MIMO-Based Forward-Looking SAR Imaging Algorithm and Simulation

Multiple-input multiple-output (MIMO) radar imaging can provide higher resolution and better sensitivity and thus can be applied to targets detection, recognition, and tracking. Missile-borne forward-looking SAR (MFL-SAR) is a new and special MIMO radar mode. It has advantage of two-dimensional (2D) imaging capability in forward direction over monostatic missile-borne SAR and airborne SAR. However, it is difficult to obtain accurate 2D frequency spectrum of the target echo signal due to the high velocity and descending height of this platform, which brings a lot of obstacles to imaging algorithm design. Therefore, a new imaging algorithm for MFL-SAR configuration based on the method of series reversion is proposed in this paper. This imaging method can implement range compression, secondary range compression (SRC), and range cell migration correction (RCMC) effectively. Finally, some simulations of point targets and comparison results confirm the efficiency of our proposed algorithm

More than Classification: A Unified Framework for Event Temporal Relation Extraction

Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events. For example, relation \textit{Includes} could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3\% on both datasets. By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios. When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations. The code is released at \url{https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE}

Directional Texture Editing for 3D Models

Texture editing is a crucial task in 3D modeling that allows users to automatically manipulate the surface materials of 3D models. However, the inherent complexity of 3D models and the ambiguous text description lead to the challenge in this task. To address this challenge, we propose ITEM3D, a \textbf{T}exture \textbf{E}diting \textbf{M}odel designed for automatic \textbf{3D} object editing according to the text \textbf{I}nstructions. Leveraging the diffusion models and the differentiable rendering, ITEM3D takes the rendered images as the bridge of text and 3D representation, and further optimizes the disentangled texture and environment map. Previous methods adopted the absolute editing direction namely score distillation sampling (SDS) as the optimization objective, which unfortunately results in the noisy appearance and text inconsistency. To solve the problem caused by the ambiguous text, we introduce a relative editing direction, an optimization objective defined by the noise difference between the source and target texts, to release the semantic ambiguity between the texts and images. Additionally, we gradually adjust the direction during optimization to further address the unexpected deviation in the texture domain. Qualitative and quantitative experiments show that our ITEM3D outperforms the state-of-the-art methods on various 3D objects. We also perform text-guided relighting to show explicit control over lighting. Our project page: https://shengqiliu1.github.io/ITEM3D.Comment: project page: https://shengqiliu1.github.io/ITEM3

Study on fractional vegetation cover dynamic in the Yellow River Basin, China from 1901 to 2100

Increasing climate change makes vegetation dynamic. At the same time, dynamic changes in vegetation not only have a feedback effect on climate change, but also affect the hydrological cycle process. Therefore, understanding the vegetation change and its response to climate change is a priority for predicting future climate change and studying the impact of vegetation change on the hydrological cycle. In this study, the Yellow River Basin in China is the study area. Based on the analysis of the evolution characteristics of meteorological elements and fractional vegetation cover (FVC), the delta downscaling Coupled Model Intercomparison Project Phase 6 (CMIP6) models are optimized. The empirical orthogonal function (EOF) and singular value decomposition (SVD) methods are used to investigate the impact of climate change on vegetation in the Yellow River Basin. The results show that: (1) in the four scenarios (SSP126, SSP245, SSP370, and SSP585), FVC in the Yellow River Basin from 2022 to 2100 shows an increasing trend, SSP370 (0.017 10a–1) > SSP126 (0.014 10a–1) > SSP245 (0.0087 10a–1) > SSP585 (0.0086 10a–1). Spatially, FVC in most regions of the Yellow River Basin show an increasing trend under the four scenarios, and the degraded areas are concentrated in a small part of the Yellow River headwaters. (2) There is a significant positive correlation between FVC and precipitation (Pre) and temperature (Tem) under four scenarios in the Yellow River Basin from 2022 to 2100. Under the same scenario, the annual average temperature can be considered as the dominant factor of FVC change in the Yellow River Basin. Under different scenarios, the impact of climate change on FVC under the high emission scenarios is greater than that under the low emission scenarios. This study will help to better understand the response of vegetation to climate change and provide a scientific basis for formulating ecological protection measures to cope with future climate change in the Yellow River Basin

