pwc-archive/papers-with-abstracts huggingface dataset:
Influence Estimation and Maximization via Neural Mean-Field Dynamics
Authors: Shushan He, Hongyuan Zha, Xiaojing Ye
Date: 2021-06-03 00:00:00

We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems on heterogeneous diffusion networks. Our new framework leverages the Mori-Zwanzig formalism to obtain an exact evolution equation of the individual node infection probabilities, which renders a delay differential equation with memory integral approximated by learnable time convolution operators. Directly using information diffusion cascade data, our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities. Connections between parameter learning and optimal control are also established, leading to a rigorous and implementable algorithm for training NMF. Moreover, we show that the projected gradient descent method can be employed to solve the challenging influence maximization problem, where the gradient is computed extremely fast by integrating NMF forward in time just once in each iteration. Extensive empirical studies show that our approach is versatile and robust to variations of the underlying diffusion network models, and significantly outperform existing approaches in accuracy and efficiency on both synthetic and real-world data.
Unsupervised Learning of General-Purpose Embeddings for Code Changes
Authors: Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev, Timofey Bryksin
Date: 2021-06-03 00:00:00

Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations during pre-training and evaluate them on two different downstream tasks - applying changes to code and commit message generation. During pre-training, the model learns to apply the given code change in a correct way. This task requires only code changes themselves, which makes it unsupervised. In the task of applying code changes, our model outperforms baseline models by 5.9 percentage points in accuracy. As for the commit message generation, our model demonstrated the same results as supervised models trained for this specific task, which indicates that it can encode code changes well and can be improved in the future by pre-training on a larger dataset of easily gathered code changes.
Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19
Authors: Vishrawas Gopalakrishnan, Sayali Navalekar, Pan Ding, Ryan Hooley, Jacob Miller, Raman Srinivasan, Ajay Deshpande, Xuan Liu, Simone Bianco, James H. Kaufman
Date: 2021-06-03 00:00:00
Tasks: Benchmarking, Decision Making

Pandemic control measures like lock-down, restrictions on restaurants and gatherings, social-distancing have shown to be effective in curtailing the spread of COVID-19. However, their sustained enforcement has negative economic effects. To craft strategies and policies that reduce the hardship on the people and the economy while being effective against the pandemic, authorities need to understand the disease dynamics at the right geo-spatial granularity. Considering factors like the hospitals' ability to handle the fluctuating demands, evaluating various reopening scenarios, and accurate forecasting of cases are vital to decision making. Towards this end, we present a flexible end-to-end solution that seamlessly integrates public health data with tertiary client data to accurately estimate the risk of reopening a community. At its core lies a state-of-the-art prediction model that auto-captures changing trends in transmission and mobility. Benchmarking against various published baselines confirm the superiority of our forecasting algorithm. Combined with the ability to extend to multiple client-specific requirements and perform deductive reasoning through counter-factual analysis, this solution provides actionable insights to multiple client domains ranging from government to educational institutions, hospitals, and commercial establishments.
Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology
Authors: Elva Luz Crespo Neira, Ashkan Ebadi, Catherine Beaudry, Andrea Schiffauerova
Date: 2021-06-03 00:00:00

Incorporating existing knowledge is vital for innovating, discovering, and generating new ideas. Knowledge production through research and invention is the key to scientific and technological development. As an emerging technology, nanotechnology has already proved its great potential for the global economy, attracting considerable federal investments. Canada is reported as one of the major players in producing nanotechnology research. In this paper, we focused on the main drivers of knowledge production and diffusion by analyzing Canadian nanotechnology researchers. We hypothesized that knowledge production in Canadian nanotechnology is influenced by three key proximity factors, namely cognitive, geographical, and collaborative. Using statistical analysis, social network analysis, and machine learning techniques we comprehensively assessed the influence of the proximity factors on academic knowledge production. Our results not only prove a significant impact of the three key proximity factors but also their predictive potential.
The Signed Cumulative Distribution Transform for 1-D Signal Analysis and Classification
Authors: Akram Aldroubi, Rocio Diaz Martin, Ivan Medri, Gustavo K. Rohde, Sumati Thareja
Date: 2021-06-03 00:00:00
Tasks: Translation

