pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting non-linear functional brain co-activations: directed, dynamic and delayed
Authors: Ignacio Cifre, Maria T. Miller Flores, Jeremi K. Ochab, Dante R. Chialvo
Date: 2020-07-30 00:00:00
Tasks: Functional Connectivity

The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In previous work we first demonstrated that the relatively stronger BOLD activations contain most of the information relevant to understand functional connectivity and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this work we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different perspective in the analysis of brain co-activation patterns. In this paper we provide for a proof of concept, based on replicating and completing existing results on an Autism database, to discuss the main features and advantages of the proposed strategy for the study of brain functional correlations. These results show new interpretations of the correlations found on this sample.
Simple RGC: ImageJ plugins for counting retinal ganglion cells and determining the transduction efficiency of viral vectors in retinal wholemounts
Authors: Tiger Cross, Rasika Navarange, Joon-Ho Son, William Burr, Arjun Singh, Kelvin Zhang, Miruna Rusu, Konstantinos Gkoutzis, Andrew Osborne, Bart Nieuwenhuis
Date: 2020-08-14 00:00:00

Simple RGC consists of a collection of ImageJ plugins to assist researchers investigating retinal ganglion cell (RGC) injury models in addition to helping assess the effectiveness of treatments. The first plugin named RGC Counter accurately calculates the total number of RGCs from retinal wholemount images. The second plugin named RGC Transduction measures the co-localisation between two channels making it possible to determine the transduction efficiencies of viral vectors and transgene expression levels. The third plugin named RGC Batch is a batch image processor to deliver fast analysis of large groups of microscope images. These ImageJ plugins make analysis of RGCs in retinal wholemounts easy, quick, consistent, and less prone to unconscious bias by the investigator. The plugins are freely available from the ImageJ update site https://sites.imagej.net/Sonjoonho/.
Systematic Biases in Link Prediction: comparing heuristic and graph embedding based methods
Authors: Aakash Sinha, Rémy Cazabet, Rémi Vaudaine
Date: 2018-10-11 00:00:00
Tasks: Fairness, Graph Embedding, Link Prediction, Prediction, Recommendation Systems

Link prediction is a popular research topic in network analysis. In the last few years, new techniques based on graph embedding have emerged as a powerful alternative to heuristics. In this article, we study the problem of systematic biases in the prediction, and show that some methods based on graph embedding offer less biased results than those based on heuristics, despite reaching lower scores according to usual quality scores. We discuss the relevance of this finding in the context of the filter bubble problem and the algorithmic fairness of recommender systems.
A decade of movement ecology
Authors: Rocío Joo, Simona Picardi, Matthew E. Boone, Thomas A. Clay, Samantha C. Patrick, Vilma S. Romero-Romero, Mathieu Basille
Date: 2020-05-29 00:00:00

Movement is fundamental to life, shaping population dynamics, biodiversity patterns, and ecosystem structure. Recent advances in tracking technology have enabled fundamental questions about movement to be tackled, leading to the development of the movement ecology framework (MEF), considered a milestone in the field [1]. The MEF introduced an integrative theory of organismal movement, linking internal state, motion capacity and navigation capacity to external factors. Here, a decade later, we investigated the current state of research in the field. Using a text mining approach on >8000 peer-reviewed papers in movement ecology, we explored the main research topics, evaluated the impact of the MEF, and assessed changes in the use of technological devices, software and statistical methods. The number of publications has increased considerably and there have been major technological changes in the past decade (i.e.~increased use of GPS devices, accelerometers and video cameras, and a convergence towards R), yet we found that research focuses on the same questions, specifically, on the effect of environmental factors on movement and behavior. In practice, it appears that movement ecology research does not reflect the MEF. We call on researchers to transform the field from technology-driven to embrace interdisciplinary collaboration, in order to reveal key processes underlying movement (e.g.~navigation), as well as evolutionary, physiological and life-history consequences of particular strategies.
Cell type nomenclature for the mammalian brain: Development and application of a systematic, extensible convention
Authors: Jeremy A. Miller, Nathan W. Gouwens, Bosiljka Tasic, Forrest Collman, Cindy T. J. van Velthoven, Trygve E. Bakken, Hongkui Zeng, Ed S. Lein, Amy Bernard
Date: 2020-06-09 00:00:00

