pwc-archive/papers-with-abstracts huggingface dataset:
Return to Lacan: an approach to digital twin mind with free energy principle
Authors: Lingyu Li, Chunbo Li
Date: 2023-09-13 00:00:00

Free energy principle (FEP) is a burgeoning theory in theoretical neuroscience that provides a universal law for modelling living systems of any scale. Expecting a digital twin mind from this first principle, we propose a macro-level interpretation that bridge neuroscience and psychoanalysis through the lens of computational Lacanian psychoanalysis. In this article, we claim three fundamental parallels between FEP and Lacanian psychoanalysis, and suggest a FEP approach to formalizing Lacan's theory. Sharing the non-linear temporal structure that combines prediction and retrospection (logical time), both of two theories focus on epistemological questions that how systems represented themselves and external world, and those elements failed to be represented (lacks and free energy) significantly influence the systems' subsequent states. Additionally, the fundamental hypothesis of FEP that the precise state of environment is always concealed, accounts for object petit a, the core concept in Lacan's theory. With neuropsychoanalytic mapping from three orders (the Real, the Symbolic, and the Imaginary, RSI) onto brain regions, we propose a brain-wide FEP model for a minimal definition of Lacanian mind - composite state of RSI that is perturbated by desire running over the logical time. The FEP-RSI model involves three FEP units connected by respective free energy with a natural compliance with logical time, mimicking core dynamics of Lacanian mind. The biological plausibility of current model is considered from perspectives of cognitive neuroscience. In conclusion, the FEP-RSI model encapsulates a unified framework for digital twin modeling at the macro level.
Discovering Dynamic Effective Connectome of Brain with Bayesian Dynamic DAG Learning
Authors: Abdolmahdi Bagheri, Mohammad Pasande, Kevin Bello, Babak Nadjar Araabi, Alireza Akhondi-Asl
Date: 2023-09-07 00:00:00
Tasks: Causal Discovery

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic causal model enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.
Modeling infant object perception as program induction
Authors: Jan-Philipp Fränken, Christopher G. Lucas, Neil R. Bramley, Steven T. Piantadosi
Date: 2023-08-28 00:00:00
Tasks: Attribute, Inductive Learning, Object, Object Recognition, Program induction

Infants expect physical objects to be rigid and persist through space and time and in spite of occlusion. Developmentists frequently attribute these expectations to a "core system" for object recognition. However, it is unclear if this move is necessary. If object representations emerge reliably from general inductive learning mechanisms exposed to small amounts of environment data, it could be that infants simply induce these assumptions very early. Here, we demonstrate that a domain general learning system, previously used to model concept learning and language learning, can also induce models of these distinctive "core" properties of objects after exposure to a small number of examples. Across eight micro-worlds inspired by experiments from the developmental literature, our model generates concepts that capture core object properties, including rigidity and object persistence. Our findings suggest infant object perception may rely on a general cognitive process that creates models to maximize the likelihood of observations
Computational limits to the legibility of the imaged human brain
Authors: James K Ruffle, Robert J Gray, Samia Mohinta, Guilherme Pombo, Chaitanya Kaul, Harpreet Hyare, Geraint Rees, Parashkev Nachev
Date: 2023-08-23 00:00:00
Tasks: Functional Connectivity, GPU

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications, and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted psychology better than the coincidence of chronic disease (p<0.05). Serology predicted chronic disease (p<0.05) and was best predicted by it (p<0.001), followed by structural neuroimaging (p<0.05). Our findings suggest either more informative imaging or more powerful models are needed to decipher individual level characteristics from the human brain.
Do Species Evolve Through Mutations Guided by Non-Coding RNAs?
Authors: Reza Rahmanzadeh
Date: 2023-09-13 00:00:00

