pwc-archive/papers-with-abstracts huggingface dataset:
Price & Choose
Authors: Federico Echenique, Matías Núñez
Date: 2022-12-12 00:00:00

We describe a two-stage mechanism that fully implements the set of efficient outcomes in two-agent environments with quasi-linear utilities. The mechanism asks one agent to set prices for each outcome, and the other agent to make a choice, paying the corresponding price: Price \& Choose. We extend our implementation result in three main directions: an arbitrary number of players, non-quasi linear utilities, and robustness to max-min behavior. Finally, we discuss how to reduce the payoff inequality between players while still achieving efficiency.
Zero-Shot Accent Conversion using Pseudo Siamese Disentanglement Network
Authors: Dongya Jia, Qiao Tian, Kainan Peng, Jiaxin Li, Yuanzhe Chen, Mingbo Ma, Yuping Wang, Yuxuan Wang
Date: 2022-12-12 00:00:00
Tasks: Data Augmentation, Disentanglement

The goal of accent conversion (AC) is to convert the accent of speech into the target accent while preserving the content and speaker identity. AC enables a variety of applications, such as language learning, speech content creation, and data augmentation. Previous methods rely on reference utterances in the inference phase or are unable to preserve speaker identity. To address these issues, we propose a zero-shot reference-free accent conversion method, which is able to convert unseen speakers' utterances into a target accent. Pseudo Siamese Disentanglement Network (PSDN) is proposed to disentangle the accent from the content representation. Experimental results show that our model generates speech samples with much higher accentedness than the input and comparable naturalness, on two-way conversion including foreign-to-native and native-to-foreign.
On Finite Difference Jacobian Computation in Deformable Image Registration
Authors: Yihao Liu, Junyu Chen, Shuwen Wei, Aaron Carass, Jerry Prince
Date: 2022-12-12 00:00:00
Tasks: Image Registration

Producing spatial transformations that are diffeomorphic is a key goal in deformable image registration. As a diffeomorphic transformation should have positive Jacobian determinant |J| everywhere, the number of voxels with |J|<0 has been used to test for diffeomorphism and also to measure the irregularity of the transformation. For digital transformations, |J| is commonly approximated using a central difference, but this strategy can yield positive |J|'s for transformations that are clearly not diffeomorphic -- even at the voxel resolution level. To show this, we first investigate the geometric meaning of different finite difference approximations of |J|. We show that to determine if a deformation is diffeomorphic for digital images, the use of any individual finite difference approximation of |J| is insufficient. We further demonstrate that for a 2D transformation, four unique finite difference approximations of |J|'s must be positive to ensure that the entire domain is invertible and free of folding at the pixel level. For a 3D transformation, ten unique finite differences approximations of |J|'s are required to be positive. Our proposed digital diffeomorphism criteria solves several errors inherent in the central difference approximation of |J| and accurately detects non-diffeomorphic digital transformations. The source code of this work is available at https://github.com/yihao6/digital_diffeomorphism.
A Bit Stream Feature-Based Energy Estimator for HEVC Software Encoding
Authors: Geetha Ramasubbu, André Kaup, Christian Herglotz
Date: 2022-12-11 00:00:00

The total energy consumption of today's video coding systems is globally significant and emphasizes the need for sustainable video coder applications. To develop such sustainable video coders, the knowledge of the energy consumption of state-of-the-art video coders is necessary. For that purpose, we need a dedicated setup that measures the energy of the encoding and decoding system. However, such measurements are costly and laborious. To this end, this paper presents an energy estimator that uses a subset of bit stream features to accurately estimate the energy consumption of the HEVC software encoding process. The proposed model reaches a mean estimation error of 4.88% when averaged over presets of the x265 encoder implementation. The results from this work help to identify properties of encoding energy-saving bit streams and, in turn, are useful for developing new energy-efficient video coding algorithms.
COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec
Authors: Théo Ladune, Pierrick Philippe, Félix Henry, Gordon Clare, Thomas Leguay
Date: 2022-12-11 00:00:00
Proceeding: ICCV 2023 1

