pwc-archive/papers-with-abstracts huggingface dataset:
Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
Authors: Eva Lagunas, Flor Ortiz, Geoffrey Eappen, Saed Daoud, Wallace Alves Martins, Jorge Querol, Symeon Chatzinotas, Nicolas Skatchkovsky, Bipin Rajendran, Osvaldo Simeone
Date: 2024-01-12 00:00:00

Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads. As such, NP represents an interesting opportunity for implementing AI tasks on board power-limited satellite communication spacecraft. In this article, we disseminate the findings of a recently completed study which targeted the comparison in terms of performance and power-consumption of different satellite communication use cases implemented on standard AI accelerators and on NPs. In particular, the article describes three prominent use cases, namely payload resource optimization, onboard interference detection and classification, and dynamic receive beamforming; and compare the performance of conventional convolutional neural networks (CNNs) implemented on Xilinx's VCK5000 Versal development card and SNNs on Intel's neuromorphic chip Loihi 2.
Multi-View Near-field Imaging in NLOS with Non-Reconfigurable EM Skins
Authors: Davide Tornielli Bellini, Dario Tagliaferri, Marouan Mizmizi, Stefano Tebaldini, Umberto Spagnolini
Date: 2024-01-12 00:00:00

This paper deals with radar imaging in non-line of sight (NLOS) with the aid of non-reconfigurable electromagnetic skins (NR-EMSs). NR-EMSs are passive metasurfaces whose reflection properties are defined during the manufacturing process, and represent a low-cost alternative to reconfigurable intelligent surfaces to implement advanced wave manipulations. We propose and discuss a multi-view near-field radar imaging system where a moving source progressively illuminates different portions of the NR-EMS, whereby each portion (\textit{module}) is purposely phase-configured to focus the impinging radiation over a desired NLOS area of interest. The source, e.g., a radar-equipped vehicle, synthesizes a wide aperture that maps onto the NR-EMS, allowing NLOS imaging with enhanced resolution compared to the standalone radar capabilities. Simulation results show the feasibility and benefits of such an imaging approach and shed light on a possible practical application of metasurfaces for sensing.
RIS-Aided NLoS Monostatic Sensing under Mobility and Angle-Doppler Coupling
Authors: Mahmut Kemal Ercan, Musa Furkan Keskin, Sinan Gezici, Henk Wymeersch
Date: 2024-01-12 00:00:00

We investigate the problem of reconfigurable intelligent surface (RIS)-aided monostatic sensing of a mobile target under line-of-sight (LoS) blockage considering a single antenna, full-duplex, and dual-functional radar-communications base station (BS). For the purpose of target detection and delay/Doppler/angle estimation, we derive a detector based on the generalized likelihood ratio test (GLRT), which entails a high-dimensional parameter search and leads to angle-Doppler coupling. To tackle these challenges, we propose a two-step algorithm for solving the GLRT detector/estimator in a low-complexity manner, accompanied by a RIS phase profile design tailored to circumvent the angle-Doppler coupling effect. Simulation results verify the effectiveness of the proposed algorithm, demonstrating its convergence to theoretical bounds and its superiority over state-of-the-art mobility-agnostic benchmarks.
On array geometry and self-interference in full-duplex massive MIMO communications
Authors: Robin Rajamäki, Risto Wichman
Date: 2024-01-12 00:00:00

This paper studies the role of the joint transmit-receive antenna array geometry in shaping the self-interference (SI) channel in full-duplex communications. We consider a simple spherical wave SI model and two prototypical linear array geometries with uniformly spaced transmit and receive antennas. We show that the resulting SI channel matrix has a regular (Toeplitz) structure in both of these cases. However, the number of significant singular values of these matrices - an indication of the severity of SI - can be markedly different. We demonstrate that both reduced SI and high angular resolution can be obtained by employing suitable sparse array configurations that fully leverage the available joint transmit-receive array aperture without suffering from angular ambiguities. Numerical electromagnetic simulations also suggest that the worst-case SI of such sparse arrays need not increase - but can actually decrease - with the number of antennas. Our findings provide preliminary insight into the extent to which the array geometry alone can mitigate SI in full-duplex massive MIMO communications systems employing a large number of antennas.
Harnessing Holes for Spatial Smoothing with Applications in Automotive Radar
Authors: Yinyan Bu, Robin Rajamäki, Pulak Sarangi, Piya Pal
Date: 2024-01-12 00:00:00