The current theory of evolution is almost the one Darwin and Wallace proposed two centuries ago and the following discoveries e.g., Mendelian genetics and neutral mutation theory have not made significant modifications. The current evolution theory relies mostly on heritable variations within species population, natural selection and genetic drift. The inability of the current theory to explain and predict biological observations, especially the emergence of evolutionary novelties, highlights the need to incorporate recent evolutionary, developmental and genetics findings in order to achieve a more comprehensive explanation of species evolution. The present paper provides significant body of evidence to substantiate a new theory to account for species evolution. The main axes of the proposed theory include: First, mutations leading to genetic novelties in a given species during evolution should be guided by the environment surrounding that species. Second, environment and germline are connected with each other via soma-germline messengers e.g., non-coding RNAs (ncRNAs). Third, based on the information that germline continuously receives in terms of epigenetic messengers three stages of heritable changes may occur in germline genome to produce more adaptable offspring: i, Epigenetic modifications, ii, Genetic mutations in the sequence of pre-existing genes in order to improve their potency, and iii, The production of new genes with distinct gene-coding regions, which may have their own regulatory regions.
Partial differential equation models for invasive species spread in the presence of spatial heterogeneity
Authors: Elliott Hughes, Miguel Moyers-Gonzalez, Rua Murray, Phillip L. Wilson
Date: 2023-09-13 00:00:00

Models of invasive species spread often assume that landscapes are spatially homogeneous; thus simplifying analysis but potentially reducing accuracy. We extend a recently developed partial differential equation model for invasive conifer spread to account for spatial heterogeneity in parameter values and introduce a method to obtain key outputs (e.g. spread rates) from computational simulations. Simulations produce patterns of spatial spread remarkably similar to observed patterns in grassland ecosystems invaded by exotic conifers, validating our spatially explicit strategy. We find that incorporating spatial variation in different parameters does not significantly affect the evolution of invasions (which are characterised by a long quiescent period followed by rapid evolution towards to a constant rate of invasion) but that distributional assumptions can have a significant impact on the spread rate of invasions. Our work demonstrates that spatial variation in site-suitability or other parameters can have a significant impact on invasions
Statistical Applications in Pain Management: A Review
Authors: Flora Fan
Date: 2023-09-12 00:00:00
Tasks: Management

Chronic pain is a widespread and debilitating condition that affects millions of individuals worldwide. Recent research has unveiled a connection between chronic pain and epigenetic processes, shedding light on the complex interplay between genetics, environment, and pain perception. This review synthesizes findings from several studies exploring the relationship between epigenetic modifications, pain intensity, disability, and various other factors in individuals with chronic pain. The studies encompass a range of chronic pain conditions, including chronic low back pain, knee osteoarthritis pain, and pain in the context of underlying health conditions. The review highlights key findings and implications from each study and discusses their collective contributions to our understanding of the epigenetic mechanisms underpinning chronic pain.
Weak Markovian Approximations of Rough Heston
Authors: Christian Bayer, Simon Breneis
Date: 2023-09-13 00:00:00

The rough Heston model is a very popular recent model in mathematical finance; however, the lack of Markov and semimartingale properties poses significant challenges in both theory and practice. A way to resolve this problem is to use Markovian approximations of the model. Several previous works have shown that these approximations can be very accurate even when the number of additional factors is very low. Existing error analysis is largely based on the strong error, corresponding to the $L^2$ distance between the kernels. Extending earlier results by [Abi Jaber and El Euch, SIAM Journal on Financial Mathematics 10(2):309--349, 2019], we show that the weak error of the Markovian approximations can be bounded using the $L^1$-error in the kernel approximation for general classes of payoff functions for European style options. Moreover, we give specific Markovian approximations which converge super-polynomially in the number of dimensions, and illustrate their numerical superiority in option pricing compared to previously existing approximations. The new approximations also work for the hyper-rough case $H > -1/2$.
Epps Effect and the Signature of Short-Term Momentum Traders
Authors: Jérôme Busca, Léon Thomir
Date: 2023-09-13 00:00:00

