Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization

The discovery of drug-target interactions (DTIs) is a very promising area of research with great potential. In general, the identification of reliable interactions among drugs and proteins can boost the development of effective pharmaceuticals. In this work, we leverage random walks and matrix factorization techniques towards DTI prediction. In particular, we take a multi-layered network perspective, where different layers correspond to different similarity metrics between drugs and targets. To fully take advantage of topology information captured in multiple views, we develop an optimization framework, called MDMF, for DTI prediction. The framework learns vector representations of drugs and targets that not only retain higher-order proximity across all hyper-layers and layer-specific local invariance, but also approximates the interactions with their inner product. Furthermore, we propose an ensemble method, called MDMF2A, which integrates two instantiations of the MDMF model that optimize surrogate losses of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC), respectively. The empirical study on real-world DTI datasets shows that our method achieves significant improvement over current state-of-the-art approaches in four different settings. Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs.

Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation

Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT), which often leads to inferior tracking results due to the missing detected objects. The common practice in multi-object tracking is re-identifying the missed objects after their reappearance. Though tracking performance can be boosted by the re-identification, the annotation of identity is required to train the model. In addition, such practice of re-identification still can not track those highly occluded objects when they are missed by the detector. In this paper, we focus on online multi-object tracking and design two novel modules, the unsupervised re-identification learning module and the occlusion estimation module, to handle these problems. Specifically, the proposed unsupervised re-identification learning module does not require any (pseudo) identity information nor suffer from the scalability issue. The proposed occlusion estimation module tries to predict the locations where occlusions happen, which are used to estimate the positions of missed objects by the detector. Our study shows that, when applied to state-of-the-art MOT methods, the proposed unsupervised re-identification learning is comparable to supervised re-identification learning, and the tracking performance is further improved by the proposed occlusion estimation module.

Stochastic Weight Averaging Revisited

Stochastic weight averaging (SWA) is recognized as a simple while one effective approach to improve the generalization of stochastic gradient descent (SGD) for training deep neural networks (DNNs). A common insight to explain its success is that averaging weights following an SGD process equipped with cyclical or high constant learning rates can discover wider optima, which then lead to better generalization. We give a new insight that does not concur with the above one. We characterize that SWA's performance is highly dependent on to what extent the SGD process that runs before SWA converges, and the operation of weight averaging only contributes to variance reduction. This new insight suggests practical guides on better algorithm design. As an instantiation, we show that following an SGD process with insufficient convergence, running SWA more times leads to continual incremental benefits in terms of generalization. Our findings are corroborated by extensive experiments across different network architectures, including a baseline CNN, PreResNet-164, WideResNet-28-10, VGG16, ResNet-50, ResNet-152, DenseNet-161, and different datasets including CIFAR-{10,100}, and Imagenet.

Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems

For autonomous driving, an essential task is to detect surrounding objects accurately. To this end, most existing systems use optical devices, including cameras and light detection and ranging (LiDAR) sensors, to collect environment data in real time. In recent years, many researchers have developed advanced machine learning models to detect surrounding objects. Nevertheless, the aforementioned optical devices are vulnerable to optical signal attacks, which could compromise the accuracy of object detection. To address this critical issue, we propose a framework to detect and identify sensors that are under attack. Specifically, we first develop a new technique to detect attacks on a system that consists of three sensors. Our main idea is to: 1) use data from three sensors to obtain two versions of depth maps (i.e., disparity) and 2) detect attacks by analyzing the distribution of disparity errors. In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method. Based on the detection scheme, we further develop an identification model that is capable of identifying up to n-2 attacked sensors in a system with one LiDAR and n cameras. We prove the correctness of our identification scheme and conduct experiments to show the accuracy of our identification method. Finally, we investigate the overall sensitivity of our framework.

Unsupervised Finetuning

This paper studies "unsupervised finetuning", the symmetrical problem of the well-known "supervised finetuning". Given a pretrained model and small-scale unlabeled target data, unsupervised finetuning is to adapt the representation pretrained from the source domain to the target domain so that better transfer performance can be obtained. This problem is more challenging than the supervised counterpart, as the low data density in the small-scale target data is not friendly for unsupervised learning, leading to the damage of the pretrained representation and poor representation in the target domain. In this paper, we find the source data is crucial when shifting the finetuning paradigm from supervise to unsupervise, and propose two simple and effective strategies to combine source and target data into unsupervised finetuning: "sparse source data replaying", and "data mixing". The motivation of the former strategy is to add a small portion of source data back to occupy their pretrained representation space and help push the target data to reside in a smaller compact space; and the motivation of the latter strategy is to increase the data density and help learn more compact representation. To demonstrate the effectiveness of our proposed ``unsupervised finetuning'' strategy, we conduct extensive experiments on multiple different target datasets, which show better transfer performance than the naive strategy.

