Non-Malleable Codes for Small-Depth Circuits

We construct efficient, unconditional non-malleable codes that are secure against tampering functions computed by small-depth circuits. For constant-depth circuits of polynomial size (i.e. $\mathsf{AC^0}$ tampering functions), our codes have codeword length $n = k^{1+o(1)}$ for a $k$-bit message. This is an exponential improvement of the previous best construction due to Chattopadhyay and Li (STOC 2017), which had codeword length $2^{O(\sqrt{k})}$. Our construction remains efficient for circuit depths as large as $\Theta(\log(n)/\log\log(n))$ (indeed, our codeword length remains $n\leq k^{1+\epsilon})$, and extending our result beyond this would require separating $\mathsf{P}$ from $\mathsf{NC^1}$. We obtain our codes via a new efficient non-malleable reduction from small-depth tampering to split-state tampering. A novel aspect of our work is the incorporation of techniques from unconditional derandomization into the framework of non-malleable reductions. In particular, a key ingredient in our analysis is a recent pseudorandom switching lemma of Trevisan and Xue (CCC 2013), a derandomization of the influential switching lemma from circuit complexity; the randomness-efficiency of this switching lemma translates into the rate-efficiency of our codes via our non-malleable reduction.Comment: 26 pages, 4 figure

The Space Experiment of the Exo-ecosystem

The experiment of exo-ecosystem and the exploration of extraterrestrial habitability aims to explore the adaptation of terrestrial life in space conditions for the manned space program and the future interstellar migration, which shows great scientific significance and public interests. By our knowledge the early life on Earth, archaea and extremophile have the ability to adapt to extreme environmental conditions and can potentially habitat in extraterrestrial environments. Here we proposed a design and framework for the experiment on exo-ecosystem and extraterrestrial habitability. The conceptual approach involves building an ecosystem based on archaea and extremophiles in a simulated extraterrestrial environment, with a focus on assessing the exobiological potential and adaptability of terrestrial life forms in such conditions through controlled experiments. Specifically, we introduce the Chinese Exo-Ecosystem Space Experiment (CHEESE), which investigates the survivability and potential for sustained growth, reproduction, and ecological interactions of methanogens under simulated Mars and Moon environments using the China Space Station (CSS) as a platform. We highlight that the space station provides unique yet relatively comprehensive conditions for simulating extraterrestrial environments. In conclusion, space experiments involving exo-ecosystems could pave the way for long-term human habitation in space, ensuring our ability to sustain colonies and settlements beyond Earth while minimizing our ecological impact on celestial bodies

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

Chinese Discourse Annotation Reference Manual

This document provides extensive guidelines and examples for Rhetorical Structure Theory (RST) annotation in Mandarin Chinese. The guideline is divided into three sections. We first introduce preprocessing steps to prepare data for RST annotation. Secondly, we discuss syntactic criteria to segment texts into Elementary Discourse Units (EDUs). Lastly, we provide examples to define and distinguish discourse relations in different genres. We hope that this reference manual can facilitate RST annotations in Chinese and accelerate the development of the RST framework across languages