OpenRaiser/Intern-Atlas huggingface dataset:
A bibliometric methodology to unveil territorial inequities in the scientific wealth to combat COVID-19
Venue: arXiv cs.DL 2020
Year: 2020

In this paper we develop a methodology to assess the scientific wealth of territories at field level. Our methodology uses a bibliometric approach based on the observation of academic research performance and overall scientific production in each territory. We apply it to assess disparities in the Italian territories in the medical specialties at the front line of the COVID-19 emergency. Italy has been the first among western countries to be severely affected by the onset of the COVID-19 pandemic. The study reveals remarkable inequities across territories, with scientific weaknesses concentrated in the south. Policies for rebalancing the north-south divide should also consider, in addition to tangible assets, the gap in production and availability of quality medical knowledge.
Foundations of Bayesian Learning from Synthetic Data
Venue: arXiv cs.LG 2020
Year: 2020

There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy or availability constraints. Despite a large number of methods for synthetic data generation, there are comparatively few results on the statistical properties of models learnt on synthetic data, and fewer still for situations where a researcher wishes to augment real data with another party's synthesised data. We use a Bayesian paradigm to characterise the updating of model parameters when learning in these settings, demonstrating that caution should be taken when applying conventional learning algorithms without appropriate consideration of the synthetic data generating process and learning task. Recent results from general Bayesian updating support a novel and robust approach to Bayesian synthetic-learning founded on decision theory that outperforms standard approaches across repeated experiments on supervised learning and inference problems.
Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease
Venue: arXiv cs.LG 2020
Year: 2020

Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data. A common approach to these problems involves clustering patients time-series information such as vital sign observations) to determine dissimilar subgroups of the patient population. Most clustering methods assume time-invariance of vital-signs and are unable to provide interpretability in clusters that is clinically relevant, for instance, event or outcome information. In this work, we evaluate three different clustering models on a large hospital dataset of vital-sign observations from patients suffering from Chronic Obstructive Pulmonary Disease. We further propose novel modifications to deal with unevenly sampled time-series data and unbalanced class distribution to improve phenotype separation. Lastly, we discuss further avenues of investigation for models to learn patient subgroups with distinct behaviour and phenotype.
Efficient and Effective Community Search on Large-scale Bipartite Graphs
Venue: arXiv cs.DB 2020
Year: 2020

Bipartite graphs are widely used to model relationships between two types of entities. Community search retrieves densely connected subgraphs containing a query vertex, which has been extensively studied on unipartite graphs. However, community search on bipartite graphs remains largely unexplored. Moreover, all existing cohesive subgraph models on bipartite graphs can only be applied to measure the structure cohesiveness between two sets of vertices while overlooking the edge weight in forming the community. In this paper, we study the significant (alpha, beta)-community search problem on weighted bipartite graphs. Given a query vertex q, we aim to find the significant (alpha, beta)-community R of q which adopts (alpha, beta)-core to characterize the engagement level of vertices, and maximizes the minimum edge weight (significance) within R. To support fast retrieval of R, we first retrieve the maximal connected subgraph of (alpha, beta)-core containing the query vertex (the (alpha, beta)-community), and the search space is limited to this subgraph with a much smaller size than the original graph. A novel index structure is presented which can be built in O(delta * m) time and takes O(delta * m) space where m is the number of edges in G, delta is bounded by the square root of m and is much smaller in practice. Utilizing the index, the (alpha, beta)-community can be retrieved in optimal time. To further obtain R, we develop peeling and expansion algorithms to conduct searches by shrinking from the (alpha, beta)-community and expanding from the query vertex, respectively. The experimental results on real graphs not only demonstrate the effectiveness of the significant (alpha, beta)-community model but also validate the efficiency of our query processing and indexing techniques.
A variational characterisation of projective spherical designs over the quaternions
Venue: arXiv cs.IT 2020
Year: 2020

