Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection

Arbitrary-oriented object detection is a relatively emerging but challenging task. Although remarkable progress has been made, there still remain many unsolved issues due to the large diversity of patterns in orientation, scale, aspect ratio, and visual appearance of objects in aerial images. Most of the existing methods adopt a coarse-grained fixed label assignment strategy and suffer from the inconsistency between the classification score and localization accuracy. First, to align the metric inconsistency between sample selection and regression loss calculation caused by fixed IoU strategy, we introduce affine transformation to evaluate the quality of samples and propose a distance-based label assignment strategy. The proposed metric-aligned selection (MAS) strategy can dynamically select samples according to the shape and rotation characteristic of objects. Second, to further address the inconsistency between classification and localization, we propose a critical feature sampling (CFS) module, which performs localization refinement on the sampling location for classification task to extract critical features accurately. Third, we present a scale-controlled smooth $L_1$ loss (SC-Loss) to adaptively select high quality samples by changing the form of regression loss function based on the statistics of proposals during training. Extensive experiments are conducted on four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016, and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed detector

Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval

In this work, by re-examining the "matching" nature of Anomaly Detection (AD), we propose a new AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed. In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion. Given a test sample, the top-K most similar training images are first selected based on a robust histogram matching process. Secondly, the nearest neighbor of each test patch is retrieved over the similar geometrical locations on those "global nearest neighbors", by using a carefully trained local metric. Finally, the anomaly score of each test image patch is calculated based on the distance to its "local nearest neighbor" and the "non-background" probability. The proposed method is termed "Cascade Patch Retrieval" (CPR) in this work. Different from the conventional patch-matching-based AD algorithms, CPR selects proper "targets" (reference images and locations) before "shooting" (patch-matching). On the well-acknowledged MVTec AD, BTAD and MVTec-3D AD datasets, the proposed algorithm consistently outperforms all the comparing SOTA methods by remarkable margins, measured by various AD metrics. Furthermore, CPR is extremely efficient. It runs at the speed of 113 FPS with the standard setting while its simplified version only requires less than 1 ms to process an image at the cost of a trivial accuracy drop. The code of CPR is available at https://github.com/flyinghu123/CPR.Comment: 13 pages,8 figure

CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGoDial, new challenging and comprehensive Chinese benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources: 1) a slot-based dialog (SBD) dataset with table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed knowledge. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing. The proposed experimental settings include the combinations of training with either the entire training set or a few-shot training set, and testing with either the standard test set or a hard test subset, which can assess model capabilities in terms of general prediction, fast adaptability and reliable robustness.Comment: EMNLP 202

An Efficient Approach to Solve the Large-Scale Semidefinite Programming Problems

Solving the large-scale problems with semidefinite programming (SDP) constraints is of great importance in modeling and model reduction of complex system, dynamical system, optimal control, computer vision, and machine learning. However, existing SDP solvers are of large complexities and thus unavailable to deal with large-scale problems. In this paper, we solve SDP using matrix generation, which is an extension of the classical column generation. The exponentiated gradient algorithm is also used to solve the special structure subproblem of matrix generation. The numerical experiments show that our approach is efficient and scales very well with the problem dimension. Furthermore, the proposed algorithm is applied for a clustering problem. The experimental results on real datasets imply that the proposed approach outperforms the traditional interior-point SDP solvers in terms of efficiency and scalability

Relational Topology-based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

ABSTRACTPredicting interactions between drugs and target proteins has become an essential task in the drug discovery process. Although the method of validation via wet-lab experiments has become available, experimental methods for drug-target interaction (DTI) identification remain either time consuming or heavily dependent on domain expertise. Therefore, various computational models have been proposed to predict possible interactions between drugs and target proteins. However, most prediction methods do not consider the topological structures characteristics of the relationship. In this paper, we propose a relational topology-based heterogeneous network embedding method to predict drug-target interactions, abbreviated as RTHNE_ DTI. We first construct a heterogeneous information network based on the interaction between different types of nodes, to enhance the ability of association discovery by fully considering the topology of the network. Then drug and target protein nodes can be represented by the other types of nodes. According to the different topological structure of the relationship between the nodes, we divide the relationship in the heterogeneous network into two categories and model them separately. Extensive experiments on the real-world drug datasets, RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods. RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs

Pretreating poplar cuttings with low nitrogen ameliorates salt stress responses by increasing stored carbohydrates and priming stress signaling pathways

