pwc-archive/papers-with-abstracts huggingface dataset:
Economic incentives for capacity reductions on interconnectors in the day-ahead market
Authors: E. Ruben van Beesten, Daan Hulshof
Date: 2022-10-13 00:00:00

We consider a zonal international power market and investigate potential economic incentives for short-term reductions of transmission capacities on existing interconnectors by the responsible transmission system operators (TSOs). We show that if a TSO aims to maximize domestic total welfare, it often has an incentive to reduce the capacity on the interconnectors to neighboring countries. In contrast with the (limited) literature on this subject, which focuses on incentives through the avoidance of future balancing costs, we show that incentives can exist even if one ignores balancing and focuses solely on welfare gains in the day-ahead market itself. Our analysis consists of two parts. In the first part, we develop an analytical framework that explains why these incentives exist. In particular, we distinguish two mechanisms: one based on price differences with neighboring countries and one based on the domestic electricity price. In the second part, we perform numerical experiments using a model of the Northern-European power system, focusing on the Danish TSO. In 97% of the historical hours tested, we indeed observe economic incentives for capacity reductions, leading to significant welfare gains for Denmark and welfare losses for the system as a whole. We show that the potential for welfare gains greatly depends on the ability of the TSO to adapt interconnector capacities to short-term market conditions. Finally, we explore the extent to which the recently introduced European "70%-rule" can mitigate the incentives for capacity reductions and their welfare effects.
Proximity, Similarity, and Friendship Formation: Theory and Evidence
Authors: A. Arda Gitmez, Román Andrés Zárate
Date: 2022-10-12 00:00:00

Can proximity make friendships more diverse? To address this question, we propose a learning-driven friendship formation model to study how proximity and similarity influence the likelihood of forming social connections. The model predicts that proximity affects more friendships between dissimilar than similar individuals, in opposition to a preference-driven version of the model. We use an experiment at selective boarding schools in Peru that generates random variation in the physical proximity between students to test these predictions. The empirical evidence is consistent with the learning model: while social networks exhibit homophily by academic achievement and poverty, proximity generates more diverse social connections.
General Manipulability Theorem for a Matching Model
Authors: Paola B. Manasero, Jorge Oviedo
Date: 2022-10-12 00:00:00
Tasks: model

In a many-to-many matching model in which agents' preferences satisfy substitutability and the law of aggregate demand, we proof the General Manipulability Theorem. We result generalizes the presented in Sotomayor (1996 and 2012) for the many-to-one model. In addition, we show General Manipulability Theorem fail when agents' preferences satisfy only substitutability.
DCANet: Differential Convolution Attention Network for RGB-D Semantic Segmentation
Authors: Lizhi Bai, Jun Yang, Chunqi Tian, Yaoru Sun, Maoyu Mao, Yanjun Xu, Weirong Xu
Date: 2022-10-13 00:00:00
Tasks: Semantic Segmentation

Combining RGB images and the corresponding depth maps in semantic segmentation proves the effectiveness in the past few years. Existing RGB-D modal fusion methods either lack the non-linear feature fusion ability or treat both modal images equally, regardless of the intrinsic distribution gap or information loss. Here we find that depth maps are suitable to provide intrinsic fine-grained patterns of objects due to their local depth continuity, while RGB images effectively provide a global view. Based on this, we propose a pixel differential convolution attention (DCA) module to consider geometric information and local-range correlations for depth data. Furthermore, we extend DCA to ensemble differential convolution attention (EDCA) which propagates long-range contextual dependencies and seamlessly incorporates spatial distribution for RGB data. DCA and EDCA dynamically adjust convolutional weights by pixel difference to enable self-adaptive in local and long range, respectively. A two-branch network built with DCA and EDCA, called Differential Convolutional Network (DCANet), is proposed to fuse local and global information of two-modal data. Consequently, the individual advantage of RGB and depth data are emphasized. Our DCANet is shown to set a new state-of-the-art performance for RGB-D semantic segmentation on two challenging benchmark datasets, i.e., NYUDv2 and SUN-RGBD.
Imrpoving Strain Estimation in Breast Ultrasound Images Using Novel 1.5D Approach (Simulation and In-vivo results
Authors: Irteza Enan Kabir
Date: 2022-10-13 00:00:00

