pwc-archive/papers-with-abstracts huggingface dataset:
The mass-conversion method: a hybrid technique for simulating well-mixed chemical reaction networks
Authors: Joshua C. Kynaston, Christian A Yates, Anna Hekkink, Chris Guiver
Date: 2022-10-21 00:00:00
Tasks: Computational Efficiency

There exist several methods for simulating biological and physical systems as represented by chemical reaction networks. Systems with low numbers of particles are frequently modelled as discrete-state Markov jump processes and are typically simulated via a stochastic simulation algorithm (SSA). An SSA, while accurate, is often unsuitable for systems with large numbers of individuals, and can become prohibitively expensive with increasing reaction frequency. Large systems are often modelled deterministically using ordinary differential equations, sacrificing accuracy and stochasticity for computational efficiency and analytical tractability. In this paper, we present a novel hybrid technique for the accurate and efficient simulation of large chemical reaction networks. This technique, which we name the mass-conversion method, couples a discrete-state Markov jump process to a system of ordinary differential equations by simulating a reaction network using both techniques simultaneously. Individual molecules in the network are represented by exactly one regime at any given time, and may switch their governing regime depending on particle density. In this manner, we model high copy-number species using the cheaper continuum method and low copy-number species using the more expensive, discrete-state stochastic method to preserve the impact of stochastic fluctuations at low copy number. The motivation, as with similar methods, is to retain the advantages while mitigating the shortfalls of each method. We demonstrate the performance and accuracy of our method for several test problems that exhibit varying degrees of inter-connectivity and complexity by comparing averaged trajectories obtained from both our method and from exact stochastic simulation.
The Idea of Exo-brain
Authors: Ankur Betageri
Date: 2022-10-16 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

In this paper I examine the process of getting affected by and the process of making sense of non-language sounds and propose the idea of the contextual cognitive apparatus or exo-brain. We are affected by a singing voice even when we do not fully understand the sounds produced by it. When the listener starts hearing words in the sung song and/or starts interpreting the heard words he recreates the sense and meaning of the song based on a context which either preexists in a culture or gets formed due to his extended engagement with a nebulous auditory stimulus that is perceived as meaningful. This is what I call the work of the creative ear. The affective power of the song comes from an affective reaction between the singing voice, especially the sung non-language sounds, and the linguistic and cultural contexts of the singer and the listener and out this affective reaction something emerges which constitutes the meaning, sensible or affective, of the non-language sound. I show how the cognitive contexts of the production and the reception of the non-language sounds, and the song, play a central role in our apprehension of the sounds and propose the existence of an extra-bodily sense making organ of our cognitive system, the extension of our brain and sense organs, which I call contextual cognitive apparatus or exo-brain.
Grid cells and their potential application in AI
Authors: Jason Toy
Date: 2022-10-12 00:00:00

Since their Nobel Prize winning discovery in 2005, grid cells have been studied extensively by neuroscientists. Their multi-scale periodic firing rates tiling the environment as the animal moves around has been shown as critical for path integration. Multiple experiments have shown that grid cells also fire for other representations such as olfactory, attention mechanisms, imagined movement, and concept organization potentially acting as a form of neural recycling and showing the possible brain mechanism for cognitive maps that Tolman envisioned in 1948. Grid cell integration into artificial neural networks may enable more robust, generalized, and smarter computers. In this paper we give an overview of grid cell research since their discovery, their role in neuroscience and cognitive science, and possible future directions of artificial intelligence research.
The quest for the definition of life
Authors: Kazem Haghnejad Azar
Date: 2022-10-10 00:00:00
Tasks: Diversity

The intricacy and diversity inherent in living organisms present a formidable obstacle to the establishment of a universally accepted definition. Life manifests in a multitude of forms, exhibiting various attributes such as growth, reproduction, responsiveness to stimuli, adaptation, and homeostasis. However, each of these characteristics can also be observed to some degree within certain non-living systems, leading to a blurring of boundaries and generating conceptual complexities. In this manuscript, I demonstrate that the transformation of a non-living entity into a living organism does not adhere to a specific temporal boundary that unequivocally designates the onset of life. Through mathematical analysis, I have demonstrated that a comprehensive definition of living beings does not exist, which means that there are no clear boundaries in the chemical processes that turn non-living entities into living ones. In other words, living organisms do not possess unique characteristics that can completely set them apart from non-living entities. Therefore, no definitive definition exists that unequivocally distinguishes living things from non-living things.
Eco-evolution from deep time to contemporary dynamics: the role of timescales and rate modulators
Authors: Emanuel A. Fronhofer, Dov Corenblit, Jhelam N. Deshpande, Lynn Govaert, Philippe Huneman, Frédérique Viard, Philippe Jarne, Sara Puijalon
Date: 2022-10-21 00:00:00

