pwc-archive/papers-with-abstracts huggingface dataset:
Repetitive Dilemma Games in Distribution Information Using Interplay of Droop Quota: Meek's Method in Impact of Maximum Compensation and Minimum Cost Routes in Information Role of Marginal Contribution in Two-Sided Matching Markets
Authors: Yasuko Kawahata
Date: 2024-02-19 00:00:00
Tasks: Fact Checking

This paper is a preliminary report of the research plan and a digest of the results and discussions. On research note explores the complex dynamics of fake news dissemination and fact-checking costs within the framework of information markets and analyzes the equilibrium between supply and demand using the concepts of droop quotas, Meek's method, and marginal contributions. By adopting a two-sided matching market perspective, we delve into scenarios in which markets are stable under the influence of fake news perceived as truth and those in which credibility prevails. Through the application of iterated dilemma game theory, we investigate the strategic choices of news providers affected by the costs associated with spreading fake news and fact-checking efforts. We further examine the maximum reward problem and strategies to minimize the cost path for spreading fake news, and consider a nuanced understanding of market segmentation into "cheap" and "premium" segments based on the nature of the information being spread. Our analysis uses mathematical models and computational processes to identify stable equilibrium points that ensure market stability in the face of deceptive information practices and provide insight into effective strategies to enhance the informational health of the market. Through this comprehensive approach, this paper aims for a more truthful and reliable perspective from which to observe information markets. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.
Blind Identification of Binaural Room Impulse Responses from Smart Glasses
Authors: Thomas Deppisch, Nils Meyer-Kahlen, Sebastià V. Amengual Garí
Date: 2024-03-28 00:00:00

Smart glasses are increasingly recognized as a key medium for augmented reality, offering a hands-free platform with integrated microphones and non-ear-occluding loudspeakers to seamlessly mix virtual sound sources into the real-world acoustic scene. To convincingly integrate virtual sound sources, the room acoustic rendering of the virtual sources must match the real-world acoustics. Information about a user's acoustic environment however is typically not available. This work uses a microphone array in a pair of smart glasses to blindly identify binaural room impulse responses (BRIRs) from a few seconds of speech in the real-world environment. The proposed method uses dereverberation and beamforming to generate a pseudo reference signal that is used by a multichannel Wiener filter to estimate room impulse responses which are then converted to BRIRs. The multichannel room impulse responses can be used to estimate room acoustic parameters which is shown to outperform baseline algorithms in the estimation of reverberation time and direct-to-reverberant energy ratio. Results from a listening experiment further indicate that the estimated BRIRs often reproduce the real-world room acoustics perceptually more convincingly than measured BRIRs from other rooms of similar size.
LV-CTC: Non-autoregressive ASR with CTC and latent variable models
Authors: Yuya Fujita, Shinji Watanabe, Xuankai Chang, Takashi Maekaku
Date: 2024-03-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition

Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to achieve high accuracy and fast inference by simplifying the autoregressive (AR) generation process of conventional models. Connectionist temporal classification (CTC) is one of the key techniques used in NAR ASR models. In this paper, we propose a new model combining CTC and a latent variable model, which is one of the state-of-the-art models in the neural machine translation research field. A new neural network architecture and formulation specialized for ASR application are introduced. In the proposed model, CTC alignment is assumed to be dependent on the latent variables that are expected to capture dependencies between tokens. Experimental results on a 100 hours subset of Librispeech corpus showed the best recognition accuracy among CTC-based NAR models. On the TED-LIUM2 corpus, the best recognition accuracy is achieved including AR E2E models with faster inference speed.
Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI
Authors: Tian Yu, Yunhe Li, Michael E. Kim, Chenyu Gao, Qi Yang, Leon Y. Cai, Susane M. Resnick, Lori L. Beason-Held, Daniel C. Moyer, Kurt G. Schilling, Bennett A. Landman
Date: 2024-03-27 00:00:00
Tasks: Diffusion  MRI

Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relationships with WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn and propagate streamlines directly from T1 and anatomical contexts. Training for this network has previously relied on high-resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical protocols. Then, we define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. Under this metric, T1 tractography generated by CoRNN reproduces diffusion tractography with approximately two millimeters of error.
Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate
Authors: Zirui Chen, Zhaoyang Zhang, Zhaohui Yang, Chongwen Huang, Merouane Debbah
Date: 2024-03-28 00:00:00

