pwc-archive/papers-with-abstracts huggingface dataset:
Simple Analysis of Johnson-Lindenstrauss Transform under Neuroscience Constraints
Authors: Maciej Skorski
Date: 2020-08-20 00:00:00
Tasks: LEMMA

The paper re-analyzes a version of the celebrated Johnson-Lindenstrauss Lemma, in which matrices are subjected to constraints that naturally emerge from neuroscience applications: a) sparsity and b) sign-consistency. This particular variant was studied first by Allen-Zhu, Gelashvili, Micali, Shavit and more recently by Jagadeesan (RANDOM'19). The contribution of this work is a novel proof, which in contrast to previous works a) uses the modern probability toolkit, particularly basics of sub-gaussian and sub-gamma estimates b) is self-contained, with no dependencies on subtle third-party results c) offers explicit constants. At the heart of our proof is a novel variant of Hanson-Wright Lemma (on concentration of quadratic forms). Of independent interest are also auxiliary facts on sub-gaussian random variables.
A comparative study of similarity-based and GNN-based link prediction approaches
Authors: Md Kamrul Islam, Sabeur Aridhi, Malika Smail-Tabbone
Date: 2020-08-20 00:00:00
Tasks: Graph Neural Network, Link Prediction, Prediction

The task of inferring the missing links in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature. They show good prediction performance in many real-world graphs though they are heuristics and lack of universal applicability. On the other hand, the success of neural networks for classification tasks in various domains leads researchers to study them in graphs. When a neural network can operate directly on the graph, then it is termed as the graph neural network (GNN). GNN is able to learn hidden features from graphs which can be used for link prediction task in graphs. Link predictions based on GNNs have gained much attention of researchers due to their convincing high performance in many real-world graphs. This appraisal paper studies some similarity and GNN-based link prediction approaches in the domain of homogeneous graphs that consists of a single type of (attributed) nodes and single type of pairwise links. We evaluate the studied approaches against several benchmark graphs with different properties from various domains.
Resource-efficient adaptive Bayesian tracking of magnetic fields with a quantum sensor
Authors: K. Craigie, E. M. Gauger, Y. Altmann, C. Bonato
Date: 2020-08-20 00:00:00

Single-spin quantum sensors, for example based on nitrogen-vacancy centres in diamond, provide nanoscale mapping of magnetic fields. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be minimised. Bayesian estimation and adaptive experiment optimisation can speed up the sensing process by reducing the number of measurements required. These protocols consist of computing and updating the probability distribution of the magnetic field based on measurement outcomes and of determining optimized acquisition settings for the next measurement. However, the computational steps feeding into the measurement settings of the next iteration must be performed quickly enough to allow for real-time updates. This article addresses the issue of computational speed by implementing an approximate Bayesian estimation technique, where probability distributions are approximated by a finite sum of Gaussian functions. Given that only three parameters are required to fully describe a Gaussian density, we find that in many cases, the magnetic field probability distribution can be described by fewer than ten parameters, achieving a reduction in computation time by factor 10 compared to existing approaches. For T2* = 1 micro second, only a small decrease in computation time is achieved. However, in these regimes, the proposed Gaussian protocol outperforms the existing one in tracking accuracy.
Generating Music with a Self-Correcting Non-Chronological Autoregressive Model
Authors: Wayne Chi, Prachi Kumar, Suri Yaddanapudi, Rahul Suresh, Umut Isik
Date: 2020-08-18 00:00:00

