Strong optical force induced by morphology dependent resonances

We consider the resonant optical force acting on a pair of transparent microspheres by the excitation of the Morphology Dependent Resonance (MDR). The bonding and anti-bonding modes of the MDR correspond to strong attractions and repulsions respectively. The dependence of the force on separation and the role of absorption are discussed. At resonance, the force can be enhanced by orders of magnitude so that it will dominate over other relevant forces. We find that a stable binding configuration can be induced by the resonant optical force.Comment: 4 pages, 4 figure

A comparative study on the performance of three color schemes in landscape preference tests

The photo color is recognised as one of the most significant but not fully understood factors influencing the results of landscape preference research. In this context, this paper compares the performances of three photo color schemes (original, rendered and white-black color schemes) frequently used in landscape preference tests to figure out which is the more suitable alternative to an original color photo. Statistics analysis results demonstrated that: 1) In general, the photo color schemes particularly the white-black scheme will significantly affect the results of landscape preference test. Compared with white-black, color in any other forms can increase the degree of preference for a given landscape. 2) The photo color scheme plays a decisive role in respondent’s judgment on some landscape attributes. Original color, White-black color and Rendered color schemes are better suited in landscape preference tests that highlight the effect of color, characteristic and naturalness respectively. 3) When the Rendered color scheme is used as an alternative to the Original color scheme, it has a much better performance than the White-black Color Scheme and is therefore recommended as the prior alternative color scheme to the Original color scheme under most scenarios in landscape preference research. Based on these results, it is suggested that color should be more carefully treated according to its different performance in landscape cognition research

PromptTTS: Controllable Text-to-Speech with Text Descriptions

Using a text description as prompt to guide the generation of text or images (e.g., GPT-3 or DALLE-2) has drawn wide attention recently. Beyond text and image generation, in this work, we explore the possibility of utilizing text descriptions to guide speech synthesis. Thus, we develop a text-to-speech (TTS) system (dubbed as PromptTTS) that takes a prompt with both style and content descriptions as input to synthesize the corresponding speech. Specifically, PromptTTS consists of a style encoder and a content encoder to extract the corresponding representations from the prompt, and a speech decoder to synthesize speech according to the extracted style and content representations. Compared with previous works in controllable TTS that require users to have acoustic knowledge to understand style factors such as prosody and pitch, PromptTTS is more user-friendly since text descriptions are a more natural way to express speech style (e.g., ''A lady whispers to her friend slowly''). Given that there is no TTS dataset with prompts, to benchmark the task of PromptTTS, we construct and release a dataset containing prompts with style and content information and the corresponding speech. Experiments show that PromptTTS can generate speech with precise style control and high speech quality. Audio samples and our dataset are publicly available.Comment: Submitted to ICASSP 202

Statistical Knowledge Assessment for Large Language Models

Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? Existing LLMs may generate distinct responses for different prompts. In this paper, we study the problem of quantifying knowledge contained in an LLM regarding a given set of facts. We propose KaRR, a statistical approach to assess factual knowledge for LLMs. The main idea is to estimate the ratio of LLM generating text corresponding to the answer entity given diverse prompts of the subject and the querying relation, versus it generating by random chances. Our assessment suite contains a comprehensive set of 994,123 entities and 600 relations, with 1,395,905 text aliases. We use our method to evaluate 20 LLMs of various sizes, including LLaMA, Alpaca, OPT, etc. Experiments show that our results have a strong correlation (0.43 Kendall's $\tau$) with the results of human assessment on LLMs. Our results reveal that the knowledge in LLMs with the same backbone architecture adheres to the scaling law, while tuning on instruction-following data sometimes compromises the model's capability to generate factually correct text reliably.Comment: Accepted by NeurIPS 202