pwc-archive/papers-with-abstracts huggingface dataset:
Local Analysis of Heterogeneous Intracellular Transport: Slow and Fast moving Endosomes
Authors: Nickolay Korabel, Daniel Han, Alessandro Taloni, Gianni Pagnini, Sergei Fedotov, Viki Allan, Thomas A. Waigh
Date: 2021-07-24 00:00:00

Trajectories of endosomes inside living eukaryotic cells are highly heterogeneous in space and time and diffuse anomalously due to a combination of viscoelasticity, caging, aggregation and active transport. Some of the trajectories display switching between persistent and anti-persistent motion while others jiggle around in one position for the whole measurement time. By splitting the ensemble of endosome trajectories into slow moving sub-diffusive and fast moving super-diffusive endosomes, we analyzed them separately. The mean squared displacements and velocity auto-correlation functions confirm the effectiveness of the splitting methods. Applying the local analysis, we show that both ensembles are characterized by a spectrum of local anomalous exponents and local generalized diffusion coefficients. Slow and fast endsomes have exponential distributions of local anomalous exponents and power law distributions of generalized diffusion coefficients. This suggests that heterogeneous fractional Brownian motion is an appropriate model for both fast and slow moving endosomes. This article is part of a Special Issue entitled: "Recent Advances In Single-Particle Tracking: Experiment and Analysis" edited by Janusz Szwabi\'nski and Aleksander Weron.
Inferring Economic Condition Uncertainty from Electricity Big Data
Authors: Haoqi Qian, Zhengyu Shi, Libo Wu
Date: 2021-07-24 00:00:00

Inferring the uncertainty in economic conditions is significant for both decision makers as well as market players. In this paper, we propose a novel approach to measure the economic uncertainties by using the Hidden Markov Model (HMM). We construct a dimensionless index, Economic Condition Uncertainty (ECU) index, which ranges from zero to one and is comparable among sectors, regions and periods. We used the daily electricity consumption data of more than 18,000 firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, whether at sectoral or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in the uncertainty of economic conditions are mainly related to regional economic structures and targeted regulatory policies faced by sectors. The ECU index can also be readily extended to measure the uncertainty of economic conditions in various realms, which has great potentials in the future.
Peace through bribing
Authors: Jingfeng Lu, Zongwei Lu, Christian Riis
Date: 2021-07-24 00:00:00

We study a model in which before a conflict between two parties escalates into a war (in the form of an all-pay auction), a party can offer a take-it-or-leave-it bribe to the other for a peaceful settlement. In contrast to the received literature, we find that peace security is impossible in our model. We characterize the necessary and sufficient conditions for peace implementability. Furthermore, we find that separating equilibria do not exist and the number of (on-path) bribes in any non-peaceful equilibria is at most two. We also consider a requesting model and characterize the necessary and sufficient conditions for the existence of robust peaceful equilibria, all of which are sustained by the identical (on-path) request. Contrary to the bribing model, peace security is possible in the requesting model.
Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection
Authors: Wanying Ge, Jose Patino, Massimiliano Todisco, Nicholas Evans
Date: 2021-07-26 00:00:00
Tasks: Face Swapping

End-to-end approaches to anti-spoofing, especially those which operate directly upon the raw signal, are starting to be competitive with their more traditional counterparts. Until recently, all such approaches consider only the learning of network parameters; the network architecture is still hand crafted. This too, however, can also be learned. Described in this paper is our attempt to learn automatically the network architecture of a speech deepfake and spoofing detection solution, while jointly optimising other network components and parameters, such as the first convolutional layer which operates on raw signal inputs. The resulting raw differentiable architecture search system delivers a tandem detection cost function score of 0.0517 for the ASVspoof 2019 logical access database, a result which is among the best single-system results reported to date.
Crowdsourcing strong labels for sound event detection
Authors: Irene Martín-Morató, Manu Harju, Annamaria Mesaros
Date: 2021-07-26 00:00:00
Tasks: Event Detection, Sound Event Detection

Strong labels are a necessity for evaluation of sound event detection methods, but often scarcely available due to the high resources required by the annotation task. We present a method for estimating strong labels using crowdsourced weak labels, through a process that divides the annotation task into simple unit tasks. Based on estimations of annotators' competence, aggregation and processing of the weak labels results in a set of objective strong labels. The experiment uses synthetic audio in order to verify the quality of the resulting annotations through comparison with ground truth. The proposed method produces labels with high precision, though not all event instances are recalled. Detection metrics comparing the produced annotations with the ground truth show 80% F-score in 1 s segments, and up to 89.5% intersection-based F1-score calculated according to the polyphonic sound detection score metrics.
Inplace Gated Convolutional Recurrent Neural Network For Dual-channel Speech Enhancement
Authors: Jinjiang Liu, Xueliang Zhang
Date: 2021-07-26 00:00:00
Tasks: Speech Enhancement

