pwc-archive/papers-with-abstracts huggingface dataset:
Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics
Authors: Katherine Benjamin, Aneesha Bhandari, Zhouchun Shang, Yanan Xing, Yanru An, Nannan Zhang, Yong Hou, Ulrike Tillmann, Katherine R. Bull, Heather A. Harrington
Date: 2022-12-13 00:00:00
Tasks: Topological Data Analysis

Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with immunofluorescent imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.
Effects of anterior temporal lobe resection on cortical morphology
Authors: Karoline Leiberg, Jane de Tisi, John S Duncan, Bethany Little, Peter N Taylor, Sjoerd B Vos, Gavin P Winston, Bruno Mota, Yujiang Wang
Date: 2022-12-13 00:00:00

Anterior temporal lobe resection (ATLR) is a surgical procedure to treat drug-resistant temporal lobe epilepsy (TLE). Resection may involve large amounts of cortical tissue. Here, we examine the effects of this surgery on cortical morphology measured in independent variables both near the resection and remotely. We studied 101 individuals with TLE (55 left, 46 right onset) who underwent ATLR. For each individual we considered one pre-surgical MRI and one follow-up MRI 2 to 13 months after surgery. We used our newly developed surface-based method to locally compute traditional morphological variables (average cortical thickness, exposed surface area, and total surface area), and the independent measures $K$, $I$, and $S$, where $K$ measures white matter tension, $I$ captures isometric scaling, and $S$ contains the remaining information about cortical shape. Data from 924 healthy controls was included to account for healthy ageing effects occurring during scans. A SurfStat random field theory clustering approach assessed changes across the cortex caused by ATLR. Compared to preoperative data, surgery had marked effects on all morphological measures. Ipsilateral effects were located in the orbitofrontal and inferior frontal gyri, the pre- and postcentral gyri and supramarginal gyrus, and the lateral occipital gyrus and lingual cortex. Contralateral effects were in the lateral occipital gyrus, and inferior frontal gyrus and frontal pole. The restructuring following ATLR is reflected in widespread morphological changes, mainly in regions near the resection, but also remotely in regions that are structurally connected to the anterior temporal lobe. The causes could include mechanical effects, Wallerian degeneration, or compensatory plasticity. The study of independent measures revealed additional effects compared to traditional measures.
Temporal dynamics of subjective sleepiness: A convergence analysis of two scales
Authors: Valeriia Demareva, Valeriia Viakhireva, Irina Zayceva, Inna Isakova, Yana Okhrimchuk, Karina Zueva, Andrey Demarev, Marina Zhukova, Nikolay Nazarov, Julia Edelevaa
Date: 2022-12-13 00:00:00
Tasks: Vocal Bursts Valence Prediction

While sleepiness assessment metrics were initially developed in medical research to study the effects of drugs on sleep, subjective sleepiness assessment is now widely used in both fundamental and applied studies. The Stanford Sleepiness Scale (SSS) and the Karolinska Sleepiness Scale (KSS) are often considered the gold standard in sleepiness research. However, only a few studies have applied both scales, and their convergence and specific features have not been sufficiently investigated. The present study aims to analyse the dynamics and convergence of subjective sleepiness as measured by the KSS and SSS in a population of adults. To achieve this, we present the Subjective Sleepiness Dynamics Dataset (SSDD), which collects evening and morning data on situational subjective sleepiness. A total of 208 adults participated in the experiment. Our findings suggest that sleepiness generally increased from the evening till night and was highest early in the morning. The SSS score appeared to be more sensitive to certain factors, such as the presence of a sleep disorder. The SSS and KSS scores strongly correlated with each other and converged on sleepiness assessment. However, the KSS showed a more even distribution of scores than the SSS. Currently, we are continuously expanding the SSDD.
Tangent functional connectomes uncover more unique phenotypic traits
Authors: Kausar Abbas, Mintao Liu, Michael Wang, Duy Duong-Tran, Uttara Tipnis, Enrico Amico, Alan D. Kaplan, Mario Dzemidzic, David Kareken, Beau M. Ances, Jaroslaw Harezlak, Joaquín Goñi
Date: 2022-12-13 00:00:00

