OpenRaiser/Intern-Atlas huggingface dataset:
MedGraph: An experimental semantic information retrieval method using knowledge graph embedding for the biomedical citations indexed in PubMed
Venue: arXiv cs.IR 2021
Year: 2021

Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our method using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TFIDF based algorithm. We use a dataset extracted from PubMed, including 30 million articles' metadata such as abstracts, author information, citation information, and extracted biological entity mentions. We do that by pulling a subset of the dataset to evaluate MedGraph using predefined queries with ground truth ranked results. To our knowledge, this technique has not been explored before in biomedical information retrieval. In addition, our results provide evidence that semantic approaches to search and relevance in biomedical digital libraries that rely on knowledge graph modeling offer better search relevance results when compared with traditional approaches in terms of objective metrics.
Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer
Venue: arXiv cs.CL 2021
Year: 2021

Given the fact of a case, Legal Judgment Prediction (LJP) involves a series of sub-tasks such as predicting violated law articles, charges and term of penalty. We propose leveraging a unified text-to-text Transformer for LJP, where the dependencies among sub-tasks can be naturally established within the auto-regressive decoder. Compared with previous works, it has three advantages: (1) it fits in the pretraining pattern of masked language models, and thereby can benefit from the semantic prompts of each sub-task rather than treating them as atomic labels, (2) it utilizes a single unified architecture, enabling full parameter sharing across all sub-tasks, and (3) it can incorporate both classification and generative sub-tasks. We show that this unified transformer, albeit pretrained on general-domain text, outperforms pretrained models tailored specifically for the legal domain. Through an extensive set of experiments, we find that the best order to capture dependencies is different from human intuitions, and the most reasonable logical order for humans can be sub-optimal for the model. We further include two more auxiliary tasks: court view generation and article content prediction, showing they can not only improve the prediction accuracy, but also provide interpretable explanations for model outputs even when an error is made. With the best configuration, our model outperforms both previous SOTA and a single-tasked version of the unified transformer by a large margin.
5th Place Solution for VSPW 2021 Challenge
Venue: arXiv cs.CV 2021
Year: 2021

In this article, we introduce the solution we used in the VSPW 2021 Challenge. Our experiments are based on two baseline models, Swin Transformer and MaskFormer. To further boost performance, we adopt stochastic weight averaging technique and design hierarchical ensemble strategy. Without using any external semantic segmentation dataset, our solution ranked the 5th place in the private leaderboard. Besides, we have some interesting attempts to tackle long-tail recognition and overfitting issues, which achieves improvement on val subset. Maybe due to distribution difference, these attempts don't work on test subset. We will also introduce these attempts and hope to inspire other researchers.
Predicting Airbnb Rental Prices Using Multiple Feature Modalities
Venue: arXiv cs.LG 2021
Year: 2021

Figuring out the price of a listed Airbnb rental is an important and difficult task for both the host and the customer. For the former, it can enable them to set a reasonable price without compromising on their profits. For the customer, it helps understand the key drivers for price and also provides them with similarly priced places. This price prediction regression task can also have multiple downstream uses, such as in recommendation of similar rentals based on price. We propose to use geolocation, temporal, visual and natural language features to create a reliable and accurate price prediction algorithm.
Contextual Exploration Using a Linear Approximation Method Based on Satisficing
Venue: arXiv cs.LG 2021
Year: 2021

Deep reinforcement learning has enabled human-level or even super-human performance in various types of games. However, the amount of exploration required for learning is often quite large. Deep reinforcement learning also has super-human performance in that no human being would be able to achieve such amounts of exploration. To address this problem, we focus on the \textit{satisficing} policy, which is a qualitatively different approach from that of existing optimization algorithms. Thus, we propose Linear RS (LinRS), which is a type of satisficing algorithm and a linear extension of risk-sensitive satisficing (RS), for application to a wider range of tasks. The generalization of RS provides an algorithm to reduce the volume of exploratory actions by adopting a different approach from existing optimization algorithms. LinRS utilizes linear regression and multiclass classification to linearly approximate both the action value and proportion of action selections required in the RS calculation. The results of our experiments indicate that LinRS reduced the number of explorations and run time compared to those of existing algorithms in contextual bandit problems. These results suggest that a further generalization of satisficing algorithms may be useful for complex environments, including those that are to be handled with deep reinforcement learning.
An Informative Tracking Benchmark
Venue: arXiv cs.CV 2021
Year: 2021

