SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction.

Drug-target binding affinity prediction is a fundamental task for drug discovery and has been studied for decades. Most methods follow the canonical paradigm that processes the inputs of the protein (target) and the ligand (drug) separately and then combines them together. In this study we demonstrate, surprisingly, that a model is able to achieve even superior performance without access to any protein-sequence-related information. Instead, a protein is characterized completely by the ligands that it interacts. Specifically, we treat different proteins separately, which are jointly trained in a multi-head manner, so as to learn a robust and universal representation of ligands that is generalizable across proteins. Empirical evidences show that the novel paradigm outperforms its competitive sequence-based counterpart, with the Mean Squared Error (MSE) of 0.4261 versus 0.7612 and the R-Square of 0.7984 versus 0.6570 compared with DeepAffinity. We also investigate the transfer learning scenario where unseen proteins are encountered after the initial training, and the cross-dataset evaluation for prospective studies. The results reveals the robustness of the proposed model in generalizing to unseen proteins as well as in predicting future data. Source codes and data are available at https://github.com/huzqatpku/SAM-DTA.

High-extinction-ratio low-voltage dual-racetrack modulator for low-power DAC-less PAM-4 modulation.

In a parallel-coupled dual-racetrack modulator, resonant light in two resonators can interfere with each other. In lieu of critical coupling, such interference is capable of producing high extinction ratios (ERs) for high-speed modulation. Experiments demonstrate ERs of over 9 dB at 50 Gb/s and 40-50% modulation depth enhancement compared with a single-resonator modulator at 50-56 Gb/s with a peak-to-peak driving voltage of 2.3 V. Furthermore, joint modulation of two racetracks offers the possibility to combine two separate bits of driving signals to generate four-level pulse-amplitude modulation (PAM-4) without an external digital-to-analog converter (DAC). To tackle the complex multi-variable transfer function of this modulator, a procedure for configuring PAM-4 states is theoretically developed. Finally, we demonstrate 100 Gb/s PAM-4 with an electro-optic modulation power consumption of < 40 fJ/bit for this device.

Study on landscape ecological risk assessment of Hooded Crane breeding and overwintering habitat.

Xinqing Wetland and Shengjin Lake Wetland are the main habitats of Hooded Crane in China. However, in recent years, the irrational land use situation of occupied wetlands has occurred frequently, resulting in the destruction of the breeding of the Hooded Crane population and the destruction of the wintering habitat. The paper takes Xinqing Wetland and Shengjin Lake Wetland as the research area, and uses landscape ecology and geostatistics as the theoretical basis to reveal the changes of land landscape in the study area, and analyze the main breeding and wintering habitat types of Hooded Crane. The landscape ecological risk assessment model is constructed by the landscape pattern index method, and the ecological risk in the study area is divided into five levels: extremely low ecological risk, low ecological risk, medium ecological risk, high ecological risk and extremely high ecological risk. The landscape ecological risk of the study area is analyzed from the aspects of single landscape type, spatial distribution characteristics and time series changes. Overall, from 1986 to 2018, the landscape ecological risks of Xinqing Wetland and Shengjin Lake Wetland all showed an upward trend, both from the low ecological risk level in 1986 to the middle ecological risk level in 2018. The ecological risk value of Xinqing Wetland increased from 0.3777 in 1986 to 0.5882 in 2018; the ecological risk value of Shengjin Lake wetland landscape increased from 0.2706 in 1986 to 0.5709 in 2018. The growth rate of the Shengjin Lake wetland is higher than that of the Xinqing Wetland. In order to better protect the ecological environment of the wetland and protect the habitat of the Hooded Crane, this paper will propose corresponding suggestions and countermeasures.

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification.

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system.

Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.

Incoherent control of locally controllable quantum systems.

An incoherent control scheme for state control of locally controllable quantum systems is proposed. This scheme includes three steps: (1) amplitude amplification of the initial state by a suitable unitary transformation, (2) projective measurement of the amplified state, and (3) final optimization by a unitary controlled transformation. The first step increases the amplitudes of some desired eigenstates and the corresponding probability of observing these eigenstates, the second step projects, with high probability, the amplified state into a desired eigenstate, and the last step steers this eigenstate into the target state. Within this scheme, two control algorithms are presented for two classes of quantum systems. As an example, the incoherent control scheme is applied to the control of a hydrogen atom by an external field. The results support the suggestion that projective measurements can serve as an effective control and local controllability information can be used to design control laws for quantum systems. Thus, this scheme establishes a subtle connection between control design and controllability analysis of quantum systems and provides an effective engineering approach in controlling quantum systems with partial controllability information.