We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image Codec. It is a learned alternative to autoencoders with 629 parameters and 680 multiplications per decoded pixel. COOL-CHIC offers compression performance close to modern conventional MPEG codecs such as HEVC and is competitive with popular autoencoder-based systems. This method is inspired by Coordinate-based Neural Representations, where an image is represented as a learned function which maps pixel coordinates to RGB values. The parameters of the mapping function are then sent using entropy coding. At the receiver side, the compressed image is obtained by evaluating the mapping function for all pixel coordinates. COOL-CHIC implementation is made open-source.
IRS-aided UAV for Future Wireless Communications: A Survey and Research Opportunities
Authors: Anas Alkhatieb, Khaled Rabie, Xingwang Li, Galymzhan Nauryzbayev, Ramez Alkhatib
Date: 2022-12-12 00:00:00

Both unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRS) are gaining traction as transformative technologies for upcoming wireless networks. The IRS-aided UAV communication, which introduces IRSs into UAV communications, has emerged in an effort to improve the system performance while also overcoming UAV communication constraints and issues. The purpose of this paper is to provide a comprehensive overview of IRSassisted UAV communications. First, we provide five examples of how IRSs and UAVs can be combined to achieve unrivaled potential in difficult situations. The technological features of the most recent relevant researches on IRS-aided UAV communications from the perspective of the main performance criteria, i.e., energy efficiency, security, spectral efficiency, etc. Additionally, previous research studies on technology adoption as machine learning algorithms. Lastly, some promising research directions and open challenges for IRS-aided UAV communication are presented.
Matrix Approximation with Side Information: When Column Sampling is Enough
Authors: Jeongmin Chae, Praneeth Narayanamurthy, Selin Bac, Shaama Mallikarjun Sharada, Urbashi Mitra
Date: 2022-12-11 00:00:00

A novel matrix approximation problem is considered herein: observations based on a few fully sampled columns and quasi-polynomial structural side information are exploited. The framework is motivated by quantum chemistry problems wherein full matrix computation is expensive, and partial computations only lead to column information. The proposed algorithm successfully estimates the column and row-space of a true matrix given a priori structural knowledge of the true matrix. A theoretical spectral error bound is provided, which captures the possible inaccuracies of the side information. The error bound proves it scales in its signal-to-noise (SNR) ratio. The proposed algorithm is validated via simulations which enable the characterization of the amount of information provided by the quasi-polynomial side information.
Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks
Authors: Zhendong Li, Wen Chen, Ziheng Zhang, Qingqing Wu, Huanqing Cao, Jun Li
Date: 2022-12-10 00:00:00

In this paper, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the non-linear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is non-convex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the non-convex original problem. In detail, the whole problem is divided into three sub-problems to solve. For the non-convexity of the objective function, successive convex approximation (SCA) is used to transform it, and penalty function method and difference-of-convex (DC) programming are applied to deal with the non-convex constraints. Finally, we alternately solve the three sub-problems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.
On signal strength-based distance estimation using UWB technology
Authors: Leo Botler, Konrad Diwold, Kay Römer
Date: 2022-12-10 00:00:00

