NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic

Reasoning has been a central topic in artificial intelligence from the beginning. The recent progress made on distributed representation and neural networks continues to improve the state-of-the-art performance of natural language inference. However, it remains an open question whether the models perform real reasoning to reach their conclusions or rely on spurious correlations. Adversarial attacks have proven to be an important tool to help evaluate the Achilles' heel of the victim models. In this study, we explore the fundamental problem of developing attack models based on logic formalism. We propose NatLogAttack to perform systematic attacks centring around natural logic, a classical logic formalism that is traceable back to Aristotle's syllogism and has been closely developed for natural language inference. The proposed framework renders both label-preserving and label-flipping attacks. We show that compared to the existing attack models, NatLogAttack generates better adversarial examples with fewer visits to the victim models. The victim models are found to be more vulnerable under the label-flipping setting. NatLogAttack provides a tool to probe the existing and future NLI models' capacity from a key viewpoint and we hope more logic-based attacks will be further explored for understanding the desired property of reasoning.Comment: Published as a conference paper at ACL 202

A note on α\alpha-permanent and loop soup

In this paper, it is shown that the $\alpha$-permanent in algebra is closely related to loop soup in probability. We obtain explicit expansions of $\alpha$-permanents of the block matrices associated to tridiagonal-like matrices. It is proved in two ways, one is the direct combinatorial proof, and the other is the probabilistic proof via loop soup.Comment: 8 pages, 1 figur

Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation

Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations. Existing methods mainly rely on single-turn datadriven learning and simple target-guided strategy without considering semantic or factual knowledge relations among candidate topics/keywords. This results in poor transition smoothness and low success rate. In this work, we adopt a structured approach that controls the intended content of system responses by introducing coarse-grained keywords, attains smooth conversation transition through turn-level supervised learning and knowledge relations between candidate keywords, and drives an conversation towards an specified target with discourse-level guiding strategy. Specially, we propose a novel dynamic knowledge routing network (DKRN) which considers semantic knowledge relations among candidate keywords for accurate next topic prediction of next discourse. With the help of more accurate keyword prediction, our keyword-augmented response retrieval module can achieve better retrieval performance and more meaningful conversations. Besides, we also propose a novel dual discourse-level target-guided strategy to guide conversations to reach their goals smoothly with higher success rate. Furthermore, to push the research boundary of target-guided open-domain conversation to match real-world scenarios better, we introduce a new large-scale Chinese target-guided open-domain conversation dataset (more than 900K conversations) crawled from Sina Weibo. Quantitative and human evaluations show our method can produce meaningful and effective target-guided conversations, significantly improving over other state-of-the-art methods by more than 20% in success rate and more than 0.6 in average smoothness score.Comment: 8 pages, 2 figues, 6tables, AAAI2020, fix our model's abbreviatio

Learning Vector Quantization-Aided Detection for MIMO Systems

In this letter, the learning vector quantization (LVQ) from machine learning (ML) is adopted into the large-scale multiple-input multiple-output (MIMO) detection to improve the detection performance. Inspired by the decision region from lattice decoding, the random Gaussian noises are applied in the proposed learning vector quantization-aided detection (LVQD) algorithm for data generation. Then, based on the classification, supervised learning is activated to update the targeted prototype vector iteratively, so as to a better detection performance. Meanwhile, the decoding radius in lattices is also used to serve as a preprocessing for LVQD, which leads to an efficient detection without performance loss. Finally, simulation results confirm that considerable performance gain can be achieved by the proposed LVQD algorithm, which suits well for suboptimal detection schemes

Changes in the expressions of E-selectin, adiponectin and serum ferritin in patients with diabetic retinopathy, and their correlations

Purpose: To study changes in the expressions of E-selectin, adiponectin (APN) and serum ferritin in patients with diabetes retinopathy (DN) and their correlations with DN.Methods: Diabetes patients (180) in Tangshan City Workers Hospital Group Rehabilitation Hospital from February 2014 to February 2015 were recruited and divided into three groups: 56 non-diabetic retinopathy (NDR) cases, 60 non-proliferative diabetic retinopathy (NPDR) cases and 64 proliferative diabetic retinopathy (PDR) cases. Serum levels of E-selectin, adiponectin (APN), ferritin (SF), fasting plasma glucose (FPG) and fasting insulin (FINS) were separately assayed, and their correlations analyzed.Results: There were no significant differences between patients in the three groups with respect to sex, age, blood pressure and blood lipids (p > 0.05). Serum E-selectin, APN and SF levels gradually and significantly increased in the order: NDR group ˂ NPDR group ˂ PDR group (p < 0.05).Conclusion: E-selectin, APN and SF levels of patients with DN increase with aggravation of the disease. The decrease in E-selectin, APN and SF levels may have a positive effect on the improvement of the status of DN patients.Keywords: Diabetic retinopathy, E-selectin, Adiponectin, Serum ferriti

Fashion Matrix: Editing Photos by Just Talking

The utilization of Large Language Models (LLMs) for the construction of AI systems has garnered significant attention across diverse fields. The extension of LLMs to the domain of fashion holds substantial commercial potential but also inherent challenges due to the intricate semantic interactions in fashion-related generation. To address this issue, we developed a hierarchical AI system called Fashion Matrix dedicated to editing photos by just talking. This system facilitates diverse prompt-driven tasks, encompassing garment or accessory replacement, recoloring, addition, and removal. Specifically, Fashion Matrix employs LLM as its foundational support and engages in iterative interactions with users. It employs a range of Semantic Segmentation Models (e.g., Grounded-SAM, MattingAnything, etc.) to delineate the specific editing masks based on user instructions. Subsequently, Visual Foundation Models (e.g., Stable Diffusion, ControlNet, etc.) are leveraged to generate edited images from text prompts and masks, thereby facilitating the automation of fashion editing processes. Experiments demonstrate the outstanding ability of Fashion Matrix to explores the collaborative potential of functionally diverse pre-trained models in the domain of fashion editing.Comment: 13 pages, 5 figures, 2 table