This paper studies spatial smoothing using sparse arrays in single-snapshot Direction of Arrival (DOA) estimation. We consider the application of automotive MIMO radar, which traditionally synthesizes a large uniform virtual array by appropriate waveform and physical array design. We explore deliberately introducing holes into this virtual array to leverage resolution gains provided by the increased aperture. The presence of these holes requires re-thinking DOA estimation, as conventional algorithms may no longer be easily applicable and alternative techniques, such as array interpolation, may be computationally expensive. Consequently, we study sparse array geometries that permit the direct application of spatial smoothing. We show that a sparse array geometry is amenable to spatial smoothing if it can be decomposed into the sum set of two subsets of suitable cardinality. Furthermore, we demonstrate that many such decompositions may exist - not all of them yielding equal identifiability or aperture. We derive necessary and sufficient conditions to guarantee identifiability of a given number of targets, which gives insight into choosing desirable decompositions for spatial smoothing. This provides uniform recovery guarantees and enables estimating DOAs at increased resolution and reduced computational complexity.
Effect of Beampattern on Matrix Completion with Sparse Arrays
Authors: Robin Rajamäki, Mehmet Can Hücümenoğlu, Pulak Sarangi, Piya Pal
Date: 2024-01-12 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

We study the problem of noisy sparse array interpolation, where a large virtual array is synthetically generated by interpolating missing sensors using matrix completion techniques that promote low rank. The current understanding is quite limited regarding the effect of the (sparse) array geometry on the angle estimation error (post interpolation) of these methods. In this paper, we make advances towards solidifying this understanding by revealing the role of the physical beampattern of the sparse array on the performance of low rank matrix completion techniques. When the beampattern is analytically tractable (such as for uniform linear arrays and nested arrays), our analysis provides concrete and interpretable bounds on the scaling of the angular error as a function of the number of sensors, and demonstrates the effectiveness of nested arrays in presence of noise and a single temporal snapshot.
Beyond Diagonal RIS for Multi-Band Multi-Cell MIMO Networks: A Practical Frequency-Dependent Model and Performance Analysis
Authors: Arthur S. de Sena, Mehdi Rasti, Nurul H. Mahmood, Matti Latva-aho
Date: 2024-01-12 00:00:00

This paper delves into the unexplored frequency-dependent characteristics of beyond diagonal reconfigurable intelligent surfaces (BD-RISs). A generalized practical frequency-dependent reflection model is proposed as a fundamental framework for configuring fully-connected and group-connected RISs in a multi-band multi-base station (BS) multiple-input multiple-output (MIMO) network. Leveraging this practical model, multi-objective optimization strategies are formulated to maximize the received power at multiple users connected to different BSs, each operating under a distinct carrier frequency. By relying on matrix theory and exploiting the symmetric structure of the reflection matrices inherent to BD-RISs, relaxed tractable versions of the challenging problems are achieved for scenarios with obstructed and unobstructed direct channel links. The relaxed solutions are then combined with codebook-based approaches to configure the practical capacitance values for the BD-RISs. Simulation results reveal the frequency-dependent behaviors of different RIS architectures and demonstrate the effectiveness of the proposed schemes. Notably, BD-RISs exhibit high reflection performance across the intended frequency range, remarkably outperforming conventional single-connected RISs. Moreover, the proposed optimization approaches prove effective in enabling the targeted operation of BD-RISs across one or more carrier frequencies. The results also shed light on the potential for harmful interference in the absence of synchronization between RISs and adjacent BSs.
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements
Authors: Muhammad Wasim Nawaz, Abdesselam Bouzerdoum, Muhammad Mahboob Ur Rahman, GHULAM ABBAS, Faizan Rashid
Date: 2024-01-12 00:00:00
Tasks: Activity Recognition, Motion Estimation, object-detection, Object Detection, Object Tracking, Optical Flow Estimation, Visual Object Tracking

Optical flow is the pattern of apparent motion of objects in a scene. The computation of optical flow is a critical component in numerous computer vision tasks such as object detection, visual object tracking, and activity recognition. Despite a lot of research, efficiently managing abrupt changes in motion remains a challenge in motion estimation. This paper proposes novel variational regularization methods to address this problem since they allow combining different mathematical concepts into a joint energy minimization framework. In this work, we incorporate concepts from signal sparsity into variational regularization for motion estimation. The proposed regularization uses a robust l1 norm, which promotes sparsity and handles motion discontinuities. By using this regularization, we promote the sparsity of the optical flow gradient. This sparsity helps recover a signal even with just a few measurements. We explore recovering optical flow from a limited set of linear measurements using this regularizer. Our findings show that leveraging the sparsity of the derivatives of optical flow reduces computational complexity and memory needs.
Unified Near-field and Far-field Localization with Holographic MIMO
Authors: Mengyuan Cao, Haobo Zhang, Boya Di, Hongliang Zhang
Date: 2024-01-12 00:00:00

