pwc-archive/papers-with-abstracts huggingface dataset:
Online unsupervised Learning for domain shift in COVID-19 CT scan datasets
Authors: Nicolas Ewen, Naimul Khan
Date: 2021-07-31 00:00:00

Neural networks often require large amounts of expert annotated data to train. When changes are made in the process of medical imaging, trained networks may not perform as well, and obtaining large amounts of expert annotations for each change in the imaging process can be time consuming and expensive. Online unsupervised learning is a method that has been proposed to deal with situations where there is a domain shift in incoming data, and a lack of annotations. The aim of this study is to see whether online unsupervised learning can help COVID-19 CT scan classification models adjust to slight domain shifts, when there are no annotations available for the new data. A total of six experiments are performed using three test datasets with differing amounts of domain shift. These experiments compare the performance of the online unsupervised learning strategy to a baseline, as well as comparing how the strategy performs on different domain shifts. Code for online unsupervised learning can be found at this link: https://github.com/Mewtwo/online-unsupervised-learning
Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors
Authors: Ender Ozturk, Chethan Kumar Anjinappa, Fatih Erden, Ismail Guvenc, Huaiyu Dai, Arupjyoti Bhuyan
Date: 2021-08-04 00:00:00

Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones. However, spatial multiplexing with drones suffers from rank deficient channels due to the unobstructed line-of-sight (LoS), especially in millimeter-wave (mmWave) frequencies that use narrow beams. One possible solution is utilizing low-cost and low-complexity metamaterial-based intelligent reflecting surfaces (IRS) to enrich the multipath environment, taking into account that the drones are restricted to fly only within well-defined drone corridors. A hurdle with this solution is placing the IRSs optimally. In this study, we propose an approach for IRS placement with a goal to improve the spatial multiplexing gains, and hence to maximize the average channel capacity in a predefined drone corridor. Our results at 6 GHz, 28 GHz and 60 GHz show that the proposed approach increases the average rates for all frequency bands for a given drone corridor, when compared with the environment where there are no IRSs present, and IRS-aided channels perform close to each other at sub-6 and mmWave bands.
Uplink Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channels
Authors: Thomas Choi, Masaaki Ito, Issei Kanno, Takeo Oseki, Kosuke Yamazaki, Andreas F. Molisch
Date: 2021-08-04 00:00:00

Cell-free massive MIMO (CF-mMIMO) is expected to provide reliable wireless services for a large number of user equipments (UEs) using access points (APs) distributed across a wide area. When the UEs are battery-powered, uplink energy efficiency (EE) becomes an important performance metric for CF-mMIMO systems. Therefore, if the "target" spectral efficiency (SE) is met, it is important to optimize the uplink EE when setting the transmit powers of the UEs. Also, such transmit power control (TPC) method must be tested on channel data from real-world measurements to prove its effectiveness. In this paper, we compare three different TPC algorithms using zero-forcing reception by applying them to 3.5 GHz channel measurement data featuring ~30,000 possible AP locations and 8 UE locations in a 200mx200m area. We show that the max-min EE algorithm is highly effective in improving the uplink EE at a target SE, especially if the number of single-antenna APs is large, circuit power consumption is low, and the maximum allowed transmit power of the UEs is high.
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
Authors: Abu Bucker Siddik, Dawson Drake, Thomas Wilkinson, Phillip L. De Leon, Steven Sandoval, Margaret Campos
Date: 2021-08-04 00:00:00

Wireless network security may be improved by identifying networked devices via traits that are tied to hardware differences, typically related to unique variations introduced in the manufacturing process. One way these variations manifest is through unique transient events when a radio transmitter is activated or deactivated. Features extracted from these signal bursts have in some cases, shown to provide a unique "fingerprint" for a wireless device. However, only recently have researchers made such data available for research and comparison. Herein, we describe a publicly-available corpus of radio frequency signals that can be used for wireless device fingerprint research. The WIDEFT corpus contains signal bursts from 138 unique devices (100 bursts per device), including Bluetooth- and WiFi-enabled devices, from 79 unique models. Additionally, to demonstrate the utility of the WIDEFT corpus, we provide four baseline evaluations using a minimal subset of previously-proposed features and a simple ensemble classifier.
1.71 Tb/s Single-Channel and 56.51 Tb/s DWDM Transmission over 96.5 km Field-Deployed SSMF
Authors: Fabio Pittala, Ralf-Peter Braun, Georg Boecherer, Patrick Schulte, Maximilian Schaedler, Stefano Bettelli, Stefano Calabro, Maxim Kuschnerov, Andreas Gladisch, Fritz-Joachim Westphal, Changsong Xie, Rongfu Chen, Qibing Wang, Bofang Zheng
Date: 2021-08-04 00:00:00

