pwc-archive/papers-with-abstracts huggingface dataset:
Circumstantial evidence and explanatory models for synapses in large-scale spike recordings
Authors: Ian H. Stevenson
Date: 2023-04-19 00:00:00
Tasks: Functional Connectivity

Whether, when, and how causal interactions between neurons can be meaningfully studied from observations of neural activity alone are vital questions in neural data analysis. Here we aim to better outline the concept of functional connectivity for the specific situation where systems neuroscientists aim to study synapses using spike train recordings. In some cases, cross-correlations between the spikes of two neurons are such that, although we may not be able to say that a relationship is causal without experimental manipulations, models based on synaptic connections provide precise explanations of the data. Additionally, there is often strong circumstantial evidence that pairs of neurons are monosynaptically connected. Here we illustrate how circumstantial evidence for or against synapses can be systematically assessed and show how models of synaptic effects can provide testable predictions for pair-wise spike statistics. We use case studies from large-scale multi-electrode spike recordings to illustrate key points and to demonstrate how modeling synaptic effects using large-scale spike recordings opens a wide range of data analytic questions.
Brain-wide functional imaging to highlight differences between the diurnal and nocturnal neuronal activity in zebrafish larvae
Authors: Giuseppe de Vito, Lapo Turrini, Chiara Fornetto, Elena Trabalzini, Pietro Ricci, Duccio Fanelli, Francesco Vanzi, Francesco Saverio Pavone
Date: 2023-04-19 00:00:00
Tasks: Rhythm

Most living organisms show highly conserved physiological changes following a 24-hour cycle which goes by the name of circadian rhythm. Among experimental models, the effects of light-dark cycle have been recently investigated in the larval zebrafish. Owing to its small size and transparency, this vertebrate enables optical access to the entire brain. Indeed, the combination of this organism with light-sheet imaging grants high spatio-temporal resolution volumetric recording of neuronal activity. This imaging technique, in its multiphoton variant, allows functional investigations without unwanted visual stimulation. Here, we employed a custom two-photon light-sheet microscope to study brain-wide differences in neuronal activity between diurnal and nocturnal periods in larval zebrafish assessed at the transition between day and night. We describe for the first time an activity increase in the low frequency domain of the pretectum and a frequency-localized activity decrease of the anterior rhombencephalic turning region during the nocturnal period. Moreover, our data confirm a nocturnal reduction in habenular activity. Furthermore, brain-wide detrended fluctuation analysis revealed a nocturnal decrease in the self-affinity of the neuronal signals in parts of the dorsal thalamus and the medulla oblongata and an increase in the pretectum. Our data show that brain-wide nonlinear light-sheet imaging represents a useful tool to investigate circadian rhythm effects on neuronal activity.
Network topology and movement cost, not updating mechanism, determine the evolution of cooperation in mobile structured populations
Authors: Diogo L. Pires, Igor Erovenko, Mark Broom
Date: 2023-04-19 00:00:00

Evolutionary models are used to study the self-organisation of collective action, often incorporating population structure due to its ubiquitous presence and long-known impact on emerging phenomena. We investigate the evolution of multiplayer cooperation in mobile structured populations, where individuals move strategically on networks and interact with those they meet in groups of variable size. We find that the evolution of multiplayer cooperation primarily depends on the network topology and movement cost while using different stochastic update rules seldom influences evolutionary outcomes. Cooperation robustly co-evolves with movement on complete networks and structure has a partially detrimental effect on it. These findings contrast an established wisdom in evolutionary graph theory that cooperation can only emerge under some update rules and if the average degree is low. We find that group-dependent movement erases the locality of interactions, suppresses the impact of evolutionary structural viscosity on the fitness of individuals, and leads to assortative behaviour that is much more powerful than viscosity in promoting cooperation. We analyse the differences remaining between update rules through a comparison of evolutionary outcomes and fixation probabilities.
A model for seagrass species competition: dynamics of the symmetric case
Authors: Pablo Moreno-Spiegelberg, Damià Gomila
Date: 2023-04-19 00:00:00