Localization which uses holographic multiple input multiple output surface such as reconfigurable intelligent surface (RIS) has gained increasing attention due to its ability to accurately localize users in non-line-of-sight conditions. However, existing RIS-enabled localization methods assume the users at either the near-field (NF) or the far-field (FF) region, which results in high complexity or low localization accuracy, respectively, when they are applied in the whole area. In this paper, a unified NF and FF localization method is proposed for the RIS-enabled localization system to overcome the above issue. Specifically, the NF and FF regions are both divided into grids. The RIS reflects the signals from the user to the base station~(BS), and then the BS uses the received signals to determine the grid where the user is located. Compared with existing NF- or FF-only schemes, the design of the location estimation method and the RIS phase shift optimization algorithm is more challenging because they are based on a hybrid NF and FF model. To tackle these challenges, we formulate the optimization problems for location estimation and RIS phase shifts, and design two algorithms to effectively solve the formulated problems, respectively. The effectiveness of the proposed method is verified through simulations.
Non-uniform Array and Frequency Spacing for Regularization-free Gridless DOA
Authors: Yifan Wu, Michael B. Wakin, Peter Gerstoft
Date: 2024-01-12 00:00:00

Gridless direction-of-arrival (DOA) estimation with multiple frequencies can be applied in acoustics source localization problems. We formulate this as an atomic norm minimization (ANM) problem and derive an equivalent regularization-free semi-definite program (SDP) thereby avoiding regularization bias. The DOA is retrieved using a Vandermonde decomposition on the Toeplitz matrix obtained from the solution of the SDP. We also propose a fast SDP program to deal with non-uniform array and frequency spacing. For non-uniform spacings, the Toeplitz structure will not exist, but the DOA is retrieved via irregular Vandermonde decomposition (IVD), and we theoretically guarantee the existence of the IVD. We extend ANM to the multiple measurement vector (MMV) cases and derive its equivalent regularization-free SDP. Using multiple frequencies and the MMV model, we can resolve more sources than the number of physical sensors for a uniform linear array. Numerical results demonstrate that the regularization-free framework is robust to noise and aliasing, and it overcomes the regularization bias.
Dynamics and numerical simulations to predict empirical antibiotic treatment of multi-resistant Pseudomonas aeruginosa infection
Authors: Javier López-de-la-Cruz, María Pérez-Aranda, Ana Alcudia, Belén Begines, Tomás Caraballo, Eloísa Pajuelo, Pedro J. Ginel
Date: 2024-01-13 00:00:00
Tasks: Decision Making

This work discloses an epidemiological mathematical model to predict an empirical treatment for dogs infected by Pseudomonas aeruginosa. This dangerous pathogen is one of the leading causes of multi-resistant infections and can be transmitted from dogs to humans. Numerical simulations and appropriated codes were developed using Matlab software to gather information concerning long-time dynamics of the susceptible, infected and recovered individuals. All data compiled from the mathematical model was used to provide an appropriated antibiotic sensitivity panel for this specific infection. In this study, several variables have been included in this model to predict which treatment should be prescribed in emergency cases, when there is no time to perform an antibiogram or the cost of it could not be assumed. In particular, we highlight the use of this model aiming to become part of the convenient toolbox of Public Health research and decision-making in the design of the mitigation strategy of bacterial pathogens.
Optimal Insurance to Maximize Exponential Utility when Premium is Computed by a Convex Functional
Authors: Jingyi Cao, Dongchen Li, Virginia R. Young, Bin Zou
Date: 2024-01-16 00:00:00

We find the optimal indemnity to maximize the expected utility of terminal wealth of a buyer of insurance whose preferences are modeled by an exponential utility. The insurance premium is computed by a convex functional. We obtain a necessary condition for the optimal indemnity; then, because the candidate optimal indemnity is given implicitly, we use that necessary condition to develop a numerical algorithm to compute it. We prove that the numerical algorithm converges to a unique indemnity that, indeed, equals the optimal policy. We also illustrate our results with numerical examples.
Pandemic infection forecasting through compartmental model and learning-based approaches
Authors: Marianna Karapitta, Andreas Kasis, Charithea Stylianides, Kleanthis Malialis, Panayiotis Kolios
Date: 2024-01-12 00:00:00

The emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing widespread infections and loss of life. The rapid spread of pandemics have made governments across the world adopt a range of actions, including non-pharmaceutical measures to contain its impact. However, the dynamic nature of pandemics makes selecting intervention strategies challenging. Hence, the development of suitable monitoring and forecasting tools for tracking infected cases is crucial for designing and implementing effective measures. Motivated by this, we present a hybrid pandemic infection forecasting methodology that integrates compartmental model and learning-based approaches. In particular, we develop a compartmental model that includes time-varying infection rates, which are the key parameters that determine the pandemic's evolution. To identify the time-dependent infection rates, we establish a hybrid methodology that combines the developed compartmental model and tools from optimization and neural networks. Specifically, the proposed methodology estimates the infection rates by fitting the model to available data, regarding the COVID-19 pandemic in Cyprus, and then predicting their future values through either a) extrapolation, or b) feeding them to neural networks. The developed approach exhibits strong accuracy in predicting infections seven days in advance, achieving low average percentage errors both using the extrapolation (9.90%) and neural network (5.04%) approaches.
Provisions and Economic Capital for Credit Losses
Authors: Dorinel Bastide, Stéphane Crépey
Date: 2024-01-15 00:00:00
Tasks: Management

Based on supermodularity ordering properties, we show that convex risk measures of credit losses are nondecreasing w.r.t. credit-credit and, in a wrong-way risk setup, credit-market, covariances of elliptically distributed latent factors. These results support the use of such setups for computing credit provisions and economic capital or for conducting stress test exercises and risk management analysis.
Identifiability of Level-1 Species Networks from Gene Tree Quartets
Authors: Elizabeth S. Allman, Hector Baños, Marina Garrote-Lopez, John A. Rhodes
Date: 2024-01-11 00:00:00

When hybridization or other forms of lateral gene transfer have occurred, evolutionary relationships of species are better represented by phylogenetic networks than by trees. While inference of such networks remains challenging, several recently proposed methods are based on quartet concordance factors -- the probabilities that a tree relating a gene sampled from the species displays the possible 4-taxon relationships. Building on earlier results, we investigate what level-1 network features are identifiable from concordance factors under the network multispecies coalescent model. We obtain results on both topological features of the network, and numerical parameters, uncovering a number of failures of identifiability related to 3-cycles in the network.
Animal-associated marine Acidobacteria with a rich natural product repertoire
Authors: Stefan Leopold-Messer, Clara Chepkirui, Mathijs F. J. Mabesoone, Joshua Mayer, Lucas Paoli, Shinichi Sunagawa, Agustinus R. Uria, Toshiyuki Wakimoto, Jörn Piel
Date: 2024-01-15 00:00:00

Sponges have long been recognized as a rich source of bioactive natural products. Various studies suggest that many of these compounds are produced by symbiotic bacteria. However, substance supplies and functional insights about the producers remain limited because cultivation remains unsuccessful. To identify alternative, sustainable sources of sponge-derived polyketides, we computationally analyzed 5289 characterized and orphan trans-acyltransferase polyketide synthases, enzymes with widespread roles in polyketide biosynthesis by bacterial symbionts. The analytical workflow predicted marine animal-derived Acidobacteria of the family Acanthopleuribacteraceae with large sets of biosynthetic gene clusters to be enriched in sponge-type chemistry. Targeted compound isolation from a chiton-associated strain yielded new congeners of the phorboxazoles and calyculins, potent and scarce cytotoxins exclusively known from sponges. These first natural products of Acidobacteria and new coral metagenomic data on a third family member suggest animal-associated Acanthopleuribacteraceae as a rich source of sponge-type as well as novel metabolites
Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization
Authors: Jiahao Qiu, Hui Yuan, Jinghong Zhang, Wentao Chen, Huazheng Wang, Mengdi Wang
Date: 2024-01-08 00:00:00

While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences, recombination of mutations, and running new rounds of screening. To enhance the efficiency of such a process, we propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model. Under simplified assumptions and a Gaussian Process prior, we provide theoretical analysis and a Bayesian regret bound, demonstrating that the combination of local search and bandit learning method can efficiently discover a near-optimal design. The full algorithm is compatible with a suite of randomized tree search heuristics, machine learning models, pre-trained embeddings, and bandit techniques. We test various instances of the algorithm across benchmark protein datasets using simulated screens. Experiment results demonstrate that the algorithm is both sample-efficient and able to find top designs using reasonably small mutation counts.
Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides
Authors: Chloé Puget, Jonathan Ganz, Julian Ostermaier, Thomas Konrad, Eda Parlak, Christof Albert Bertram, Matti Kiupel, Katharina Breininger, Marc Aubreville, Robert Klopfleisch
Date: 2024-01-02 00:00:00
Tasks: whole slide images

Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides.