pwc-archive/papers-with-abstracts huggingface dataset:
The DELICES project: Indexing scientific literature through semantic expansion
Authors: Florian Boudin, Béatrice Daille, Evelyne Jacquey, Jian-Yun Nie
Date: 2021-06-28 00:00:00
Tasks: Articles

Scientific digital libraries play a critical role in the development and dissemination of scientific literature. Despite dedicated search engines, retrieving relevant publications from the ever-growing body of scientific literature remains challenging and time-consuming. Indexing scientific articles is indeed a difficult matter, and current models solely rely on a small portion of the articles (title and abstract) and on author-assigned keyphrases when available. This results in a frustratingly limited access to scientific knowledge. The goal of the DELICES project is to address this pitfall by exploiting semantic relations between scientific articles to both improve and enrich indexing. To this end, we will rely on the latest advances in semantic representations to both increase the relevance of keyphrases extracted from the documents, and extend indexing to new terms borrowed from semantically similar documents.
On the Capacity of Quantum Private Information Retrieval from MDS-Coded and Colluding Servers
Authors: Matteo Allaix, Seunghoan Song, Lukas Holzbaur, Tefjol Pllaha, Masahito Hayashi, Camilla Hollanti
Date: 2021-06-28 00:00:00
Tasks: Information Retrieval, Retrieval

In quantum private information retrieval (QPIR), a user retrieves a classical file from multiple servers by downloading quantum systems without revealing the identity of the file. The QPIR capacity is the maximal achievable ratio of the retrieved file size to the total download size. In this paper, the capacity of QPIR from MDS-coded and colluding servers is studied for the first time. Two general classes of QPIR, called stabilizer QPIR and dimension-squared QPIR induced from classical strongly linear PIR are defined, and the related QPIR capacities are derived. For the non-colluding case, the general QPIR capacity is derived when the number of files goes to infinity. A general statement on the converse bound for QPIR with coded and colluding servers is derived showing that the capacities of stabilizer QPIR and dimension-squared QPIR induced from any class of PIR are upper bounded by twice the classical capacity of the respective PIR class. The proposed capacity-achieving scheme combines the star-product scheme by Freij-Hollanti et al. and the stabilizer QPIR scheme by Song et al. by employing (weakly) self-dual Reed--Solomon codes.
Intent Disentanglement and Feature Self-supervision for Novel Recommendation
Authors: Tieyun Qian, Yile Liang, Qing Li, Xuan Ma, Ke Sun, Zhiyong Peng
Date: 2021-06-28 00:00:00
Tasks: Disentanglement, Recommendation Systems, Self-Supervised Learning

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing methods has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experimental results on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of accuracy, novelty, coverage, and trade-off.
Sequential Recommendation with Graph Neural Networks
Authors: Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang song, Depeng Jin, Yong Li
Date: 2021-06-27 00:00:00
Tasks: Graph Neural Network, Metric Learning, Sequential Recommendation

Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation. First, user behaviors in their rich historical sequences are often implicit and noisy preference signals, they cannot sufficiently reflect users' actual preferences. In addition, users' dynamic preferences often change rapidly over time, and hence it is difficult to capture user patterns in their historical sequences. In this work, we propose a graph neural network model called SURGE (short for SeqUential Recommendation with Graph neural nEtworks) to address these two issues. Specifically, SURGE integrates different types of preferences in long-term user behaviors into clusters in the graph by re-constructing loose item sequences into tight item-item interest graphs based on metric learning. This helps explicitly distinguish users' core interests, by forming dense clusters in the interest graph. Then, we perform cluster-aware and query-aware graph convolutional propagation and graph pooling on the constructed graph. It dynamically fuses and extracts users' current activated core interests from noisy user behavior sequences. We conduct extensive experiments on both public and proprietary industrial datasets. Experimental results demonstrate significant performance gains of our proposed method compared to state-of-the-art methods. Further studies on sequence length confirm that our method can model long behavioral sequences effectively and efficiently.
Detecting race and gender bias in visual representation of AI on web search engines
Authors: Mykola Makhortykh, Aleksandra Urman, Roberto Ulloa
Date: 2021-06-26 00:00:00
Tasks: Image Retrieval, Information Retrieval, Retrieval

Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems.
ICDAR 2021 Competition on Scientific Literature Parsing
Authors: Antonio Jimeno Yepes, Xu Zhong, Douglas Burdick
Date: 2021-06-08 00:00:00
Tasks: document understanding, object-detection, Object Detection, Table Recognition

Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. However, scientific literature is often available in unstructured PDF format. While PDF is great for preserving basic visual elements, such as characters, lines, shapes, etc., on a canvas for presentation to humans, automatic processing of the PDF format by machines presents many challenges. With over 2.5 trillion PDF documents in existence, these issues are prevalent in many other important application domains as well. Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims to drive the advances specifically in document understanding. ICDAR2021-SLP leverages the PubLayNet and PubTabNet datasets, which provide hundreds of thousands of training and evaluation examples. In Task A, Document Layout Recognition, submissions with the highest performance combine object detection and specialised solutions for the different categories. In Task B, Table Recognition, top submissions rely on methods to identify table components and post-processing methods to generate the table structure and content. Results from both tasks show an impressive performance and opens the possibility for high performance practical applications.
Polyconvex anisotropic hyperelasticity with neural networks
Authors: Dominik K. Klein, Mauricio Fernández, Robert J. Martin, Patrizio Neff, Oliver Weeger
Date: 2021-06-20 00:00:00
Tasks: Data Augmentation

In the present work, two machine learning based constitutive models for finite deformations are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic and fulfill the polyconvexity condition, which implies ellipticity and thus ensures material stability. The first constitutive model is based on a set of polyconvex, anisotropic and objective invariants. The second approach is formulated in terms of the deformation gradient, its cofactor and determinant, uses group symmetrization to fulfill the material symmetry condition, and data augmentation to fulfill objectivity approximately. The extension of the dataset for the data augmentation approach is based on mechanical considerations and does not require additional experimental or simulation data. The models are calibrated with highly challenging simulation data of cubic lattice metamaterials, including finite deformations and lattice instabilities. A moderate amount of calibration data is used, based on deformations which are commonly applied in experimental investigations. While the invariant-based model shows drawbacks for several deformation modes, the model based on the deformation gradient alone is able to reproduce and predict the effective material behavior very well and exhibits excellent generalization capabilities. In addition, the models are calibrated with transversely isotropic data, generated with an analytical polyconvex potential. For this case, both models show excellent results, demonstrating the straightforward applicability of the polyconvex neural network constitutive models to other symmetry groups.
Zero-shot learning approach to adaptive Cybersecurity using Explainable AI
Authors: Dattaraj Rao, Shraddha Mane
Date: 2021-06-21 00:00:00
Tasks: Intrusion Detection, Management, Zero-Shot Learning

Cybersecurity is a domain where there is constant change in patterns of attack, and we need ways to make our Cybersecurity systems more adaptive to handle new attacks and categorize for appropriate action. We present a novel approach to handle the alarm flooding problem faced by Cybersecurity systems like security information and event management (SIEM) and intrusion detection (IDS). We apply a zero-shot learning method to machine learning (ML) by leveraging explanations for predictions of anomalies generated by a ML model. This approach has huge potential to auto detect alarm labels generated in SIEM and associate them with specific attack types. In this approach, without any prior knowledge of attack, we try to identify it, decipher the features that contribute to classification and try to bucketize the attack in a specific category - using explainable AI. Explanations give us measurable factors as to what features influence the prediction of a cyber-attack and to what degree. These explanations generated based on game-theory are used to allocate credit to specific features based on their influence on a specific prediction. Using this allocation of credit, we propose a novel zero-shot approach to categorize novel attacks into specific new classes based on feature influence. The resulting system demonstrated will get good at separating attack traffic from normal flow and auto-generate a label for attacks based on features that contribute to the attack. These auto-generated labels can be presented to SIEM analyst and are intuitive enough to figure out the nature of attack. We apply this approach to a network flow dataset and demonstrate results for specific attack types like ip sweep, denial of service, remote to local, etc. Paper was presented at the first Conference on Deployable AI at IIT-Madras in June 2021.
AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data
Authors: Francisco Barreras, Mikhail Hayhoe, Hamed Hassani, Victor M. Preciado
Date: 2021-06-28 00:00:00
Tasks: Bayesian Inference