Asymmetric Graph Representation Learning

Despite the enormous success of graph neural networks (GNNs), most existing GNNs can only be applicable to undirected graphs where relationships among connected nodes are two-way symmetric (i.e., information can be passed back and forth). However, there is a vast amount of applications where the information flow is asymmetric, leading to directed graphs where information can only be passed in one direction. For example, a directed edge indicates that the information can only be conveyed forwardly from the start node to the end node, but not backwardly. To accommodate such an asymmetric structure of directed graphs within the framework of GNNs, we propose a simple yet remarkably effective framework for directed graph analysis to incorporate such one-way information passing. We define an incoming embedding and an outgoing embedding for each node to model its sending and receiving features respectively. We further develop two steps in our directed GNN model with the first one to aggregate/update the incoming features of nodes and the second one to aggregate/update the outgoing features. By imposing the two roles for each node, the likelihood of a directed edge can be calculated based on the outgoing embedding of the start node and the incoming embedding of the end node. The log-likelihood of all edges plays a natural role of regularization for the proposed model, which can alleviate the over-smoothing problem of the deep GNNs. Extensive experiments on multiple real-world directed graphs demonstrate outstanding performances of the proposed model in both node-level and graph-level tasks.

ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation

Co-occurrent visual pattern makes aggregating contextual information a common paradigm to enhance the pixel representation for semantic image segmentation. The existing approaches focus on modeling the context from the perspective of the whole image, i.e., aggregating the image-level contextual information. Despite impressive, these methods weaken the significance of the pixel representations of the same category, i.e., the semantic-level contextual information. To address this, this paper proposes to augment the pixel representations by aggregating the image-level and semantic-level contextual information, respectively. First, an image-level context module is designed to capture the contextual information for each pixel in the whole image. Second, we aggregate the representations of the same category for each pixel where the category regions are learned under the supervision of the ground-truth segmentation. Third, we compute the similarities between each pixel representation and the image-level contextual information, the semantic-level contextual information, respectively. At last, a pixel representation is augmented by weighted aggregating both the image-level contextual information and the semantic-level contextual information with the similarities as the weights. Integrating the image-level and semantic-level context allows this paper to report state-of-the-art accuracy on four benchmarks, i.e., ADE20K, LIP, COCOStuff and Cityscapes.

Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks

This paper is concerned with image classification based on deep convolutional neural networks (CNNs). The focus is centered around the following question: given a set of candidate CNN models, how to select the right one that has the best generalization property for the current task? Present model selection methods require access to a batch of labeled data for defining a performance metric, such as the cross-entropy loss, the classification error rate, the negative log-likelihood, and so on. In many practical cases, however, labeled data are not available in time as labeling itself is a time-consuming and expensive task. To this end, this paper presents an approach to CNN model selection using only unlabeled data. This method is developed based on a principle termed consistent relative confidence (CRC). The effectiveness and efficiency of the presented method are demonstrated by extensive experimental studies based on datasets MNIST and FasionMNIST.

Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach

Social media such as Instagram and Twitter have become important platforms for marketing and selling illicit drugs. Detection of online illicit drug trafficking has become critical to combat the online trade of illicit drugs. However, the legal status often varies spatially and temporally; even for the same drug, federal and state legislation can have different regulations about its legality. Meanwhile, more drug trafficking events are disguised as a novel form of advertising commenting leading to information heterogeneity. Accordingly, accurate detection of illicit drug trafficking events (IDTEs) from social media has become even more challenging. In this work, we conduct the first systematic study on fine-grained detection of IDTEs on Instagram. We propose to take a deep multimodal multilabel learning (DMML) approach to detect IDTEs and demonstrate its effectiveness on a newly constructed dataset called multimodal IDTE(MM-IDTE). Specifically, our model takes text and image data as the input and combines multimodal information to predict multiple labels of illicit drugs. Inspired by the success of BERT, we have developed a self-supervised multimodal bidirectional transformer by jointly fine-tuning pretrained text and image encoders. We have constructed a large-scale dataset MM-IDTE with manually annotated multiple drug labels to support fine-grained detection of illicit drugs. Extensive experimental results on the MM-IDTE dataset show that the proposed DMML methodology can accurately detect IDTEs even in the presence of special characters and style changes attempting to evade detection.

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion

Illicit drug trafficking via social media sites such as Instagram has become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge due to the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Totally nearly 4,000 user accounts, of which over 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.