Track-track association (TTA) in complex environment for multi-sensor fusion is a challenging topic due to the uncertainty of measurements, biased data, mismatch caused by different resolution etc. In this work, we proposed an end-to-end deep learning model, named the spatial-temporal attention network (STAN) for TTA tasks in complex scenarios. Three modules in the backbone of STAN for intra-track and inter-track feature representation are based on self-attention mechanism, e.g., the motion mode encoder (MME) module to encode the motion pattern of single moving targets, the spatial structure extraction (SSE) module for capturing the inter-track spatial interaction relation of an individual sensor, and the spatialtemporal fusion (STF) module for intra-track modeling on temporal dimensions, respectively. A relation reasoning head (RRH) is built for track-track relation reasoning based on the encoded track features. Experimental results on different tasks show that our proposed method achieved superior performance for track-track association compared with previous methods.

Liver cancer is one of the most common malignant diseases worldwide. Segmentation and reconstruction of liver tumors and vessels in CT images can provide convenience for physicians in preoperative planning and surgical intervention. In this paper, we introduced a TransFusionNet framework, which consists of a semantic feature extraction module, a local spatial feature extraction module, an edge feature extraction module, and a multi-scale feature fusion module to achieve fine-grained segmentation of liver tumors and vessels. In addition, we applied the transfer learning approach to pre-train using public datasets and then fine-tune the model to further improve the fitting effect. Furthermore, we proposed an intelligent quantization scheme to compress the model weights and achieved high performance inference on JetsonTX2. The TransFusionNet framework achieved mean IoU of 0.854 in vessel segmentation task, and achieved mean IoU of 0.927 in liver tumor segmentation task. When profiling the Computational Performance of the quantized inference, our quantized model achieved 4TFLOPs on Node with NVIDIA RTX3090 and 132GFLOPs on JetsonTX2. This unprecedented segmentation effect solves the accuracy and performance bottleneck of automated segmentation to a certain extent.

The task of a deep learning (DL) program is to train a model with high precision and apply it to different scenarios. A DL program often involves massive numerical calculations. Therefore, the robustness and stability of the numerical calculations are dominant in the quality of DL programs. Indeed, numerical bugs are common in DL programs, producing NaN (Not-a-Number) and INF (Infinite). A numerical bug may render the DL models inaccurate, causing the DL applications unusable. In this work, we conduct the first empirical study on numerical bugs in DL programs by analyzing the programs implemented on the top of two popular DL libraries (i.e., TensorFlow and PyTorch). Specifically, We collect a dataset of 400 numerical bugs in DL programs. Then, we classify these numerical bugs into nine categories based on their root causes and summarize two findings. Finally, we provide the implications of our study on detecting numerical bugs in DL programs.

Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug-drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects. Traditional DDIs prediction methods use only drug sequence information or drug graph information, which ignores information about the position of atoms and edges in the spatial structure. In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information. Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the Siamese network architecture to serve as the architecture of Molormer, which can make full use of the limited data to train the model for better performance and also limit the differences to some extent between networks dealing with drug features. Experiments show that our proposed method outperforms state-of-the-art methods in Accuracy, Precision, Recall and F1 on multi-label DDIs dataset. In the case study section, we used Molormer to make predictions of new interactions for the drugs Aliskiren, Selexipag and Vorapaxar and validated parts of the predictions. Code and models are available at https://github.com/IsXudongZhang/Molormer.

Traditional Chinese medicines (TCMs) have been used to treat diseases for thousands of years. The application of traditional Chinese medicine provides new ideas for the treatment of cancer and other intractable diseases. Phosphoinositide-3kinase (PI3K) pathway is an important way to regulate tumor cells, such as cervical cancer. Deep learning provides a powerful application in calculating interactions between drugs and targets. In this study, we try to use the method of deep learning to reposition molecules of TCMs and 21 targets on PI3K pathway, and predict the TCMs that can regulate PI3K pathway, so as to achieve the purpose of cancer treatment. A deep convolutional neural network (DCNN) is constructed and trained on KIBA dataset. The accuracy of predicting the binding affinity of drug-target pairs is 85.3%. DCNN ranked 433 molecules of 35 TCMs with 21 PI3K pathway target proteins. We find that Gancao and Huangqin have strong binding affinity with more than half of PI3K pathway targets. Meanwhile, Renshen, Zhizi, Mahuang, etc. are also effective on multiple targets.

Numerical computation is dominant in deep learning (DL) programs. Consequently, numerical bugs are one of the most prominent kinds of defects in DL programs. Numerical bugs can lead to exceptional values such as NaN (Not-a-Number) and INF (Infinite), which can be propagated and eventually cause crashes or invalid outputs. They occur when special inputs cause invalid parameter values at internal mathematical operations such as log(). In this paper, we propose the first dynamic technique, called GRIST, which automatically generates a small input that can expose numerical bugs in DL programs. GRIST piggy-backs on the built-in gradient computation functionalities of DL infrastructures. Our evaluation on 63 real-world DL programs shows that GRIST detects 78 bugs including 56 unknown bugs. By submitting them to the corresponding issue repositories, eight bugs have been confirmed and three bugs have been fixed. Moreover, GRIST can save 8.79X execution time to expose numerical bugs compared to running original programs with its provided inputs. Compared to the state-of-the-art technique DEBAR (which is a static technique), DEBAR produces 12 false positives and misses 31 true bugs (of which 30 bugs can be found by GRIST), while GRIST only misses one known bug in those programs and no false positive. The results demonstrate the effectiveness of GRIST.

We propose a scheme of multi-channel quantum random generator based on superluminescent LED (SLED), which can use only one laser source to generate independent random bit sequences parallelly. By using dense wavelength division multiplexing (DWDM) module with 100 GHz interval, we produced 10 random bit streams in different channels, every stream's generating speed reaching 2.5 Gbps. All the random bit sequences can pass the DIEHARD randomness test and NIST-STS test, and there is no apparent correlation between every two bit streams. This multi-channel generator can satisfy the different components' need of random numbers in QKD systems and has advantages in cost of devices.

The authors present a pipeline method for detection and tracking of pixel-sized moving targets with unknown trajectories from a time sequence of highly noisy images. The pipeline target detection algorithm uses the temporal continuity of the smooth trajectories of moving targets and successfully detects and simultaneously tracks all the target trajectories by mapping them from the image sequence onto a single target frame. The pipeline method overcomes the constraint of a straight line trajectory that most other algorithms require for similar tasks. The algorithm is a complete parallel distributed processing type process, and therefore is highly time-efficient-ideal for real-time detection and tracking of arbitrary target trajectories in high-noise environments.>