Sparse Recovery for Bistatic MIMO Radar Imaging in the Presence of Array Gain Uncertainties

A sparse recovery based transmit-receive angle imaging scheme is proposed for bistatic multiple-input multiple-output (MIMO) radar. The redundancy of the transmit and receive angles in the same range cell is exploited to construct the sparse model. The imaging is then performed by compressive sensing method with consideration of both the transmit and receive array gain uncertainties. An additional constraint is imposed on the inverse of the transmit and receive array gain errors matrices to make the optimization problem of the CS solvable. The image of the targets can be reconstructed using small number of snapshots in the case of large array gain uncertainties. Simulation results confirm the effectiveness of the proposed scheme

Effects of magnetic field pretreatment and chloride salt stress on physio-biochemical changes and γ-aminobutyric acid accumulation in germinated brown rice

Germinated brown rice is a staple food with high nutritional value and market prospects. Gamma-Aminobutyric Acid (GABA), abundantly present in germinated brown rice, has attracted significant attention due to its multiple active functions on the human body. This study aimed to enrich GABA in germinated brown rice by using static magnetic field pretreatment and NaCl, CaCl2 and KCl stress. After selecting Nanjing9108, which had the highest GABA content among the nine cultivars, a single-factor experiment was conducted and optimized the pretreatment condition as 10 mT static magnetic field for 40 min. Under this condition, the GABA content in brown rice germinated for 36 h was 66.35 mg/100 g, which was 13.88% higher than the control group. Simultaneously, the germination rate and early growth of germinated brown rice were also promoted. The optimal combination of culture medium for GABA enrichment obtained by response surface experimental design was NaCl 37.23 mmol/L, CaCl2 4.71 mmol/L, and KCl 5.75 mmol/L, with a GABA content of 69.783 mg/100 g. Under this condition, the changes in nutrients and the expression of glutamic acid decarboxylase (GAD) and GABA transaminase (GABA-T) related genes during the 0-48 h germination process of brown rice were studied. The relative expression of GAD was promoted and the relative expression of GABA-T was inhibited, resulting in the accumulation of GABA. This indicates that the combination of static magnetic field and salt treatment is an effective method to increase the GABA content in germinated brown rice

Identification of genes regulated by Wnt/β-catenin pathway and involved in apoptosis via microarray analysis

BACKGROUND: Wnt/β-catenin pathway has critical roles in development and oncogenesis. Although significant progress has been made in understanding the downstream signaling cascade of this pathway, little is known regarding Wnt/β-catenin pathway modification of the cellular apoptosis. METHODS: To identify potential genes regulated by Wnt/β-catenin pathway and involved in apoptosis, we used a stably integrated, inducible RNA interference (RNAi) vector to specific inhibit the expression and the transcriptional activity of β-catenin in HeLa cells. Meanwhile, we designed an oligonucleotide microarray covering 1384 apoptosis-related genes. Using oligonucleotide microarrays, a series of differential expression of genes was identified and further confirmed by RT-PCR. RESULTS: Stably integrated inducible RNAi vector could effectively suppress β-catenin expression and the transcriptional activity of β-catenin/TCF. Meanwhile, depletion of β-catenin in this manner made the cells more sensitive to apoptosis. 130 genes involved in some important cell-apoptotic pathways, such as PTEN-PI3K-AKT pathway, NF-κB pathway and p53 pathway, showed significant alteration in their expression level after the knockdown of β-catenin. CONCLUSION: Coupling RNAi knockdown with microarray and RT-PCR analyses proves to be a versatile strategy for identifying genes regulated by Wnt/β-catenin pathway and for a better understanding the role of this pathway in apoptosis. Some of the identified β-catenin/TCF directed or indirected target genes may represent excellent targets to limit tumor growth