pwc-archive/papers-with-abstracts huggingface dataset:
Small-Angle X-Ray Scattering Signatures of Conformational Heterogeneity and Homogeneity of Disordered Protein Ensembles
Authors: Jianhui Song, Jichen Li, Hue Sun Chan
Date: 2021-05-27 00:00:00

Physically, disordered ensembles of non-homopolymeric polypeptides are expected to be heterogeneous; i.e., they should differ from those homogeneous ensembles of homopolymers that harbor an essentially unique relationship between average values of end-to-end distance $R_{\rm EE}$ and radius of gyration $R_{\rm g}$. It was posited recently, however, that small-angle X-ray scattering (SAXS) data on conformational dimensions of disordered proteins can be rationalized almost exclusively by homopolymer ensembles. Assessing this perspective, chain-model simulations are used to evaluate the discriminatory power of SAXS-determined molecular form factors (MFFs) with regard to homogeneous versus heterogeneous ensembles. The general approach adopted here is not bound by any assumption about ensemble encodability, in that the postulated heterogeneous ensembles we evaluated are not restricted to those entailed by simple interaction schemes. Our analysis of MFFs for certain heterogeneous ensembles with more narrowly distributed $R_{\rm EE}$ and $R_{\rm g}$ indicates that while they deviates from MFFs of homogeneous ensembles, the differences can be rather small. Remarkably, some heterogeneous ensembles with asphericity and $R_{\rm EE}$ drastically different from those of homogeneous ensembles can nonetheless exhibit practically identical MFFs, demonstrating that SAXS MFFs do not afford unique characterizations of basic properties of conformational ensembles in general. In other words, the ensemble to MFF mapping is practically many-to-one and likely non-smooth. Heteropolymeric variations of the $R_{\rm EE}$--$R_{\rm g}$ relationship were further showcased using an analytical perturbation theory developed here for flexible heteropolymers. Ramifications of our findings for interpretation of experimental data are discussed.
Geometry of Gene Regulatory Dynamics
Authors: David A. Rand, Archishman Raju, Meritxell Saez, Francis Corson, Eric D. Siggia
Date: 2021-05-28 00:00:00

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types, that elaborates this complexity, result from the activity of gene networks and was likened by Waddington to a flow through a landscape in which valleys represent alternative fates. Geometric methods allow the formal representation of such landscapes and codify the types of behaviors that result from systems of differential equations. Results from Smale and coworkers imply that systems encompassing gene network models can be represented as potential gradients with a Riemann metric, justifying the Waddington metaphor. Here, we extend this representation to include parameter dependence and enumerate all 3-way cellular decisions realisable by tuning at most two parameters, which can be generalized to include spatial coordinates in a tissue. All diagrams of cell states vs model parameters are thereby enumerated. We unify a number of standard models for spatial pattern formation by expressing them in potential form. Turing systems appear non-potential yet in suitable variables the dynamics are low dimensional, potential, and a time independent embedding recovers the biological variables. Lateral inhibition is described by a saddle point with many unstable directions. A model for the patterning of the Drosophila eye appears as relaxation in a bistable potential. Geometric reasoning provides intuitive dynamic models for development that are well adapted to fit time-lapse data.
The Complex-Pole Filter Representation (COFRE) for spectral modeling of fNIRS signals
Authors: Marco A. Pinto Orellana, Peyman Mirtaheri, Hugo L. Hammer
Date: 2021-05-13 00:00:00

