pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Server Private Linear Computation with Joint and Individual Privacy Guarantees
Authors: Nahid Esmati, Anoosheh Heidarzadeh
Date: 2021-08-20 00:00:00
Tasks: Information Retrieval, Retrieval

This paper considers the problem of multi-server Private Linear Computation, under the joint and individual privacy guarantees. In this problem, identical copies of a dataset comprised of $K$ messages are stored on $N$ non-colluding servers, and a user wishes to obtain one linear combination of a $D$-subset of messages belonging to the dataset. The goal is to design a scheme for performing the computation such that the total amount of information downloaded from the servers is minimized, while the privacy of the $D$ messages required for the computation is protected. When joint privacy is required, the identities of all of these $D$ messages must be kept private jointly, and when individual privacy is required, the identity of every one of these $D$ messages must be kept private individually. In this work, we characterize the capacity, which is defined as the maximum achievable download rate, under both joint and individual privacy requirements. In particular, we show that when joint privacy is required the capacity is given by ${(1+1/N+\dots+1/N^{K-D})^{-1}}$, and when individual privacy is required the capacity is given by ${(1+1/N+\dots+1/N^{\lceil K/D\rceil-1})^{-1}}$ assuming that $D$ divides $K$, or $K\pmod D$ divides $D$. Our converse proofs are based on reduction from two variants of the multi-server Private Information Retrieval problem in the presence of side information. Our achievability schemes build up on our recently proposed schemes for single-server Private Linear Transformation and the multi-server private computation scheme proposed by Sun and Jafar. Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute $L$ (potentially more than one) linear combinations.
Supervised Contrastive Learning for Interpretable Long-Form Document Matching
Authors: Akshita Jha, Vineeth Rakesh, Jaideep Chandrashekar, Adithya Samavedhi, Chandan K. Reddy
Date: 2021-08-20 00:00:00
Tasks: Articles, Contrastive Learning, Form, Semantic Text Matching, Text Matching

Recent advancements in deep learning techniques have transformed the area of semantic text matching. However, most state-of-the-art models are designed to operate with short documents such as tweets, user reviews, comments, etc. These models have fundamental limitations when applied to long-form documents such as scientific papers, legal documents, and patents. When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of "similarity"), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content. In this paper, we describe CoLDE: Contrastive Long Document Encoder - a transformer-based framework that addresses these challenges and allows for interpretable comparisons of long documents. CoLDE uses unique positional embeddings and a multi-headed chunkwise attention layer in conjunction with a supervised contrastive learning framework to capture similarity at three different levels: (i) high-level similarity scores between a pair of documents, (ii) similarity scores between different sections within and across documents, and (iii) similarity scores between different chunks in the same document and across other documents. These fine-grained similarity scores aid in better interpretability. We evaluate CoLDE on three long document datasets namely, ACL Anthology publications, Wikipedia articles, and USPTO patents. Besides outperforming the state-of-the-art methods on the document matching task, CoLDE is also robust to changes in document length and text perturbations and provides interpretable results.
Is News Recommendation a Sequential Recommendation Task?
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Date: 2021-08-20 00:00:00
Tasks: Diversity, News Recommendation, Sequential Recommendation

News recommendation is often modeled as a sequential recommendation task, which assumes that there are rich short-term dependencies over historical clicked news. However, in news recommendation scenarios users usually have strong preferences on the temporal diversity of news information and may not tend to click similar news successively, which is very different from many sequential recommendation scenarios such as e-commerce recommendation. In this paper, we study whether news recommendation can be regarded as a standard sequential recommendation problem. Through extensive experiments on two real-world datasets, we find that modeling news recommendation as a sequential recommendation problem is suboptimal. To handle this challenge, we further propose a temporal diversity-aware news recommendation method that can promote candidate news that are diverse from recently clicked news, which can help predict future clicks more accurately. Experiments show that our approach can consistently improve various news recommendation methods.
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
Authors: Gokul Krishnan, Sumit K. Mandal, Manvitha Pannala, Chaitali Chakrabarti, Jae-sun Seo, Umit Y. Ogras, Yu Cao
Date: 2021-08-14 00:00:00
Tasks: Benchmarking

