pwc-archive/papers-with-abstracts huggingface dataset:
WHITE PAPER: Protecting GNSS Against Intentional Interference
Authors: Arul Elango, Ahmed Al-Tahmeesschi, Mikko Saukkoriipi, Titti Malmivirta, Laura Ruotsalainen
Date: 2022-08-24 00:00:00
Tasks: Autonomous Vehicles, Management

The vulnerabilities associated with modern systems relying on Global Navigation Satellite Systems (GNSS) due to intentional and unintentional interference is an increasing threat. Since radio frequency interference (RFI) significantly degrades the performance of a GNSS receiver. Several traditional critical applications such as aviation, maritime and rail transport systems to more recent applications such as autonomous vehicles, can be severely affected by such undetected nor mitigated RFIs. Moreover, critical infrastructures such as power supply and money transfer, are becoming more and more dependent on the accurate timing information provided by GNSS. Thus, interference detection and management techniques are crucial to be utilised in order to reduce interference effects. This paper offers a state-of-the-art review of several proposed methods for interference detection and mitigation with solutions ranging from traditional to machine learning-based approaches. In addition, to be able to characterise the RFI threats and develop mitigation techniques, it is essential to monitor RFI systematically and share recorded data with interested entities. Therefore, three GNSS threat monitoring systems are briefly described. This White paper is a compilation of the seminar presentations given at a seminar on "Protecting GNSS against intentional interference" in March 2022 in Finland.
RIS-Aided Near-Field Localization and Channel Estimation for the Sub-Terahertz System
Authors: Yijin Pan, Cunhua Pan, Shi Jin, Jiangzhou Wang
Date: 2022-08-24 00:00:00

The low hardware cost makes ultra-large (XL) reconfigurable intelligent surface (RIS) an attractive solution for the performance enhancement of localization and communication systems, but it results in near-field propagation channels, especially for the high-frequency communication systems, e.g. mmWave and sub-terahertz (subTHz) systems. This makes localization and channel estimation in the near field of the RIS-aided subTHz system more challenging. In this paper, we consider the spherical wavefront propagation in the near field of the subTHz system with the assistance of a RIS. A near-field channel estimation and localization (NF-JCEL) algorithm is proposed based on the derived second-order Fresnel approximation of the near-field channel model. To be specific, we first decouple the user equipment (UE) distances and angles of arrival (AoAs) through a down-sampled Toeplitz covariance matrix, so that the vertical and azimuth AoAs in the array steering vectors can be estimated separately with low complexities. Then, the UE distance can be estimated by the simple one-dimensional search, and the channel attenuation coefficients are obtained through the orthogonal matching pursuit (OMP) method. Simulation results validate the superiority of the proposed NF-JCEL algorithm to the conventional far-field algorithm, and show that higher resolution accuracy can be obtained by the proposed algorithm.
Temporary Laser Inter-Satellite Links in Free-Space Optical Satellite Networks
Authors: Aizaz U. Chaudhry, Halim Yanikomeroglu
Date: 2022-08-23 00:00:00

Laser inter-satellite links (LISLs) between satellites in a free-space optical satellite network (FSOSN) can be divided into two classes: permanent LISLs (PLs) and temporary LISLs (TLs). TLs are not desirable in next-generation FSOSNs (NG-FSOSNs) due to high LISL setup time, but they may become feasible in next-next-generation FSOSNs (NNG-FSOSNs). Using the satellite constellation for Phase I of Starlink, we study the impact of TLs on network latency in an NG-FSOSN (which has only PLs) versus an NNG-FSOSN (which has PLs and TLs) under different long-distance inter-continental data communications scenarios, including Sydney-Sao Paulo, Toronto-Istanbul, Madrid-Tokyo, and New York-Jakarta, and different LISL ranges for satellites, including 659.5 km, 1,319 km, 1,500 km, 1,700 km, 2,500 km, 3,500 km, and 5,016 km. It is observed from the results that TLs provide higher satellite connectivity and thereby higher network connectivity, and they lead to lower average network latency for the NNG-FSOSN compared to the NG-FSOSN in all scenarios at all LISL ranges. In comparison with the NG-FSOSN, the improvement in latency with the NNG-FSOSN is significant at LISL ranges of 1,500 km, 1,700 km, and 2,500 km, where the improvement is 16.83 ms, 23.43 ms, and 18.20 ms, respectively, for the Sydney-Sao Paulo inter-continental connection. For the Toronto-Istanbul, Madrid-Tokyo, and New York-Jakarta inter-continental connections, the improvement is 14.58 ms, 23.35 ms, and 23.52 ms, respectively, at the 1,700 km LISL range.
Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions
Authors: Cristiano Gratton, Naveen K. D. Venkategowda, Reza Arablouei, Stefan Werner
Date: 2022-08-23 00:00:00
Tasks: Distributed Optimization