Functional connectomes (FCs) contain pairwise estimations of functional couplings based on pairs of brain regions activity. FCs are commonly represented as correlation matrices that are symmetric positive definite (SPD) lying on or inside the SPD manifold. Since the geometry on the SPD manifold is non-Euclidean, the inter-related entries of FCs undermine the use of Euclidean-based distances. By projecting FCs into a tangent space, we can obtain tangent functional connectomes (tangent-FCs). Tangent-FCs have shown a higher predictive power of behavior and cognition, but no studies have evaluated the effect of such projections with respect to fingerprinting. We hypothesize that tangent-FCs have a higher fingerprint than regular FCs. Fingerprinting was measured by identification rates (ID rates) on test-retest FCs as well as on monozygotic and dizygotic twins. Our results showed that identification rates are systematically higher when using tangent-FCs. Specifically, we found: (i) Riemann and log-Euclidean matrix references systematically led to higher ID rates. (ii) In tangent-FCs, Main-diagonal regularization prior to tangent space projection was critical for ID rate when using Euclidean distance, whereas barely affected ID rates when using correlation distance. (iii) ID rates were dependent on condition and fMRI scan length. (iv) Parcellation granularity was key for ID rates in FCs, as well as in tangent-FCs with fixed regularization, whereas optimal regularization of tangent-FCs mostly removed this effect. (v) Correlation distance in tangent-FCs outperformed any other configuration of distance on FCs or on tangent-FCs across the fingerprint gradient (here sampled by assessing test-retest, Monozygotic and Dizygotic twins). (vi)ID rates tended to be higher in task scans compared to resting-state scans when accounting for fMRI scan length.
A Tribute to Phil Bourne -- Scientist and Human
Authors: Cameron Mura, Emma Candelier, Lei Xie
Date: 2022-12-08 00:00:00
Tasks: ARC

This Special Issue of Biomolecules, commissioned in honor of Dr. Philip E. Bourne, focuses on a new field of biomolecular data science. In this brief retrospective, we consider the arc of Bourne's 40-year scientific and professional career, particularly as it relates to the origins of this new field.
A Dynamic Theory of the Area of Distribution
Authors: Jorge Soberón, Luis Osorio-Olvera
Date: 2022-12-13 00:00:00

Aims To propose and analyze a general, dynamic, process-oriented theory of the area of distribution. Methods The area of distribution is modelled by combining (by multiplication) three matrices: one matrix represents movements, another niche tolerances, and a third, biotic interactions. Results are derived from general properties of this product and from simulation of a cellular automaton defined in terms of the matrix operations. Everything is implemented practically in an R package. Results Results are obtained by simulation and by mathematical analysis. We show that the mid-domain effect is a direct consequence of dispersal; that to include movements to Ecological Niche Modeling significantly affects results, but cannot be done without choosing an ancestral area of distribution. We discuss ways of estimating such ancestral areas. We show that, in our approach, movements and niche effects are mixed in ways almost impossible to disentangle, and show this is a consequence of the singularity of a matrix. We introduce a tool (the Connectivity-Suitability-Dispersal plot) to extend the results of simple niche modeling to understand the effects of dispersal. Main conclusions The conceptually straightforward scheme we present for the area of distribution integrates, in a mathematically sound and computationally feasible way, several key ideas in biogeography: the geographic and environmental matrix, the Grinnellian niche, dispersal capacity and the ancestral area of origin of groups of species. We show that although full simulations are indispensable to obtain the dynamics of an area of distribution, interesting results can be derived simply by analyzing the matrices representing the dynamics.
Xylem embolism and bubble formation during freezing suggest complex dynamics of pressure-tension in Betula pendula stems
Authors: Katline Charra-Vaskou, Anna Lintunen, Thierry Améglio, Eric Badel, Hervé Cochard, Stefan Mayr, Yann Salmon, Heikki Suhonen, Mahaut van Rooij, Guillaume Charrier
Date: 2022-12-13 00:00:00

Freeze-thaw-induced embolism is a key limiting factor for perennial plants in frost-exposed environments. Gas bubbles are formed during freezing and expand during thawing resulting in xylem embolism. However, when water freezes, its volume increases by 9%, generating local pressures, which can affect the formation of bubbles. To characterize local dynamic of pressure-tension and physical state of the sap during freeze-thaw cycles, we simultaneously used ultrasonic acoustic emissions analysis and synchrotron-based high resolution computed tomography on the diffuse-porous species Betula pendula. Visualization of individual air-filled vessels was performed to measure freeze-thaw induced embolism after successive freeze-thaw cycles down to -10{\deg}C or -20{\deg}C during the leafy and the leafless periods. We also measured the distribution of gas bubbles in frozen xylem of Betula pendula, and made additional continuous monitoring of embolism spreading during one freeze-thaw cycle using a dedicated cooling system that allowed X-ray scanning during freezing and thawing. Experiments confirmed that ultrasonic emissions occurred after the onset of ice formation, together with bubble formation, whereas the development of embolism took place after thawing in all cases. The pictures of frozen tissues indicated that the positive pressure induced by the volumetric increase of ice can provoke inward flow from the cell wall toward the lumen of the vessels. We found no evidence that wider vessels within a tissue were more prone to embolism although the occurrence of gas bubbles in larger conduits would make them prone to earlier embolism. These results highlight the need to monitor local pressure as well as ice and air distribution during xylem freezing to understand the mechanism leading to frost-induced embolism.
Profit and loss decomposition in continuous time and approximations
Authors: Gero Junike, Hauke Stier, Marcus C. Christiansen
Date: 2022-12-13 00:00:00