It is a well-documented fact that the correlation function of the returns on two "related" assets is generally increasing as a function of the horizon $h$ of these returns. This phenomenon, termed the Epps Effect, holds true in a wide variety of markets, and there is a large body of literature devoted to its theoretical justification. Our focus here is to describe and understand a deviation to the Epps effect, observed in the context of the foreign exchange and cryptocurrency markets. Specifically, we document a sharp local maximum of the cross-correlation function of returns on the Euro EUR/USD and Bitcoin BTC/USD pairs as a function of $h$. Our claim is that this anomaly reveals the activity of short-term momentum traders.
Synthetic Text Generation using Hypergraph Representations
Authors: Natraj Raman, Sameena Shah
Date: 2023-09-06 00:00:00
Tasks: Hypergraph representations, Text Generation

Generating synthetic variants of a document is often posed as text-to-text transformation. We propose an alternate LLM based method that first decomposes a document into semantic frames and then generates text using this interim sparse format. The frames are modeled using a hypergraph, which allows perturbing the frame contents in a principled manner. Specifically, new hyperedges are mined through topological analysis and complex polyadic relationships including hierarchy and temporal dynamics are accommodated. We show that our solution generates documents that are diverse, coherent and vary in style, sentiment, format, composition and facts.
Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences
Authors: Sai Koneru, Jian Wu, Sarah Rajtmajer
Date: 2023-09-07 00:00:00
Tasks: Articles

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies in the social sciences. We compare the performance of LLMs to several state-of-the-art benchmarks and highlight opportunities for future research in this area. The dataset is available at https://github.com/Sai90000/ScientificHypothesisEvidencing.git
Can humans help BERT gain "confidence"?
Authors: Piyush Agrawal
Date: 2023-08-31 00:00:00
Tasks: Benchmarking, EEG

The advancements in artificial intelligence over the last decade have opened a multitude of avenues for interdisciplinary research. Since the idea of artificial intelligence was inspired by the working of neurons in the brain, it seems pretty practical to combine the two fields and take the help of cognitive data to train AI models. Not only it will help to get a deeper understanding of the technology, but of the brain as well. In this thesis, I conduct novel experiments to integrate cognitive features from the Zurich Cognitive Corpus (ZuCo) (Hollenstein et al., 2018) with a transformer-based encoder model called BERT. I show how EEG and eye-tracking features from ZuCo can help to increase the performance of the NLP model. I confirm the performance increase with the help of a robustness-checking pipeline and derive a word-EEG lexicon to use in benchmarking on an external dataset that does not have any cognitive features associated with it. Further, I analyze the internal working mechanism of BERT and explore a potential method for model explainability by correlating it with a popular model-agnostic explainability framework called LIME (Ribeiro et al., 2016). Finally, I discuss the possible directions to take this research forward.
Narrative as a Dynamical System
Authors: Isidoros Doxas, James Meiss, Steven Bottone, Tom Strelich, Andrew Plummer, Adrienne Breland, Simon Dennis, Kathy Garvin-Doxas, Michael Klymkowsky
Date: 2023-09-06 00:00:00

There is increasing evidence that human activity in general, and narrative in particular, can be treated as a dynamical system in the physics sense; a system whose evolution is described by an action integral, such that the average of all possible paths from point A to point B is given by the extremum of the action. We create by construction three such paths by averaging about 500 different narratives, and we show that the average path is consistent with an action principle.
Measurement of refractive index of single microparticles
Authors: Gregor Knoener, Simon Parkin, Timo A. Nieminen, Norman R. Heckenberg, Halina Rubinsztein-Dunlop
Date: 2006-10-09 00:00:00
Tasks: Optical Tweezers Simulations, Physical Simulations