In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes. 2.5D integration or chiplet-based architectures interconnect multiple small chips (i.e., chiplets) to form a large computing system, presenting a feasible solution beyond a monolithic IMC architecture to accelerate large deep learning models. This paper presents a new benchmarking simulator, SIAM, to evaluate the performance of chiplet-based IMC architectures and explore the potential of such a paradigm shift in IMC architecture design. SIAM integrates device, circuit, architecture, network-on-chip (NoC), network-on-package (NoP), and DRAM access models to realize an end-to-end system. SIAM is scalable in its support of a wide range of deep neural networks (DNNs), customizable to various network structures and configurations, and capable of efficient design space exploration. We demonstrate the flexibility, scalability, and simulation speed of SIAM by benchmarking different state-of-the-art DNNs with CIFAR-10, CIFAR-100, and ImageNet datasets. We further calibrate the simulation results with a published silicon result, SIMBA. The chiplet-based IMC architecture obtained through SIAM shows 130$\times$ and 72$\times$ improvement in energy-efficiency for ResNet-50 on the ImageNet dataset compared to Nvidia V100 and T4 GPUs.
Device-Free Sensing in OFDM Cellular Network
Authors: Qin Shi, Liang Liu, Shuowen Zhang, Shuguang Cui
Date: 2021-08-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper considers device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular network to enable integrated sensing and communication (ISAC). A novel two-phase sensing framework is proposed to localize the passive targets that cannot transmit/receive reference signals to/from the base stations (BSs), where the ranges of the targets are estimated based on their reflected OFDM signals to the BSs in Phase I, and the location of each target is estimated based on its ranges to different BSs in Phase II. Specifically, in Phase I, we design a model-free range estimation approach by leveraging the OFDM channel estimation technique for determining the delay values of all the two-way BS-target-BS paths, which does not rely on any BS-target channel model. In Phase II, we reveal that ghost targets may be falsely detected in some cases as all the targets reflect the same signals to the BSs, which thus do not know how to match each estimated range with the right target. Interestingly, we show that the above data association issue is not a fundamental limitation for device-free sensing: under the ideal case of perfect range estimation in Phase I, the probability for ghost targets to exist is proved to be negligible when the targets are randomly located. Moreover, under the practical case of imperfect range estimation in Phase I, we propose an efficient algorithm for joint data association and target localization in Phase II. Numerical results show that our proposed two-phase framework can achieve very high accuracy in the localization of passive targets, which increases with the system bandwidth.
Latency-Constrained Highly-Reliable mmWave Communication via Multi-point Connectivity
Authors: Dileep Kumar, Satya Joshi, Antti Tölli
Date: 2021-08-20 00:00:00

The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving low-latency and ultra-reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We propose a novel blockage-aware algorithm for the sum-power minimization problem under the user-specific latency requirements in a dynamic mobile access network. We use the Lyapunov optimization framework, and provide a dynamic control algorithm, which efficiently transforms a time-average stochastic problem into a sequence of deterministic subproblems. A robust beamformer design is then proposed by exploiting the queue backlogs and channel information, that efficiently allocates the required radio and cooperation resources, and proactively leverages the multi-antenna spatial diversity according to the instantaneous needs of the users. Further, to adapt to the uncertainties of the mmWave channel, we consider a pessimistic estimate of the rates over link blockage combinations and an adaptive selection of the CoMP serving set from the available remote radio units (RRUs). Moreover, after the relaxation of coupled and non-convex constraints via the Fractional Program (FP) techniques, a low-complexity closed-form iterative algorithm is provided by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. The simulation results manifest that, in the presence of random blockages, the proposed methods outperform the baseline scenarios and provide power-efficient, high-reliable, and low-latency mmWave communication.
Agentization of Two Population-Driven Models of Mathematical Biology
Authors: John C. Stevenson
Date: 2021-08-16 00:00:00
Tasks: Vocal Bursts Valence Prediction

Single species population models and discrete stochastic gene frequency models are two standards of mathematical biology important for the evolution of populations. An agent based model is presented which reproduces these models and then explores where these models agree and disagree under relaxed specifications. For the population models, the requirement of homogeneous mixing prevents prediction of extinctions due to local resource depletion. These models also suggest equilibrium based on attainment of constant population levels though underlying population characteristics may be nowhere close to equilibrium. The discrete stochastic gene frequency models assume well mixed populations at constant levels. The models' predictions for non-constant populations in strongly oscillating and chaotic regimes are surprisingly good, only diverging from the ABM at the most chaotic levels.
Abnormal Road Surface Detection Using Wheel Sensor Data
Authors: Tamás Dózsa, János Radó, János Volk, Ádám Kisari, Alexandros Soumelidis, Péter Kovács
Date: 2021-08-20 00:00:00
Tasks: Anomaly Detection, Binary Classification

