pwc-archive/papers-with-abstracts huggingface dataset:
Deep-Learning Assisted IoT Based RIS for Cooperative Communications
Authors: Bulent Sagir, Erdogan Aydin, Haci Ilhan
Date: 2022-01-25 00:00:00
Tasks: Deep Learning

Reconfigurable intelligent surfaces (RISs) are software-controlled passive devices that can be used as relay (R) systems to reflect incoming signals from a source (S) to a destination (D) in a cooperative manner with optimum signal strength to improve the performance of wireless communication networks. The configurability and flexibility of an RIS deployed in an Internet-of-Things (IoT)-based network can enable network designers to devise stand-alone or cooperative configurations that have considerable advantages over conventional networks. In this paper, two new deep neural network (DNN)-assisted cooperative RIS models, namely, DNN_R-CRIS and DNN_{R, D}-CRIS, are proposed for cooperative communications. In DNN_R-CRIS model, the potential of RIS deployment as an IoT-based relay element in a next-generation cooperative network is investigated using deep learning (DL) techniques for RIS phase optimization. In addition, to reduce the maximum likelihood (ML) complexity at D, a new DNN-based symbol detection method is presented with the DNN_{R,D}-CRIS model combined with DNN-assisted phase optimization. For a different number of relays and receiver configurations, the bit error rate (BER) performance results of the proposed DNN_R-CRIS and DNN_{R, D}-CRIS models and traditional cooperative RIS (CRIS) scheme (without a DNN) are presented for a multi-relay cooperative communication scenario with path loss effects. It is revealed that the proposed DNN-based models show promising results in terms of BER, even in high-noise environments with low system complexity.
Rate-Splitting Multiple Access for Multi-Antenna Joint Radar and Communications with Partial CSIT: Precoder Optimization and Link-Level Simulations
Authors: Rafael Cerna Loli, Onur Dizdar, Bruno Clerckx
Date: 2022-01-25 00:00:00
Tasks: Fairness

Dual-Functional Radar-Communication (DFRC) systems have been investigated to manage the inter-system interference between radar and communication systems. However, the studies in literature often assume that the DFRC possesses perfect Channel State Information at the Transmitter (CSIT), which is an unrealistic assumption due to the inevitable CSIT errors in practical deployments. In this work, we aim to design a DFRC system under the practical assumption of partial CSIT. To achieve this, the proposed DFRC marries the capabilities of a Multiple-Input Multiple-Output (MIMO) radar with Rate-Splitting Multiple Access (RSMA). RSMA is a powerful downlink communication scheme based on linearly precoded Rate-Splitting (RS) that partially decodes multi-user interference (MUI) and partially treats it as noise and is inherently robust to partial CSIT. Using RSMA, the DFRC precoders are optimized in the presence of partial CSIT to simultaneously maximize the Average Weighted Sum-Rate (AWSR) under Quality-of-Service (QoS) constraints and minimize the DFRC beampattern Mean Squared Error (MSE) against an ideal MIMO radar beampattern. Simulation results demonstrate that the RSMA-based DFRC largely outperforms DFRCs based on other commonly used strategies such as Space Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA). Specifically, the common stream unique in the RSMA-based DFRC allows for flexible rate partitioning to guarantee user rate fairness with partial CSIT while also being the main contributor to generating a directional beampattern for effective radar sensing. The practical performance of the DFRC is then further assessed through Link-Level simulations (LLS) to take into account the effects of coding and modulation in the finite length regime as well as the channel aging due to mobility and latency, where the superiority of RSMA is again corroborated.
Covert Communications through Imperfect Cancellation
Authors: Daniel Chew, Christine Nguyen, Samuel Berhanu, Chris Baumgart, A. Brinton Cooper
Date: 2022-01-25 00:00:00
Tasks: parameter estimation