We describe a novel approach for generating music using a self-correcting, non-chronological, autoregressive model. We represent music as a sequence of edit events, each of which denotes either the addition or removal of a note---even a note previously generated by the model. During inference, we generate one edit event at a time using direct ancestral sampling. Our approach allows the model to fix previous mistakes such as incorrectly sampled notes and prevent accumulation of errors which autoregressive models are prone to have. Another benefit is a finer, note-by-note control during human and AI collaborative composition. We show through quantitative metrics and human survey evaluation that our approach generates better results than orderless NADE and Gibbs sampling approaches.
asya: Mindful verbal communication using deep learning
Authors: Evalds Urtans, Ariel Tabaks
Date: 2020-08-20 00:00:00
Tasks: Deep Learning, speaker-diarization, Speaker Diarization

asya is a mobile application that consists of deep learning models which analyze spectra of a human voice and do noise detection, speaker diarization, gender detection, tempo estimation, and classification of emotions using only voice. All models are language agnostic and capable of running in real-time. Our speaker diarization models have accuracy over 95% on the test data set. These models can be applied for a variety of areas like customer service improvement, sales effective conversations, psychology and couples therapy.
Generative chemistry: drug discovery with deep learning generative models
Authors: Yuemin Bian, Xiang-Qun Xie
Date: 2020-08-20 00:00:00
Tasks: Deep Learning, Drug Discovery, Generative Adversarial Network

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for the generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.
A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards
Authors: Mekaal Swerhun, Jasmine Foley, Brandon Massop, Vijay Mago
Date: 2020-08-20 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Evolutionary Algorithms

In recent years, machine learning has seen an increasing presencein a large variety of fields, especially in health care and bioinformatics.More specifically, the field where machine learning algorithms have found most applications is Genetic Algorithms.The objective of this paper is to conduct a survey of articles published from 2015 onwards that deal with Genetic Algorithms(GA) and how they are used in bioinformatics.To achieve the objective, a scoping review was conducted that utilized Google Scholar alongside Publish or Perish and the Scimago Journal & CountryRank to search for respectable sources. Upon analyzing 31 articles from the field of bioinformatics, it became apparent that genetic algorithms rarely form a full application, instead they rely on other vital algorithms such as support vector machines.Indeed, support vector machines were the most prevalent algorithms used alongside genetic algorithms; however, while the usage of such algorithms contributes to the heavy focus on accuracy by GA programs, it often sidelines computation times in the process. In fact, most applications employing GAs for classification and feature selectionare nearing or at 100% success rate, and the focus of future GA development should be directed elsewhere. Population-based searches, like GA, are often combined with other machine learning algorithms. In this scoping review, genetic algorithms combined with Support Vector Machines were found to perform best. The performance metric that was evaluated most often was accuracy. Measuring the accuracy avoids measuring the main weakness of GAs, which is computational time. The future of genetic algorithms could be open-ended evolutionary algorithms, which attempt to increase complexity and find diverse solutions, rather than optimize a fitness function and converge to a single best solution from the initial population of solutions.
Detecting Aedes Aegypti Mosquitoes through Audio Classification with Convolutional Neural Networks
Authors: Marcelo Schreiber Fernandes, Weverton Cordeiro, Mariana Recamonde-Mendoza
Date: 2020-08-19 00:00:00
Tasks: Audio Classification, General Classification

The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and used them to train a convolutional neural network (CNN) through supervised learning. As a feature, we used the recording spectrogram to represent the mosquito wingbeat frequency over time visually. We trained and compared three classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In our evaluation, the binary and ensemble models achieved accuracy of 97.65% ($\pm$ 0.55) and 94.56% ($\pm$ 0.77), respectively, whereas the multiclass had an accuracy of 78.12% ($\pm$ 2.09). The best sensitivity was observed in the ensemble approach (96.82% $\pm$ 1.62), followed by the multiclass for the particular case of Aedes aegypti (90.23% $\pm$ 3.83) and the binary (88.49% $\pm$ 6.68). The binary classifier and the multiclass classifier presented the best balance between precision and recall, with F1-measure close to 90%. Although the ensemble classifier achieved the lowest precision, thus impairing its F1-measure (79.95% $\pm$ 2.13), it was the most powerful classifier to detect Aedes aegypti in our dataset.
On transversality of bent hyperplane arrangements and the topological expressiveness of ReLU neural networks
Authors: J. Elisenda Grigsby, Kathryn Lindsey
Date: 2020-08-20 00:00:00
Tasks: Binary Classification