This paper presents a new mathematical signal transform that is especially suitable for decoding information related to non-rigid signal displacements. We provide a measure theoretic framework to extend the existing Cumulative Distribution Transform [ACHA 45 (2018), no. 3, 616-641] to arbitrary (signed) signals on $\overline{\mathbb{R}}$. We present both forward (analysis) and inverse (synthesis) formulas for the transform, and describe several of its properties including translation, scaling, convexity, linear separability and others. Finally, we describe a metric in transform space, and demonstrate the application of the transform in classifying (detecting) signals under random displacements.
Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation
Authors: Nuttapong Attrapadung, Koki Hamada, Dai Ikarashi, Ryo Kikuchi, Takahiro Matsuda, Ibuki Mishina, Hiraku Morita, Jacob C. N. Schuldt
Date: 2021-06-04 00:00:00
Tasks: Privacy Preserving

Privacy-preserving machine learning (PPML) aims at enabling machine learning (ML) algorithms to be used on sensitive data. We contribute to this line of research by proposing a framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation (MPC). This is in contrast to most prior works, which substitute ML algorithms with approximated "MPC-friendly" variants. A drawback of the latter approach is that fine-tuning of the combined ML and MPC algorithms is required, which might lead to less efficient algorithms or inferior quality ML. This is an issue for secure deep neural networks (DNN) training in particular, as this involves arithmetic algorithms thought to be "MPC-unfriendly", namely, integer division, exponentiation, inversion, and square root. In this work, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations. Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This framework enables us to efficiently and securely compute modern ML algorithms such as Adam and the softmax function "as is", without resorting to approximations. As a result, we obtain secure DNN training that outperforms state-of-the-art three-party systems; our full training is up to 6.7 times faster than just the online phase of the recently proposed FALCON@PETS'21 on a standard benchmark network. We further perform measurements on real-world DNNs, AlexNet and VGG16. The performance of our framework is up to a factor of about 12-14 faster for AlexNet and 46-48 faster for VGG16 to achieve an accuracy of 70% and 75%, respectively, when compared to FALCON.
Intelligent Transportation Systems to Mitigate Road Traffic Congestion
Authors: Nizar Hamadeh, Ali Karouni, Zeinab Farhat, Hussein El Ghor, Mohamad El Ghor, Israa Katea
Date: 2021-06-04 00:00:00
Tasks: Management

Intelligent transport systems have efficiently and effectively proved themselves in settling up the problem of traffic congestion around the world. The multi-agent based transportation system is one of the most important intelligent transport systems, which represents an interaction among the neighbouring vehicles, drivers, roads, infrastructure and vehicles. In this paper, two traffic management models have been created to mitigate congestion and to ensure that emergency vehicles arrive as quickly as possible. A tool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing the interactions of traffic. The simulation model has showed a significant reduction of at least 50% in the average time delay and thus a real improvement in the entire journey time.
Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex
Authors: Keitaro Tanaka, Ryosuke Sawata, Shusuke Takahashi
Date: 2021-06-04 00:00:00
Tasks: Clustering, Deep Clustering

This paper presents a new deep clustering (DC) method called manifold-aware DC (M-DC) that can enhance hyperspace utilization more effectively than the original DC. The original DC has a limitation in that a pair of two speakers has to be embedded having an orthogonal relationship due to its use of the one-hot vector-based loss function, while our method derives a unique loss function aimed at maximizing the target angle in the hyperspace based on the nature of a regular simplex. Our proposed loss imposes a higher penalty than the original DC when the speaker is assigned incorrectly. The change from DC to M-DC can be easily achieved by rewriting just one term in the loss function of DC, without any other modifications to the network architecture or model parameters. As such, our method has high practicability because it does not affect the original inference part. The experimental results show that the proposed method improves the performances of the original DC and its expansion method.
COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
Authors: Ilia Kamyshev, Sahar Moghimian Hoosh, Dmitrii Kriukov, Elena Gryazina, Henni Ouerdane
Date: 2021-06-04 00:00:00
Tasks: Non-Intrusive Load Monitoring

The global effort toward renewable energy and the electrification of energy-intensive sectors have significantly increased the demand for electricity, making energy efficiency a critical focus. Non-intrusive load monitoring (NILM) enables detailed analyses of household electricity usage by disaggregating the total power consumption into individual appliance-level data. In this paper, we propose COLD (Concurrent Loads Disaggregator), a transformer-based model specifically designed to address the challenges of disaggregating high-frequency data with multiple simultaneously working devices. COLD supports up to 42 devices and accurately handles scenarios with up to 11 concurrent loads, achieving 95% load identification accuracy and 82% disaggregation performance on the test data. In addition, we introduce a new fully labeled high-frequency NILM dataset for load disaggregation derived from the UK-DALE 16 kHz dataset. Finally, we analyze the decline in NILM model performance as the number of concurrent loads increases.
Ambulatory blood pressure monitoring versus office blood pressure measurement: Are there sex differences?
Authors: Aleksandar Miladinović, Miloš Ajčević, Giulia Siveri, Laura Liguori, Lorenzo Pascazio, Agostino Accardo
Date: 2021-06-04 00:00:00
Tasks: Management