We propose a general population dynamics model for two seagrass species growing and interacting in two spatial dimensions. The model includes spatial terms accounting for the clonal growth characteristics of seagrasses, and coupling between species through the net mortality rate. We consider both intraspecies and interspecies facilitative and competitive interactions, allowing density-dependent interaction mechanisms. Here we study the case of very similar species with reciprocal interactions, which allows reducing the number of the model parameters to just four, and whose bifurcation structure can be considered the backbone of the complete system. We find that the parameter space can be divided into ten regions with qualitatively different bifurcation diagrams. These regimes can be further grouped into just five regimes with different ecological interpretations. Our analysis allows the classifying of all possible density distributions and dynamical behaviors of meadows with two coexisting species.
Exact solutions for diffusive transport on heterogeneous growing domains
Authors: Stuart T. Johnston, Matthew J. Simpson
Date: 2023-04-19 00:00:00
Tasks: valid

From the smallest biological systems to the largest cosmological structures, spatial domains undergo expansion and contraction. Within these growing domains, diffusive transport is a common phenomenon. Mathematical models have been widely employed to investigate diffusive processes on growing domains. However, a standard assumption is that the domain growth is spatially uniform. There are many relevant examples where this is not the case, such as the colonisation of growing gut tissue by neural crest cells. As such, it is not straightforward to disentangle the individual roles of heterogeneous growth and diffusive transport. Here we present exact solutions to models of diffusive transport on domains undergoing spatially non-uniform growth. The exact solutions are obtained via a combination of transformation, convolution and superposition techniques. We verify the accuracy of these solutions via comparison with simulations of a corresponding lattice-based random walk. We explore various domain growth functions, including linear growth, exponential growth and contraction, and oscillatory growth. Provided the domain size remains positive, we find that the derived solutions are valid. The exact solutions reveal the relationship between model parameters, such as the diffusivity and the type and rate of domain growth, and key statistics, such as the survival and splitting probabilities.
The ghost of ecology in chaos, combining intransitive and higher order effects
Authors: John Vandermeer, Ivette Perfecto
Date: 2023-04-18 00:00:00
Tasks: Triplet

Historically, musings about the structure of ecological communities has revolved around the structure of pairwise interactions, competition, predation, mutualism, etc. . . Recently a growing literature acknowledges that the baseline assumption that the pair of species is not necessarily the metaphorical molecule of community ecology, and that certain structures containing three or more species may not be usefully divisible into pairwise components. Two examples are intransitive competition (species A dominates species B dominates species C dominates species A), and nonlinear higher-order effects. While these two processes have been discussed extensively, the explicit analysis of how the two of them behave when simultaneously part of the same dynamic system has not yet appeared in the literature. A concrete situation exists on coffee farms in Puerto Rico in which three ant species, at least on some farms, form an intransitive competitive triplet, and that triplet is strongly influenced, nonlinearly, by a fly parasitoid that modifies the competitive ability of one of the species in the triplet. Using this arrangement as a template we explore the dynamical consequences with a simple ODE model. Results are complicated and include include alternative periodic and chaotic attractors. The qualitative structures of those complications, however, may be retrieved easily from a reflection on the basic natural history of the system.
The Evolution of Sociality and the Polyvagal Theory
Authors: J. Sean Doody, Gordon Burghardt, Vladimir Dinets
Date: 2023-04-18 00:00:00

The polyvagal theory (PT), offered by Porges (2021), proposes that the autonomic nervous system (ANS) was repurposed in mammals, via a second vagal nerve, to suppress defensive strategies and support the expression of sociality. Three critical assumptions of this theory are that (1) the transition of the ANS was associated with the evolution of social mammals from asocial reptiles; (2) the transition enabled mammals, unlike their reptilian ancestors, to derive a biological benefit from social interactions; and (3) the transition forces a less parsimonious explanation (convergence) for the evolution of social behavior in birds and mammals, since birds evolved from a reptilian lineage. Two recently published reviews, however, provided compelling evidence that the social asocial dichotomy is overly simplistic, neglects the diversity of vertebrate social systems, impedes our understanding of the evolution of social behavior, and perpetuates the erroneous belief that one group, non-avian reptiles, is incapable of complex social behavior. In the worst case, if PT depends upon a transition from asocial reptiles to social mammals, then the ability of PT to explain the evolution of the mammalian ANS is highly questionable. A great number of social behaviors occur in both reptiles and mammals. In the best case, PT has misused the terms social and asocial. Even here, however, the theory would still need to identify a particular suite of behaviors found in mammals and not reptiles that could be associated with, or explain, the transition of the ANS, and then replace the asocial and social labels with more specific descriptors.
Modulating human brain responses via optimal natural image selection and synthetic image generation
Authors: Zijin Gu, Keith Jamison, Mert R. Sabuncu, Amy Kuceyeski
Date: 2023-04-18 00:00:00
Tasks: Image Generation

Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images predicted to achieve maximal activations using a group level encoding model evoke higher responses than images predicted to achieve average activations, and the activation gain is positively associated with the encoding model accuracy. Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images. In our second experiment, we found that synthetic images derived using a personalized encoding model elicited higher responses compared to synthetic images from group-level or other subjects' encoding models. The finding of aTLfaces favoring synthetic images than natural images was also replicated. Our results indicate the possibility of using data-driven and generative approaches to modulate macro-scale brain region responses and probe inter-individual differences in and functional specialization of the human visual system.
How human-derived brain organoids are built differently from brain organoids derived from genetically-close relatives: A multi-scale hypothesis
Authors: Tao Zhang, Sarthak Gupta, Madeline A. Lancaster, J. M. Schwarz
Date: 2023-04-17 00:00:00

How genes affect tissue scale organization remains a longstanding biological puzzle. As experimental efforts aim to quantify gene expression, chromatin organization, cellular structure, and tissue structure, computational modeling lags behind. To address this gap, we merge a cellular-based tissue model with a nuclear model that includes a deformable lamina shell and chromatin to test multiscale hypotheses linking chromatin and tissue scales. We propose a multiscale hypothesis focusing on brain organoids to explain structural differences between brain organoids built from induced-pluripotent human stem cells and induced-pluripotent gorilla and chimpanzee cells. Recent experiments discover that a cell fate transition from neuroepithelial to radial glial cells includes a new intermediate state delayed in human organoids, which narrows and lengthens cells on the apical side. Experiments show that the transcription factor ZEB2 plays a major role in the emergence of this intermediate state with ZEB2 mRNA levels peaking. We postulate that the enhancement of ZEB2 expression is potentially due to chromatin reorganization in response to mechanical deformations of the nucleus. A larger critical mechanical strain triggers reorganization in human-derived stem cells, causing delayed ZEB2 upregulation compared with genetically close relatives. We test this by exploring how slightly different initial configurations of chromatin reorganize under applied strain, with greater representing less genetically-close relatives. We find that larger configuration discrepancies produce increased differences in the magnitude of chromatin displacement that rise faster than linearly yet slower than exponentially. Changes in chromatin strain and contact maps can reveal species-specific differences, aiding our understanding of how one species differs in structure from another.
Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies
Authors: Nick James, Max Menzies
Date: 2023-04-18 00:00:00

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as: an increase in the market's collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a "best value" portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market, and to what extent. Results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.
Reference-based Image Composition with Sketch via Structure-aware Diffusion Model
Authors: Kangyeol Kim, Sunghyun Park, Junsoo Lee, Jaegul Choo
Date: 2023-03-31 00:00:00
Tasks: Image Manipulation

Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and enable fine-grained generation, we introduce a multi-input-conditioned image composition model that incorporates a sketch as a novel modal, alongside a reference image. Thanks to the edge-level controllability using sketches, our method enables a user to edit or complete an image sub-part with a desired structure (i.e., sketch) and content (i.e., reference image). Our framework fine-tunes a pre-trained diffusion model to complete missing regions using the reference image while maintaining sketch guidance. Albeit simple, this leads to wide opportunities to fulfill user needs for obtaining the in-demand images. Through extensive experiments, we demonstrate that our proposed method offers unique use cases for image manipulation, enabling user-driven modifications of arbitrary scenes.
Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning
Authors: Geoffery Agorku, Divine Agbobli, Vuban Chowdhury, Kwadwo Amankwah-Nkyi, Adedolapo Ogungbire, Portia Ankamah Lartey, Armstrong Aboah
Date: 2023-04-14 00:00:00
Tasks: Data Augmentation, Deep Learning, Ensemble Learning, image-classification, Image Classification