We propose a method for covert communications using an IEEE 802.11 OFDM/QAM packet as a carrier. We show how to hide the covert message so that the transmitted signal does not violate the spectral mask specified by the standard, and we determine its impact on the OFDM packet error rate. We show conditions under which the hidden signal is not usable and those under which it can be retrieved with a usable bit error rate (BER). The hidden signal is extracted by cancellation of the OFDM signal in the covert receiver. We explore the effects of the hidden signal on OFDM parameter estimation and the covert signal BER. We conclude with an experiment using Over-The-Air recordings of 802.11 packets, inject the hidden
Fast, Flexible, and Exact Minimum Flow Decompositions via ILP
Authors: Fernando H. C. Dias, Lucia Williams, Brendan Mumey, Alexandru I. Tomescu
Date: 2022-01-26 00:00:00

Minimum flow decomposition (MFD) (the problem of finding a minimum set of paths that perfectly decomposes a flow) is a classical problem in Computer Science, and variants of it are powerful models in multiassembly problems in Bioinformatics (e.g. RNA assembly). However, because this problem and its variants are NP-hard, practical multiassembly tools either use heuristics or solve simpler, polynomial-time solvable versions of the problem, which may yield solutions that are not mini-mal or do not perfectly decompose the flow. Many RNA assemblers also use integer linear programming(ILP) formulations of such practical variants, having the major limitation they need to encode all the potentially exponentially many solution paths. Moreover, the only exact solver for MFD does not scale to large instances and cannot be efficiently generalized to practical MFD variants. In this work, we provide the first practical ILP formulation for MFD (and thus the first fast and exact solver for MFD), based on encoding all of the exponentially many solution paths using only a quadratic number of variables. On both simulated and real flow graphs, our approach solves any instance in under 13 seconds. We also show that our ILP formulation can be easily and efficiently adapted for many practical variants, such as incorporating longer or paired-end reads or minimizing flow errors. We hope that our results can remove the current tradeoff between the complexity of a multi assembly model and its tractability and can lie at the core of future practical RNA assembly tools.
The positive-negative mode link between brain connectivity, demographics, and behavior: A pre-registered replication of Smith et al. 2015
Authors: Nikhil Goyal1, Dustin Moraczewski, Peter A. Bandettini, Emily S. Finn, Adam G. Thomas
Date: 2022-01-25 00:00:00
Tasks: Functional Connectivity

In mental health research, it has proven difficult to find measures of brain function that provide reliable indicators of mental health and well-being, including susceptibility to mental health disorders. Recently, a family of data-driven analyses have provided such reliable measures when applied to large, population-level datasets. In the current pre-registered replication study, we show that the canonical correlation analysis (CCA) methods previously developed using resting-state MRI functional connectivity and subject measures of cognition and behavior from healthy adults are also effective in measuring well-being (a "positive-negative axis") in an independent developmental dataset. Our replication was successful in two out of three of our pre-registered criteria, such that a primary CCA mode's weights displayed a significant positive relationship and explained a significant amount of variance in both functional connectivity and subject measures. The only criteria that was not successful was that compared to other modes the magnitude of variance explained by the primary CCA mode was smaller than predicted, a result which could indicate a developmental trajectory of a primary mode. This replication establishes a signature neurotypical relationship between connectivity and phenotype, opening new avenues of research in neuroscience with clear clinical applications.
Two Results about the Sackin and Colless Indices for Phylogenetic Trees and Their Shapes
Authors: Gary Goh, Michael Fuchs, Louxin Zhang
Date: 2022-01-26 00:00:00

The Sackin and Colless indices are two widely-used metrics for measuring the balance of trees and for testing evolutionary models in phylogenetics. This short paper contributes two results about the Sackin and Colless indices of trees. One result is the asymptotic analysis of the expected Sackin and Colless indices of a tree shape (which are full binary rooted unlabelled trees) under the uniform model where tree shapes are sampled with equal probability. Another is a short elementary proof of the closed formula for the expected Sackin index of phylogenetic trees (which are full binary rooted trees with leaves being labelled with taxa) under the uniform model.
Repetition and reproduction of preclinical medical studies: taking a leaf from the plant sciences with consideration of generalised systematic errors
Authors: Jeremy S. C. Clark, Anna Salacka, Agnieszka Boron, Thierry van de Wetering, Konrad Podsiadlo, Kamila Rydzewska, Krzysztof Safranow, Kazimierz Ciechanowski, Leszek Domanski, Andrzej Ciechanowicz
Date: 2022-01-26 00:00:00
Tasks: Articles