Financial institutions and insurance companies that analyze the evolution and sources of profits and losses often look at risk factors only at discrete reporting dates, ignoring the detailed paths. Continuous-time decompositions avoid this weakness and also make decompositions consistent across different reporting grids. We construct a large class of continuous-time decompositions from a new extended version of It\^o's formula and uniquely identify a preferred decomposition from the axioms of exactness, symmetry and normalization. This unique decomposition turns out to be a stochastic limit of recursive Shapley values, but it suffers from a curse of dimensionality as the number of risk factors increases. We develop an approximation that breaks this curse when the risk factors almost surely have no simultaneous jumps.
On LASSO for High Dimensional Predictive Regression
Authors: Ziwei Mei, Zhentao Shi
Date: 2022-12-14 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

This paper examines LASSO, a widely-used $L_{1}$-penalized regression method, in high dimensional linear predictive regressions, particularly when the number of potential predictors exceeds the sample size and numerous unit root regressors are present. The consistency of LASSO is contingent upon two key components: the deviation bound of the cross product of the regressors and the error term, and the restricted eigenvalue of the Gram matrix. We present new probabilistic bounds for these components, suggesting that LASSO's rates of convergence are different from those typically observed in cross-sectional cases. When applied to a mixture of stationary, nonstationary, and cointegrated predictors, LASSO maintains its asymptotic guarantee if predictors are scale-standardized. Leveraging machine learning and macroeconomic domain expertise, LASSO demonstrates strong performance in forecasting the unemployment rate, as evidenced by its application to the FRED-MD database.
ECON: Explicit Clothed humans Optimized via Normal integration
Authors: Yuliang Xiu, Jinlong Yang, Xu Cao, Dimitrios Tzionas, Michael J. Black
Date: 2022-12-14 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Human Reconstruction, Surface Reconstruction

The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is enabling the creation of detailed, clothed, 3D humans from images. However, existing methods are far from perfect. IF-based methods recover free-form geometry, but produce disembodied limbs or degenerate shapes for novel poses or clothes. To increase robustness for these cases, existing work uses an explicit parametric body model to constrain surface reconstruction, but this limits the recovery of free-form surfaces such as loose clothing that deviates from the body. What we want is a method that combines the best properties of implicit representation and explicit body regularization. To this end, we make two key observations: (1) current networks are better at inferring detailed 2D maps than full-3D surfaces, and (2) a parametric model can be seen as a "canvas" for stitching together detailed surface patches. Based on these, our method, ECON, has three main steps: (1) It infers detailed 2D normal maps for the front and back side of a clothed person. (2) From these, it recovers 2.5D front and back surfaces, called d-BiNI, that are equally detailed, yet incomplete, and registers these w.r.t. each other with the help of a SMPL-X body mesh recovered from the image. (3) It "inpaints" the missing geometry between d-BiNI surfaces. If the face and hands are noisy, they can optionally be replaced with the ones of SMPL-X. As a result, ECON infers high-fidelity 3D humans even in loose clothes and challenging poses. This goes beyond previous methods, according to the quantitative evaluation on the CAPE and Renderpeople datasets. Perceptual studies also show that ECON's perceived realism is better by a large margin. Code and models are available for research purposes at econ.is.tue.mpg.de
Domain Generalization by Learning and Removing Domain-specific Features
Authors: Yu Ding, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, Fang Chen
Date: 2022-12-14 00:00:00
Proceeding: Advances in Neural Information Processing Systems 2022 12
Tasks: Decoder, Domain Generalization, image-classification, Image Classification

Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a model that can generalize to unseen domains. In this paper, we propose a new approach that aims to explicitly remove domain-specific features for domain generalization. Following this approach, we propose a novel framework called Learning and Removing Domain-specific features for Generalization (LRDG) that learns a domain-invariant model by tactically removing domain-specific features from the input images. Specifically, we design a classifier to effectively learn the domain-specific features for each source domain, respectively. We then develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed. With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification. Extensive experiments demonstrate that our framework achieves superior performance compared with state-of-the-art methods.
RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging
Authors: Heng Li, Zhaopeng Cui, Shuaicheng Liu, Ping Tan
Date: 2022-12-14 00:00:00
Proceeding: CVPR 2022 1

