Enforcing Paraphrase Generation via Controllable Latent Diffusion

Paraphrase generation aims to produce high-quality and diverse utterances of a given text. Though state-of-the-art generation via the diffusion model reconciles generation quality and diversity, textual diffusion suffers from a truncation issue that hinders efficiency and quality control. In this work, we propose \textit{L}atent \textit{D}iffusion \textit{P}araphraser~(LDP), a novel paraphrase generation by modeling a controllable diffusion process given a learned latent space. LDP achieves superior generation efficiency compared to its diffusion counterparts. It facilitates only input segments to enforce paraphrase semantics, which further improves the results without external features. Experiments show that LDP achieves improved and diverse paraphrase generation compared to baselines. Further analysis shows that our method is also helpful to other similar text generations and domain adaptations. Our code and data are available at https://github.com/NIL-zhuang/ld4pg

Moving Risk of Crowds in the Entrance Confluence Area in the Presence of Channelizing Facilities

In recent years, the measures to interfere the crowds movement with physical facilities (such as channelizing, separation railing) have become more and more common, but how they affect the crowd movement and what moving risks exist in the entrance confluence area have not been fully revealed. Therefore, this paper analyzes the moving risk of the crowds before the bottleneck entrance area, in the presence of the channelizing barriers by controllable laboratory experiments. The visual color cloud charts of the local density, speed and confusion degree of moving directions within the entrance confluence area are analyzed in the presence of different gaps (1.05m and 0.7m) channelizing barriers, to further quantify the motion risk of the crowds. The study finds that the narrower gaps of the channelizing railings, the larger area of high-risk zones, and they have clear ‘lane formation’ effect in shaping the risk zones. The both ends of the channelizing barriers are higher moving risk zones for multi-entry sides conditions, but the area before the middle channels also needs to be closely concerned when the participants entering from two opposite entering sides. The study will provide theoretical basis for evaluating the safety of the setting conditions of the channelizing barriers and conducting scientific crowd management decisions

Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture.

Color calibration is a critical step for unmanned aerial vehicle (UAV) remote sensing, especially in precision agriculture, which relies mainly on correlating color changes to specific quality attributes, e.g. plant health, disease, and pest stresses. In UAV remote sensing, the exemplar-based color transfer is popularly used for color calibration, where the automatic search for the semantic correspondences is the key to ensuring the color transfer accuracy. However, the existing attention mechanisms encounter difficulties in building the precise semantic correspondences between the reference image and the target one, in which the normalized cross correlation is often computed for feature reassembling. As a result, the color transfer accuracy is inevitably decreased by the disturbance from the semantically unrelated pixels, leading to semantic mismatch due to the absence of semantic correspondences. In this article, we proposed an unsupervised object-based attention mechanism (OBAM) to suppress the disturbance of the semantically unrelated pixels, along with a further introduced weight-adjusted Adaptive Instance Normalization (AdaIN) (WAA) method to tackle the challenges caused by the absence of semantic correspondences. By embedding the proposed modules into a photorealistic style transfer method with progressive stylization, the color transfer accuracy can be improved while better preserving the structural details. We evaluated our approach on the UAV data of different crop types including rice, beans, and cotton. Extensive experiments demonstrate that our proposed method outperforms several state-of-the-art methods. As our approach requires no annotated labels, it can be easily embedded into the off-the-shelf color transfer approaches. Relevant codes and configurations will be available at https://github.com/huanghsheng/object-based-attention-mechanis

Citrus fruit detection based on an improved YOLOv5 under natural orchard conditions.

Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions, where some specific information of citrus might be lost due to the resultant complex occlusion. Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets. To solve this problem, an improved deep learning algorithm based on YOLOv5, named IYOLOv5, was proposed for accurate detection of citrus fruits. An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network, which aims to reduce the miss detection rate. Subsequently, the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features. A coordinate attention mechanism module was then introduced into the network's detection layer. The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images. The results show that the proposed IYOLOv5 achieved the highest mean average precision (93.5%) and F1-score (95.6%), compared to the traditional deep learning models including Faster R-CNN, CenterNet, YOLOv3, YOLOv5, and YOLOv7. In particular, the proposed IYOLOv5 obtained a decrease of missed detection rate (at least 13.1%) on the specific task of detecting heavily occluded citrus, compared to other models. Therefore, the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard

Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2–9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization

Incidence and Etiology of Drug-Induced Liver Injury in Mainland China

Background & Aims: We performed a nationwide, retrospective study to determine the incidence and causes of drug-induced liver injury (DILI) in mainland China.Methods: We collected data on a total of 25,927 confirmed DILI cases, hospitalized from 2012 through 2014 at 308 medical centers in mainland China. We collected demographic, medical history, treatment, laboratory, disease severity, and mortality data from all patients. Investigators at each site were asked to complete causality assessments for each case whose diagnosis at discharge was DILI (n=29,478) according to the Roussel Uclaf Causality Assessment Method.Results: Most cases of DILI presented with hepatocellular injury (51.39%; 95% CI, 50.76–52.03), followed by mixed injury (28.30%; 95% CI, 27.73–28.87) and cholestatic injury (20.31%; 95% CI, 19.80–20.82). The leading single classes of implicated drugs were traditional Chinese medicines or herbal and dietary supplements (26.81%) and anti-tuberculosis medications (21.99%). Chronic DILI occurred in 13.00% of the cases and, although 44.40% of the hepatocellular DILI cases fulfilled Hy’s Law criteria, only 280 cases (1.08%) progressed to hepatic failure, 2 cases underwent liver transplantation (0.01%), and 102 patients died (0.39%). Among deaths, DILI was judged to have a primary role in 72 (70.59%), a contributory role in 21 (20.59%), and no role in 9 (8.82%). Assuming the proportion of DILI in the entire hospitalized population of China was represented by that observed in the 66 centers where DILI capture was complete, we estimated the annual incidence in the general population to be 23.80 per 100,000 persons (95% CI, 20.86–26.74). Only hospitalized patients were included in this analysis, so the true incidence is likely to be higher.Conclusions: In a retrospective study to determine the incidence and causes of drug-induced liver injury (DILI) in mainland China, the annual incidence in the general population was estimated to be 23.80 per 100,000 persons—higher than that reported from western countries. Traditional Chinese medicines, herbal and dietary supplements, and anti-tuberculosis drugs were the leading causes of DILI in mainland Chin

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

JUNO Sensitivity to Invisible Decay Modes of Neutrons

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $n \rightarrow { inv}$ and $nn \rightarrow { inv}$. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$.Comment: 28 pages, 7 figures, 4 table