pwc-archive/papers-with-abstracts huggingface dataset:
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?
Authors: Vahid Salari, Serafim Rodrigues, Erhan Saglamyurek, Christoph Simon, Daniel Oblak
Date: 2021-11-22 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

The present paper examines the viability of a radically novel idea for brain-computer interface (BCI), which could lead to novel technological, experimental and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. BCIs read-out brain signals and transduce them into task commands, which are performed by a machine. In closed-loop, the machine can stimulate the brain with appropriate signals. In recent years, it has been shown that there is some ultraweak light emission from neurons within or close to the visible and near-infrared parts of the optical spectrum. Such ultraweak photon emission (UPE) reflects the cellular (and body) oxidative status, and compelling pieces of evidence are beginning to emerge that UPE may well play an informational role in neuronal functions. In fact, several experiments point to a direct correlation between UPE intensity and neural activity, oxidative reactions, EEG activity, cerebral blood flow, cerebral energy metabolism, and release of glutamate. Here, we propose a novel skull implant BCI that uses UPE. We suggest that a photonic integrated chip installed on the interior surface of the skull may enable a new form of extraction of the relevant features from the UPE signals. In the current technology landscape, photonic technologies advance rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturization, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. For our proposed BCI, we make some major conjectures, which need to be experimentally verified, and hence we discuss the controversial parts, feasibility of technology and limitations, and potential impact of this envisaged technology if successfully implemented in the future.
Multilayer heat equations and their solutions via oscillating integral transforms
Authors: Andrey Itkin, Alexander Lipton, Dmitry Muravey
Date: 2021-12-02 00:00:00

By expanding the Dirac delta function in terms of the eigenfunctions of the corresponding Sturm-Liouville problem, we construct some new (oscillating) integral transforms. These transforms are then used to solve various finance, physics, and mathematics problems, which could be characterized by the existence of a multilayer spatial structure and moving (time-dependent) boundaries (internal interfaces) between the layers. Thus, constructed solutions are semi-analytical and extend the authors' previous work (Itkin, Lipton, Muravey, Multilayer heat equations: application to finance, FMF, 1, 2021). However, our new method doesn't duplicate the previous one but provides alternative representations of the solution which have different properties and serve other purposes.
Combining Global and Local Attention with Positional Encoding for Video Summarization
Authors: Evlampios Apostolidis, Georgios Balaouras, Vasileios Mezaris, Ioannis Patras
Date: 2021-12-01 00:00:00
Proceeding: IEEE International Symposium on Multimedia (ISM) 2021 12
Tasks: Supervised Video Summarization, Video Summarization

This paper presents a new method for supervised video summarization. To overcome drawbacks of existing RNN-based summarization architectures, that relate to the modeling of long-range frames' dependencies and the ability to parallelize the training process, the developed model relies on the use of self-attention mechanisms to estimate the importance of video frames. Contrary to previous attention-based summarization approaches that model the frames' dependencies by observing the entire frame sequence, our method combines global and local multi-head attention mechanisms to discover different modelings of the frames' dependencies at different levels of granularity. Moreover, the utilized attention mechanisms integrate a component that encodes the temporal position of video frames - this is of major importance when producing a video summary. Experiments on two datasets (SumMe and TVSum) demonstrate the effectiveness of the proposed model compared to existing attention-based methods, and its competitiveness against other state-of-the-art supervised summarization approaches. An ablation study that focuses on our main proposed components, namely the use of global and local multi-head attention mechanisms in collaboration with an absolute positional encoding component, shows their relative contributions to the overall summarization performance.
Simple Alternatives to the Common Correlated Effects Model
Authors: Nicholas L. Brown, Peter Schmidt, Jeffrey M. Wooldridge
Date: 2021-12-02 00:00:00
Tasks: model