Ultra-wideband (UWB) technology has become very popular for indoor positioning and distance estimation (DE) systems due to its decimeter-level accuracy achieved when using time-of-flight-based techniques. Techniques for DE relying on signal strength (DESS) received less attention. As a consequence, existing benchmarks consist of simple channel characterizations rather than methods aiming to increase accuracy. Further development in DESS may enable lower-cost transceivers to applications that can afford lower accuracies than those based on time-of-flight. Moreover, it is a fundamental building block used by a recently proposed approach that can enable security against cyberattacks on DE which could not be avoided using only time-of-flight-based techniques. In this paper, we evaluate the suitability of several machine-learning models trained in different real-world environments to increase UWB-based DESS accuracy. Additionally, aiming for implementation in commercial off-the-shelf (COTS) transceivers, we propose and evaluate an approach to resolve ambiguities comprising DESS in these devices. Our results show that the proposed DE approaches have sub-decimeter accuracy when testing the models in the same environment and positions in which they have been trained, and achieved an average MAE of 24 cm when tested in a different environment. 3 datasets obtained from our experiments are made publicly available.
POCS-based framework of signal reconstruction from generalized non-uniform samples
Authors: Nguyen T. Thao, Dominik Rzepka, Marek Miśkowicz
Date: 2022-12-10 00:00:00

We formalize the use of projections onto convex sets (POCS) for the reconstruction of signals from non-uniform samples in their highest generality. This covers signals in any Hilbert space $\mathscr H$, including multi-dimensional and multi-channel signals, and samples that are most generally inner products of the signals with given kernel functions in $\mathscr H$. An attractive feature of the POCS method is the unconditional convergence of its iterates to an estimate that is consistent with the samples of the input, even when these samples are of very heterogeneous nature on top of their non-uniformity, and/or under insufficient sampling. Moreover, the error of the iterates is systematically monotonically decreasing, and their limit retrieves the input signal whenever the samples are uniquely characteristic of this signal. In the second part of the paper, we focus on the case where the sampling kernel functions are orthogonal in $\mathscr H$, while the input may be confined in a smaller closed space $\mathscr A$ (of bandlimitation for example). This covers the increasingly popular application of time encoding by integration, including multi-channel encoding. We push the analysis of the POCS method in this case by giving a special parallelized version of it, showing its connection with the pseudo-inversion of the linear operator defined by the samples, and giving a multiplierless discrete-time implementation of it that paradoxically accelerates the convergence of the iteration.
Complex systems in Ecology: a guided tour with large Lotka-Volterra models and random matrices
Authors: Imane Akjouj, Matthieu Barbier, Maxime Clenet, Walid Hachem, Mylène Maïda, François Massol, Jamal Najim, Viet Chi Tran
Date: 2022-12-12 00:00:00

Ecosystems represent archetypal complex dynamical systems, often modelled by coupled differential equations of the form $$ \frac{d x_i}{d t} = x_i \varphi_i(x_1,\cdots, x_N)\ , $$ where $N$ represents the number of species and $x_i$, the abundance of species $i$. Among these families of coupled diffential equations, Lotka-Volterra (LV) equations $$ \frac{d x_i}{d t} = x_i ( r_i - x_i +(\Gamma \mathbf{x})_i)\ , $$ play a privileged role, as the LV model represents an acceptable trade-off between complexity and tractability. Here, $r_i$ represents the intrinsic growth of species $i$ and $\Gamma$ stands for the interaction matrix: $\Gamma_{ij}$ represents the effect of species $j$ over species $i$. For large $N$, estimating matrix $\Gamma$ is often an overwhelming task and an alternative is to draw $\Gamma$ at random, parametrizing its statistical distribution by a limited number of model features. Dealing with large random matrices, we naturally rely on Random Matrix Theory (RMT). The aim of this review article is to present an overview of the work at the junction of theoretical ecology and large random matrix theory. It is intended to an interdisciplinary audience spanning theoretical ecology, complex systems, statistical physics and mathematical biology.
FMO Study of the Interaction Energy between Human Estrogen Receptor $α$ and Selected Ligands
Authors: Ricardo Ugarte
Date: 2022-12-09 00:00:00

