pwc-archive/papers-with-abstracts huggingface dataset:
OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach
Authors: Fahad Alhomayani, Mohammad H. Mahoor
Date: 2022-05-30 00:00:00
Tasks: Outdoor Localization

In recent years, fingerprint-based positioning has gained researchers attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions constitutes a high entry barrier for studies. As an effort to overcome this barrier and foster new research efforts, this paper presents OutFin, a novel dataset of outdoor location fingerprints that were collected using two different smartphones. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is different in terms of its visibility to the Global Navigation Satellite System and reference points number, arrangement, and spacing. Before OutFin was made available to the public, several experiments were conducted to validate its technical quality.
Convergence Analysis of Consensus-ADMM for General QCQP
Authors: Huiping Huang, Hing Cheung So, Abdelhak M. Zoubir
Date: 2022-05-30 00:00:00

We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs. We prove that the augmented Lagrangian function value is monotonically non-increasing as long as the augmented Lagrangian parameter is chosen to be sufficiently large. Simulation results show that the augmented Lagrangian function is bounded from below when the matrix in the quadratic term of the objective function is positive definite. In such a case, the consensus-ADMM is convergent.
Effect of Prefix/Suffix Configurations on OTFS Systems with Rectangular Waveforms
Authors: Salah Eddine Zegrar, Hüseyin Arslan
Date: 2022-05-30 00:00:00

Recently, orthogonal time-frequency-space (OTFS) modulation is used as a promising candidate waveform for high mobility communication scenarios. In practical transmission, OTFS with rectangular pulse shaping is implemented using different prefix/suffix configurations including reduced-cyclic prefix (RCP), full-CP (FCP), full-zero suffix (FZS), and reduced-zero padded (RZP). However, for each prefix/suffix type, different effective channel are seen at the receiver side resulting in dissimilar performance of the various OTFS configurations given a specific communication scenario. To fulfill this gap, in this paper, we study and model the effective channel in OTFS systems using various prefix/suffix configurations. Then, from the input-output relation analysis of the received signal, we show that the OTFS has a simple sparse structure for all prefix/suffix types, where the only difference is the phase term introduced when extending quasi-periodically in the delay-Doppler grid. We provide a comprehensive comparison between all OTFS types in terms of channel estimation/equalization complexity, symbol detection performance, power and spectral efficiencies, which helps in deciding the optimal prefix/suffix configuration for a specific scenario. Finally, we propose a novel OTFS structure namely reduced-FCP (RFCP) where the information of the CP block is decodable.
Underwater Acoustic Communication Channel Modeling using Reservoir Computing
Authors: Oluwaseyi Onasami, Ming Feng, Hao Xu, Mulugeta Haile, Lijun Qian
Date: 2022-05-30 00:00:00
Tasks: Transfer Learning

Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communications, modeling and understanding the UWA channel is indispensable. However, there exist many challenges due to the high uncertainties of the underwater environment and the lack of real-world measurement data. In this work, the capability of reservoir computing and deep learning has been explored for modeling the UWA communication channel accurately using real underwater data collected from a water tank with disturbance and from Lake Tahoe. We leverage the capability of reservoir computing for modeling dynamical systems and provided a data-driven approach to modeling the UWA channel using Echo State Network (ESN). In addition, the potential application of transfer learning to reservoir computing has been examined. Experimental results show that ESN is able to model chaotic UWA channels with better performance compared to popular deep learning models in terms of mean absolute percentage error (MAPE), specifically, ESN has outperformed deep neural network by 2% and as much as 40% in benign and chaotic UWA respectively.
QoE-Aware Resource Allocation for Semantic Communication Networks
Authors: Lei Yan, Zhijin Qin, Rui Zhang, Yongzhao Li, Geoffrey Ye Li
Date: 2022-05-28 00:00:00
Tasks: Semantic Communication

With the aim of accomplishing intelligence tasks, semantic communications transmit task-related information only, yielding significant performance gains over conventional communications. To guarantee user requirements for different types of tasks, we perform the semantic-aware resource allocation in a multi-cell multi-task network in this paper. Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed. We formulate the QoE-aware semantic resource allocation in terms of the number of transmitted semantic symbols, channel assignment, and power allocation. To solve this problem, we first decouple it into two independent subproblems. The first one is to optimize the number of transmitted semantic symbols with given channel assignment and power allocation, which is solved by the exhaustive searching method. The second one is the channel assignment and power allocation subproblem, which is modeled as a many-to-one matching game and solved by the proposed low-complexity matching algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method on the overall QoE.
Transfer Learning-based Channel Estimation in Orthogonal Frequency Division Multiplexing Systems Using Data-nulling Superimposed Pilots
Authors: Chaojin Qing, Lei Dong, Li Wang, Guowei Ling, Jiafan Wang
Date: 2022-05-28 00:00:00
Tasks: Transfer Learning