Ultrasound elastography is the method to image the elasticity of compliant tissues due to a mechanical compression applied to it. In elastography, the local strain of explored tissue is estimated by analyzing the echo signals. This is accomplished by a sonographer who uses ultrasound transducer to apply pressure on the tissue area causing displacement of the tissue. A set of data is obtained before and after the compression. This stress along the axial direction not only causes deformation in that direction but it can also cause lateral deformation in the tissue. This relative deformation is later calculated using tracking algorithms. Finally, estimating the elasticity, images are formed. In this paper, we introduce a novel strain estimator. The proposed 1.5D strain estimator uses 1D windows for swift computations and searches the lateral direction for taking the non-axial movement into account. We have investigated the performance of our estimator using simulated and in-vivo data of breast tissue. The performance of our method is verified by comparing with different methods up to 16\% applied strain. The proposed estimator has exhibited a superior performance compared to the conventional methods. The image quality significantly improves, especially in the presence of higher applied strain, when the non-axial motion is significant.
Subject-specific quantitative susceptibility mapping using patch based deep image priors
Authors: Arvind Balachandrasekaran, Davood Karimi, Camilo Jaimes, Ali Gholipour
Date: 2022-10-10 00:00:00

Quantitative Susceptibility Mapping is a parametric imaging technique to estimate the magnetic susceptibilities of biological tissues from MRI phase measurements. This problem of estimating the susceptibility map is ill posed. Regularized recovery approaches exploiting signal properties such as smoothness and sparsity improve reconstructions, but suffer from over-smoothing artifacts. Deep learning approaches have shown great potential and generate maps with reduced artifacts. However, for reasonable reconstructions and network generalization, they require numerous training datasets resulting in increased data acquisition time. To overcome this issue, we proposed a subject-specific, patch-based, unsupervised learning algorithm to estimate the susceptibility map. We make the problem well-posed by exploiting the redundancies across the patches of the map using a deep convolutional neural network. We formulated the recovery of the susceptibility map as a regularized optimization problem and adopted an alternating minimization strategy to solve it. We tested the algorithm on a 3D invivo dataset and, qualitatively and quantitatively, demonstrated improved reconstructions over competing methods.
Measurement Testbed for Radar and Emitter Localization of UAV at 3.75 GHz
Authors: Julia Beuster, Carsten Andrich, Michael Döbereiner, Steffen Schieler, Maximilian Engelhardt, Christian Schneider, Reiner Thomä
Date: 2022-10-13 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper presents an experimental measurement platform for the research and development of unmanned aerial vehicles (UAVs) localization algorithms using radio emission and reflectivity. We propose a cost-effective, flexible testbed made from commercial off-the-shelf (COTS) devices to allow academic research regarding the upcoming integration of UAV surveillance in existing mobile radio networks in terms of integrated sensing and communication (ISAC). The system enables nanosecond-level synchronization accuracy and centimeter-level positioning accuracy for multiple distributed sensor nodes and a mobile UAV-mounted node. Results from a real-world measurement in a 16 km2 urban area demonstrate the system's performance with both emitter localization as well as with the radar setup.
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation
Authors: HanCong Feng, XinHai Yan, Kaili Jiang, Xinyu Zhao, Bin Tang
Date: 2022-10-13 00:00:00
Tasks: Classification, Clustering, Data Augmentation, Deep Learning, Pseudo Label

The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM).Recent supervised deep learning-based methods have achieved great success in a such classification task.However, those methods require enough labeled samples to work properly and in many circumstances, it is not available.To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels.Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features.Next,the pseudo-supervised contrastive training is involved to further promote the separation between the extracted class-dependent features.And finally, the unsupervised problem is converted to a semi-supervised classification problem via pseudo label generation. The simulation results show that the proposed algorithm can effectively extract class-dependent features, outperforming several unsupervised clustering methods, even reaching performance on par with the supervised deep learning-based methods.
Switch-based Hybrid Beamforming Transceiver Design for Wideband Communications with Beam Squint
Authors: Mengyuan Ma, Nhan Thanh Nguyen, Markku Juntti
Date: 2022-10-13 00:00:00

Hybrid beamforming (HBF) transceiver architectures based on frequency-independent phase shifters (PS-HBF) are sensitive to the phases and physical directions with limited capability to compensate for the detrimental effects of the beam squint. Motivated by the fact that switches are phase-independent and more power/cost efficient than PSs, we consider the switch-based HBF (SW-HBF) for wideband large-scale multiple-input multiple-output systems in this paper. We first derive a closed-form expression of the beam squint ratio and compare the expected array gains of both SW-HBF and PS-HBF architectures. The results show that SW-HBF is more robust to the beam squint effect. We then focus on the SW-HBF designs to maximize the spectral efficiency (SE) in both single-user and multiuser systems, which are both non-convex mixed-integer problems. For the former, by combining the tabu search (TS) method and projected gradient ascend (PGA), we propose an efficient heuristic PGA-TS algorithm to design analog beamformers while the digital ones admit closed-form solutions. For the latter, we develop a two-step algorithm based on fractional programming and the PGA-TS method. Simulations show that the proposed SW-HBF schemes are efficient and can outperform PS-based HBF architectures in terms of both SE and energy efficiency in terahertz communication systems.
A Study on mm-wave Propagation in and around Buildings
Authors: Leonardo Possenti, Marina Barbiroli, Enrico M. Vitucci, Franco Fuschini, Mattia Fosci, Vittorio Degli-Esposti
Date: 2022-10-13 00:00:00

