pwc-archive/papers-with-abstracts huggingface dataset:
On Cache-Aided Multi-User Private Information Retrieval with Small Caches
Authors: Charul Rajput, B. Sundar Rajan
Date: 2022-12-25 00:00:00
Tasks: All, Information Retrieval, Retrieval

In this paper, we propose a scheme for the problem of cache-aided multi-user private information retrieval with small caches, in which $K$ users are connected to $S$ non-colluding databases via shared links. Each database contains a set of $N$ files, and each user has a dedicated cache of size equivalent to the size of $M$ files. All the users want to retrieve a file without revealing their demands to the databases. During off-peak hours, all the users will fill their caches, and when required, users will demand their desired files by cooperatively generating query sets for each database. After receiving the transmissions from databases, all the users should get their desired files using transmitted data and their cache contents. This problem has been studied in [X. Zhang, K. Wan, H. Sun, M. Ji and G. Caire, \tqt{Fundamental limits of cache-aided multiuser private information retrieval}, IEEE Trans. Commun., 2021], in which authors proposed a product design scheme. In this paper, we propose a scheme that gives a better rate for a particular value of $M$ than the product design scheme. We consider a slightly different approach for the placement phase. Instead of a database filling the caches of all users directly, a database will broadcast cache content for all users on a shared link, and then the users will decide unitedly which part of the broadcasted content will be stored in the cache of each user. This variation facilitates maintaining the privacy constraint at a reduced rate.
A Large-scale Friend Suggestion Architecture
Authors: Lin Zhang, Rui Li
Date: 2022-12-24 00:00:00

Online social as an extension of traditional life plays an important role in our daily lives. Users often seek out new friends that have significant similarities such as interests and habits, motivating us to exploit such online information to suggest friends to users. In this work, we focus on friend suggestion in online game platforms because in-game social quality significantly correlates with player engagement, determining game experience. Unlike a typical recommendation system that depends on item-user interactions, in our setting, user-user interactions do not depend on each other. Meanwhile, user preferences change rapidly due to fast changing game environment. There has been little work on designing friend suggestion when facing these difficulties, and for the first time we aim to tackle this in large scale online games. Motivated by the fast changing online game environment, we formulate this problem as friend ranking by modeling the evolution of similarity among users, exploiting the long-term and short-term feature of users in games. Our experiments on large-scale game datasets with several million users demonstrate that our proposed model achieves superior performance over other competing baselines.
IR-ORAM: Path Access Type Based Memory Intensity Reduction for Path-ORAM
Authors: Mehrnoosh Raoufi, Youtao Zhang, Jun Yang
Date: 2022-05-17 00:00:00
Proceeding: IEEE International Symposium on High-Performance Computer Architecture (HPCA) 2022 5
Tasks: Computer Architecture and Systems, Computer Security, Position, Privacy Preserving

Path ORAM is an effective ORAM (Oblivious RAM) primitive for protecting memory access patterns. Path ORAM converts each off-chip memory request from user program to tens to hundreds of memory accesses. While several schemes have been proposed to mitigate the total number of memory accesses, Path ORAM remains a highly memory intensive primitive that leads to large memory bandwidth occupation and performance degradation.In this paper, we propose IR-ORAM to reduce the memory intensity based on path access types in Path ORAM. Path accesses in Path ORAM, while being kept oblivious to ensure privacy protection, can be categorized to three types: paths for requested data blocks, paths for position map blocks, and dummy paths. We develop a set of techniques to reduce the memory intensity of each type while ensuring the obliviousness at the same time — we reduce the number of data blocks to access for each tree path, reduce the number of path accesses for position maps, and convert many dummy path accesses to early write-backs of dirty data in LLC. Our experimental results show that IR-ORAM achieves on average 42% performance improvement over the state-of-the-art while effectively enforcing the memory access obliviousness and the same level of security protection.
Dynamic Circular Formation Of Multi-Agent Systems With Obstacle Avoidance And Size Scaling: A Flocking Approach
Authors: Seyed Mohammad Mahdi Seyed Sajadi, Hajar Atrianfar
Date: 2022-12-23 00:00:00

Formation control with the flocking approach is an efficient method that can reach the formation without determining the agent's position. This paper focuses on reaching the circular formation around the leader or target with a specific geometric pattern for the second-order multi-agent system. This means that the polygon formation is formed with arbitrary initial conditions. To create the circular formation, two potential function terms have been used for agent-agent and leader-agent interaction. In our approach, if some faults occur during the circular formation and some agents fail, the regular polygon formation will still form with fewer agents. Obstacle avoidance for a single-circle formation and collision-free motion is guaranteed. A circular formation with size scaling is proposed to better maneuver and pass through obstacles. Also, several circles with the desired radius can be reached with changes in the agent-leader potential function. In this work, optimization algorithms with different scenarios are compared to calculate the parameters of our algorithm.
Learned Lossless Image Compression Through Interpolation With Low Complexity
Authors: Fatih Kamisli
Date: 2022-12-26 00:00:00
Tasks: Image Compression

