Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models

Recent work studies the cognitive capabilities of language models through psychological tests designed for humans. While these studies are helpful for understanding the general capabilities of these models, there is no guarantee that a model possessing sufficient capabilities to pass those tests would actually use those capabilities in performing real-life tasks. In this work, we formulate task-oriented cognitive capabilities, which are human-like cognitive capabilities that language models leverage to perform tasks. These capabilities are (i) the ability to quickly generate good candidate utterances (the search capability) (ii) the ability to predict how a listener interprets those utterances and choose the most appropriate one (the pragmatic capability). We design an evaluation scheme for comparing these capabilities of a language model with those of a human. Applying this scheme to examine various models in a navigation instruction generation problem, we find that their pragmatic capability is severely lacking. This insight leads us to augment them with better models of the listener and obtain a significant boost of 11% in success rate in guiding real humans. Our work advocates for having a principled procedure for aligning language models with humans that involves (i) formulating task-oriented capabilities, (ii) devising a method to quantify their deficiency, and (iii) iteratively improving them.Comment: Findings of ACL 202

Hallucination Detection for Grounded Instruction Generation

We investigate the problem of generating instructions to guide humans to navigate in simulated residential environments. A major issue with current models is hallucination: they generate references to actions or objects that are inconsistent with what a human follower would perform or encounter along the described path. We develop a model that detects these hallucinated references by adopting a model pre-trained on a large corpus of image-text pairs, and fine-tuning it with a contrastive loss that separates correct instructions from instructions containing synthesized hallucinations. Our final model outperforms several baselines, including using word probability estimated by the instruction-generation model, and supervised models based on LSTM and Transformer

Successfully Guiding Humans with Imperfect Instructions by Highlighting Potential Errors and Suggesting Corrections

This paper addresses the challenge of leveraging imperfect language models to guide human decision-making in the context of a grounded navigation task. We show that an imperfect instruction generation model can be complemented with an effective communication mechanism to become more successful at guiding humans. The communication mechanism we build comprises models that can detect potential hallucinations in instructions and suggest practical alternatives, and an intuitive interface to present that information to users. We show that this approach reduces the human navigation error by up to 29% with no additional cognitive burden. This result underscores the potential of integrating diverse communication channels into AI systems to compensate for their imperfections and enhance their utility for humans

CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation

In the field of 3D object detection for autonomous driving, LiDAR-Camera (LC) fusion is the top-performing sensor configuration. Still, LiDAR is relatively high cost, which hinders adoption of this technology for consumer automobiles. Alternatively, camera and radar are commonly deployed on vehicles already on the road today, but performance of Camera-Radar (CR) fusion falls behind LC fusion. In this work, we propose Camera-Radar Knowledge Distillation (CRKD) to bridge the performance gap between LC and CR detectors with a novel cross-modality KD framework. We use the Bird's-Eye-View (BEV) representation as the shared feature space to enable effective knowledge distillation. To accommodate the unique cross-modality KD path, we propose four distillation losses to help the student learn crucial features from the teacher model. We present extensive evaluations on the nuScenes dataset to demonstrate the effectiveness of the proposed CRKD framework. The project page for CRKD is https://song-jingyu.github.io/CRKD.Comment: Accepted to CVPR 202

YUAN 2.0: A Large Language Model with Localized Filtering-based Attention

In this work, we develop and release Yuan 2.0, a series of large language models with parameters ranging from 2.1 billion to 102.6 billion. The Localized Filtering-based Attention (LFA) is introduced to incorporate prior knowledge of local dependencies of natural language into Attention. A data filtering and generating system is presented to build pre-training and fine-tuning dataset in high quality. A distributed training method with non-uniform pipeline parallel, data parallel, and optimizer parallel is proposed, which greatly reduces the bandwidth requirements of intra-node communication, and achieves good performance in large-scale distributed training. Yuan 2.0 models display impressive ability in code generation, math problem-solving, and chatting compared with existing models. The latest version of YUAN 2.0, including model weights and source code, is accessible at Github

SYNERGISTIC EFFECT OF RADIATION AND TRADITIONAL CHINESE MEDICINE RHIZOMA TYPHONII ETHANOL EXTRACTS DEPENDS ON P53 EXPRESSION IN TREATMENT OF LEWIS MOUSE LUNG CANCER CELLS

Background: Lung cancer is the leading cause of cancer-related death, and it is the most common cancer in terms of both incidence and mortality. There is an urgent need on novel therapeutic strategies for lung cancer. Traditional Chinese herbal medicines (CHM) have potential valuable for cancer treatment. Materials and Methods: Lewis mouse lung cancer cell line and Lewis cells tumors xenograft were used in this experiment. MTT assay was used to detect cell proliferation, flow cytometry (FCM) analysis and Western blotting to detect cell apoptosis, and colony formation assay to evaluate the effect of combined therapy of RT+IR. Results: Our data showed that Rhizoma typhonii (RT) obviously inhibited the proliferations of Lewis cells in time and dose dependent manners by MTT assay and enhanced radiosensitivity by colony formation assay. The effects of RT to Ionizing radiation (IR) therapy were demonstrated radiosensitivity on tumors xenograft experiment. In our study, RT induced apoptotic in Lewis cells directly and enhanced the pro-apoptotic effect of IR by regulating the expression of p53. Conclusions: These data suggested that RT may be a great potential anti-tumor medicine and the combination of RT and IR may provide a new therapeutic strategy for the treatment of Lewis lung cancer

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to a undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds-10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases

A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually

Enhancing regulatory T-cell function via inhibition of high mobility group box 1 protein signaling in immune thrombocytopenia

Primary immune thrombocytopenia (ITP) is the most common acquired autoimmune bleeding disorder. Abnormally increased levels of High Mobility Group Box 1 (HMGB1) protein associate with thrombocytopenia and therapeutic outcome in ITP. Previous studies proposed that a natural inhibitor of HMGB1, 18β-glycyrrhetinic acid (18β-GA), could be used for its anti-inflammatory and immune-modulatory effects, although its ability to correct immune balance in ITP is unclear. In this study, we showed that plasma HMGB1 correlated negatively with platelet counts in ITP patients, and confirmed that 18β-GA stimulated the production of regulatory T cells (Treg), restored the balance of CD4+ T-cell subsets and enhanced the suppressive function of Treg through blocking the effect on HMGB1 in patients with ITP. HMGB1 short hairpin RNA interference masked the effect of 18β-GA in Treg of ITP patients. Furthermore, we found that 18β-GA alleviated thrombocytopenia in mice with ITP. Briefly, anti-CD61 immune-sensitized splenocytes were transferred into severe combined immunodeficient mice to induce a murine model of severe ITP. The proportion of circulating Treg increased significantly, while the level of plasma HMGB1 and serum antiplatelet antibodies decreased significantly in ITP mice along 18β-GA treatment. In addition, 18β-GA reduced phagocytic activity of macrophages towards platelets both in ITP patients and ITP mice. These results indicate that 18β-GA has the potential to restore immune balance in ITP via inhibition of HMGB1 signaling. In short, this study reveals the role of HMGB1 in ITP, which may serve as a potential target for thrombocytopenia therapy