Unlocking Second Language Mastery Navigating the Complex Terrain of Adult Language Acquisition

This paper mainly focuses on the more effective way that language learners who “escaped” during the critical period of acquiring a second language. In the paper, we use the methodology of bibliography to compare the essential differences between children and adult learners from the perspectives of linguistics, neurolinguistics, society and culturology, and further study SLA theory based on the author’s own educational experience. It is an indisputable fact that older language learners have less flexibility and plasticity than children due to their mature brains. However, it can be partially compensated for by cognitive ability and social experience thus overtaking a corner. Nonetheless, some points are especially worth paying attention to, such as the necessity to “design” the grammar into the structure of the paragraph to be the “guiding framework” of thinking; Learners should not be excessively concerned about the accuracy of grammar and words at academic level, so as to avoid negative emotions such as anxiety, which affect language learning, and is partly consistent with the SLA’s Affective Filter Hypothesis. This research offers fresh guidelines for students and teachers of second languages to consider contemporary methods of teaching and learning, particularly the “fanatics” of “natural acquired” or “academically structured education.” Additionally, researchers and teachers who consider upgrading teaching methods offer evidence and inspiration owing to the growing popularity of artificial intelligence

Prognostic significance of HALP score and combination of peripheral blood multiple indicators in patients with early breast cancer

BackgroundTo assess the prognostic significance of preoperative hemoglobin, albumin, lymphocyte, and platelet (HALP) score combined with multiple peripheral blood indicators in patients with early breast cancer (EBC).MethodsA total of 411 patients with early invasive breast cancer underwent breast-conserving surgery or radical surgery at Changzhou No.2 People’s Hospital from January 2015 to December 2020. The cut-off values of HALP, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and prognostic nutritional index (PNI) were calculated using the software X-tile. The primary outcomes were recurrence-free survival (RFS), which was analyzed using the Kaplan Meier (K-M) method, while log-rank was used to test the differences between high and low curves. Cox regression analysis was used to analyze the prognostic significance of HALP. Furthermore, the prognostic predictive value of independent prognostic factors was determined using the receiver operating characteristic (ROC) curve.ResultsLow HALP score (P<0.0001), high PLR (P<0.0001), and low LMR (P = 0.0345) were significantly associated with worse RFS. Body mass index (BMI)<24 (P = 0.0036), no diabetes (P = 0.0205), earlier TNM stage (P = 0.0005), and no lymph node metastasis (P = 0.0022) were positively correlated with longer survival HALP scores (hazard ratio [HR] 95% confidence interval [CI]: 0.08 (0.024–0.265), P<0.0001), BMI (HR 95%CI: 0.254 (0.109–0.589), P = 0.001), TNM stage (HR 95%CI: 0.153 (0.041–0.571), P = 0.005), and diabetes (HR 95%CI: 0.259 (0.085–0.785), P = 0.017) were demonstrated as independent prognostic factors by Cox regression analysis. The ROC curves depicted that the two most valuable factors were TNM stage and HALP, and combined independent factors were more accurate in prognostic prediction than any single factor. This further indicated that the TNM stage combined HALP or BMI were more valuable combinations.ConclusionThe HALP score was an independent prognostic factor for EBC and was significantly associated with worse RFS. This score may predict the probability of postoperative tumor recurrence or metastasis before surgery

Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement

We present a new method, PARsing And visual GrOuNding (ParaGon), for grounding natural language in object placement tasks. Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding. For compositionality, ParaGon parses a language instruction into an object-centric graph representation to ground objects individually. For ambiguity, ParaGon uses a novel particle-based graph neural network to reason about object placements with uncertainty. Essentially, ParaGon integrates a parsing algorithm into a probabilistic, data-driven learning framework. It is fully differentiable and trained end-to-end from data for robustness against complex, ambiguous language input.Comment: To appear in ICRA 202

Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

Large-scale task planning is a major challenge. Recent work exploits large language models (LLMs) directly as a policy and shows surprisingly interesting results. This paper shows that LLMs provide a commonsense model of the world in addition to a policy that acts on it. The world model and the policy can be combined in a search algorithm, such as Monte Carlo Tree Search (MCTS), to scale up task planning. In our new LLM-MCTS algorithm, the LLM-induced world model provides a commonsense prior belief for MCTS to achieve effective reasoning; the LLM-induced policy acts as a heuristic to guide the search, vastly improving search efficiency. Experiments show that LLM-MCTS outperforms both MCTS alone and policies induced by LLMs (GPT2 and GPT3.5) by a wide margin, for complex, novel tasks. Further experiments and analyses on multiple tasks -- multiplication, multi-hop travel planning, object rearrangement -- suggest minimum description length (MDL) as a general guiding principle: if the description length of the world model is substantially smaller than that of the policy, using LLM as a world model for model-based planning is likely better than using LLM solely as a policy.Comment: In Proceedings of NeurIPS 202

Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.Comment: Accepted by 2019 China Symposium on Cognitive Computing and Hybrid Intelligence(CCHI'19

Mitigating Shortcuts in Language Models with Soft Label Encoding

Recent research has shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. In this work, we aim to answer the following research question: Can we reduce spurious correlations by modifying the ground truth labels of the training data? Specifically, we propose a simple yet effective debiasing framework, named Soft Label Encoding (SoftLE). We first train a teacher model with hard labels to determine each sample's degree of relying on shortcuts. We then add one dummy class to encode the shortcut degree, which is used to smooth other dimensions in the ground truth label to generate soft labels. This new ground truth label is used to train a more robust student model. Extensive experiments on two NLU benchmark tasks demonstrate that SoftLE significantly improves out-of-distribution generalization while maintaining satisfactory in-distribution accuracy

LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF

Neural Radiance Fields (NeRFs) have made great success in representing complex 3D scenes with high-resolution details and efficient memory. Nevertheless, current NeRF-based pose estimators have no initial pose prediction and are prone to local optima during optimization. In this paper, we present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter, which introduces a two-stage localization mechanism in city-scale NeRF. In place recognition stage, we train a regressor through images generated from trained NeRFs, which provides an initial value for global localization. In pose optimization stage, we minimize the residual between the observed image and rendered image by directly optimizing the pose on tangent plane. To avoid convergence to local optimum, we introduce a Truncated Dynamic Low-pass Filter (TDLF) for coarse-to-fine pose registration. We evaluate our method on both synthetic and real-world data and show its potential applications for high-precision navigation in large-scale city scenes. Codes and data will be publicly available at https://github.com/jike5/LATITUDE.Comment: 7 pages, 6 figures, submitted to ICRA 202