Discourse-Aware Graph Networks for Textual Logical Reasoning

Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are unexplored as current QA systems focus on entity-based relations. In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs). The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features. This pipeline is applied to a general encoder, whose fundamental features are joined with the high-level logic features for answer prediction. Experiments on three textual logical reasoning datasets demonstrate the reasonability of the logical structures built in DAGNs and the effectiveness of the learned logic features. Moreover, zero-shot transfer results show the features' generality to unseen logical texts

REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. While recent works retrieve supporting facts/evidence from commonsense knowledge bases to supply additional information to each question, there is still ample opportunity to advance it on the quality of the evidence. It is crucial since the quality of the evidence is the key to answering commonsense questions, and even determines the upper bound on the QA systems performance. In this paper, we propose a recursive erasure memory network (REM-Net) to cope with the quality improvement of evidence. To address this, REM-Net is equipped with a module to refine the evidence by recursively erasing the low-quality evidence that does not explain the question answering. Besides, instead of retrieving evidence from existing knowledge bases, REM-Net leverages a pre-trained generative model to generate candidate evidence customized for the question. We conduct experiments on two commonsense question answering datasets, WIQA and CosmosQA. The results demonstrate the performance of REM-Net and show that the refined evidence is explainable.Comment: Accepted by AAAI 202

Thermodynamic Cycle Analysis and Experimental Investigate on a Two-stage Vapor Injection Low Temperature Air Source Heat Pump with a Variable Displacement Ratio Rotary Compressor

Two-stage vapor injection compression cycle with flash tank was thermodynamically analyzed, the results showed that there existed the optimum theoretical displacement ratio of high stage to low stage corresponding to the maximum coefficient of performance(COP), the optimum displacement ratio and the volumetric heating capacity decreased with evaporation temperature decreasing. An optimum theoretical displacement ratio correlation for R290, R32 and R410A was given. A new type two-stage vapor injection low temperature air source heat pump (ASHP) was designed, which had a variable speed triple-cylinder rotary compressor with two cylinders in low stage and one cylinder in high stage. The experimental results of the new type ASHP showed that the heating capacity under 20℃/-20℃(inside room /outside room) could reach the rated heating capacity under 20℃/7℃, improving 96% compared to conventional one-stage ASHP, the heating capacity under 20℃/-30℃ could reach 80% of the rated one. COP of the new type ASHP could improve 5%~10% when the heating capacity was comparable to the conventional ASHP, and the heating capacity of the new type ASHP could improve 30%~50% when COP was comparable to the conventional ASHP

Improving the sensitivity of a near-infrared nanocomposite photodetector by enhancing trap induced hole injection

We report the enhancement of the photoconductive gain of nanocomposite near-infrared photodetectors by a zinc oxide nanoparticles (ZnO NPs) rich surface at the nanocomposite/cathode interface. An argon plasma etching process was used to remove polymer at the surface of nanocomposite films, which resulted in a ZnO NPs rich surface. The other way is to spin-coat a thin layer of ZnO NPs onto the nanocomposite layer. The ZnO NPs rich surface, which acts as electron traps to induce secondary hole injection under reverse bias, increased hole injection, and thus the external quantum efficiency by 2–3 times. The darkcurrent declined one order of magnitude simultaneously as a result of etching the top nanocomposite layer. The specific detectivity at 800 nm was increased by 7.4 times to 1.11x1010 Jones due to the simultaneously suppressed noise and enhanced gain

How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?

In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently shown impressive performance. However, the security aspects of these systems have received relatively less attention. This paper pioneers the exploration of vulnerabilities in LLM-based navigation models in urban outdoor environments, a critical area given the technology's widespread application in autonomous driving, logistics, and emergency services. Specifically, we introduce a novel Navigational Prompt Suffix (NPS) Attack that manipulates LLM-based navigation models by appending gradient-derived suffixes to the original navigational prompt, leading to incorrect actions. We conducted comprehensive experiments on an LLMs-based navigation model that employs various LLMs for reasoning. Our results, derived from the Touchdown and Map2Seq street-view datasets under both few-shot learning and fine-tuning configurations, demonstrate notable performance declines across three metrics in the face of both white-box and black-box attacks. These results highlight the generalizability and transferability of the NPS Attack, emphasizing the need for enhanced security in LLM-based navigation systems. As an initial countermeasure, we propose the Navigational Prompt Engineering (NPE) Defense strategy, concentrating on navigation-relevant keywords to reduce the impact of adversarial suffixes. While initial findings indicate that this strategy enhances navigational safety, there remains a critical need for the wider research community to develop stronger defense methods to effectively tackle the real-world challenges faced by these systems