The complex-pole frequency representation (COFRE) is introduced in this paper as a new approach for spectrum modeling in biomedical signals. Our method allows us to estimate the spectral power density at precise frequencies using an array of narrow band-pass filters with single complex poles. Closed-form expressions for the frequency resolution and transient time response of the proposed filters have also been formulated. In addition, COFRE filters have a constant time and space complexity allowing their use in real-time environments. Our model was applied to identify frequency markers that characterize tinnitus in very-low-frequency oscillations within functional near-infrared spectroscopy (fNIRS) signals. We examined data from six patients with subjective tinnitus and seven healthy participants as a control group. A significant decrease in the spectrum power was observed in tinnitus patients in the left temporal lobe. In particular, we identified several tinnitus signatures in the spectral hemodynamic information, including (a.) a significant spectrum difference in one specific harmonic in the metabolic/endothelial frequency region, at 7mHz, for both chromophores and hemispheres; and (b.) a significant differences in the range 30-50mHz in the neurogenic/myogenic band.
Cybersecurity and Sustainable Development
Authors: Adam Sulich, Malgorzata Rutkowska, Agnieszka Krawczyk-Jezierska, Jaroslaw Jezierski, Tomasz Zema
Date: 2021-05-28 00:00:00
Tasks: Management

Growing interdependencies between organizations lead them towards the creation of inter-organizational networks where cybersecurity and sustainable development have become one of the most important issues. The Environmental Goods and Services Sector (EGSS) is one of the fastest developing sectors of the economy fueled by the growing relationships between network entities based on ICT usage. In this sector, Green Cybersecurity is an emerging issue because it secures processes related directly and indirectly to environmental management and protection. In the future, the multidimensional development of the EGSS can help European Union to overcome the upcoming crises. At the same time, computer technologies and cybersecurity can contribute to the implementation of the concept of sustainable development. The development of environmental technologies along with their cybersecurity is one of the aims of the realization of sustainable production and domestic security concepts among the EU countries. Hence, the aim of this article is a theoretical discussion and research on the relationships between cybersecurity and sustainable development in inter-organizational networks. Therefore, the article is an attempt to give an answer to the question about the current state of the implementation of cybersecurity in relation to the EGSS part of the economy in different EU countries.
Three Remarks On Asset Pricing
Authors: Victor Olkhov
Date: 2021-05-24 00:00:00
Tasks: Time Series, Time Series Analysis

We consider the consumption-based asset pricing model, derive a new modified basic pricing equation, and present its successive approximations using the Taylor series expansions of the investor's utility during the averaging time interval. For linear and quadratic Taylor approximations, we derive new expressions for the mean price, mean payoff, volatility, skewness, and the asset's amount that define the maximum of the investor's utility. We discuss the market-based origin of price probability. We use volume weighted average price (VWAP) as a market-based average price and introduce market-based price volatility. The use of VWAP results in zero correlations between the price p and trade volume U. We derive a correlation between price p and squares of trade volume {U^2} and between squares of price {p^2} and volume {U^2}. To predict market-based price volatility, one should forecast the 2-d statistical moments of the market trade values and volumes at the same horizon T.
Perfect bidder collusion through bribe and request
Authors: Jingfeng Lu, Zongwei Lu, Christian Riis
Date: 2019-12-08 00:00:00

We study collusion in a second-price auction with two bidders in a dynamic environment. One bidder can make a take-it-or-leave-it collusion proposal, which consists of both an offer and a request of bribes, to the opponent. We show that there always exists a robust equilibrium in which the collusion success probability is one. In the equilibrium, for each type of initiator the expected payoff is generally higher than the counterpart in any robust equilibria of the single-option model (Es\"{o} and Schummer (2004)) and any other separating equilibria in our model.
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems
Authors: Irene Martín-Morató, Toni Heittola, Annamaria Mesaros, Tuomas Virtanen
Date: 2021-05-28 00:00:00
Tasks: Acoustic Scene Classification, Quantization, Scene Classification