Reproduction of pre-clinical results has a high failure rate. The fundamental methodology including replication ("protocol") for hypothesis testing/validation to a state allowing inference, varies within medical and plant sciences with little justification. Here, five protocols are distinguished which deal differently with systematic/random errors and vary considerably in result veracity. Aim: to compare prevalence of protocols (defined in text). Medical/plant science articles from 2017/2019 were surveyed: 713 random articles assessed for eligibility for counts: first (with p-values): 1) non-replicated; 2) global; 3) triple-result protocols; second: 4) replication-error protocol; 5) meta-analyses. Inclusion criteria: human/plant/fungal studies with categorical groups. Exclusion criteria: phased clinical trials, pilot studies, cases, reviews, technology, rare subjects, -omic studies. Abbreviated PICOS question: which protocol was evident for a main result with categorically distinct group difference(s) ? Electronic sources: Journal Citation Reports 2017/2019, Google. Triplication prevalence differed dramatically between sciences (both years p<10-16; cluster-adjusted chi-squared tests): From 320 studies (80/science/year): in 2017, 53 (66%, 95% confidence interval (C.I.) 56%:77%) and in 2019, 48 (60%, C.I. 49%:71%) plant studies had triple-result or triplicated global protocols, compared with, in both years, 4 (5%, C.I. 0.19%:9.8%) medical studies. Plant sciences had a higher prevalence of protocols more likely to counter generalised systematic errors (the most likely cause of false positives) and random error than non-replicated protocols, without suffering from serious flaws found with random-Institutes protocols. It is suggested that a triple-result (organised-reproduction) protocol, with Institute consortia, is likely to solve most problems connected with the replicability crisis.
A novel sustainable role of compost as a universal protective substitute for fish, chicken, pig, and cattle, and its estimation by structural equation modeling
Authors: Hirokuni Miyamoto, Wataru Suda, Hiroaki Kodama, Hideyuki Takahashi, Yumiko Nakanishi, Shigeharu Moriya, Kana Adachi, Nao Kiriyama, Masaya Wada, Daisuke Sudo, Shunsuke Ito, Minami Shibata, Shinji Wada, Takako Murano, Hitoshi Taguchi, Chie Shindo, Arisa Tsuboi, Naoko Tsuji, Makiko Matsuura, Chitose Ishii, Teruno Nakaguma, Toshiyuki Ito, Toru Okada, Teruo Matsushita, Tamotsu Kato, Atsushi Kurotani, Hideaki Shima, Yudai Inabu, Yukihiro Tashiro, Kenji Sakai, Kenichi Mori, Takashi Satoh, Kenta Suzuki, Takeshi Miura, Hidetoshi Morita, Shinji Fukuda, Jun Kikuchi, Hisashi Miyamoto, Masahira Hattori, Naoki Yamamoto, Hiroshi Ohno
Date: 2022-01-26 00:00:00

Natural decomposition of organic matter is essential in food systems, and compost is used worldwide as an organic fermented fertilizer. However, as a feature of the ecosystem, its effects on the animals are poorly understood. Here we show that oral administration of compost and/or its derived thermophilic Bacillaceae, i.e., Caldibacillus hisashii and Weizmannia coagulans, can modulate the prophylactic activities of various industrial animals. The fecal omics analyses in the modulatory process showed an improving trend dependent upon animal species, environmental conditions, and administration. However, structural equation modeling (SEM) estimated the grouping candidates of bacteria and metabolites as standard key components beyond the animal species. In particular, the SEM model implied a strong relationship among partly digesting fecal amino acids, increasing genus Lactobacillus as inhabitant beneficial bacteria and 2-aminoisobutyric acid involved in lantibiotics. These results highlight the potential role of compost for sustainable protective control in agriculture, fishery, and livestock industries.
The theory of quantitative trading
Authors: Andrea Berdondini
Date: 2021-12-27 00:00:00
Tasks: Articles, Logical Reasoning