How to reduce the pilot overhead required for channel estimation? How to deal with the channel dynamic changes and error propagation in channel prediction? To jointly address these two critical issues in next-generation transceiver design, in this paper, we propose a novel framework named channel deduction for high-dimensional channel acquisition in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Specifically, it makes use of the outdated channel information of past time slots, performs coarse estimation for the current channel with a relatively small number of pilots, and then fuses these two information to obtain a complete representation of the present channel. The rationale is to align the current channel representation to both the latent channel features within the past samples and the coarse estimate of current channel at the pilots, which, in a sense, behaves as a complementary combination of estimation and prediction and thus reduces the overall overhead. To fully exploit the highly nonlinear correlations in time, space, and frequency domains, we resort to learning-based implementation approaches. By using the highly efficient complex-domain multilayer perceptron (MLP)-mixer for crossing space-frequency domain representation and the recurrence-based or attention-based mechanisms for the past-present interaction, we respectively design two different channel deduction neural networks (CDNets). We provide a general procedure of data collection, training, and deployment to standardize the application of CDNets. Comprehensive experimental evaluations in accuracy, robustness, and efficiency demonstrate the superiority of the proposed approach, which reduces the pilot overhead by up to 88.9% compared to state-of-the-art estimation approaches and enables continuous operating even under unknown user movement and error propagation.
Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systems
Authors: Minje Kim, In-soo Kim, Junil Choi
Date: 2024-03-28 00:00:00
Tasks: CPU, Fairness, Quantization

Limited capacity of fronthaul links in a cell-free massive multiple-input multiple-output (MIMO) system can cause quantization errors at a central processing unit (CPU) during data transmission, complicating the centralized rate optimization problem. Addressing this challenge, we propose a harmony search (HS)-based algorithm that renders the combinatorial non-convex problem tractable. One of the distinctive features of our algorithm is its hierarchical structure: it first allocates resources at the access point (AP) level and subsequently optimizes for user equipment (UE), ensuring a more efficient and structured approach to resource allocation. Our proposed algorithm deals with rigorous conditions, such as asymmetric fronthaul bit allocation and distinct quantization error levels at each AP, which were not considered in previous works. We derive a closed-form expression of signal-to-interference-plusnoise ratio (SINR), in which additive quantization noise model (AQNM) based distortion error is taken into account, to define the mathematical expression of spectral efficiency (SE) for each UE. Also, we provide analyses on computational complexity and convergence to investigate the practicality of proposed algorithm. By leveraging various performance metrics such as total SE and max-min fairness, we demonstrate that the proposed algorithm can adaptively optimize the fronthaul bit allocation depending on system requirements. Finally, simulation results show that the proposed algorithm can achieve satisfactory performance while maintaining low computational complexity, as compared to the exhaustive search method
Low-Complexity Estimation Algorithm and Decoupling Scheme for FRaC System
Authors: Mengjiang Sun, Peng Chen, Zhenxin Cao, Fei Shen
Date: 2024-03-27 00:00:00
Tasks: Autonomous Vehicles, parameter estimation

With the leaping advances in autonomous vehicles and transportation infrastructure, dual function radar-communication (DFRC) systems have become attractive due to the size, cost and resource efficiency. A frequency modulated continuous waveform (FMCW)-based radar-communication system (FRaC) utilizing both sparse multiple-input and multiple-output (MIMO) arrays and index modulation (IM) has been proposed to form a DFRC system specifically designed for vehicular applications. In this paper, the three-dimensional (3D) parameter estimation problem in the FRaC is considered. Since the 3D-parameters including range, direction of arrival (DOA) and velocity are coupled in the estimating matrix of the FRaC system, the existing estimation algorithms cannot estimate the 3D-parameters accurately. Hence, a novel decomposed decoupled atomic norm minimization (DANM) method is proposed by splitting the 3D-parameter estimating matrix into multiple 2D matrices with sparsity constraints. Then, the 3D-parameters are estimated and efficiently and separately with the optimized decoupled estimating matrix. Moreover, the Cram\'{e}r-Rao lower bound (CRLB) of the 3D-parameter estimation are derived, and the computational complexity of the proposed algorithm is analyzed. Simulation results show that the proposed decomposed DANM method exploits the advantage of the virtual aperture in the existence of coupling caused by IM and sparse MIMO array and outperforms the co-estimation algorithm with lower computation complexity.
Design principles, growth laws, and competition of minimal autocatalysts
Authors: Yann Sakref, Olivier Rivoire
Date: 2024-03-27 00:00:00