mm-waves are envisaged as key enabler for 5G and 6G wireless communications, thanks to the wider bandwidth and to the possibility of implementing large-scale antenna arrays and new advanced transmission techniques, such as massive MIMO and beamforming, that can take advantage of the multidimensional properties of the wireless channel. In order to further study the mm-wave wireless channel, where propagation shows different characteristics compared to the sub-6 GHz band, a joint measurement and simulation campaigns in indoor and outdoor microcellular environments has been carried out. The investigation highlights that the traditional assumption that mm-wave NLoS propagation is problematic is not true since significant reflections, scattering and even transmission mechanisms provide good NLoS coverage in most indoor and outdoor scenarios. This also reflects in the limited angle-spread differences between LoS and NLoS locations in some cases. Finally, the contribution of different propagation mechanisms (reflection, diffraction, scattering and combination of them) to the received power is analyzed in the paper with the help of ray tracing simulations.
SNN-SC: A Spiking Semantic Communication Framework for Collaborative Intelligence
Authors: Mengyang Wang, Jiahui Li, Mengyao Ma, Xiaopeng Fan
Date: 2022-10-13 00:00:00
Tasks: Classification, Quantization, Semantic Communication

Collaborative Intelligence (CI) has emerged as a promising framework for deploying Artificial Intelligence (AI) models on resource-constrained edge devices. In CI, the AI model is partitioned between the edge device and the cloud, with intermediate features transmitted from the edge sub-model to the cloud sub-model to complete the inference task. However, reducing feature transmission overhead while maintaining task performance remains a challenge, particularly in the case of noisy wireless channels. In this paper, we propose a Spiking Neural Network (SNN)-based Semantic Communication (SC) model, SNN-SC, which extracts compact semantic information from features and transmits it through digital binary channels. Compared to the Deep Neural Network (DNN)-based SC model, whose output is floating-point, the binary output of SNN makes SNN-SC directly applicable to digital binary channels without the need for extra quantization. Moreover, we introduce a novel spiking neuron called IHF to enhance the reconstruction capability of the SNN-SC decoder. Finally, we enhance the performance of SNN-SC by maximizing the entropy of semantic information. SNN-SC achieves a higher compression ratio and overcomes the `cliff effect' compared to the traditional separate source and channel coding method. In addition, SNN-SC has lower computational complexity than the DNN-based SC model and maintains higher task performance under poor channel conditions.
Distributed Reconfigurable Intelligent Surfaces for Energy Efficient Indoor Terahertz Wireless Communications
Authors: Yiming Huo, Xiaodai Dong, Nuwan Ferdinand
Date: 2022-10-12 00:00:00
Tasks: Integrated sensing and communication

With the fifth-generation (5G) networks widely commercialized and fast deployed, the sixth-generation (6G) wireless communication is envisioned to provide competitive quality of service (QoS) in multiple aspects to global users. The critical and underlying research of the 6G is, firstly, highly dependent on the precise modeling and characterization of the wireless propagation when the spectrum is believed to expand to the terahertz (THz) domain. Moreover, future networks' power consumption and energy efficiency are critical factors to consider. In this research, based on a review of the fundamental mechanisms of reconfigurable intelligent surface (RIS) assisted wireless communications, we utilize the 3D ray-tracing method to analyze a realistic indoor THz propagation environment with the existence of human blockers. Furthermore, we propose a distributed RISs framework (DRF) to assist the indoor THz wireless communication to achieve overall energy efficiency. The numerical analysis of simulation results based on more than 2,900 indoor THz wireless communication sub-scenarios has demonstrated the significant efficacy of applying distributed RISs to overcome the mobile human blockage issue, improve the THz signal coverage, increase signal-to-noise ratios (SNRs), and QoS. With practical hardware design constraints investigated, we eventually envision how to utilize the existing integrated sensing and communication techniques to deploy and operate such a system in reality. Such a distributed RISs framework can also lay the foundation of efficient THz communications for Internet-of-Things (IoT) networks.
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
Authors: Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
Date: 2022-10-05 00:00:00
Tasks: Diversity, Language Modelling, Specificity, Text Infilling

Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine. Computational design of antibodies involves generating novel and diverse sequences, while maintaining structural consistency. Unique to antibodies, designing the complementarity-determining region (CDR), which determines the antigen binding affinity and specificity, creates its own unique challenges. Recent deep learning models have shown impressive results, however the limited number of known antibody sequence/structure pairs frequently leads to degraded performance, particularly lacking diversity in the generated sequences. In our work we address this challenge by leveraging Model Reprogramming (MR), which repurposes pretrained models on a source language to adapt to the tasks that are in a different language and have scarce data - where it may be difficult to train a high-performing model from scratch or effectively fine-tune an existing pre-trained model on the specific task. Specifically, we introduce ReprogBert in which a pretrained English language model is repurposed for protein sequence infilling - thus considers cross-language adaptation using less data. Results on antibody design benchmarks show that our model on low-resourced antibody sequence dataset provides highly diverse CDR sequences, up to more than a two-fold increase of diversity over the baselines, without losing structural integrity and naturalness. The generated sequences also demonstrate enhanced antigen binding specificity and virus neutralization ability. Code is available at https://github.com/IBM/ReprogBERT
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning
Authors: Seongok Ryu, Sumin Lee
Date: 2022-09-29 00:00:00
Tasks: Bayesian Inference, Decision Making, Drug Discovery

In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico prediction of solubility has been studied for its utility in virtual screening and lead optimization. Recently, machine learning (ML) methods using experimental data has been popular because physics-based methods like quantum mechanics and molecular dynamics are not suitable for high-throughput tasks due to its computational costs. However, ML method can exhibit over-fitting problem in a data-deficient condition, and this is the case for most chemical property datasets. In addition, ML methods are regarded as a black box function in that it is difficult to interpret contribution of hidden features to outputs, hindering analysis and modification of structure-activity relationship. To deal with mentioned issues, we developed Bayesian graph neural networks (GNNs) with the self-attention readout layer. Unlike most GNNs using self-attention in node updates, self-attention applied at readout layer enabled a model to improve prediction performance as well as to identify atom-wise importance, which can help lead optimization as exemplified for three FDA-approved drugs. Also, Bayesian inference enables us to separate more or less accurate results according to uncertainty in solubility prediction task We expect that our accurate, reliable and interpretable model can be used for more careful decision-making and various applications in the development of drugs.
Fast genomic optical map assembly algorithm using binary representation
Authors: Przemysław Stawczyk, Robert Nowak
Date: 2022-10-13 00:00:00

Reducing the cost of sequencing genomes provided by next-generation sequencing technologies has greatly increased the number of genomic projects. As a result, there is a growing need for better assembly and assembly validation methods. One promising idea is to use heterogeneous data in assembly projects. Optical Mapping (OM) is beneficial in validating genomic assemblies, correction and scaffolding. Single raw OM read describes a DNA molecule's long fragment, up to 1Mbp. Raw OM data from the same genome could be assembled to create consensus maps that span an entire chromosome. The assembly process is computationally hard because of the large number of errors in input data. This work describes a new algorithm and computer program to assemble OM reads without a reference genome. In our algorithm, we explored binary representation for genome maps. We focused on the efficiency of data structures and algorithms and scale on parallel platforms. The algorithm consists of several steps, of which the most important are : (1) conversion of the restriction maps into binary strings, (2) detection of overlaps between restriction maps, (3) determining the layout of restriction maps set, (4) creation of consensus genomic maps. Our algorithm deals with optical mapping data with low error levels but fails with high-level error reads. We developed a software library, console application and module for Python language. The approach presented in this paper proved to be faster than a dynamic programming approach and performed well on error-free data. It could be used as a step of \textit{de~novo} assembly pipelines or to detect misassemblies.The software is freely available in a public repository under GNU LGPL v3 license (\url{https://sourceforge.net/p/binary-genome-maps/code}).
D-CryptO: Deep learning-based analysis of colon organoid morphology from brightfield images
Authors: Lyan Abdul, Jocelyn Xu, Alexander Sotra, Abbas Chaudary, Jerry Gao, Shravanthi Rajasekar, Nicky Anvari, Hamidreza Mahyar, Boyang Zhang
Date: 2022-10-12 00:00:00
Tasks: Morphological Analysis

Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While analyzing the structural development of organoids can be a valuable readout, using traditional image analysis tools makes it challenging because of the heterogeneities and the abstract nature of organoid morphologies. To address this limitation, we developed and validated a deep learning-based image analysis tool, named D-CryptO, for the classification of organoid morphology. D-CryptO can automatically assess the crypt formation and opacity of colorectal organoids from brightfield images to determine the extent of organoid structural maturity. To validate this tool, changes in organoid morphology were analyzed during organoid passaging and short-term forskolin stimulation. To further demonstrate the potential of D-CryptO for drug testing, organoid structures were analyzed following treatments with a panel of chemotherapeutic drugs. With D-CryptO, subtle variations in how colon organoids responded to the different chemotherapeutic drugs were detected, which suggest potentially distinct mechanisms of action. This tool could be expanded to other organoid types, like intestinal organoids, to facilitate 3D tissue morphological analysis.