The recent advent of single cell RNA-sequencing and other high-throughput technologies has led to an explosion of cell type definitions across multiple organ systems. Consortia like the BRAIN Initiative Cell Census Network (BICCN) and the Human Cell Atlas (HCA) have begun to standardize and centralize the intake of data and associated metadata from these projects; however, the naming and organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies derived from overlapping datasets. To facilitate cross-dataset alignment and comparison, we created a working nomenclature convention for matching and tracking cell types across studies. The convention is qualitatively similar to how gene transcripts are tracked across different versions of GENCODE genome builds, allowing comparison of matched types with a common reference or any other taxonomy. It augments but does not change existing cell type names provided in original publications and can be directly applied to data from new or published studies. We applied this nomenclature to a series of published cell type data from the Allen Institute, highlighting its extensibility to classifications defined using multiple quantifiable modalities. Finally, we propose conventions for assigning accurate yet flexible cell types names in the mammalian cortex as a step towards a much larger community-wide effort to organize multi-source, data-driven information related to cell types. The convention presented here can be easily applied to cell type taxonomies from any organ system or organism.
Dynamic change of gene-to-gene regulatory networks in response to SARS-CoV-2 infection
Authors: Yoshihisa Tanaka, Kako Higashihara, Mai Adachi Nakazawa, Fumiyoshi Yamashita, Yoshinori Tamada, Yasushi Okuno
Date: 2020-08-21 00:00:00

The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable for an understanding of how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effect of SARS-CoV-2 infection on human host cells. Here, we have presented gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes of the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, revealing that interferon signaling gradually switches to the subsequent inflammatory-cytokine signaling cascades. Furthermore, we have succeeded in capturing a COVID-19 patient-specific network in which transduction of these signalings is coincidently induced. This enabled us to explore local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insight into SARS-CoV-2 research from the perspective of cellular systems.
Safe-by-Design Control for Euler-Lagrange Systems
Authors: Wenceslao Shaw Cortez, Dimos V. Dimarogonas
Date: 2020-09-08 00:00:00
Tasks: Position

Safety-critical control is characterized as ensuring constraint satisfaction for a given dynamical system. Recent developments in zeroing control barrier functions (ZCBFs) have provided a framework for ensuring safety of a superlevel set of a single constraint function. Euler-Lagrange systems represent many real-world systems including robots and vehicles, which must abide by safety-regulations, especially for use in human-occupied environments. These safety regulations include state constraints (position and velocity) and input constraints that must be respected at all times. ZCBFs are valuable for satisfying system constraints for general nonlinear systems, however their construction to satisfy state and input constraints is not straightforward. Furthermore, the existing barrier function methods do not address the multiple state constraints that are required for safety of Euler-Lagrange systems. In this paper, we propose a methodology to construct multiple, non-conflicting control barrier functions for Euler-Lagrange systems subject to input constraints to satisfy safety regulations, while concurrently taking into account robustness margins and sampling-time effects. The proposed approach consists of a sampled-data controller and an algorithm for barrier function construction to enforce safety (i.e satisfy position and velocity constraints). The proposed method is validated in simulation on a 2-DOF planar manipulator.
Spectral Method for Multiplexed Phase Retrieval and Application in Optical Imaging in Complex Media
Authors: Jonathan Dong, Florent Krzakala, Sylvain Gigan
Date: 2018-10-30 00:00:00
Tasks: Retrieval

We introduce a generalized version of phase retrieval called multiplexed phase retrieval. We want to recover the phase of amplitude-only measurements from linear combinations of them. This corresponds to the case in which multiple incoherent sources are sampled jointly, and one would like to recover their individual contributions. We show that a recent spectral method developed for phase retrieval can be generalized to this setting, and that its performance follows a phase transition behavior. We apply this new technique to light focusing at depth in a complex medium. Experimentally, although we only have access to the sum of the intensities on multiple targets, we are able to separately focus on each ones, thus opening potential applications in deep fluorescence imaging and light delivery
Dual Attention in Time and Frequency Domain for Voice Activity Detection
Authors: Joohyung Lee, Youngmoon Jung, Hoirin Kim
Date: 2020-03-27 00:00:00
Tasks: Action Detection, Activity Detection

Voice activity detection (VAD) is a challenging task in low signal-to-noise ratio (SNR) environment, especially in non-stationary noise. To deal with this issue, we propose a novel attention module that can be integrated in Long Short-Term Memory (LSTM). Our proposed attention module refines each LSTM layer's hidden states so as to make it possible to adaptively focus on both time and frequency domain. Experiments are conducted on various noisy conditions using Aurora 4 database. Our proposed method obtains the 95.58 % area under the ROC curve (AUC), achieving 22.05 % relative improvement compared to baseline, with only 2.44 % increase in the number of parameters. Besides, we utilize focal loss for alleviating the performance degradation caused by imbalance between speech and non-speech sections in training sets. The results show that the focal loss can improve the performance in various imbalance situations compared to the cross entropy loss, a commonly used loss function in VAD.
SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm
Authors: Muhammad Saif-ur-Rehman, Omair Ali, Robin Lienkaemper, Sussane Dyck, Marita Metzler, Yaroslav Parpaley, Joerg Wellmer, Charles Liu, Brian Lee, Spencer Kellis, Richard Andersen, Ioannis Iossifidis, Tobias Glasmachers, Christian Klaes
Date: 2019-12-23 00:00:00
Tasks: Clustering, Spike Sorting