With the increasing popularity of deep learning in image processing, many learned lossless image compression methods have been proposed recently. One group of algorithms that have shown good performance are based on learned pixel-based auto-regressive models, however, their sequential nature prevents easily parallelized computations and leads to long decoding times. Another popular group of algorithms are based on scale-based auto-regressive models and can provide competitive compression performance while also enabling simple parallelization and much shorter decoding times. However, their major drawback are the used large neural networks and high computational complexity. This paper presents an interpolation based learned lossless image compression method which falls in the scale-based auto-regressive models group. The method achieves better than or on par compression performance with the recent scale-based auto-regressive models, yet requires more than 10x less neural network parameters and encoding/decoding computation complexity. These achievements are due to the contributions/findings in the overall system and neural network architecture design, such as sharing interpolator neural networks across different scales, using separate neural networks for different parameters of the probability distribution model and performing the processing in the YCoCg-R color space instead of the RGB color space.
Deep Quadratic Hedging
Authors: Alessandro Gnoatto, Silvia Lavagnini, Athena Picarelli
Date: 2022-12-24 00:00:00

We propose a novel computational procedure for quadratic hedging in high-dimensional incomplete markets, covering mean-variance hedging and local risk minimization. Starting from the observation that both quadratic approaches can be treated from the point of view of backward stochastic differential equations (BSDEs), we (recursively) apply a deep learning-based BSDE solver to compute the entire optimal hedging strategies paths. This allows us to overcome the curse of dimensionality, extending the scope of applicability of quadratic hedging in high dimension. We test our approach with a classic Heston model and with a multiasset and multifactor generalization thereof, showing that this leads to high levels of accuracy.
Frenet-Cartesian Model Representations for Automotive Obstacle Avoidance within Nonlinear MPC
Authors: Rudolf Reiter, Armin Nurkanović, Jonathan Frey, Moritz Diehl
Date: 2022-12-22 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

In recent years, nonlinear model predictive control (NMPC) has been extensively used for solving automotive motion control and planning tasks. In order to formulate the NMPC problem, different coordinate systems can be used with different advantages. We propose and compare formulations for the NMPC related optimization problem, involving a Cartesian and a Frenet coordinate frame (CCF/ FCF) in a single nonlinear program (NLP). We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints. With this approach, we exploit the simpler formulation of opponent vehicle constraints in the CCF, as well as road aligned costs and constraints related to the FCF. Comparisons to other approaches in a simulation framework highlight the advantages of the proposed approaches.
Adaptive Control of Surge Impedance for Electric Motors in Motor Drive Systems
Authors: Fahmid Sadeque, Fariba Fateh, JiangBiao He, Behrooz Mirafzal
Date: 2022-12-26 00:00:00

This article studies the possibility of controlling the surge impedance of the electric motor in motor drives. The existing solution to suppress (or eliminate) the reflected wave impact on the motor insulation run by a Si-IGBT or SiC-MOSFET-based drive is to use either a sinewave filter or dv/dt filter. The alternative solution suggested in this article is to implement a high-bandwidth electronic circuit at the end of the cable or at the motor terminals to match the surge impedance of the cable and motor. The high-frequency voltage ringing due to the reflected waves in motor drives is around 1 MHz, depending on the cable parameters and the length of the cable. In the proposed method, the electronic circuit can quickly detect the dv/dt rise and fall edges and adjust the electronic circuit equivalent impedance when pulses arrive the motor terminals. Thus, the cable and motor surge impedances can be matched over a short time to prevent reflected waves. As a result, the leakage currents passing through the ball bearing and overvoltage stress on the motor insulation can be suppressed significantly.
Dynamic Speed Harmonization
Authors: Ozgenur Kavas-Torris, Levent Guvenc
Date: 2022-12-25 00:00:00

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to accommodate an ever-increasing number of vehicles every day. However, some roadways, during specific hours of the day, had already been on the brink of reaching their capacity to withstand the number of vehicles travelling on them. Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the speed of a vehicle to prevent it from being affected by bottlenecks has been presented. First, co-simulations were run between MATLAB Simulink and CarSim to test different deceleration profiles. Then, Hardware-in-the-Loop (HIL) simulations were run with a Road Side Unit (RSU), which emulated a roadside detector that spotted bottlenecks and sent information to the Connected Vehicle about the position of the queue and the average speed of the vehicles at the queue. The DSH algorithm was also tested on a track to compare the performance of the different deceleration profiles in terms of ride comfort.
Bounded Disturbance Amplification for Mass Chains with Passive Interconnection
Authors: Kaoru Yamamoto, Malcolm C. Smith
Date: 2022-12-24 00:00:00