We present an Extended Kalman Filter framework for system identification and control of a stochastic high-dimensional epidemic model. The scale and severity of the COVID-19 emergency have highlighted the need for accurate forecasts of the state of the pandemic at a high resolution. Mechanistic compartmental models are widely used to produce such forecasts and assist in the design of control and relief policies. Unfortunately, the scale and stochastic nature of many of these models often makes the estimation of their parameters difficult. With the goal of calibrating a high dimensional COVID-19 model using low-level mobility data, we introduce a method for tractable maximum likelihood estimation that combines tools from Bayesian inference with scalable optimization techniques from machine learning. The proposed approach uses automatic backward-differentiation to directly compute the gradient of the likelihood of COVID-19 incidence and death data. The likelihood of the observations is estimated recursively using an Extended Kalman Filter and can be easily optimized using gradient-based methods to compute maximum likelihood estimators. Our compartmental model is trained using GPS mobility data that measures the mobility patterns of millions of mobile phones across the United States. We show that, after calibrating against incidence and deaths data from the city of Philadelphia, our model is able to produce an accurate 30-day forecast of the evolution of the pandemic.
Graphical Economies with Resale
Authors: Gabriel P. Andrade, Rafael Frongillo, Elliot Gorokhovsky, Sharadha Srinivasan
Date: 2021-06-28 00:00:00

Kakade, Kearns, and Ortiz (KKO) introduce a graph-theoretic generalization of the classic Arrow--Debreu (AD) exchange economy. Despite its appeal as a networked version of AD, we argue that the KKO model is too local, in the sense that goods cannot travel more than one hop through the network. We introduce an alternative model in which agents may purchase goods on credit in order to resell them. In contrast to KKO, our model allows for long-range trade, and yields equilibria in more settings than KKO, including sparse endowments. Our model smoothly interpolates between the KKO and AD equilibrium concepts: we recover KKO when the resale capacity is zero, and recover AD when it is sufficiently large. We give general equilibrium existence results, and an auction-based algorithm to compute approximate equilibria when agent utilities satisfy the weak gross-substitutes property.
Analysis and Control of Autonomous Mobility-on-Demand Systems
Authors: Gioele Zardini, Nicolas Lanzetti, Marco Pavone, Emilio Frazzoli
Date: 2021-06-28 00:00:00
Tasks: Autonomous Vehicles

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.
Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
Authors: Pulkit Tandon, Shubham Chandak, Pat Pataranutaporn, Yimeng Liu, Anesu M. Mapuranga, Pattie Maes, Tsachy Weissman, Misha Sra
Date: 2021-06-26 00:00:00
Tasks: Talking Face Generation, Talking Head Generation, Video Compression, Voice Cloning

Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth (~100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos ("talking-head videos") to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users (n = 242) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git.
An Audio Envelope Generator Derived from Industrial Process Control
Authors: Ashwin Pillay
Date: 2021-06-26 00:00:00

Audio envelopes serve a crucial role in ensuring the versatility of synthesizers in producing timbres. To this end, the Attack, Decay, Release and Sustain (ADSR) envelope generator and its derivatives have been established as a mainstay in modern music. However, there may be merit in exploring alternate techniques to produce envelopes that could not only resemble ADSR but also be used to create novel timbres. Consequently, an attempt is made in this research to formulate the framework of a new envelope generator by redefining the Proportional-Integral-Derivative (PID) algorithm used in feedback-based process control. Additionally, a detailed analysis is made on the modes of operation and the nature of envelopes thus generated to establish it as a potential harbinger of distinctive styles of music.
Topology Learning of Linear Dynamical Systems with Latent Nodes using Matrix Decomposition
Authors: Mishfad S. V., Harish Doddi, Murti V. Salapaka
Date: 2019-12-16 00:00:00
Tasks: Retrieval