Eco-evolutionary dynamics, or eco-evolution for short, are thought to involve rapid demography (ecology) and equally rapid phenotypic changes (evolution) leading to novel, emergent system behaviours. This focus on contemporary dynamics is likely due to accumulating evidence for rapid evolution, from classical laboratory microcosms and natural populations, including the iconic Trinidadian guppies. We argue that this view is too narrow, preventing the successful integration of ecology and evolution. While maintaining that eco-evolution involves emergence, we highlight that this may also be true for slow ecology and evolution which unfold over thousands or millions of years, such as the feedbacks between riverine geomorphology and plant evolution. We thereby integrate geomorphology and biome-level feedbacks into eco-evolution, significantly extending its scope. Most importantly, we emphasize that eco-evolutionary systems need not be frozen in state-space: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can synchronize or desynchronize ecology and evolution. We speculate that global change may increase the occurrence of eco-evolution and emergent system behaviours which represents substantial challenges for prediction. Our perspective represents an attempt to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from contemporary dynamics to deep time.
A survey of open questions in adaptive therapy: bridging mathematics and clinical translation
Authors: Jeffrey West, Fred Adler, Jill Gallaher, Maximilian Strobl, Renee Brady-Nicholls, Joel S. Brown, Mark Robertson-Tessi, Eunjung Kim, Robert Noble, Yannick Viossat, David Basanta, Alexander R. A. Anderson
Date: 2022-10-18 00:00:00

Adaptive therapy is a dynamic cancer treatment protocol that updates (or "adapts") treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: 1) the necessary components of mathematical models of adaptive therapy 2) design and validation of dosing protocols, and 3) challenges and opportunities in clinical translation.
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?
Authors: Ítalo Santana, Andrea Lodi, Thibaut Vidal
Date: 2022-09-09 00:00:00
Tasks: Combinatorial Optimization, Heuristic Search

Extensive research has been conducted, over recent years, on various ways of enhancing heuristic search for combinatorial optimization problems with machine learning algorithms. In this study, we investigate the use of predictions from graph neural networks (GNNs) in the form of heatmaps to improve the Hybrid Genetic Search (HGS), a state-of-the-art algorithm for the Capacitated Vehicle Routing Problem (CVRP). The crossover and local-search components of HGS are instrumental in finding improved solutions, yet these components essentially rely on simple greedy or random choices. It seems intuitive to attempt to incorporate additional knowledge at these levels. Throughout a vast experimental campaign on more than 10,000 problem instances, we show that exploiting more sophisticated strategies using measures of node relatedness (heatmaps, or simply distance) within these algorithmic components can significantly enhance performance. However, contrary to initial expectations, we also observed that heatmaps did not present significant advantages over simpler distance measures for these purposes. Therefore, we faced a common -- though rarely documented -- situation of overkill: GNNs can indeed improve performance on an important optimization task, but an ablation analysis demonstrated that simpler alternatives perform equally well.
What do Large Language Models Learn beyond Language?
Authors: Avinash Madasu, Shashank Srivastava
Date: 2022-10-21 00:00:00
Tasks: Inductive Learning

