pwc-archive/papers-with-abstracts huggingface dataset:
Izhikevich-Inspired Optoelectronic Neurons with Excitatory and Inhibitory Inputs for Energy-Efficient Photonic Spiking Neural Networks
Authors: Yun-jhu Lee, Mehmet Berkay On, Xian Xiao, Roberto Proietti, S. J. Ben Yoo
Date: 2021-05-03 00:00:00

We designed, prototyped, and experimentally demonstrated, for the first time to our knowledge, an optoelectronic spiking neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of three transistors acting as electrical spiking circuits, a vertical-cavity surface-emitting laser (VCSEL) for optical spiking outputs, and two photodetectors for excitatory and inhibitory optical spiking inputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. Utilizing the simulated neuron model, we conducted simulations using fully connected (FC) and convolutional neural networks (CNN). The simulation results using MNIST handwritten digits recognition show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. We further designed a nanoscale optoelectronic neuron utilizing quantum impedance conversion where a 200 aJ/spike input can trigger the output from on-chip nanolasers with 10 fJ/spike. The nanoscale neuron can support a fanout of ~80 or overcome 19 dB excess optical loss while running at 10 GSpikes/second in the neural network, which corresponds to 100x throughput and 1000x energy-efficiency improvement compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid.
A Unifying and Canonical Description of Measure-Preserving Diffusions
Authors: Alessandro Barp, So Takao, Michael Betancourt, Alexis Arnaudon, Mark Girolami
Date: 2021-05-06 00:00:00

A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework. In this paper, we develop a geometric theory that improves and generalises this construction to any manifold. We thereby demonstrate that the completeness result is a direct consequence of the topology of the underlying manifold and the geometry induced by the target measure $P$; there is no need to introduce other structures such as a Riemannian metric, local coordinates, or a reference measure. Instead, our framework relies on the intrinsic geometry of $P$ and in particular its canonical derivative, the deRham rotationnel, which allows us to parametrise the Fokker--Planck currents of measure-preserving diffusions using potentials. The geometric formalism can easily incorporate constraints and symmetries, and deliver new important insights, for example, a new complete recipe of Langevin-like diffusions that are suited to the construction of samplers. We also analyse the reversibility and dissipative properties of the diffusions, the associated deterministic flow on the space of measures, and the geometry of Langevin processes. Our article connects ideas from various literature and frames the theory of measure-preserving diffusions in its appropriate mathematical context.
Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories
Authors: Carlo Manzo
Date: 2021-05-06 00:00:00
Tasks: Feature Engineering

The study of the dynamics of natural and artificial systems has provided several examples of deviations from Brownian behavior, generally defined as anomalous diffusion. The investigation of these dynamics can provide a better understanding of diffusing objects and their surrounding media, but a quantitative characterization from individual trajectories is often challenging. Efforts devoted to improving anomalous diffusion detection using classical statistics and machine learning have produced several new methods. Recently, the anomalous diffusion challenge (AnDi, https://www.andi-challenge.org) was launched to objectively assess these approaches on a common dataset, focusing on three aspects of anomalous diffusion: the inference of the anomalous diffusion exponent; the classification of the diffusion model; and the segmentation of trajectories. In this article, I describe a simple approach to tackle the tasks of the AnDi challenge by combining extreme learning machine and feature engineering (AnDi-ELM). The method reaches satisfactory performance while offering a straightforward implementation and fast training time with limited computing resources, making a suitable tool for fast preliminary screening.
Information Limits for Detecting a Subhypergraph
Authors: Mingao Yuan, Zuofeng Shang
Date: 2021-05-05 00:00:00

We consider the problem of recovering a subhypergraph based on an observed adjacency tensor corresponding to a uniform hypergraph. The uniform hypergraph is assumed to contain a subset of vertices called as subhypergraph. The edges restricted to the subhypergraph are assumed to follow a different probability distribution than other edges. We consider both weak recovery and exact recovery of the subhypergraph, and establish information-theoretic limits in each case. Specifically, we establish sharp conditions for the possibility of weakly or exactly recovering the subhypergraph from an information-theoretic point of view. These conditions are fundamentally different from their counterparts derived in hypothesis testing literature.
Evaluating e-Government Services in Kurdistan Institution for Strategic Studies and Scientific Research Using the EGOVSAT Model
Authors: Bryar A. Hassan, Aram M. Ahmed, Soran A. Saeed, Awin A. Saeed
Date: 2021-05-06 00:00:00