We give an inequality on the packing of vectors/lines in quaternionic Hilbert space $\Hd$, which generalises those of Sidelnikov and Welch for unit vectors in $\Rd$ and $\Cd$. This has a parameter $t$, and depends only on the vectors up to projective unitary equivalence. The sequences of vectors in ${\mathbb{F}}^d={\mathbb{R}}^d,{\mathbb{C}}^d,{\mathbb{H}}^d$ that give equality, which we call spherical $(t,t)$-designs, are seen to satisfy a cubature rule on the unit sphere in ${\mathbb{F}}^d$ for a suitable polynomial space $\Hom_{\Fd}(t,t)$. Using this, we show that the projective spherical $t$-designs on the Delsarte spaces $\FF P^{d-1}$ coincide with the spherical $(t,t)$-designs of unit vectors in ${\mathbb{F}}^d$. We then explore a number of examples in quaternionic space. The unitarily invariant polynomial space ${\mathop{\rm Hom}\nolimits}_{\mathbb{H}^d}(t,t)$ and the inner product that we define on it so the reproducing kernel has a simple form are of independent interest.
Revising the classic computing paradigm and its technological implementations
Venue: arXiv cs.AR 2020
Year: 2020

Today's computing is told to be based on the classic paradigm, proposed by von Neumann, a three-quarter century ago. However, that paradigm was justified (for the timing relations of) vacuum tubes only. The technological development invalidated the classic paradigm (but not the model!) and led to catastrophic performance losses in computing systems, from operating gate level to large networks, including the neuromorphic ones. The paper reviews the critical points of the classic paradigm and scrutinizes the confusion made around it. It discusses some of the consequences of improper technological implementation, from the shared media to the parallelized operation. The model is perfect, but it is applied outside of its range of validity. The paradigm is extended by providing the "procedure" that enables computing science to work with cases where the transfer time is not negligible apart from processing time.
Learn2Sing: Target Speaker Singing Voice Synthesis by learning from a Singing Teacher
Venue: arXiv cs.SD 2020
Year: 2020

Singing voice synthesis has been paid rising attention with the rapid development of speech synthesis area. In general, a studio-level singing corpus is usually necessary to produce a natural singing voice from lyrics and music-related transcription. However, such a corpus is difficult to collect since it's hard for many of us to sing like a professional singer. In this paper, we propose an approach -- Learn2Sing that only needs a singing teacher to generate the target speakers' singing voice without their singing voice data. In our approach, a teacher's singing corpus and speech from multiple target speakers are trained in a frame-level auto-regressive acoustic model where singing and speaking share the common speaker embedding and style tag embedding. Meanwhile, since there is no music-related transcription for the target speaker, we use log-scale fundamental frequency (LF0) as an auxiliary feature as the inputs of the acoustic model for building a unified input representation. In order to enable the target speaker to sing without singing reference audio in the inference stage, a duration model and an LF0 prediction model are also trained. Particularly, we employ domain adversarial training (DAT) in the acoustic model, which aims to enhance the singing performance of target speakers by disentangling style from acoustic features of singing and speaking data. Our experiments indicate that the proposed approach is capable of synthesizing singing voice for target speaker given only their speech samples.
Beyond Cell-free MIMO: Energy Efficient Reconfigurable Intelligent Surface Aided Cell-free MIMO Communications
Venue: arXiv cs.IT 2020
Year: 2020

Cell-free systems can effectively eliminate the inter-cell interference by enabling multiple base stations (BSs) to cooperatively serve users without cell boundaries at the expense of high costs of hardware and power sources due to the large-scale deployment of BSs. To tackle this issue, the low-cost reconfigurable intelligent surface (RIS) can serve as a promising technique to improve the energy efficiency of cell-free systems. In this paper, we consider an RIS aided cell-free MIMO system where multiple RISs are deployed around BSs and users to create favorable propagation conditions via reconfigurable reflections in a low-cost way, thereby enhancing cell-free MIMO communications. To maximize the energy efficiency, a hybrid beamforming (HBF) scheme consisting of the digital beamforming at BSs and the RIS-based analog beamforming is proposed. The energy efficiency maximization problem is formulated and an iterative algorithm is designed to solve this problem. The impact of the transmit power, the number of RIS, and the RIS size on energy efficiency are investigated. Both theoretical analysis and simulation results reveal that the optimal energy efficiency depends on the numbers of RISs and the RIS size. Numerical evaluations also show that the proposed system can achieve a higher energy efficiency than conventional ones.
Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19
Venue: arXiv cs.SI 2020
Year: 2020