This paper introduces the problem of passive control of a chain of N identical masses in which there is an identical passive connection between neighbouring masses and a similar connection to a movable point. The problem arises in the design of multi-storey buildings which are subjected to earthquake disturbances, but applies in other situations, for example vehicle platoons. The paper studies the scalar transfer functions from the disturbance to a given intermass displacement. It is shown that these transfer functions can be conveniently represented in the form of complex iterative maps and that these maps provide a method to establish boundedness in N of the H-infinity norm of these transfer functions for certain choices of interconnection impedance.
Hypertracking and Hyperrejection: Control of Signals beyond the Nyquist Frequency
Authors: Kaoru Yamamoto, Yutaka Yamamoto, Masaaki Nagahara
Date: 2022-12-24 00:00:00

This paper studies the problem of signal tracking and disturbance rejection for sampled-data control systems, where the pertinent signals can reside beyond the so-called Nyquist frequency. In light of the sampling theorem, it is generally understood that manipulating signals beyond the Nyquist frequency is either impossible or at least very difficult. On the other hand, such control objectives often arise in practice, and control of such signals is much desired. This paper examines the basic underlying assumptions in the sampling theorem and pertinent sampled-data control schemes, and shows that the limitation above can be removed by assuming a suitable analog signal generator model. Detailed analysis of multirate closed-loop systems, zeros and poles are given, which gives rise to tracking or rejection conditions. Robustness of the new scheme is fully characterized; it is shown that there is a close relationship between tracking/rejection frequencies and the delay length introduced for allowing better performance. Examples are discussed to illustrate the effectiveness of the proposed method here.
Transmission Congestion Management with Generalized Generation Shift Distribution Factors
Authors: Shutong Pu, Guangchun Ruan, Xinfei Yan, Haiwang Zhong
Date: 2022-12-24 00:00:00
Tasks: Management

A major concern in modern power systems is that the popularity and fluctuating characteristics of renewable energy may cause more and more transmission congestion events. Traditional congestion management modeling involves AC or DC power flow equations, while the former equation always accompanies great amount of computation, and the latter cannot consider voltage amplitude and reactive power. Therefore, this paper proposes a congestion management approach incorporating a specially-designed generalized generator shift distribution factor (GSDF) to derive a computationally-efficient and accurate management strategies. This congestion management strategy involves multiple balancing generators for generation shift operation. The proposed model is superior in a low computational complexity (linear equation) and versatile modeling representation with full consideration of voltage amplitude and reactive power.
ROS-Based Multi-Agent Systems COntrol Simulation Testbed (MASCOT)
Authors: Arvind Pandit, Akash Njattuvetty, Ameer K. Mulla
Date: 2022-12-24 00:00:00

This paper presents a simulation testbed developed for testing and demonstration of decentralized control algorithms designed for multi-agent systems. Aimed at bridging a gap between theory and practical deployment of such algorithms, this testbed provides a simulator with multi-agent systems having quadcopter agents. It is used to test the control algorithms designed assuming simple agent dynamics like a single or double integrator. This is based on the fact that under certain assumptions, quadcopter dynamics can be modeled as a double integrator system. A gazebo simulator with a physics engine as ODE (Open Dynamics Engine) is used for simulating the dynamics of the quadcopter. Robot Operating System is used to develop communication networks and motion control algorithms for the different agents in the simulation testbed. The performance of the test bed is analyzed by implementing linear control laws such as position control, leaderless consensus, leader-follower consensus, and non-linear control law for min-max time consensus. This work is published as an open-source ROS package under MIT license at https://github.com/Avi241/mascot. A docker image is also developed for easy setup of the system.
The fate of the American dream: A first passage under resetting approach to income dynamics
Authors: Petar Jolakoski, Arnab Pal, Trifce Sandev, Ljupco Kocarev, Ralf Metzler, Viktor Stojkoski
Date: 2022-12-26 00:00:00

