pwc-archive/papers-with-abstracts huggingface dataset:
Constrained and unconstrained deep image prior optimization models with automatic regularization
Authors: Pasquale Cascarano, Giorgia Franchini, Erich Kobler, Federica Porta, Andrea Sebastiani
Date: 2022-07-27 00:00:00
Proceeding: Computational Optimization and Applications 2022 7
Tasks: Deblurring, Denoising

Deep Image Prior (DIP) is currently among the most efficient unsupervised deep learning based methods for ill-posed inverse problems in imaging. This novel framework relies on the implicit regularization provided by representing images as the output of generative Convolutional Neural Network (CNN) architectures. So far, DIP has been shown to be an effective approach when combined with classical and novel regularizers. Unfortunately, to obtain appropriate solutions, all the models proposed up to now require an accurate estimate of the regularization parameter. To overcome this difficulty, we consider a locally adapted regularized unconstrained model whose local regularization parameters are automatically estimated for additively separable regularizers. Moreover, we propose a novel constrained formulation in analogy to Morozov’s discrepancy principle which enables the application of a broader range of regularizers. Both the unconstrained and the constrained models are solved via the proximal gradient descent-ascent method. Numerical results demonstrate the robustness with respect to image content, noise levels and hyperparameters of the proposed models on both denoising and deblurring of simulated as well as real natural and medical images.
[Reproducibility Report] Path Planning using Neural A* Search
Authors: Shreya Bhatt, Aayush Jain, Parv Maheshwari, Animesh Jha, Debashish Chakravarty
Date: 2022-07-16 00:00:00
Tasks: Motion Planning

The following paper is a reproducibility report for "Path Planning using Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility Challenge 2021. The original paper proposes the Neural A* planner, and claims it achieves an optimal balance between the reduction of node expansions and path accuracy. We verify this claim by reimplementing the model in a different framework and reproduce the data published in the original paper. We have also provided a code-flow diagram to aid comprehension of the code structure. As extensions to the original paper, we explore the effects of (1) generalizing the model by training it on a shuffled dataset, (2) introducing dropout, (3) implementing empirically chosen hyperparameters as trainable parameters in the model, (4) altering the network model to Generative Adversarial Networks (GANs) to introduce stochasticity, (5) modifying the encoder from Unet to Unet++, (6) incorporating cost maps obtained from the Neural A* module in other variations of A* search.
IoT-Shield: A Novel DDoS Detection Approach for IoT-Based Devices
Authors: Ghazaleh Shirvani; Saeid Ghasemshirazi; Behzad Beigzadeh
Date: 2021-12-07 00:00:00
Proceeding: 11th Smart Grid Conference (SGC) 2021 12
Tasks: Management

The widespread deployment of sensors and linked items contributes to the rising interest in the Internet-of-Things (IoT). These are used in conjunction with other Online services to develop highly sophisticated and profitable cloud-based services. Despite significant attempts to secure them, security management remains a crucial problem for these devices, Because of their intricacy, heterogeneous nature, and resource constraints. This paper presents IoT-Shield, a data mining technique that coincides with a process mining approach for identifying misbehavior in of the kind. IoT-Shield enables the characterization of IoT devices' behavioral models and the detection of possible threats, even in the presence of diverse protocols and platforms. The underlying architecture and components are then described and formalized, and a proof-of-concept prototype is detailed. A real-world traffic dataset known as KDD-NSL is used to evaluate the performance of our technique in a series of comprehensive trials. We conclude that XGBoost is the best available data mining algorithm for the IoT DDoS prediction method. We highlight how consolidating process mining with data mining helps in covering heterogeneity and lowering the computational burden.
Persuading Risk-Conscious Agents: A Geometric Approach
Authors: Jerry Anunrojwong, Krishnamurthy Iyer, David Lingenbrink
Date: 2022-08-07 00:00:00

We consider a persuasion problem between a sender and a receiver whose utility may be nonlinear in her belief; we call such receivers risk-conscious. Such utility models arise when the receiver exhibits systematic biases away from expected-utility-maximization, such as uncertainty aversion (e.g., from sensitivity to the variance of the waiting time for a service). Due to this nonlinearity, the standard approach to finding the optimal persuasion mechanism using revelation principle fails. To overcome this difficulty, we use the underlying geometry of the problem to develop a convex optimization framework to find the optimal persuasion mechanism. We define the notion of full persuasion and use our framework to characterize conditions under which full persuasion can be achieved. We use our approach to study binary persuasion, where the receiver has two actions and the sender strictly prefers one of them at every state. Under a convexity assumption, we show that the binary persuasion problem reduces to a linear program, and establish a canonical set of signals where each signal either reveals the state or induces in the receiver uncertainty between two states. Finally, we discuss the broader applicability of our methods to more general contexts, and illustrate our methodology by studying information sharing of waiting times in service systems.
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model
Authors: Ahmet M. Elbir, Wei Shi, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas
Date: 2022-08-07 00:00:00
Tasks: Federated Learning