Office automation is an initiative used to digitally deliver services to citizens, private and public sectors. It is used to digitally collect, store, create, and manipulate office information as a need of accomplishing basic tasks. Azya Office Automation has been implemented as a pilot project in Kurdistan Institution for Strategic Studies and Scientific Research (KISSR) since 2013. The efficiency of governance in Kurdistan Institution for Strategic Studies and Scientific Research has been improved, thanks to its implementation. The aims of this research paper is to evaluate user satisfaction of this software and identify its significant predictors using EGOVSAT Model. The user satisfaction of this model encompasses five main parts, which are utility, reliability, efficiency, customization, and flexibility. For that purpose, a detailed survey is conducted to measure the level of user satisfaction. A total of sixteen questions have distributed among forty one users of the software in KISSR. In order to evaluate the software, three measurement have been used which are reliability test, regression analysis and correlation analysis. The results indicate that the software is successful to a decent extent based on user satisfaction feedbacks obtained by using EGOVSAT Model.
Learning Early Exit Strategies for Additive Ranking Ensembles
Authors: Francesco Busolin, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani
Date: 2021-05-06 00:00:00

Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the average number of trees traversed by documents to accumulate the scores, thus reducing the overall query response time. LEAR exploits a classifier that predicts whether a document can early exit the ensemble because it is unlikely to be ranked among the final top-k results. The early exit decision occurs at a sentinel point, i.e., after having evaluated a limited number of trees, and the partial scores are exploited to filter out non-promising documents. We evaluate LEAR by deploying it in a production-like setting, adopting a state-of-the-art algorithm for ensembles traversal. We provide a comprehensive experimental evaluation on two public datasets. The experiments show that LEAR has a significant impact on the efficiency of the query processing without hindering its ranking quality. In detail, on a first dataset, LEAR is able to achieve a speedup of 3x without any loss in NDCG1@0, while on a second dataset the speedup is larger than 5x with a negligible NDCG@10 loss (< 0.05%).
JMNet: A joint matting network for automatic human matting
Authors: Xian Wu, Xiao-Nan Fang, Tao Chen, Fang-Lue Zhang
Date: 2020-04-14 00:00:00
Tasks: Image Matting, Pose Estimation

We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images. We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module, which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques; the shared encoder leads to better performance and lower memory costs. Our model can process real images downloaded from the Internet for use in composition applications.
A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Media
Authors: BiCheng Yan, Dylan Robert Harp, Rajesh J. Pawar
Date: 2021-04-30 00:00:00
Tasks: Management

Simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing partial-differential equations (PDEs) into algebraic systems via numerical methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint. Therefore, developing cost-effective, data-driven models can become a practical choice since deep learning approaches are considered to be universal approximations. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO2 storage sites, where CO2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained from physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses including the temporal-spatial evolution of the pressure and saturation plumes. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enables development of data-driven models with higher fidelity.
Cyclic Arbitrage in Decentralized Exchanges
Authors: Ye Wang, Yan Chen, Haotian Wu, Liyi Zhou, Shuiguang Deng, Roger Wattenhofer
Date: 2021-04-21 00:00:00

Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discrepancies open up arbitrage possibilities with trading through different cryptocurrencies cyclically. In this paper, we conduct a systematic investigation on cyclic arbitrages in DEXes. We propose a theoretical framework for studying cyclic arbitrage. With our framework, we analyze the profitability conditions and optimal trading strategies of cyclic transactions. We further examine exploitable arbitrage opportunities and the market size of cyclic arbitrages with transaction-level data of Uniswap V2. We find that traders have executed 292,606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue. However, the revenue of the most profitable unexploited opportunity is persistently higher than 1 ETH (4,000 USD), which indicates that DEX markets may not be efficient enough. By analyzing how traders implement cyclic arbitrages, we find that traders can utilize smart contracts to issue atomic transactions and the atomic implementations could mitigate users' financial loss in cyclic arbitrage from the price impact.
The Homogenous Properties of Automated Market Makers
Authors: Johannes Rude Jensen, Mohsen Pourpouneh, Kurt Nielsen, Omri Ross
Date: 2021-03-31 00:00:00