For dual-channel speech enhancement, it is a promising idea to design an end-to-end model based on the traditional array signal processing guideline and the manifold space of multi-channel signals. We found that the idea above can be effectively implemented by the classical convolutional recurrent neural networks (CRN) architecture. We propose a very compact in place gated convolutional recurrent neural network (inplace GCRN) for end-to-end multi-channel speech enhancement, which utilizes inplace-convolution for frequency pattern extraction and reconstruction. The inplace characteristics efficiently preserve spatial cues in each frequency bin for channel-wise long short-term memory neural networks (LSTM) tracing the spatial source. In addition, we come up with a new spectrum recovery method by predict amplitude mask, mapping, and phase, which effectively improves the speech quality.
Utilizing the Structure of the Curvelet Transform with Compressed Sensing
Authors: Nicholas Dwork, Peder E. Z. Larson
Date: 2021-07-24 00:00:00
Tasks: compressed sensing, Denoising, Image Reconstruction

The discrete curvelet transform decomposes an image into a set of fundamental components that are distinguished by direction and size as well as a low-frequency representation. The curvelet representation is approximately sparse; thus, it is a useful sparsifying transformation to be used with compressed sensing. However, the low-frequency portion is seldom sparse. This manuscript presents a method to modify the redundant sparsifying transformation comprised of the wavelet and curvelet transforms to take advantage of this fact with compressed sensing image reconstruction. Instead of relying on sparsity for this low-frequency estimate, the Nyquist-Shannon theorem specifies a square region centered on the $0$ frequency to be collected, which is used to generate a blurry estimate. A Basis Pursuit Denoising problem is solved to determine the missing details after modifying the sparsifying transformation to take advantage of the known fully sampled region. Improvements in quality are shown on magnetic resonance and optical images.
Lightweight Hardware Transform Design for the Versatile Video Coding 4K ASIC Decoders
Authors: Ibrahim Farhat, Wassim Hamidouche, Adrien Grill, Daniel Ménard, Olivier Déforges
Date: 2021-07-24 00:00:00
Tasks: 4k, Decoder

Versatile Video Coding (VVC) is the next generation video coding standard finalized in July 2020. VVC introduces new coding tools enhancing the coding efficiency compared to its predecessor High Efficiency Video Coding (HEVC). These new tools have a significant impact on the VVC software decoder complexity estimated to 2 times HEVC decoder complexity. In particular, the transform module includes in VVC separable and non-separable transforms named Multiple Transform Selection (MTS) and Low Frequency Non-Separable Transform (LFNST) tools, respectively. In this paper, we present an area-efficient hardware architecture of the inverse transform module for a VVC decoder. The proposed design uses a total of 64 regular multipliers in a pipelined architecture targeting Application-Specific Integrated Circuit (ASIC) platforms. It consists in a multi-standard architecture that supports the transform modules of recent MPEG standards including Advanced Video Coding (AVC), HEVC and VVC. The architecture leverages all primary and secondary transforms optimisations including butterfly de-composition, coefficients zeroing and the inherent linear relationship between the transforms. The synthesized results show that the proposed method sustains a constant throughput of 1sample per cycle and a constant latency for all block sizes. The proposed hardware inverse transform module operates at 600MHz frequency enabling to decode in real-time 4K video at 30 frames per second in 4:2:2 chroma sub-sampling format. The proposed module has been integrated in an ASIC UHD decoder targeting energy-aware decoding of VVC videos on consumer devices.
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization
Authors: Xinlin Zhang, Hengfa Lu, Di Guo, Zongying Lai, Huihui Ye, Xi Peng, Bo Zhao, Xiaobo Qu
Date: 2021-07-24 00:00:00
Tasks: MRI Reconstruction