We develop a new consensus-based distributed algorithm for solving learning problems with feature partitioning and non-smooth convex objective functions. Such learning problems are not separable, i.e., the associated objective functions cannot be directly written as a summation of agent-specific objective functions. To overcome this challenge, we redefine the underlying optimization problem as a dual convex problem whose structure is suitable for distributed optimization using the alternating direction method of multipliers (ADMM). Next, we propose a new method to solve the minimization problem associated with the ADMM update step that does not rely on any conjugate function. Calculating the relevant conjugate functions may be hard or even unfeasible, especially when the objective function is non-smooth. To obviate computing any conjugate function, we solve the optimization problem associated with each ADMM iteration in the dual domain utilizing the block coordinate descent algorithm. Unlike the existing related algorithms, the proposed algorithm is fully distributed and does away with the conjugate of the objective function. We prove theoretically that the proposed algorithm attains the optimal centralized solution. We also confirm its network-wide convergence via simulations.
A simple and intuitive method to calculate $R_0$ in complex epidemic models
Authors: Carlos Hernandez-Suarez, Osval Montesinos Lopez
Date: 2022-08-22 00:00:00
Tasks: Decision Making

Epidemic models are a valuable tool in the decision making process. Once a mathematical model for an epidemics has been established, the very next step is calculating a mathematical expression for the basic reproductive number, $R_0$, which is the average number of infections caused by an individual that is introduced in a population of susceptibles. Finding a mathematical expression for $R_0$ is important because it allows to analyze the effect of the different parameters in the model on $R_0$ so that we can act on them to keep $R_0 < 1$, so that the epidemic fades out. In this work we show how to calculate $R_0$ in complicated epidemic models by using only basic concepts of Markov chains.
An Extension of Goldman-Hodgkin-Katz Equations by Charges from Ionic Solution and Ion Channel Protein
Authors: Dexuan Xie
Date: 2022-08-24 00:00:00

The Goldman-Hodgkin-Katz (GHK) equations have been widely applied to ion channel studies, simulations, and model developments. However, they are constructed under a constant electric field, causing them to have a low degree of approximation in the prediction of ionic fluxes, electric currents, and membrane potentials. In this paper, the equations are extended from the constant electric field to the nonlinear electric field induced by charges from an ionic solution and an ion channel protein. Furthermore, a novel numerical quadrature scheme is developed to estimate one major parameter, called the extension parameter, of the extended GHK equations in terms of a set of electrostatic potential values. To this end, the extended GHK equations become a bridge between the "macroscopic" ion channel kinetics and the "microscopic" electrostatic potential values across a cell membrane. To generate a set of required electrostatic potential values, a nonlinear finite element iterative scheme for solving a one-dimensional Poisson-Nernst-Planck ion channel model is developed and implemented as a Python software package. This package is then used to do numerical studies on the extended GHK equations, the numerical quadrature scheme, and the nonlinear iterative scheme. Numerical results confirm the importance of considering charge effects in the calculation of ionic fluxes. They also validate the high numerical accuracy of the numerical quadrature scheme, the fast convergence rate of the nonlinear iterative scheme, and the high performance of the software package.
A structurally informed model for modulating functional connectivity
Authors: Andrew C. Murphy, Romain Duprat, Theodore D. Satterthwaite, Desmond J. Oathes, Dani S. Bassett
Date: 2022-08-24 00:00:00
Tasks: Functional Connectivity, model

Functional connectivity (FC) between brain regions tracks symptom severity in many neuropsychiatric disorders. Transcranial magnetic stimulation (TMS) directly alters regional activity and indirectly alters FC. Predicting how FC will change following TMS is difficult, but would allow novel therapies that target FC to improve symptoms. We address this challenge by proposing a predictive model that explains how TMS-induced activation can change the strength of FC. Here, we focus on the FC of the frontoparietal (FPS) and default mode (DMS) systems given the importance of their FC in executive function and affective disorders. We fit this model to neuroimaging data in 29 individuals who received TMS to the frontal cortex and evaluated the FC between the FPS and DMS. For each individual, we measured the TMS-induced change in FC between the FPS and DMS (the FC network), and the structural coupling between the stimulated area and the FPS and DMS (the structural context network (SCN)). We find that TMS-induced FC changes are best predicted when the model accounts for white matter fibers from the stimulated area to the two systems. We find that the correlation between these two networks (structure-function coupling) - and therefore the predictability of the TMS-induced modulation - was highest when the SCN contained a dense core of intraconnected regions, indicating that the stimulated area had ample access to an anatomical module. Further, we found that when the core of the SCN overlapped with the FPS and DMS, we observed the greatest change in the strength of their FC. Broadly, our findings explain how the structural connectivity of a stimulated region modulates TMS-induced changes in the brain's functional network. Efforts to account for such structural connections could improve predictions of TMS response, further informing the development of TMS protocols for clinical translation.
Collective targeted migrations: a balancing act involving aggregation, group size and environmental clues: a simulation study
Authors: Carlos Hernandez-Suarez
Date: 2022-08-23 00:00:00
Tasks: Navigate