This paper presents the details of Task 1A Acoustic Scene Classification in the DCASE 2021 Challenge. The task targeted development of low-complexity solutions with good generalization properties. The provided baseline system is based on a CNN architecture and post-training quantization of parameters. The system is trained using all the available training data, without any specific technique for handling device mismatch, and obtains an overall accuracy of 47.7%, with a log loss of 1.473. The task received 99 submissions from 30 teams, and most of the submitted systems outperformed the baseline. The most used techniques among the submissions were residual networks and weight quantization, with the top systems reaching over 70% accuracy, and log loss under 0.8. The acoustic scene classification task remained a popular task in the challenge, despite the increasing difficulty of the setup.
An Unsupervised Optical Flow Estimation For LiDAR Image Sequences
Authors: Xuezhou Guo, Xuhu Lin, Lili Zhao, Zezhi Zhu, Jianwen Chen
Date: 2021-05-28 00:00:00
Tasks: 3D Semantic Segmentation, Motion Estimation, Optical Flow Estimation, Semantic Segmentation

In recent years, the LiDAR images, as a 2D compact representation of 3D LiDAR point clouds, are widely applied in various tasks, e.g., 3D semantic segmentation, LiDAR point cloud compression (PCC). Among these works, the optical flow estimation for LiDAR image sequences has become a key issue, especially for the motion estimation of the inter prediction in PCC. However, the existing optical flow estimation models are likely to be unreliable for LiDAR images. In this work, we first propose a light-weight flow estimation model for LiDAR image sequences. The key novelty of our method lies in two aspects. One is that for the different characteristics (with the spatial-variation feature distribution) of the LiDAR images w.r.t. the normal color images, we introduce the attention mechanism into our model to improve the quality of the estimated flow. The other one is that to tackle the lack of large-scale LiDAR-image annotations, we present an unsupervised method, which directly minimizes the inconsistency between the reference image and the reconstructed image based on the estimated optical flow. Extensive experimental results have shown that our proposed model outperforms other mainstream models on the KITTI dataset, with much fewer parameters.
Augmented Networks for Faster Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI
Authors: Engin Dikici, Xuan V. Nguyen, Matthew Bigelow, Luciano M. Prevedello
Date: 2021-05-27 00:00:00
Tasks: Sensitivity

Early detection of brain metastases (BM) is one of the determining factors for the successful treatment of patients with cancer; however, the accurate detection of small BM lesions (< 15mm) remains a challenging task. We previously described a framework for the detection of small BM in single-sequence gadolinium-enhanced T1-weighted 3D MRI datasets. It combined classical image processing (IP) with a dedicated convolutional neural network, taking approximately 30 seconds to process each dataset due to computation-intensive IP stages. To overcome the speed limitation, this study aims to reformulate the framework via an augmented pair of CNNs (eliminating the IP) to reduce the processing times while preserving the BM detection performance. Our previous implementation of the BM detection algorithm utilized Laplacian of Gaussians (LoG) for the candidate selection portion of the solution. In this study, we introduce a novel BM candidate detection CNN (cdCNN) to replace this classical IP stage. The network is formulated to have (1) a similar receptive field as the LoG method, and (2) a bias for the detection of BM lesion loci. The proposed CNN is later augmented with a classification CNN to perform the BM detection task. The cdCNN achieved 97.4% BM detection sensitivity when producing 60K candidates per 3D MRI dataset, while the LoG achieved 96.5% detection sensitivity with 73K candidates. The augmented BM detection framework generated on average 9.20 false-positive BM detections per patient for 90% sensitivity, which is comparable with our previous results. However, it processes each 3D data in 1.9 seconds, presenting a 93.5% reduction in the computation time.
Differential Chaos Shift Keying-based Wireless Power Transfer with Nonlinearities
Authors: Priyadarshi Mukherjee, Constantinos Psomas, Ioannis Krikidis
Date: 2021-05-28 00:00:00