This paper proposes a deep recurrent Rotation Averaging Graph Optimizer (RAGO) for Multiple Rotation Averaging (MRA). Conventional optimization-based methods usually fail to produce accurate results due to corrupted and noisy relative measurements. Recent learning-based approaches regard MRA as a regression problem, while these methods are sensitive to initialization due to the gauge freedom problem. To handle these problems, we propose a learnable iterative graph optimizer minimizing a gauge-invariant cost function with an edge rectification strategy to mitigate the effect of inaccurate measurements. Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function. Besides, our approach iteratively rectifies relative rotations to make them more consistent with the current camera orientations and observed relative rotations. Furthermore, we employ a gated recurrent unit to improve the result by tracing the temporal information of the cost graph. Our framework is a real-time learning-to-optimize rotation averaging graph optimizer with a tiny size deployed for real-world applications. RAGO outperforms previous traditional and deep methods on real-world and synthetic datasets. The code is available at https://github.com/sfu-gruvi-3dv/RAGO
Self-Supervised Geometry-Aware Encoder for Style-Based 3D GAN Inversion
Authors: Yushi Lan, Xuyi Meng, Shuai Yang, Chen Change Loy, Bo Dai
Date: 2022-12-14 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Face Reconstruction, Face Reconstruction

StyleGAN has achieved great progress in 2D face reconstruction and semantic editing via image inversion and latent editing. While studies over extending 2D StyleGAN to 3D faces have emerged, a corresponding generic 3D GAN inversion framework is still missing, limiting the applications of 3D face reconstruction and semantic editing. In this paper, we study the challenging problem of 3D GAN inversion where a latent code is predicted given a single face image to faithfully recover its 3D shapes and detailed textures. The problem is ill-posed: innumerable compositions of shape and texture could be rendered to the current image. Furthermore, with the limited capacity of a global latent code, 2D inversion methods cannot preserve faithful shape and texture at the same time when applied to 3D models. To solve this problem, we devise an effective self-training scheme to constrain the learning of inversion. The learning is done efficiently without any real-world 2D-3D training pairs but proxy samples generated from a 3D GAN. In addition, apart from a global latent code that captures the coarse shape and texture information, we augment the generation network with a local branch, where pixel-aligned features are added to faithfully reconstruct face details. We further consider a new pipeline to perform 3D view-consistent editing. Extensive experiments show that our method outperforms state-of-the-art inversion methods in both shape and texture reconstruction quality. Code and data will be released.
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
Authors: Jennifer J. Sun, Lili Karashchuk, Amil Dravid, Serim Ryou, Sonia Fereidooni, John Tuthill, Aggelos Katsaggelos, Bingni W. Brunton, Georgia Gkioxari, Ann Kennedy, Yisong Yue, Pietro Perona
Date: 2022-12-14 00:00:00
Proceeding: CVPR 2023 1
Tasks: Decoder

Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses without annotations. However, current keypoint discovery approaches commonly process single 2D views and do not operate in the 3D space. We propose a new method to perform self-supervised keypoint discovery in 3D from multi-view videos of behaving agents, without any keypoint or bounding box supervision in 2D or 3D. Our method, BKinD-3D, uses an encoder-decoder architecture with a 3D volumetric heatmap, trained to reconstruct spatiotemporal differences across multiple views, in addition to joint length constraints on a learned 3D skeleton of the subject. In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.
Policy Adaptation from Foundation Model Feedback
Authors: Yuying Ge, Annabella Macaluso, Li Erran Li, Ping Luo, Xiaolong Wang
Date: 2022-12-14 00:00:00
Proceeding: CVPR 2023 1
Tasks: Decision Making, model

Recent progress on vision-language foundation models have brought significant advancement to building general-purpose robots. By using the pre-trained models to encode the scene and instructions as inputs for decision making, the instruction-conditioned policy can generalize across different objects and tasks. While this is encouraging, the policy still fails in most cases given an unseen task or environment. In this work, we propose Policy Adaptation from Foundation model Feedback (PAFF). When deploying the trained policy to a new task or a new environment, we first let the policy play with randomly generated instructions to record the demonstrations. While the execution could be wrong, we can use the pre-trained foundation models to provide feedback to relabel the demonstrations. This automatically provides new pairs of demonstration-instruction data for policy fine-tuning. We evaluate our method on a broad range of experiments with the focus on generalization on unseen objects, unseen tasks, unseen environments, and sim-to-real transfer. We show PAFF improves baselines by a large margin in all cases. Our project page is available at https://geyuying.github.io/PAFF/