Soil salinity is a widespread stress in semi-arid forests worldwide, but how to manage nitrogen (N) nutrition to improve plant saline tolerance remains unclear. Here, the cuttings of a widely distributed poplar from central Asia, Populus russikki Jabl., were exposed to either normal or low nitrogen (LN) concentrations for two weeks in semi-controlled greenhouse, and then they were added with moderate salt solution or not for another two weeks to evaluate their physiological, biochemical, metabolites and transcriptomic profile changes. LN-pretreating alleviated the toxicity caused by the subsequent salt stress in the poplar plants, demonstrated by a significant reduction in the influx of Na+ and Cl- and improvement of the K+/Na+ ratio. The other salt-stressed traits were also ameliarated, indicated by the variations of chlorophyll content, PSII photochemical activity and lipid peroxidation. Stress alleviation resulted from two different processes. First, LN pretreatment caused a significant increase of non-structural carbohydrates (NSC), allowed for an increased production of osmolytes and a higher potential fueling ion transport under subsequent salt condition, along with increased transcript levels of the cation/H+ ATPase. Second, LN pretreatment enhanced the transcript levels of stress signaling components and phytohormones pathway as well as antioxidant enzyme activities. The results indicate that early restrictions of N supply could enhance posterior survival under saline stress in poplar plants, which is important for plantation programs and restoration activities in semi-arid areas.This research was supported by Natural Science Foundation of China ( 31770644 and 31270660 ), Project of Innovation research team in Sichuan Education Administration in China (No. 13TD0023 )

Investigating dosimetric effect of rotational setup errors in IMPT planning of synchronous bilateral lung cancer

Purpose: The purpose of this study is to evaluate the dosimetric effect of rotational setup errors on the synchronous bi-lateral lung cancer plans generated by the intensity modulated proton therapy (IMPT) technique.Methods: The original IMPT plans were generated in for the left planning target volume (PTV) and right PTV of the left lung and right lung, respectively. Each plan was generated using two beams (lateral and posterior-anterior) with an isocenter placed at the center of the corresponding PTV. The IMPT plans were optimized for a total dose of 74 Gy[RBE] prescribed to each PTV with 2 Gy(RBE) per fraction. Original plans were recalculated by introducing simulated rotational errors. For each PTV, 18 rotational plans (±1⁰, ±2⁰, and ±3⁰) for each of the yaw, roll, and pitch rotations were generated. Results: Rotational errors caused the reduction in the clinical target volume (CTV) and PTV coverage in new rotational IMPT plans when compared to the original IMPT lung plans. The CTV D99 was reduced by up to 13.3%, 9.1%, and 5.9% for the yaw (+3⁰), roll (-3⁰), and pitch (+3⁰), respectively. The PTV D95 was reduced by up to 8.7%, 7.3%, and 4.6% for the yaw (+3⁰), roll (-3⁰), and pitch (+3⁰), respectively. The PTV V100 showed the highest deviation with a reduction of dose coverage by up to 40.1%, 31.8%, and 33.9% for the yaw (-3⁰), roll (-3⁰), and pitch (+3⁰) respectively. Conclusion: The rotational setup errors with magnitude of ≥2⁰ can produce a significant loss of dose coverage to the target volume in the IMPT of a synchronous bi-lateral lung cancer. The yaw had the most severe impact on the dosimetric results when compared to other two rotational errors (roll and pitch)

Investigating dosimetric effect of rotational setup errors in IMPT planning of synchronous bilateral lung cancer

Purpose: The purpose of this study is to evaluate the dosimetric effect of rotational setup errors on the synchronous bi-lateral lung cancer plans generated by the intensity modulated proton therapy (IMPT) technique.Methods: The original IMPT plans were generated in for the left planning target volume (PTV) and right PTV of the left lung and right lung, respectively. Each plan was generated using two beams (lateral and posterior-anterior) with an isocenter placed at the center of the corresponding PTV. The IMPT plans were optimized for a total dose of 74 Gy[RBE] prescribed to each PTV with 2 Gy(RBE) per fraction. Original plans were recalculated by introducing simulated rotational errors. For each PTV, 18 rotational plans (±1⁰, ±2⁰, and ±3⁰) for each of the yaw, roll, and pitch rotations were generated. Results: Rotational errors caused the reduction in the clinical target volume (CTV) and PTV coverage in new rotational IMPT plans when compared to the original IMPT lung plans. The CTV D99 was reduced by up to 13.3%, 9.1%, and 5.9% for the yaw (+3⁰), roll (-3⁰), and pitch (+3⁰), respectively. The PTV D95 was reduced by up to 8.7%, 7.3%, and 4.6% for the yaw (+3⁰), roll (-3⁰), and pitch (+3⁰), respectively. The PTV V100 showed the highest deviation with a reduction of dose coverage by up to 40.1%, 31.8%, and 33.9% for the yaw (-3⁰), roll (-3⁰), and pitch (+3⁰) respectively. Conclusion: The rotational setup errors with magnitude of ≥2⁰ can produce a significant loss of dose coverage to the target volume in the IMPT of a synchronous bi-lateral lung cancer. The yaw had the most severe impact on the dosimetric results when compared to other two rotational errors (roll and pitch).</p