Along with the rapid progress of visual tracking, existing benchmarks become less informative due to redundancy of samples and weak discrimination between current trackers, making evaluations on all datasets extremely time-consuming. Thus, a small and informative benchmark, which covers all typical challenging scenarios to facilitate assessing the tracker performance, is of great interest. In this work, we develop a principled way to construct a small and informative tracking benchmark (ITB) with 7% out of 1.2 M frames of existing and newly collected datasets, which enables efficient evaluation while ensuring effectiveness. Specifically, we first design a quality assessment mechanism to select the most informative sequences from existing benchmarks taking into account 1) challenging level, 2) discriminative strength, 3) and density of appearance variations. Furthermore, we collect additional sequences to ensure the diversity and balance of tracking scenarios, leading to a total of 20 sequences for each scenario. By analyzing the results of 15 state-of-the-art trackers re-trained on the same data, we determine the effective methods for robust tracking under each scenario and demonstrate new challenges for future research direction in this field.
Toward Democratizing Access to Facilities Data: A Framework for Intelligent Data Discovery and Delivery
Venue: arXiv cs.DC 2021
Year: 2021

Data collected by large-scale instruments, observatories, and sensor networks are key enablers of scientific discoveries in many disciplines. However, ensuring that these data can be accessed, integrated, and analyzed in a democratized and timely manner remains a challenge. In this article, we explore how state-of-the-art techniques for data discovery and access can be adapted to facility data and develop a conceptual framework for intelligent data access and discovery.
On the Physical Layer Security Performance over RIS-aided Dual-hop RF-UOWC Mixed Network
Venue: arXiv cs.IT 2021
Year: 2021

Since security has been one of the crucial issues for high-yield communications such as 5G and 6G, the researchers continuously come up with newer techniques to enhance the security and performance of these progressive wireless communications. Reconfigurable intelligent surface (RIS) is one of those techniques that artificially rearrange and optimize the propagation environment of electromagnetic waves to improve both spectrum and energy efficiency of wireless networks. Besides, in underwater communication, underwater optical wireless communication (UOWC) is a better alternative/replacement for conventional acoustic and radio frequency (RF) technologies. Hence, mixed RIS-aided RF-UOWC can be treated as a promising technology for future wireless networks. This work focuses on the secrecy performance of mixed dual-hop RIS-aided RF-UOWC networks under the intercepting effort of a probable eavesdropper. The RF link operates under generalized Gamma fading distribution; likewise, the UOWC link experiences the mixture exponential generalized Gamma distribution. The secrecy analysis subsumes the derivations of closed-form expressions for average secrecy capacity, exact and lower bound of secrecy outage probability, and strictly positive secrecy capacity, all in terms of Meijer G functions. Capitalizing on these derivations, the effects of heterodyne and intensity modulation/direct detection systems, underwater turbulence resulting from air bubble levels, temperature gradients, and salinity gradients, are measured. Unlike conventional models that merely deal with thermally uniform scenarios, this proposed model is likely to be unique in terms of dealing with secrecy analysis of a temperature gradient RIS-aided RF-UOWC network. Lastly, the derivations are validated via Monte-Carlo simulations.
Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed Cross-Modal Info-Max Hashing (CMIMH). First, to learn informative representations that can preserve both intra- and inter-modal similarities, we leverage the recent advances in estimating variational lower-bound of MI to maximize the MI between the binary representations and input features and between binary representations of different modalities. By jointly maximizing these MIs under the assumption that the binary representations are modelled by multivariate Bernoulli distributions, we can learn binary representations, which can preserve both intra- and inter-modal similarities, effectively in a mini-batch manner with gradient descent. Furthermore, we find out that trying to minimize the modality gap by learning similar binary representations for the same instance from different modalities could result in less informative representations. Hence, balancing between reducing the modality gap and losing modality-private information is important for the cross-modal retrieval tasks. Quantitative evaluations on standard benchmark datasets demonstrate that the proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation
Venue: arXiv cs.CV 2021
Year: 2021