For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cram\'er-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.
Prediction-based Hybrid Slicing Framework for Service Level Agreement Guarantee in Mobility Scenarios: A Deep Learning Approach
Authors: Heng Zhang, Guangjin Pan, Shugong Xu, Shunqing Zhang, Zhiyuan Jiang
Date: 2022-08-06 00:00:00

Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Inter-slice radio resource allocation (IS-RRA) in the radio access network (RAN) is very important. However, user mobility brings new challenges for optimal IS-RRA. This paper first proposes a soft and hard hybrid slicing framework where a common slice is introduced to realize a trade-off between isolation and spectrum efficiency (SE). To address the challenges posed by user mobility, we propose a two-step deep learning-based algorithm: joint long short-term memory (LSTM)-based network state prediction and deep Q network (DQN)-based slicing strategy. In the proposal, LSTM networks are employed to predict traffic demand and the location of each user in a slicing window level. Moreover, channel gain is mapped by location and a radio map. Then, the predicted channel gain and traffic demand are input to the DQN to output the precise slicing adjustment. Finally, experiment results confirm the effectiveness of our proposed slicing framework: the slices' SLA can be guaranteed well, and the proposed algorithm can achieve near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and SE.
POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning
Authors: Shalini Jain, Yashas Andaluri, S. VenkataKeerthy, Ramakrishna Upadrasta
Date: 2022-07-27 00:00:00
Tasks: CPU, reinforcement-learning, Reinforcement Learning (RL)

The ever increasing memory requirements of several applications has led to increased demands which might not be met by embedded devices. Constraining the usage of memory in such cases is of paramount importance. It is important that such code size improvements should not have a negative impact on the runtime. Improving the execution time while optimizing for code size is a non-trivial but a significant task. The ordering of standard optimization sequences in modern compilers is fixed, and are heuristically created by the compiler domain experts based on their expertise. However, this ordering is sub-optimal, and does not generalize well across all the cases. We present a reinforcement learning based solution to the phase ordering problem, where the ordering improves both the execution time and code size. We propose two different approaches to model the sequences: one by manual ordering, and other based on a graph called Oz Dependence Graph (ODG). Our approach uses minimal data as training set, and is integrated with LLVM. We show results on x86 and AArch64 architectures on the benchmarks from SPEC-CPU 2006, SPEC-CPU 2017 and MiBench. We observe that the proposed model based on ODG outperforms the current Oz sequence both in terms of size and execution time by 6.19% and 11.99% in SPEC 2017 benchmarks, on an average.
FRA-RIR: Fast Random Approximation of the Image-source Method
Authors: Yi Luo, Jianwei Yu
Date: 2022-08-08 00:00:00
Tasks: Denoising, GPU, Room Impulse Response (RIR), Speech Denoising

The training of modern speech processing systems often requires a large amount of simulated room impulse response (RIR) data in order to allow the systems to generalize well in real-world, reverberant environments. However, simulating realistic RIR data typically requires accurate physical modeling, and the acceleration of such simulation process typically requires certain computational platforms such as a graphics processing unit (GPU). In this paper, we propose FRA-RIR, a fast random approximation method of the widely-used image-source method (ISM), to efficiently generate realistic RIR data without specific computational devices. FRA-RIR replaces the physical simulation in the standard ISM by a series of random approximations, which significantly speeds up the simulation process and enables its application in on-the-fly data generation pipelines. Experiments show that FRA-RIR can not only be significantly faster than other existing ISM-based RIR simulation tools on standard computational platforms, but also improves the performance of speech denoising systems evaluated on real-world RIR when trained with simulated RIR. A Python implementation of FRA-RIR is available online\footnote{\url{https://github.com/yluo42/FRA-RIR}}.
SSDPT: Self-Supervised Dual-Path Transformer for Anomalous Sound Detection in Machine Condition Monitoring
Authors: Jisheng Bai, Jianfeng Chen, Mou Wang, Muhammad Saad Ayub, Qingli Yan
Date: 2022-08-06 00:00:00
Tasks: Self-Supervised Learning