In this article, we present a novel approach to reconstruct the topology of networked linear dynamical systems with latent nodes. The network is allowed to have directed loops and bi-directed edges. The main approach relies on the unique decomposition of the inverse of power spectral density matrix (IPSDM) obtained from observed nodes as a sum of sparse and low-rank matrices. We provide conditions and methods for decomposing the IPSDM of the observed nodes into sparse and low-rank components. The sparse component yields the moral graph associated with the observed nodes, and the low-rank component retrieves parents, children and spouses (the Markov Blanket) of the hidden nodes. The article provides necessary and sufficient conditions for the unique decomposition of a given skew symmetric matrix into sum of a sparse skew symmetric and a low-rank skew symmetric matrices. It is shown that for a large class of systems, the unique decomposition of imaginary part of the IPSDM of observed nodes, a skew symmetric matrix, into the sparse and the low-rank components is sufficient to identify the moral graph of the observed nodes as well as the Markov Blanket of latent nodes. For a large class of systems, all spurious links in the moral graph formed by the observed nodes can be identified. Assuming conditions on hidden nodes required for identifiability, links between the hidden and observed nodes can be reconstructed, resulting in the retrieval of the exact topology of the network from the availability of IPSDM. Moreover, for finite number of data samples, we provide concentration bounds on the entry-wise distance between the true IPSDM and the estimated IPSDM.
Hybrid zonotopes: a new set representation for reachability analysis of mixed logical dynamical systems
Authors: Trevor J. Bird, Herschel C. Pangborn, Neera Jain, Justin P. Koeln
Date: 2021-06-28 00:00:00

This article presents a new set representation named the hybrid zonotope that is equivalent to the union of $2^N$ constrained zonotopes -- convex polytopes -- through the addition of $N$ binary zonotope factors. The major contribution of this manuscript is a closed-form solution for exact forward reachable sets of discrete-time, linear hybrid systems modeled as mixed logical dynamical systems. The proposed approach captures the worst-case exponential growth in the number of convex sets required to represent the nonconvex reachable set while exhibiting only linear growth in the complexity of the hybrid zonotope set representation. Redundancy removal techniques are provided that leverage binary trees to store the combinations of binary factors of the hybrid zonotope that map to nonempty convex subsets. Numerical examples show the hybrid zonotope's ability to compactly represent nonconvex reachable sets with an exponential number of features. Furthermore, the hybrid zonotope is shown to be closed under linear mappings, Minkowski sums, generalized intersections, and halfspace intersections.
Analyzing Power Quality Implications of High Level Charging Rates of Electric Vehicle Within Distribution Networks
Authors: Arash Farokhi Soofi, Reza Bayani, Saeed D. Manshadi
Date: 2021-06-28 00:00:00

This paper investigates the impact of the charging level of high penetration level of Electric Vehicles (EVs) on the power quality of the electricity distribution network. The EV owners tend to charge their EVs as fast as possible. The charging levels of EVs within the distribution network affect the voltage profile of buses of the network. In this paper, an exact Second-Order Cone Programming (SOCP) formulation of the full AC optimal power flow (ACOPF) problem of the distribution network is presented. The network includes solar generation units and EVs as Distributed Energy Resources (DERs). Different charging levels are considered to analyze the impact of EVs on the distribution network. The performance of the proposed model is illustrated for the modified IEEE-33 bus system for different charging levels for EVs. Besides, the impact of available solar power and battery degradation cost of EVs on the distribution network is investigated. It is illustrated that how EV charging will cause voltage deviation challenges for the distribution network.