We study estimation of factor models in a fixed-T panel data setting and significantly relax the common correlated effects (CCE) assumptions pioneered by Pesaran (2006) and used in dozens of papers since. In the simplest case, we model the unobserved factors as functions of the cross-sectional averages of the explanatory variables and show that this is implied by Pesaran's assumptions when the number of factors does not exceed the number of explanatory variables. Our approach allows discrete explanatory variables and flexible functional forms in the covariates. Plus, it extends to a framework that easily incorporates general functions of cross-sectional moments, in addition to heterogeneous intercepts and time trends. Our proposed estimators include Pesaran's pooled correlated common effects (CCEP) estimator as a special case. We also show that in the presence of heterogeneous slopes our estimator is consistent under assumptions much weaker than those previously used. We derive the fixed-T asymptotic normality of a general estimator and show how to adjust for estimation of the population moments in the factor loading equation.
RIF Regression via Sensitivity Curves
Authors: Javier Alejo, Gabriel Montes-Rojas, Walter Sosa-Escudero
Date: 2021-12-02 00:00:00
Tasks: regression, Sensitivity

This paper proposes an empirical method to implement the recentered influence function (RIF) regression of Firpo, Fortin and Lemieux (2009), a relevant method to study the effect of covariates on many statistics beyond the mean. In empirically relevant situations where the influence function is not available or difficult to compute, we suggest to use the \emph{sensitivity curve} (Tukey, 1977) as a feasible alternative. This may be computationally cumbersome when the sample size is large. The relevance of the proposed strategy derives from the fact that, under general conditions, the sensitivity curve converges in probability to the influence function. In order to save computational time we propose to use a cubic splines non-parametric method for a random subsample and then to interpolate to the rest of the cases where it was not computed. Monte Carlo simulations show good finite sample properties. We illustrate the proposed estimator with an application to the polarization index of Duclos, Esteban and Ray (2004).
Can Education Motivate Individual Health Demands? Dynamic Pseudo-panel Evidence from China's Immigration
Authors: Shixi Kang, Jingwen Tan
Date: 2021-12-02 00:00:00
Tasks: Decision Making

Enhancing residents' willingness to participate in basic health services is a key initiative to optimize the allocation of health care resources and promote equitable improvements in group health. This paper investigates the effect of education on resident health record completion rates using a system GMM model based on pseudo-panel that consisting of five-year cross-sectional data. To mitigate possible endogeneity, this paper controls for cohort effects while also attenuating dynamic bias in the estimation from a dynamic perspective and provides robust estimates based on multi-model regression. The results show that (1) education can give positive returns on health needs to the mobile population under the static perspective, and such returns are underestimated when cohort effects are ignored; (2) there is a significant cumulative effect of file completion rate under the dynamic perspective, and file completion in previous years will have a positive effect on the current year. (3)The positive relationship between education and willingness to make health decisions is also characterized by heterogeneity by gender, generation, and education level itself. Among them, education is more likely to promote decision-making intentions among men and younger groups, and this motivational effect is more significant among those who received basic education.
Directional Lifting Wavelet Transform for Image Edge Analysis
Authors: Kensuke Fujinoki, Keita Ashizawa
Date: 2021-12-02 00:00:00
Tasks: Edge Detection

In this paper, we propose a new two-dimensional directional discrete wavelet transform that can decompose an image into 12 multiscale directional edge components. The proposed transform is designed in a fully discrete setting and thus is easy to implement in actual computations. The proposed transform is viewed as a category of redundant discrete wavelet transforms implemented by fast in-place computational algorithms by a lifting scheme that has been modified to incorporate redundancy. The redundancy is limited to $(N \times J+1)/4$, where $N=12$ is the directional selectivity and $J$ is a decomposition level of the multiscale transform. Numerical experiments in edge detection using various images demonstrate the advantages of the proposed method over some conventional standard methods. The proposed method outperforms several conventional edge detection methods in identifying both the location and orientation of edges, and well captures the directional and geometrical features of images.
Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning
Authors: Guiqiu Liao, Oscar Caravaca-Mora, Benoit Rosa, Philippe Zanne, Alexandre Asch, Diego Dall Alba, Paolo Fiorini, Michel de Mathelin, Florent Nageotte, Michalina J. Gora
Date: 2021-12-02 00:00:00

Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning. By improving the scanning process, this instability can be partially reduced. To further correct the rotational distortion in the OCT image, a volumetric data stabilization algorithm is proposed. The algorithm first estimates the Non-Uniform Rotational Distortion (NURD) for each B-scan by using a Convolutional Neural Network (CNN). A correlation map between two successive B-scans is computed and provided as input to the CNN. To solve the problem of accumulative error in iterative frame stream processing, we deploy an overall rotation estimation between reference orientation and actual OCT image orientation. We train the network with synthetic OCT videos by intentionally adding rotational distortion into real OCT images. As part of this article we discuss the proposed method in two different scanning modes: the first is a conventional pullback mode where the optical components move along the protection sheath, and the second is a self-designed scanning mode where the catheter is globally translated by using an external actuator. The efficiency and robustness of the proposed method are evaluated with synthetic scans as well as real scans under two scanning modes.
Ghost Imaging Based on Recurrent Neural Network
Authors: Yuchen He, Sihong Duan, Jianxing Li, Hui Chen, Huaibin Zheng, Jianbin Liu, Yu Zhou, Zhuo Xu
Date: 2021-12-01 00:00:00
Tasks: compressed sensing, Image Reconstruction

Benefit from the promising features of second-order correlation, ghost imaging (GI) has received extensive attentions in recent years. Simultaneously, GI is affected by the poor trade-off between sampling rate and imaging quality. The traditional image reconstruction method in GI is to accumulate the action result of each speckle and the corresponding bucket signal. We found that the image reconstruction process of GI is very similar to the Recurrent Neural Network (RNN), which is one of the deep learning algorithm. In this paper, we proposed a novel method that effectively implements GI on the RNN architecture, called GI-RNN. The state of each layer in RNN is determined by the output of the previous layer and the input of this layer, and the output of the network is the sum of all previous states. Therefore, we take the speckle of each illumination and the corresponding bucket signal as the input of each layer, and the output of the network is the sum of all previous speckle and bucket signal, which is the image of the target. The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0.38$\%$). Moreover, we compare GI-RNN with traditional GI algorithm and compressed sensing algorithm. The results of different targets show that GI-RNN is 6.61 dB higher than compressed sensing algorithm and 12.58 dB higher than traditional GI algorithm on average. In our view, the proposed method makes an important step to applications of GI.
Time-Series Estimation from Randomly Time-Warped Observations
Authors: İlker Bayram
Date: 2021-12-02 00:00:00
Tasks: Time Series, Time Series Analysis

We consider the problem of estimating a signal from its warped observations. Such estimation is commonly performed by altering the observations through some inverse-warping, or solving a computationally demanding optimization formulation. While these may be unavoidable if observations are few, when large amounts of warped observations are available, the cost of running such algorithms can be prohibitive. We consider the scenario where we have many observations, and propose a computationally simple algorithm for estimating the function of interest. We demonstrate the utility of the algorithm on streaming biomedical signals.
A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing
Authors: Zhong Yang, Yaru Fu, Yuanwei Liu, Yue Chen, Junshan Zhang
Date: 2021-12-02 00:00:00
Tasks: Edge-computing