The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured approaches demand considerable memory consumption and are time-consuming due to a noticeable number of matrix operations performed on the huge-size block Hankel-like matrix. In this work, we proposed a novel framework to utilize the low-rank property but meanwhile to achieve faster reconstructions and promising results. The framework allows us to enforce the low-rankness of Hankel matrices constructing from 1D vectors instead of 2D matrices from 1D vectors and thus avoid the construction of huge block Hankel matrix for 2D k-space matrices. Moreover, under this framework, we can easily incorporate other information, such as the smooth phase of the image and the low-rankness in the parameter dimension, to further improve the image quality. We built and validated two models for parallel and parameter magnetic resonance imaging experiments, respectively. Our retrospective in-vivo results indicate that the proposed approaches enable faster reconstructions than the state-of-the-art approaches, e.g., about 8x faster than STDLRSPIRiT, and faithful removal of undersampling artifacts.
Deep Transfer Clustering of Radio Signals
Authors: Qi Xuan, Xiaohui Li, Zhuangzhi Chen, Dongwei Xu, Shilian Zheng, Xiaoniu Yang
Date: 2021-07-26 00:00:00
Tasks: Clustering, Deep Clustering, Transfer Learning

Modulation recognition is an important task in radio signal processing. Most of the current researches focus on supervised learning. However, in many real scenarios, it is difficult and cost to obtain the labels of signals. In this letter, we turn to the more challenging problem: can we cluster the modulation types just based on a large number of unlabeled radio signals? If this problem can be solved, we then can also recognize modulation types by manually labeling a very small number of samples. To answer this problem, we propose a deep transfer clustering (DTC) model. DTC naturally integrates feature learning and deep clustering, and further adopts a transfer learning mechanism to improve the feature extraction ability of an embedded convolutional neural network (CNN) model. The experiments validate that our DTC significantly outperforms a number of baselines, achieving the state-of-the-art performance in clustering radio signals for modulation recognition.
Superconducting Quantum Amplifier-Integrator in Ultra-High Speed Continuous-time Delta-Sigma Converter
Authors: Debopam Banerjee
Date: 2021-07-26 00:00:00

Present semiconductor research is increasingly focusing on either higher speeds or higher linearity or both. Applications range from consumer, industrial, healthcare and military. Typically such circuits are fabricated in today's low-voltage CMOS processes using silicon and in few cases BJT-CMOS combined like Gallium-Arsenide or Indium-Phosphide. These technology nodes face a plethora of problems like reduction of dynamic range of the circuit due to mismatch, distortion, noise, thermal and electromigration issues due to excessive currents, etc. Compounding these problems is the issue with lower achievable gain from an amplifier which often gets limited due to lower supply voltages in such technology nodes. Slowly circuit techniques like chopping, cascoding, cascading and calibration are nearing their limits. In this paper we present a radically different approach to our regular analog design building blocks using macroscopic quantum effects which have hitherto not found favour with the design community. We will solely focus on the effect of superconductivity and adopting its macroscopic phenomena to amplifiers, integrators and comparators. Using staggered superconductors we can achieve a gain which depends only on physical quantum constants and remains invariant under process, temperature, supply, interference, etc. This robustness of gain in an amplifier goes a long way in attaining higher linearity. The comparator can resolve a minimum of 2.07fT magnetic flux but when embedded inside a Delta-Sigma loop can typically attain 100 times smaller resolution pushing the boundaries of sensing.
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems
Authors: Ly V. Nguyen, Duy H. N. Nguyen, A. Lee Swindlehurst
Date: 2021-07-26 00:00:00
Tasks: Quantization

Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of the domain knowledge in the few-bit quantization process. While the first data detection network, namely B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, namely FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the vanishing gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into its weight matrices of the channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gain in estimation accuracy by the proposed deep learning framework.
Distributed and Autonomous Aerial Data Collection in Smart City Surveillance Applications
Authors: Haemin Lee, Soyi Jung, Joongheon Kim
Date: 2021-07-25 00:00:00

The massive growth of Smart City and Internet of Things applications enables safety and security. The data those are produced from surveillance cameras in aerial devices such as unmanned aerial networks (UAVs) are needed to be transferred to ground stations for secure data analysis. When the scale of network is relatively large compare to the wireless communication coverage of device, it is not always available to transmit the data to the ground stations, thus distributed and autonomous algorithms are essentially desired. Based on the needs, we propose a novel algorithm that is for collecting surveillance data under the consideration of mobility and flexibility of UAV networks. Due to the battery limitation in UAVs, we selectively collect data from the UAVs by setting rules under the consideration of distance and similarity. As a sequence, the UAV devices have to compete for a chance to get data processing. For this purpose, this paper designs a Myerson auction-based deep learning algorithm to leverage the UAV's revenue compare to traditional second-price auction while preserving truthfulness. Based on simulation results, we verify that our proposed algorithm achieves desired performance improvements.
Reflection and Relay Dual-Functional RIS Assisted MU-MISO Systems
Authors: Yanan Ma, Rang Liu, Yang Liu, Ming Li, Qian Liu
Date: 2021-07-24 00:00:00