Anomalous sound detection for machine condition monitoring has great potential in the development of Industry 4.0. However, these anomalous sounds of machines are usually unavailable in normal conditions. Therefore, the models employed have to learn acoustic representations with normal sounds for training, and detect anomalous sounds while testing. In this article, we propose a self-supervised dual-path Transformer (SSDPT) network to detect anomalous sounds in machine monitoring. The SSDPT network splits the acoustic features into segments and employs several DPT blocks for time and frequency modeling. DPT blocks use attention modules to alternately model the interactive information about the frequency and temporal components of the segmented acoustic features. To address the problem of lack of anomalous sound, we adopt a self-supervised learning approach to train the network with normal sound. Specifically, this approach randomly masks and reconstructs the acoustic features, and jointly classifies machine identity information to improve the performance of anomalous sound detection. We evaluated our method on the DCASE2021 task2 dataset. The experimental results show that the SSDPT network achieves a significant increase in the harmonic mean AUC score, in comparison to present state-of-the-art methods of anomalous sound detection.
High-gain observer for the nitrification process including sensor dynamics
Authors: Patrick Schmidt, Arne-Jens Hempel, Stefan Streif
Date: 2022-08-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Fertilization is commonly used to increase harvests. The lack of knowledge of soil properties and the excessive use of fertilizers can result in overfertilization. Current sensor technology is able to measure the concentrations of some of the involved substances only at selected locations and depths. Point measurements of adjacent sensors in coarse sensor networks can be used to infer upon the state of nitrate concentrations in the sensor surroundings. For this purpose, a high gain observer is proposed. Models of the nitrification process as well as the measurement dynamics for the observer design are derived and discretized on a grid to obtain a system of ordinary differential equations. It is shown that the nonlinearities of the model can be bounded and how the observer gain can be computed via linear matrix inequalities. Furthermore, a model reduction is proposed, which allows the consideration of more grid points. A simulation study demonstrates the proposed approach.
MIMO Super-Twisting Controller using a passivity-based design
Authors: Juan F. Garcia-Mathey, Jaime A. Moreno
Date: 2022-08-08 00:00:00

A novel MIMO homogeneous Super-Twisting Algorithm is proposed in this paper for nonlinear systems with relative degree one, having a time and state-varying uncertain control matrix. The uncertainty is represented by a constant but unknown left matrix factor. Sufficient conditions for stability with full-matrix control gains are established, in contrast to the usual scalar gains. For this a smooth Lyapunov function, based on a passivity interpretation, is used. Moreover, continuous and homogeneous approximations of the classical discontinuous Super-Twisting algorithm are obtained, using a unified analysis method.
Exponentially Stable MRAC of MIMO Switched Systems with Matched Uncertainty and Completely Unknown Control Matrix
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2022-08-08 00:00:00

In this paper an attempt is made to extend the concept of the exponentially stable adaptive control to one class of multi-input-multi-output (MIMO) plants with matched nonlinearity and unknown piecewise constant parameters. Within the intervals between two consecutive parameter switches, the proposed adaptive control system ensures: 1) exponential convergence to zero of the parameter and reference model tracking errors, 2) the monotonicity of the control law adjustable parameters. Both properties are guaranteed in case the regressor is finitely exciting somewhere inside each of such intervals. Compared to the existing methods, the proposed one is applicable to systems with unknown switching signal function and completely unknown control matrix. The theoretical results are supported by the numerical experiments.
Towards Analytical Electromagnetic Models for Reconfigurable Intelligent Surfaces
Authors: Tiebin Mi, Jianan Zhang, Rujing Xiong, Zhengyu Wang, Robert Caiming Qiu
Date: 2022-08-08 00:00:00

Physically accurate and mathematically tractable models are presented to characterize scattering and reflection properties of reconfigurable intelligent surfaces (RISs). We take continuous and discrete strategies to model a single patch and patch array and their interactions with multiple incident electromagnetic (EM) waves. The proposed models consider the effect of the incident and scattered angles, polarization features, and the topology and geometry of RISs. Particularly, a simple system of linear equations can describe the multiple-input multiple-output (MIMO) behaviors of RISs under reasonable assumptions. It can serve as a fundamental model for analyzing and optimizing the performance of RIS-aided systems in the far-field regime. The proposed models are employed to identify the advantages and limitations of three typical configurations. One important finding is that complicated beam reshaping functionality can not be endowed by popular phase compensation configurations. A possible solution is the simultaneous configurations of collecting area and phase shifting. Numerical simulations verify the effectiveness of the proposed configuration schemes.
Flocks, Games, and Cognition: A Geometric Approach
Authors: Udit Halder, Vidya Raju, Matteo Mischiati, Biswadip Dey, P. S. Krishnaprasad
Date: 2022-08-07 00:00:00

