Improved SNARK Frontend for Highly Repetitive Computations

Modern SNARK designs typically feature a frontend-backend paradigm: The frontend compiles a user\u27s program into some equivalent circuit representation, while the backend calls for a SNARK specifically made for proving satisfiability of the circuit. While the circuit may be defined over small fields, the backend prover often needs to lift the computation to much larger fields for achieving soundness. This gap results in concrete overheads, for example, when representing a SHA-256 program as a circuit with pairing-based backend SNARKs. For a class of computations that are $\textit{highly repetitive}$, we propose an improved frontend that partially bridges this gap. Compared with existing works, our frontend yields circuit representations defined over larger fields but of smaller size. Our implementation shows that for SIMD computation with $\approx 180$ SHA-256 instances, our improved frontend improves prover runtime by over $2.6 \times$ and reduces memory usage by over $1.3 \times$. Central to our result and of independent interest, is an efficient technique for proving non-native modulo arithmetic

RAFT based wireless blockchain networks in the presence of malicious jamming

Blockchain shows great potential to be applied in wireless IoT ecosystems for establishing the trust and consensus mechanisms without central authority’s involvement. Based on RAFT consensus mechanism, this paper investigates the security performance of wireless blockchain networks in the presence of malicious jamming. We first map and model the blockchain transaction as a wireless network composed of uplink and downlink transmissions by assuming the follower nodes’ position as a Poisson Point Process (PPP) with selected leader location. The probability of achieving successful blockchain transactions is derived and verified by extensive simulations. The results provide analytical guidance for the practical deployment of wireless blockchain networks

When Do Program-of-Thoughts Work for Reasoning?

The reasoning capabilities of Large Language Models (LLMs) play a pivotal role in the realm of embodied artificial intelligence. Although there are effective methods like program-of-thought prompting for LLMs which uses programming language to tackle complex reasoning tasks, the specific impact of code data on the improvement of reasoning capabilities remains under-explored. To address this gap, we propose complexity-impacted reasoning score (CIRS), which combines structural and logical attributes, to measure the correlation between code and reasoning abilities. Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity by considering the difficulty and the cyclomatic complexity. Through an empirical analysis, we find not all code data of complexity can be learned or understood by LLMs. Optimal level of complexity is critical to the improvement of reasoning abilities by program-aided prompting. Then we design an auto-synthesizing and stratifying algorithm, and apply it to instruction generation for mathematical reasoning and code data filtering for code generation tasks. Extensive results demonstrates the effectiveness of our proposed approach. Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.Comment: Work in progres

Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization

Human intelligence exhibits compositional generalization (i.e., the capacity to understand and produce unseen combinations of seen components), but current neural seq2seq models lack such ability. In this paper, we revisit iterative back-translation, a simple yet effective semi-supervised method, to investigate whether and how it can improve compositional generalization. In this work: (1) We first empirically show that iterative back-translation substantially improves the performance on compositional generalization benchmarks (CFQ and SCAN). (2) To understand why iterative back-translation is useful, we carefully examine the performance gains and find that iterative back-translation can increasingly correct errors in pseudo-parallel data. (3) To further encourage this mechanism, we propose curriculum iterative back-translation, which better improves the quality of pseudo-parallel data, thus further improving the performance.Comment: accepted in AAAI 202

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. Code and datasets are available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.Comment: Work in progres

Novel biomass-based polymeric dyes: preparation and performance assessment in the dyeing of biomass-derived aldehyde-tanned leather

High-performance chrome-free leather production is currently one of the most concerning needs to warrant the sustainable development of the leather industry due to the serious chrome pollution. Driven by these research challenges, this work explores using biobased polymeric dyes (BPDs) based on dialdehyde starch and reactive small-molecule dye (reactive red 180, RD-180) as novel dyeing agents for leather tanned using a chrome-free, biomass-derived aldehyde tanning agent (BAT). FTIR, 1H NMR, XPS, and UV-visible spectrometry analyses indicated that a Schiff base structure was generated between the aldehyde group of dialdehyde starch (DST) and the amino group of RD-180, resulting in the successful load of RD-180 on DST to produce BPD. The BPD could first penetrate the BAT-tanned leather efficiently and then be deposited on the leather matrix, thus exhibiting a high uptake ratio. Compared with the crust leathers prepared using a conventional anionic dye (CAD), dyeing, and RD-180 dyeing, the BPD-dyed crust leather not only had better coloring uniformity and fastness but it also showed a higher tensile strength, elongation at break, and fullness. These data suggest that BPD has the potential to be used as a novel sustainable polymeric dye for the high-performance dyeing of organically tanned chrome-free leather, which is paramount to ensuring and promoting the sustainable development of the leather industry