What is behind the \emph{wisdom of the crowds} described by Simons (2004)? It has been showed that insects may use gravitational fields to travel (Dreyer et al 2018) and we may ask whether the use of gravitational fields is enough to secure the arrival of an individual to a relatively narrow spot thousands of kilometers away, as it is the case for example of monarch butterflies, which may travel 4,500 km to land in Mexico within an area of about 1/8 the size of Long Island. Here we show that if individuals budget a fraction of time to seek the target and the rest to maintain aggregation, the chances of landing within a narrow spot even under a weak signal are increased. Our model exhibits the existence of an equilibrium, the tradeoff that comes from maintaining group size, aggregation, and targeting. Whenever this balance is broken, the population may behave erratically. If this strategic balance between targeting and aggregation is prevalent then the possibility of destabilizing may be used to control or regulate the spread of undesirable organisms that navigate under this balancing set of mechanisms.
Revised calculation of the coefficient of parentage in plant breeding
Authors: Carlos Hernandez-Suarez, Osval Montesinos Lopez
Date: 2022-08-23 00:00:00

The Coefficient of Parentage (COP) between two individuals is the expected inbreeding of their offspring. Originally exploited by animal breeders, is now a routine calculation among plant breeders as part of crop improvement programs. Here we show that the COP between strains requires a different calculation than the used to calculate the COP between individuals. Failure to do so may result in an overestimation of the amount of inbreeding. Here we provide a simple methodology to calculate the correct coefficient of parentage between strains.
Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers
Authors: Anran Liu, Xingyu Li, Hongyi Wu, Bangwei Guo, Jitendra Jonnagaddala, Hong Zhang, Xu Steven Xu
Date: 2022-08-23 00:00:00

Purpose Tumor-infiltrating lymphocytes (TILs) have significant prognostic values in cancers. However, very few automated, deep-learning-based TIL scoring algorithms have been developed for colorectal cancers (CRC). Methods We developed an automated, multiscale LinkNet workflow for quantifying cellular-level TILs for CRC tumors using H&E-stained images. The predictive performance of the automatic TIL scores (TIL) for disease progression and overall survival was evaluate using two international datasets, including 554 CRC patients from The Cancer Genome Atlas (TCGA) and 1130 CRC patients from Molecular and Cellular Oncology (MCO). Results The LinkNet model provided an outstanding precision (0.9508), recall (0.9185), and overall F1 score (0.9347). Clear dose-response relationships were observed between TILs and risk of disease progression or death decreased in both TCGA and MCO cohorts. Both univariate and multivariate Cox regression analyses for the TCGA data demonstrated that patients with high TILs had significant (approx. 75%) reduction of risk for disease progression. In both MCO and TCGA studies, the TIL-high group was significantly associated with improved overall survival in univariate analysis (30% and 54% reduction in risk, respectively). However, potential confounding was observed in the MCO dataset. The favorable effects of high TILs were consistently observed in different subgroups according to know risk factors. Conclusion A deep-learning workflow for automatic TIL quantification based on LinkNet was successfully developed.
MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing
Authors: Laura Gwilliams, Graham Flick, Alec Marantz, Liina Pylkkanen, David Poeppel, Jean-Remi King
Date: 2022-07-26 00:00:00

The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked related fields such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.
Ensnarled: On the topological linkage of spatially embedded network pairs
Authors: Felix Kramer, Carl D Modes
Date: 2022-08-24 00:00:00
Tasks: Attribute

The observation, design and analysis of mesh-like networks in bionics, polymer physics and biological systems has brought forward an extensive catalog of fascinating structures of which a subgroup share a particular, yet critically under appreciated attribute: being embedded in space such that one wouldn't be able to pull them apart without prior removal of a subset of edges, a state which we here call ensnarled. In this study we elaborate on a graph theoretical method to analyze ensnarled finite, 2-component nets on the basis of Hopf-link identification. Doing so we are able to construct an edge priority operator, derived from the linking numbers of the spatial graphs' cycle bases, which highlights critical edges. On its basis we developed a greedy algorithm which identifies optimal edge removals to achieve unlinking, allowing for the establishment of a new topological metric characterizing the state of ensnarled network pairs.
Statistical exploration of the Manifold Hypothesis
Authors: Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
Date: 2022-08-24 00:00:00