Automated market makers (AMM) have grown to obtain significant market share within the cryptocurrency ecosystem, resulting in a proliferation of new products pursuing exotic strategies for horizontal differentiation. Yet, their theoretical properties are curiously homogeneous when a set of basic assumptions are met. In this paper, we start by presenting a universal approach to deriving a formula for liquidity provisioning for AMMs. Next, we show that the constant function market maker and token swap market maker models are theoretically equivalent when liquidity reserves are uniform. Proceeding with an examination of AMM market microstructure, we show how non-linear price effect translates into slippage for traders and impermanent losses for liquidity providers. We proceed by showing how impermanent losses are a function of both volatility and market depth and discuss the implications of these findings within the context of the literature.
Impact of digital economic activity on regional economic growth: A Case study from northern Minas Gerais between 2009 To 2018
Authors: Dr. Cesar R Salas-Guerra
Date: 2021-05-06 00:00:00

At present, the economic measurement of the national statistical offices has not defined or captured the benefits of the digital economy activity due to the low quality or inexistence of methodologies. Currently, there is a relevant debate on the capacity of the digital economy activity to generate productivity, economic growth, and well-being through innovation and knowledge. For this reason, this research identified and studied specialized knowledge, human settlement, and digital economic activity as the factors that influence regional economic growth. As a result, the impact generated by a new business operating models based on information technology was measured. Furthermore, this research used an empirical measurement model that made it possible to identify certain phenomena such as regional poles of regional economic development (PRDE) that surround economically flourishing regions. In addition, it showed that municipalities with high degrees of economic growth were impacted by digital economic activity and specialized knowledge. This finding is consistent with economic growth theories that point to technological evolution as the main factor of modern economic growth. Consequently, this study contributed beneficial results to the local government to develop strategies framed in solving industrial cooperation of economically flourishing regions with their neighbors, facing the problem of agglomeration of resources and capital reflected in human settlement promote an imbalance in economic growth and social development.
Should You Take Investment Advice From WallStreetBets? A Data-Driven Approach
Authors: Tolga Buz, Gerard de Melo
Date: 2021-05-06 00:00:00

Reddit's WallStreetBets (WSB) community has come to prominence in light of its notable role in affecting the stock prices of what are now referred to as meme stocks. Yet very little is known about the reliability of the highly speculative investment advice disseminated on WSB. This paper analyses WSB data spanning from January 2019 to April 2021 in order to assess how successful an investment strategy relying on the community's recommendations could have been. We detect buy and sell advice and identify the community's most popular stocks, based on which we define a WSB portfolio. Our evaluation shows that this portfolio has grown approx. 200% over the last three years and approx. 480% over the last year, significantly outperforming the S&P500. The average short-term accuracy of buy and sell signals, in contrast, is not found to be significantly better than randomly or equally distributed buy decisions within the same time frame. However, we present a technique for estimating whether posts are proactive as opposed to reactive and show that by focusing on a subset of more promising buy signals, a trader could have made investments yielding higher returns than the broader market or the strategy of trusting all posted buy signals. Lastly, the analysis is also conducted specifically for the period before 2021 in order to factor out the effects of the GameStop hype of January 2021 - the results confirm the conclusions and suggest that the 2021 hype merely amplified pre-existing characteristics.
Rate Maximizations for Reconfigurable Intelligent Surface-Aided Wireless Networks: A Unified Framework via Block Minorization-Maximization
Authors: Zepeng Zhang, Ziping Zhao
Date: 2021-05-06 00:00:00

The reconfigurable intelligent surface (RIS) has arose an upsurging research interest recently due to its promising outlook in 5G-and-beyond wireless networks. With the assistance of RIS, the wireless propagation environment is no longer static and could be customized to support diverse service requirements. In this paper, we will approach the rate maximization problems in RIS-aided wireless networks by considering the beamforming and reflecting design jointly. Three representative design problems from different system settings are investigated based on a proposed unified algorithmic framework via the block minorization-maximization (BMM) method. Extensions and generalizations of the proposed framework in dealing with some other related problems are further presented. Merits of the proposed algorithms are demonstrated through numerical simulations in comparison with the state-of-the-art methods.
USM-SED - A Dataset for Polyphonic Sound Event Detection in Urban Sound Monitoring Scenarios
Authors: Jakob Abeßer
Date: 2021-05-06 00:00:00
Tasks: Dataset Generation, Event Detection, Sound Event Detection