The novel coronavirus pandemic continues to ravage communities across the US. Opinion surveys identified importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. Here, we use social media data to study complexity of polarization. We analyze a large dataset of tweets related to the pandemic collected between January and May of 2020, and develop methods to classify the ideological alignment of users along the moderacy (hardline vs moderate), political (liberal vs conservative) and science (anti-science vs pro-science) dimensions. While polarization along the science and political dimensions are correlated, politically moderate users are more likely to be aligned with the pro-science views, and politically hardline users with anti-science views. Contrary to expectations, we do not find that polarization grows over time; instead, we see increasing activity by moderate pro-science users. We also show that anti-science conservatives tend to tweet from the Southern US, while anti-science moderates from the Western states. Our findings shed light on the multi-dimensional nature of polarization, and the feasibility of tracking polarized opinions about the pandemic across time and space through social media data.
Digging Deeper into CRNN Model in Chinese Text Images Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Automatic text image recognition is a prevalent application in computer vision field. One efficient way is use Convolutional Recurrent Neural Network(CRNN) to accomplish task in an end-to-end(End2End) fashion. However, CRNN notoriously fails to detect multi-row images and excel-like images. In this paper, we present one alternative to first recognize single-row images, then extend the same architecture to recognize multi-row images with proposed multiple methods. To recognize excel-like images containing box lines, we propose Line-Deep Denoising Convolutional AutoEncoder(Line-DDeCAE) to recover box lines. Finally, we present one Knowledge Distillation(KD) method to compress original CRNN model without loss of generality. To carry out experiments, we first generate artificial samples from one Chinese novel book, then conduct various experiments to verify our methods.
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions. These systems, however, rely on complex handcrafted heuristics for sequential matching - which are applied on top of a pre-computed pairwise similarity matrix between reference and query image sequences of a single route - to further reduce false-positive rates compared to single-frame retrieval methods. As a result, performing multi-frame place recognition can be extremely slow for deployment on autonomous vehicles or evaluation on large datasets, and fail when using relatively short parameter values such as a sequence length of 2 frames. In this paper, we propose DeepSeqSLAM: a trainable CNN+RNN architecture for jointly learning visual and positional representations from a single monocular image sequence of a route. We demonstrate our approach on two large benchmark datasets, Nordland and Oxford RobotCar - recorded over 728 km and 10 km routes, respectively, each during 1 year with multiple seasons, weather, and lighting conditions. On Nordland, we compare our method to two state-of-the-art sequence-based methods across the entire route under summer-winter changes using a sequence length of 2 and show that our approach can get over 72% AUC compared to 27% AUC for Delta Descriptors and 2% AUC for SeqSLAM; while drastically reducing the deployment time from around 1 hour to 1 minute against both. The framework code and video are available at https://mchancan.github.io/deepseqslam
Are University Rankings Statistically Significant? A Comparison among Chinese Universities and with the USA
Venue: arXiv cs.DL 2020
Year: 2020

Purpose: We address the question of whether differences are statistically significant in the rankings of universities. We propose methods measuring the statistical significance among different universities and illustrate the results by empirical data. Design/methodology/approach: Based on z-testing and overlapping confidence intervals, and using data about 205 Chinese universities included in the Leiden Rankings 2020, we argue that three main groups of Chinese research universities can be distinguished. Findings: When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020, the results similarly indicate three main groups: high, middle, low. Using this data (Leiden Rankings and Web-of-Science), the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap. Research limitations: We show empirically that differences in ranking may be due to changes in the data, the models, or the modeling effects on the data. The scientometric groupings are not always stable when we use different methods. R&D policy implications: Differences among universities can be tested for their statistical significance. The statistics relativize the values of decimals in the rankings. One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings. Originality/value: In the discussion about the rankings of universities, the question of whether differences are statistically significant, is, in our opinion, insufficiently addressed.
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
Venue: arXiv cs.LG 2020
Year: 2020