In this paper, we investigate conventional communication-based chaotic waveforms in the context of wireless power transfer (WPT). Particularly, we present a differential chaos shift keying (DCSK)-based WPT architecture, that employs an analog correlator at the receiver, in order to boost the energy harvesting (EH) performance. We take into account the nonlinearities of the EH process and derive closed-form analytical expressions for the harvested direct current (DC) under a generalized Nakagami-m block fading model. We show that, in this framework, both the peak-to-average-power-ratio of the received signal and the harvested DC, depend on the parameters of the transmitted waveform. Furthermore, we investigate the case of deterministic unmodulated chaotic waveforms and demonstrate that, in the absence of a correlator, modulation does not affect the achieved harvested DC. On the other hand, it is shown that for scenarios with a correlator-aided receiver, DCSK significantly outperforms the unmodulated case. Based on this observation, we propose a novel DCSK-based signal design, which further enhances the WPT capability of the proposed architecture; corresponding analytical expressions for the harvested DC are also derived. Our results demonstrate that the proposed architecture and the associated signal design, can achieve significant EH gains in DCSK-based WPT systems. Furthermore, we also show that, even by taking into account the nonlinearities at the transmitter amplifier, the proposed chaotic waveform performs significantly better in terms of EH, when compared with the existing multisine signals.
Grouped Variable Selection for Generalized Eigenvalue Problems
Authors: Jonathan Dan, Simon Geirnaert, Alexander Bertrand
Date: 2021-05-28 00:00:00
Tasks: Variable Selection

Many problems require the selection of a subset of variables from a full set of optimization variables. The computational complexity of an exhaustive search over all possible subsets of variables is, however, prohibitively expensive, necessitating more efficient but potentially suboptimal search strategies. We focus on sparse variable selection for generalized Rayleigh quotient optimization and generalized eigenvalue problems. Such problems often arise in the signal processing field, e.g., in the design of optimal data-driven filters. We extend and generalize existing work on convex optimization-based variable selection using semidefinite relaxations toward group-sparse variable selection using the $\ell_{1,\infty}$-norm. This group-sparsity allows, for instance, to perform sensor selection for spatio-temporal (instead of purely spatial) filters, and to select variables based on multiple generalized eigenvectors instead of only the dominant one. Furthermore, we extensively compare our method to state-of-the-art methods for sensor selection for spatio-temporal filter design in a simulated sensor network setting. The results show both the proposed algorithm and backward greedy selection method best approximate the exhaustive solution. However, the backward greedy selection has more specific failure cases, in particular for ill-conditioned covariance matrices. As such, the proposed algorithm is the most robust currently available method for group-sparse variable selection in generalized eigenvalue problems.
Hybrid Beamforming for Intelligent Reflecting Surface Aided Millimeter Wave MIMO Systems
Authors: Sung Hyuck Hong, Jaeyong Park, Sung-Jin Kim, Junil Choi
Date: 2021-05-28 00:00:00

As communication systems that employ millimeter wave (mmWave) frequency bands must use large antenna arrays to overcome the severe propagation loss of mmWave signals, hybrid beamforming has been considered as an integral component of mmWave communications. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative technology that can significantly improve the performance of mmWave communication systems through the use of low-cost passive reflecting elements. In this paper, we study IRS-aided mmWave multiple-input multiple-output (MIMO) systems with hybrid beamforming architectures. We first exploit the sparse-scattering structure and large dimension of mmWave channels to develop the joint design of IRS reflection matrix and hybrid beamformer for narrowband MIMO systems. Then, we generalize the proposed joint design to broadband MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation by leveraging the angular sparsity of frequency-selective mmWave channels. Simulation results demonstrate that the proposed joint designs can significantly enhance the spectral efficiency of the systems of interest and achieve superior performance over the existing designs.
A Low-Complexity Method for FFT-based OFDM Sensing
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Y. Jay Guo
Date: 2021-05-28 00:00:00

OFDM sensing is gaining increasing popularity in wideband radar applications as well as in joint communication and radar/radio sensing (JCAS). As JCAS will potentially be integrated into future mobile networks where OFDM is crucial, OFDM sensing is envisioned to be ubiquitously deployed. A fast Fourier transform (FFT) based OFDM sensing (FOS) method was proposed a decade ago and has been regarded as a de facto standard given its simplicity. In this article, we introduce an easy trick -- a pre-processing on target echo -- to further reduce the computational complexity of FOS without degrading key sensing performance. Underlying the trick is a newly disclosed feature of the target echo in OFDM sensing which, to the best of our knowledge, has not been effectively exploited yet.
Accurate Frequency Estimation with Fewer DFT Interpolations based on Padé Approximation
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Y. Jay Guo
Date: 2021-05-28 00:00:00

