PARADIGMS FOR FOREIGN TECH-PLATFORMS REGULATION: U.S. OPTIONS AFTER THE TIKTOK SAGA

The heated discussion stirred up by the U.S. regulatory actions against TikTok continues to this day. The nearly predatory popularity of this Chinese application has raised people’s awareness that the country is in urgent need of a fully developed policy in order to deal with the surge of robust foreign digital platforms. This article gives the contour of the latest development of theories regarding the foreign tech-platforms regulation. Three contemporary frameworks are reviewed. The first laissez faire paradigm inherits the values of early neoliberalism to prevent a “Splinternet,” but its inaction fails to deal with novel security threats ranging from data privacy to economic competitiveness nowadays. The second case-by-case restrictions paradigm is presently the most mainstream and frequently-discussed scheme. It recognizes the blurriness of the existing non-systemic actions and has been flourished with risk assessment methods proposed by scholars. However, the inconsistency, unpredictability and the complexity of rules constitute its inborn deficiency. The last platform-utilities paradigm is a newly-developed innovative approach, which identifies the similarity between the tech-platforms and traditional utilities platforms of political-economy features, and thus provides legitimacy and viability of the sectoral regulation. Nonetheless, the differences between the internet platforms and the traditional utilities platforms, the shift from the U.S. long standing open attitude, and the risk of second order effects, all require further reflection. All of the proposals are an inspiration for policymakers to rethink the tension between internet freedom and national security. The article concludes by briefly reviewing the TikTok saga chronologically and analyzing the latest regulation attempt of Executive Orders of different States

Exploring New Paths for the International Outreach of Zigong Dinosaur Lantern Festival on the Foundation of Chinese and Western Folk Cultural Exchange

This article aims to delineate the history and significance of the Zigong Lantern Festival, delving into its current limitations. It seeks to explore novel avenues for the international promotion of Chinese traditional folk culture, facilitating a more dynamic integration of the Zigong Dinosaur Lantern Festival into global cultural exchanges. The objective is to enhance the vibrancy of international cultural interactions and illuminate a new radiance for Chinese culture on the world stage

Language Models Represent Beliefs of Self and Others

Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social reasoning. While Large Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms underlying these capabilities remain elusive. In this study, we discover that it is possible to linearly decode the belief status from the perspectives of various agents through neural activations of language models, indicating the existence of internal representations of self and others' beliefs. By manipulating these representations, we observe dramatic changes in the models' ToM performance, underscoring their pivotal role in the social reasoning process. Additionally, our findings extend to diverse social reasoning tasks that involve different causal inference patterns, suggesting the potential generalizability of these representations.Comment: project page: https://walter0807.github.io/RepBelief

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

In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

<p>Abstract</p> <p>Background</p> <p>MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions.</p> <p>Results</p> <p>Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments.</p> <p>Conclusions</p> <p>Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.</p

JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models

The proliferation of large language models (LLMs) has underscored concerns regarding their security vulnerabilities, notably against jailbreak attacks, where adversaries design jailbreak prompts to circumvent safety mechanisms for potential misuse. Addressing these concerns necessitates a comprehensive analysis of jailbreak prompts to evaluate LLMs' defensive capabilities and identify potential weaknesses. However, the complexity of evaluating jailbreak performance and understanding prompt characteristics makes this analysis laborious. We collaborate with domain experts to characterize problems and propose an LLM-assisted framework to streamline the analysis process. It provides automatic jailbreak assessment to facilitate performance evaluation and support analysis of components and keywords in prompts. Based on the framework, we design JailbreakLens, a visual analysis system that enables users to explore the jailbreak performance against the target model, conduct multi-level analysis of prompt characteristics, and refine prompt instances to verify findings. Through a case study, technical evaluations, and expert interviews, we demonstrate our system's effectiveness in helping users evaluate model security and identify model weaknesses.Comment: Submitted to VIS 202

UADB: Unsupervised Anomaly Detection Booster

Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to its wide real-world applications. Due to the complete absence of supervision signals, UAD methods rely on implicit assumptions about anomalous patterns (e.g., scattered/sparsely/densely clustered) to detect anomalies. However, real-world data are complex and vary significantly across different domains. No single assumption can describe such complexity and be valid in all scenarios. This is also confirmed by recent research that shows no UAD method is omnipotent. Based on above observations, instead of searching for a magic universal winner assumption, we seek to design a general UAD Booster (UADB) that empowers any UAD models with adaptability to different data. This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods. To achieve this, we dive deep into the UAD problem and find that compared to normal data, anomalies (i) lack clear structure/pattern in feature space, thus (ii) harder to learn by model without a suitable assumption, and finally, leads to (iii) high variance between different learners. In light of these findings, we propose to (i) distill the knowledge of the source UAD model to an imitation learner (booster) that holds no data assumption, then (ii) exploit the variance between them to perform automatic correction, and thus (iii) improve the booster over the original UAD model. We use a neural network as the booster for its strong expressive power as a universal approximator and ability to perform flexible post-hoc tuning. Note that UADB is a model-agnostic framework that can enhance heterogeneous UAD models in a unified way. Extensive experiments on over 80 tabular datasets demonstrate the effectiveness of UADB

Microwave regeneration of spent activated carbon for the treatment of ester-containing wastewater

In this study, an integrated granular activated carbon (GAC) adsorption/microwave (MW) irradiation process was used for the treatment of ester-containing wastewater from a lithium-ion battery (LIB) factory.</p