This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based uncertainty inference (UI) in deep neural network (DNN)-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs' distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probability density of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/-distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI.
Exploring Self-Attention for Visual Odometry
Venue: arXiv cs.CV 2020
Year: 2020

Visual odometry networks commonly use pretrained optical flow networks in order to derive the ego-motion between consecutive frames. The features extracted by these networks represent the motion of all the pixels between frames. However, due to the existence of dynamic objects and texture-less surfaces in the scene, the motion information for every image region might not be reliable for inferring odometry due to the ineffectiveness of dynamic objects in derivation of the incremental changes in position. Recent works in this area lack attention mechanisms in their structures to facilitate dynamic reweighing of the feature maps for extracting more refined egomotion information. In this paper, we explore the effectiveness of self-attention in visual odometry. We report qualitative and quantitative results against the SOTA methods. Furthermore, saliency-based studies alongside specially designed experiments are utilized to investigate the effect of self-attention on VO. Our experiments show that using self-attention allows for the extraction of better features while achieving a better odometry performance compared to networks that lack such structures.
On $k$-rainbow domination in middle graphs
Venue: arXiv cs.DM 2020
Year: 2020

Let $G$ be a finite simple graph with vertex set $V(G)$ and edge set $E(G)$. A function $f : V(G) \rightarrow \mathcal{P}(\{1, 2, \dotsc, k\})$ is a \textit{$k$-rainbow dominating function} on $G$ if for each vertex $v \in V(G)$ for which $f(v)= \emptyset$, it holds that $\bigcup_{u \in N(v)}f(u) = \{1, 2, \dotsc, k\}$. The weight of a $k$-rainbow dominating function is the value $\sum_{v \in V(G)}|f(v)|$. The \textit{$k$-rainbow domination number} $γ_{rk}(G)$ is the minimum weight of a $k$-rainbow dominating function on $G$. In this paper, we initiate the study of $k$-rainbow domination numbers in middle graphs. We define the concept of a middle $k$-rainbow dominating function, obtain some bounds related to it and determine the middle $3$-rainbow domination number of some classes of graphs. We also provide upper and lower bounds for the middle $3$-rainbow domination number of trees in terms of the matching number. In addition, we determine the $3$-rainbow domatic number for the middle graph of paths and cycles.
A Tale of Two Referees
Venue: arXiv cs.DL 2020
Year: 2020

Success in academia hinges on publishing in top tier journals. This requires innovative results. And this requires clear and convincing presentation of said results. Presentation can make the difference of one tier in journal level. A lot of useful advice on this topic is available online from well-respected outlets; see, for example, El-Omar (2014); Gould (2014); Neiles et al. (2015); Notz and Kafadar (2011); or Sachdeva (2020). This text provides a different angle.
Occams Razor for Big Data? On Detecting Quality in Large Unstructured Datasets
Venue: arXiv cs.DB 2020
Year: 2020

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony or Occams Razor in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns, generate new information, or store and further process large amounts of sensor data is then reviewed; examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence aimed at coping with the big data deluge in the near future.
Fisher Information of a Family of Generalized Normal Distributions
Venue: arXiv cs.IT 2020
Year: 2020

In this brief note we compute the Fisher information of a family of generalized normal distributions. Fisher information is usually defined for regular distributions, i.e. continuously differentiable (log) density functions whose support does not depend on the family parameter $θ$. Although the uniform distribution in $[-θ, + θ]$ does not satisfy the regularity requirements, as a special case of our result, we will obtain the Fisher information for this family.
Global Road Damage Detection: State-of-the-art Solutions
Venue: arXiv cs.CV 2020
Year: 2020

This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from several countries registered for this competition. The submitted solutions were evaluated using two datasets test1 and test2, comprising 2,631 and 2,664 images. This paper encapsulates the top 12 solutions proposed by these teams. The best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2. The paper concludes with a review of the facets that worked well for the presented challenge and those that could be improved in future challenges.