This paper introduces a novel dataset for polyphonic sound event detection in urban sound monitoring use-cases. Based on isolated sounds taken from the FSD50k dataset, 20,000 polyphonic soundscapes are synthesized with sounds being randomly positioned in the stereo panorama using different loudness levels. The paper gives a detailed discussion of possible application scenarios, explains the dataset generation process in detail, and discusses current limitations of the proposed USM-SED dataset.
Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets
Authors: Subhroshekhar Ghosh, Meixia Lin, Dongfang Sun
Date: 2021-05-06 00:00:00

Spectrograms are fundamental tools in time-frequency analysis, being the squared magnitude of the so-called short time Fourier transform (STFT). Signal analysis via spectrograms has traditionally explored their peaks, i.e. their maxima. This is complemented by a recent interest in their zeros or minima, following seminal work by Flandrin and others, which exploits connections with Gaussian analytic functions (GAFs). However, the zero sets (or extrema) of GAFs have a complicated stochastic structure, complicating any direct theoretical analysis. Standard techniques largely rely on statistical observables from the analysis of spatial data, whose distributional properties for spectrograms are mostly understood only at an empirical level. In this work, we investigate spectrogram analysis via an examination of the stochastic geometric properties of their level sets. We obtain rigorous theorems demonstrating the efficacy of a spectrogram level sets based approach to the detection and estimation of signals, framed in a concrete inferential set-up. Exploiting these ideas as theoretical underpinnings, we propose a level sets based algorithm for signal analysis that is intrinsic to given spectrogram data, and substantiate its effectiveness via extensive empirical studies. Our results also have theoretical implications for spectrogram zero based approaches to signal analysis. To our knowledge, these results are arguably among the first to provide a rigorous statistical understanding of signal detection and reconstruction in this set up, complemented with provable guarantees on detection thresholds and rates of convergence.
Model reduction in acoustic inversion by artificial neural network
Authors: Janne Koponen, Timo Lähivaara, Jari Kaipio, Marko Vauhkonen
Date: 2021-05-05 00:00:00
Tasks: Image Reconstruction

In ultrasound tomography, the speed of sound inside an object is estimated based on acoustic measurements carried out by sensors surrounding the object. An accurate forward model is a prominent factor for high-quality image reconstruction, but it can make computations far too time-consuming in many applications. Using approximate forward models, it is possible to speed up the computations, but the quality of the reconstruction may have to be compromised. In this paper, a neural network -based approach is proposed, that can compensate for modeling errors caused by the approximate forward models. The approach is tested with various different imaging scenarios in a simulated two-dimensional domain. The results show that with fairly small training datasets, the proposed approach can be utilized to approximate the modelling errors, and to significantly improve the image reconstruction quality in ultrasound tomography, compared to commonly used inversion algorithms.
Simulating the DFT Algorithm for Audio Processing
Authors: Omkar Deshpande, Kharanshu Solanki, Sree Pujitha Suribhatla, Sanya Zaveri, Luv Ghodasara
Date: 2021-04-29 00:00:00

Since the evolution of digital computers, the storage of data has always been in terms of discrete bits that can store values of either 1 or 0. Hence, all computer programs (such as MATLAB), convert any input continuous signal into a discrete dataset. Applying this to oscillating signals, such as audio, opens a domain for processing as well as editing. The Fourier transform, which is an integral over infinite limits, for the use of signal processing is discrete. The essential feature of the Fourier transform is to decompose any signal into a combination of multiple sinusoidal waves that are easy to deal with. The discrete Fourier transform (DFT) can be represented as a matrix, with each data point acting as an orthogonal point, allowing one to perform complicated transformations on individual frequencies. Due to this formulation, all the concepts of linear algebra and linear transforms prove to be extremely useful here. In this paper, we first explain the theoretical basis of audio processing using linear algebra, and then focus on a simulation coded in MATLAB, to process and edit various audio samples. The code is open ended and easily expandable by just defining newer matrices which can transform over the original audio signal. Finally, this paper attempts to highlight and briefly explain the results that emerge from the simulation