IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions

Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA tasks. However, common knowledge bases are inherently constrained by limited coverage and noisy information, making retrieval-based approaches inadequate to answer implicit reasoning questions. In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning. We leverage large language models (LLMs) for deriving such knowledge via a novel prompting method based on inductive reasoning patterns. On top of this, we implement two versions of IAG named IAG-GPT and IAG-Student, respectively. IAG-GPT directly utilizes the knowledge generated by GPT-3 for answer prediction, while IAG-Student gets rid of dependencies on GPT service at inference time by incorporating a student inductor model. The inductor is firstly trained via knowledge distillation and further optimized by back-propagating the generator feedback via differentiable beam scores. Experimental results show that IAG outperforms RAG baselines as well as ChatGPT on two Open-Domain QA tasks. Notably, our best models have won the first place in the official leaderboards of CSQA2.0 (since Nov 1, 2022) and StrategyQA (since Jan 8, 2023)

Detecting and Tracking Moving Airplanes from Space Based on Normalized Frame Difference Labeling and Improved Similarity Measures

The emerging satellite videos provide the opportunity to detect moving objects and track their trajectories, which were not possible for remotely sensed imagery with limited temporal resolution. So far, most studies using satellite video data have been concentrated on traffic monitoring through detecting and tracking moving cars, whereas the studies on other moving objects such as airplanes are limited. In this paper, an integrated method for monitoring moving airplanes from a satellite video is proposed. First, we design a normalized frame difference labeling (NFDL) algorithm to detect moving airplanes, which adopts a non-recursive strategy to deliver stable detection throughout the whole video. Second, the template matching (TM) technique is utilized for tracking the detected moving airplanes in the frame sequence by improved similarity measures (ISMs) with better rotation invariance and model drift suppression ability. Template matching with improved similarity measures (TM-ISMs) is further implemented to handle the leave-the-scene problem. The developed method is tested on a satellite video to detect and track eleven moving airplanes. Our NFDL algorithm successfully detects all the moving airplanes with the highest F1 score of 0.88 among existing algorithms. The performance of TM-ISMs is compared with both its traditional counterparts and other state-of-the-art tracking algorithms. The experimental results show that TM-ISMs can handle both rotation and leave-the-scene problems. Moreover, TM-ISMs achieve a very high tracking accuracy of 0.921 and the highest tracking speed of 470.62 frames per second

A Method to Detect and Track Moving Airplanes from a Satellite Video

In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F1 score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated

Carbon‐anchored Sb nanoparticles as high‐capacity and stable anode for aqueous alkaline batteries

Abstract Antimony (Sb) holds a high theoretic capacity and suitable redox potential as a promising anode for aqueous alkaline batteries (AABs). However, the uncontrollable nucleation for SbO2− and promiscuous water‐induced side reactions severely degrade the reversibility of Sb anode. Herein, the carbon‐anchored Sb nanoparticles are constructed to induce uniform Sb plating/stripping for high‐performance AABs. The experimental results reveal that the enhanced interaction between carbon and antimony as well as defective carbon can significantly improve the electrical conductivity and decrease the Sb nucleation overpotential. Accordingly, the as‐prepared Sb anode enables preferential plating of Sb rather than parasitic side reactions. As a result, the cycle life of A‐Sb/CF is sustained over 500 cycles at 10 mA cm−2/2 mAh cm−2. Even at the high capacity of 4 mAh cm−2, this anode can cycle stably for 225 cycles, which is significantly better than the Sb/CF counterpart. Furthermore, the assembled Ni3S2@Ni(OH)2//A‐Sb/CF full battery demonstrates a high capacity of 2.17 mAh cm−2 and a stable cycle life of over 500 cycles

SETDB1-mediated CD147-K71 di-methylation promotes cell apoptosis in non-small cell lung cancer

Protein post-translational modifications (PTMs) are at the heart status of cellular signaling events and broadly involved in tumor progression. CD147 is a tumor biomarker with various PTMs, promoting tumor metastasis and metabolism reprogramming. Nevertheless, the relationship between the PTMs of CD147 and apoptosis has not been reported. In our study, we produced a specific anti-CD147-K71 di-methylation (CD147-K71me2) antibody by immunizing with a di-methylated peptide and observed that the level of CD147-K71me2 in non-small cell lung cancer (NSCLC) tissues were lower than that in NSCLC adjacent tissues. SETDB1 was identified as the methyltransferase catalyzing CD147 to generate CD147-K71me2. RNA-seq showed that FOSB was the most significant differentially expressed gene (DEG) between wild-type CD147 (CD147-WT) and K71-mutant CD147 (CD147-K71R) groups. Subsequently, we found that CD147-K71me2 promoted the expression of FOSB by enhancing the phosphorylation of p38, leading to tumor cell apoptosis. In vivo experiments showed that CD147-K71me2 significantly inhibited tumor progression by promoting cell apoptosis. Taken together, our findings indicate the inhibitory role of CD147-K71me2 in tumor progression from the perspective of post-translational modification, which is distinct from the pro-cancer function of CD147 itself, broadening our perspective on tumor-associated antigen CD147

Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data

The digital mapping of road environment is an important task for road infrastructure inventory and urban planning. Automatic extraction and classification of pole-like objects can remarkably reduce mapping cost and enhance work efficiency. Therefore, this paper proposes a voxel-based method that automatically extracts and classifies three-dimensional (3-D) pole-like objects by analyzing the spatial characteristics of objects. First, a voxel-based shape recognition is conducted to generate a set of pole-like object candidates. Second, according to their isolation and vertical continuity, the pole-like objects are detected and individualized using the proposed circular model with an adaptive radius and the vertical region growing algorithm. Finally, several semantic rules, consisting of shape features and spatial topological relationships, are derived for further classifying the extracted pole-like objects into four categories (i.e., lamp posts, utility poles, tree trunks, and others). The proposed method was evaluated using three datasets from mobile LiDAR point cloud data. The experimental results demonstrate that the proposed method efficiently extracted the pole-like objects from the three datasets, with extraction rates of 85.3%, 94.1%, and 92.3%. Moreover, the proposed method can achieve robust classification results, especially for classifying tree trunks.Optical and Laser Remote Sensin

Mobile emergency (surgical) hospital: Development and application in medical relief of “4.20” Lushan earthquake in Sichuan Province, China

In the 21st century, natural disasters and emergencies occur frequently worldwide, which leads to the loss of hundreds of thousands of lives as well as the direct and indirect economic losses. China has a vast territory frequently struck by natural disasters. However, the reality is not optimistic. Poor organization and management during the rescue actions, the lack of large-scale, systematic medical rescue equipment were all great barriers to the outcomes. Mobile hospitals are expected to provide better health care. We were inspired by the concept of mobile hospital. Chongqing Emergency Medical Center, has set up trauma care system since 1988, in which prehospital care, intensive care, and in-hospital treatment is fully integrated. As a major advantage, such a system provided assurance of “golden hour” rescue treatment. Providing mobile intensive care and prehospital surgical service for severe trauma patients could reduce mortality significantly. Based on the civilian experiences in Chongqing Emergency Medical Center, the mobile emergency (surgical) hospital was developed

Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR

Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m2, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg–Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was Populus euphratica. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (R2 = 0.741, RMSE = 0.487, %RMSE = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI)

KCNN4 Promotes the Stemness Potentials of Liver Cancer Stem Cells by Enhancing Glucose Metabolism

The presence of liver cancer stem cells (LCSCs) is one of the reasons for the treatment failure of hepatocellular carcinoma (HCC). For LCSCs, one of their prominent features is metabolism plasticity, which depends on transporters and ion channels to exchange metabolites and ions. The K+ channel protein KCNN4 (Potassium Calcium-Activated Channel Subfamily N Member 4) has been reported to promote cell metabolism and malignant progression of HCCs, but its influence on LCSC stemness has remained unclear. Here, we demonstrated that KCNN4 was highly expressed in L-CSCs by RT-PCR and Western blot. Then, we illustrated that KCNN4 promoted the stemness of HC-C cells by CD133+CD44+ LCSC subpopulation ratio analysis, in vitro stemness transcription factor detection, and sphere formation assay, as well as in vivo orthotopic liver tumor formation and limiting dilution tumorigenesis assays. We also showed that KCNN4 enhanced the glucose metabolism in LCSCs by metabolic enzyme detections and seahorse analysis, and the KCNN4-promoted increase in LCSC ratios was abolished by glycolysis inhibitor 2-DG or OXPHOS inhibitor oligomycin. Collectively, our results suggested that KCNN4 promoted LCSC stemness via enhancing glucose metabolism, and that KCNN4 would be a potential molecular target for eliminating LCSCs in HCC

SRSF1 Facilitates Cytosolic DNA-Induced Production of Type I Interferons Recognized by RIG-I

<div><p>Background</p><p>Evidence has shown that psoriasis is closely associated with infection; however, the mechanism of this association remains unclear. In mammalian cells, viral or bacterial infection is accompanied by the release of cytosolic DNA, which in turn triggers the production of type-I interferons (IFNs). Type I IFNs and their associated genes are significantly upregulated in psoriatic lesions. RIG-I is also highly upregulated in psoriatic lesions and is responsible for IFN production. However, RIG-I mediated regulatory signaling in psoriasis is poorly understood.</p><p>Methods</p><p>We screened a cDNA library and identified potential RIG-I interacting partners that may play a role in psoriasis.</p><p>Results</p><p>We found that serine/arginine-rich splicing factor 1 (SRSF1) could specifically interact with RIG-I to facilitate RIG-I mediated production of type-I IFN that is triggered by cytosolic DNA. We found SRSF1 associates with RNA polymerase III and RIG-I in a DNA-dependent manner. In addition, treatment with a TNFα inhibitor downregulated SRSF1 expression in peripheral blood mononuclear cells (PBMCs) from psoriasis vulgaris patients.</p><p>Discussion</p><p>Based on the abundance of pathogenic cytosolic DNA that is detected in psoriatic lesions, our finding that RIG-I interacts with SRSF1 to regulate type-I IFN production reveals a critical link regarding how cytosolic DNA specifically activates aberrant IFN expression. These data may provide new therapeutic targets for the treatment of psoriasis.</p></div