We report an industry leading optical dense wavelength division multiplexing (DWDM) field trial with line rates per channel exceeding 1.66 Tb/s using 130 GBaud dual-polarization probabilistic constellation shaping 256-ary quadrature amplitude modulation (DP-PCS256QAM) in a high capacity data center interconnect (DCI) scenario. This research trial was performed on 96.5 km of field-deployed standard single mode G.652 fiber infrastructure of Deutsche Telekom in Germany employing Erbium-doped fiber amplifier (EDFA)-only amplification. A total of 34 channels were transmitted with 150 GHz spacing for a total fiber capacity of 56.51 Tb/s and a spectral efficiency higher than 11bit/s/Hz. In the single-channel transmission scenario 1.71 Tb/s was achieved over the same link. In addition, we successfully demonstrate record net bitrates of 1.88 Tb/s in back-to-back (B2B) using 130 GBaud DP-PCS400QAM.
Destination-Feedback Free Distributed Transmit Beamforming using Guided Directionality
Authors: Samer Hanna, Enes Krijestorac, Danijela Cabric
Date: 2021-08-04 00:00:00

Distributed transmit beamforming enables cooperative radios to act as one virtual antenna array, extending their communications' range beyond the capabilities of a single radio. Most existing distributed beamforming approaches rely on the destination radio sending feedback to adjust the transmitters' signals for coherent combining. However, relying on the destination radio's feedback limits the communications range to that of a single radio. Existing destination-feedback-free approaches rely on phase synchronization and knowing the node locations with sub-wavelength accuracy, which becomes impractical for radios mounted on high-mobility platforms like UAVs. In this work, we propose and demonstrate a destination-feedback-free distributed beamforming approach that leverages the radio's mobility and coarse location information in a dominant line-of-sight channel. In the proposed approach, one radio acts as a guide and moves to point the beam of the remaining radios towards the destination. We specify the radios' position requirements and verify their relation to the combined signal at the destination using simulations. A proof of concept demo was implemented using software defined radios, showing up to 9dB SNR improvement in the beamforming direction just by relying on the coarse placement of four radios.
TreCap: A wearable device to measure and assess tremor data of visually guided hand movements in real time
Authors: R. P. Bremm, A. Werle, C. Auer, F. Hertel, J. Gonçalves, K. P. Koch
Date: 2021-08-03 00:00:00

The assessment and treatment of motor symptoms such as tremor in Parkinson's disease depends exclusively on the physician's visual observation of standardised movements (i.e. motor tasks). Wearable sensors such as accelerometers are able to detect some manifestations of these pathological signs in movement disorders. Sensor data from motor tasks, however, must be processed sequentially with annotated data from clinical experts. Hence, we designed TreCap, a custom-built wearable device with new software to capture and evaluate motor symptoms such as tremor in real time. Inertial sensor data is systematically processed, stored and tailored to each motor task by this software, including annotated data from clinical rating scores and deep brain stimulation parameters. For prototype testing, the wearable device was validated in a pilot study on subjects with physiological hand tremor. The processed data sets are suitable for machine learning to classify motor tasks. Results on healthy subjects demonstrate an accuracy of 95% with support vector machine algorithms. TreCap software is expandable and allows full access to the configuration of all sensors via Bluetooth. Finally, the functions of the entire device provide a platform to be apt for future clinical trials.
Unrolled Wirtinger Flow with Deep Decoding Priors for Phaseless Imaging
Authors: Samia Kazemi, Bariscan Yonel, Birsen Yazıcı
Date: 2021-08-03 00:00:00
Tasks: Rolling Shutter Correction

We introduce a deep learning (DL) based network and an associated exact recovery theory for imaging from intensity-only measurements. The network architecture uses a recurrent structure that unrolls the Wirtinger Flow (WF) algorithm with a deep decoding prior that enables performing the algorithm updates in a lower dimensional encoded image space. We use a separate deep network (DN), referred to as the encoding network, for transforming the spectral initialization used in the WF algorithm to an appropriate initial value for the encoded domain. The unrolling scheme models a fixed number of iterations of the underlying optimization algorithm into a recurrent neural network (RNN). Furthermore, it facilitates simultaneous learning of the parameters of the decoding and encoding networks and the RNN. We establish a sufficient condition to guarantee exact recovery under deterministic forward models. Additionally, we demonstrate the relation between the Lipschitz constants of the trained decoding prior and encoding networks to the convergence rate of the WF algorithm. We show the practical applicability of our method in synthetic aperture imaging using high fidelity simulation data from the PCSWAT software. Our numerical study shows that the decoding prior and the encoding network facilitate improvements in sample complexity.
Exact and Heuristic Approaches to Drone Delivery Problems
Authors: Júlia C. Freitas, Puca Huachi V. Penna, Túlio A. M. Toffolo
Date: 2021-07-29 00:00:00
Tasks: Traveling Salesman Problem

