pwc-archive/papers-with-abstracts huggingface dataset:
Parallel APSM for Fast and Adaptive Digital SIC in Full-Duplex Transceivers with Nonlinearity
Authors: M. Hossein Attar, Omid Taghizadeh, Kaxin Chang, Ramez Askar, Matthias Mehlhose, Slawomir Stanczak
Date: 2022-07-12 00:00:00

This paper presents a kernel-based adaptive filter that is applied for the digital domain self-interference cancellation (SIC) in a transceiver operating in full-duplex (FD) mode. In FD, the benefit of simultaneous transmission and receiving of signals comes at the price of strong self-interference (SI). In this work, we are primarily interested in suppressing the SI using an adaptive filter namely adaptive projected subgradient method (APSM) in a reproducing kernel Hilbert space (RKHS) of functions. Using the projection concept as a powerful tool, APSM is used to model and consequently remove the SI. A low-complexity and fast-tracking algorithm is provided taking advantage of parallel projections as well as the kernel trick in RKHS. The performance of the proposed method is evaluated on real measurement data. The method illustrates the good performance of the proposed adaptive filter, compared to the known popular benchmarks. They demonstrate that the kernel-based algorithm achieves a favorable level of digital SIC while enabling parallel computation-based implementation within a rich and nonlinear function space, thanks to the employed adaptive filtering method.
Edge Augmentation on Disconnected Graphs via Eigenvalue Elevation
Authors: Tianyi Li
Date: 2022-07-12 00:00:00
Tasks: Community Detection

The graph-theoretical task of determining most likely inter-community edges based on disconnected subgraphs' intra-community connectivity is proposed. An algorithm is developed for this edge augmentation task, based on elevating the zero eigenvalues of graph's spectrum. Upper bounds for eigenvalue elevation amplitude and for the corresponding augmented edge density are derived and are authenticated with simulation on random graphs. The algorithm works consistently across synthetic and real networks, yielding desirable performance at connecting graph components. Edge augmentation reverse-engineers graph partition under different community detection methods (Girvan-Newman method, greedy modularity maximization, label propagation, Louvain method, and fluid community), in most cases producing inter-community edges at >50% frequency.
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
Authors: T. Lucas Makinen, Tom Charnock, Pablo Lemos, Natalia Porqueres, Alan Heavens, Benjamin D. Wandelt
Date: 2022-07-11 00:00:00
Tasks: Clustering, Graph Neural Network

We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter constraints using mock dark matter halo catalogues. We employ Information Maximising Neural Networks (IMNNs) to quantify Fisher information extraction as a function of graph representation. We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference. We reduce the area of joint $\Omega_m, \sigma_8$ parameter constraints with small ($\sim$100 object) halo catalogues by a factor of 42 over the two-point correlation function, and demonstrate that the networks automatically combine mass and clustering information. This work utilises a new IMNN implementation over graph data in Jax, which can take advantage of either numerical or auto-differentiability. We also show that graph IMNNs successfully compress simulations away from the fiducial model at which the network is fitted, indicating a promising alternative to n-point statistics in catalogue simulation-based analyses.
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval
Authors: Meilin Yang, Jian Xu, Yang Liu, Wenbo Ding
Date: 2022-07-12 00:00:00
Tasks: Deep Hashing, Federated Learning, Retrieval

Deep hashing has been widely applied in large-scale data retrieval due to its superior retrieval efficiency and low storage cost. However, data are often scattered in data silos with privacy concerns, so performing centralized data storage and retrieval is not always possible. Leveraging the concept of federated learning (FL) to perform deep hashing is a recent research trend. However, existing frameworks mostly rely on the aggregation of the local deep hashing models, which are trained by performing similarity learning with local skewed data only. Therefore, they cannot work well for non-IID clients in a real federated environment. To overcome these challenges, we propose a novel federated hashing framework that enables participating clients to jointly train the shared deep hashing model by leveraging the prototypical hash codes for each class. Globally, the transmission of global prototypes with only one prototypical hash code per class will minimize the impact of communication cost and privacy risk. Locally, the use of global prototypes are maximized by jointly training a discriminator network and the local hashing network. Extensive experiments on benchmark datasets are conducted to demonstrate that our method can significantly improve the performance of the deep hashing model in the federated environments with non-IID data distributions.
TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge Devices
Authors: Mohammad Loni, Hamid Mousavi, Mohammad Riazati, Masoud Daneshtalab, and Mikael Sjodin
Date: 2022-03-14 00:00:00
Proceeding: Design, Automation and Test in Europe Conference (DATE) 2022 3
Tasks: Neural Architecture Search, Quantization