Global trends and prospects about synaptic plasticity in Alzheimer’s disease: a bibliometric analysis

Background and purposeIn recent years, synaptic plasticity disorders have been identified as one of the key pathogenic factors and the early pathological characteristics of Alzheimer’s disease (AD). In this study, we tried to use bibliometric analysis to gain a systematic understanding about synaptic plasticity in Alzheimer’s disease.MethodsWe extracted relevant publications from the Web of Science Core Collection (WoSCC) on August 29th, 2022. Then, we used CiteSpace, VOSviewer and other online bibliometric platforms1 to further analyze the obtained data.ResultsA total of 2,348 published articles and reviews about synaptic plasticity in AD from 2002 to 2022 were identified. During the past two decades, the overall trends of the numbers and citations of manuscripts were on the rise. The United States was the leading country with the largest number of publications which showed its crucial role in this field. The collaboration network analysis showed that the United States and China had the most frequent collaboration. In addition, Harvard University was the institution with the greatest number of publications and cited times. Among all authors, Selkoe DJ was the most influential author with the greatest cited times. The journal of Alzheimer’s disease published the maximum number of documents in the field of synaptic plasticity in AD within 20 years. Furthermore, the results of keywords burst detection showed that the hot topics have shifted from the synaptic transmission, precursor protein and plaque formation to neuroinflammation, microglia and alpha synuclein.ConclusionThis study analyzed 2,348 publications with 82,025 references covering the topic of synaptic plasticity in AD and presented the research trends. The results indicated that neuroinflammation, microglia and alpha synuclein were the current research hotspots, which implied the potential clinical applications to AD

Genome-wide association study in Alzheimer’s disease: a bibliometric and visualization analysis

BackgroundThousands of research studies concerning genome-wide association studies (GWAS) in Alzheimer’s disease (AD) have been published in the last decades. However, a comprehensive understanding of the current research status and future development trends of GWAS in AD have not been clearly shown. In this study, we tried to gain a systematic overview of GWAS in AD by bibliometric and visualization analysis.MethodsThe literature search terms are: (“genome-wide analysis” or “genome-wide association study” or “whole-genome analysis”) AND (“Alzheimer’s Disease” or “Alzheimer Disease”). Relevant publications were extracted from the Web of Science Core Collection (WoSCC) database. Collected data were further analyzed using VOSviewer, CiteSpace and R package Bibliometrix. The countries, institutions, authors and scholar collaborations were investigated. The co-citation analysis of publications was visualized. In addition, research hotspots and fronts were examined.ResultsA total of 1,350 publications with 59,818 citations were identified. The number of publications and citations presented a significant rising trend since 2013. The United States was the leading country with an overwhelming number of publications (775) and citations (42,237). The University of Washington and Harvard University were the most prolific institutions with 101 publications each. Bennett DA was the most influential researcher with the highest local H-index. Neurobiology of Aging was the journal with the highest number of publications. Aβ, tau, immunity, microglia and DNA methylation were research hotspots. Disease and causal variants were research fronts.ConclusionThe most frequently studied AD pathogenesis and research hotspots are (1) Aβ and tau, (2) immunity and microglia, with TREM2 as a potential immunotherapy target, and (3) DNA methylation. The research fronts are (1) looking for genetic similarities between AD and other neurological diseases and syndromes, and (2) searching for causal variants of AD. These hotspots suggest noteworthy directions for future studies on AD pathogenesis and genetics, in which basic research regarding immunity is promising for clinical conversion. The current under-researched directions are (1) GWAS in AD biomarkers based on large sample sizes, (2) studies of causal variants of AD, and (3) GWAS in AD based on non-European populations, which need to be strengthened in the future