The Flying Sidekick Traveling Salesman Problem (FSTSP) considers a delivery system composed by a truck and a drone. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel Mixed Integer Programming (MIP) formulation and a heuristic approach to address the problem. The proposedMIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.
Identifiability of species network topologies from genomic sequences using the logDet distance
Authors: Elizabeth S. Allman, Hector Baños, John A. Rhodes
Date: 2021-08-03 00:00:00

Inference of network-like evolutionary relationships between species from genomic data must address the interwoven signals from both gene flow and incomplete lineage sorting. The heavy computational demands of standard approaches to this problem severely limit the size of datasets that may be analyzed, in both the number of species and the number of genetic loci. Here we provide a theoretical pointer to more efficient methods, by showing that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case. This result is obtained under the Network Multispecies Coalescent model combined with a mixture of General Time-Reversible sequence evolution models across individual gene trees, but does not depend on partitioning sequences by genes. Thus under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees.
Fast frequency modulation is encoded according to the listener expectations in the human subcortical auditory pathway
Authors: Alejandro Tabas, Stefan Kiebel, Michael Marxen, Katharina von Kriegstein
Date: 2021-08-04 00:00:00
Tasks: speech-recognition, Speech Recognition

Expectations aid and bias our perception. In speech, expected words are easier to recognise than unexpected words, particularly in noisy environments, and incorrect expectations can make us misunderstand our conversational partner. Expectations are combined with the output from the sensory pathways to form representations of speech in the cerebral cortex. However, it is unclear whether expectations are propagated further down to subcortical structures to aid the encoding of the basic dynamic constituent of speech: fast frequency-modulation (FM). Fast FM-sweeps are the basic invariant constituent of consonants, and their correct encoding is fundamental for speech recognition. Here we tested the hypothesis that subjective expectations drive the encoding of fast FM-sweeps characteristic of speech in the human subcortical auditory pathway. We used fMRI to measure neural responses in the human auditory midbrain (inferior colliculus) and thalamus (medial geniculate body). Participants listened to sequences of FM-sweeps for which they held different expectations based on the task instructions. We found robust evidence that the responses in auditory midbrain and thalamus encode the difference between the acoustic input and the subjective expectations of the listener. The results indicate that FM-sweeps are already encoded at the level of the human auditory midbrain and that encoding is mainly driven by subjective expectations. We conclude that the subcortical auditory pathway is integrated in the cortical network of predictive speech processing and that expectations are used to optimise the encoding of even the most basic acoustic constituents of speech.
CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis
Authors: David R. Stirling, Anne E. Carpenter, Beth A. Cimini
Date: 2021-08-04 00:00:00
Tasks: BIG-bench Machine Learning, Object

Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualisation tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses.
An age-structured model of hepatitis B viral infection highlights the potential of different therapeutic strategies
Authors: Farzad Fatehi, Richard J. Bingham, Eric C. Dykeman, Peter G. Stockley, Reidun Twarock
Date: 2021-08-04 00:00:00

Hepatitis B virus is a global health threat, and its elimination by 2030 has been prioritised by the World Health Organisation. Here we present an age-structured model for the immune response to an HBV infection, which takes into account contributions from both cell-mediated and humoral immunity. The model has been validated using published patient data recorded during acute infection. It has been adapted to the scenarios of chronic infection, clearance of infection, and flare-ups via variation of the immune response parameters. The impacts of immune response exhaustion and non-infectious subviral particles on the immune response dynamics are analysed. A comparison of different treatment options in the context of this model reveals that drugs targeting aspects of the viral life cycle are more effective than exhaustion therapy, a form of therapy mitigating immune response exhaustion. Our results suggest that antiviral treatment is best started when viral load is declining rather than in a flare-up. The model suggests that a fast antibody production rate always lead to viral clearance, highlighting the promise of antibody therapies currently in clinical trials.
Therapeutic Interfering Particles Exploiting Viral Replication and Assembly Mechanisms Show Promising Performance: A Modelling Study
Authors: Farzad Fatehi, Richard J. Bingham, Pierre-Philippe Dechant, Peter G. Stockley, Reidun Twarock
Date: 2021-08-04 00:00:00