Ternary Neural Networks (TNNs) compress network
weights and activation functions into 2-bit representation resulting in remarkable network compression and energy efficiency.
However, there remains a significant gap in accuracy between
TNNs and full-precision counterparts. Recent advances in Neural
Architectures Search (NAS) promise opportunities in automated
optimization for various deep learning tasks. Unfortunately, this
area is unexplored for optimizing TNNs. This paper proposes
TAS, a framework that drastically reduces the accuracy gap
between TNNs and their full-precision counterparts by integrating quantization into the network design. We experienced
that directly applying NAS to the ternary domain provides
accuracy degradation as the search settings are customized for
full-precision networks. To address this problem, we propose (i) a
new cell template for ternary networks with maximum gradient
propagation; and (ii) a novel learnable quantizer that adaptively
relaxes the ternarization mechanism from the distribution of the
weights and activation functions. Experimental results reveal that
TAS delivers 2.64% higher accuracy and ≈2.8× memory saving
over competing methods with the same bit-width resolution on the
CIFAR-10 dataset. These results suggest that TAS is an effective
method that paves the way for the efficient design of the next
generation of quantized neural networks.
A Lottery-Demand-Based Explanation of the Beta Anomaly
Authors: Turan G. Bali, Stephen J. Brown, Scott Murray, and Yi Tang
Date: 2017-12-01 00:00:00
Proceeding: JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 52, No. 6 2017 12
Tasks: regression

The low (high) abnormal returns of stocks with high (low) beta, which we refer to as the beta anomaly, is one of the most persistent anomalies in empirical asset pricing research. This article demonstrates that investors’ demand for lottery-like stocks is an important driver of the beta anomaly. The beta anomaly is no longer detected when beta-sorted portfolios are neutralized to lottery demand, regression specifications control for lottery demand, or factor models include a lottery demand factor. The beta anomaly is concentrated in stocks with low levels of institutional ownership and it exists only when the price impact of lottery demand is concentrated in high-beta stocks.
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization
Authors: Xianxin Song, Jie Xu, Fan Liu, Tony Xiao Han, Yonina C. Eldar
Date: 2022-07-12 00:00:00

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is dedicatedly deployed to assist an access point (AP) to sense a target at its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. We consider two types of target models, namely the point and extended targets, for which the AP aims to estimate the target's direction-of-arrival (DoA) and the target response matrix with respect to the IRS, respectively, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cram\'er-Rao bound (CRB) on the estimation error. Towards this end, we first obtain the CRB expressions for the two target models in closed form. It is shown that in the point target case, the CRB for estimating the DoA depends on both the transmit and reflective beamformers; while in the extended target case, the CRB for estimating the target response matrix only depends on the transmit beamformers. Next, for the point target case, we optimize the joint beamforming design to minimize the CRB, via alternating optimization, semi-definite relaxation, and successive convex approximation. For the extended target case, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form. Finally, numerical results show that for both cases, the proposed designs based on CRB minimization achieve improved sensing performance in terms of mean squared error, as compared to other traditional schemes.
Herd Routes: A Preventative IoT-Based System for Improving Female Pedestrian Safety on City Streets
Authors: Madeleine Woodburn, Wynita M. Griggs, Jakub Marecek, Robert N. Shorten
Date: 2022-07-12 00:00:00

Over two thirds of women of all ages in the UK have experienced some form of sexual harassment in a public space. Recent tragic incidents involving female pedestrians have highlighted some of the personal safety issues that women still face in cities today. There exist many popular location-based safety applications as a result of this; however, these applications tend to take a reactive approach where action is taken only after an incident has occurred. This paper proposes a preventative approach to the problem by creating safer public environments through societal incentivisation. The proposed system, called "Herd Routes", improves the safety of female pedestrians by generating busier pedestrian routes as a result of route incentivisation. A novel application of distributed ledgers is proposed to provide security and trust, a record of system users' locations and IDs, and a platform for token exchange. A proof-of-concept was developed using the simulation package SUMO (Simulation of Urban Mobility), and a smartphone app. was built in Android Studio so that pedestrian Hardware-in-the-Loop testing could be carried out to validate the technical feasibility and desirability of the system. With positive results from the initial testing of the proof-of-concept, further development could significantly contribute towards creating safer pedestrian routes through cities, and tackle the societal change that is required to improve female pedestrian safety in the long term.
Cluster-Based Control of Transition-Independent MDPs
Authors: Carmel Fiscko, Soummya Kar, Bruno Sinopoli
Date: 2022-07-11 00:00:00
Tasks: Clustering

