pwc-archive/papers-with-abstracts huggingface dataset:
A comparative study of two-dimensional vocal tract acoustic modeling based on Finite-Difference Time-Domain methods
Authors: Debasish Ray Mohapatra, Victor Zappi, Sidney Fels
Date: 2021-02-09 00:00:00
Tasks: Acoustic Modelling

The two-dimensional (2D) numerical approaches for vocal tract (VT) modelling can afford a better balance between the low computational cost and accurate rendering of acoustic wave propagation. However, they require a high spatio-temporal resolution in the numerical scheme for a precise estimation of acoustic formants at the simulation run-time expense. We have recently proposed a new VT acoustic modelling technique, known as the 2.5D Finite-Difference Time-Domain (2.5D FDTD), which extends the existing 2D FDTD approach by adding tube depth to its acoustic wave solver. In this work, first, the simulated acoustic outputs of our new model are shown to be comparable with the 2D FDTD and a realistic 3D FEM VT model at a low spatio-temporal resolution. Next, a radiation model is developed by including a circular baffle around the VT as head geometry. The transfer functions of the radiation model are analyzed using five different vocal tract shapes for vowel sounds /a/, /e/, /i/, /o/ and /u/.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Authors: Shuai Lu, Daya Guo, Shuo Ren, JunJie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu
Date: 2021-02-09 00:00:00
Tasks: BIG-bench Machine Learning, Clone Detection, Cloze Test, Code Completion, Code Generation, Code Repair, Code Search, Code Summarization, Code Translation, Decoder, Defect Detection, Document Translation, Text-to-Code Generation

Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine learning research for program understanding and generation. CodeXGLUE includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison. CodeXGLUE also features three baseline systems, including the BERT-style, GPT-style, and Encoder-Decoder models, to make it easy for researchers to use the platform. The availability of such data and baselines can help the development and validation of new methods that can be applied to various program understanding and generation problems.
DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING MACHINE LEARNING
Authors: Aryan Kokane1, Gourhari Sharma1, Akash Raina1, Shubham Narole1, Prof. Pramila M. Chawan2
Date: 2020-11-07 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

The objective of this paper is to perform a 
survey of different literatures where a comprehensive study 
on Diabetic Retinopathy (DR) is done and different Machine 
learning techniques are used to detect DR. Diabetic 
Retinopathy (DR) is an eye disease in humans with diabetes 
which may harm the retina of the eye and may cause total 
visual impairment. Therefore it is critical to detect diabetic 
retinopathy in the early phase to avoid blindness in humans. 
Our aim is to detect the presence of diabetic retinopathy by 
applying machine learning classifying algorithms. Hence we 
try and summarize the various models and techniques used 
along with methodologies used by them and analyze the 
accuracies and results. It will give us exactness of which 
algorithm will be appropriate and more accurate for 
prediction.
Turkey Behavior Identification System with a GUI Using Deep Learning and Video Analytics
Authors: Shengtai Ju, Sneha Mahapatra, Marisa A. Erasmus, Amy R. Reibman, Fengqing Zhu
Date: 2021-02-09 00:00:00
Tasks: object-detection, Object Detection

In this paper, we propose a video analytics system to identify the behavior of turkeys. Turkey behavior provides evidence to assess turkey welfare, which can be negatively impacted by uncomfortable ambient temperature and various diseases. In particular, healthy and sick turkeys behave differently in terms of the duration and frequency of activities such as eating, drinking, preening, and aggressive interactions. Our system incorporates recent advances in object detection and tracking to automate the process of identifying and analyzing turkey behavior captured by commercial grade cameras. We combine deep-learning and traditional image processing methods to address challenges in this practical agricultural problem. Our system also includes a web-based user interface to create visualization of automated analysis results. Together, we provide an improved tool for turkey researchers to assess turkey welfare without the time-consuming and labor-intensive manual inspection.
Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network
Authors: Qun Wang, Han Hu, Haijian Sun, Rose Qingyang Hu
Date: 2021-02-09 00:00:00
Tasks: CPU, Edge-computing

Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existing attackers, it is very challenging to offload computation tasks securely and efficiently. In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations. To tackle the problem, a dynamic secure task offloading and resource allocation algorithm is proposed based on Lyapunov optimization theory. A stochastic non-convex problem is formulated to jointly optimize the local-CPU frequency and transmit power, aiming at maximizing the network energy efficiency, which is defined as the ratio of the long-term average secure rate to the long-term average power consumption of all users. The formulated problem is decomposed into the deterministic sub-problems in each time slot. The optimal local CPU-cycle and the transmit power of each user can be given in the closed-from. Simulation results evaluate the impacts of different parameters on the efficiency metrics and demonstrate that the proposed method can achieve better performance compared with other benchmark methods in terms of energy efficiency.
Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of Conditional Tail Expectation
Authors: Nawaf Mohammed, Edward Furman, Jianxi Su
Date: 2021-02-09 00:00:00
Tasks: Management

Risk capital allocations (RCAs) are an important tool in quantitative risk management, where they are utilized to, e.g., gauge the profitability of distinct business units, determine the price of a new product, and conduct the marginal economic capital analysis. Nevertheless, the notion of RCA has been living in the shadow of another, closely related notion, of risk measure (RM) in the sense that the latter notion often shapes the fashion in which the former notion is implemented. In fact, as the majority of the RCAs known nowadays are induced by RMs, the popularity of the two are apparently very much correlated. As a result, it is the RCA that is induced by the Conditional Tail Expectation (CTE) RM that has arguably prevailed in scholarly literature and applications. Admittedly, the CTE RM is a sound mathematical object and an important regulatory RM, but its appropriateness is controversial in, e.g., profitability analysis and pricing. In this paper, we address the question as to whether or not the RCA induced by the CTE RM may concur with alternatives that arise from the context of profit maximization. More specifically, we provide exhaustive description of all those probabilistic model settings, in which the mathematical and regulatory CTE RM may also reflect the risk perception of a profit-maximizing insurer.
Orbital Stabilization of Point-to-Point Maneuvers in Underactuated Mechanical Systems
Authors: Christian Fredrik Sætre, Anton S. Shiriaev
Date: 2021-02-09 00:00:00

The task of inducing, via continuous static state-feedback control, an asymptotically stable heteroclinic orbit in a nonlinear control system is considered in this paper. The main motivation comes from the problem of ensuring convergence to a so-called point-to-point maneuver in an underactuated mechanical system. Namely, to a smooth curve in its state--control space, which is consistent with the system dynamics and connects two (linearly) stabilizable equilibrium points. The proposed method uses a particular parameterization, together with a state projection onto the maneuver as to combine two linearization techniques for this purpose: the Jacobian linearization at the equilibria on the boundaries and a transverse linearization along the orbit. This allows for the computation of stabilizing control gains offline by solving a semidefinite programming problem. The resulting nonlinear controller, which simultaneously asymptotically stabilizes both the orbit and the final equilibrium, is time-invariant, locally Lipschitz continuous, requires no switching, and has a familiar feedforward plus feedback--like structure. The method is also complemented by synchronization function--based arguments for planning such maneuvers for mechanical systems with one degree of underactuation. Numerical simulations of the non-prehensile manipulation task of a ball rolling between two points upon the "butterfly" robot demonstrates the efficacy of the synthesis.
The Effect of Social Distancing on the Reach of an Epidemic in Social Networks
Authors: Gregory Gutin, Tomohiro Hirano, Sung-Ha Hwang, Philip R. Neary, Alexis Akira Toda
Date: 2020-05-06 00:00:00

How does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a capacity constrained Susceptible-Infected-Removed (SIR) model. The key modelling feature is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing always reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network. Our results also reveal the importance of coordinating social distancing policies at the global level. In particular, the public health benefits from social distancing to a group (e.g., a country) may be completely undone if that group maintains connections with outside groups that are not following suit.
Reduction of the Beam Pointing Error for Improved Free-Space Optical Communication Link Performance
Authors: I. N'Doye, W. Cai, A. Alalwan, X. Sun, W. G. Headary, M. -S. Alouini, B. -S. Ooi, T. -M. Laleg-Kirati
Date: 2021-02-09 00:00:00