Large language models (LMs) have rapidly become a mainstay in Natural Language Processing. These models are known to acquire rich linguistic knowledge from training on large amounts of text. In this paper, we investigate if pre-training on text also confers these models with helpful `inductive biases' for non-linguistic reasoning. On a set of 19 diverse non-linguistic tasks involving quantitative computations, recognizing regular expressions and reasoning over strings. We find that pretrained models significantly outperform comparable non-pretrained neural models. This remains true also in experiments with training non-pretrained models with fewer parameters to account for model regularization effects. We further explore the effect of text domain on LMs by pretraining models from text from different domains and provenances. Our experiments surprisingly reveal that the positive effects of pre-training persist even when pretraining on multi-lingual text or computer code, and even for text generated from synthetic languages. Our findings suggest a hitherto unexplored deep connection between pre-training and inductive learning abilities of language models.
Graphemic Normalization of the Perso-Arabic Script
Authors: Raiomond Doctor, Alexander Gutkin, Cibu Johny, Brian Roark, Richard Sproat
Date: 2022-10-21 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation

Since its original appearance in 1991, the Perso-Arabic script representation in Unicode has grown from 169 to over 440 atomic isolated characters spread over several code pages representing standard letters, various diacritics and punctuation for the original Arabic and numerous other regional orthographic traditions. This paper documents the challenges that Perso-Arabic presents beyond the best-documented languages, such as Arabic and Persian, building on earlier work by the expert community. We particularly focus on the situation in natural language processing (NLP), which is affected by multiple, often neglected, issues such as the use of visually ambiguous yet canonically nonequivalent letters and the mixing of letters from different orthographies. Among the contributing conflating factors are the lack of input methods, the instability of modern orthographies, insufficient literacy, and loss or lack of orthographic tradition. We evaluate the effects of script normalization on eight languages from diverse language families in the Perso-Arabic script diaspora on machine translation and statistical language modeling tasks. Our results indicate statistically significant improvements in performance in most conditions for all the languages considered when normalization is applied. We argue that better understanding and representation of Perso-Arabic script variation within regional orthographic traditions, where those are present, is crucial for further progress of modern computational NLP techniques especially for languages with a paucity of resources.
Ares: A System-Oriented Wargame Framework for Adversarial ML
Authors: Farhan Ahmed, Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati
Date: 2022-10-24 00:00:00

Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved into an eternal war between defenders, who seek to increase the robustness of ML models against adversarial attacks, and adversaries, who seek to develop better attacks capable of weakening or defeating these defenses. This domain, however, has found little buy-in from ML practitioners, who are neither overtly concerned about these attacks affecting their systems in the real world nor are willing to trade off the accuracy of their models in pursuit of robustness against these attacks. In this paper, we motivate the design and implementation of Ares, an evaluation framework for adversarial ML that allows researchers to explore attacks and defenses in a realistic wargame-like environment. Ares frames the conflict between the attacker and defender as two agents in a reinforcement learning environment with opposing objectives. This allows the introduction of system-level evaluation metrics such as time to failure and evaluation of complex strategies such as moving target defenses. We provide the results of our initial exploration involving a white-box attacker against an adversarially trained defender.
Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs
Authors: Paul K. Mandal, Rakesh Mahto
Date: 2022-10-22 00:00:00
Tasks: Alzheimer's Detection, Alzheimer's Disease Detection

Alzheimer's disease (AD) is a neuro-degenerative disease that can cause dementia and result severe reduction in brain function inhibiting simple tasks especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD induced dementia and unpaid care for people with AD related dementia is valued at $271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for early detection of AD. We then give an overview of our dataset that was from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and propose a deep Convolutional Neural Network (CNN) architecture consisting of 7,866,819 parameters. This model has three different convolutional branches with each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three class accuracy.
Deep Kronecker Network
Authors: Long Feng, Guang Yang
Date: 2022-10-24 00:00:00
Tasks: regression