This book consists of a selection of articles divided into three main themes: Statistics, Quantitative Trading, Psychology. These three arguments are indispensable for the development of a quantitative trading system. The order of the articles was chosen so as to constitute a single logical reasoning that develops progressively.
Fat Tails and Optimal Liability Driven Portfolios
Authors: Jan Rosenzweig
Date: 2022-01-26 00:00:00

We look at optimal liability-driven portfolios in a family of fat-tailed and extremal risk measures, especially in the context of pension fund and insurance fixed cashflow liability profiles, but also those arising in derivatives books such as delta one books or options books in the presence of stochastic volatilities. In the extremal limit, we recover a new tail risk measure, Extreme Deviation (XD), an extremal risk measure significantly more sensitive to extremal returns than CVaR. Resulting optimal portfolios optimize the return per unit of XD, with portfolio weights consisting of a liability hedging contribution, and a risk contribution seeking to generate positive risk-adjusted return. The resulting allocations are analyzed qualitatively and quantitatively in a number of different limits.
Multivariate matrix-exponential affine mixtures and their applications in risk theory
Authors: Eric C. K. Cheung, Oscar Peralta, Jae-Kyung Woo
Date: 2021-12-17 00:00:00

In this paper, a class of multivariate matrix-exponential affine mixtures with matrix-exponential marginals is proposed. The class is shown to possess various attractive properties such as closure under size-biased Esscher transform, order statistics, residual lifetime and higher order equilibrium distributions. This allows for explicit calculations of various actuarial quantities of interest. The results are applied in a wide range of actuarial problems including multivariate risk measures, aggregate loss, large claims reinsurance, weighted premium calculations and risk capital allocation. Furthermore, a multiplicative background risk model with dependent risks is considered and its capital allocation rules are provided as well. We finalize by discussing a calibration scheme based on complete data and potential avenues of research.
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer
Authors: Kaiqi Zhao, Yitao Chen, Ming Zhao
Date: 2022-01-22 00:00:00
Tasks: image-classification, Image Classification, Incremental Learning, Model Compression, speech-recognition, Speech Recognition, Transfer Learning

Deep learning models have introduced various intelligent applications to edge devices, such as image classification, speech recognition, and augmented reality. There is an increasing need of training such models on the devices in order to deliver personalized, responsive, and private learning. To address this need, this paper presents a new solution for deploying and training state-of-the-art models on the resource-constrained devices. First, the paper proposes a novel filter-pruning-based model compression method to create lightweight trainable models from large models trained in the cloud, without much loss of accuracy. Second, it proposes a novel knowledge transfer method to enable the on-device model to update incrementally in real time or near real time using incremental learning on new data and enable the on-device model to learn the unseen categories with the help of the in-cloud model in an unsupervised fashion. The results show that 1) our model compression method can remove up to 99.36% parameters of WRN-28-10, while preserving a Top-1 accuracy of over 90% on CIFAR-10; 2) our knowledge transfer method enables the compressed models to achieve more than 90% accuracy on CIFAR-10 and retain good accuracy on old categories; 3) it allows the compressed models to converge within real time (three to six minutes) on the edge for incremental learning tasks; 4) it enables the model to classify unseen categories of data (78.92% Top-1 accuracy) that it is never trained with.
Generalization Error Bounds on Deep Learning with Markov Datasets
Authors: Lan V. Truong
Date: 2021-12-23 00:00:00
Tasks: Deep Learning