The apparent difficulty of designing simple autocatalysts that grow exponentially in the absence of enzymes, external drives or ingenious internal mechanisms severely constrains scenarios for the emergence of evolution by natural selection in chemical and physical systems. Here, we systematically analyze these difficulties in the context of one of the simplest and most generic autocatalysts: a dimeric molecule that duplicates by templated ligation. We show that despite its simplicity, such an autocatalyst can achieve exponential growth autonomously. This only requires that the rate of the spontaneous dimerization, the interactions between molecules, and the concentrations of substrates and products are in appropriate ranges. We also show, however, that it is possible to design as simple sub-exponential autocatalysts that have an advantage over exponential autocatalysts when competing for a common resource. We reach these conclusions by developing a general theoretical framework based on kinetic barrier diagrams. Besides challenging commonly accepted assumptions in the field of the origin of life, our results provide a blueprint for the experimental realization of elementary autocatalysts exhibiting a form of natural selection, whether on a molecular or colloidal scale.
Navigating Eukaryotic Genome Annotation Pipelines: A Route Map to BRAKER, Galba, and TSEBRA
Authors: Tomáš Brůna, Lars Gabriel, Katharina J. Hoff
Date: 2024-03-28 00:00:00

Annotating the structure of protein-coding genes represents a major challenge in the analysis of eukaryotic genomes. This task sets the groundwork for subsequent genomic studies aimed at understanding the functions of individual genes. BRAKER and Galba are two fully automated and containerized pipelines designed to perform accurate genome annotation. BRAKER integrates the GeneMark-ETP and AUGUSTUS gene finders, employing the TSEBRA combiner to attain high sensitivity and precision. BRAKER is adept at handling genomes of any size, provided that it has access to both transcript expression sequencing data and an extensive protein database from the target clade. In particular, BRAKER demonstrates high accuracy even with only one type of these extrinsic evidence sources, although it should be noted that accuracy diminishes for larger genomes under such conditions. In contrast, Galba adopts a distinct methodology utilizing the outcomes of direct protein-to-genome spliced alignments using miniprot to generate training genes and evidence for gene prediction in AUGUSTUS. Galba has superior accuracy in large genomes if protein sequences are the only source of evidence. This chapter provides practical guidelines for employing both pipelines in the annotation of eukaryotic genomes, with a focus on insect genomes.
Just-DNA-Seq, open-source personal genomics platform: longevity science for everyone
Authors: Kulaga Anton, Borysova Olga, Karmazin Alexey, Koval Maria, Usanov Nikolay, Fedorova Alina, Evfratov Sergey, Pushkareva Malvina, Ryangguk Kim, Tacutu Robi
Date: 2024-03-28 00:00:00

Genomic data has become increasingly accessible to the general public with the advent of companies offering whole genome sequencing at a relatively low cost. However, their reports are not verifiable due to a lack of crucial details and transparency: polygenic risk scores do not always mention all the polymorphisms involved. Simultaneously, tackling the manual investigation and interpretation of data proves challenging for individuals lacking a background in genetics. Currently, there is no open-source or commercial solution that provides comprehensive longevity reports surpassing a limited number of polymorphisms. Additionally, there are no ready-made, out-of-the-box solutions available that require minimal expertise to generate reports independently. To address these issues, we have developed the Just-DNA-Seq open-source genomic platform. Just-DNA-Seq aims to provide a user-friendly solution to genome annotation by allowing users to upload their own VCF files and receive annotations of their genetic variants and polygenic risk scores related to longevity. We also created GeneticsGenie custom GPT that can answer genetics questions based on our modules. With the Just-DNA-Seq platform, we want to provide full information regarding the genetics of long life: disease-predisposing variants, that can reduce lifespan and manifest at different age (cardiovascular, oncological, neurodegenerative diseases, etc.), pro-longevity variants and longevity drug pharmacokinetics. In this research article, we will discuss the features and capabilities of Just-DNA-Seq, and how it can benefit individuals looking to understand and improve their health. It's crucial to note that the Just-DNA-Seq platform is exclusively intended for scientific and informational purposes and is not suitable for medical applications.
In the driver's mind: modeling the dynamics of human overtaking decisions in interactions with oncoming automated vehicles
Authors: Samir H. A. Mohammad, Haneen Farah, Arkady Zgonnikov
Date: 2024-03-28 00:00:00