Avian flocks display a wide variety of flight behaviors, including steady directed translation of center of mass, rapid change of overall morphology, re-shuffling of positions of individuals within a persistent form, etc. These behaviors may be viewed as flock-scale strategies, emerging from interactions between individuals, accomplishing some collective adaptive purpose such as finding a roost, or mitigating the danger from predator attacks. While we do not conceive the flock as a single cognitive agent, the moment-to-moment decisions of individuals, influenced by their neighbors, appear as if to realize collective strategies that are cognizant of purpose. In this paper, we identify the actions of the flock as allocation of energetic resources, and thereby associate a cognitive cost to behavior. Our notion of cognitive cost reflects the burden arising from rapid re-allocation of resource. Using a recently developed natural geometric approach to kinetic energy allocation, we map the flock behavior to a temporal signature on the standard (probability) simplex. Given the signature of a flocking event, we calculate the cognitive cost as a solution to an optimal control problem based on a game-theoretic model. Alternatively, one can associate to a signature an entropic cost. These two cost measures, when applied to data on starling flocks, show a consistent spread in value across events, and we suggest the possibility that higher cost may arise from predator attacks.
Optimal Parking Planning for Shared Autonomous Vehicles
Authors: Seongjin Choi, Jinwoo Lee
Date: 2022-08-07 00:00:00
Tasks: Autonomous Vehicles

Parking is a crucial element of the driving experience in urban transportation systems. Especially in the coming era of Shared Autonomous Vehicles (SAVs), parking operations in urban transportation networks will inevitably change. Parking stations will serve as storage places for unused vehicles and depots that control the level-of-service of SAVs. This study presents an Analytical Parking Planning Model (APPM) for the SAV environment to provide broader insights into parking planning decisions. Two specific planning scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which considers the target area as a single homogeneous zone, and (ii) Two-zone APPM (T-APPM), which considers the target area as two different zones, such as city center and suburban area. S-APPM offers a closed-form solution to find the optimal density of parking stations and parking spaces and the optimal number of SAV fleets, which is beneficial for understanding the explicit relationship between planning decisions and the given environments, including demand density and cost factors. In addition, to incorporate different macroscopic characteristics across two zones, T-APPM accounts for inter- and intra-zonal passenger trips and the relocation of vehicles. We conduct a case study to demonstrate the proposed method with the actual data collected in Seoul Metropolitan Area, South Korea. Sensitivity analyses with respect to cost factors are performed to provide decision-makers with further insights. Also, we find that the optimal densities of parking stations and spaces in the target area are much lower than the current situations.
Energy Efficiency Analysis of Charging Pads-powered UAV-enabled Wireless Networks
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2022-08-07 00:00:00

This paper analyzes the energy efficiency of a novel system model where unmanned aerial vehicles (UAVs) are used to provide coverage for user hotspots (user clusters) and are deployed on charging pads to enhance the flight time. We introduce a new notion of "cluster pairs" to capture the dynamic nature of the users' spatial distribution in order to exploit one of the top advantages of UAVs, which is the mobility and relocation flexibility. Using tools from stochastic geometry, we first derive a new distance distribution that is vital for energy efficiency analysis. Next, we compute the coverage probability under two deployment strategies: (i) one UAV per cluster pair, and (ii) one UAV per cluster. Finally, we compute the energy efficiency for both strategies. Our numerical results reveal which of the two strategies is better for different system parameters. Our work investigates some new aspects of the UAV-enabled communication system such as the dynamic density of users and the advantages or disadvantages of one- or two-UAV deployment strategies per cluster pair. By considering the relationships between the densities of user cluster pairs and the charging pads, it is shown that an optimal cluster pair density exists to maximize energy efficiency.
Optimal Operation of HVDC Interconnector: Irish Case
Authors: Md Umar Hashmi
Date: 2022-08-06 00:00:00

In September 2018 EirGrid launched the new electricity market. These new market arrangements integrate the all island electricity market with European electricity markets, making optimal use of cross border transmission assets. Ireland operates three operational HVDC interconnectors: Moyle, East-West, and Greenlink and one in development Celtic interconnector which connect Ireland to Scotland, Wales, and France respectively. Irish market operator, EirGrid, can maximize their operational profit by using the price difference in these electricity markets. We propose a profit maximization modelling which considers the line losses and price difference in these different electricity markets and identifies the optimal import/export of power using HVDC interconnectors. These models in future should incorporate the distribution losses, renewable energy curtailment, and Irish power network congestion levels. The proposed modeling is the first step towards implementing a multi-objective HVDC interconnector operating strategy.