The refractive index of single microparticles is derived from precise measurement and rigorous modeling of the stiffness of a laser trap. We demonstrate the method for particles of four different materials with diameters from 1.6 to 5.2 microns and achieve an accuracy of better than 1%. The method greatly contributes as a new characterization technique because it works best under conditions (small particle size, polydispersion) where other methods, such as absorption spectroscopy, start to fail. Particles need not be transferred to a particular fluid, which prevents particle degradation or alteration common in index matching techniques. Our results also show that advanced modeling of laser traps accurately reproduces experimental reality.
SLAMB: Accelerated Large Batch Training with Sparse Communication
Authors: Hang Xu, Wenxuan Zhang, Jiawei Fei, Yuzhe Wu, Tingwen Xie, Jun Huang, Yuchen Xie, Mohamed Elhoseiny, Panos Kalnis
Date: 2023-06-15 00:00:00
Proceeding: The International Conference on Machine Learning (ICML) 2023 6

Distributed training of large deep neural networks requires frequent exchange of massive data between machines, thus communication efficiency is a major concern. Existing compressed communication methods are either not compatible with large batch optimization algorithms, or do not provide sufficient speedup in large scale. In this paper, we combine sparsification-based gradient compression with the layer-wise adaptive moments optimizer for large batch training (LAMB). We propose SLAMB, a novel communication-efficient optimizer that supports large batch sizes and scales to thousands of GPUs. SLAMB employs momentum masking, local error compensation, and element-wise adaptive rescaling to achieve accurate layer-wise weight updates, which translates to fast convergence for very large batches. Our empirical results show that, compared to the state-of-the-art, SLAMB transmits half the amount of data in large-batch BERT pre-training, without sacrificing accuracy. Moreover, SLAMB achieves excellent scalability in large computing infrastructures. For instance, SLAMB with 128 GPUs reduces the training time of Swin Transformer pre-training on ImageNet to 5.35 hours, which is 2 hours faster than the state-of-the-art. At the extreme, we trained BERT-XL (2.8B parameters) on 1,024 NVIDIA A100 GPUs, where SLAMB achieved 90% scaling efficiency.
Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders
Authors: Matthew B. Webster, Joonnyong Lee
Date: 2023-08-31 00:00:00
Tasks: blind source separation, Decoder, Self-Supervised Learning

The task of blind source separation (BSS) involves separating sources from a mixture without prior knowledge of the sources or the mixing system. Single-channel mixtures and non-linear mixtures are a particularly challenging problem in BSS. In this paper, we propose a novel method for addressing BSS with single-channel non-linear mixtures by leveraging the natural feature subspace specialization ability of multi-encoder autoencoders. During the training phase, our method unmixes the input into the separate encoding spaces of the multi-encoder network and then remixes these representations within the decoder for a reconstruction of the input. Then to perform source inference, we introduce a novel encoding masking technique whereby masking out all but one of the encodings enables the decoder to estimate a source signal. To this end, we also introduce a sparse mixing loss that encourages sparse remixing of source encodings throughout the decoder and a so-called zero reconstruction loss on the decoder for coherent source estimations. To analyze and evaluate our method, we conduct experiments on a toy dataset, designed to demonstrate this property of feature subspace specialization, and with real-world biosignal recordings from a polysomnography sleep study for extracting respiration from electrocardiogram and photoplethysmography signals.
EnCodecMAE: Leveraging neural codecs for universal audio representation learning
Authors: Leonardo Pepino, Pablo Riera, Luciana Ferrer
Date: 2023-09-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition

The goal of universal audio representation learning is to obtain foundational models that can be used for a variety of downstream tasks involving speech, music and environmental sounds. To approach this problem, methods inspired by works on self-supervised learning for NLP, like BERT, or computer vision, like masked autoencoders (MAE), are often adapted to the audio domain. In this work, we propose masking representations of the audio signal, and training a MAE to reconstruct the masked segments. The reconstruction is done by predicting the discrete units generated by EnCodec, a neural audio codec, from the unmasked inputs. We evaluate this approach, which we call EnCodecMAE, on a wide range of tasks involving speech, music and environmental sounds. Our best model outperforms various state-of-the-art audio representation models in terms of global performance. Additionally, we evaluate the resulting representations in the challenging task of automatic speech recognition (ASR), obtaining decent results and paving the way for a universal audio representation.