Fragment molecular orbital (FMO) calculations were performed in aqueous media which allowed us to obtain the interaction energy between the human estrogen receptor $\alpha$ ligand-binding domain (ER) and the selected ligands (L): 17$\beta$-estradiol (E2), 17$\alpha$-estradiol (17$\alpha$-E2), estriol (E3), genistein (GNT), diethylstilbestrol (DES), bisphenol A (BPA), bisphenol AF (BPAF), hydroxychlor (HPTE) and methoxychlor (DMDT). These calculations were carried out on representative structures of L-ER complexes obtained from molecular dynamics simulations. The MP2/6-31G(d) L-ER FMO interaction energy in kcal/mol is as follows: E3 (-100.1) < GNT (-95.8) < E2 (-88.5) < BPA (-84.7) < DES (-82.6) < BPAF (-80.6) < 17$\alpha$-E2 (-78.7) < HPTE (-75.9) < DMDT (-46.3) The central hydrophobic core of the ligands interacts attractively with several apolar amino acid residues of ER. Glu 353 and His 524 interacts strongly with most ligands through a hydrogen bond with the hydroxyl group of the phenol A-ring and the terminal hydroxylated ring, respectively. Water molecules were found at the binding site of receptor. In our model systems we have demonstrated what is generally observed in ligand-receptor complexes: the steric and chemical complementarity of the groups on the ligand and binding site surfaces.
Neural Replicator Analysis for virus genomes binomial systematics in metagenomics
Authors: Alexandr A. Ezhov
Date: 2022-12-11 00:00:00
Tasks: Classification

We have presented some arguments to substantiate the usefulness of neural replicator analysis (NRA) for constructing variants of the natural binomial classification of virus genomes based only on knowledge of their complete genomic sequences, without involving other data on the phenotype, functions, encoded proteins, etc., and also without the need of genomic sequences alignment. Perhaps this will make sense when processing metagenomic data. This makes it possible to construct the binomial classification accepted for the viruses themselves. We restrict ourselves to three families of viruses having dsDNA circular genomes (Papillomaviridae, Polyomaviridae and Caulimoviridae) and partly to the family Geminiviridae having ssDNA genomes though the approach presented can be also applied to genomes of other dsDNA, ssDNA and ssRNA viruses, including linear ones (some results for Mitoviridae are also presented). It is argued that binomial classification of virus genomes which is difficult to apply in all cases can nevertheless be informative tool of revealing virus properties, areal of hosts, forms of diseases and can also show the connections of the viruses belonging to different families and even to different kingdoms.
Reconstructing gene expression and knockout effect scores from DNA mutation (Mut2Ex): methodology and application to cancer prediction problems
Authors: Maya Ramchandran, Maayan Baron
Date: 2022-12-10 00:00:00

Building prediction models for outcomes of clinical relevance when only a limited number of mutational features are available causes considerable challenges due to the sparseness and low-dimensionality of the data. In this article, we present a method to augment the predictive power of these features by leveraging multi-modal associative relationships between an individual's mutational profile and their corresponding gene expression or knockout effect profiles. We can thus reconstruct expression or effect scores for genes of interest from the available mutation features and then use this reconstructed representation directly to model and predict clinical outcomes. We show that our method produces significant improvements in predictive accuracy compared to models utilizing only the raw mutational data, and results in conclusions comparable to those obtained using real expression or effect profiles.
MiNAA: Microbiome Network Alignment Algorithm
Authors: Reed Nelson, Rosa Aghdam, Claudia Solis-Lemus
Date: 2022-12-12 00:00:00

Our Microbiome Network Alignment Algorithm (MiNAA) aligns two microbial networks using a combination of the GRAph ALigner (GRAAL) algorithm and the Hungarian algorithm. Network alignment algorithms find pairs of nodes (one node from the first network and the other node from the second network) that have the highest similarity. Traditionally, similarity has been defined as topological similarity such that the neighborhoods around the two nodes are similar. Recent implementations of network alignment methods such as NETAL and L-GRAAL also include measures of biological similarity, yet these methods are restricted to one specific type of biological similarity (e.g. sequence similarity in L-GRAAL). Our work extends existing network alignment implementations by allowing any type of biological similarity to be input by the user. This flexibility allows the user to choose whatever measure of biological similarity is suitable for the study at hand. In addition, unlike most existing network alignment methods that are tailored for protein or gene interaction networks, our work is the first one suited for microbiome networks.
Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution
Authors: Dominic RW Burrows
Date: 2022-12-12 00:00:00
Tasks: Causal Inference