The 360°imaging has recently gained great attention; however, its angular resolution is relatively lower than that of a narrow field-of-view (FOV) perspective image as it is captured by using fisheye lenses with the same sensor size. Therefore, it is beneficial to super-resolve a 360°image. Some attempts have been made but mostly considered the equirectangular projection (ERP) as one of the way for 360°image representation despite of latitude-dependent distortions. In that case, as the output high-resolution(HR) image is always in the same ERP format as the low-resolution (LR) input, another information loss may occur when transforming the HR image to other projection types. In this paper, we propose SphereSR, a novel framework to generate a continuous spherical image representation from an LR 360°image, aiming at predicting the RGB values at given spherical coordinates for super-resolution with an arbitrary 360°image projection. Specifically, we first propose a feature extraction module that represents the spherical data based on icosahedron and efficiently extracts features on the spherical surface. We then propose a spherical local implicit image function (SLIIF) to predict RGB values at the spherical coordinates. As such, SphereSR flexibly reconstructs an HR image under an arbitrary projection type. Experiments on various benchmark datasets show that our method significantly surpasses existing methods.
A Structured Analysis of Information Security Incidents in the Maritime Sector
Venue: arXiv cs.CR 2021
Year: 2021

Cyber attacks in the maritime sector can have a major impact on world economy. However, the severity of this threat can be underestimated because many attacks remain unknown or unnoticed. We present an overview about publicly known cyber incidents in the maritime sector from the past 20 years. In total, we found 90 publicly reported attacks and 15 proof of concepts. Furthermore, we interviewed five IT security experts from the maritime sector. The interviews put the results of our research in perspective and confirm that our view is comprehensive. However, the interviewees highlight that there is a high dark figure of unreported incidents and argue that threat information sharing may potentially be helpful for attack prevention. From these results, we extract threats for players in the maritime sector.
On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters
Venue: arXiv cs.LG 2021
Year: 2021

Bloom Filters are a fundamental and pervasive data structure. Within the growing area of Learned Data Structures, several Learned versions of Bloom Filters have been considered, yielding advantages over classic Filters. Each of them uses a classifier, which is the Learned part of the data structure. Although it has a central role in those new filters, and its space footprint as well as classification time may affect the performance of the Learned Filter, no systematic study of which specific classifier to use in which circumstances is available. We report progress in this area here, providing also initial guidelines on which classifier to choose among five classic classification paradigms.
Extension of Convolutional Neural Network along Temporal and Vertical Directions for Precipitation Downscaling
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning has been utilized for the statistical downscaling of climate data. Specifically, a two-dimensional (2D) convolutional neural network (CNN) has been successfully applied to precipitation estimation. This study implements a three-dimensional (3D) CNN to estimate watershed-scale daily precipitation from 3D atmospheric data and compares the results with those for a 2D CNN. The 2D CNN is extended along the time direction (3D-CNN-Time) and the vertical direction (3D-CNN-Vert). The precipitation estimates of these extended CNNs are compared with those of the 2D CNN in terms of the root-mean-square error (RMSE), Nash-Sutcliffe efficiency (NSE), and 99th percentile RMSE. It is found that both 3D-CNN-Time and 3D-CNN-Vert improve the model accuracy for precipitation estimation compared to the 2D CNN. 3D-CNN-Vert provided the best estimates during the training and test periods in terms of RMSE and NSE.
How to Find a Good Explanation for Clustering?
Venue: arXiv cs.DS 2021
Year: 2021

$k$-means and $k$-median clustering are powerful unsupervised machine learning techniques. However, due to complicated dependences on all the features, it is challenging to interpret the resulting cluster assignments. Moshkovitz, Dasgupta, Rashtchian, and Frost [ICML 2020] proposed an elegant model of explainable $k$-means and $k$-median clustering. In this model, a decision tree with $k$ leaves provides a straightforward characterization of the data set into clusters. We study two natural algorithmic questions about explainable clustering. (1) For a given clustering, how to find the "best explanation" by using a decision tree with $k$ leaves? (2) For a given set of points, how to find a decision tree with $k$ leaves minimizing the $k$-means/median objective of the resulting explainable clustering? To address the first question, we introduce a new model of explainable clustering. Our model, inspired by the notion of outliers in robust statistics, is the following. We are seeking a small number of points (outliers) whose removal makes the existing clustering well-explainable. For addressing the second question, we initiate the study of the model of Moshkovitz et al. from the perspective of multivariate complexity. Our rigorous algorithmic analysis sheds some light on the influence of parameters like the input size, dimension of the data, the number of outliers, the number of clusters, and the approximation ratio, on the computational complexity of explainable clustering.
Analyzing the influence of prolific collaborations on authors productivity and visibility
Venue: arXiv cs.DL 2021
Year: 2021

