges injected from the electrodes which results in a net decrease of free charge carriers and a negative photocurrent. The photocurrent switching phenomenon in GQD and PANI-GQD devices may open up novel applications in optoelectronics