Data-nulling superimposed pilot (DNSP) effectively alleviates the superimposed interference of superimposed training (ST)-based channel estimation (CE) in orthogonal frequency division multiplexing (OFDM) systems, while facing the challenges of the estimation accuracy and computational complexity. By developing the promising solutions of deep learning (DL) in the physical layer of wireless communication, we fuse the DNSP and DL to tackle these challenges in this paper. Nevertheless, due to the changes of wireless scenarios, the model mismatch of DL leads to the performance degradation of CE, and thus faces the issue of network retraining. To address this issue, a lightweight transfer learning (TL) network is further proposed for the DL-based DNSP scheme, and thus structures a TL-based CE in OFDM systems. Specifically, based on the linear receiver, the least squares estimation is first employed to extract the initial features of CE. With the extracted features, we develop a convolutional neural network (CNN) to fuse the solutions of DLbased CE and the CE of DNSP. Finally, a lightweight TL network is constructed to address the model mismatch. To this end, a novel CE network for the DNSP scheme in OFDM systems is structured, which improves its estimation accuracy and alleviates the model mismatch. The experimental results show that in all signal-to-noise-ratio (SNR) regions, the proposed method achieves lower normalized mean squared error (NMSE) than the existing DNSP schemes with minimum mean square error (MMSE)-based CE. For example, when the SNR is 0 decibel (dB), the proposed scheme achieves similar NMSE as that of the MMSE-based CE scheme at 20 dB, thereby significantly improving the estimation accuracy of CE. In addition, relative to the existing schemes, the improvement of the proposed scheme presents its robustness against the impacts of parameter variations.
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control
Authors: Gregory Canal, Yancy Diaz-Mercado, Magnus Egerstedt, Christopher Rozell
Date: 2022-05-27 00:00:00
Tasks: Brain Computer Interface

A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user's brain activity, which is then processed into commands that drive a system end effector. BCIs involve either invasive measurements which allow for high-complexity control but are generally infeasible, or noninvasive measurements which offer lower quality signals but are more practical to use. In general, BCI systems have not been developed that efficiently, robustly, and scalably perform high-complexity control while retaining the practicality of noninvasive measurements. Here we leverage recent results from feedback information theory to fill this gap by modeling BCIs as a communications system and deploying a human-implementable interaction algorithm for noninvasive control of a high-complexity robot swarm. We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models. Our results provide a proof of concept for how a large class of high-complexity effectors (even beyond robotics) can be effectively controlled by a BCI system with low-complexity and noisy inputs.
Functional and evolutionary genomics of the Streptomyces metabolism
Authors: Pablo Cruz-Morales
Date: 2022-05-30 00:00:00

This thesis is focused in the study of the evolution of the metabolic repertoire of Streptomyces, which are renowned as proficient producers of bioactive Natural Products (NPs). The main goal of my work was to contribute into the understanding of the evolutionary mechanisms behind the evolution of NP biosynthetic pathways. Specifically, the development of a bioinformatic method that helps into the discovery of new NP biosynthetic pathways from actinobacterial genome sequences with emphasis on members of the genus Streptomyces. I developed this method using a comparative and functional genomics perspective. My studies indicate that central metabolic enzymes were expanded in a genus-specific manner in Actinobacteria, and that they have been extensively recruited for the biosynthesis of NPs. Based in these observations, I developed EvoMining, a bioinformatic pipeline for the identificatoon of novel biosynthetic pathways in microbial genomes. Using EvoMining several new NP biosynthetic pathways have been predicted in different members of the phylum Actinobacteria, including the model organism S. lividans 66. To test this approach, the genome sequence of this model strain was obtained, and its analysis led to the discovery of an unprecedented system for peptide bond formation, as well as a biosynthetic pathway for an arsenic-containing metabolite. Moreover, this work also led to the identification of expansions on a conserved metabolic node in the glycolytic pathway of Streptomyces. These expansions occurred before the radiation of Streptomyces and are concomitant with the evolution of their capability to produce NPs. Experimental analyses indicate that this node evolved to mediate the interplay between central an NP metabolism.
Change in structural brain network abnormalities after traumatic brain injury determines post-injury recovery
Authors: James J Gugger, Nishant Sinha, Yiming Huang, Alexa Walter, Cillian Lynch, Justin Morrison, Nathan Smyk, Danielle Sandsmark, Ramon Diaz-Arrastia, Kathryn A Davis
Date: 2022-05-29 00:00:00