Epileptic seizures are characterised by abnormal brain dynamics at multiple scales, engaging single neurons, neuronal ensembles and coarse brain regions. Key to understanding the cause of such emergent population dynamics, is capturing the collective behaviour of neuronal activity at multiple brain scales. In this thesis I make use of the larval zebrafish to capture single cell neuronal activity across the whole brain during epileptic seizures. Firstly, I make use of statistical physics methods to quantify the collective behaviour of single neuron dynamics during epileptic seizures. Here, I demonstrate a population mechanism through which single neuron dynamics organise into seizures: brain dynamics deviate from a phase transition. Secondly, I make use of single neuron network models to identify the synaptic mechanisms that actually cause this shift to occur. Here, I show that the density of neuronal connections in the network is key for driving generalised seizure dynamics. Interestingly, such changes also disrupt network response properties and flexible dynamics in brain networks, thus linking microscale neuronal changes with emergent brain dysfunction during seizures. Thirdly, I make use of non-linear causal inference methods to study the nature of the underlying neuronal interactions that enable seizures to occur. Here I show that seizures are driven by high synchrony but also by highly non-linear interactions between neurons. Interestingly, these non-linear signatures are filtered out at the macroscale, and therefore may represent a neuronal signature that could be used for microscale interventional strategies. This thesis demonstrates the utility of studying multi-scale dynamics in the larval zebrafish, to link neuronal activity at the microscale with emergent properties during seizures.
Neural Underpinnings of Decoupled Ethical Behavior in Adolescents as an Interaction of Peer and Personal Values
Authors: Manvi Jain, Karsheet Negi, Pooja S. Sahni, Mannu Brahmi, Neha Singh, Dayal Pyari Srivastava, Jyoti Kumar
Date: 2022-12-10 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

In the present study, we are trying to understand how peer unethical behavior stimulates the decoupling of emotions in adolescents. We have simulated an interactive game-based environment in order to stimulate participants to make decisions that are found to be correlated with their virtual partner decisions. The responses given by participants were also recorded as neural signals using an EEG to study neurophysiological correlates of different decision-making behavioral patterns. There was an active correlation between personality values and decision-making. Preliminary analysis was focused on studying the differences in lower brain frequencies (0.1-4Hz) when the participants developed frustration, in contrast to when they experienced gratitude. The study presents three case studies in which delta frequencies increased in cases when frustration was experienced and decreased when gratitude was experienced. The study focused on understanding the neural underpinnings of corresponding modified behavior in adolescents. The findings highlight an increase in delta frequencies when apparent frustration was developed in adolescents due to their peer unethical behavior. The delta frequencies lowered when participants were tested for ethical behavior. The results concluded that based on personality value types, adolescents tend to develop frustration toward perceived unethical behavior and carry it over to other unrelated peers. This study is highly explorative in nature, with preliminary analysis using only three case studies, having a small sample size. However, the novelty of this study brings about new dimensions to social cognition and personality studies.
Decomposition of the Leinster-Cobbold Diversity Index
Authors: Bingzhang Chen, Michael Grinfeld
Date: 2022-12-11 00:00:00
Tasks: Diversity

The Leinster and Cobbold diversity index possesses a number of merits; in particular, it generalises many existing indices and defines an effective number. We present a scheme to quantify the contribution of richness, evenness, and taxonomic similarity to this index. Compared to the work of van Dam (2019), our approach gives unbiased estimates of both evenness and similarity in a non-homogeneous community. We also introduce a notion of taxonomic tree equilibration which should be of use in the description of community structure.