Objective. Recent advancements in electrode designs and micro-fabrication technology has allowed existence of microelectrode arrays with hundreds of channels for single-cell recordings. In such electrophysiological recordings, each implanted micro-electrode can record the activities of more than one neuron in its vicinity. Recording the activities of multiple neurons may also be referred to as multiple unit activity. However, for any further analysis, the main goal is to isolate the activity of each recorded neuron and thus called single-unit activity. This process may also be referred to as spike sorting or spike classification. Recent approaches to extract SUA are time consuming, mainly due to the requirement of human intervention at various stages of spike sorting pipeline. Lack of standardization is another drawback of the current available approaches. Therefore, in this study we proposed a standard spike sorter: SpikeDeep-Classifier, a fully automatic spike sorting algorithm. Approach. We proposed a novel spike sorting pipeline, based on a set of supervised and unsupervised learning algorithms. We used supervised, deep learning-based algorithms for extracting meaningful channels and removing background activities (noise) from the extracted channels. We also showed that the process of clustering becomes straight-forward, once the noise/artifact is completely removed from the data. Therefore, in the next stage, we applied a simple clustering algorithm (K-mean) with predefined maximum number of clusters. Lastly, we used a similarity-based criterion to keep distinct clusters and merge similar-looking clusters. Main results. We evaluated our algorithm on a dataset collected from two different species (humans and non-human primates (NHPs)) without any retraining. We also validated our algorithm on two publicly available labeled datasets.
Reconfiguring motor circuits for a joint manual and BCI task
Authors: Benjamin Lansdell, Ivana Milovanovic, Cooper Mellema, Eberhard E Fetz, Adrienne L Fairhall, Chet T Moritz
Date: 2017-02-23 00:00:00
Tasks: Brain Computer Interface

Designing brain-computer interfaces (BCIs) that can be used in conjunction with ongoing motor behavior requires an understanding of how neural activity co-opted for brain control interacts with existing neural circuits. For example, BCIs may be used to regain lost motor function after stroke. This requires that neural activity controlling unaffected limbs is dissociated from activity controlling the BCI. In this study we investigated how primary motor cortex accomplishes simultaneous BCI control and motor control in a task that explicitly required both activities to be driven from the same brain region (i.e. a dual-control task). Single-unit activity was recorded from intracortical, multi-electrode arrays while a non-human primate performed this dual-control task. Compared to activity observed during naturalistic motor control, we found that both units used to drive the BCI directly (control units) and units that did not directly control the BCI (non-control units) significantly changed their tuning to wrist torque. Using a measure of effective connectivity, we observed that control units decrease their connectivity. Through an analysis of variance we found that the intrinsic variability of the control units has a significant effect on task proficiency. When this variance is accounted for, motor cortical activity is flexible enough to perform novel BCI tasks that require active decoupling of natural associations to wrist motion. This study provides insight into the neural activity that enables a dual-control brain-computer interface.
Reforming the State-Based Forward Guidance through Wage Growth Rate Threshold: Evidence from FRB/US Simulations
Authors: Sudiksha Joshi
Date: 2020-08-19 00:00:00
Tasks: Time Series, Time Series Analysis

I have analyzed the practicality of the Evans Rule in the state based forward guidance and possible ways to reform it. I examined the biases, measurement errors, and other limitations extant in the unemployment and the inflation rate in the Evans Rule. Using time series analysis, I calibrated the thresholds of ECI wage growth and the employment to population ratio and investigated the relationship between other labor utilization variables. Then I imposed various shocks and constructed impulse response functions to contrast the paths of eight macroeconomic variables under three scenarios. The results suggest that under the wage growth rate scenario, the federal funds rate lift off earlier than under the current Evans Rule.
Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks
Authors: Shahrokh Abbasi-Rad, Kieran O'Brien, Samuel Kelly, Viktor Vegh, Anders Rodell, Yasvir Tesiram, Jin Jin, Markus Barth, Steffen Bollmann
Date: 2019-11-19 00:00:00

Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pulse power and scaling the amplitude in a slice-wise fashion. Methods: We used a TR-FOCI adiabatic pulse for spin inversion in a T2-FLAIR sequence to improve B1+ homogeneity and calculate the pulse power required for adiabaticity slice-by-slice to minimize the SAR. Drawing on the implicit B1+ inhomogeneity present in a standard localizer scan, 3D AutoAlign localizers and SA2RAGE B1+ maps were acquired in eight volunteers. A convolutional neural network (CNN) was then trained to predict the B1+ profile from the localizers and scale factors for the pulse power for each slice were calculated. The ability to predict the B1+ profile as well as how the derived pulse scale factors affected the FLAIR inversion efficiency were assessed in transverse, sagittal, and coronal orientations. Results: The predicted B1+ maps matched the measured B1+ maps with a mean difference of 4.45% across all slices. The acquisition in the transverse orientation was shown to be most effective for this method and delivered a 40% reduction in SAR along with 1min and 30-sec reduction in scan time (28%) without degradation of image quality. Conclusion: We propose a SAR reduction technique based on the prediction of B1+ profiles from standard localizer scans using a CNN and show that scaling the inversion pulse power slice-by-slice for FLAIR sequences at 7T reduces SAR and scan time without compromising image quality.
Evolution of the 21 cm signal throughout cosmic history
Authors: Jonathan R. Pritchard, Abraham Loeb
Date: 2008-02-15 00:00:00

The potential use of the redshifted 21 cm line from neutral hydrogen for probing the epoch of reionization is motivating the construction of several low-frequency interferometers. There is also much interest in the possibility of constraining the initial conditions from inflation and the nature of the dark matter and dark energy by probing the power-spectrum of density perturbations in three dimensions and on smaller scales than probed by the microwave background anisotropies. Theoretical understanding of the 21 cm signal has been fragmented into different regimes of physical interest. In this paper, we make the first attempt to describe the full redshift evolution of the 21 cm signal between 0<z<300. We include contributions to the 21 cm signal from fluctuations in the gas density, temperature and neutral fraction, as well as the Lyman alpha flux, and allow for a post-reionization signal from damped Ly alpha systems. Our comprehensive analysis provides a useful foundation for optimizing the design of future arrays whose goal is to separate the particle physics from the astrophysics, either by probing the peculiar velocity distortion of the 21 cm power spectrum, or by extending the 21 cm horizon to z > 25 before the first galaxies had formed, or to z < 6 when the residual pockets of hydrogen trace large scale structure.
Speaker Diarization with Region Proposal Network
Authors: Zili Huang, Shinji Watanabe, Yusuke Fujita, Paola Garcia, Yiwen Shao, Daniel Povey, Sanjeev Khudanpur
Date: 2020-02-14 00:00:00
Tasks: Region Proposal, speaker-diarization, Speaker Diarization

Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem. Although the standard diarization systems can achieve satisfactory results in various scenarios, they are composed of several independently-optimized modules and cannot deal with the overlapped speech. In this paper, we propose a novel speaker diarization method: Region Proposal Network based Speaker Diarization (RPNSD). In this method, a neural network generates overlapped speech segment proposals, and compute their speaker embeddings at the same time. Compared with standard diarization systems, RPNSD has a shorter pipeline and can handle the overlapped speech. Experimental results on three diarization datasets reveal that RPNSD achieves remarkable improvements over the state-of-the-art x-vector baseline.
Trends in bird abundance differ among protected forests but not bird guilds
Authors: Jeffrey W. Doser, Aaron S. Weed, Elise F. Zipkin, Kathryn M. Miller, Andrew O. Finley
Date: 2020-08-27 00:00:00
Tasks: Management

Improved monitoring and associated inferential tools to efficiently identify declining bird populations, particularly of rare or sparsely distributed species, is key to informed conservation and management across large spatio-temporal regions. We assess abundance trends for 106 bird species in a network of eight national park forests located within the northeast USA from 2006-2019 using a novel hierarchical model. We develop a multi-species, multi-region removal sampling model that shares information across species and parks to enable inference on rare species and sparsely sampled parks and to evaluate the effects of local forest structure. Trends in bird abundance over time varied widely across parks, but species showed similar trends within parks. Three parks (Acadia, Marsh-Billings-Rockefeller, and Morristown) decreased in bird abundance across all species, while three parks (Saratoga, Roosevelt-Vanderbilt, and Weir-Farm) increased in abundance. Bird abundance peaked at medium levels of basal area and high levels of percent forest and forest regeneration, with percent forest having the largest effect. Variation in these effects across parks could be a result of differences in forest structural stage and diversity. Our novel hierarchical model enables estimates of abundance at the network, park, guild, and species levels. We found large variation in abundance trends across parks but not across bird guilds, suggesting that local forest condition may have a broad and consistent effect on the entire bird community within a given park. Management should target the three parks with overall decreasing trends in bird abundance to further identify what specific factors are driving observed declines across the bird community. Understanding how bird communities respond to local forest structure and other stressors is crucial for informed and lasting management.