Detailed knowledge of individual income dynamics is crucial for investigating the existence of the American dream: Are we able to improve our income status during our working life? This key question simply boils down to observing individual status and how it moves between two thresholds: the current income and the desired income. Yet, our knowledge of these temporal properties of income remains limited since we rely on estimates coming from transition matrices which simplify income dynamics by aggregating the individual changes into quantiles and thus overlooking significant microscopic variations. Here, we bridge this gap by employing First Passage Time concepts in a baseline stochastic process with resetting used for modeling income dynamics and developing a framework that is able to crucially disaggregate the temporal properties of income to the level of an individual worker. We find analytically and illustrate numerically that our framework is orthogonal to the transition matrix approach and leads to improved and more granular estimates. Moreover, to facilitate empirical applications of the framework, we introduce a publicly available statistical methodology, and showcase the application using the USA income dynamics data. These results help to improve our understanding on the temporal properties of income in real economies and provide a set of tools for designing policy interventions.
Neural Structure Fields with Application to Crystal Structure Autoencoders
Authors: Naoya Chiba, Yuta Suzuki, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku, Kotaro Saito, Kanta Ono
Date: 2022-12-08 00:00:00

Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure estimation. Among these applications, the inverse design of materials can contribute to explore materials with desired properties without relying on luck or serendipity. We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks. Inspired by the concepts of vector fields in physics and implicit neural representations in computer vision, the proposed NeSF considers a crystal structure as a continuous field rather than as a discrete set of atoms. Unlike existing grid-based discretized spatial representations, the NeSF overcomes the tradeoff between spatial resolution and computational complexity and can represent any crystal structure. We propose an autoencoder of crystal structures that can recover various crystal structures, such as those of perovskite structure materials and cuprate superconductors. Extensive quantitative results demonstrate the superior performance of the NeSF compared with the existing grid-based approach.
An Effective Treatment Approach to Difference-in-Differences with General Treatment Patterns
Authors: Takahide Yanagi
Date: 2022-12-26 00:00:00

We consider a general difference-in-differences model in which the treatment variable of interest may be non-binary and its value may change in each period. It is generally difficult to estimate treatment parameters defined with the potential outcome given the entire path of treatment adoption, because each treatment path may be experienced by only a small number of observations. We propose an alternative approach using the concept of effective treatment, which summarizes the treatment path into an empirically tractable low-dimensional variable, and develop doubly robust identification, estimation, and inference methods. We also provide a companion R software package.
New trends in South-South migration: The economic impact of COVID-19 and immigration enforcement
Authors: Roxana Gutiérrez-Romero, Nayeli Salgado
Date: 2022-12-24 00:00:00

This paper evaluates the impact of the pandemic and enforcement at the US and Mexican borders on the emigration of Guatemalans during 2017-2020. During this period, the number of crossings from Guatemala fell by 10%, according to the Survey of Migration to the Southern Border of Mexico. Yet, there was a rise of nearly 30% in the number of emigration crossings of male adults travelling with their children. This new trend was partly driven by the recent reduction in the number of children deported from the US. For a one-point reduction in the number of children deported from the US to Guatemalan municipalities, there was an increase of nearly 14 in the number of crossings made by adult males leaving from Guatemala for Mexico; and nearly 0.5 additional crossings made by male adults travelling with their children. However, the surge of emigrants travelling with their children was also driven by the acute economic shock that Guatemala experienced during the pandemic. During this period, air pollution in the analysed Guatemalan municipalities fell by 4%, night light per capita fell by 15%, and homicide rates fell by 40%. Unlike in previous years, emigrants are fleeing poverty rather than violence. Our findings suggest that a reduction in violence alone will not be sufficient to reduce emigration flows from Central America, but that economic recovery is needed.
Violence in Guatemala pushes adults and children to seek work in Mexico
Authors: Roxana Gutiérrez-Romero
Date: 2022-12-24 00:00:00

This article estimates the impact of violence on emigration crossings from Guatemala to Mexico as final destination during 2009-2017. To identify causal effects, we use as instruments the variation in deforestation in Guatemala, and the seizing of cocaine in Colombia. We argue that criminal organizations deforest land in Guatemala, fueling violence and leading to emigration, particularly during exogenous supply shocks to cocaine. A one-point increase in the homicide rate differential between Guatemalan municipalities and Mexico, leads to 211 additional emigration crossings made by male adults. This rise in violence, also leads to 20 extra emigration crossings made by children.
Knowledge Management in Management of Social and Economic Development of Municipalities: Highlights
Authors: Maria A. Shishanina, Anatoly A. Sidorov
Date: 2022-12-24 00:00:00
Tasks: Management

The paper discusses the process of social and economic development of municipalities. A conclusion is made that developing an adequate model of social and economic development using conventional approaches presents a considerable challenge. It is proposed to use semantic modeling to represent the social and economic development of municipalities, and cognitive mapping to identify the set of connections that occur among indicators and that have a direct impact on social and economic development.