The accurate measurement of blood pressure (BP) is an important prerequisite for the reliable diagnosis and efficient management of hypertension and other medical conditions. Office Blood Pressure Measurement (OBP) is a technique performed in-office with the sphygmomanometer, while Ambulatory Blood Pressure Monitoring (ABPM) is a technique that measures blood pressure during 24h. The BP fluctuations also depend on other factors such as physical activity, temperature, mood, age, sex, any pathologies, a hormonal activity that may intrinsically influence the differences between OBP and ABPM. The aim of this study is to examine the possible influence of sex on the discrepancies between OBP and ABPM in 872 subjects with known or suspected hypertension. A significant correlation was observed between OBP and ABPM mean values calculated during the day, night and 24h (ABPMday, ABPMnight, ABPM24h) in both groups (p<0.0001). The main finding of this study is that no difference between sexes was observed in the relation between OBP and mean ABMP values except between systolic OBP and systolic ABPM during the night. In addition, this study showed a moderate correlation between BPs obtained with the two approaches with a great dispersion around the regression line which suggests that the two approaches cannot be used interchangeably.
A Learning-based Optimal Market Bidding Strategy for Price-Maker Energy Storage
Authors: Mathilde D. Badoual, Scott J. Moura
Date: 2021-06-04 00:00:00
Tasks: Model Predictive Control

Load serving entities with storage units reach sizes and performances that can significantly impact clearing prices in electricity markets. Nevertheless, price endogeneity is rarely considered in storage bidding strategies and modeling the electricity market is a challenging task. Meanwhile, model-free reinforcement learning such as the Actor-Critic are becoming increasingly popular for designing energy system controllers. Yet implementation frequently requires lengthy, data-intense, and unsafe trial-and-error training. To fill these gaps, we implement an online Supervised Actor-Critic (SAC) algorithm, supervised with a model-based controller -- Model Predictive Control (MPC). The energy storage agent is trained with this algorithm to optimally bid while learning and adjusting to its impact on the market clearing prices. We compare the supervised Actor-Critic algorithm with the MPC algorithm as a supervisor, finding that the former reaps higher profits via learning. Our contribution, thus, is an online and safe SAC algorithm that outperforms the current model-based state-of-the-art.
An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment
Authors: Emna Baccour, Fatima Haouari, Aiman Erbad, Amr Mohamed, Kashif Bilal, Mohsen Guizani, Mounir Hamdi
Date: 2021-06-04 00:00:00
Tasks: BIG-bench Machine Learning

Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu and Twitch are gaining more momentum recently. Allocating the limited resources in a cost-effective manner while maximizing the Quality of Service (QoS) through real-time delivery and the provision of the appropriate representations for all viewers is a challenging problem. In our paper, we introduce a machine-learning based predictive resource allocation framework for geo-distributed cloud sites, considering the delay and quality constraints to guarantee the maximum QoS for viewers and the minimum cost for content providers. First, we present an offline optimization that decides the required transcoding resources in distributed regions near the viewers with a trade-off between the QoS and the overall cost. Second, we use machine learning to build forecasting models that proactively predict the approximate transcoding resources to be reserved at each cloud site ahead of time. Finally, we develop a Greedy Nearest and Cheapest algorithm (GNCA) to perform the resource allocation of real-time broadcasted videos on the rented resources. Extensive simulations have shown that GNCA outperforms the state-of-the art resource allocation approaches for crowdsourced live streaming by achieving more than 20% gain in terms of system cost while serving the viewers with relatively lower latency.
How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning
Authors: Namiko Saito, Tetsuya OGATA, Satoshi Funabashi, Hiroki Mori, Shigeki SUGANO
Date: 2021-06-04 00:00:00
Tasks: Multimodal Deep Learning, Object

