药研云医生——基于深度学习的药物疗效预测系统

The drug efficacy prediction system can link the pharmacological big data with the disease target protein through in-depth learning and other methods, and then build an artificial intelligence model of drugs and diseases, explore the originally seemingly “irrelevant” diseases and syndromes and the potential efficacy of drugs, and greatly reduce the drug development cost and time limit through drug reuse

YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective

Unmanned Aerial Vehicles (UAVs), specifically drones equipped with remote sensing object detection technology, have rapidly gained a broad spectrum of applications and emerged as one of the primary research focuses in the field of computer vision. Although UAV remote sensing systems have the ability to detect various objects, small-scale objects can be challenging to detect reliably due to factors such as object size, image degradation, and real-time limitations. To tackle these issues, a real-time object detection algorithm (YOLO-Drone) is proposed and applied to two new UAV platforms as well as a specific light source (silicon-based golden LED). YOLO-Drone presents several novelties: 1) including a new backbone Darknet59; 2) a new complex feature aggregation module MSPP-FPN that incorporated one spatial pyramid pooling and three atrous spatial pyramid pooling modules; 3) and the use of Generalized Intersection over Union (GIoU) as the loss function. To evaluate performance, two benchmark datasets, UAVDT and VisDrone, along with one homemade dataset acquired at night under silicon-based golden LEDs, are utilized. The experimental results show that, in both UAVDT and VisDrone, the proposed YOLO-Drone outperforms state-of-the-art (SOTA) object detection methods by improving the mAP of 10.13% and 8.59%, respectively. With regards to UAVDT, the YOLO-Drone exhibits both high real-time inference speed of 53 FPS and a maximum mAP of 34.04%. Notably, YOLO-Drone achieves high performance under the silicon-based golden LEDs, with a mAP of up to 87.71%, surpassing the performance of YOLO series under ordinary light sources. To conclude, the proposed YOLO-Drone is a highly effective solution for object detection in UAV applications, particularly for night detection tasks where silicon-based golden light LED technology exhibits significant superiority

Boundary-Aware Proposal Generation Method for Temporal Action Localization

The goal of Temporal Action Localization (TAL) is to find the categories and temporal boundaries of actions in an untrimmed video. Most TAL methods rely heavily on action recognition models that are sensitive to action labels rather than temporal boundaries. More importantly, few works consider the background frames that are similar to action frames in pixels but dissimilar in semantics, which also leads to inaccurate temporal boundaries. To address the challenge above, we propose a Boundary-Aware Proposal Generation (BAPG) method with contrastive learning. Specifically, we define the above background frames as hard negative samples. Contrastive learning with hard negative mining is introduced to improve the discrimination of BAPG. BAPG is independent of the existing TAL network architecture, so it can be applied plug-and-play to mainstream TAL models. Extensive experimental results on THUMOS14 and ActivityNet-1.3 demonstrate that BAPG can significantly improve the performance of TAL

FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems

Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, the federated learning (FL) framework allows multiple users to train a global model on the basis of respecting individual data privacy. However, it has not yet been systematically evaluated how robust FL-based NIDSs are against existing privacy attacks under existing defenses. To address this issue, we propose two privacy evaluation metrics designed for FL-based NIDSs, including (1) privacy score that evaluates the similarity between the original and recovered traffic features using reconstruction attacks, and (2) evasion rate against NIDSs using Generative Adversarial Network-based adversarial attack with the reconstructed benign traffic. We conduct experiments to show that existing defenses provide little protection that the corresponding adversarial traffic can even evade the SOTA NIDS Kitsune. To defend against such attacks and build a more robust FL-based NIDS, we further propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. It achieves both high utility by minimizing the gradient distance and strong privacy protection by maximizing the input distance. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection with up to 7 times higher privacy score, while maintaining model accuracy loss within 3% under optimal parameter combination.Comment: 14 pages, 9 figures, submitted to TIF

VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal finetuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset.Comment: CVPR 2023, https://github.com/google-research/google-research/tree/master/vil

A method review of the climate change impact on crop yield

Climate change significantly impacts global agricultural production, giving rise to considerable uncertainties. To explore these climate impacts, three independent methods have been employed: manipulated experiments, process-based crop models, and empirical statistical models. However, the uncertainty stemming from the use of different methods has received insufficient attention, and its implications remain unclear, necessitating a systematic review. In this study, we conducted a comprehensive review of numerous previous studies to summarize the historic development and current status of each method. Through a method comparison, we identified their respective strengths, limitations, and ideal areas of application. Additionally, we outlined potential prospects and suggested directions for future improvements, including clarifying the response mechanisms, updating simulation technologies, and developing multi-method ensembles. By addressing the knowledge gap regarding method differences, this review could contribute to a more accurate assessment of climate impacts on agriculture

Global Three-Dimensional Radiation Magnetohydrodynamic Simulations of Accretion onto a Stellar Mass Black Hole at Sub- and Near-critical Accretion Rates

We present global 3D radiation magnetohydrodynamical simulations of accretion onto a 6.62 solar mass black hole with quasi-steady state accretion rates reaching 0.016 to 0.9 times the critical accretion rate, which is defined as the accretion rate to power the Eddington luminosity assuming a 10% radiative efficiency, in different runs. The simulations show no sign of thermal instability over hundreds of thermal timescales at 10 $r_{\rm g}$. The energy dissipation happens close to the mid-plane in the near-critical runs and near the disk surface in the low accretion rate run. The total radiative luminosity inside $\sim$20 $r_{\rm g}$ is about 1% to 30% the Eddington limit, with a radiative efficiency of about 6% and 3%, respectively, in the sub- and near-critical accretion regimes. In both cases, self-consistent turbulence generated by the magnetorotational instability (MRI) leads to angular momentum transfer, and the disk is supported by magnetic pressure. Outflows from the central low-density funnel with a terminal velocity of $\sim$0.1$c$ are seen only in the near-critical runs. We conclude that these magnetic pressure dominated disks are thermally stable and thicker than the $\alpha$ disk, and the effective temperature profiles are much flatter than that in the $\alpha$ disks. The magnetic pressure of these disks are comparable within an order of magnitude with the previous analytical magnetic pressure dominated disk model.Comment: 17 pages, 13 figures, 3 tables, accepted for publication in Ap

Plasma kinetics: Discrete Boltzmann modelling and Richtmyer-Meshkov instability

A discrete Boltzmann model (DBM) for plasma kinetics is proposed. The constructing of DBM mainly considers two aspects. The first is to build a physical model with sufficient physical functions before simulation. The second is to present schemes for extracting more valuable information from massive data after simulation. For the first aspect, the model is equivalent to a magnetohydrodynamic model plus a coarse-grained model for the most relevant TNE behaviors including the entropy production rate. A number of typical benchmark problems including Orszag-Tang (OT) vortex problem are used to verify the physical functions of DBM. For the second aspect, the DBM use non-conserved kinetic moments of (f-feq) to describe non-equilibrium state and behaviours of complex system. The OT vortex problem and the Richtmyer-Meshkov instability (RMI) are practical applications of the second aspect. For RMI with interface inverse and re-shock process, it is found that, in the case without magnetic field, the non-organized momentum flux shows the most pronounced effects near shock front, while the non-organized energy flux shows the most pronounced behaviors near perturbed interface. The influence of magnetic field on TNE effects shows stages: before the interface inverse, the TNE strength is enhanced by reducing the interface inverse speed; while after the interface inverse, the TNE strength is significantly reduced. Both the global average TNE strength and entropy production rate contributed by non-organized energy flux can be used as physical criteria to identify whether or not the magnetic field is sufficient to prevent the interface inverse.Comment: 20 pages, 15 figure