This work studies efficient solution methods for cluster-based control policies of transition-independent Markov decision processes (TI-MDPs). We focus on control of multi-agent systems, whereby a central planner (CP) influences agents to select desirable group behavior. The agents are partitioned into disjoint clusters whereby agents in the same cluster receive the same controls but agents in different clusters may receive different controls. Under mild assumptions, this process can be modeled as a TI-MDP where each factor describes the behavior of one cluster. The action space of the TI-MDP becomes exponential with respect to the number of clusters. To efficiently find a policy in this rapidly scaling space, we propose a clustered Bellman operator that optimizes over the action space for one cluster at any evaluation. We present Clustered Value Iteration (CVI), which uses this operator to iteratively perform "round robin" optimization across the clusters. CVI converges exponentially faster than standard value iteration (VI), and can find policies that closely approximate the MDP's true optimal value. A special class of TI-MDPs with separable reward functions are investigated, and it is shown that CVI will find optimal policies on this class of problems. Finally, the optimal clustering assignment problem is explored. The value functions TI-MDPs with submodular reward functions are shown to be submodular functions, so submodular set optimization may be used to find a near optimal clustering assignment. We propose an iterative greedy cluster splitting algorithm, which yields monotonic submodular improvement in value at each iteration. Finally, simulations offer empirical assessment of the proposed methods.
Safe Drone Flight with Time-Varying Backup Controllers
Authors: Andrew Singletary, Aiden Swann, Ivan Dario Jimenez Rodriguez, Aaron D. Ames
Date: 2022-07-11 00:00:00
Tasks: Computational Efficiency

The weight, space, and power limitations of small aerial vehicles often prevent the application of modern control techniques without significant model simplifications. Moreover, high-speed agile behavior, such as that exhibited in drone racing, make these simplified models too unreliable for safety-critical control. In this work, we introduce the concept of time-varying backup controllers (TBCs): user-specified maneuvers combined with backup controllers that generate reference trajectories which guarantee the safety of nonlinear systems. TBCs reduce conservatism when compared to traditional backup controllers and can be directly applied to multi-agent coordination to guarantee safety. Theoretically, we provide conditions under which TBCs strictly reduce conservatism, describe how to switch between several TBC's and show how to embed TBCs in a multi-agent setting. Experimentally, we verify that TBCs safely increase operational freedom when filtering a pilot's actions and demonstrate robustness and computational efficiency when applied to decentralized safety filtering of two quadrotors.
Statistics of the interaural parameters for dichotic tones in diotic noise ($N_0 S_ψ$)
Authors: Jörg Encke, Mathias Dietz
Date: 2022-07-12 00:00:00

Stimuli consisting of an interaurally phase-shifted tone in diotic noise -- often referred to as $N_0 S_\psi$ -- are commonly used in the field of binaural hearing. As a consequence of mixing diotic noise with a dichotic tone, this type of stimulus contains random fluctuations in both interaural phase- and level-difference. This study reports the joint probability density functions of the two interaural differences as a function of amplitude and interaural phase of the tone. Furthermore, a second joint probability density function for interaural phase differences and the instantaneous power of the stimulus is derived.
Set input-to-state stability under input delays for nonlinear systems with disturbances
Authors: Pallavi Sinha, Irinel-Constantin Morărescu, Sukumar Srikant
Date: 2022-07-12 00:00:00

The study proposes new results on the set input-to-state stability (ISS) subject to a small input time delay for compact, invariant sets that contains the origin. First, using the nonlinear small-gain theory, we prove a Razumikhin-type theorem that ensures ISS for sets in the setup of functional differential equations (FDEs) with disturbances. Next we show how this theorem can be used to ensure set ISS for nonlinear systems with input delays and disturbances. In comparison to the existing research on set ISS robustness with respect to small time delays at the input, our results are rather broad, retaining the ISS gain and without any constraints on time delayed states. The effectiveness of the method is illustrated through two case-studies addressing set stability for classes of nonlinear oscillators of practical interest.
Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence
Authors: Dulip Madurasinghe, Ganesh K. Venayagamoorthy
Date: 2022-07-12 00:00:00
Tasks: energy management, Management

Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition.
Age-of-Information in Clocked Networks
Authors: R. Schoeffauer, G. Wunder
Date: 2022-07-12 00:00:00

We derive key features of the Age-of-Information distribution in a system whose activities are strictly limited to periodic instances on a global time grid. In particular, one agent periodically generates updates while the other agent periodically uses the most recently received of those updates. Likewise, transmission of those updates over a network can only occur periodically. All periods may differ. We derive results for two different models: a basic one in which the mathematical problems can be handled directly and an extended model which, among others, can also account for stochastic transmission failure, making the results applicable to instances with wireless communication. For both models, a suitable approximation for the expected Age-of-Information and an upper bound for its largest occurring value are developed. For the extended model (which is the more relevant one from a practical standpoint) we also present numerical results for the distribution of the approximation error for numerous parameter choices.
Adaptive and Robust Cross-Voltage-Level Power Flow Control of Active Distribution Networks
Authors: Jannik Zwartscholten, Christian Rehtanz
Date: 2022-07-12 00:00:00
Tasks: Scheduling

The large-scale integration of Distributed Energy Resources (DERs) into the electric power system offers new opportunities to ensure stability. For example, Active Distribution Networks (ADNs) can be used in (sub-)transmission systems in the emergency state, as far as high robustness and performance of the ADN control are guaranteed. This paper presents an adaptive control system for ADN's cross-voltage-level power flow control. For this purpose, the gain scheduling approach is used. Furthermore, this work introduces a method for control parameter tuning. In order to validate the control parameter tuning, the adaptive control system is analyzed regarding robustness and performance using an exemplary medium voltage grid. In addition, the influence of uncertainties is examined. Finally, the operation of the adaptive control system is demonstrated by performing time-domain simulations.
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures
Authors: Mostafa Mohammadkarimi, Geert Leus, Raj Thilak Rajan
Date: 2022-07-12 00:00:00
Tasks: Combinatorial Optimization

A new method for joint ranging and Phase Offset (PO) estimation of multiple drones/aircrafts is proposed in this paper. The proposed method employs the superimposed uncoordinated Automatic Dependent Surveillance Broadcast (ADS-B) packets broadcasted by drones/aircrafts for joint range and PO estimation. It jointly estimates range and PO prior to ADS-B packet decoding; thus, it can improve air safety when packet decoding is infeasible due to packet collision. Moreover, it enables coherent detection of ADS-B packets, which can result in more reliable multiple target tracking in aviation systems using cooperative sensors for detect and avoid (DAA). By minimizing the Kullback Leibler Divergence (KLD) statistical distance measure, we show that the received complex baseband signal coming from K uncoordinated drones corrupted by Additive White Gaussian Noise (AWGN) at a single antenna receiver can be approximated by an independent and identically distributed Gaussian Mixture (GM) with 2 power K mixture components in the two dimensional (2D) plane. While direct joint Maximum Likelihood Estimation (MLE) of range and PO from the derived GM Probability Density Function (PDF) leads to an intractable maximization, our proposed method employs the Expectation Maximization (EM) algorithm to estimate the modes of the 2D Gaussian mixture followed by a reordering estimation technique through combinatorial optimization to estimate range and PO. An extension to a multiple antenna receiver is also investigated in this paper. While the proposed estimator can estimate the range of multiple drones with a single receive antenna, a larger number of drones can be supported with higher accuracy by the use of multiple antennas at the receiver. The effectiveness of the proposed estimator is supported by simulation results. We show that the proposed estimator can jointly estimate the range of three drones accurately.
AC-feasible Local Flexibility Market with Continuous Trading
Authors: Aikaterini A. Forouli, Georgios K. Papazoglou, Emmanouil A. Bakirtzis, Pandelis N. Biskas, Anastasios G. Bakirtzis
Date: 2022-07-12 00:00:00

This paper proposes a novel continuous Local Flexibility Market where active power flexibility located in the distribution system can be traded. The market design engages the Market Operator, the Distribution System Operator and Market Participants with dispatchable assets. The proposed market operates in a single distribution system and considers network constraints via AC network sensitivities, calculated at an initial network operating point. Trading is possible when AC network constraints are respected and when anticipated network violations are alleviated or resolved. The implementation allows for partial bid matching and is computationally light, therefore, suitable for continuous trading applications. The proposed design is thoroughly described and is demonstrated in a test distribution system. It is shown that active power trading in the proposed market design can lead to resolution of line overloads.