Selection of appropriate tools and use of them when performing daily tasks is a critical function for introducing robots for domestic applications. In previous studies, however, adaptability to target objects was limited, making it difficult to accordingly change tools and adjust actions. To manipulate various objects with tools, robots must both understand tool functions and recognize object characteristics to discern a tool-object-action relation. We focus on active perception using multimodal sensorimotor data while a robot interacts with objects, and allow the robot to recognize their extrinsic and intrinsic characteristics. We construct a deep neural networks (DNN) model that learns to recognize object characteristics, acquires tool-object-action relations, and generates motions for tool selection and handling. As an example tool-use situation, the robot performs an ingredients transfer task, using a turner or ladle to transfer an ingredient from a pot to a bowl. The results confirm that the robot recognizes object characteristics and servings even when the target ingredients are unknown. We also examine the contributions of images, force, and tactile data and show that learning a variety of multimodal information results in rich perception for tool use.
Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models
Authors: Steve Kommrusch, Théo Barollet, Louis-Noël Pouchet
Date: 2021-06-01 00:00:00
Tasks: Graph-to-Sequence, valid

We target the problem of provably computing the equivalence between two complex expression trees. To this end, we formalize the problem of equivalence between two such programs as finding a set of semantics-preserving rewrite rules from one into the other, such that after the rewrite the two programs are structurally identical, and therefore trivially equivalent.We then develop a graph-to-sequence neural network system for program equivalence, trained to produce such rewrite sequences from a carefully crafted automatic example generation algorithm. We extensively evaluate our system on a rich multi-type linear algebra expression language, using arbitrary combinations of 100+ graph-rewriting axioms of equivalence. Our machine learning system guarantees correctness for all true negatives, and ensures 0 false positive by design. It outputs via inference a valid proof of equivalence for 93% of the 10,000 equivalent expression pairs isolated for testing, using up to 50-term expressions. In all cases, the validity of the sequence produced and therefore the provable assertion of program equivalence is always computable, in negligible time.
Quantum Perceptron Revisited: Computational-Statistical Tradeoffs
Authors: Mathieu Roget, Giuseppe Di Molfetta, Hachem Kadri
Date: 2021-06-04 00:00:00
Tasks: Quantum Machine Learning

Quantum machine learning algorithms could provide significant speed-ups over their classical counterparts; however, whether they could also achieve good generalization remains unclear. Recently, two quantum perceptron models which give a quadratic improvement over the classical perceptron algorithm using Grover's search have been proposed by Wiebe et al. arXiv:1602.04799 . While the first model reduces the complexity with respect to the size of the training set, the second one improves the bound on the number of mistakes made by the perceptron. In this paper, we introduce a hybrid quantum-classical perceptron algorithm with lower complexity and better generalization ability than the classical perceptron. We show a quadratic improvement over the classical perceptron in both the number of samples and the margin of the data. We derive a bound on the expected error of the hypothesis returned by our algorithm, which compares favorably to the one obtained with the classical online perceptron. We use numerical experiments to illustrate the trade-off between computational complexity and statistical accuracy in quantum perceptron learning and discuss some of the key practical issues surrounding the implementation of quantum perceptron models into near-term quantum devices, whose practical implementation represents a serious challenge due to inherent noise. However, the potential benefits make correcting this worthwhile.
Price graphs: Utilizing the structural information of financial time series for stock prediction
Authors: Junran Wu, Ke Xu, Xueyuan Chen, Shangzhe Li, Jichang Zhao
Date: 2021-06-04 00:00:00
Tasks: Stock Prediction, Time Series, Time Series Analysis

Great research efforts have been devoted to exploiting deep neural networks in stock prediction. While long-range dependencies and chaotic property are still two major issues that lower the performance of state-of-the-art deep learning models in forecasting future price trends. In this study, we propose a novel framework to address both issues. Specifically, in terms of transforming time series into complex networks, we convert market price series into graphs. Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the chaotic property. We take graph embeddings to represent the associations among temporal points as the prediction model inputs. Node weights are used as a priori knowledge to enhance the learning of temporal attention. The effectiveness of our proposed framework is validated using real-world stock data, and our approach obtains the best performance among several state-of-the-art benchmarks. Moreover, in the conducted trading simulations, our framework further obtains the highest cumulative profits. Our results supplement the existing applications of complex network methods in the financial realm and provide insightful implications for investment applications regarding decision support in financial markets.
Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements
Authors: Michael D. Vander Wal, Ryan G. McClarren, Kelli D. Humbird
Date: 2021-06-04 00:00:00

Simulations of high energy density physics are expensive in terms of computational resources. In particular, the computation of opacities of plasmas in the non-local thermal equilibrium (NLTE) regime can consume as much as 90\% of the total computational time of radiation hydrodynamics simulations for high energy density physics applications. Previous work has demonstrated that a combination of fully-connected autoencoders and a deep jointly-informed neural network (DJINN) can successfully replace the standard NLTE calculations for the opacity of krypton. This work expands this idea to combining multiple elements into a single surrogate model with the focus here being on the autoencoder.