Defective interfering particles arise spontaneously during a viral infection as mutants lacking essential parts of the viral genome. Their ability to replicate in the presence of the wild-type (WT) virus (at the expense of viable viral particles) is mimicked and exploited by therapeutic interfering particles. We propose a strategy for the design of therapeutic interfering RNAs (tiRNAs) against positive-sense single-stranded RNA viruses that assemble via packaging signal-mediated assembly. These tiRNAs contain both an optimised version of the virus assembly manual that is encoded by multiple dispersed RNA packaging signals and a replication signal for viral polymerase, but lack any protein coding information. We use an intracellular model for hepatitis C viral (HCV) infection that captures key aspects of the competition dynamics between tiRNAs and viral genomes for virally produced capsid protein and polymerase. We show that only a small increase in the assembly and replication efficiency of the tiRNAs compared with WT virus is required in order to achieve a treatment efficacy greater than 99%. This demonstrates that the proposed tiRNA design could be a promising treatment option for RNA viral infections.
Calibrating the Nelson-Siegel-Svensson Model by Genetic Algorithm
Authors: Asif Lakhany, Andrej Pintar, Amber Zhang
Date: 2021-08-03 00:00:00

Accurately fitting the term structure of interest rates is critical to central banks and other market participants. The Nelson-Siegel and Nelson-Siegel-Svensson models are probably the best-known models for this purpose due to their intuitive appeal and simple representation. However, this simplicity comes at a price. The difficulty in calibrating these models is twofold. Firstly, the objective function being minimized during the calibration procedure is nonlinear and has multiple local optima. Secondly, there is strong co-dependence among the model parameters. As a result, their estimated values behave erratically over time. To avoid these problems, we apply a heuristic optimization method, specifically the Genetic Algorithm approach, and show that it is able to construct reliable interest rate curves and stable model parameters over time, regardless of the shape of the curves.
On simulation of rough Volterra stochastic volatility models
Authors: Jan Matas, Jan Pospíšil
Date: 2021-07-26 00:00:00

Rough Volterra volatility models are a progressive and promising field of research in derivative pricing. Although rough fractional stochastic volatility models already proved to be superior in real market data fitting, techniques used in simulation of these models are still inefficient in terms of speed and accuracy. This paper aims to present accurate and efficient tools and techniques for Monte-Carlo simulations for a wide range of rough volatility models. In particular, we compare three commonly used simulation methods: the Cholesky method, the Hybrid scheme, and the rDonsker scheme. We also comment on the implementation of variance reduction techniques. In particular, we show the obstacles of the so-called turbocharging technique whose performance is sometimes counter-productive. To overcome these obstacles, we suggest several modifications.
On Modelling of Crude Oil Futures in a Bivariate State-Space Framework
Authors: Peilun He, Karol Binkowski, Nino Kordzakhia, Pavel Shevchenko
Date: 2021-08-04 00:00:00

We study a bivariate latent factor model for the pricing of commodity fu- tures. The two unobservable state variables representing the short and long term fac- tors are modelled as Ornstein-Uhlenbeck (OU) processes. The Kalman Filter (KF) algorithm has been implemented to estimate the unobservable factors as well as unknown model parameters. The estimates of model parameters were obtained by maximising a Gaussian likelihood function. The algorithm has been applied to WTI Crude Oil NYMEX futures data.
On the Parameter Estimation in the Schwartz-Smiths Two-Factor Model
Authors: Karol Binkowski, Peilun He, Nino Kordzakhia, Pavel Shevchenko
Date: 2021-08-04 00:00:00
Tasks: parameter estimation, Vocal Bursts Valence Prediction

The two unobservable state variables representing the short and long term factors introduced by Schwartz and Smith in [16] for risk-neutral pricing of futures contracts are modelled as two correlated Ornstein-Uhlenbeck processes. The Kalman Filter (KF) method has been implemented to estimate the short and long term factors jointly with un- known model parameters. The parameter identification problem arising within the likelihood function in the KF has been addressed by introduc- ing an additional constraint. The obtained model parameter estimates are the conditional Maximum Likelihood Estimators (MLEs) evaluated within the KF. Consistency of the conditional MLEs is studied. The methodology has been tested on simulated data.
Unified Signal Compression Using Generative Adversarial Networks
Authors: Bowen Liu, Ang Cao, Hun-Seok Kim
Date: 2019-12-08 00:00:00

We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals. The compressed signal is represented by a latent vector fed into a generator network which is trained to produce high quality signals that minimize a target objective function. To efficiently quantize the compressed signal, non-uniformly quantized optimal latent vectors are identified by iterative back-propagation with ADMM optimization performed for each iteration. Our experiments show that the proposed algorithm outperforms prior signal compression methods for both image and speech compression quantified in various metrics including bit rate, PSNR, and neural network based signal classification accuracy.