We propose Deep Kronecker Network (DKN), a novel framework designed for analyzing medical imaging data, such as MRI, fMRI, CT, etc. Medical imaging data is different from general images in at least two aspects: i) sample size is usually much more limited, ii) model interpretation is more of a concern compared to outcome prediction. Due to its unique nature, general methods, such as convolutional neural network (CNN), are difficult to be directly applied. As such, we propose DKN, that is able to i) adapt to low sample size limitation, ii) provide desired model interpretation, and iii) achieve the prediction power as CNN. The DKN is general in the sense that it not only works for both matrix and (high-order) tensor represented image data, but also could be applied to both discrete and continuous outcomes. The DKN is built on a Kronecker product structure and implicitly imposes a piecewise smooth property on coefficients. Moreover, the Kronecker structure can be written into a convolutional form, so DKN also resembles a CNN, particularly, a fully convolutional network (FCN). Furthermore, we prove that with an alternating minimization algorithm, the solutions of DKN are guaranteed to converge to the truth geometrically even if the objective function is highly nonconvex. Interestingly, the DKN is also highly connected to the tensor regression framework proposed by Zhou et al. (2010), where a CANDECOMP/PARAFAC (CP) low-rank structure is imposed on tensor coefficients. Finally, we conduct both classification and regression analyses using real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to demonstrate the effectiveness of DKN.
Language Model Pre-Training with Sparse Latent Typing
Authors: Liliang Ren, Zixuan Zhang, Han Wang, Clare R. Voss, ChengXiang Zhai, Heng Ji
Date: 2022-10-23 00:00:00
Tasks: Few-shot NER, Language Modeling, Language Modelling, model, Sentence

Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences. In this paper, we manage to push the language models to obtain a deeper understanding of sentences by proposing a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge. Besides, the language model pre-trained with such an objective also significantly improves Information Extraction related downstream tasks in both supervised and few-shot settings. Our code is publicly available at: https://github.com/renll/SparseLT.
Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts
Authors: Hongli Zhan, Tiberiu Sosea, Cornelia Caragea, Junyi Jessy Li
Date: 2022-10-22 00:00:00
Tasks: Emotion Detection and Trigger Summarization

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people's emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences. This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion. To support this goal, we introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of ~1,900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.
Tucker-O-Minus Decomposition for Multi-view Tensor Subspace Clustering
Authors: Yingcong Lu, Yipeng Liu, Zhen Long, Zhangxin Chen, Ce Zhu
Date: 2022-10-23 00:00:00
Tasks: Clustering, Tensor Decomposition

With powerful ability to exploit latent structure of self-representation information, different tensor decompositions have been employed into low rank multi-view clustering (LRMVC) models for achieving significant performance. However, current approaches suffer from a series of problems related to those tensor decomposition, such as the unbalanced matricization scheme, rotation sensitivity, deficient correlations capture and so forth. All these will lead to LRMVC having insufficient access to global information, which is contrary to the target of multi-view clustering. To alleviate these problems, we propose a new tensor decomposition called Tucker-O-Minus Decomposition (TOMD) for multi-view clustering. Specifically, based on the Tucker format, we additionally employ the O-minus structure, which consists of a circle with an efficient bridge linking two weekly correlated factors. In this way, the core tensor in Tucker format is replaced by the O-minus architecture with a more balanced structure, and the enhanced capacity of capturing the global low rank information will be achieved. The proposed TOMD also provides more compact and powerful representation abilities for the self-representation tensor, simultaneously. The alternating direction method of multipliers is used to solve the proposed model TOMD-MVC. Numerical experiments on six benchmark data sets demonstrate the superiority of our proposed method in terms of F-score, precision, recall, normalized mutual information, adjusted rand index, and accuracy.
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking
Authors: Bahman Moraffah, Antonia Papandreou-Suppappola
Date: 2022-10-20 00:00:00
Tasks: Object, Object Tracking, Transfer Learning

The problem of sequentially transferring from a source object track and a model to another Bayesian filter has become ubiquitous. Due to the lack of a structural model that can capture the dependence among different models, the transfer may not be fully specified. In this paper, we introduce a novel Bayesian model that accounts for the model-jump from which the object can choose a model and follow. We aim to track the trajectory of the object while sequentially transferring from the source object to the target object. The main idea is to impute the dynamical model while tracking the object and estimating the state parameters of the moving object according to discretized dynamic systems. We demonstrate this procedure can handle the model mismatch as it sequentially corrects the predictive model. Particularly, for a fixed number of motion models, the object can learn what motion to follow at each time step. We employ a prior model for each model and then adaptively correct for changing one model to another to robustly estimate object trajectory under various motions. More concretely, we propose a robust Bayesian recipe to handle the model-jump and then integrate it with a Markov chain Monte Carlo (MCMC) approach to sample from the posterior distribution. We demonstrate through experiments the advantage of accounting for model-jump in our proposed method for knowledge transfer between learning tasks in Bayesian transfer learning.