Intelligent tires can be used for a wide array of applications ranging from tire pressure monitoring to analyzing tire/road interactions, wheel loading, and tread wear monitoring. In this article, we develop a measurement system for intelligent tires equipped with a 3-D piezoresistive force sensor. The output of the sensor is segmented into tire revolution cycles, which are then represented by a transformation relying on adaptive Hermite functions. The underlying idea behind this step is to extract relevant features which capture tire dynamics. Then we evaluate the proposed measurement system in a potential vehicle application, that is, abnormal road surface detection. We deal with the corresponding binary classification problem by developing both low-complexity analytical and data-driven machine learning algorithms, which are tested on real-world measurement data. Our experiments showed that the proposed methods are able to detect abnormalities on the road surface with a mean accuracy of over 97%.
The Effect of Dust and Sand on the 5G Terrestrial Links
Authors: Esmail M M Abuhdima, Gurcan Comert, Pierluigi Pisu, Chin-Tser Huang, Ahmed El Qaouaq, Chunheng Zhao, Shakendra Alston, Kirk Ambrose, Jian Liu
Date: 2021-08-20 00:00:00
Tasks: Sand

Wireless connections are a communication channel used to support different applications in our life such as microwave connections, mobile cellular networks, and intelligent transportation systems. The wireless communication channels are affected by different weather factors such as rain, snow, fog, dust, and sand. This effect is more evident in the high frequencies of the millimeter-wave (mm-wave) band. Recently, the 5G opened the door to support different applications with high speed and good quality. A recent study investigates the effect of rain and snow on the 5G communication channel to reduce the challenge of using high millimeter-wave frequencies. This research investigates the impact of dust and sand on the communication channel of 5G mini links using Mie scattering model to estimate the propagating wave's attenuation by computing the free space loss of a dusty region. Also, the cross-polarization of the propagating wave with dust and sand is taken into account at different distances of the propagating length. Two kinds of mini links, ML-6363, and ML-6352, are considered to demonstrate the effect of dust and sand in these specific operating frequency bands. The 73.5 GHz (V-band) and (21.5GHz (K-band) are the ML-6352 and ML-6363 radio frequency, respectively. Also, signal depolarization is another important radio frequency transmission parameter that is considered heroin. The numerical and simulation results show that the 5G ML-6352 is more effect by dust and sand than ML6363. The 5G toolbox is used to build the communication system and simulate the effect of the dust and sand on the different frequency bands.
Uncertainties and output feedback in rollout event-triggered control
Authors: Stefan Wildhagen, Frank Allgöwer
Date: 2021-08-20 00:00:00
Tasks: Model Predictive Control

The fact that event-triggered control (ETC) often exhibits an improved performance-communication tradeoff over time-triggered control renders it especially useful for Networked Control Systems (NCSs). However, it has proven difficult to characterize the traffic produced by ETC a priori. Rollout ETC addresses this issue by using a triggering and control law that is implicitly defined by the solution to an optimal control problem (OCP), instead of an explicit one as in classical ETC. This allows to directly incorporate predefined constraints on the transmission traffic as well as on states and inputs. In this article, we examine the practically relevant case when output instead of state measurements are available, and measurements as well as the LTI plant are subject to uncertainties. To address these challenges, we adapt methods from robust tube-based model predictive control and propose three different strategies to implement an error feedback in an NCSs setup, the applicability of which depends on the capabilities of the actuator. We establish recursive feasibility, robust constraint satisfaction and convergence. Finally, we illustrate our results in a numerical example.
Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints
Authors: Cephas Samende, Jun Cao, Zhong Fan
Date: 2021-08-20 00:00:00
Tasks: energy trading

In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading. Due to (i) uncertainties caused by renewable energy generation and consumption, (ii) difficulties in developing an accurate and efficient energy trading model, and (iii) the need to satisfy distribution network constraints, it is challenging for prosumers to obtain optimal energy trading decisions that minimize their individual energy costs. To address the challenge, we first formulate the above problem as a Markov decision process and propose a multi-agent deep deterministic policy gradient algorithm to learn optimal energy trading decisions. To satisfy the distribution network constraints, we propose distribution network tariffs which we incorporate in the algorithm as incentives to incentivize energy trading decisions that help to satisfy the constraints and penalize the decisions that violate them. The proposed algorithm is model-free and allows the agents to learn the optimal energy trading decisions without having prior information about other agents in the network. Simulation results based on real-world datasets show the effectiveness and robustness of the proposed algorithm.
Impact of Aviation Electrification on Airports: Flight Scheduling and Charging
Authors: Boya Hou, Subhonmesh Bose, Lavanya Marla, Kiruba Haran
Date: 2021-08-20 00:00:00
Tasks: Scheduling

Electrification can help to reduce the carbon footprint of aviation. The transition away from jet fuel-powered conventional airplane towards battery-powered electrified aircraft will impose extra charging requirements on airports. In this paper, we first quantify the increase in energy demands at several airports across the United States (US), when commercial airline carriers partially deploy hybrid electric aircraft (HEA). We then illustrate that smart charging and minor modifications to flight schedules can substantially reduce peak power demands, and in turn the needs for grid infrastructure upgrade. Motivated by our data analysis, we then formulate an optimization problem for slot allocation that incorporates HEA charging considerations. This problem jointly decides flight schedules and charging profiles to manage airport congestion and peak power demands. We illustrate the efficacy of our formulation through a case study on the John F. Kennedy International Airport.
Real-time Transient Simulation and Studies of Offshore Wind Turbines
Authors: Thai-Thanh Nguyen, Tuyen Vu, Thomas Ortmeyer, George Stefopoulos, Greg Pedrick, Jason MacDowell
Date: 2021-08-19 00:00:00

This paper presents developed real-time simulation models for offshore wind turbine generators in compliance with industry standards. The critical control functions such as negative sequence injection, sequence current limit, voltage ride through, and power curtailments are designed to meet the industry requirements for future electromagnetic transient (EMT) testing and controls of offshore wind farms. Average-value and switching detailed models are developed in the Opal-RT real-time simulator. Real-time capabilities of these models are compared to show the effectiveness of the average-value model in terms of accuracy and computation efficiency. Studies of balanced and unbalanced faults illustrate the ability of the proposed turbine models to inject active and reactive currents during fault events. The models are validated against the second-generation generic wind turbine model proposed by Western Electricity Coordinating Council (WECC). Validation results reveal that the proposed models are aligned with the WECC generic model. In addition, the models provide an extended capability in mitigating the active power oscillation during unbalanced fault conditions.
Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
Authors: Bingzhuo Zhong, Abolfazl Lavaei, Hongpeng Cao, Majid Zamani, Marco Caccamo
Date: 2021-02-10 00:00:00

High performance but unverified controllers, e.g., artificial intelligence-based (a.k.a. AI-based) controllers, are widely employed in cyber-physical systems (CPSs) to accomplish complex control missions. However, guaranteeing the safety and reliability of CPSs with this kind of controllers is currently very challenging, which is of vital importance in many real-life safety-critical applications. To cope with this difficulty, we propose in this work a Safe-visor architecture for sandboxing unverified controllers in CPSs operating in noisy environments (a.k.a. stochastic CPSs). The proposed architecture contains a history-based supervisor, which checks inputs from the unverified controller and makes a compromise between functionality and safety of the system, and a safety advisor that provides fallback when the unverified controller endangers the safety of the system. Both the history-based supervisor and the safety advisor are designed based on an approximate probabilistic relation between the original system and its finite abstraction. By employing this architecture, we provide formal probabilistic guarantees on preserving the safety specifications expressed by accepting languages of deterministic finite automata (DFA). Meanwhile, the unverified controllers can still be employed in the control loop even though they are not reliable. We demonstrate the effectiveness of our proposed results by applying them to two (physical) case studies.
Sparse-Denoising Methods for Extracting Desaturation Transients in Cerebral Oxygenation Signals of Preterm Infants
Authors: Minoo Ashoori, Eugene M. Dempsey, Fiona B. McDonald, John M. O'Toole
Date: 2021-08-20 00:00:00
Tasks: Denoising

Preterm infants are at high risk of developing brain injury in the first days of life as a consequence of poor cerebral oxygen delivery. Near-infrared spectroscopy (NIRS) is an established technology developed to monitor regional tissue oxygenation. Detailed waveform analysis of the cerebral NIRS signal could improve the clinical utility of this method in accurately predicting brain injury. Frequent transient cerebral oxygen desaturations are commonly observed in extremely preterm infants, yet their clinical significance remains unclear. The aim of this study was to examine and compare the performance of two distinct approaches in isolating and extracting transient deflections within NIRS signals. We optimized three different simultaneous low-pass filtering and total variation denoising (LPF_TVD) methods and compared their performance with a recently proposed method that uses singular-spectrum analysis and the discrete cosine transform (SSA_DCT). Parameters for the LPF_TVD methods were optimized over a grid search using synthetic NIRS-like signals. The SSA_DCT method was modified with a post-processing procedure to increase sparsity in the extracted components. Our analysis, using a synthetic NIRS-like dataset, showed that a LPF_TVD method outperformed the modified SSA_DCT method: median mean-squared error of 0.97 (95% CI: 0.86 to 1.07) was lower for the LPF_TVD method compared to the modified SSA_DCT method of 1.48 (95% CI: 1.33 to 1.63), P<0.001. The dual low-pass filter and total variation denoising methods are considerably more computational efficient, by 3 to 4 orders of magnitude, than the SSA_DCT method. More research is needed to examine the efficacy of these methods in extracting oxygen desaturation in real NIRS signals.