Non-orthogonal multiple access (NOMA) enabled fog radio access networks (NOMA-F-RANs) have been taken as a promising enabler to release network congestion, reduce delivery latency, and improve fog user equipments' (F-UEs') quality of services (QoS). Nevertheless, the effectiveness of NOMA-F-RANs highly relies on the charted feature information (preference distribution, positions, mobilities, etc.) of F-UEs as well as the effective caching, computing, and resource allocation strategies. In this article, we explore how artificial intelligence (AI) techniques are utilized to solve foregoing tremendous challenges. Specifically, we first elaborate on the NOMA-F-RANs architecture, shedding light on the key modules, namely, cooperative caching and cache-aided mobile edge computing (MEC). Then, the potentially applicable AI-driven techniques in solving the principal issues of NOMA-F-RANs are reviewed. Through case studies, we show the efficacy of AI-enabled methods in terms of F-UEs' latent feature extraction and cooperative caching. Finally, future trends of AI-driven NOMA-F-RANs, including open research issues and challenges, are identified.
Wide-Sense Stationarity in Generalized Graph Signal Processing
Authors: Xingchao Jian, Wee Peng Tay
Date: 2021-12-02 00:00:00
Tasks: Denoising

We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. This general model encompasses various signals such as the traditional scalar-valued graph signal, multichannel graph signal, and discrete- and continuous-time graph signals, allowing us to build a unified theory of graph random processes. We introduce the notion of joint wide-sense stationarity in this generalized GSP framework, which allows us to characterize a graph random process as a combination of uncorrelated oscillation modes across both the vertex and Hilbert space domains. We elucidate the relationship between the notions of wide-sense stationarity in different domains, and derive the Wiener filters for denoising and signal completion under this framework. Numerical experiments on both real and synthetic datasets demonstrate the utility of our generalized approach in achieving better estimation performance compared to traditional GSP or the time-vertex framework.
Coded Estimation: Design of Backscatter Array Codes for 3D Orientation Estimation
Authors: Mohamad Rida Rammal, Suhas Diggavi, Ashutosh Sabharwal
Date: 2021-12-01 00:00:00
Tasks: TAG

We consider the problem of estimating the orientation of a 3D object with the assistance of configurable backscatter tags. We explore the idea of designing tag response codes to improve the accuracy of orientation estimation. To minimize the difference between the true and estimated orientation, we propose two code design criteria. We also derive a lower bound on the worst-case error using Le Cam's method and provide simulation results for multiple scenarios including line-of-sight only and multipath, comparing the theoretical bounds to those achieved by the designs.
Analysing high-throughput sequencing data in Python with HTSeq 2.0
Authors: Givanna H Putri, Simon Anders, Paul Theodor Pyl, John E Pimanda, Fabio Zanini
Date: 2021-12-02 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Summary: HTSeq 2.0 provides a more extensive API including a new representation for sparse genomic data, enhancements in htseq-count to suit single cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes, and Python 3 support. Availability and implementation: HTSeq 2.0 is released as an open-source software under the GNU General Public Licence and available from the Python Package Index at https://pypi.python.org/pypi/HTSeq. The source code is available on Github at https://github.com/htseq/htseq. Contact: fabio.zanini@unsw.edu.au
Extraction of diverse gene groups with individual relationship from gene co-expression networks
Authors: Iori Azuma, Tadahaya Mizuno, Hiroyuki Kusuhara
Date: 2021-12-02 00:00:00