Free-space optical communication is emerging as a low-power, low-cost, and high data rate alternative to radio-frequency communication in short-to medium-range applications. However, it requires a close-to-line-of-sight link between the transmitter and the receiver. This paper proposes a robust $\cHi$ control law for free-space optical (FSO) beam pointing error systems under controlled weak turbulence conditions. The objective is to maintain the transmitter-receiver line, which means the center of the optical beam as close as possible to the center of the receiving aperture within a prescribed disturbance attenuation level. First, we derive an augmented nonlinear discrete-time model for pointing error loss due to misalignment caused by weak atmospheric turbulence. We then investigate the $\cHi$-norm optimization problem that guarantees the closed-loop pointing error is stable and ensures the prescribed weak disturbance attenuation. Furthermore, we evaluate the closed-loop outage probability error and bit error rate (BER) that quantify the free-space optical communication performance in fading channels. Finally, the paper concludes with a numerical simulation of the proposed approach to the FSO link's error performance.
Leadership through influence: what mechanisms allow leaders to steer a swarm?
Authors: Sara Bernardi, Raluca Eftimie, Kevin J. Painter
Date: 2021-02-09 00:00:00

Collective migration of cells and animals often relies on a specialised set of "leaders", whose role is to steer a population of naive followers towards some target. We formulate a continuous model to understand the dynamics and structure of such groups, splitting a population into separate follower and leader types with distinct orientation responses. We incorporate "leader influence" via three principal mechanisms: a bias in the orientation of leaders according to the destination, distinct speeds of movement and distinct levels of conspicuousness. Using a combination of analysis and numerical computation on a sequence of models of increasing complexity, we assess the extent to which leaders successfully shepherd the swarm. While all three mechanisms can lead to a successfully steered swarm, parameter regime is crucial with non successful choices generating a variety of unsuccessful attempts, including movement away from the target, swarm splitting or swarm dispersal.
Vehicle Localization via Cooperative Channel Mapping
Authors: Xinghe Chu, Zhaoming Lu, David Gesbert, Luhan Wang, Xiangming Wen
Date: 2021-02-09 00:00:00
Tasks: Autonomous Driving

This paper addresses vehicle positioning, a topic whose importance has risen dramatically in the context of future autonomous driving systems. While classical methods that use GPS and/or beacon signals from network infrastructure for triangulation tend to be sensitive to multi-paths and signal obstruction, our method exhibits robustness with respect to such phenomena. Our approach builds on the recently proposed Channel-SLAM method which first enabled leveraging of multi-path so as to improve (single) vehicle positioning. Here, we propose a cooperative mapping approach which builds upon the Channel-SLAM concept, referred to here as Team Channel-SLAM. Team Channel-SLAM not only exploits the stationary nature of many reflecting objects around the vehicle, but also capitalizes on the multi-vehicle nature of road traffic. The key intuition behind our method is the exploitation for the first time of the correlation between reflectors around multiple neighboring vehicles. An algorithm is derived for reflector selection and estimation, combined with a team particle filter (TPF) so as to achieve high precision simultaneous multiple vehicle positioning. We obtain large improvement over the single-vehicle positioning scenario, with gains being already noticeable for moderate vehicle densities, such as over 40% improvement for a vehicle density as low as 4 vehicles in 132 meters' length road.
An open debate on SARS-CoV-2's proximal origin is long overdue
Authors: Rossana Segreto, Yuri Deigin, Kevin McCairn, Alejandro Sousa, Dan Sirotkin, Karl Sirotkin, Jonathan J. Couey, Adrian Jones, Daoyu Zhang
Date: 2021-02-07 00:00:00

There is a near consensus view that SARS-CoV-2 has a natural zoonotic origin; however, several characteristics of SARS-CoV-2 taken together are not easily explained by a natural zoonotic origin hypothesis. These include: a low rate of evolution in the early phase of transmission; the lack of evidence of recombination events; a high pre-existing binding to human ACE2; a novel furin cleavage site insert; a flat glycan binding domain of the spike protein which conflicts with host evasion survival patterns exhibited by other coronaviruses, and high human and mouse peptide mimicry. Initial assumptions against a laboratory origin, by contrast, have remained unsubstantiated. Furthermore, over a year after the initial outbreak in Wuhan, there is still no clear evidence of zoonotic transfer from a bat or intermediate species. Given the immense social and economic impact of this pandemic, identifying the true origin of SARS-CoV-2 is fundamental to preventing future outbreaks. The search for SARS-CoV-2's origin should include an open and unbiased inquiry into a possible laboratory origin.
Fast and Accurate Amplitude Demodulation of Wideband Signals
Authors: Mantas Gabrielaitis
Date: 2021-02-09 00:00:00
Tasks: Computational Efficiency

Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using natural speech and synthetic signals.
Stability Analysis and State-Feedback Stabilization of LPV Time-Delay Systems with Piecewise Constant Parameters subject to Spontaneous Poissonian Jumps
Authors: Muhammad Zakwan
Date: 2021-02-09 00:00:00

This paper discusses the stability analysis of linear parameter varying systems with a parameter-dependent delay where the parameters are assumed to be stochastic piecewise constants under spontaneous Poissonian jumps. Based on stochastic Lyapunov-Krasovskii functionals, we also provide sufficient synthesis conditions for the gain-scheduled state-feedback controller with memory in terms of parameter-dependent linear matrix inequalities (LMIs). Such synthesis conditions are computationally intractable due to the presence of integral terms. However, we show that these LMIs can be equivalently represented by integral-free LMIs, which are computationally tractable. Finally, we illustrate the applicability of the results through examples.
A modelling study across the Italian regions: Lockdown, testing strategy, colored zones, and skew-normal distributions. How a numerical index of pandemic criticality could be useful in tackling the CoViD-19
Authors: Stefano De Leo, Manoel P. Araujo
Date: 2021-02-05 00:00:00

As Europe is facing the second wave of the CoViD-19 pandemic, each country should carefully review how it dealt with the first wave of outbreak. Lessons from the first experience should be useful to avoid indiscriminate closures and, above all, to determine universal (understandable) parameters to guide the introduction of containment measures to reduce the spreading of the virus. The use of few (effective) parameters is indeed of extreme importance to create a link between authorities and population, allowing the latter to understand the reason for some restrictions and, consequently, to allow an active participation in the fight against the pandemic. Testing strategies, fitting skew parameters (as mean, mode, standard deviation, and skewness), mortality rates, and weekly CoViD-19 spreading data, as more people are getting infected, were used to compare the first wave of the outbreak in the Italian regions and to determine which parameters have to be checked before introducing restrictive containment measures. We propose few \textit{universal} parameters that, once appropriately weighed, could be useful to correctly differentiate the pandemic situation in the national territory and to rapidly assign the properly pandemic risk to each region.
Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images
Authors: Jiaxuan Li, Peiyao Jin, Jianfeng Zhu, Haidong Zou, Xun Xu, Min Tang, Minwen Zhou, Yu Gan, Jiangnan He, Yuye Ling, Yikai Su
Date: 2021-02-09 00:00:00
Tasks: Decoder, Medical Image Segmentation, Retinal OCT Layer Segmentation, Segmentation, Spatial Reasoning

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we developed a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conducted experiments on human peripapillary retinal OCT images. The Dice score of the proposed segmentation network is 0.820$\pm$0.001 and the pixel accuracy is 0.830$\pm$0.002, both of which outperform those from other state-of-the-art techniques.
Forecasting the Evolution of Hydrogen Vehicle Fleet in the UK usingGrowth and Lotka-Volterra Models
Authors: Florimond Gueniat, Sahdia Maryam
Date: 2021-02-09 00:00:00

Road vehicles play an important role in the UK's energy systems and are a critical component in reducing the reliance on fossil fuels and mitigating emissions. A dynamic model of vehicle fleet, based on predator-prey concepts, is presented. It allows to predict the evolution of the hydrogen based vehicle's role in the UK's vehicle fleet and the sensitivity of this growth to the supply chain. In addition to this, this model is used to predict the demand of hydrogen for the passenger vehicle fleet for various scenarios. A key result is that the amount of hydrogen required to support a huge network of hydrogen based vehicles is currently not practical, regarding the infrastructure required and the cost to build such an infrastructure. In order to mitigate that, the policy focus should move primarily from hydrogen based vehicles to first encompass range extenders in the transport energy system.
Autophosphorylation and the dynamics of the activation of Lck
Date: 2021-01-22 00:00:00

Lck (lymphocyte-specific protein tyrosine kinase) is an enzyme which plays a number of important roles in the function of immune cells. It belongs to the Src family of kinases which are known to undergo autophosphorylation. It turns out that this leads to a remarkable variety of dynamical behaviour which can occur during their activation. We prove that in the presence of autophosphorylation one phenomenon, bistability, already occurs in a mathematical model for a protein with a single phosphorylation site. We further show that a certain model of Lck exhibits oscillations. Finally we discuss the relations of these results to models in the literature which involve Lck and describe specific biological processes, such as the early stages of T cell activation and the stimulation of T cell responses resulting from the suppression of PD-1 signalling which is important in immune checkpoint therapy for cancer.