Reconfigurable intelligent surfaces (RISs) have been deemed as one of potential components of future wireless communication systems because they can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to double fading effect, the passive RIS can offer sufficient signal strength only when receivers are nearby and located at the same side as the incident signals. Moreover, RIS cannot provide service coverage for the users at the back side of it. In this paper we introduce a novel reflection and relay dual-functional RIS architecture, which can simultaneously realize passive reflection and active relay functionalities to enhance the coverage. The problem of joint transmit beamforming and dual-functional RIS design is investigated to maximize the achievable sum-rate of a multiuser multiple-input single-output (MU-MISO) system. Based on fractional programming (FP) theory and majorization-minimization (MM) technique, we propose an efficient iterative transmit beamforming and RIS design algorithm. Simulation results demonstrate the superiority of the introduced dual-functional RIS architecture and the effectiveness of the proposed algorithm.
Multiradar Data Fusion for Respiratory Measurement of Multiple People
Authors: Shunsuke Iwata, Takato Koda, Takuya Sakamoto
Date: 2021-07-24 00:00:00

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system.
Low Complexity Hybrid Precoding Designs for Multiuser mmWave/THz Ultra Massive MIMO Systems
Authors: Joao Pedro Pavia, Vasco Velez, Renato Ferreira, Nuno Souto, Marco Ribeiro, Joao Silva, Rui Dinis
Date: 2021-07-24 00:00:00

Millimeter-wave and terahertz technologies have been attracting attention from the wireless research community since they can offer large underutilized bandwidths which can enable the support of ultra-high-speed connections in future wireless communication systems. While the high signal attenuation occurring at these frequencies requires the adoption of very large (or the so-called ultra-massive) antenna arrays, in order to accomplish low complexity and low power consumption, hybrid analog/digital designs must be adopted. In this paper we present a hybrid design algorithm suitable for both mmWave and THz multiuser multiple-input multiple-output (MIMO) systems, which comprises separate computation steps for the digital precoder, analog precoder and multiuser interference mitigation. The design can also incorporate different analog architectures such as phase shifters, switches and inverters, antenna selection and so on. Furthermore, it is also applicable for different structures namely, fully connected, arrays of subarrays (AoSA) and dynamic arrays of subarrays (DAoSA), making it suitable for the support of ultra-massive MIMO (UM-MIMO) in severely hardware constrained THz systems. We will show that, by using the proposed approach, it is possible to achieve good trade-offs between spectral efficiency and simplified implementation, even as the number of users and data streams increases.)
Liquidity Provision with Adverse Selection and Inventory Costs
Authors: Martin Herdegen, Johannes Muhle-Karbe, Florian Stebegg
Date: 2021-07-26 00:00:00

We study one-shot Nash competition between an arbitrary number of identical dealers that compete for the order flow of a client. The client trades either because of proprietary information, exposure to idiosyncratic risk, or a mix of both trading motives. When quoting their price schedules, the dealers do not know the client's type but only its distribution, and in turn choose their price quotes to mitigate between adverse selection and inventory costs. Under essentially minimal conditions, we show that a unique symmetric Nash equilibrium exists and can be characterized by the solution of a nonlinear ODE.
How sticky are our proteins? Quantifying hydrophobicity of the human proteome
Authors: Juami Hermine Mariama van Gils, Dea Gogishvili, Jan van Eck, Robbin Bouwmeester, Erik van Dijk, Sanne Abeln
Date: 2021-07-25 00:00:00

Proteins tend to bury hydrophobic residues inside their core during the folding process to provide stability to the protein structure and to prevent aggregation. Nevertheless, proteins do expose some 'sticky' hydrophobic residues to the solvent. These residues can play an important functional role, for example in protein-protein and membrane interactions. Here, we investigate how hydrophobic protein surfaces are by providing three measures for surface hydrophobicity: the total hydrophobic surface area, the relative hydrophobic surface area, and - using our MolPatch method - the largest hydrophobic patch. Secondly, we analyse how difficult it is to predict these measures from sequence: by adapting solvent accessibility predictions from NetSurfP2.0, we obtain well-performing prediction methods for the THSA and RHSA, while predicting LHP is more difficult. Finally, we analyse implications of exposed hydrophobic surfaces: we show that hydrophobic proteins typically have low expression, suggesting cells avoid an overabundance of sticky proteins.