The trajectory of an individual's recovery after traumatic brain injury (TBI) is heterogeneous, with complete recovery in some cases but persistent disability in others. We hypothesized that changes in structural brain network abnormalities guide the trajectory of an individual's recovery post-injury. Our objective was to characterize the variability in recovery post-TBI by identifying a putative neuroimaging biomarker of traumatic axonal injury (TAI) in individuals with mild TBI. We analyzed 70 T1-weighted and diffusion MRIs longitudinally collected from 35 individuals during the subacute and chronic post-injury periods. Each individual underwent longitudinal blood work to characterize blood protein biomarkers of axonal and glial injury and assessment of post-injury recovery in the subacute and chronic periods. By comparing the MRI data of individual cases with 35 controls, we estimated the longitudinal change in structural brain network abnormalities. We validated this proxy measure of TAI with independent measures of acute intracranial injury estimated from head CT and blood protein biomarkers. Post-injury structural network abnormality was significantly higher than controls in both subacute and chronic periods, associated with an acute CT lesion and subacute blood levels of glial fibrillary acid protein (r=0.5, p=0.008) and neurofilament light (r=0.41, p=0.02). Longitudinal change in abnormality associated with change in functional outcome status (r=-0.51, p=0.003) and post-concussive symptoms (BSI: r=0.46, p=0.03; RPQ:r = 0.46, p=0.02). Brain regions that most closely mapped onto symptom change over time corresponded to structural network hubs or areas susceptible to neurotrauma. Structural network abnormalities might be a biomarker of TAI. Assessing changes in brain network abnormality might enable better patient stratification for monitoring recovery after neurotrauma.
Unsupervised learning of features and object boundaries from local prediction
Authors: Heiko H. Schütt, Wei Ji Ma
Date: 2022-05-27 00:00:00
Tasks: Contrastive Learning

A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects. Those two aspects are usually dealt with separately, although predictability is discussed as a cue for both. To incorporate features and boundaries into the same model, we model a layer of feature maps with a pairwise Markov random field model in which each factor is paired with an additional binary variable, which switches the factor on or off. Using one of two contrastive learning objectives, we can learn both the features and the parameters of the Markov random field factors from images without further supervision signals. The features learned by shallow neural networks based on this loss are local averages, opponent colors, and Gabor-like stripe patterns. Furthermore, we can infer connectivity between locations by inferring the switch variables. Contours inferred from this connectivity perform quite well on the Berkeley segmentation database (BSDS500) without any training on contours. Thus, computing predictions across space aids both segmentation and feature learning, and models trained to optimize these predictions show similarities to the human visual system. We speculate that retinotopic visual cortex might implement such predictions over space through lateral connections.
Modeling the Dynamics of the Coronavirus SARS-CoV-2 Pandemic using Modified SIR Model with the 'Damped-Oscillator' Dynamics of the Effective Reproduction Number
Authors: Anne V. Ginzburg, Valeriy V. Ginzburg, Julia O. Ginzburg, Ana Garcia Arias, Leela Rakesh
Date: 2022-05-29 00:00:00

The COVID-19 pandemic has been a great catastrophe that upended human lives and caused millions of deaths all over the world. The rapid spread of the virus, with its early-stage exponential growth and subsequent 'waves', caught many medical professionals and decision-makers unprepared. Even though epidemiological models have been known for almost a century (since the 'Spanish Influenza' pandemic of 1918-20), the real-life spread of the SARS-CoV-2 virus often confounded the modelers. While the general framework of epidemiological models like SEIR (susceptible-exposed-infected-recovered) or SIR (susceptible-exposed-infected) was not in question, the behavior of model parameters turned out to be unpredictable and complicated. In particular, while the 'basic' reproduction number, R0, can be considered a constant (for the original SARS-CoV-2 virus, prior to the emergence of variants, R0 is between 2.5 and 3.0), the 'effective' reproduction number, R(t), was a complex function of time, influenced by human behavior in response to the pandemic (e.g., masking, lockdowns, transition to remote work, etc.) To better understand these phenomena, we model the first year of the pandemic (between February 2020 and February 2021) for a number of localities (fifty US states, as well as several countries) using a simple SIR model. We show that the evolution of the pandemic can be described quite successfully by assuming that R(t) behaves in a 'viscoelastic' manner, as a sum of two or three 'damped oscillators' with different natural frequencies and damping coefficients. These oscillators likely correspond to different sub-populations having different reactions to proposed mitigation measures. The proposed approach can offer future data modelers new ways to fit the reproduction number evolution with time (as compared to the purely data-driven approaches most prevalent today).
DNA Storage Error Simulator: A Tool for Simulating Errors in Synthesis, Storage, PCR and Sequencing
Authors: Jamie J. Alnasir, Thomas Heinis, Louis Carteron
Date: 2022-05-28 00:00:00