Frequency estimation is a fundamental problem in many areas. The well-known A&M and its variant estimators have established an estimation framework by iteratively interpolating the discrete Fourier transform (DFT) coefficients. In general, those estimators require two DFT interpolations per iteration, have uneven initial estimation performance against frequencies, and are incompetent for small sample numbers due to low-order approximations involved. Exploiting the iterative estimation framework of A&M, we unprecedentedly introduce the Pad\'e approximation to frequency estimation, unveil some features about the updating function used for refining the estimation in each iteration, and develop a simple closed-form solution to solving the residual estimation error. Extensive simulation results are provided, validating the superiority of the new estimator over the state-the-art estimators in wide ranges of key parameters.
Millimeter Wave Analog Beamforming Codebooks Robust to Self-Interference
Authors: Ian P. Roberts, Hardik B. Jain, Sriram Vishwanath, Jeffrey G. Andrews
Date: 2021-05-27 00:00:00

This paper develops a novel methodology for designing analog beamforming codebooks for full-duplex millimeter wave (mmWave) transceivers, the first such codebooks to the best of our knowledge. Our design reduces the self-interference coupled by transmit-receive beam pairs and simultaneously delivers high beamforming gain over desired coverage regions, allowing mmWave full-duplex systems to support beam alignment while minimizing self-interference. To do so, our methodology allows some variability in beamforming gain to strategically shape beams that reject self-interference while still having substantial gain. We present an algorithm for approximately solving our codebook design problem while accounting for the non-convexity posed by digitally-controlled phase shifters and attenuators. Numerical results suggest that our design can outperform or nearly match existing codebooks in sum spectral efficiency across a wide range of self-interference power levels. Results show that our design offers an extra 20-50 dB of robustness to self-interference, depending on hardware constraints.
SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging
Authors: Ziyu Jia, Youfang Lin, Jing Wang, Xuehui Wang, Peiyi Xie, Yingbin Zhang
Date: 2021-05-24 00:00:00
Tasks: object-detection, Object Detection, Salient Object Detection, Sleep Staging

Sleep staging is fundamental for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively extract salient waves in multimodal sleep data; 2) How to capture the multi-scale transition rules among sleep stages; 3) How to adaptively seize the key role of specific modality for sleep staging. To address these challenges, we propose SalientSleepNet, a multimodal salient wave detection network for sleep staging. Specifically, SalientSleepNet is a temporal fully convolutional network based on the $\rm U^2$-Net architecture that is originally proposed for salient object detection in computer vision. It is mainly composed of two independent $\rm U^2$-like streams to extract the salient features from multimodal data, respectively. Meanwhile, the multi-scale extraction module is designed to capture multi-scale transition rules among sleep stages. Besides, the multimodal attention module is proposed to adaptively capture valuable information from multimodal data for the specific sleep stage. Experiments on the two datasets demonstrate that SalientSleepNet outperforms the state-of-the-art baselines. It is worth noting that this model has the least amount of parameters compared with the existing deep neural network models.
Cysteine post-translational modifications: ten years from chemical proteomics to bioinformatics
Authors: Yanzheng Meng, Lei LI
Date: 2021-05-28 00:00:00

As the only thiol-bearing amino acid, cysteine (Cys) residues in proteins have the reactive thiol side chain, which is susceptible to a series of post-translational modifications (PTMs). These PTMs participate in a wide range of biological activities including the alteration of enzymatic reactions, protein-protein interactions and protein stability. Here we summarize the advance of cysteine PTM identification technologies and the features of the various kinds of the PTMs. We also discuss in silico approaches for the prediction of the different types of cysteine modified sites, giving directions for future study.