Motivation: Modules in gene coexpression networks (GCN) can be regarded as gene groups with individual relationships. No studies have optimized module detection methods to extract diverse gene groups from GCN, especially for data from clinical specimens. Results: Here, we optimized the flow from transcriptome data to gene modules, aiming to cover diverse gene relationships. We found the prediction accuracy of relationships in benchmark networks of non-mammalian was not always suitable for evaluating gene relationships of human and employed network based metrics. We also proposed a module detection method involving a combination of graphical embedding and recursive partitioning, and confirmed its stable and high performance in biological plausibility of gene groupings. Analysis of differentially ex-pressed genes of several reported cancers using the extracted modules successfully added relational information consistent with previous reports, confirming the usefulness of our framework.
Extracting seizure onset from surface EEG with Independent Component Analysis: insights from simultaneous scalp and intracerebral EEG
Authors: Andrei Barborica, Ioana Mindruta, Laurent Sheybani, Laurent Spinelli, Irina Oane, Constantin Pistol, Cristian Donos, Victor J López-Madrona, Serge Vulliemoz, Christian Bénar, Víctor López-Madrona, Christian-George Bénar
Date: 2021-12-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The success of stereoelectroencephalographic (SEEG) investigations depends crucially on the hypotheses on the putative location of the seizure onset zone. This information is derived from non-invasive data either based on visual analysis or advanced source localization algorithms. While source localization applied to interictal spikes recorded on scalp is the classical method, it does not provide unequivocal information regarding the seizure onset zone. Raw ictal activity contains a mixture of signals originating from several regions of the brain as well as EMG artifacts, hampering direct input to the source localization algorithms. We therefore introduce a methodology that disentangles the various sources contributing to the scalp ictal activity using independent component analysis and uses equivalent current dipole localization as putative locus of ictal sources. We validated the results of our analysis pipeline by performing long-term simultaneous scalp -- intracerebral (SEEG) recordings in 14 patients and analyzing the wavelet coherence between the independent component encoding the ictal discharge and the SEEG signals in 8 patients passing the inclusion criteria. Our results show that invasively recorded ictal onset patterns, including low-voltage fast activity, can be captured by the independent component analysis of scalp EEG. The visibility of the ictal activity strongly depends on the depth of the sources. The equivalent current dipole localization can point to the seizure onset zone (SOZ) with an accuracy that can be as high as 10mm for superficially located sources, that gradually decreases for deeper seizure generators, averaging at 47 mm in the 8 analyzed patients. Independent component analysis is therefore shown to have a promising SOZ localizing value, indicating whether the seizure onset zone is neocortical, and its approximate location, or located in mesial structures. That may contribute to a better crafting of the hypotheses used as basis of the stereo-EEG implantations.
Matrix metalloproteinases as new targets in Alzheimer's disease: Opportunities and Challenges
Authors: Pauline Zipfel, Christophe Rochais, Kévin Baranger, Santiago Rivera, Patrick Dallemagne
Date: 2021-12-02 00:00:00

Although matrix metalloproteinases (MMPs) are implicated in the regulation of numerous physiological processes, evidences of their pathological roles have also been obtained in the last decades, making MMPs attractive therapeutic targets for several diseases. Recent discoveries of their involvement in central nervous system (CNS) disorders, and in particular in Alzheimer's disease (AD), have paved the way to consider MMP modulators as promising therapeutic strategies. Over the past few decades, diverse approaches have been undertaken in the design of
Finding Cortical Subregions Regarding the Dorsal Language Pathway Based on the Structural Connectivity
Authors: Young-Eun Hwang, Young-Bo Kim, Young-Don Son
Date: 2021-12-01 00:00:00

Although the language-related fiber pathways in the human brain, such as superior longitudinal fasciculus (SLF) and arcuate fasciculus (AF), are already well-known, understanding more sophisticated cortical regions connected by the fiber tracts is essential to scrutinizing the structural connectivity of language circuits. With the regions of interest that were selected based on the Brainnetome atlas, the fiber orientation distribution estimation method for tractography was used to produce further elaborate connectivity information. The results indicated that both fiber bundles had two distinct connections with the prefrontal corte (PFC). The SLF-II and dorsal AF are mainly connected to the rostrodorsal part of the inferior parietal cortex (IPC) and lateral part of the fusiform gyrus with the inferior frontal junction (IFJ), respectively. In contrast, the SLF-III and ventral AF were primary linked to the anterior part of the supramarginal gyrus and superior part of the temporal cortex with the inferior frontal cortex, including the Broca's area. Moreover, the IFJ in the PFC, which has rarely been emphasized as a language-related subretion, also had the strongest connectivity with the previously known language-related subregions among the PFC; consequently, we proposed that these specific regions are interconnected via the SLF and AF within the PFC, IPC, and temporal cortex as language-related circuitry.