DNA has many valuable characteristics that make it suitable for a long-term storage medium, in particular its durability and high information density. DNA can be stored safely for hundreds of years with virtually no degradation, in contrast to hard disk drives which typically last for about 5 years. Furthermore, the duration of DNA-Storage can be extended to potentially up to thousands of years if it is desiccated and cooled in storage. Advances in DNA technologies have made it possible to store the entirety of Wikipedia in a test tube and read that information using a handheld sequencing device, although imperfections in writing (synthesis) and reading (sequencing) need to be mitigated for it to be viable as a mainstream storage medium. New sequencing technologies, such as nanopore sequencing, aim to penetrate the consumer world, thanks to their affordability and size. However, the error characteristics of nanopore sequencing are not yet well characterised. DNA Storage Error Simulator models errors that can be introduced in all the phases of DNA storage workflow, including synthesis, storage, PCR for amplification and finally sequencing. The error characteristics for sequencing and synthesis can be configured in all necessary detail or can be chosen from a predefined set of values based on available literature and our own analysis. Availability: DNA Storage Error Simulator can be accessed online from: https://master.dbahb2jho41s4.amplifyapp.com (https://dnastorage.doc.ic.ac.uk/DNA-error-simulator)
Managing Risk in DeFi Portfolios
Authors: Hugo Inzirillo, Stanislas de Quenetain
Date: 2022-05-29 00:00:00
Tasks: Management

Decentralized Finance (DeFi) is a new financial industry built on blockchain technologies. Decentralized financial services have consequently increased the ability to lend, borrow, and invest in decentralized investment vehicles, allowing investors to bypass third party intermediaries. DeFi's promise is to reduce the cost of transaction and management fees whilst increasing trust between agents of the Financial Industry 3.0. This paper provides an overview of the different components of DeFi, as well as the risks involved in investing through these new vehicles. We will also propose an allocation methodology which will integrate and quantify these risks.
Multimodal Masked Autoencoders Learn Transferable Representations
Authors: Xinyang Geng, Hao liu, Lisa Lee, Dale Schuurmans, Sergey Levine, Pieter Abbeel
Date: 2022-05-27 00:00:00
Tasks: Contrastive Learning

Building scalable models to learn from diverse, multimodal data remains an open challenge. For vision-language data, the dominant approaches are based on contrastive learning objectives that train a separate encoder for each modality. While effective, contrastive learning approaches introduce sampling bias depending on the data augmentations used, which can degrade performance on downstream tasks. Moreover, these methods are limited to paired image-text data, and cannot leverage widely-available unpaired data. In this paper, we investigate whether a large multimodal model trained purely via masked token prediction, without using modality-specific encoders or contrastive learning, can learn transferable representations for downstream tasks. We propose a simple and scalable network architecture, the Multimodal Masked Autoencoder (M3AE), which learns a unified encoder for both vision and language data via masked token prediction. We provide an empirical study of M3AE trained on a large-scale image-text dataset, and find that M3AE is able to learn generalizable representations that transfer well to downstream tasks. Surprisingly, we find that M3AE benefits from a higher text mask ratio (50-90%), in contrast to BERT whose standard masking ratio is 15%, due to the joint training of two data modalities. We also provide qualitative analysis showing that the learned representation incorporates meaningful information from both image and language. Lastly, we demonstrate the scalability of M3AE with larger model size and training time, and its flexibility to train on both paired image-text data as well as unpaired data.
Exemplar Free Class Agnostic Counting
Authors: Viresh Ranjan, Minh Hoai
Date: 2022-05-27 00:00:00
Tasks: Density Estimation, Exemplar-Free, Region Proposal, Test-time Adaptation

We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category. All previous class agnostic counting methods cannot work in a fully automated setting, and require computationally expensive test time adaptation. To address these challenges, we propose a visual counter which operates in a fully automated setting and does not require any test time adaptation. Our proposed approach first identifies exemplars from repeating objects in an image, and then counts the repeating objects. We propose a novel region proposal network for identifying the exemplars. After identifying the exemplars, we obtain the corresponding count by using a density estimation based Visual Counter. We evaluate our proposed approach on FSC-147 dataset, and show that it achieves superior performance compared to the existing approaches.