Let F:R^n -> R be a feedforward ReLU neural network. It is well-known that for any choice of parameters, F is continuous and piecewise (affine) linear. We lay some foundations for a systematic investigation of how the architecture of F impacts the geometry and topology of its possible decision regions for binary classification tasks. Following the classical progression for smooth functions in differential topology, we first define the notion of a generic, transversal ReLU neural network and show that almost all ReLU networks are generic and transversal. We then define a partially-oriented linear 1-complex in the domain of F and identify properties of this complex that yield an obstruction to the existence of bounded connected components of a decision region. We use this obstruction to prove that a decision region of a generic, transversal ReLU network F: R^n -> R with a single hidden layer of dimension (n + 1) can have no more than one bounded connected component.
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Authors: Rilwan Adewoyin, Peter Dueben, Peter Watson, Yulan He, Ritabrata Dutta
Date: 2020-08-20 00:00:00
Tasks: Decoder

Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and strong precipitation events. However, these numerical simulators have difficulties representing precipitation events accurately, mainly due to limited spatial resolution when simulating multi-scale dynamics in the atmosphere. To improve the prediction of high resolution precipitation we apply a Deep Learning (DL) approach using an input of CM simulations of the model fields (weather variables) that are more predictable than local precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an encoder-decoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers to effectively model multi-scale spatio-temporal weather processes. We use a conditional-continuous loss function to capture the zero-skewed %extreme event patterns of rainfall. Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction and improves upon the rainfall predictions of a state-of-the-art dynamical weather model. Moreover, by evaluating the performance of our model under various, training and testing, data formulation strategies, we show that there is enough data for our deep learning approach to output robust, high-quality results across seasons and varying regions.
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Authors: Tao Yang, Shikai Fang, Shibo Li, Yulan Wang, Qingyao Ai
Date: 2020-08-20 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval

Leveraging biased click data for optimizing learning to rank systems has been a popular approach in information retrieval. Because click data is often noisy and biased, a variety of methods have been proposed to construct unbiased learning to rank (ULTR) algorithms for the learning of unbiased ranking models. Among them, automatic unbiased learning to rank (AutoULTR) algorithms that jointly learn user bias models (i.e., propensity models) with unbiased rankers have received a lot of attention due to their superior performance and low deployment cost in practice. Despite their differences in theories and algorithm design, existing studies on ULTR usually use uni-variate ranking functions to score each document or result independently. On the other hand, recent advances in context-aware learning-to-rank models have shown that multivariate scoring functions, which read multiple documents together and predict their ranking scores jointly, are more powerful than uni-variate ranking functions in ranking tasks with human-annotated relevance labels. Whether such superior performance would hold in ULTR with noisy data, however, is mostly unknown. In this paper, we investigate existing multivariate scoring functions and AutoULTR algorithms in theory and prove that permutation invariance is a crucial factor that determines whether a context-aware learning-to-rank model could be applied to existing AutoULTR framework. Our experiments with synthetic clicks on two large-scale benchmark datasets show that AutoULTR models with permutation-invariant multivariate scoring functions significantly outperform those with uni-variate scoring functions and permutation-variant multivariate scoring functions.
PARADE: Passage Representation Aggregation for Document Reranking
Authors: Canjia Li, Andrew Yates, Sean MacAvaney, Ben He, Yingfei Sun
Date: 2020-08-20 00:00:00
Tasks: Ad-Hoc Information Retrieval, Document Ranking, Knowledge Distillation, Reranking

Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking. Due to inherent sequence length limits of these models, they need to be run over a document's passages, rather than processing the entire document sequence at once. Although several approaches for aggregating passage-level signals have been proposed, there has yet to be an extensive comparison of these techniques. In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score. We find that passage representation aggregation techniques can significantly improve over techniques proposed in prior work, such as taking the maximum passage score. We call this new approach PARADE. In particular, PARADE can significantly improve results on collections with broad information needs where relevance signals can be spread throughout the document (such as TREC Robust04 and GOV2). Meanwhile, less complex aggregation techniques may work better on collections with an information need that can often be pinpointed to a single passage (such as TREC DL and TREC Genomics). We also conduct efficiency analyses, and highlight several strategies for improving transformer-based aggregation.
Associating Ridesourcing with Road Safety Outcomes: Insights from Austin Texas
Authors: Eleftheria Kontou, Noreen C. McDonald
Date: 2020-01-08 00:00:00

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nation's sustainable development goals and vision zero efforts around the globe. The advent of transportation network companies, such as ridesourcing, expands mobility options in cities and may impact road safety outcomes. In this study, we analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes (p<0.05), a 0.25% decrease in road injuries (p<0.001), and a 0.36% decrease in DWI offenses (p<0.0001) in Travis County. Ridesourcing use is not associated with road fatalities at a 0.05 significance level. This study augments existing work because it moves beyond binary indicators of ridesourcing presence or absence and analyzes patterns within an urbanized area rather than metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on our transportation system's safety, which may serve as a template for future analyses of other US cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety, while helping identify sets of actions to achieve safer and more efficient shared mobility systems.
Finding structural hole spanners based on community forest model and diminishing marginal utility in large scale social networks
Authors: Yan Zhang, Hua Xu, Yunfeng Xu, Junhui Deng, Juan Gu, Rui Ma, Jie Lai, Jiangtao Hu, Xiaoshuai Yu, Lei Hou, Lidong Gu, Yanling Wei, Yichao Xiao, Junhao Lu
Date: 2020-04-24 00:00:00
Proceeding: Knowledge-Based Systems, 105916. 2020 4
Tasks: Community Detection, Link Prediction, Marketing, Sociology

Structural hole spanners play key role in information diffusion, community detection, epidemic
diseases and rumors spreading, link prediction and viral marketing, the discovery for them is a key
research work in the area of social networks. Some scholars have proposed inspired models and
methods based on Mathematics, Sociology, and Economics. In this paper, we try to give a more
visual and detailed definition of structural hole spanner based on the existing work, and propose a
novel algorithm to identify structural hole spanner based on community forest model and diminishing
marginal utility. Our work includes following four folds. Firstly we revealed the diminishing marginal
utility phenomenon in the process of community reconstruction. Secondly we proved that metrics
based on local or one-sided features cannot be used as a criterion for judging structural hole spanner.
Thirdly we proved that the influence of SHS is not related with the distribution of SHS in the network.
Fourthly we develop a novel algorithm to identify SHS. Our algorithm has slightly better performance
than the state-of-the-art algorithms. It worked well on Zachary’s karate club, American College
Football, ground-truth samples sampled from DBLP, ground-truth samples sampled from Youtube and
large-scale collaboration network DBLP.
Finding overlapping community from social networks based on community forest model
Authors: Yunfeng Xu, Hua Xu, Dongwen Zhang, Yan Zhang
Date: 2016-12-29 00:00:00
Proceeding: Knowledge-Based Systems. 109. 10.1016 2016 12
Tasks: Community Detection

Overlapping community detection is the key research work to discover and
explore the social networks. A great deal of work has been devoted to detect
overlapping communities, but no one can give a clear formula definition of
community from the internal structure to the external boundary. More in
depth, there are four challenges to existing research works. In this paper,
firstly we propose overlapping community forest model and disjoint commu-
nity forest model based on the community forest model, secondly give a clear
formula definition of overlapping community and disjoint community based
on the backbone degree and expansion, thirdly propose a novel algorithm to
find overlapping communities based on the backbone degree and expansion
to resolve the four challenges. This algorithm has better performance than
four related algorithms mentioned by this paper in large scale social net-
works. It works well on American college football, Zachary’s Karate Club,
Netscience-coauthor, Condensed matter collaborations, LFR etc. data sets.