How to Work with Honest but Curious Judges? (Preliminary Report)

The three-judges protocol, recently advocated by Mclver and Morgan as an example of stepwise refinement of security protocols, studies how to securely compute the majority function to reach a final verdict without revealing each individual judge's decision. We extend their protocol in two different ways for an arbitrary number of 2n+1 judges. The first generalisation is inherently centralised, in the sense that it requires a judge as a leader who collects information from others, computes the majority function, and announces the final result. A different approach can be obtained by slightly modifying the well-known dining cryptographers protocol, however it reveals the number of votes rather than the final verdict. We define a notion of conditional anonymity in order to analyse these two solutions. Both of them have been checked in the model checker MCMAS

Cultural values reflected within Chinese children's stories

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on August 19, 2009)Thesis (M.S.) University of Missouri-Columbia 2008.A total of 145 Chinese children's stories published from the 1980s to the present were examined to explore the changes in Chinese culture as a result of modernization and globalization in Chinese society. The selected stories were rated assessing the reflection of Chinese, Western and social moral values. Western cultural values were found increasingly in children's stories over the past 20 years. Some Chinese traditional values were consistently reflected in stories across all decades, while some of traditional values were occurring less. Chinese children's stories were also used as a tool to teach moral education. The genres of children's stories also impact the reflection of cultural values in the stories. These results suggest the influence of Western culture, as well as the continuity of Chinese traditional culture in Chinese children's stories.Includes bibliographical references

Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions

Quantum algorithms for optimization problems are of general interest. Despite recent progress in classical lower bounds for nonconvex optimization under different settings and quantum lower bounds for convex optimization, quantum lower bounds for nonconvex optimization are still widely open. In this paper, we conduct a systematic study of quantum query lower bounds on finding $\epsilon$-approximate stationary points of nonconvex functions, and we consider the following two important settings: 1) having access to $p$-th order derivatives; or 2) having access to stochastic gradients. The classical query lower bounds is $\Omega\big(\epsilon^{-\frac{1+p}{p}}\big)$ regarding the first setting, and $\Omega(\epsilon^{-4})$ regarding the second setting (or $\Omega(\epsilon^{-3})$ if the stochastic gradient function is mean-squared smooth). In this paper, we extend all these classical lower bounds to the quantum setting. They match the classical algorithmic results respectively, demonstrating that there is no quantum speedup for finding $\epsilon$-stationary points of nonconvex functions with $p$-th order derivative inputs or stochastic gradient inputs, whether with or without the mean-squared smoothness assumption. Technically, our quantum lower bounds are obtained by showing that the sequential nature of classical hard instances in all these settings also applies to quantum queries, preventing any quantum speedup other than revealing information of the stationary points sequentially.Comment: 32 pages, 0 figures. To appear in the Fortieth International Conference on Machine Learning (ICML 2023

The Physics of Nanoaperture Optical Traps: Design, Fabrication and Experimentation

Recent progress in nano optics, spurred by progress in nanofabrication, has allowed us to overcome these challenges. We use surface plasmon polaritons to break the optical diffraction limit and squeeze the photon energy into a local hot spot. The small mode volume of a plasmonic antenna or nanoaperature significantly enhances the local field and can be designed to resonate at a desired wavelength. By designing, fabricating, and testing these nanoapertures, I trap single nanoparticles with significantly reduced laser power by measuring the monochromatic transmission change of a resonant aperture. A freely diffused nanoparticle, behaving like a dipole antenna, interacts with the nanoaperture when trapped and shifts the resonance of the nanoaperture. By only monitoring a single wavelength, the presence of the particle changes the transmission signal. The effect of particle-induced transmission spectrum shift is called the self-induced back-action effect. This particle-induced spectrum change increases the transmission amplitude and variance once trapped. Furthermore, the monochromatic transmission measurement is a faster detection method than the spectrum measurement. It is able to follow up the diffusion, folding or conformation change of the trapped particle

New Threats to Privacy-preserving Text Representations

The users’ privacy concerns mandate data publishers to protect privacy by anonymizing the data before sharing it with data consumers. Thus, the ultimate goal of privacy-preserving representation learning is to protect user privacy while ensuring the utility, e.g., the accuracy of the published data, for future tasks and usages. Privacy-preserving embeddings are usually functions that are encoded to low-dimensional vectors to protect privacy while preserving important semantic information about an input text. We demonstrate that these embeddings still leak private information, even though the low dimensional embeddings encode generic semantics. We develop two classes of attacks, i.e., adversarial classification attack and adversarial generation attack, to study the threats for these embeddings. In particular, the threats are (1) these embeddings may reveal sensitive attributes letting alone if they explicitly exist in the input text, and (2) the embedding vectors can be partially recovered via generation models. Besides, our experimental results show that our approach can produce higher-performing adversary models than other adversary baselines

Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster

In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself. FastCoT uses a size-varying context window whose size changes with position to conduct parallel decoding and auto-regressive decoding simultaneously, thus fully utilizing GPU computation resources. In FastCoT, the parallel decoding part provides the LLM with a quick glance of the future composed of approximate tokens, which could lead to faster answers compared to regular autoregressive decoding used by causal transformers. We also provide an implementation of parallel decoding within LLM, which supports KV-cache generation and batch processing. Through extensive experiments, we demonstrate that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach. Additionally, we show that the context window size exhibits considerable robustness for different tasks

Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions from seen states and objects. The disparity between the manually labeled semantic information and its actual visual features causes a significant imbalance of visual deviation in the distribution of various object classes and state classes, which is ignored by existing methods. To ameliorate these issues, we consider the CZSL task as an unbalanced multi-label classification task and propose a novel method called MUtual balancing in STate-object components (MUST) for CZSL, which provides a balancing inductive bias for the model. In particular, we split the classification of the composition classes into two consecutive processes to analyze the entanglement of the two components to get additional knowledge in advance, which reflects the degree of visual deviation between the two components. We use the knowledge gained to modify the model's training process in order to generate more distinct class borders for classes with significant visual deviations. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art on MIT-States, UT-Zappos, and C-GQA when combined with the basic CZSL frameworks, and it can improve various CZSL frameworks. Our codes are available on https://anonymous.4open.science/r/MUST_CGE/