Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust

Legal judgment Prediction (LJP), aiming to predict a judgment based on fact descriptions, serves as legal assistance to mitigate the great work burden of limited legal practitioners. Most existing methods apply various large-scale pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent improvements. However, we discover the fact that the state-of-the-art (SOTA) model makes judgment predictions according to wrong (or non-casual) information, which not only weakens the model's generalization capability but also results in severe social problems like discrimination. Here, we analyze the causal mechanism misleading the LJP model to learn the spurious correlations, and then propose a framework to guide the model to learn the underlying causality knowledge in the legal texts. Specifically, we first perform open information extraction (OIE) to refine the text having a high proportion of causal information, according to which we generate a new set of data. Then, we design a model learning the weights of the refined data and the raw data for LJP model training. The extensive experimental results show that our model is more generalizable and robust than the baselines and achieves a new SOTA performance on two commonly used legal-specific datasets

Automating Method Naming with Context-Aware Prompt-Tuning

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still sub-optimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract function of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency. In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of PL and NL through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous similarity-based consistency checking approaches. The experimental results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.Comment: Accepted by ICPC-202

Layer orientation and size effects on micropillar compression of Al/SiC nanolaminates

Nanolaminates consisting of alternating layers of two dissimilar materials can possess extraordinary mechanical properties compared to their bulk counterparts, making them promising for engineering applications. Extremely high room temperature strengths and damage tolerance have been reported when the individual layer thicknesses are less than 100 nm, and this has been attributed to the large density of interfaces and grain boundaries that act as barriers for pinned dislocations [1–3]. Micropillar compression tests have been extensively employed to study nanolaminate deformation with the force generally applied perpendicular to the individual layers [1,4,5]. However, studies covering the effect of pillar size and layer orientation with respect to the pillar axis in metal-ceramic nanolaminates are still scarce. This work is mainly focused on the study, by micropillar compression, of the deformation and failure mechanisms of metal-ceramic Al/SiC nanolaminates, with layer thickness between 10 and 100 nm, as a function of layer orientation and pillar size,. Finite element modeling (FEM) was used to support the experimental observations, when needed. Deformation mechanisms and stress-strain behavior were determined for layers oriented at 0º, 45 º and 90º, for two different pillar sizes. The results revealed that the main initial deformation mechanism at room temperature was plasticity of the Al layers, constrained by the ceramic SiC layers, but that the final failure is very dependent on layer orientation and other microstructural features apart from layer thickness. While the micropillars loaded parallel (0º) and at 45º to the layers failed by the formation of shear and kink bands, triggered by the pre-existing layer waviness, micropillar loaded in the perpendicular direction fail by cracking of the SiC layers, without any appreciable effect of layer waviness. Two size effects were observed, one intrinsic and related to the individual thickness and the other, extrinsic, related to the pillar size. The origin and competition between these two size effects will be discussed

Microstructure and high temperature mechanical properties of hard TaSiN coatings

Hard nitride coatings are widely used in the cutting tool industry, where coatings are commonly exposed to high temperature under service conditions. The addition of Si into nitride coatings, such as the TaSiN system, has been shown to enhance their oxidation resistance [1], which coupled with its high hardness, make this system of great interest for many engineering applications involving high temperatures. In this study, the room and high temperature mechanical properties of magnetron sputtered TaSiN coatings were measured using nanoindentation (between 25°C and 500°C). Fracture toughness was also evaluated at a similar temperature range using the micro-pillar splitting method (see Figure 1). The effects of N2 flow ratios on the composition, evolving phases and microstructure of the obtained nanocrystalline TaSiN coatings before and after the high temperature testing were examined by RBS, XRD and TEM analysis. Hardness was observed to increase with N content as we approach stoichiometries that allow higher degrees of crystallization of the TaN hard phases, which are embedded in an amorphous matrix. Coatings with an optimal composition of Ta55Si10N35 retain a hardness value of up to 30 GPa at 500°C, being also the toughest. Please click Additional Files below to see the full abstract

Autoantibody from women with preeclampsia induces soluble Fms-like tyrosine kinase-1 production via angiotensin type 1 receptor and calcineurin/nuclear factor of activated T-cells signaling

Preeclampsia is a pregnancy-specific hypertensive syndrome that causes substantial maternal and fetal morbidity and mortality. Recent evidence indicates that maternal endothelial dysfunction in preeclampsia results from increased soluble Fms-like tyrosine kinase-1 (sFlt-1), a circulating antiangiogenic protein. Factors responsible for excessive production of sFlt-1 in preeclampsia have not been identified. We tested the hypothesis that angiotensin II type 1 (AT1) receptor activating autoantibodies, which occur in women with preeclampsia, contribute to increased production of sFlt-1. IgG from women with preeclampsia stimulates the synthesis and secretion of sFlt-1 via AT1 receptor activation in pregnant mice, human placental villous explants, and human trophoblast cells. Using FK506 or short-interfering RNA targeted to the calcineurin catalytic subunit mRNA, we determined that calcineurin/nuclear factor of activated T-cells signaling functions downstream of the AT1 receptor to induce sFlt-1 synthesis and secretion by AT1-receptor activating autoantibodies. AT1-receptor activating autoantibody–induced sFlt-1 secretion resulted in inhibition of endothelial cell migration and capillary tube formation in vitro. Overall, our studies demonstrate that an autoantibody from women with preeclampsia induces sFlt-1 production via angiotensin receptor activation and downstream calcineurin/nuclear factor of activated T-cells signaling. These autoantibodies represent potentially important targets for diagnosis and therapeutic intervention