The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted. This paper presents the development and evaluation of a real-time YOLOv5 Deep Learning (DL) model for detecting riders and passengers on motorbikes, identifying whether the detected person is wearing a helmet. We trained the model on 100 videos recorded at 10 fps, each for 20 seconds. Our study demonstrated the applicability of DL models to accurately detect helmet regulation violators even in challenging lighting and weather conditions. We employed several data augmentation techniques in the study to ensure the training data is diverse enough to help build a robust model. The proposed model was tested on 100 test videos and produced an mAP score of 0.5267, ranking 11th on the AI City Track 5 public leaderboard. The use of deep learning techniques for image classification tasks, such as identifying helmet-wearing riders, has enormous potential for improving road safety. The study shows the potential of deep learning models for application in smart cities and enforcing traffic regulations and can be deployed in real-time for city-wide monitoring.
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos
Authors: Kelvin Kwakye, Younho Seong, Armstrong Aboah, Sun Yi
Date: 2023-04-13 00:00:00
Tasks: Anomaly Detection

In recent years, distracted driving has garnered considerable attention as it continues to pose a significant threat to public safety on the roads. This has increased the need for innovative solutions that can identify and eliminate distracted driving behavior before it results in fatal accidents. In this paper, we propose a Signal-Based anomaly detection algorithm that segments videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to precisely estimate the start and end times of an anomalous driving event. In the phase of anomaly detection and analysis, driver pose background estimation, mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM classifier was applied to candidate anomalies to detect and classify final anomalies. The proposed method achieved an overlap score of 0.5424 and ranked 9th on the public leader board in the AI City Challenge 2023, according to experimental validation results.
Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5
Authors: Elham Soltanikazemi, Ashwin Dhakal, Bijaya Kumar Hatuwal, Imad Eddine Toubal, Armstrong Aboah, Kannappan Palaniappan
Date: 2023-04-13 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

This research focuses on real-time surveillance systems as a means for tackling the issue of non-compliance with helmet regulations, a practice that considerably amplifies the risk for motorcycle drivers or riders. Despite the well-established advantages of helmet usage, achieving widespread compliance remains challenging due to diverse contributing factors. To effectively address this concern, real-time monitoring and enforcement of helmet laws have been proposed as a plausible solution. However, previous attempts at real-time helmet violation detection have been hindered by their limited ability to operate in real-time. To overcome this limitation, the current paper introduces a novel real-time helmet violation detection system that utilizes the YOLOv5 single-stage object detection model. This model is trained on the 2023 NVIDIA AI City Challenge 2023 Track 5 dataset. The optimal hyperparameters for training the model are determined using genetic algorithms. Additionally, data augmentation and various sampling techniques are implemented to enhance the model's performance. The efficacy of the models is evaluated using precision, recall, and mean Average Precision (mAP) metrics. The results demonstrate impressive precision, recall, and mAP scores of 0.848, 0.599, and 0.641, respectively for the training data. Furthermore, the model achieves notable mAP score of 0.6667 for the test datasets, leading to a commendable 4th place rank in the public leaderboard. This innovative approach represents a notable breakthrough in the field and holds immense potential to substantially enhance motorcycle safety. By enabling real-time monitoring and enforcement capabilities, this system has the capacity to contribute towards increased compliance with helmet laws, thereby effectively reducing the risks faced by motorcycle riders and passengers.
Regularizing Second-Order Influences for Continual Learning
Authors: Zhicheng Sun, Yadong Mu, Gang Hua
Date: 2023-04-20 00:00:00
Proceeding: CVPR 2023 1
Tasks: Continual Learning

Continual learning aims to learn on non-stationary data streams without catastrophically forgetting previous knowledge. Prevalent replay-based methods address this challenge by rehearsing on a small buffer holding the seen data, for which a delicate sample selection strategy is required. However, existing selection schemes typically seek only to maximize the utility of the ongoing selection, overlooking the interference between successive rounds of selection. Motivated by this, we dissect the interaction of sequential selection steps within a framework built on influence functions. We manage to identify a new class of second-order influences that will gradually amplify incidental bias in the replay buffer and compromise the selection process. To regularize the second-order effects, a novel selection objective is proposed, which also has clear connections to two widely adopted criteria. Furthermore, we present an efficient implementation for optimizing the proposed criterion. Experiments on multiple continual learning benchmarks demonstrate the advantage of our approach over state-of-the-art methods. Code is available at https://github.com/feifeiobama/InfluenceCL.