Science has become more collaborative over the past years, a phenomenon that is related to the increase in the number of authors per paper and the emergence of interdisciplinary works featuring specialists of different fields. In such a environment, it is not trivial to quantify the individual impact of researchers. Here we analyze how the most prolific collaboration tie (in terms of co-produced works) of an established researcher influences their productivity and visibility metrics. In particular, we check how the number of produced works, citations and h-index rank of an researcher changes when their works also coauthored by their prolific collaborators are not considered. We observed different patterns of prolific collaborator influence across the major fields of knowledge. More specifically, in formal and applied sciences, the prolific collaborators seem to play an important role to the visibility metrics of authors even when they are among the highly cited. Such a results can help stakeholders to better understand the collaboration patterns and draw measures of success that also consider collaboration ties.
On Homotopy of Walks and Spherical Maps in Homotopy Type Theory
Venue: arXiv cs.LO 2021
Year: 2021

We work with combinatorial maps to represent graph embeddings into surfaces up to isotopy. The surface in which the graph is embedded is left implicit in this approach. The constructions herein are proof-relevant and stated with a subset of the language of homotopy type theory. This article presents a refinement of one characterisation of embeddings in the sphere, called spherical maps, of connected and directed multigraphs with discrete node sets. A combinatorial notion of homotopy for walks and the normal form of walks under a reduction relation is introduced. The first characterisation of spherical maps states that a graph can be embedded in the sphere if any pair of walks with the same endpoints are merely walk-homotopic. The refinement of this definition filters out any walk with inner cycles. As we prove in one of the lemmas, if a spherical map is given for a graph with a discrete node set, then any walk in the graph is merely walk-homotopic to a normal form. The proof assistant Agda contributed to formalising the results recorded in this article.
Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis
Venue: arXiv cs.CV 2021
Year: 2021

The field of neural networks has seen significant advances in recent years with the development of deep and convolutional neural networks. Although many of the current works address real-valued models, recent studies reveal that neural networks with hypercomplex-valued parameters can better capture, generalize, and represent the complexity of multidimensional data. This paper explores the quaternion-valued convolutional neural network application for a pattern recognition task from medicine, namely, the diagnosis of acute lymphoblastic leukemia. Precisely, we compare the performance of real-valued and quaternion-valued convolutional neural networks to classify lymphoblasts from the peripheral blood smear microscopic images. The quaternion-valued convolutional neural network achieved better or similar performance than its corresponding real-valued network but using only 34% of its parameters. This result confirms that quaternion algebra allows capturing and extracting information from a color image with fewer parameters.
Learning Semantic-Aligned Feature Representation for Text-based Person Search
Venue: arXiv cs.CV 2021
Year: 2021

Text-based person search aims to retrieve images of a certain pedestrian by a textual description. The key challenge of this task is to eliminate the inter-modality gap and achieve the feature alignment across modalities. In this paper, we propose a semantic-aligned embedding method for text-based person search, in which the feature alignment across modalities is achieved by automatically learning the semantic-aligned visual features and textual features. First, we introduce two Transformer-based backbones to encode robust feature representations of the images and texts. Second, we design a semantic-aligned feature aggregation network to adaptively select and aggregate features with the same semantics into part-aware features, which is achieved by a multi-head attention module constrained by a cross-modality part alignment loss and a diversity loss. Experimental results on the CUHK-PEDES and Flickr30K datasets show that our method achieves state-of-the-art performances.
Probability Density Estimation Based Imitation Learning
Venue: arXiv cs.LG 2021
Year: 2021

Imitation Learning (IL) is an effective learning paradigm exploiting the interactions between agents and environments. It does not require explicit reward signals and instead tries to recover desired policies using expert demonstrations. In general, IL methods can be categorized into Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL). In this work, a novel reward function based on probability density estimation is proposed for IRL, which can significantly reduce the complexity of existing IRL methods. Furthermore, we prove that the theoretically optimal policy derived from our reward function is identical to the expert policy as long as it is deterministic. Consequently, an IRL problem can be gracefully transformed into a probability density estimation problem. Based on the proposed reward function, we present a "watch-try-learn" style framework named Probability Density Estimation based Imitation Learning (PDEIL), which can work in both discrete and continuous action spaces. Finally, comprehensive experiments in the Gym environment show that PDEIL is much more efficient than existing algorithms in recovering rewards close to the ground truth.