In this paper, we derive upper bounds on generalization errors for deep neural networks with Markov datasets. These bounds are developed based on Koltchinskii and Panchenko's approach for bounding the generalization error of combined classifiers with i.i.d. datasets. The development of new symmetrization inequalities in high-dimensional probability for Markov chains is a key element in our extension, where the spectral gap of the infinitesimal generator of the Markov chain plays a key parameter in these inequalities. We also propose a simple method to convert these bounds and other similar ones in traditional deep learning and machine learning to Bayesian counterparts for both i.i.d. and Markov datasets. Extensions to $m$-order homogeneous Markov chains such as AR and ARMA models and mixtures of several Markov data services are given.
Using a Novel COVID-19 Calculator to Measure Positive U.S. Socio-Economic Impact of a COVID-19 Pre-Screening Solution (AI/ML)
Authors: Richard Swartzbaugh, Amil Khanzada, Praveen Govindan, Mert Pilanci, Ayomide Owoyemi, Les Atlas, Hugo Estrada, Richard Nall, Michael Lotito, Rich Falcone, Jennifer Ranjani J
Date: 2022-01-21 00:00:00

The COVID-19 pandemic has been a scourge upon humanity, claiming the lives of more than 5.1 million people worldwide; the global economy contracted by 3.5% in 2020. This paper presents a COVID-19 calculator, synthesizing existing published calculators and data points, to measure the positive U.S. socio-economic impact of a COVID-19 AI/ML pre-screening solution (algorithm & application).
Learning Norms via Natural Language Teachings
Authors: Taylor Olson, Ken Forbus
Date: 2022-01-20 00:00:00

To interact with humans, artificial intelligence (AI) systems must understand our social world. Within this world norms play an important role in motivating and guiding agents. However, very few computational theories for learning social norms have been proposed. There also exists a long history of debate on the distinction between what is normal (is) and what is normative (ought). Many have argued that being capable of learning both concepts and recognizing the difference is necessary for all social agents. This paper introduces and demonstrates a computational approach to learning norms from natural language text that accounts for both what is normal and what is normative. It provides a foundation for everyday people to train AI systems about social norms.
Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis
Authors: Carey Shenkman, Dhanaraj Thakur, Emma Llansó
Date: 2021-12-15 00:00:00

The ever-increasing amount of user-generated content online has led, in recent years, to an expansion in research and investment in automated content analysis tools. Scrutiny of automated content analysis has accelerated during the COVID-19 pandemic, as social networking services have placed a greater reliance on these tools due to concerns about health risks to their moderation staff from in-person work. At the same time, there are important policy debates around the world about how to improve content moderation while protecting free expression and privacy. In order to advance these debates, we need to understand the potential role of automated content analysis tools. This paper explains the capabilities and limitations of tools for analyzing online multimedia content and highlights the potential risks of using these tools at scale without accounting for their limitations. It focuses on two main categories of tools: matching models and computer prediction models. Matching models include cryptographic and perceptual hashing, which compare user-generated content with existing and known content. Predictive models (including computer vision and computer audition) are machine learning techniques that aim to identify characteristics of new or previously unknown content.
How to Transform Kernels for Scale-Convolutions
Authors: Ivan Sosnovik, Artem Moskalev, Arnold Smeulders
Date: 2021-07-26 00:00:00
Proceeding: ICCVW 2021 7

Scale is often seen as a given, disturbing factor in many vision tasks. When doing so it is one of the factors why we need more data during learning. In recent work scale equivariance was added to convolutional neural networks. It was shown to be effective for a range of tasks. We aim for accurate scale-equivariant convolutional neural networks (SE-CNNs) applicable for problems where high granularity of scale and small kernel sizes are required. Current SE-CNNs rely on weight sharing and kernel rescaling, the latter of which is accurate for integer scales only. To reach accurate scale equivariance, we derive general constraints under which scale-convolution remains equivariant to discrete rescaling. We find the exact solution for all cases where it exists, and compute the approximation for the rest. The discrete scale-convolution pays off, as demonstrated in a new state-of-the-art classification on MNIST-scale and on STL-10 in the supervised learning setting.