The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space. This phenomenon is observed empirically in many real world situations, has led to development of a wide range of statistical methods in the last few decades, and has been suggested as a key factor in the success of modern AI technologies. We show that rich and sometimes intricate manifold structure in data can emerge from a generic and remarkably simple statistical model -- the Latent Metric Model -- via elementary concepts such as latent variables, correlation and stationarity. This establishes a general statistical explanation for why the Manifold Hypothesis seems to hold in so many situations. Informed by the Latent Metric Model we derive procedures to discover and interpret the geometry of high-dimensional data, and explore hypotheses about the data generating mechanism. These procedures operate under minimal assumptions and make use of well known graph-analytic algorithms.
BORON: an ultra-lightweight and low power encryption design for pervasive computing
Authors: Gaurav BANSOD, Narayan PISHAROTY, Abhijit PATIL
Date: 2017-03-21 00:00:00
Proceeding: Frontiers of Information Technology & Electronic Engineering 2017 3

We propose an ultra-lightweight, compact, and low power block cipher BORON. BORON is a substitution and 
permutation based network, which operates on a 64-bit plain text and supports a key length of 128/80 bits. BORON has a compact 
structure which requires 1939 gate equivalents (GEs) for a 128-bit key and 1626 GEs for an 80-bit key. The BORON cipher 
includes shift operators, round permutation layers, and XOR operations. Its unique design helps generate a large number of active 
S-boxes in fewer rounds, which thwarts the linear and differential attacks on the cipher. BORON shows good performance on both 
hardware and software platforms. BORON consumes less power as compared to the lightweight cipher LED and it has a higher 
throughput as compared to other existing SP network ciphers. We also present the security analysis of BORON and its 
performance as an ultra-lightweight compact cipher. BORON is a well-suited cipher design for applications where both a small 
footprint area and low power dissipation play a crucial role.
Unbiased Multi-Modality Guidance for Image Inpainting
Authors: Yongsheng Yu, Dawei Du, Libo Zhang, Tiejian Luo
Date: 2022-08-25 00:00:00
Tasks: Image Inpainting, Semantic Segmentation

Image inpainting is an ill-posed problem to recover missing or damaged image content based on incomplete images with masks. Previous works usually predict the auxiliary structures (e.g., edges, segmentation and contours) to help fill visually realistic patches in a multi-stage fashion. However, imprecise auxiliary priors may yield biased inpainted results. Besides, it is time-consuming for some methods to be implemented by multiple stages of complex neural networks. To solve this issue, we develop an end-to-end multi-modality guided transformer network, including one inpainting branch and two auxiliary branches for semantic segmentation and edge textures. Within each transformer block, the proposed multi-scale spatial-aware attention module can learn the multi-modal structural features efficiently via auxiliary denormalization. Different from previous methods relying on direct guidance from biased priors, our method enriches semantically consistent context in an image based on discriminative interplay information from multiple modalities. Comprehensive experiments on several challenging image inpainting datasets show that our method achieves state-of-the-art performance to deal with various regular/irregular masks efficiently.
The ReprGesture entry to the GENEA Challenge 2022
Authors: Sicheng Yang, Zhiyong Wu, Minglei Li, Mengchen Zhao, Jiuxin Lin, Liyang Chen, Weihong Bao
Date: 2022-08-25 00:00:00
Tasks: Decoder, Gesture Generation, Representation Learning, Rhythm

This paper describes the ReprGesture entry to the Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022. The GENEA challenge provides the processed datasets and performs crowdsourced evaluations to compare the performance of different gesture generation systems. In this paper, we explore an automatic gesture generation system based on multimodal representation learning. We use WavLM features for audio, FastText features for text and position and rotation matrix features for gesture. Each modality is projected to two distinct subspaces: modality-invariant and modality-specific. To learn inter-modality-invariant commonalities and capture the characters of modality-specific representations, gradient reversal layer based adversarial classifier and modality reconstruction decoders are used during training. The gesture decoder generates proper gestures using all representations and features related to the rhythm in the audio. Our code, pre-trained models and demo are available at https://github.com/YoungSeng/ReprGesture.
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm
Authors: Xinyi Wang, Simon Yusuf Enoch, Dong Seong Kim
Date: 2022-08-25 00:00:00
Tasks: Adversarial Attack

Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of high-performance attack generation methods, most of them directly add perturbations to original data and measure them using L_p norms; this can break the major structure of data, thus, creating invalid attacks. In this paper, we propose a black-box attack, which, instead of modifying original data, modifies latent features of data extracted by an autoencoder; then, we measure noises in semantic space to protect the semantics of data. We trained autoencoders on MNIST and CIFAR-10 datasets and found optimal adversarial perturbations using a genetic algorithm. Our approach achieved a 100% attack success rate on the first 100 data of MNIST and CIFAR-10 datasets with less perturbation than FGSM.