Understanding human behavior in overtaking scenarios is crucial for enhancing road safety in mixed traffic with automated vehicles (AVs). Computational models of behavior play a pivotal role in advancing this understanding, as they can provide insight into human behavior generalizing beyond empirical studies. However, existing studies and models of human overtaking behavior have mostly focused on scenarios with simplistic, constant-speed dynamics of oncoming vehicles, disregarding the potential of AVs to proactively influence the decision-making process of the human drivers via implicit communication. Furthermore, so far it remained unknown whether overtaking decisions of human drivers are affected by whether they are interacting with an AV or a human-driven vehicle (HDV). To address these gaps, we conducted a "reverse Wizard-of-Oz" driving simulator experiment with 30 participants who repeatedly interacted with oncoming AVs and HDVs, measuring the drivers' gap acceptance decisions and response times. The oncoming vehicles featured time-varying dynamics designed to influence the overtaking decisions of the participants by briefly decelerating and then recovering to their initial speed. We found that participants did not alter their overtaking behavior when interacting with oncoming AVs compared to HDVs. Furthermore, we did not find any evidence of brief decelerations of the oncoming vehicle affecting the decisions or response times of the participants. Cognitive modeling of the obtained data revealed that a generalized drift-diffusion model with dynamic drift rate and velocity-dependent decision bias best explained the gap acceptance outcomes and response times observed in the experiment. Overall, our findings highlight the potential of cognitive models for further advancing the ongoing development of safer interactions between human drivers and AVs during overtaking maneuvers.
Structural Brain Connectivity and Treatment Improvement in Mood Disorder
Authors: Sébastien Dam, Jean-Marie Batail, Gabriel H Robert, Dominique Drapier, Pierre Maurel, Julie Coloigner
Date: 2024-03-22 00:00:00

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions,adverse events, and lost therapeutic opportunities.Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) -- and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the MD patients went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved. First, the threshold-free network-based statistics was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free network-based statistics revealed impaired connections involvingregions of the basal ganglia in MD patients compared to HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula and amygdala in the MD group. Compared with the not-improved, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe and rostral anterior cingulate.Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.
Are Colors Quanta of Light for Human Vision? A Quantum Cognition Study of Visual Perception
Authors: Jonito Aerts Arguëlles
Date: 2024-03-14 00:00:00

We show that colors are light quanta for human visual perception in a similar way as photons are light quanta for physical measurements of light waves. Our result relies on the identification in the quantum measurement process itself of the warping mechanism which is characteristic of human perception. This warping mechanism makes stimuli classified into the same category perceived as more similar, while stimuli classified into different m categories are perceived as more different. In the quantum measurement process, the warping takes place between the pure states, which play the role played for human perception by the stimuli, and the density states after decoherence, which play the role played for human perception by the percepts. We use the natural metric for pure states, namely the normalized Fubini Study metric to measure distances between pure states, and the natural metric for density states, namely the normalized trace-class metric, to measure distances between density states. We then show that when pure states lie within a well-defined region surrounding an eigenstate, the quantum measurement, namely the process of decoherence, contracts the distance between these pure states, while the reverse happens for pure states lying in a well-defined region between two eigenstates, for which the quantum measurement causes a dilation. We elaborate as an example the situation of a two-dimensional quantum measurement described by the Bloch model and apply it to the situation of two colors 'Light' and 'Dark'. We argue that this analogy of warping, on the one hand in human perception and on the other hand in the quantum measurement process, makes colors to be quanta of light for human vision.